CN111783486A - Maintenance early warning method and device for card reader equipment - Google Patents

Maintenance early warning method and device for card reader equipment Download PDF

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Publication number
CN111783486A
CN111783486A CN202010585021.XA CN202010585021A CN111783486A CN 111783486 A CN111783486 A CN 111783486A CN 202010585021 A CN202010585021 A CN 202010585021A CN 111783486 A CN111783486 A CN 111783486A
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card
reading module
card reading
data
early warning
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CN111783486B (en
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高伟
张磊
施好健
刘金山
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/0095—Testing the sensing arrangement, e.g. testing if a magnetic card reader, bar code reader, RFID interrogator or smart card reader functions properly
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G08B21/185—Electrical failure alarms
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Time Recorders, Dirve Recorders, Access Control (AREA)

Abstract

The invention provides a maintenance early warning method and a device of card reader equipment, wherein the method comprises the following steps: acquiring state data of card reader equipment, wherein the state data comprises a plurality of first state parameters; obtaining at least one early warning parameter according to each first state parameter; and if the early warning parameters are judged to exceed the corresponding threshold value range, first early warning information is sent. The device is used for executing the method. The maintenance early warning method and the maintenance early warning device for the card reader equipment, provided by the embodiment of the invention, improve the reliability of the card reader equipment.

Description

Maintenance early warning method and device for card reader equipment
Technical Field
The invention relates to the technical field of equipment maintenance, in particular to a maintenance early warning method and device for card reader equipment.
Background
The card reader device can be configured on the counter of a bank outlet and used for reading information of a bank card and an identity card.
In the prior art, a maintenance mode of a card reader device used by a counter of a bank outlet is fault maintenance, namely, the card reader device is maintained when the card reader device fails, but the card reader device is maintained when the card reader device fails, so that the use of the card reader device is influenced, time is required for replacement and maintenance of the card reader device, service cannot be provided for a customer within a certain time, the handling of customer service is influenced, and customer service experience of the bank outlet is influenced.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a method and a device for maintaining and early warning of card reader equipment, which can at least partially solve the problems in the prior art.
On one hand, the invention provides a maintenance early warning method of card reader equipment, which comprises the following steps:
acquiring state data of card reader equipment, wherein the state data comprises a plurality of first state parameters;
obtaining at least one early warning parameter according to each first state parameter;
and if the early warning parameters are judged to exceed the corresponding threshold value range, first early warning information is sent.
In another aspect, the present invention provides a maintenance early warning device for a card reader device, including:
the card reader comprises a state data acquisition unit, a state data acquisition unit and a control unit, wherein the state data acquisition unit is used for acquiring state data of the card reader equipment, and the state data comprises a plurality of first state parameters;
the early warning parameter obtaining unit is used for obtaining at least one early warning parameter according to each first state parameter;
and the first early warning judgment unit is used for sending first early warning information after judging that the early warning parameters exceed the corresponding threshold value range.
In another aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for performing maintenance and warning of a card reader device according to any of the embodiments described above when executing the computer program.
In yet another aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the maintenance warning method for a card reader device according to any one of the above embodiments.
According to the maintenance early warning method and device for the card reader equipment, provided by the embodiment of the invention, the state data of the card reader equipment can be obtained, at least one early warning parameter is obtained according to each first state parameter included in the state data, the early warning parameter is judged and known to exceed the corresponding threshold range, the first early warning information is sent, the maintenance early warning can be carried out when the card reader equipment does not meet the functional requirements, so that the card reader equipment is maintained by taking action in advance, the active maintenance of the card reader equipment is realized, and the reliability of the card reader equipment is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a schematic flowchart of a maintenance warning method for a card reader device according to a first embodiment of the present invention.
Fig. 2 is a flowchart illustrating a maintenance warning method for a card reader device according to a second embodiment of the present invention.
Fig. 3 is a flowchart illustrating a maintenance warning method for a card reader device according to a third embodiment of the present invention.
Fig. 4 is a flowchart illustrating a maintenance warning method for a card reader device according to a fourth embodiment of the present invention.
Fig. 5 is a flowchart illustrating a maintenance warning method for a card reader device according to a fifth embodiment of the present invention.
Fig. 6 is a schematic structural diagram of a maintenance warning device for a card reader device according to a sixth embodiment of the present invention.
Fig. 7 is a schematic structural diagram of a maintenance warning device for a card reader device according to a seventh embodiment of the present invention.
Fig. 8 is a schematic structural diagram of a maintenance warning device for a card reader device according to an eighth embodiment of the present invention.
Fig. 9 is a schematic structural diagram of a maintenance warning device for a card reader device according to a ninth embodiment of the present invention.
Fig. 10 is a schematic structural diagram of a maintenance warning device for a card reader device according to a tenth embodiment of the present invention.
Fig. 11 is a schematic structural diagram of a maintenance warning device for a card reader device according to an eleventh embodiment of the present invention.
Fig. 12 is a schematic physical structure diagram of an electronic device according to a twelfth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
Fig. 1 is a schematic flow chart of a maintenance early warning method for a card reader device according to a first embodiment of the present invention, and as shown in fig. 1, the maintenance early warning method for a card reader device according to the embodiment of the present invention includes:
s101, acquiring state data of card reader equipment, wherein the state data comprises a plurality of first state parameters;
specifically, when the card reader device is used, the real-time recording data of the card reader device may be sent to a server, and the server may acquire the state data of the card reader device according to the real-time recording data of the card reader device. The card reader equipment can comprise at least one of a magnetic stripe type reading and writing module, a contact type IC card reading and writing module, a radio frequency IC card reading and writing module and an identity card reading module. The status data may include a plurality of first status parameters, and the first status parameters may include parameters such as the number of times of card swiping success of the magnetic stripe, the total number of times of card swiping of the magnetic stripe, the number of times of card swiping success of the contact IC card, the total number of times of card swiping of the contact IC card, the response time of card swiping of each time of the radio frequency IC card, the number of times of success of reading the identification card, the total number of times of reading the identification card, the response time of reading each time of the identification card, and the like. The real-time recording data is real-time data generated when the card reader device works, such as response time of card swiping, whether card swiping succeeds, reading time of an identity card, whether the identity card succeeds, the working temperature of the whole machine, the working voltage of the whole machine and the like, and is set according to actual needs, and the embodiment of the invention is not limited. The executing body of the maintenance early warning method of the card reader device provided by the embodiment of the invention comprises but is not limited to a server.
S102, obtaining at least one early warning parameter according to each first state parameter;
specifically, after the server obtains that the state data includes a plurality of first state parameters, at least one early warning parameter may be obtained according to each first state parameter. The early warning parameters may be a card swiping success rate of the magnetic stripe card, a card swiping success rate of the contact IC card, an average response time of card swiping of the radio frequency IC card, a reading success rate of the identity card, and an average response time of reading the identity card, and are set according to actual situations, which is not limited in the embodiments of the present invention.
S103, if the early warning parameters are judged to exceed the corresponding threshold value range, first early warning information is sent.
Specifically, after the server obtains the at least one early warning parameter, for each early warning parameter, the server may determine whether the early warning parameter exceeds a threshold range corresponding to the early warning parameter, and if the early warning parameter exceeds the corresponding threshold range, send first early warning information, and may notify relevant operation and maintenance personnel through an email or a short message, so as to maintain the card reader device corresponding to the early warning parameter in advance. Wherein the first warning information may indicate which module of the card reader device needs to be maintained.
According to the maintenance early warning method for the card reader equipment, provided by the embodiment of the invention, the state data of the card reader equipment can be obtained, at least one early warning parameter is obtained according to each first state parameter included in the state data, after the early warning parameter is judged and obtained to exceed the corresponding threshold range, the first early warning information is sent, maintenance early warning can be carried out when the card reader equipment does not meet the functional requirements, so that action is taken in advance to maintain the card reader equipment, passive maintenance is avoided when the card reader equipment breaks down, active maintenance of the card reader equipment is realized, and the reliability of the card reader equipment is improved.
On the basis of the above embodiments, further, the first state parameter includes the number of times that the magnetic stripe is successfully swiped in the card and the total number of times that the magnetic stripe is swiped in the card within a first preset time period; correspondingly, the obtaining at least one early warning parameter according to each first state parameter includes:
and if the total number of times of swiping the magnetic stripe card is larger than a first threshold value, calculating to obtain the card swiping success rate of the magnetic stripe card according to the successful number of times of swiping the magnetic stripe card and the total number of times of swiping the magnetic stripe card.
Specifically, when the card reader device includes the magnetic stripe type read-write module, the magnetic stripe card swiping record of the magnetic stripe type read-write module in use can be sent to the server in real time, the server can count the magnetic stripe card swiping success times of the magnetic stripe type read-write module in a first preset time period, obtain the magnetic stripe card swiping success times in the first preset time period, count the sum of the magnetic stripe card swiping times of the magnetic stripe type read-write module in the first preset time period, and obtain the magnetic stripe card swiping total times in the first preset time period. The first state parameters comprise the number of successful card swiping times of the magnetic stripe and the total number of card swiping times of the magnetic stripe in a first preset time period. The first preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited.
After obtaining the card swiping success times and the magnetic stripe card swiping total times, the server judges whether the magnetic stripe card swiping total times is larger than a first threshold value, if the magnetic stripe card swiping total times is larger than the first threshold value, the server divides the card swiping success times by the magnetic stripe card swiping total times, and the card swiping success rate of the magnetic stripe card can be calculated. If the total number of times of swiping the magnetic stripe card is less than or equal to the first threshold value, the number of times of swiping the magnetic stripe card by the magnetic stripe type read-write module is too small, and the card swiping success rate of the magnetic stripe card does not need to be calculated. The first threshold is set according to practical experience, for example, set to 100, and the embodiment of the present invention is not limited.
After the card swiping success rate of the magnetic stripe card is obtained through calculation, whether the card swiping success rate of the magnetic stripe card is larger than or equal to a first card swiping threshold value or not can be judged, if the card swiping success rate of the magnetic stripe card is larger than or equal to the first card swiping threshold value, the card swiping success rate of the magnetic stripe type read-write module meets the functional requirement, and maintenance and early warning are not needed. If the card swiping success rate of the magnetic stripe card is smaller than the first card swiping threshold, the card swiping success rate of the magnetic stripe card is beyond the corresponding threshold range, and first early warning information needs to be sent.
