CN115893142A - Elevator maintenance-on-demand management system and method based on Internet of things and big data - Google Patents
Elevator maintenance-on-demand management system and method based on Internet of things and big data Download PDFInfo
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- CN115893142A CN115893142A CN202211607836.9A CN202211607836A CN115893142A CN 115893142 A CN115893142 A CN 115893142A CN 202211607836 A CN202211607836 A CN 202211607836A CN 115893142 A CN115893142 A CN 115893142A
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Abstract
The invention relates to an elevator maintenance-on-demand management system and method based on the Internet of things and big data, wherein the management system comprises a basic information module, an elevator front-end unit, a fault detection module, a data statistics module and a period calculation module; the basic information module is used for storing basic information of the elevator; the data statistics module is used for carrying out statistics on the operation data and the failure times of the elevator; the period calculation module is used for creating a period measurement and calculation model of the maintenance of the elevator according to the need according to the basic information of the elevator and the data counted by the data counting module and calculating the period of the maintenance of the elevator according to the need. In the invention, the minimum period of the elevator maintenance as required can be calculated according to the basic information, the operation data and the fault condition of the elevator by establishing the period measuring and calculating model of the elevator maintenance as required, so that a maintenance unit and a using unit can be guided to make a more reasonable maintenance as required task plan.
Description
Technical Field
The invention belongs to the technical field of elevator maintenance, and relates to an elevator on-demand maintenance management system and method based on the Internet of things and big data.
Background
At present, the time constraint on elevator maintenance in China is divided into four categories, namely half month, quarter, half year, year and the like according to the sixth elevator maintenance rule (TSG-T5002-2017). The maintenance company maintains the elevator regularly in fifteen days as one period according to the signed maintenance contract, and the maintenance result is submitted to a user unit in the form of a maintenance order to be filed. The maintenance mode can theoretically realize comprehensive coverage inspection, eliminate hidden dangers, reduce risks and ensure normal operation of the elevator. However, with the increase of the number of elevators in use, the number of persons for maintaining and maintaining the elevator cannot keep up with the increase of the elevators due to various reasons, so that the man-machine ratio is increased continuously, and the problems of low quality, low efficiency, high risk, high danger and the like of the maintenance of the existing elevator are caused.
According to the stipulation of TSG T5002-2017 elevator maintenance rules, maintenance time and maintenance items have the most basic requirements. The method has positive effects on standardizing elevator maintenance behaviors and finding potential safety hazards in time, but the adoption of the same maintenance time and maintenance projects for elevators with different conditions is obviously not scientific enough, and the adoption of a maintenance mode of fixed time and projects for elevators with better equipment running conditions is waste. With the technical progress, the reliability of elevator parts is greatly improved, the waste of resources is caused by maintaining the existing requirements, and the maintenance is needed according to the actual working condition of the elevator.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problems to be solved by the invention are as follows: the elevator on-demand maintenance management system and method based on the Internet of things and big data are provided.
In order to achieve the purpose, the invention provides the following technical scheme:
the utility model provides an elevator maintenance as required management system based on thing networking and big data, includes:
the basic information module is used for storing basic information of the elevator;
the elevator preposition unit is used for acquiring the operation data of the elevator in real time and transmitting the operation data to the fault detection module and the data statistics module through the Internet of things;
the fault detection module is used for judging whether the elevator has faults or not and the specific fault type according to the operation data of the elevator;
the data statistics module is used for carrying out statistics on the operation data and the failure times of the elevator;
and the period calculation module is used for creating a period measurement and calculation model for maintaining the elevator according to the need and substituting the period for calculating the maintenance of the elevator according to the need according to the basic information of the elevator and the data counted by the data counting module.
