CN113077613A - Alarm for preventing sensor misjudgment and judgment method thereof - Google Patents
Alarm for preventing sensor misjudgment and judgment method thereof Download PDFInfo
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Abstract
The invention discloses an alarm for preventing a sensor from misjudging and a judging method thereof, which are used for solving the problem that no tool can carry out misjudging judgment on the sensor on the premise of not optimizing the sensor at present; establishing data connection with the sensor to acquire working data of the sensor; reading a preset condition comparison table of the alarm, comparing the preset condition comparison table with the working data of the sensor, and selecting the working environment and the working content of the current sensor; establishing a data cleaning pool corresponding to a working environment; the working data of the sensor is used as an input value and sent to a data cleaning pool for data cleaning, wherein the data cleaning result is abnormal data and normal data; if abnormal data appears, the alarm generates alarm information, and the check equipment collects the environmental information of sensor work simultaneously, and the signal conditioning makes and prevents the sensor misjudgment relatively tradition, and this application embodiment can carry out the misjudgment to the sensor under the prerequisite of not optimizing the sensor.
Description
Technical Field
The invention belongs to the field of sensors, relates to a sensor misjudgment technology, and particularly relates to an alarm for preventing sensor misjudgment and a judgment method thereof.
Background
When traditional sensor is preventing to judge by mistake, the signal that produces sensor circuit is carried out signal conditioning usually, but can lead to the size grow of sensor or sensor circuit board technology degree of difficulty increase when production like this, lead to the production and processing cost to rise, simultaneously because sensor operational environment is different, operating personnel are different, the mounted position is different and maintenance difference etc. can all be according to the degree of difficulty increase that the signal was taked care, the trade company can not carry out signal conditioning to the condition of every enterprise, this just leads to the sensor when using, probably from different to different, the erroneous judgement appears, at present, there is not the instrument can carry out the erroneous judgement to the sensor under the prerequisite that does not optimize to the sensor and judge by mistake.
Disclosure of Invention
The invention aims to provide an alarm for preventing a sensor from misjudging and a judging method thereof, which are used for solving the problem that no tool can carry out misjudging judgment on the sensor on the premise of not optimizing the sensor at present.
In a first aspect, the object of the present invention can be achieved by the following technical solutions:
a judgment method of an alarm for preventing erroneous judgment of a sensor, the judgment method comprising:
establishing data connection with a sensor to acquire working data of the sensor;
reading a preset condition comparison table of the alarm, comparing the preset condition comparison table with the working data of the sensor, and selecting the working environment and the working content of the current sensor;
establishing a data cleaning pool corresponding to a working environment;
sending working data of a sensor as an input value to a data cleaning pool for data cleaning, wherein the data cleaning result is abnormal data and normal data;
if the abnormal data appears, the alarm generates alarm information, and meanwhile, the checking equipment acquires the working environment information of the sensor;
and if the normal data appear, continuing to clean the data.
In one implementation, the working data of the sensor includes model sub-information and working sub-information.
In one implementation, before reading the alarm preset condition comparison table, the method further includes:
the method comprises the steps of collecting working data of a plurality of types of sensors, wherein the working data of the plurality of types of sensors comprise model sub information and worker sub information;
screening integrated data from the working data of the plurality of types of sensors, wherein the integrated data are the model sub information and the working sub information;
and inputting the integrated data corresponding to the working data of the plurality of types of sensors into a deep neural network for learning training to obtain the preset condition comparison table.
In one implementation, before the establishing the data washing pool corresponding to the working environment, the method further includes:
constructing a basic frame of a data cleaning pool, wherein the basic frame of the data cleaning pool comprises a plurality of corresponding grooves, abnormal grooves and outflow grooves;
filling experience barrier strips into the basic frame of the data cleaning pool, wherein the experience barrier strips are arranged in corresponding grooves and are provided with a plurality of experience barrier strips;
the abnormal groove and the outflow groove are both connected with the corresponding groove, but the abnormal groove and the outflow groove are not communicated with each other.