For example, the card swiping success rate C of the magnetic stripe card in the first preset time period1Assuming C is the number of successful magnetic stripe card swipes/total number of magnetic stripe card swipes1When the current value is less than or equal to 75 percent, the failure of the magnetic stripe type read-write module is represented; when it is 75%<C1<When 85%, the magnetic stripe type read-write module is in an unstable working state, and maintenance early warning prompt is required; when C is present1More than or equal to 85 percent, which means that the magnetic stripe type read-write module works normally. Setting the first card swiping threshold to be 85 percent at C1When the number of the read-write modules is less than 85%, first early warning information is sent, so that active maintenance is carried out when the magnetic stripe type read-write modules are in an unstable working state, and passive maintenance is not carried out when the magnetic stripe type read-write modules are in failure, so that the reliability of the magnetic stripe type read-write modules is improved, and the influence on business handling of customers is reduced.
On the basis of the above embodiments, further, the first state parameter includes the number of times of card swiping success of contacting the IC card and the total number of times of card swiping of contacting the IC card in a second preset time period, and/or the response time of each card swiping of contacting the IC card in a third preset time period; correspondingly, the obtaining at least one early warning parameter according to each first state parameter includes:
if the total card swiping times of the contact IC card are judged to be larger than a second threshold value, calculating the card swiping success rate of the contact IC card according to the card swiping success times of the contact IC card and the total card swiping times of the contact IC card; and/or
And calculating and obtaining the average response time of the card swiping of the contact IC card according to the response time of each card swiping of the contact IC card in the third preset time period.
Specifically, when the card reader device includes the contact IC card reading and writing module, the contact card reading record of the contact IC card reading and writing module during use and the response time of each card reading can be sent to the server in real time, and the server can count the card reading success times of the contact IC card reading and writing module in a second preset time period, obtain the card reading success times of the contact IC card in the second preset time period, count the sum of the card reading times of the contact IC card reading and writing module in the second preset time period, and obtain the total card reading times of the contact IC card in the second preset time period. The server can also acquire the response time of each card swiping when the IC card is contacted in a third preset time period. The first state parameter can comprise the card swiping success times of contacting the IC card and the total card swiping times of contacting the IC card in a second preset time period, and/or the card swiping response time of contacting the IC card in a third preset time period. The second preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited. The third preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited.
After obtaining the card swiping success times of the contact IC card and the card swiping total times of the contact IC card, the server judges whether the card swiping total times of the contact IC card is larger than a second threshold value or not, if the card swiping total times of the contact IC card is larger than the second threshold value, the server divides the card swiping success times of the contact IC card by the card swiping total times of the contact IC card, and the card swiping success rate of the contact IC card can be calculated. If the total card swiping times of the contact IC card are less than or equal to the second threshold value, the card swiping times of the contact IC card read-write module are too few, and the card swiping success rate of the contact IC card does not need to be calculated. The second threshold is set according to practical experience, for example, set to 100, and the embodiment of the present invention is not limited.
After the card swiping success rate of the contact IC card is obtained through calculation, whether the card swiping success rate of the contact IC card is larger than or equal to a second card swiping threshold value or not can be judged, if the card swiping success rate of the contact IC card is larger than or equal to the second card swiping threshold value, the card swiping success rate of the contact IC card read-write module meets the functional requirement, and maintenance and early warning are not needed. If the card swiping success rate of the magnetic stripe card contacting the IC card is smaller than the second card swiping threshold, the card swiping success rate of the magnetic stripe card contacting the IC card is beyond the corresponding threshold range, and first early warning information needs to be sent. The second card swiping threshold is set according to practical experience, and the embodiment of the invention is not limited.
After obtaining the response time of each card swiping of the IC card in the third preset time period, the server can calculate the average value of the response time of each card swiping of the IC card in the third preset time period, and obtain the average response time of the card swiping of the IC card. The server can compare the average response time of the card swiping of the contact IC card with the first response time, and if the average response time of the card swiping of the contact IC card is greater than or equal to the first response time, it indicates that the average response time of the card swiping of the contact IC card exceeds the corresponding threshold range, and first early warning information needs to be sent. If the average response time of the card swiping of the contact IC card is shorter than the first response time, the response time of the contact IC card reading and writing module meets the functional requirement, and maintenance early warning is not needed. The first response time is set according to practical experience, and the embodiment of the present invention is not limited.
The maintenance early warning of the contact type IC card read-write module can be set according to actual needs by adopting the card swiping success rate of the contact type IC card and/or the average response time of card swiping of the contact type IC card, and the embodiment of the invention is not limited.
For example, the card-swiping success rate C of contacting the IC card in the second preset time period2Assuming that C is the number of successful card swipes for contacting the IC card/the total number of card swipes for contacting the IC card2When the current value is less than or equal to 80%, the contact type IC card read-write module is in failure; when the content is 80 percent<C2<When 90%, the contact type IC card read-write module is in an unstable working state, and maintenance early warning prompt is required; when C is present2More than or equal to 90 percent, which means that the contact type IC card read-write module works normally. Setting the second card swiping threshold to be 90 percent at C2And sending first early warning information just less than 90%, so that active maintenance is carried out when the contact type IC card read-write module is in an unstable working state, instead of passive maintenance after the contact type IC card read-write module is in a fault, the reliability of the contact type IC card read-write module is improved, and the influence on the handling of customer services is reduced.
For example, the average response time of the card swiping of the contact IC card is calculated to be t1Suppose when t is1<500 milliseconds, representing that the contact type IC card read-write module works normally; when the time is less than or equal to 500 milliseconds and less than or equal to t1Less than or equal to 1000 milliseconds, the contact type IC card read-write module is in an unstable working state, and maintenance early warning prompt is required; when t is1>1000 milliseconds, representing that the contact IC card read-write module has a fault. Setting the first response time to 500 milliseconds at t1The first early warning information is sent just when the time is more than or equal to 500 milliseconds, so that active maintenance is carried out when the contact type IC card read-write module is in an unstable working state, instead of passive maintenance after the contact type IC card read-write module is in a fault, the reliability of the contact type IC card read-write module is improved, and the influence on the business handling of customers is reduced.
On the basis of the above embodiments, further, the first state parameter includes a response time of each card swiping of the radio frequency IC card in a fourth preset time period; correspondingly, the obtaining at least one early warning parameter according to each first state parameter includes:
and calculating to obtain the average response time of the radio frequency IC card swiping according to the response time of the radio frequency IC card swiping each time in the fourth preset time period.
Specifically, when the card reader device includes the radio frequency IC card read-write module, the response time of each card swiping of the radio frequency IC card read-write module in use can be sent to the server in real time, and the server can obtain the response time of each card swiping of the radio frequency IC card in a fourth preset time period. The first state parameter may include a response time of each card swiping of the radio frequency IC card within a fourth preset time period. The fourth preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited.
After obtaining the response time of each card swiping of the radio frequency IC card in the fourth preset time period, the server may calculate an average value of the response time of each card swiping of the radio frequency IC card in the fourth preset time period, so as to obtain the average response time of the card swiping of the radio frequency IC card. The server can compare the average response time of the radio frequency IC card swiping with a second response time, and if the average response time of the radio frequency IC card swiping is greater than or equal to the second response time, it indicates that the average response time of the radio frequency IC card swiping exceeds a corresponding threshold range, and first early warning information needs to be sent. And if the average response time of the radio frequency IC card swiping is less than the second response time, the response time of the radio frequency IC card reading and writing module meets the functional requirement, and maintenance early warning is not needed. The second response time is set according to practical experience, and the embodiment of the present invention is not limited.
For example, the average response time of the card swiping of the contact IC card is calculated to be t2Suppose when t is2<280 milliseconds, representing that the radio frequency IC card read-write module works normally; when the time is less than or equal to 280 milliseconds and less than or equal to t2Less than or equal to 360 milliseconds, the radio frequency IC card read-write module is in an unstable working state, and maintenance early warning prompt is required; when t is2>And 360 milliseconds, which represents that the radio frequency IC card read-write module has a fault. Setting the second response time to 280 milliseconds at t2The first early warning information is sent just when the time is more than or equal to 280 milliseconds, so that the radio frequency IC card read-write module is actively maintained in an unstable working state, and the radio frequency IC card read-write module is not passively maintained after the radio frequency IC card read-write module is in a faultAnd the reliability of the radio frequency IC card read-write module is improved, and the influence on the business handling of the client is reduced.
On the basis of the foregoing embodiments, further, the first state parameter includes the number of successful id card readings and the total number of id card readings in a fifth preset time period, and/or the response time of each id card reading in a sixth preset time period; correspondingly, the obtaining at least one early warning parameter according to each first state parameter includes:
if the total reading times of the identity card are judged to be larger than a third threshold value, calculating to obtain the reading success rate of the identity card according to the successful reading times of the identity card and the total reading times of the identity card; and/or
And calculating to obtain the average response time of reading the identity card according to the response time of reading the identity card each time in the sixth preset time period.
Specifically, when the card reader device includes the identity card reading module, the card reading record of the identity card reading module during use and the response time for reading the identity card each time can be sent to the server in real time, the server can count the number of times of success of reading the identity card by the identity card reading module in a fifth preset time period, obtain the number of times of success of reading the identity card in the fifth preset time period, count the sum of the number of times of reading the identity card by the identity card reading module in the fifth preset time period, and obtain the total number of times of reading the identity card in the fifth preset time period. The server can also acquire the response time of each reading of the identity card in a sixth preset time period. The first state parameter may include the number of successful id card readings and the total number of id card readings in a fifth preset time period, and/or the response time of each id card reading in a sixth preset time period. The fifth preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited. The sixth preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited.
After obtaining the number of successful reading times of the identity card and the total number of reading times of the identity card, the server judges whether the total number of reading times of the identity card is greater than a third threshold value, and if the total number of reading times of the identity card is greater than the third threshold value, the server divides the total number of reading times of the identity card by the number of successful reading times of the identity card, so that the success rate of reading the identity card can be calculated. If the total number of times of reading the identity card is less than or equal to the third threshold, the number of times of reading the identity card reading module is too small, and the success rate of reading the identity card does not need to be calculated. The third threshold is set according to practical experience, for example, set to 100, and the embodiment of the present invention is not limited.