Further, the algorithm formula of the periodic measurement model of the elevator maintenance as required is as follows:
T n ＝T Δ +T 1 ×(1-α)+T 2 ×(1-β)
wherein, T n Representing the maintenance period of the elevator calculated by the maintenance period model according to the requirement; t is Δ Representing an initial elevator maintenance period; t is a unit of 1 The initial influence value of the fatigue degree of the elevator on the maintenance period is represented; t is 2 Representing an initial influence value of the elevator fault rate on the maintenance period; alpha represents an elevator working fatigue coefficient, and the value of the alpha is determined according to two dimensions of the elevator running time and the mileage; beta represents the failure rate of the elevator after the last maintenance, and the value of beta is determined according to the failure times of the elevator and the risk coefficient of each failure type.
Further, the calculation formula of the elevator working fatigue coefficient alpha is as follows:
wherein D is 0 Representing the single average travel distance of the elevator; d Δ The average distance of the elevator during single cutting is represented, and is obtained by averaging the running distance of the elevator sampled within a specified time length and removing part of jump values, or is obtained by analyzing big data; s 0 Representing the average running time of the elevator in a single time; s. the Δ The average cutting time length of the elevator in one time is represented, and the average cutting time length is obtained by sampling the running time length of the elevator in the specified time length, removing part of jump values and then averaging the average cutting time length or is obtained by analyzing big data; f. of d () And f s () Are all linear functions, and the parameters are obtained by big data analysis.
Further, the calculation formula of the failure rate β of the elevator after the last maintenance is as follows:
wherein m represents the number of elevator fault categories; f. of v Representing the number of times of the fault occurrence of each type of elevator in a calculation period; e m Coefficient of influence of elevator faults of each category on elevator operation safety, E m The values of (a) were obtained by big data analysis.
Further, the elevator front unit includes:
a base layer sensor module for calibrating the operational data;
a speed sensor module for detecting the rising and falling speeds of the elevator;
the leveling sensor module is used for judging whether the elevator stops at a leveling level or not and the running state and direction of the elevator;
a door sensor module for sensing whether an elevator car door is closed;
the jitter sensor module is used for detecting whether the elevator shakes or not and the frequency and amplitude of the elevator shaking;
the human body sensor module is used for detecting whether a person stays in the elevator car; and
and the Internet of things module is used for transmitting data through the Internet of things.
Further, the method also comprises the following steps:
and the dispatching list module is used for dynamically generating a service list through big data analysis and sending the service list to an elevator maintenance worker when the fault detection module detects an elevator fault or reaches the detection date of the elevator maintenance cycle.
Further, the method also comprises the following steps:
the first response detection module is used for detecting whether the elevator maintenance personnel respond within the specified time;
the first automatic notification module is used for automatically notifying elevator use units and elevator maintenance unit responsible persons when elevator maintenance personnel do not respond within the specified time;
the second response detection module is used for detecting whether a responsible person of the elevator maintenance unit responds within a specified time after the first automatic notification module issues the notification; and
and the second automatic notification module is used for automatically notifying the elevator using unit and the elevator maintenance unit responsible person when the elevator maintenance unit responsible person does not respond within the specified time.
An elevator on-demand maintenance management method based on the Internet of things and big data adopts an elevator on-demand maintenance management system based on the Internet of things and the big data, and the management method comprises the following steps:
s1, collecting elevator operation data;
s2, calculating an elevator maintenance period through a period measuring and calculating model;
s3, generating a service work order and sending the service work order to an elevator maintenance worker when the elevator needs to be maintained;
s5, the elevator maintenance personnel maintain or repair the elevator according to the service work order;
and S6, counting the failure times of the elevator in a preset period, and returning to execute the step S2 when the failure times of the elevator reach or exceed the preset times.
Further, the method also comprises the following steps:
s4, detecting whether the elevator breaks down, and generating a service work order to send to an elevator maintenance worker when the elevator breaks down; and executing the step S5;
after the step S5 is finished, the following steps are also executed:
and S7, detecting whether the fault of the elevator is recovered after maintenance, if so, ending the service work order, and otherwise, informing an elevator maintenance worker to continue maintenance.