In one implementation, the abnormal data is data of working data flowing into an abnormal slot;
the normal data is data of working data flowing into the outflow slot;
in one implementation, the verification device includes one or more of a camera, a backup sensor, an internet of things device, and a smart device.
In another aspect, the alarm includes:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
Compared with the prior art, the invention has the beneficial effects that:
establishing data connection with a sensor to acquire working data of the sensor; reading a preset condition comparison table of the alarm, comparing the preset condition comparison table with the working data of the sensor, and selecting the working environment and the working content of the current sensor; establishing a data cleaning pool corresponding to a working environment; sending working data of a sensor as an input value to a data cleaning pool for data cleaning, wherein the data cleaning result is abnormal data and normal data; if the abnormal data appears, the alarm generates alarm information, and meanwhile, the checking equipment acquires the working environment information of the sensor; if the normal data appear, then continue to carry out data cleaning, use signal conditioning to make and prevent the sensor misjudgment relatively the tradition, this application embodiment can carry out the misjudgment to the sensor under the prerequisite of not optimizing the sensor.
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.
FIG. 1 is a schematic block diagram of the present invention;
fig. 2 is a schematic block diagram of the alarm of the present invention.
Detailed Description
The embodiments of the present invention will be described below with reference to the drawings.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
When traditional sensor is preventing to judge by mistake, the signal that produces sensor circuit is carried out signal conditioning usually, but can lead to the size grow of sensor or sensor circuit board technology degree of difficulty increase when production like this, lead to the production and processing cost to rise, simultaneously because sensor operational environment is different, operating personnel are different, the mounted position is different and maintenance difference etc. can all be according to the degree of difficulty increase that the signal was taked care, the trade company can not carry out signal conditioning to the condition of every enterprise, this just leads to the sensor when using, probably from different to different, the erroneous judgement appears, at present, there is not the instrument can carry out the erroneous judgement to the sensor under the prerequisite that does not optimize to the sensor and judge by mistake.
In order to solve the technical problems, the application provides a judgment method, which is used for collecting working data of a plurality of types of sensors, wherein the working data of the plurality of types of sensors comprise model sub-information and worker sub-information; screening integrated data from the working data of the sensors of a plurality of types, wherein the integrated data are model sub information and working sub information; and inputting integrated data corresponding to the working data of the plurality of types of sensors into a deep neural network for learning training to obtain a preset condition comparison table. Establishing data connection with the sensor to acquire working data of the sensor; reading a preset condition comparison table of the alarm, comparing the preset condition comparison table with the working data of the sensor, and selecting the working environment and the working content of the current sensor; constructing a basic frame of the data cleaning pool, wherein the basic frame of the data cleaning pool comprises a plurality of corresponding grooves, abnormal grooves and outflow grooves; filling experience barrier strips into the basic frame of the data cleaning pool, wherein the experience barrier strips are arranged in the corresponding grooves and are provided with a plurality of experience barrier strips; the abnormal groove and the outflow groove are connected with the corresponding groove, but the abnormal groove and the outflow groove are not communicated with each other. Establishing a data cleaning pool corresponding to a working environment; the working data of the sensor is used as an input value and sent to a data cleaning pool for data cleaning, wherein the data cleaning result is abnormal data and normal data; if abnormal data appear, the alarm generates alarm information, and meanwhile, the checking equipment acquires the working environment information of the sensor; if normal data appear, the data cleaning is continued,
compared with the traditional method for conditioning signals, the method and the device can prevent the sensor from misjudging, and the embodiment of the application can carry out misjudgment on the sensor on the premise of not optimizing the sensor.
The sensor (english name: transducer/sensor) is a detection device, which can sense the measured information and convert the sensed information into an electric signal or other information in a required form according to a certain rule to output, so as to meet the requirements of information transmission, processing, storage, display, recording, control and the like.