After the reading success rate of the identity card is obtained through calculation, whether the reading success rate of the identity card is larger than or equal to a third card swiping threshold value or not can be judged, if the reading success rate of the identity card is larger than or equal to the third card swiping threshold value, the card swiping success rate of the identity card reading module meets the functional requirements, and maintenance and early warning are not needed. If the reading success rate of the identity card is smaller than the third card swiping threshold, the fact that the reading success rate of the identity card exceeds the corresponding threshold range is indicated, and first early warning information needs to be sent. The third card swiping threshold is set according to practical experience, and the embodiment of the invention is not limited.
After obtaining the response time of each reading of the identity card in the sixth preset time period, the server may calculate an average value of the response time of each reading of the identity card in the sixth preset time period, and obtain the average response time of the reading of the identity card. The server may compare the average response time read by the identity card with a third response time, and if the average response time read by the identity card is greater than or equal to the third response time, it indicates that the average response time read by the identity card exceeds a corresponding threshold range, and first warning information needs to be sent. And if the average response time of reading the identity card is less than the third response time, the response time of the identity card reading module meets the functional requirement, and the maintenance and early warning are not needed. The third response time is set according to practical experience, and the embodiment of the present invention is not limited.
For the maintenance and early warning of the identity card reading module, the reading success rate of the identity card and/or the average response time of the identity card reading can be adopted and set according to actual needs, and the embodiment of the invention is not limited.
For example, the success rate C of reading the identity card in the fifth preset time period3Assume C is the number of successful id card reads/total id card reads3When the content is less than or equal to 50%, representing that the identity card reading module has a fault; when the content is 50 percent<C3<When 85%, representing that the identity card reading module is in an unstable working state, and needing maintenance early warning prompt; when C is present3More than or equal to 85 percent, which represents that the identity card reading module works normally. Setting the third card swiping threshold to 85 percent at C3The first early warning information is sent just less than 85%, so that active maintenance is carried out when the identity card reading module is in an unstable working state, instead of passive maintenance after the identity card reading module is in a fault, the reliability of the identity card reading module is improved, and the influence on the business handling of a client is reduced.
For example, the average response time of the obtained ID card reading is calculated as t3Suppose when t is3<1200 milliseconds, which represents that the identity card reading module works normally; when 1200 milliseconds is less than or equal to t3The time is less than or equal to 1700 milliseconds, which represents that the identity card reading module is in an unstable working state and needs maintenance early warning prompt; when t is3>1700 milliseconds, representing a failure of the identification card reading module. Setting the third response time to 1200 milliseconds at t3The first early warning information is sent just when the time is more than or equal to 1200 milliseconds, so that active maintenance is carried out when the identity card reading module is in an unstable working state, instead of passive maintenance after the identity card reading module is in a fault, the reliability of the identity card reading module is improved, and the influence on the business handling of a client is reduced.
It will be appreciated that the statistical time period may be set shorter, for example 1 day, for a busy banking site where the card reader device is used frequently, and longer, for example 3 or 5 days, for a less busy banking site where the card reader device is used less frequently. The statistical time period here refers to a first preset time period, a second preset time period, a third preset time period, a fourth preset time period, a fifth preset time period and a sixth preset time period.
Fig. 2 is a schematic flow chart of a maintenance warning method for a card reader device according to a second embodiment of the present invention, and as shown in fig. 2, on the basis of the foregoing embodiments, further, the maintenance warning method for a card reader device according to the embodiment of the present invention further includes:
s201, obtaining prediction data of each card reading module of the card reader equipment;
specifically, by preprocessing the historical record data of the past seventh preset time period of each card reading module of the card reader device, the prediction data of each card reading module can be obtained, and the server can obtain the prediction data of each card reading module of the card reader device. The card reading module can be any one of a magnetic stripe type reading and writing module, a contact type IC card reading and writing module, a radio frequency IC card reading and writing module and an identity card reading module, and the card reader equipment comprises at least one card reading module. The historical record data of each card reading module may include device data, periodic data, salary and social security data, holiday data, usage times statistical data, contemporaneous usage times data, current usage times data, average usage times data, cycle usage times data, and the like, and is set according to actual needs, which is not limited in the embodiments of the present invention. The seventh preset time period is, for example, 3 months, and is set according to actual needs, which is not limited in the embodiment of the present invention.
For example, historical data of each card reading module of the card reader device can be acquired by an internal system of a bank or a web crawler. The device data may include information about the location of the home, the home network site, the number of card reader devices in the network site, the usage window (for public or private), etc. The periodic data may include month, beginning/middle/end of month, number of days of the month, week number of the month, day of the week, etc. information. The salary and social security data may include information such as whether it is a salary day, whether it is salary for the first N days, whether it is salary for the next N days, whether it is a social security day, whether it is social security for the first N days, whether it is social security for the next N days, etc. The holiday data includes information on whether it is a holiday, whether several days in the previous N days are holidays, and whether several days in the next N days are holidays. The usage statistics may include information such as a maximum of the number of uses on the first N days, a minimum of the number of uses in the first N days, an average of the number of uses on the first N days, a median of the number of uses on the first N days, and a variance of the number of uses on the first N days. The data of the number of usage times in the same period can comprise the number of usage times in the same day in the last week, the number of usage times in the same day in the last month, the number of usage times in the same period in the last year and the like. The past usage data may include yesterday usage, previous day usage, previous three days usage, previous four days usage, and the like. The average usage data may include information such as average usage of previous week, average usage of previous month, average usage of same week of previous month, etc. The ring ratio usage times data may include information such as a day-to-ring ratio for the first N days, a week-to-ring ratio for the first N days, a month-to-ring ratio for the first N days, and a year-to-ring ratio for the first N days. In order to predict the predicted use times of the card reading module in the next day, the value range of N is greater than or equal to 1 and less than or equal to 7, and the value range is set according to the actual situation, which is not limited in the embodiment of the invention.
For example, preprocessing the history data for each card reading module may include correcting inconsistencies in the history data by filling in missing values, smoothing noise, and identifying outliers. When filling missing values, filling by adopting a mean value and a median; when the noise data and the outliers are processed, the noise data are determined through the upper edge and the lower edge of a box line graph, the outliers are detected through a clustering algorithm, then the noise data and the outliers are labeled according to specific business experience, and finally the average values of the previous period and the later period are calculated to repair the noise data and the outliers.
The preprocessing of the history data of each card reading module may comprise integrating the history data. Because the data acquired by the system has various sources, and attributes representing the same concept may have different names or units in different data sources, which may cause data inconsistency and redundancy, in the embodiment of the present invention, a method of correlation analysis may be adopted to integrate the data.
Preprocessing the history data of each card reading module can include performing a specification on the history data of each card reading module. Simplified representation of data can be obtained through reduction technology, the occupied space of the simplified data becomes smaller, but approximately the same analysis result can be generated, and the data processing efficiency of the maintenance early warning method of the card reader equipment can be improved.
The preprocessing of the historical record data of each card reading module can comprise data transformation of the historical record data of each card reading module, and the historical record data of each card reading module is more suitable for data mining through the data transformation. For example, the conversion of the geographical position information classifies the geographical position information, and the same category uses the same number to represent, so that the text data is converted into discrete numerical data.
S202, performing characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module;
specifically, after obtaining the prediction data of each card reading module, the server may perform feature processing on the prediction data of each card reading module to obtain the prediction feature data of each card reading module.
For example, characterizing the prediction data for each card reading module may include feature construction. The feature construction is used for constructing different types of features from the prediction data of each card reading module, the device features can be constructed based on the device data, the period features can be constructed based on the periodic data, the salary starting social security features can be constructed based on the salary sending and social security data, the holiday saving features can be constructed based on the holiday data, the use times statistical features can be constructed based on the use times statistical data, the use times in the same period can be constructed based on the use times in the same period, the use times in the later period can be constructed based on the use times in the later period, the average use features can be constructed based on the average use times data, and the use times in the ring ratio can be constructed based on the use times in the ring ratio.
S203, obtaining the predicted using times of each card reading module according to the predicted characteristic data of each card reading module and the using time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module;
specifically, after obtaining the predicted feature data of each card reading module, the server may input the predicted feature data of each card reading module into the usage number prediction model of each card reading module, and output the predicted usage number of each card reading module through the processing of the usage number prediction model of each card reading module. The use number prediction model of each card reading module comprises a set number of prediction submodels, and the set number of prediction submodels are obtained according to the use number training data and the historical use number training of each card reading module. The set number is set according to actual conditions, and the embodiment of the invention is not limited.
S204, obtaining the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module;
specifically, after obtaining the predicted number of usage times of each card reading module, the server may further obtain the current accumulated number of usage times of each card reading module, where each card reading module is recorded during usage, and the current accumulated number of usage times of each card reading module may be obtained through statistics. For each card reading module, the server calculates the sum of the predicted use times and the current accumulated use times of the card reading module, and the sum result is used as the predicted accumulated use times of the card reading module. The current accumulated use times of the card reading module refers to the total use times of the card reading module from the beginning of use to the time of use prediction.
And S205, if the predicted accumulated use times of the card reading module are judged to be larger than the corresponding use threshold, sending second early warning information.
Specifically, after obtaining the predicted accumulated use times of each card reading module, the server compares the predicted accumulated use times of the card reading module with the use threshold corresponding to the card reading module for each card reading module, and if the predicted accumulated use times of the card reading module is greater than the use threshold corresponding to the card reading module, it is indicated that the card reading module is about to malfunction, the server may send second warning information, so that a worker can perform operation and maintenance preparation work. And if the predicted accumulated use times of the card reading module is less than or equal to the use threshold corresponding to the card reading module, the card reading module can still be used without maintenance. The use threshold corresponding to each card reading module is set according to actual conditions, and the embodiment of the invention is not limited. It can be understood that the predicted accumulated use times of any card reading module of the card reader device is greater than the corresponding use threshold, and maintenance early warning information can be sent out, wherein the maintenance early warning information can indicate the card reading module which needs to be maintained.
For example, the card reading module is a magnetic stripe type read-write module, which is expected to be able to be refreshed 2000 times, that is, the usage threshold corresponding to the magnetic stripe type read-write module is 2000 times, if the server obtains that the predicted usage number of the next magnetic stripe type read-write module is 300 times and obtains that the current accumulated usage number of the magnetic stripe type read-write module is 1800 times, the server calculates that the predicted accumulated usage number of the magnetic stripe type read-write module is 1800+300 times to 2100 times, because the predicted accumulated usage number 2100 of the magnetic stripe type read-write module is greater than the usage threshold 2000 corresponding to the magnetic stripe type read-write module, the server may send maintenance warning information, for example, to notify a worker in a manner of a mobile phone short message, and the magnetic stripe type read-write module may reach the usage.