Further, after the service work order is sent to the elevator maintenance personnel, the following steps are also executed:
s501, detecting whether an elevator maintenance worker responds within a specified time, and if not, executing the step S502;
s502, automatically informing a person in charge of an elevator use unit and an elevator maintenance unit;
s503, detecting whether the responsible person of the elevator maintenance unit responds within a specified time, and if not, executing the step S504;
and S504, automatically informing an elevator management department.
According to the elevator maintenance-on-demand task planning method, the elevator maintenance-on-demand period measuring model is obtained through big data analysis, and the minimum period of the elevator maintenance-on-demand can be calculated by substituting the model according to basic information of the elevator and data such as the running time, the running distance and the failure times of the elevator, so that a maintenance unit and a using unit can be guided to make a more reasonable maintenance-on-demand task plan. In addition, a service work order can be dynamically generated through elevator real-time operation data when an elevator fails, and maintenance personnel can be ensured to deal with the elevator failure in time; aiming at the elevator which has faults repeatedly after processing, the maintenance period can be recalculated through the fault data so as to ensure the reasonability of the maintenance period. Therefore, the phenomenon that maintenance human resources cannot be reasonably utilized due to excessive maintenance can be avoided, the maintenance efficiency is effectively improved, the maintenance cost is reduced, and the equipment failure rate is reduced; the elevator maintenance new mode can be realized by landing on the ground according to needs.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a block diagram of a preferred embodiment of an elevator on-demand maintenance management system based on the internet of things and big data according to the present invention.
Fig. 2 is a flow chart of a preferred embodiment of the elevator on-demand maintenance management method based on the internet of things and big data.
Detailed Description
The embodiments of the invention are explained below by means of specific examples, the illustrations provided in the following examples merely illustrate the basic idea of the invention in a schematic manner, and the features in the following examples and examples can be combined with one another without conflict.
As shown in fig. 1, a preferred embodiment of the elevator on-demand maintenance management system based on the internet of things and big data of the present invention includes a basic information module, an elevator front-end unit, a fault detection module, a data statistics module, and a period calculation module.
The basic information module is used for storing basic information of the elevator; including the service life of the elevator, the floor number, the risk level, whether the original factory is maintained, and the like.
The elevator front unit is used for collecting operation data of the elevator in real time and transmitting the operation data to the fault detection module and the data statistics module through the Internet of things. The elevator front unit can comprise a basic sensor module, a speed sensor module, a flat bed sensor module, a door magnetic sensor module, a shake sensor module, a human body sensor module and an internet of things module. The base layer sensor module is used for calibrating operation data; the speed sensor module is used for detecting the ascending and descending speeds of the elevator; the leveling sensor module is used for judging whether the elevator stops at a leveling level or not and the running state and direction of the elevator; the door magnetic sensor module is used for sensing whether the elevator car door is closed or not so as to judge whether a door opening and car moving fault exists or not by combining the leveling sensor module; the vibration sensor module is used for detecting whether the elevator shakes or not and the frequency and amplitude of the elevator shaking; the human body sensor module is used for detecting whether a person stays in the elevator car; the Internet of things module is used for uploading data acquired by the module through the Internet of things.
The fault detection module is used for judging whether the elevator has faults and specific fault types according to the running data of the elevator, which is acquired by the basic sensor module, the speed sensor module, the leveling sensor module, the door magnetic sensor module, the shake sensor module and the human body sensor module.
The data statistical module is used for counting the operation data (such as the operation distance and the operation time length of each operation, the total operation distance and the total operation time length) of the elevator, and counting the failure times of the elevator (including the failure times of the elevator after the last maintenance, the failure times of each failure type, the total failure times of the elevator and the like) for the period calculation module to use.
The period calculation module is used for creating a period measurement and calculation model of the elevator maintenance according to the need, which is obtained through big data analysis, and substituting the period of the elevator maintenance according to the need, which is calculated according to the basic information of the elevator and the data counted by the data statistics module.