The sensor features include: miniaturization, digitalization, intellectualization, multifunction, systematization and networking. The method is the first link for realizing automatic detection and automatic control. The existence and development of the sensor enable the object to have the senses of touch, taste, smell and the like, and the object slowly becomes alive. Generally, the sensor is classified into ten categories, such as a thermosensitive element, a photosensitive element, a gas-sensitive element, a force-sensitive element, a magnetic-sensitive element, a humidity-sensitive element, a sound-sensitive element, a radiation-sensitive element, a color-sensitive element and a taste-sensitive element, according to the basic sensing function;
the sensor generally comprises a sensitive element, a conversion circuit and an auxiliary power supply, wherein the sensitive element directly senses a measured object and outputs a physical quantity signal which has a determined relation with the measured object; the conversion element converts the physical quantity signal output by the sensitive element into an electric signal; the conversion circuit is responsible for amplifying and modulating the electric signal output by the conversion element; the conversion element and the conversion circuit generally need an auxiliary power supply for power supply;
based on the above description, an embodiment of the present invention provides a method for determining an alarm to prevent a sensor from misjudging as shown in fig. 1, where the method includes:
establishing data connection with a sensor to acquire working data of the sensor;
reading a preset condition comparison table of the alarm, comparing the preset condition comparison table with the working data of the sensor, and selecting the working environment and the working content of the current sensor;
establishing a data cleaning pool corresponding to a working environment;
sending working data of a sensor as an input value to a data cleaning pool for data cleaning, wherein the data cleaning result is abnormal data and normal data;
if the abnormal data appears, the alarm generates alarm information, and meanwhile, the checking equipment acquires the working environment information of the sensor;
and if the normal data appear, continuing to clean the data.
The following is a detailed explanation with reference to specific embodiments of the present disclosure;
example 1
The embodiment takes a pyroelectric infrared sensor KP500B installed on an elevator as an example;
establishing data connection with a sensor to acquire working data of the sensor;
in the specific implementation, the alarm establishes data connection with the sensor in a wireless or wired mode, and acquires working data of the sensor, wherein the wireless or wired mode includes but is not limited to wifi or network cable, and the working data of the sensor includes but is not limited to model sub-information and working sub-information;
illustratively, the alarm establishes data connection with the sensor through a network cable and acquires model sub-information and worker sub-information, wherein the model sub-information includes all model information of the sensor, for example, the model sub-information of the pyroelectric infrared sensor KP500B includes: the model is as follows: KP 500B; packaging: TO-5; area of sensitive element: 2.0mm × 1.1mm × Gap0.9mmDual, binary; substrate material: silicon Si; window size: 4 x 3 mm; thickness of the substrate: 0.5 mm;
the worker information includes: the working wavelength is as follows: 5-14 μm; the average transmittance is more than 75 percent; output signal [ Vp-p ] is more than 2.2V (420 degree k blackbody 1Hz modulation frequency 0.3-3.0Hz bandwidth 72.5db gain); sensitivity: 3300V/W; detection rate: (D) 1.5 x 10^8cmHz ^ 1/2/W; noise < 200mV (mVp-p) (25 ℃); the output balance degree is less than 20 percent; working voltage: 2.2-15V; working current: 8.5-24 μm (VD =10V, Rs =47k Ω, 25 ℃); source voltage: 0.4-1.1V (VD =10V, Rs =47k Ω, 25 ℃); working temperature: -20 ℃ to +70 ℃; the preservation temperature is-35 ℃ to +80 ℃; the field of view is 138 ° x 125 °.
Reading a preset condition comparison table of the alarm, comparing the preset condition comparison table with the working data of the sensor, and selecting the working environment and the working content of the current sensor;
during specific implementation, firstly, collecting working data of a plurality of types of sensors, wherein the working data of the plurality of types of sensors comprises model sub-information and worker sub-information;
exemplarily, the working data of different models of pyroelectric infrared sensors of a plurality of different manufacturers are collected;
screening integrated data from the working data of the sensors of a plurality of types, wherein the integrated data are model sub information and working sub information;
and inputting integrated data corresponding to the working data of the plurality of types of sensors into a deep neural network for learning training to obtain a preset condition comparison table.