The maintenance early warning method of the card reader equipment provided by the embodiment of the invention can acquire the prediction data of each card reading module of the card reader equipment, perform characteristic processing on the prediction data of each card reading module to acquire the prediction characteristic data of each card reading module, acquire the prediction using times of each card reading module according to the prediction characteristic data of each card reading module and the using time prediction model of each card reading module, acquire the prediction accumulated using times of each card reading module according to the prediction using times and the current accumulated using times of each card reading module, send second early warning information after judging that the prediction accumulated using times of the card reading module is larger than the corresponding using threshold value, perform maintenance early warning before the service life of the card reading module expires, take action in advance to maintain or replace the card reading module, and realize active maintenance on the card reader equipment, the reliability of the card reader device is further improved.
According to the maintenance early warning method for the card reader equipment, on one hand, when the functions of the card reader equipment do not meet the requirements, the first early warning information is sent, on the other hand, the second early warning information is sent before the service life of the card reading module of the card reader equipment expires, the maintenance early warning can be carried out on the card reader equipment more comprehensively, and the comprehensiveness and the accuracy of the maintenance early warning on the card reader equipment are improved.
Fig. 3 is a schematic flow chart of a maintenance early warning method for a card reader device according to a third embodiment of the present invention, and as shown in fig. 3, on the basis of the foregoing embodiments, further, the obtaining the predicted number of usage times of each card reading module according to the predicted feature data of each card reading module and the usage time prediction model of each card reading module includes:
s2031, obtaining the number of use times of the card reading module according to the predicted characteristic data of the card reading module and the set number of prediction submodels;
specifically, the server inputs the predicted feature data of the card reading module into each prediction submodel, and may output the number of times of use of the card reading module in each prediction submodel, where the number of the prediction submodels is set, and the server may obtain the number of times of use of the card reading module in the set number.
S2032, calculating the predicted using times of the card reading module according to the set number of using times of the card reading module and the weight corresponding to each using time; and the weight corresponding to each use frequency of the card reading module is obtained in advance.
Specifically, after obtaining the set number of usage times of the card reading module, the server calculates the predicted usage times of the card reading module according to the set number of usage times of the card reading module and the weight corresponding to each usage time. The weight corresponding to each usage number is obtained in advance, for example, set in advance or generated in advance.
For example, the card reading module is an identity card reading module, and the server obtains n usage times of the identity card reading module according to a formula
Figure BDA0002554327810000141
Calculating and obtaining the predicted use times P of the identity card reading module, wherein QiFor the ith number of uses of the ID card reading module, kiIs QiCorresponding weights, i is a positive integer and i is less than n.
The weight corresponding to each use number of each card reading module can be generated in advance according to the number of the predictor models and the weight vector generation algorithm. A first step, given N and H, where N is the number of predictor models, i.e. a set number, 1/H represents the granularity of weight change, and the set M ═ 1,1,1,. 1,1, and the set M contains H1 in total; secondly, dividing 1 in the set M into N groups by using an interpolation method, and obtaining a common matrix by a permutation and combination idea
Figure BDA0002554327810000142
A seed distribution mode; thirdly, adding 1 of each group in each distribution mode and dividing by H to obtain the total
Figure BDA0002554327810000143
A set of evenly distributed weight vectors.
For example, in the first step, assume that N-2 and H-3, i.e., the number of predictor models is 2, and the granularity of weight change is 3. And secondly, obtaining 4 allocation modes of zero 1, three 1, two 1, and zero 1 according to a null insertion method in the arrangement idea. Thirdly, 1 in each allocation mode is added to obtain { {0,3}, {1,2}, {2,1}, {3,0} }, and a weight vector set obtained by dividing H ═ 3 respectively is as follows: { {0,1}, {1/3,2/3}, {2/3,1/3}, {1,0} }, four sets of weights.
After the vector set of weights is obtained, the effect of each predictor model on each group of weight vectors in the weight vector set can be evaluated according to the verification set of the card reading module and the historical use times corresponding to the verification set, and an optimal group of weights is selected. Firstly, obtaining the use times corresponding to each predictor model according to the verification set and each predictor model; then, calculating the combined use times of the card reading module under each group of weights according to the use times corresponding to each predictor model and each group of weights; and finally, calculating the RMSE value of the combined use times and the historical use times corresponding to each group of weights by using an RMSE (standard error) method, namely calculating the sum of the root mean square error of the combined use times and the historical use times of the card reading module corresponding to the verification set for each group of weights, wherein the group of weights with the minimum RMSE value is the obtained optimal weight vector. Wherein, the acquisition process of the verification set of the card reading module is described below.
Fig. 4 is a schematic flow chart of a maintenance early warning method for a card reader device according to a fourth embodiment of the present invention, and as shown in fig. 4, on the basis of the foregoing embodiments, further obtaining a usage number prediction model of each card reader module according to the usage number training data and the historical usage number training of each card reader module includes:
s401, acquiring use frequency training data and historical use frequency of the card reading module;
specifically, the use number training data of each card reading module of the card reader device can be obtained by preprocessing the historical record data of the past eighth preset time period of each card reading module. The historical use times can be the use times of each card reading module in the past eighth preset time period each day, and are set according to actual needs, and the embodiment of the invention is not limited. The server can obtain the use times training data and the historical use times of the card reading module. The eighth preset time period is, for example, 3 years, and is set according to actual needs, which is not limited in the embodiment of the present invention. Understandably, if the historical use times are the use times of each day, the use time prediction model of the card reading module obtained by training is used for predicting the use times of the next day of the card reading module; if the historical usage is every three days, then training the obtained usage prediction model of the card-reading module predicts the usage of the card-reading module for the next three days.
S402, obtaining use time characteristic data of the card reading module according to the use time training data of the card reading module;
specifically, after obtaining the use number training data of the card reading module, the server may perform feature processing on the use number training data of the card reading module to obtain the use number feature data of the card reading module.
For example, different types of features are constructed from the use time training data of the card reading module, the device features can be constructed based on the device data, the cycle features can be constructed based on the periodic data, the departure salary social security features can be constructed based on the salary and social security data, the holiday features can be constructed based on the holiday data, the use time statistical features can be constructed based on the use time statistical data, the contemporaneous use features can be constructed based on the contemporaneous use time data, the current use features can be constructed based on the current use time data, the average use features can be constructed based on the average use time data, and the ring ratio use features can be constructed based on the ring ratio use time data.
S403, dividing the use frequency characteristic data of the card reading module into a training set and a verification set;
specifically, after obtaining the feature data of the number of times of use of the card reading module, the server may divide the feature data of the number of times of use of the card reading module into a training set and a verification set, where the training set is used for model training and the verification set is used for model verification.
For example, the training set accounts for 80% of the usage times characteristic data of the card reading module, the verification set accounts for 20% of the usage times characteristic data of the card reading module, and the training set and the verification set do not overlap.
S404, training to obtain a preset number of to-be-determined prediction sub-models according to the training set, the historical use times corresponding to the training set and a preset number of preset models; wherein the preset number is greater than or equal to the set number;
specifically, the server performs model training on each preset model according to the training set and the historical use times corresponding to the training set to obtain each to-be-determined prediction sub-model. The number of the to-be-determined predictor models is preset, and the preset to-be-determined predictor models can be obtained. Wherein the preset number is greater than or equal to the set number. The preset number is set according to actual needs, and the embodiment of the invention is not limited. The preset model includes, but is not limited to, a support vector machine regression algorithm, a K-nearest neighbor regression algorithm, a random forest regression algorithm, a GBDT regression algorithm, an xgboost regression algorithm, a Long Short-Term Memory network (LSTM) algorithm, etc., and is set according to actual needs, which is not limited in the embodiments of the present invention. The LSTM algorithm is a neural network algorithm, and the model can be trained in a back-propagation manner. It can be understood that, in the training process of each preset model, the hyper-parameter can be automatically adjusted and optimized until a parameter which enables the model prediction effect to be globally optimal or locally optimal is obtained, in the hyper-parameter adjusting and optimizing process, the hyper-parameter which enables the model prediction effect to be optimal is dynamically searched based on the preset parameter value range and value change step length, the used hyper-parameter search algorithm includes grid search (GridSearchCV) and random search (randomized searchcv), and the hyper-parameter search algorithm is set according to actual needs, and the embodiment of the invention is not limited.
S405, verifying the preset number of to-be-determined prediction submodels according to the verification set and the historical use times corresponding to the verification set to obtain an R square value of each to-be-determined prediction submodel;
specifically, after the server obtains the preset number of undetermined predictor models, for each undetermined predictor model, the verification set is input into the undetermined predictor model, and the estimated use times corresponding to each feature data in the verification set are output. The server may calculate an R-squared (R-squared) value of the to-be-determined predictor model according to the estimated number of uses corresponding to each feature data in the verification set and the historical number of uses corresponding to each feature data in the verification set. The server may obtain R-squared values of the preset number of to-be-determined predictor models. The larger the R square value is, the better the using frequency prediction effect of the undetermined predictor model is.
S406, selecting the set number of predictor models from the preset number of undetermined predictor models according to the R square value of each undetermined predictor model.
Specifically, after obtaining the R square values of the predetermined number of undetermined predictor models, the server may sort the R square values of the predetermined number of undetermined predictor models according to the magnitude of the R square values, and if the absolute value of the difference between the R square values of all two adjacent undetermined predictor models is smaller than a predetermined value, it indicates that the R square values of the predetermined number of undetermined predictor models are not greatly different, and may take the predetermined number of undetermined predictor models as the set number of predictor models. The preset value is set according to actual experience, and the embodiment of the invention is not limited.
If the absolute value of the difference value of the R square values of the two adjacent to-be-determined predictor models is larger than or equal to the preset value in the difference values of the R square values of the two adjacent to-be-determined predictor models, the difference of the R square values of the preset number of to-be-determined predictor models is large, the to-be-determined predictor models with the largest set number of R square values can be used as the set number of sales predictor models, and the set number is larger than or equal to 4.
The undetermined predictor model with the R square value meeting the requirement is selected from the preset number of undetermined predictor models to serve as the predictor model for predicting the use times, and the accuracy of the use time prediction of the card reading module can be improved.