The algorithm formula of the periodic measurement model of the elevator maintenance as required is as follows:
T n ＝T Δ +T 1 ×(1-α)+T 2 ×(1-β)
wherein, T n And the maintenance period of the elevator calculated by the maintenance period model according to the requirement is shown.
T Δ The maintenance period of the initial elevator is represented by a hyperparameter; t is Δ The initial value of the elevator is determined by big data analysis according to factors such as the service life of the elevator, the floor number, the risk level, whether the original factory maintenance is carried out or not and the like, and generally the time does not exceed 45 days; the later stage can be adjusted according to the running condition of the elevator.
T 1 Is a hyperparameter, and represents the initial influence value of the fatigue degree of the elevator on the maintenance period, T 1 The initial value of (a) is determined by big data analysis, generally set to 30 days; the later stage can be adjusted according to the running condition of the elevator.
T 2 Is a hyperparameter, and represents the initial influence value of elevator fault rate on maintenance cycle, T 2 The initial value of (a) is determined by big data analysis, generally set to 15 days; the later stage can be adjusted according to the running condition of the elevator.
Alpha represents the working fatigue coefficient of the elevator, and the value of the alpha is determined according to two dimensions of the running time length and the mileage of the elevator; it should be noted that the value of α may be greater than 1.
Beta represents the failure rate of the elevator after the last maintenance, and the value of beta is determined according to the failure times of the elevator and the risk coefficient of each failure type, and the value is between 0 and 1.
The calculation formula of the elevator working fatigue coefficient alpha is as follows:
D 0 the average running distance of the elevator in a single time is represented, and the running distance of one elevator in one sampling period is divided by the running times.
D Δ The average distance of the elevator during single cutting is represented, and the average distance is obtained by acquiring the data of the elevator during single running distance within the specified time length, removing part of jump values and then averaging or is obtained by analyzing big data. The specific calculation method of the average distance of the elevator during single cutting comprises the following steps: firstly, running the elevator for a period of time, collecting the running distance of the elevator running each time in the period of time, and removing the average values of the maximum and minimum part of jump values; such as: and (4) removing 10% of the minimum value and 10% of the maximum value in the collected single-time travel distance data of the elevator and then obtaining an average value. When the running data of the elevator does not exist, the cutting average distance can be calculated by analyzing the single running distance data of the elevators of the same type through big data and averaging after partial jump values are removed.
S 0 The average running time of the elevator in one time is represented, and the running time of one elevator in one sampling period is divided by the running times.
S Δ The average time length of the elevator during single cutting is represented, and the average value is obtained by long data collected in the specified time length during single operation of the elevator, removing part of jump values and then obtaining the average value, or obtained by big data analysis. The specific calculation method of the average time length of the elevator single cutting comprises the following steps: firstly, running the elevator for a period of time, collecting the running time of each running of the elevator in the period of time, and removing the average values of the maximum and minimum part of jump values; such as: and (4) removing 10% of the minimum value and 10% of the maximum value in the collected long data during single-time running of the elevator. When the running data of the elevator does not exist, the average cutting time length can be calculated by analyzing the single-time running length data of the elevators of the same type through big data and averaging after partial jump values are removed.
f d () And f s () Are all linear functions, and the parameters are obtained by big data analysis.
The calculation formula of the failure rate beta of the elevator after the last maintenance is as follows:
wherein m represents the number of elevator fault categories, the elevator fault categories comprising: the elevator is electrified and does not work, the door of the elevator cannot be closed after the door is opened, the door of the elevator cannot be opened after the elevator stops, the stopping position of the elevator is not on a flat floor, the elevator does not run after the door is closed, the elevator is trapped, the elevator rapidly shakes during running, the elevator suddenly stops suddenly and the like. f. of v Indicating the number of times each type of elevator fault occurred during one counting cycle. E m Coefficient of influence of elevator faults of each category on elevator operation safety, E m The value of (A) is obtained by big data analysis, and each type of elevator fault corresponds to one E m The value of (c).