Specifically, the preset condition comparison table includes, but is not limited to, the following contents, which are shown as examples only:
comparison table for preset conditions
Establishing a data cleaning pool corresponding to a working environment;
in specific implementation, a basic frame of the data cleaning pool is constructed, wherein the basic frame of the data cleaning pool comprises a plurality of corresponding grooves, abnormal grooves and outflow grooves;
for example, the corresponding slots correspond to sensors of different models, so the KP500A, KP500B and KP500C correspond to three different corresponding slots, and each corresponding slot stores working data of the sensor;
filling experience barrier strips into the basic frame of the data cleaning pool, wherein the experience barrier strips are arranged in the corresponding grooves and are provided with a plurality of experience barrier strips;
illustratively, taking the above KP500B installed in an elevator as an example, the empirical barrier is set empirically by a serviceman or KP500B sensor developer in the elevator installation area;
for example, in the long-term maintenance of a maintenance worker in the area, the moisture of 1 o ' clock to 3 o ' clock at midnight in the local 3-6 month area is relatively high, so that the KP500B sensor can be covered with water mist, the working view field is blocked, the detection is carried out, the working view field is changed to 92 degrees multiplied by 31 degrees, meanwhile, the Monday to Friday noon is 12 o ' clock to 12 o ' clock 30 at 12 o ' clock, the afternoon is 5 o ' clock 30 to 6 o ' clock 00 is more, the number of people getting on and off the elevator is increased, the space is crowded due to the fact that the number of people in the elevator is increased, the working view field of the KP500B sensor in the elevator is blocked, the investigation records;
then based on the above findings, the service person sets the experienced barrier strip to:
the working field of view is less than 92 degrees x 31 degrees and water mist is present during 1 o 'clock to 3 o' clock at midnight in 3 to 6 months;
the field of view is less than 20 ° × 75 ° during monday through friday noon 12 o 'clock to 12 o' clock 30 and afternoon 5 o 'clock 30 to 6 o' clock 00;
meanwhile, when the developers of the KP500B sensors perform quality inspection on the KP500B sensors in the batch, the KP500B sensors in the batch are subjected to water mist covering in the KP500B sensors under the wet condition due to the packaging technology, and an alarm is generated when the working view field is lower than 35 degrees multiplied by 30 degrees;
then based on the above findings, the KP500B sensor developer sets the empirical barrier strip to be:
alarming when the working visual field is lower than 35 degrees multiplied by 30 degrees under the wet condition;
at the moment, the working visual field of KP500B corresponding to the inner side of the groove is less than 92 degrees multiplied by 31 degrees in the period from 1 point to 3 points at midnight in 3-6 months, and water mist exists;
the field of view is less than 20 ° × 75 ° during monday through friday noon 12 o 'clock to 12 o' clock 30 and afternoon 5 o 'clock 30 to 6 o' clock 00;
alarming when the working visual field is lower than 35 degrees multiplied by 30 degrees under the wet condition;
the three empirical barrier strips;
and if the following information exists in the working data of the sensor, the time is as follows: 4, month and 25 days: time: midnight 2 o' clock 14, current working field of view: 25 ° × 10 °; generating alarm information;
when the working data input KP500B corresponds to the groove, the working visual field is lower than 92 degrees multiplied by 31 degrees and the water fog degree exists during 1 point to 3 points at midnight of 3 to 6 months; the field of view is less than 20 ° × 75 ° during monday through friday noon 12 o 'clock to 12 o' clock 30 and afternoon 5 o 'clock 30 to 6 o' clock 00; and when the working visual field of the wet condition is lower than 35 degrees multiplied by 30 degrees, the three empirical barrier strips are alarmed, and the working data time of the sensor is as follows: 4, month and 25 days: time: midnight 2 o' clock 14, current working field of view: 25 ° × 10 °; generating alarm information, wherein the data is blocked in the corresponding groove and flows out of the outflow groove;
and if the following information exists in the working data of the sensor, the time is as follows: 4, month and 25 days: time: noon 2 o' clock 14, current working field of view: 25 ° × 10 °; generating alarm information;