Fig. 5 is a schematic flow chart of a maintenance early warning method for a card reader device according to a fifth embodiment of the present invention, and as shown in fig. 5, on the basis of the foregoing embodiments, further, the obtaining the usage number feature data of the card reader module according to the usage number training data of the card reader module includes:
s4021, performing feature construction on the use times training data of the card reading module to obtain multiple types of training feature data;
specifically, after obtaining the use number training data of the card reading module, the server may perform feature construction on the use number training data of the card reading module to obtain multiple types of training feature data. The multi-class training feature data may include a device self feature, a period feature, a salary social security feature, a holiday feature, a usage frequency statistic feature, a contemporaneous usage feature, a past usage feature, an average usage feature, a cyclic usage feature, and the like, and may be set according to actual needs, which is not limited in the embodiment of the present invention.
S4022, selecting the use frequency characteristic data of the card reading module from the multi-class training characteristic data according to a characteristic selection algorithm.
Specifically, after obtaining the multiple types of training feature data, the server may select the feature data of the number of uses of the card reading module from the multiple types of training feature data by using a feature selection algorithm. The feature selection algorithm includes, but is not limited to, directional search, optimal priority search, sequence forward selection, sequence backward selection, sequence floating selection, and the like, and is set according to an actual situation, which is not limited in the embodiments of the present invention. And selecting the use frequency characteristic data of the card reading module from the multi-class training characteristic data through a characteristic selection algorithm, so that the use frequency of the card reading module can be more accurately predicted. It is understood that the usage characteristic data includes at least a usage statistic.
It can be understood that after the maintenance early warning method for the card reader device provided by the embodiment of the invention is put into use, the actual use times of each card reading module can be collected, when a certain number of the actual use times and the predicted use times of the card reading modules are collected, the R square value of the use time prediction model of the card reading module can be calculated according to the actual use times and the predicted use times of the card reading modules, and the use time prediction model of the card reading module is evaluated through the R square value. And when the R square value of the use time prediction model of the card reading module is smaller than a set value, retraining to obtain the use time prediction model of the card reading module. The certain number and the set value are set according to actual experience, and the embodiment of the invention is not limited.
On the basis of the above embodiments, further, the state data further includes a second state parameter, where the second state parameter includes a complete machine operating temperature and/or a complete machine operating voltage;
and if the working temperature of the whole machine is judged to be larger than the temperature threshold value and/or if the working voltage of the whole machine is judged to be larger than the normal voltage range, sending third early warning information.
Specifically, the state data further includes a second state parameter, and the second state parameter may include at least one of a complete machine operating temperature and a complete machine operating voltage. The working temperature of the whole machine is the real-time working temperature of the card reader equipment and can be acquired through the temperature sensor. The working voltage of the whole machine is the real-time working voltage of the card reader equipment and can be acquired through a Hall voltage sensor.
And after obtaining the working temperature of the whole machine, the server compares the working temperature of the whole machine with a temperature threshold, and if the working temperature of the whole machine is greater than or equal to the temperature threshold, third early warning information is sent, and related operation and maintenance personnel can be informed through mails or short messages so as to maintain the card reader equipment in advance. The third early warning information may indicate that the maintenance early warning is caused by an excessively high working temperature of the whole machine. The temperature threshold is set according to actual needs, and the embodiment of the invention is not limited.
And after obtaining the working voltage of the whole machine, the server judges whether the working voltage of the whole machine exceeds a normal voltage range, and if the working voltage of the whole machine exceeds the normal voltage range, third early warning information is sent, and related operation and maintenance personnel can be informed through mails or short messages so as to maintain the card reader equipment. The third early warning information may indicate that the maintenance early warning is caused by the over-range of the working voltage of the whole machine. The normal voltage range is set according to actual needs, and the embodiment of the invention is not limited.
For example, the working temperature of the whole machine is T, the normal working temperature of the card reader equipment is 0-40 ℃, the temperature of the card reader equipment gradually rises after the card reader equipment starts to work, the temperature rise of 10 ℃ is assumed to be normal work after the card reader equipment starts to work, namely, when the temperature T is more than or equal to 0 ℃ and less than or equal to 50 ℃, the card reader equipment normally works; when the temperature is 50 ℃ and T is less than 60 ℃, the card reader equipment is in an unstable working state, and maintenance early warning is needed; when T is more than or equal to 60 ℃, the card reader equipment is in failure. The temperature threshold is set to be 50 ℃, and third early warning information is sent when T is just higher than 50 ℃, so that active maintenance is carried out when the card reader equipment is in an unstable working state, instead of passive maintenance after the card reader equipment is in a fault, the reliability of the card reader equipment is improved, and the influence on the business handling of customers is reduced.
For example, the operating voltage of the whole device is U, and the normal operating voltage range of the card reader device is as follows: 4.5-5.5V, and when the U exceeds the normal voltage range, the state of the card reader equipment is abnormal, and maintenance early warning prompt is required.
Fig. 6 is a schematic structural diagram of a maintenance early-warning device for a card reader device according to a sixth embodiment of the present invention, and as shown in fig. 6, the maintenance early-warning device for a card reader device according to the embodiment of the present invention includes a state data obtaining unit 601, an early-warning parameter obtaining unit 602, and a first early-warning determining unit 603, where:
the state data acquiring unit 601 is configured to acquire state data of the card reader device, where the state data includes a plurality of first state parameters; the early warning parameter obtaining unit 602 is configured to obtain at least one early warning parameter according to each first state parameter; the first warning judgment unit 603 is configured to send first warning information after judging that the warning parameter exceeds the corresponding threshold range.
Specifically, when the card reader device is used, the real-time recording data of the card reader device may be sent to the status data obtaining unit 601, and the status data obtaining unit 601 may obtain the status data of the card reader device according to the real-time recording data of the card reader device. The card reader equipment can comprise at least one of a magnetic stripe type reading and writing module, a contact type IC card reading and writing module, a radio frequency IC card reading and writing module and an identity card reading module. The status data may include a plurality of first status parameters, and the first status parameters may include parameters such as the number of times of card swiping success of the magnetic stripe, the total number of times of card swiping of the magnetic stripe, the number of times of card swiping success of the contact IC card, the total number of times of card swiping of the contact IC card, the response time of card swiping of each time of the radio frequency IC card, the number of times of success of reading the identification card, the total number of times of reading the identification card, the response time of reading each time of the identification card, and the like. The real-time recording data is real-time data generated when the card reader device works, such as response time of card swiping, whether card swiping succeeds, reading time of an identity card, whether the identity card succeeds, the working temperature of the whole machine, the working voltage of the whole machine and the like, and is set according to actual needs, and the embodiment of the invention is not limited.
After obtaining that the state data includes a plurality of first state parameters, the early warning parameter obtaining unit 602 may obtain at least one early warning parameter according to each first state parameter. The early warning parameters may be a card swiping success rate of the magnetic stripe card, a card swiping success rate of the contact IC card, an average response time of card swiping of the radio frequency IC card, a reading success rate of the identity card, and an average response time of reading the identity card, and are set according to actual situations, which is not limited in the embodiments of the present invention.
After the at least one early warning parameter is obtained, for each early warning parameter, the first early warning determining unit 603 may determine whether the early warning parameter exceeds a threshold range corresponding to the early warning parameter, and if the early warning parameter exceeds the corresponding threshold range, send first early warning information, and may notify relevant operation and maintenance personnel through an email or a short message, so as to maintain the card reader device corresponding to the early warning parameter in advance. Wherein the first warning information may indicate which module of the card reader device needs to be maintained.
The maintenance early warning device for the card reader equipment provided by the embodiment of the invention can acquire the state data of the card reader equipment, acquire at least one early warning parameter according to each first state parameter included in the state data, send the first early warning information after judging that the early warning parameter exceeds the corresponding threshold range, perform maintenance early warning when the card reader equipment does not meet the functional requirements, take action in advance to maintain the card reader equipment, avoid performing passive maintenance after the card reader equipment fails, realize active maintenance on the card reader equipment, and improve the reliability of the card reader equipment.
On the basis of the above embodiments, further, the first state parameter includes the number of times that the magnetic stripe is successfully swiped in the card and the total number of times that the magnetic stripe is swiped in the card within a first preset time period; correspondingly, the early warning parameter obtaining unit 602 is specifically configured to:
and after judging that the total number of times of swiping the magnetic stripe card is larger than a first threshold value, calculating to obtain the card swiping success rate of the magnetic stripe card according to the number of times of swiping the magnetic stripe card and the total number of times of swiping the magnetic stripe card.
Specifically, when the card reader device includes the magnetic stripe type read-write module, the magnetic stripe card swiping record of the magnetic stripe type read-write module in use can be sent to the status data obtaining unit 601 in real time, the status data obtaining unit 601 can count the number of times of successful card swiping of the magnetic stripe type read-write module in a first preset time period, obtain the number of times of successful card swiping of the magnetic stripe in the first preset time period, count the sum of the number of times of card swiping of the magnetic stripe type read-write module in the first preset time period, and obtain the total number of times of card swiping of the magnetic stripe in the first preset time period. The first state parameters comprise the number of successful card swiping times of the magnetic stripe and the total number of card swiping times of the magnetic stripe in a first preset time period. The first preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited.
After obtaining the card swiping success times and the magnetic stripe card swiping total times, the early warning parameter obtaining unit 602 may determine whether the magnetic stripe card swiping total times is greater than a first threshold, and if the magnetic stripe card swiping total times is greater than the first threshold, the early warning parameter obtaining unit 602 divides the card swiping success times by the magnetic stripe card swiping total times, so as to calculate and obtain the card swiping success rate of the magnetic stripe card. If the total number of times of swiping the magnetic stripe card is less than or equal to the first threshold value, the number of times of swiping the magnetic stripe card by the magnetic stripe type read-write module is too small, and the card swiping success rate of the magnetic stripe card does not need to be calculated. The first threshold is set according to practical experience, for example, set to 100, and the embodiment of the present invention is not limited.
On the basis of the above embodiments, further, the first state parameter includes the number of times of card swiping success of contacting the IC card and the total number of times of card swiping of contacting the IC card in a second preset time period, and/or the response time of each card swiping of contacting the IC card in a third preset time period; correspondingly, the early warning parameter obtaining unit 602 is specifically configured to:
after judging that the total card swiping times of the contact IC card are larger than a second threshold value, calculating the card swiping success rate of the contact IC card according to the card swiping success times of the contact IC card and the total card swiping times of the contact IC card; and/or
And calculating and obtaining the average response time of the card swiping of the contact IC card according to the response time of each card swiping of the contact IC card in the third preset time period.