In order to automatically generate a dispatch list when the maintenance of the elevator is required and the elevator is in failure, the on-demand maintenance management system of the elevator can further comprise a dispatch list module, a first response detection module, a first automatic notification module, a second response detection module and a second automatic notification module. And the dispatch list module is used for dynamically generating a service work list after big data analysis is carried out on the collected elevator running data, fault data and the like when the fault detection module detects an elevator fault or the detection date of the maintenance cycle of the elevator is reached. According to the severity of elevator faults and different response times, the elevator faults are mainly divided into three levels, namely emergency faults, more emergency faults and common faults. For an emergency failure, the maintenance personnel is required to respond immediately, for example: the elevator is trapped in people and the like. For more urgent failures, the maintenance personnel are required to process the faults before the next maintenance. For common faults, maintenance personnel are required to process the faults in the next maintenance.
The first response detection module is used for detecting whether the elevator maintenance personnel respond within the set time. The first automatic notification module is used for automatically notifying elevator use units and elevator maintenance unit responsible persons when elevator maintenance personnel do not respond within the specified time. And the second response detection module is used for detecting whether a responsible person of the elevator maintenance unit responds within a specified time after the first automatic notification module issues the notification. And the second automatic notification module is used for automatically notifying the elevator use unit and the elevator maintenance unit responsible person when the elevator maintenance unit responsible person does not respond within the specified time.
In the embodiment, the period measuring and calculating model of the maintenance of the elevator according to the requirement is obtained through big data analysis, and the minimum period of the maintenance of the elevator according to the requirement can be calculated by substituting the period measuring and calculating model into the basic information of the elevator and the data such as the running time, the running distance, the failure times and the like of the elevator, so that a maintenance unit and a using unit can be guided to make a more reasonable maintenance task plan according to the requirement. In addition, the service work order can be dynamically generated by combining the real-time operation data, the fault data and the maintenance data of the elevator and analyzing the big data, and the maintenance personnel can process the service work order in time after receiving the service work order. Aiming at the elevator which has faults repeatedly after processing, the maintenance period can be recalculated through the fault data so as to ensure the reasonability of the maintenance period. Therefore, the phenomenon that maintenance human resources cannot be reasonably utilized due to excessive maintenance can be avoided, the maintenance efficiency is effectively improved, the maintenance cost is reduced, and the equipment failure rate is reduced; the elevator maintenance new mode can be realized by landing on the ground according to needs.
As shown in fig. 2, a preferred embodiment of the elevator on-demand maintenance management method based on the internet of things and big data of the present invention comprises the following steps:
s1, collecting elevator operation data to provide data required by a periodic measurement model and judging whether the elevator has a fault.
S2, calculating an elevator maintenance period through a period measuring and calculating model; the calculation formula is as follows:
T n ＝T Δ +T 1 ×(1-α)+T 2 ×(1-β)。
and S3, generating a service work order and sending the service work order to an elevator maintenance worker when the elevator needs to be maintained.
And S5, after the elevator maintenance personnel receive the service work order, the elevator is maintained or repaired according to the service work order.
S6, counting the failure times of the elevator in a preset period, and when the failure times of the elevator reach or exceed the preset times, indicating that the safe operation of the elevator cannot be ensured in the current elevator maintenance period and the adjustment is needed; and returning to the step S2, and recalculating the elevator maintenance cycle through the cycle measuring and calculating model.
In order to facilitate timely handling when the elevator fails between two maintenance periods, the method also comprises the following steps:
s4, detecting whether the elevator breaks down, and when the elevator breaks down, generating a service work order by utilizing elevator real-time operation data and fault data collected by the Internet of things and combining big data analysis and sending the service work order to an elevator maintenance worker; and executing the step S5;
after the step S5 is finished, the following steps are also executed:
s7, detecting whether the fault of the elevator is recovered after maintenance, for example, the system can automatically compare the running condition of three days after the service work order is processed with the running condition before the processing, so as to judge whether the fault is effectively processed and recover to normal, and if the fault is recovered, ending the service work order; otherwise, the elevator maintenance personnel is informed to continue maintenance, and the step S5 is executed.