when the working data input KP500B corresponds to the groove, the working visual field is lower than 92 degrees multiplied by 31 degrees and the water fog degree exists during 1 point to 3 points at midnight of 3 to 6 months; the field of view is less than 20 ° × 75 ° during monday through friday noon 12 o 'clock to 12 o' clock 30 and afternoon 5 o 'clock 30 to 6 o' clock 00; and when the working visual field of the wet condition is lower than 35 degrees multiplied by 30 degrees, the three empirical barrier strips are alarmed, and the working data time of the sensor is as follows: 4, month and 25 days: time: noon 2 o' clock 14, current working field of view: 25 ° × 10 °; generating alarm information, wherein the data is blocked in the corresponding groove and flows out of the outflow groove;
and if the following information exists in the working data of the sensor, the time is as follows: 4, month and 25 days: time: noon 2 o' clock 14, current working field of view: 189 ° × 331 °; generating alarm information;
when the working data input KP500B corresponds to the groove, the working visual field is lower than 92 degrees multiplied by 31 degrees and the water fog degree exists during 1 point to 3 points at midnight of 3 to 6 months; the field of view is less than 20 ° × 75 ° during monday through friday noon 12 o 'clock to 12 o' clock 30 and afternoon 5 o 'clock 30 to 6 o' clock 00; and when the working visual field of the wet condition is lower than 35 degrees multiplied by 30 degrees, the three empirical barrier strips are alarmed, and the working data time of the sensor is as follows: 4, month and 25 days: time: noon 2 o' clock 14, current working field of view: 25 ° × 10 °;
at the moment, the three experience barrier strips do not block data to cause data overflow and flow into the abnormal groove, the alarm generates alarm information, and meanwhile, the verification equipment acquires the working environment information of the sensor;
specifically, the verification equipment comprises one or more of a camera, a standby sensor, internet of things equipment and intelligent equipment.
At the moment, a maintenance worker can remotely check the running condition of the elevator through a camera in the elevator, and two conditions occur at the moment;
in the first situation, the elevator is in failure, and the alarm information is true;
in the first case, the elevator is not faulty, but 14 am, current working field: 189 ° × 331 °; the alarm information is generated for a plurality of times, at the moment, a maintainer can inquire or consult professional personnel of a sensor research and development personnel to dispose, and if the working field of view is 189 degrees multiplied by 331 degrees after inquiry, the alarm information is generated;
at this time, the maintenance personnel can increase the working visual field to 189 degrees multiplied by 331 degrees; generating alarm information; an empirical barrier strip of (1);
meanwhile, the data cleaning pool is stored by a cloud platform, and access authority is not set, so that users in the same region can download the content of the experience barrier bar.
The abnormal groove and the outflow groove are connected with the corresponding groove, but the abnormal groove and the outflow groove are not communicated with each other.
More specifically, the abnormal groove is provided on the periphery of the data washing tank.
Example 2
The embodiment takes a pyroelectric infrared sensor KP500B installed in an escalator as an example;
establishing data connection with a sensor to acquire working data of the sensor;
in the specific implementation, the alarm establishes data connection with the sensor in a wireless or wired mode, and acquires working data of the sensor, wherein the wireless or wired mode includes but is not limited to wifi or network cable, and the working data of the sensor includes but is not limited to model sub-information and working sub-information;
illustratively, the alarm establishes data connection with the sensor through a network cable and acquires model sub-information and worker sub-information, wherein the model sub-information includes all model information of the sensor, for example, the model sub-information of the pyroelectric infrared sensor KP500B includes: the model is as follows: KP 500B; packaging: TO-5; area of sensitive element: 2.0mm × 1.1mm × Gap0.9mmDual, binary; substrate material: silicon Si; window size: 4 x 3 mm; thickness of the substrate: 0.5 mm;
the worker information includes: the working wavelength is as follows: 5-14 μm; the average transmittance is more than 75 percent; output signal [ Vp-p ] is more than 2.2V (420 degree k blackbody 1Hz modulation frequency 0.3-3.0Hz bandwidth 72.5db gain); sensitivity: 3300V/W; detection rate: (D) 1.5 x 10^8cmHz ^ 1/2/W; noise < 200mV (mVp-p) (25 ℃); the output balance degree is less than 20 percent; working voltage: 2.2-15V; working current: 8.5-24 μm (VD =10V, Rs =47k Ω, 25 ℃); source voltage: 0.4-1.1V (VD =10V, Rs =47k Ω, 25 ℃); working temperature: -20 ℃ to +70 ℃; the preservation temperature is-35 ℃ to +80 ℃; the field of view is 138 ° x 125 °.