Specifically, when the card reader device includes the contact IC card reading and writing module, the contact card reading record of the contact IC card reading and writing module during use and the response time of each card reading can be sent to the status data obtaining unit 601 in real time, the status data obtaining unit 601 can count the number of times of card reading success of the contact IC card reading and writing module in a second preset time period, obtain the number of times of card reading success of the contact IC card in the second preset time period, count the sum of the number of times of card reading of the contact IC card in the second preset time period, and obtain the total number of times of card reading of the contact IC card in the second preset time period. The state data acquisition unit 601 may also acquire a response time per card swipe of the contact IC card within a third preset time period. The first state parameter can comprise the card swiping success times of contacting the IC card and the total card swiping times of contacting the IC card in a second preset time period, and/or the card swiping response time of contacting the IC card in a third preset time period. The second preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited. The third preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited.
After obtaining the number of times of successful card swiping of the contact IC card and the total number of times of card swiping of the contact IC card, the warning parameter obtaining unit 602 may determine whether the total number of times of card swiping of the contact IC card is greater than a second threshold, and if the total number of times of card swiping of the contact IC card is greater than the second threshold, the warning parameter obtaining unit 602 may calculate the card swiping success rate of the contact IC card by dividing the number of times of successful card swiping of the contact IC card by the total number of times of card swiping of the contact IC card. If the total card swiping times of the contact IC card are less than or equal to the second threshold value, the card swiping times of the contact IC card read-write module are too few, and the card swiping success rate of the contact IC card does not need to be calculated. The second threshold is set according to practical experience, for example, set to 100, and the embodiment of the present invention is not limited. After obtaining the response time of each time the IC card is contacted within the third preset time period, the warning parameter obtaining unit 602 may calculate an average value of the response time of each time the IC card is contacted within the third preset time period, and obtain the average response time of the card swiping of the IC card.
On the basis of the above embodiments, further, the first state parameter includes a response time of each card swiping of the radio frequency IC card in a fourth preset time period; correspondingly, the early warning parameter obtaining unit 602 is specifically configured to:
and calculating to obtain the average response time of the radio frequency IC card swiping according to the response time of the radio frequency IC card swiping each time in the fourth preset time period.
Specifically, when the card reader device includes the radio frequency IC card read-write module, the response time of each card swiping of the radio frequency IC card read-write module during use may be sent to the status data obtaining unit 601 in real time, and the status data obtaining unit 601 may obtain the response time of each card swiping of the radio frequency IC card in a fourth preset time period. The first state parameter may include a response time of each card swiping of the radio frequency IC card within a fourth preset time period. The fourth preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited.
After obtaining the response time of each card swiping of the radio frequency IC card in the fourth preset time period, the warning parameter obtaining unit 602 may calculate an average value of the response time of each card swiping of the radio frequency IC card in the fourth preset time period, so as to obtain the average response time of the card swiping of the radio frequency IC card.
On the basis of the foregoing embodiments, further, the first state parameter includes the number of successful id card readings and the total number of id card readings in a fifth preset time period, and/or the response time of each id card reading in a sixth preset time period; correspondingly, the early warning parameter obtaining unit 602 is specifically configured to:
after the total reading times of the identity card are judged and known to be larger than a third threshold value, calculating the reading success rate of the identity card according to the successful reading times of the identity card and the total reading times of the identity card; and/or
And calculating to obtain the average response time of reading the identity card according to the response time of reading the identity card each time in the sixth preset time period.
Specifically, when the card reader device includes the identity card reading module, the identity card swiping record of the identity card reading module in use and the response time of reading the identity card each time can be sent to the state data obtaining unit 601 in real time, the state data obtaining unit 601 can count the number of times of success of reading the identity card in the fifth preset time period by the identity card reading module, obtain the number of times of success of reading the identity card in the fifth preset time period, count the sum of the number of times of reading the identity card in the fifth preset time period by the identity card reading module, and obtain the total number of times of reading the identity card in the fifth preset time period. The status data acquisition unit 601 may further acquire the response time of each reading of the identity card within a sixth preset time period. The first state parameter may include the number of successful id card readings and the total number of id card readings in a fifth preset time period, and/or the response time of each id card reading in a sixth preset time period. The fifth preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited. The sixth preset time period is set according to actual needs, for example, set to 1 day, and the embodiment of the present invention is not limited.
After obtaining the number of successful id card readings and the total number of id card readings, the early warning parameter obtaining unit 602 may determine whether the total number of id card readings is greater than a third threshold, and if the total number of id card readings is greater than the third threshold, the early warning parameter obtaining unit 602 divides the number of successful id card readings by the total number of id card readings, so as to calculate the reading success rate of the id card. If the total number of times of reading the identity card is less than or equal to the third threshold, the number of times of reading the identity card reading module is too small, and the success rate of reading the identity card does not need to be calculated. The third threshold is set according to practical experience, for example, set to 100, and the embodiment of the present invention is not limited. After obtaining the response time of each reading of the identity card in the sixth preset time period, the early warning parameter obtaining unit 602 may calculate an average value of the response time of each reading of the identity card in the sixth preset time period, and obtain an average response time of the reading of the identity card.
Fig. 7 is a schematic structural diagram of a maintenance early-warning device for a card reader device according to a seventh embodiment of the present invention, and as shown in fig. 7, on the basis of the foregoing embodiments, further, the maintenance early-warning device for a card reader device according to the embodiment of the present invention further includes a predicted data obtaining unit 604, a feature processing unit 605, a predicting unit 606, a calculating unit 607, and a second early-warning judging unit 608, where:
the predicted data obtaining unit 604 is configured to obtain predicted data of each card reading module of the card reader device; the feature processing unit 605 is configured to perform feature processing on the prediction data of each card reading module to obtain prediction feature data of each card reading module; the prediction unit 606 is configured to obtain the predicted number of times of use of each card reading module according to the predicted feature data of each card reading module and the usage number prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module; the calculating unit 607 is configured to calculate the predicted cumulative number of times of use of each card reading module according to the predicted number of times of use of each card reading module and the current cumulative number of times of use; the second warning judgment unit 608 is configured to send second warning information after judging that the predicted cumulative number of times of use of the card reading module is greater than the corresponding use threshold.
Specifically, by preprocessing the historical data of the past seventh preset time period of each card reading module of the card reader device, the prediction data of each card reading module can be obtained, and the prediction data obtaining unit 604 can obtain the prediction data of each card reading module of the card reader device. The card reading module can be any one of a magnetic stripe type reading and writing module, a contact type IC card reading and writing module, a radio frequency IC card reading and writing module and an identity card reading module, and the card reader equipment comprises at least one card reading module. The historical record data of each card reading module may include device data, periodic data, salary and social security data, holiday data, usage times statistical data, contemporaneous usage times data, current usage times data, average usage times data, cycle usage times data, and the like, and is set according to actual needs, which is not limited in the embodiments of the present invention. The seventh preset time period is, for example, 3 months, and is set according to actual needs, which is not limited in the embodiment of the present invention.
After obtaining the predicted data of each card reading module, the feature processing unit 605 may perform feature processing on the predicted data of each card reading module to obtain the predicted feature data of each card reading module.
After obtaining the predicted feature data of each card reading module, the prediction unit 606 may input the predicted feature data of each card reading module into the usage number prediction model of each card reading module, and output the predicted usage number of each card reading module through the processing of the usage number prediction model of each card reading module. The use number prediction model of each card reading module comprises a set number of prediction submodels, and the set number of prediction submodels are obtained according to the use number training data and the historical use number training of each card reading module. The set number is set according to actual conditions, and the embodiment of the invention is not limited.
After obtaining the predicted usage times of each card reading module, the calculating unit 607 may further obtain the current accumulated usage times of each card reading module, where each card reading module is recorded during usage, and may obtain the current accumulated usage times of each card reading module through statistics. For each card reading module, the calculation unit 607 calculates the sum of the predicted usage number of the card reading module and the current accumulated usage number, and takes the sum as the predicted accumulated usage number of the card reading module. The current accumulated use times of the card reading module refers to the total use times of the card reading module from the beginning of use to the time of use prediction.
After the predicted accumulated usage times of each card reading module are obtained, for each card reading module, the second warning judgment unit 608 compares the predicted accumulated usage times of the card reading module with the usage threshold corresponding to the card reading module, and if the predicted accumulated usage times of the card reading module is greater than the usage threshold corresponding to the card reading module, it indicates that the card reading module is about to fail, the second warning judgment unit 608 may send second warning information, so that a worker may perform operation and maintenance preparation work. And if the predicted accumulated use times of the card reading module is less than or equal to the use threshold corresponding to the card reading module, the card reading module can still be used without maintenance. The use threshold corresponding to each card reading module is set according to actual conditions, and the embodiment of the invention is not limited. It can be understood that the predicted accumulated use times of any card reading module of the card reader device is greater than the corresponding use threshold, and maintenance early warning information can be sent out, wherein the maintenance early warning information can indicate the card reading module which needs to be maintained.
The maintenance early warning device of the card reader equipment provided by the embodiment of the invention can acquire the prediction data of each card reading module of the card reader equipment, perform characteristic processing on the prediction data of each card reading module to acquire the prediction characteristic data of each card reading module, acquire the prediction using times of each card reading module according to the prediction characteristic data of each card reading module and the using time prediction model of each card reading module, acquire the prediction accumulated using times of each card reading module according to the prediction using times and the current accumulated using times of each card reading module, send second early warning information after judging that the prediction accumulated using times of the card reading module is larger than the corresponding using threshold value, perform maintenance early warning before the service life of the card reading module expires, take action in advance to maintain or replace the card reading module, and realize active maintenance on the card reader equipment, the reliability of the card reader device is further improved.
Fig. 8 is a schematic structural diagram of a maintenance early warning apparatus for a card reader device according to an eighth embodiment of the present invention, and as shown in fig. 8, on the basis of the foregoing embodiments, further, the prediction unit 606 includes a prediction sub-unit 6061 and a calculation sub-unit 6062, where:
the predictor 6061 is configured to obtain a set number of times of use of the card reading module according to the predicted feature data of the card reading module and the set number of predictor models; the calculation subunit 6062 is configured to calculate the predicted number of times of use of the card reading module according to the set number of times of use of the card reading module and the weight corresponding to each number of times of use; and the weight corresponding to each use frequency of the card reading module is obtained in advance.