In order to urge an elevator maintenance worker to maintain or repair the elevator in time according to the service work order, the following steps are also executed after the service work order is sent to the elevator maintenance worker:
s501, whether the elevator maintenance personnel respond within a specified time (namely whether the elevator maintenance personnel perform maintenance or not) is detected, if so, the step S5 is executed, and if not, the step S502 is executed.
And S502, automatically informing the elevator using unit and a responsible person of the elevator maintenance unit to remind a manager of the maintenance unit to arrange maintenance personnel for processing.
S503, detecting whether the responsible person of the elevator maintenance unit responds within a specified time (namely whether the manager of the maintenance unit arranges the maintenance person for processing), and if so, executing the step S5; if not, the step of S504 is executed.
S504, an elevator management department is automatically notified, and the elevator management department carries out punishment on a maintenance unit according to actual conditions, and even can carry out maintenance on the elevator as required when the elevator stops.
In the embodiment, the maintenance period of the healthy elevator is reasonably set, so that the phenomenon that maintenance human resources cannot be reasonably utilized due to excessive maintenance can be avoided, the maintenance efficiency is effectively improved, the maintenance cost is reduced, and the equipment failure rate is reduced; the elevator maintenance new mode can be realized by landing on the ground according to needs.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (10)
1. The utility model provides an elevator maintenance as required management system based on thing networking and big data which characterized in that includes:
the basic information module is used for storing basic information of the elevator;
the elevator preposition unit is used for acquiring the operation data of the elevator in real time and transmitting the operation data to the fault detection module and the data statistics module through the Internet of things;
the fault detection module is used for judging whether the elevator has faults and specific fault types according to the operation data of the elevator;
the data statistics module is used for carrying out statistics on the operation data and the failure times of the elevator;
and the period calculation module is used for creating a period measurement and calculation model for maintaining the elevator according to the need and substituting the period for calculating the maintenance of the elevator according to the need according to the basic information of the elevator and the data counted by the data counting module.
2. The on-demand maintenance management system for the elevator based on the internet of things and the big data as claimed in claim 1, wherein the algorithm formula of the periodic measurement and calculation model for the on-demand maintenance of the elevator is as follows:
T n ＝T Δ +T 1 ×(1-α)+T 2 ×(1-β)
wherein, T n Representing the maintenance period of the elevator calculated by the maintenance period model according to the requirement; t is Δ Representing an initial elevator maintenance period; t is a unit of 1 The initial influence value of the fatigue degree of the elevator on the maintenance period is represented; t is 2 Representing an initial influence value of the elevator fault rate on the maintenance period; alpha represents the working fatigue coefficient of the elevator, and the value of the alpha is determined according to two dimensions of the running time length and the mileage of the elevator; beta represents the failure rate of the elevator after the last maintenance, and the value of beta is determined according to the failure times of the elevator and the risk coefficient of each failure type.
3. The elevator on-demand maintenance management system based on the internet of things and big data as claimed in claim 2, wherein the calculation formula of the elevator working fatigue coefficient α is as follows:
wherein D is 0 Representing the single average travel distance of the elevator; d Δ The average distance of the elevator in single cutting is represented, and the average distance is obtained by sampling the elevator running distance in a specified time length, removing part of jump values and then taking the average value, or is obtained by analyzing big data; s 0 Representing the single average running time of the elevator; s Δ The average cutting time length of the elevator in one time is represented, and the average cutting time length is obtained by sampling the running time length of the elevator in the specified time length, removing part of jump values and then averaging the average cutting time length or is obtained by analyzing big data; f. of d () And f s () Are all linear functions, and the parameters are obtained by big data analysis.
4. The on-demand maintenance management system for the elevator based on the internet of things and the big data as claimed in claim 2, wherein the failure rate β of the elevator after the last maintenance is calculated as follows:
wherein m represents the number of elevator fault categories; f. of v Representing the number of times of the fault occurrence of each type of elevator in a calculation period; e m Coefficient of influence of elevator faults of each category on elevator operation safety, E m The values of (a) were obtained by big data analysis.