Reading a preset condition comparison table of the alarm, comparing the preset condition comparison table with the working data of the sensor, and selecting the working environment and the working content of the current sensor;
during specific implementation, firstly, collecting working data of a plurality of types of sensors, wherein the working data of the plurality of types of sensors comprises model sub-information and worker sub-information;
exemplarily, the working data of different models of pyroelectric infrared sensors of a plurality of different manufacturers are collected;
screening integrated data from the working data of the sensors of a plurality of types, wherein the integrated data are model sub information and working sub information;
and inputting integrated data corresponding to the working data of the plurality of types of sensors into a deep neural network for learning training to obtain a preset condition comparison table.
Specifically, the preset condition comparison table includes, but is not limited to, the following contents, which are shown as examples only:
comparison table for preset conditions
Establishing a data cleaning pool corresponding to a working environment;
in specific implementation, a basic frame of the data cleaning pool is constructed, wherein the basic frame of the data cleaning pool comprises a plurality of corresponding grooves, abnormal grooves and outflow grooves;
for example, the corresponding slots correspond to sensors of different models, so the KP500A, KP500B and KP500C correspond to three different corresponding slots, and each corresponding slot stores working data of the sensor;
filling experience barrier strips into the basic frame of the data cleaning pool, wherein the experience barrier strips are arranged in the corresponding grooves and are provided with a plurality of experience barrier strips;
illustratively, taking the pyroelectric infrared sensor KP500B installed in the escalator as an example, the experience barrier strip is set by a maintenance person or a KP500B sensor developer in the escalator installation area according to experience;
such as found by maintenance personnel in the area for extended maintenance,
in the time period from 00 pm to 7 pm and 30 pm at night, after the elevator is stopped, because the position of the stop pyroelectric infrared sensor KP500B is uncertain, when the pyroelectric infrared sensor KP500B runs to the gap between the escalator and the escalator well and the time exceeds 20 seconds, alarm information is generated;
meanwhile, when a pedestrian standing on the elevator station board installed with the pyroelectric infrared sensor KP500B is 3mm away from the glass partition board on one side of the escalator, alarm information is generated;
then based on the above findings, the service person sets the experienced barrier strip to:
during the period from 00 pm to 7 pm and 30 pm, the pyroelectric infrared sensor KP500B runs to the seam between the escalator and the escalator well to generate alarm information;
standing pedestrians are positioned at one side of the escalator by 3mm, and alarm information is generated;
meanwhile, when the developers of the KP500B sensors perform quality inspection on the KP500B sensors in the batch, the KP500B sensors in the batch are subjected to water mist covering in the KP500B sensors under the wet condition due to the packaging technology, and an alarm is generated when the working view field is lower than 35 degrees multiplied by 30 degrees;
then based on the above findings, the KP500B sensor developer sets the empirical barrier strip to be:
alarming when the working visual field is lower than 35 degrees multiplied by 30 degrees under the wet condition;
at this time, the pyroelectric infrared sensor KP500B exists corresponding to the inner edge of the groove
During the period from 00 pm to 7 pm and 30 pm, the pyroelectric infrared sensor KP500B runs to the seam between the escalator and the escalator well to generate alarm information;
standing pedestrians are positioned at one side of the escalator by 3mm, and alarm information is generated;
alarming when the working visual field is lower than 35 degrees multiplied by 30 degrees under the wet condition;
the three empirical barrier strips;
and if the following information exists in the working data of the sensor, the time is as follows: 4, month and 25 days: time: midnight 2 o' clock 14, generating alarm information;
when working data are input into a groove corresponding to KP500B, because the working data exist in the period from 00 pm to 7 pm 30, a pyroelectric infrared sensor KP500B runs to the gap between the escalator and the escalator well to generate alarm information; standing pedestrians are positioned at one side of the escalator by 3mm, and alarm information is generated; alarming when the working visual field is lower than 35 degrees multiplied by 30 degrees under the wet condition; generating alarm information, wherein the data is blocked in the corresponding groove and flows out of the outflow groove;
and if the following information exists in the working data of the sensor, the time is as follows: 4, month and 25 days: time: 14 at noon, generating alarm information;
when working data are input into a groove corresponding to KP500B, because the working data exist in the period from 00 pm to 7 pm 30, a pyroelectric infrared sensor KP500B runs to the gap between the escalator and the escalator well to generate alarm information; standing pedestrians are positioned at one side of the escalator by 3mm, and alarm information is generated; alarming when the working visual field is lower than 35 degrees multiplied by 30 degrees under the wet condition; at the moment, the three experience barrier strips do not block data to cause data overflow and flow into the abnormal groove, the alarm generates alarm information, and meanwhile, the verification equipment acquires the working environment information of the sensor;
specifically, the verification equipment comprises one or more of a camera, a standby sensor, internet of things equipment and intelligent equipment.