Specifically, the prediction subunit 6061 inputs the predicted feature data of the card reading module to each prediction submodel, and may output the number of times of use of the card reading module in each prediction submodel, where the number of the prediction submodels is set, and then the server may obtain the number of times of use of the card reading module set.
After the set number of usage times of the card reading module is obtained, the calculation subunit 6062 calculates the predicted usage times of the card reading module according to the set number of usage times of the card reading module and the weight corresponding to each usage time. The weight corresponding to each usage number is obtained in advance, for example, set in advance or generated in advance.
Fig. 9 is a schematic structural diagram of a maintenance early-warning apparatus for a card reader device according to a ninth embodiment of the present invention, and as shown in fig. 9, on the basis of the foregoing embodiments, further, the maintenance early-warning apparatus for a card reader device according to the embodiment of the present invention further includes a training data obtaining unit 609, a feature data obtaining unit 610, a dividing unit 611, a training unit 612, a verification unit 613, and a selection unit 614, where:
the training data acquisition unit 609 is configured to acquire use number training data and historical use number of the card reading module; the characteristic data obtaining unit 610 is configured to obtain the usage time characteristic data of the card reading module according to the usage time training data of the card reading module; the dividing unit 611 is configured to divide the feature data of the number of times of use of the card reading module into a training set and a verification set; the training unit 612 is configured to train to obtain a preset number of to-be-predicted sub-models according to the training set, the historical usage times corresponding to the training set, and a preset number of preset models; wherein the preset number is greater than or equal to the set number; the verifying unit 613 is configured to verify the predetermined number of to-be-determined predictor models according to the verification set and the historical usage times corresponding to the verification set, and obtain an R square value of each to-be-determined predictor model; the selecting unit 614 is configured to select the set number of predictor models from the preset number of undetermined predictor models according to the R-square value of each undetermined predictor model.
Specifically, the use number training data of each card reading module of the card reader device can be obtained by preprocessing the historical record data of the past eighth preset time period of each card reading module. The historical use times can be the use times of each card reading module in the past eighth preset time period each day, and are set according to actual needs, and the embodiment of the invention is not limited. The training data obtaining unit 609 may obtain the number of use training data of the card reading module and the historical number of use. The eighth preset time period is, for example, 3 years, and is set according to actual needs, which is not limited in the embodiment of the present invention. Understandably, if the historical use times are the use times of each day, the use time prediction model of the card reading module obtained by training is used for predicting the use times of the next day of the card reading module; if the historical usage is every three days, then training the obtained usage prediction model of the card-reading module predicts the usage of the card-reading module for the next three days.
After obtaining the use number training data of the card reading module, the feature data obtaining unit 610 may perform feature processing on the use number training data of the card reading module to obtain the use number feature data of the card reading module.
After obtaining the usage time characteristic data of the card reading module, the dividing unit 611 may divide the usage time characteristic data of the card reading module into a training set and a verification set, where the training set is used for model training and the verification set is used for model verification.
The training unit 612 performs model training on each preset model according to the training set and the historical use times corresponding to the training set to obtain each to-be-determined predictor model. The number of the to-be-determined predictor models is preset, and the preset to-be-determined predictor models can be obtained. Wherein the preset number is greater than or equal to the set number. The preset number is set according to actual needs, and the embodiment of the invention is not limited. The preset model includes, but is not limited to, a support vector machine regression algorithm, a K-nearest neighbor regression algorithm, a random forest regression algorithm, a GBDT regression algorithm, an xgboost regression algorithm, an LSTM algorithm, etc., and is set according to actual needs, which is not limited in the embodiments of the present invention. The LSTM algorithm is a neural network algorithm, and the model can be trained in a back-propagation manner. It can be understood that, during the training process of each preset model, the hyper-parameter can be automatically adjusted and optimized until the parameters which enable the model prediction effect to be globally optimal or locally optimal are obtained, during the hyper-parameter adjustment and optimization process, the hyper-parameters which enable the model prediction effect to be optimal are dynamically searched based on the preset parameter value range and the value change step length, the used hyper-parameter search algorithm is not limited to grid search, random search and the like, and the setting is carried out according to the actual needs, and the embodiment of the invention is not limited.
After obtaining the predetermined number of undetermined predictor models, for each undetermined predictor model, the verifying unit 613 inputs the verification set into the undetermined predictor model, and outputs the estimated number of times of use corresponding to each feature data in the verification set. The verifying unit 613 may calculate an R-squared (R-squared) value of the to-be-determined predictor model according to the estimated number of times of use corresponding to each feature data in the verification set and the historical number of times of use corresponding to each feature data in the verification set. The server may obtain R-squared values of the preset number of to-be-determined predictor models. The larger the R square value is, the better the using frequency prediction effect of the undetermined predictor model is.
After obtaining the R square values of the predetermined number of undetermined predictor models, the selecting unit 614 may sort the R square values of the predetermined number of undetermined predictor models according to the magnitude of the R square values, and if the absolute value of the difference between the R square values of all two adjacent undetermined predictor models is smaller than a predetermined value, it indicates that the R square values of the predetermined number of undetermined predictor models are not greatly different, and may take the predetermined number of undetermined predictor models as the predetermined number of predictor models. The preset value is set according to actual experience, and the embodiment of the invention is not limited.
If the absolute value of the difference value of the R square values of the two adjacent to-be-determined predictor models is larger than or equal to the preset value in the difference values of the R square values of the two adjacent to-be-determined predictor models, the difference of the R square values of the preset number of to-be-determined predictor models is large, the to-be-determined predictor models with the largest set number of R square values can be used as the set number of sales predictor models, and the set number is larger than or equal to 4.
Fig. 10 is a schematic structural diagram of a maintenance early warning apparatus for a card reader device according to a tenth embodiment of the present invention, as shown in fig. 10, on the basis of the foregoing embodiments, further, the feature data obtaining unit 610 includes a feature constructing subunit 6101 and a selecting subunit 6102, where:
the feature construction subunit 6101 is configured to perform feature construction on the use times training data of the card reading module, so as to obtain multiple types of training feature data; the selecting subunit 6102 is configured to select the feature data of the number of times of use of the card reading module from the multiple types of training feature data according to a feature selection algorithm.
Specifically, after obtaining the training data of the number of times of use of the card reading module, the feature construction subunit 6101 may perform feature construction on the training data of the number of times of use of the card reading module, so as to obtain multiple types of training feature data. The multi-class training feature data may include a device self feature, a period feature, a salary social security feature, a holiday feature, a usage frequency statistic feature, a contemporaneous usage feature, a past usage feature, an average usage feature, a cyclic usage feature, and the like, and may be set according to actual needs, which is not limited in the embodiment of the present invention.
After obtaining the multiple types of training feature data, the selecting subunit 6102 may select the usage times feature data of the card reading module from the multiple types of training feature data by using a feature selection algorithm. The feature selection algorithm includes, but is not limited to, directional search, optimal priority search, sequence forward selection, sequence backward selection, sequence floating selection, and the like, and is set according to an actual situation, which is not limited in the embodiments of the present invention. And selecting the use frequency characteristic data of the card reading module from the multi-class training characteristic data through a characteristic selection algorithm, so that the use frequency of the card reading module can be more accurately predicted. It is understood that the usage characteristic data includes at least a usage statistic.
Fig. 11 is a schematic structural diagram of a maintenance warning device for a card reader device according to an eleventh embodiment of the present invention, and as shown in fig. 11, the maintenance warning device for a card reader device according to the embodiment of the present invention further includes a third warning judgment unit 615, where:
the state data also comprises a second state parameter, and the second state parameter comprises the working temperature and/or the working voltage of the whole machine; the third warning judgment unit 615 is configured to send third warning information after judging that the operating temperature of the whole machine is greater than the temperature threshold and/or if judging that the operating voltage of the whole machine is greater than or equal to the normal voltage range.
Specifically, the state data further includes a second state parameter, and the second state parameter may include at least one of a complete machine operating temperature and a complete machine operating voltage. The working temperature of the whole machine is the real-time working temperature of the card reader equipment and can be acquired through the temperature sensor. The working voltage of the whole machine is the real-time working voltage of the card reader equipment and can be acquired through a Hall voltage sensor.
After the working temperature of the whole machine is obtained, the third warning judgment unit 615 compares the working temperature of the whole machine with a temperature threshold, and if the working temperature of the whole machine is greater than or equal to the temperature threshold, third warning information is sent, and relevant operation and maintenance personnel can be informed through mails or short messages, so that the card reader equipment can be maintained in advance. The third early warning information may indicate that the maintenance early warning is caused by an excessively high working temperature of the whole machine. The temperature threshold is set according to actual needs, and the embodiment of the invention is not limited.
After the working voltage of the whole machine is obtained, the third warning judgment unit 615 judges whether the working voltage of the whole machine exceeds a normal voltage range, and if the working voltage of the whole machine exceeds the normal voltage range, third warning information is sent, and related operation and maintenance personnel can be informed through mails or short messages so as to maintain the card reader equipment. The third early warning information may indicate that the maintenance early warning is caused by the over-range of the working voltage of the whole machine. The normal voltage range is set according to actual needs, and the embodiment of the invention is not limited.
The embodiment of the apparatus provided in the embodiment of the present invention may be specifically configured to execute the processing flows of the above method embodiments, and the functions of the apparatus are not described herein again, and refer to the detailed description of the above method embodiments.
Fig. 12 is a schematic physical structure diagram of an electronic device according to a twelfth embodiment of the present invention, and as shown in fig. 12, the electronic device may include: a processor (processor)1201, a communication Interface (Communications Interface)1202, a memory (memory)1203 and a communication bus 1204, wherein the processor 1201, the communication Interface 1202 and the memory 1203 communicate with each other through the communication bus 1204. The processor 1201 may call logic instructions in the memory 1203 to perform the following method: acquiring state data of card reader equipment, wherein the state data comprises a plurality of first state parameters; obtaining at least one early warning parameter according to each first state parameter; and if the early warning parameters are judged to exceed the corresponding threshold value range, first early warning information is sent.
In addition, the logic instructions in the memory 1203 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments, for example, comprising: acquiring state data of card reader equipment, wherein the state data comprises a plurality of first state parameters; obtaining at least one early warning parameter according to each first state parameter; and if the early warning parameters are judged to exceed the corresponding threshold value range, first early warning information is sent.