5. The on-demand maintenance management system for elevators based on the internet of things and big data according to claim 1, wherein the elevator front unit comprises:
a base layer sensor module for calibrating the operational data;
a speed sensor module for detecting the rising and falling speeds of the elevator;
the leveling sensor module is used for judging whether the elevator stops at a leveling level or not and the running state and direction of the elevator;
the door magnetic sensor module is used for sensing whether the elevator car door is closed or not;
the jitter sensor module is used for detecting whether the elevator shakes or not and the frequency and amplitude of the elevator shaking;
the human body sensor module is used for detecting whether people are detained in the elevator car; and
and the Internet of things module is used for carrying out data transmission through the Internet of things.
6. The elevator on-demand maintenance management system based on the internet of things and big data of claim 1, further comprising:
and the dispatching list module is used for dynamically generating a service list through big data analysis and sending the service list to an elevator maintenance worker when the fault detection module detects an elevator fault or reaches the detection date of the elevator maintenance cycle.
7. The elevator on-demand maintenance management system based on the internet of things and big data of claim 6, further comprising:
the first response detection module is used for detecting whether the elevator maintenance personnel respond within the specified time;
the first automatic notification module is used for automatically notifying elevator use units and elevator maintenance unit responsible persons when elevator maintenance personnel do not respond within the specified time;
the second response detection module is used for detecting whether the responsible person of the elevator maintenance unit responds within the specified time after the first automatic notification module issues the notification; and
and the second automatic notification module is used for automatically notifying the elevator using unit and the elevator maintenance unit responsible person when the elevator maintenance unit responsible person does not respond within the specified time.
8. An elevator on-demand maintenance management method based on the Internet of things and big data is characterized in that the elevator on-demand maintenance management system based on the Internet of things and big data as claimed in any one of claims 1 to 7 is adopted, and the management method comprises the following steps:
s1, collecting elevator operation data;
s2, calculating an elevator maintenance period through a period measuring and calculating model;
s3, generating a service work order and sending the service work order to an elevator maintenance worker when the elevator needs to be maintained;
s5, the elevator maintenance personnel maintain or repair the elevator according to the service work order;
and S6, counting the failure times of the elevator in a preset period, and returning to execute the step S2 when the failure times of the elevator reach or exceed the preset times.
9. The on-demand maintenance management method for the elevator based on the Internet of things and the big data according to claim 8, further comprising the following steps:
s4, detecting whether the elevator breaks down, and generating a service work order to send to an elevator maintenance worker when the elevator breaks down; and executing the step S5;
after the step S5 is finished, the following steps are also executed:
and S7, detecting whether the fault of the elevator is recovered after maintenance, if so, ending the service work order, and otherwise, informing an elevator maintenance worker to continue maintenance.
10. The on-demand maintenance management method for the elevator based on the internet of things and the big data according to claim 8 or 9, characterized in that after the service order is sent to the maintenance personnel of the elevator, the following steps are further executed:
s501, detecting whether an elevator maintenance worker responds within a specified time, and if not, executing the step S502;
s502, automatically informing a person in charge of an elevator use unit and an elevator maintenance unit;
s503, detecting whether the responsible person of the elevator maintenance unit responds within a specified time, and if not, executing the step S504;
and S504, automatically informing an elevator management department.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117499216A (en) * | 2023-12-29 | 2024-02-02 | 珠海格力电器股份有限公司 | State early warning method, device, equipment and medium of Internet of things equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117499216A (en) * | 2023-12-29 | 2024-02-02 | 珠海格力电器股份有限公司 | State early warning method, device, equipment and medium of Internet of things equipment |
| CN117499216B (en) * | 2023-12-29 | 2024-04-12 | 珠海格力电器股份有限公司 | State early warning method, device, equipment and medium of Internet of things equipment |
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