The present embodiment provides a computer-readable storage medium, which stores a computer program, where the computer program causes the computer to execute the method provided by the above method embodiments, for example, the method includes: acquiring state data of card reader equipment, wherein the state data comprises a plurality of first state parameters; obtaining at least one early warning parameter according to each first state parameter; and if the early warning parameters are judged to exceed the corresponding threshold value range, first early warning information is sent.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the description herein, reference to the description of the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," "an example," "a particular example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (22)

1. A maintenance early warning method for card reader equipment is characterized by comprising the following steps:
acquiring state data of card reader equipment, wherein the state data comprises a plurality of first state parameters;
obtaining at least one early warning parameter according to each first state parameter;
and if the early warning parameters are judged to exceed the corresponding threshold value range, first early warning information is sent.
2. The method of claim 1, wherein the first status parameter comprises a number of successful magnetic stripe swipes and a total number of magnetic stripe swipes within a first preset time period; correspondingly, the obtaining at least one early warning parameter according to each first state parameter includes:
and if the total number of times of swiping the magnetic stripe card is larger than a first threshold value, calculating to obtain the card swiping success rate of the magnetic stripe card according to the successful number of times of swiping the magnetic stripe card and the total number of times of swiping the magnetic stripe card.
3. The method according to claim 1, wherein the first status parameter comprises the number of successful card swiping times of contacting the IC card and the total card swiping times of contacting the IC card in a second preset time period, and/or the response time of each card swiping time of contacting the IC card in a third preset time period; correspondingly, the obtaining at least one early warning parameter according to each first state parameter includes:
if the total card swiping times of the contact IC card are judged and obtained to be larger than a second threshold value, calculating the card swiping success rate of the contact IC card according to the card swiping success times of the contact IC card and the total card swiping times of the contact IC card; and/or
And calculating and obtaining the average response time of the card swiping of the contact IC card according to the response time of each card swiping of the contact IC card in the third preset time period.
4. The method according to claim 1, wherein the first state parameter comprises a response time of each card swiping of the radio frequency IC card in a fourth preset time period; correspondingly, the obtaining at least one early warning parameter according to each first state parameter includes:
and calculating to obtain the average response time of the radio frequency IC card swiping according to the response time of the radio frequency IC card swiping each time in the fourth preset time period.
5. The method according to claim 1, wherein the first status parameter comprises the number of successful id card readings and the total number of id card readings in a fifth preset time period, and/or the response time of each id card reading in a sixth preset time period; correspondingly, the obtaining at least one early warning parameter according to each first state parameter includes:
if the total reading times of the identity card are judged to be larger than a third threshold value, calculating the reading success rate of the identity card according to the successful reading times of the identity card and the total reading times of the identity card; and/or
And calculating to obtain the average response time of reading the identity card according to the response time of reading the identity card each time in the sixth preset time period.
6. The method of claim 1, further comprising:
acquiring prediction data of each card reading module of the card reader equipment;
performing characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module;
obtaining the predicted use times of each card reading module according to the predicted characteristic data of each card reading module and the use time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module;
calculating the predicted accumulative use times of each card reading module according to the predicted use times and the current accumulative use times of each card reading module;
and if the predicted accumulated use times of the card reading module are judged to be larger than the corresponding use threshold, sending second early warning information.
7. The method of claim 6, wherein obtaining the predicted number of uses for each card reading module based on the predicted characteristic data for each card reading module and the predicted number of uses for each card reading module comprises:
obtaining the use times of the card reading module with the set number according to the predicted characteristic data of the card reading module and the prediction submodels with the set number;
calculating the predicted use times of the card reading module according to the set number of use times of the card reading module and the weight corresponding to each use time; and the weight corresponding to each use frequency of the card reading module is obtained in advance.
8. The method of claim 6, wherein obtaining the usage prediction model for each card reading module based on the usage training data for each card reading module and the historical usage training comprises:
acquiring use times training data and historical use times of the card reading module;
obtaining the use times characteristic data of the card reading module according to the use times training data of the card reading module;
dividing the use frequency characteristic data of the card reading module into a training set and a verification set;
training to obtain a preset number of to-be-determined prediction submodels according to the training set, the historical use times corresponding to the training set and a preset number of preset models; wherein the preset number is greater than or equal to the set number;
verifying the preset number of to-be-determined predictor models according to the verification set and the historical use times corresponding to the verification set to obtain an R square value of each to-be-determined predictor model;
and selecting the set number of predictor models from the preset number of undetermined predictor models according to the R square value of each undetermined predictor model.
9. The method according to claim 8, wherein the obtaining the usage number characteristic data of the card reading module according to the usage number training data of the card reading module comprises:
performing feature construction on the use times training data of the card reading module to obtain various types of training feature data;
and selecting the use frequency characteristic data of the card reading module from the multi-class training characteristic data according to a characteristic selection algorithm.
10. The method of any one of claims 1 to 9, wherein the status data further comprises a second status parameter, the second status parameter comprising a machine operating temperature and/or a machine operating voltage;
and if the working temperature of the whole machine is judged to be larger than the temperature threshold value and/or if the working voltage of the whole machine is judged to be larger than the normal voltage range, sending third early warning information.
11. A maintenance early warning device of a card reader device, comprising:
the card reader comprises a state data acquisition unit, a state data acquisition unit and a control unit, wherein the state data acquisition unit is used for acquiring state data of the card reader equipment, and the state data comprises a plurality of first state parameters;
the early warning parameter obtaining unit is used for obtaining at least one early warning parameter according to each first state parameter;
and the first early warning judgment unit is used for sending first early warning information after judging that the early warning parameters exceed the corresponding threshold value range.
12. The apparatus of claim 11, wherein the first status parameter comprises a number of successful magnetic stripe swipes and a total number of magnetic stripe swipes within a first preset time period; correspondingly, the early warning parameter obtaining unit is specifically configured to:
and after judging that the total number of times of swiping the magnetic stripe card is larger than a first threshold value, calculating to obtain the card swiping success rate of the magnetic stripe card according to the number of times of swiping the magnetic stripe card and the total number of times of swiping the magnetic stripe card.
13. The device according to claim 11, wherein the first status parameter comprises the number of successful card swipes of contacting the IC card and the total number of card swipes of contacting the IC card in a second preset time period, and/or the response time of each card swipes of contacting the IC card in a third preset time period; correspondingly, the early warning parameter obtaining unit is specifically configured to:
after judging that the total card swiping times of the contact IC card are larger than a second threshold value, calculating the card swiping success rate of the contact IC card according to the card swiping success times of the contact IC card and the total card swiping times of the contact IC card; and/or
And calculating and obtaining the average response time of the card swiping of the contact IC card according to the response time of each card swiping of the contact IC card in the third preset time period.
14. The apparatus according to claim 11, wherein the first status parameter comprises a response time of each card swiping of the radio frequency IC card within a fourth preset time period; correspondingly, the early warning parameter obtaining unit is specifically configured to:
and calculating to obtain the average response time of the radio frequency IC card swiping according to the response time of the radio frequency IC card swiping each time in the fourth preset time period.
15. The apparatus according to claim 11, wherein the first status parameter includes the number of successful id card readings and the total number of id card readings in a fifth preset time period, and/or the response time of each id card reading in a sixth preset time period; correspondingly, the early warning parameter obtaining unit is specifically configured to:
after the total reading times of the identity card are judged and known to be larger than a third threshold value, calculating the reading success rate of the identity card according to the successful reading times of the identity card and the total reading times of the identity card; and/or
And calculating to obtain the average response time of reading the identity card according to the response time of reading the identity card each time in the sixth preset time period.
16. The apparatus of claim 11, further comprising:
a prediction data acquisition unit for acquiring prediction data of each card reading module of the card reader device;
the characteristic processing unit is used for carrying out characteristic processing on the predicted data of each card reading module to obtain the predicted characteristic data of each card reading module;
the prediction unit is used for obtaining the predicted using times of each card reading module according to the predicted characteristic data of each card reading module and the using time prediction model of each card reading module; the use frequency prediction model of each card reading module comprises a set number of prediction submodels, wherein the set number of prediction submodels are obtained according to use frequency training data and historical use frequency training of each card reading module;
the calculating unit is used for calculating the predicted accumulated use times of each card reading module according to the predicted use times and the current accumulated use times of each card reading module;
and the second early warning judgment unit is used for sending second early warning information after judging that the predicted accumulated use times of the card reading module is larger than the corresponding use threshold value.
17. The apparatus of claim 16, wherein the prediction unit comprises:
the prediction subunit is used for obtaining the use times of the card reading module with the set number according to the prediction characteristic data of the card reading module and the prediction submodels with the set number;
the calculation subunit is used for calculating the predicted use times of the card reading module according to the set number of use times of the card reading module and the weight corresponding to each use time; and the weight corresponding to each use frequency of the card reading module is obtained in advance.
18. The apparatus of claim 16, further comprising:
the training data acquisition unit is used for acquiring the use times training data and the historical use times of the card reading module;
the characteristic data obtaining unit is used for obtaining the use time characteristic data of the card reading module according to the use time training data of the card reading module;
the dividing unit is used for dividing the use frequency characteristic data of the card reading module into a training set and a verification set;
the training unit is used for training to obtain a preset number of to-be-predicted submodels according to the training set, the historical use times corresponding to the training set and a preset number of preset models; wherein the preset number is greater than or equal to the set number;
the verification unit is used for verifying the preset number of to-be-determined prediction submodels according to the verification set and the historical using times corresponding to the verification set to obtain an R square value of each to-be-determined prediction submodel;
and the selection unit is used for selecting the set number of the predictor models from the preset number of the undetermined predictor models according to the R square value of each undetermined predictor model.
19. The apparatus of claim 18, wherein the feature data obtaining unit comprises:
the characteristic construction subunit is used for carrying out characteristic construction on the use times training data of the card reading module to obtain various types of training characteristic data;
and the selecting subunit is used for selecting the use frequency characteristic data of the card reading module from the multiple types of training characteristic data according to a characteristic selection algorithm.
20. The device according to any one of claims 11 to 19, further comprising a third early warning judgment unit, wherein the status data further comprises a second status parameter, and the second status parameter comprises a complete machine working temperature and/or a complete machine working voltage;
and the third early warning judgment unit is used for sending third early warning information after judging that the working temperature of the whole machine is larger than a temperature threshold value and/or if judging that the working voltage of the whole machine is larger than a normal voltage range.
21. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 10 are implemented by the processor when executing the computer program.
22. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 10.
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