At the moment, a maintenance worker can remotely check the operation condition of the escalator through a camera in a corresponding area of the escalator, and two conditions occur at the moment;
in the first situation, the elevator is in failure, and the alarm information is true;
in the first situation, the elevator does not have a fault but 14 am, the alarm information is generated for a plurality of times, at the moment, a maintainer can perform treatment by inquiring or consulting professional personnel of a sensor research and development personnel, and if 14 am is found after inquiry, the alarm information is generated;
at this point, the maintenance personnel may increase 14 pm; generating alarm information; an empirical barrier strip of (1);
meanwhile, the data cleaning pool is stored by a cloud platform, and access authority is not set, so that users in the same region can download the content of the experience barrier bar.
The abnormal groove and the outflow groove are connected with the corresponding groove, but the abnormal groove and the outflow groove are not communicated with each other.
More specifically, the abnormal groove is provided on the periphery of the data washing tank.
In one implementation, the relevant functions implemented by the various modules in FIG. 2 may be implemented in connection with a processor. Referring to fig. 2, fig. 2 is a schematic structural diagram of an alarm provided in an embodiment of the present invention, where the alarm includes a processor and a memory, and the processor and the memory are connected through one or more communication buses.
The processor is configured to support the audio feature extraction device to perform the corresponding functions of the alarm in the method described in fig. 1. The processor may be a Central Processing Unit (CPU), a Network Processor (NP), a hardware chip, or any combination thereof.
The memory is used for storing program codes, audio signals and the like. The memory may include volatile memory (volatile memory), such as Random Access Memory (RAM); the memory may also include a non-volatile memory (non-volatile memory), such as a read-only memory (ROM), a flash memory (flash memory), a Hard Disk Drive (HDD), or a solid-state drive (SSD); the memory may also comprise a combination of memories of the kind described above.
The processor may call program code stored in the memory to perform the following:
establishing data connection with a sensor to acquire working data of the sensor;
reading a preset condition comparison table of the alarm, comparing the preset condition comparison table with the working data of the sensor, and selecting the working environment and the working content of the current sensor;
establishing a data cleaning pool corresponding to a working environment;
sending working data of a sensor as an input value to a data cleaning pool for data cleaning, wherein the data cleaning result is abnormal data and normal data;
if the abnormal data appears, the alarm generates alarm information, and meanwhile, the checking equipment acquires the working environment information of the sensor;
and if the normal data appear, continuing to clean the data.
Further, the processor may also execute operations corresponding to the alarm in the embodiment shown in fig. 1, which may specifically refer to the description in the method embodiment and will not be described herein again.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several 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 above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (7)
1. A judgment method of an alarm for preventing misjudgment of a sensor is characterized by comprising the following steps:
establishing data connection with a sensor to acquire working data of the sensor;
reading a preset condition comparison table of the alarm, comparing the preset condition comparison table with the working data of the sensor, and selecting the working environment and the working content of the current sensor;
establishing a data cleaning pool corresponding to a working environment;
sending working data of a sensor as an input value to a data cleaning pool for data cleaning, wherein the data cleaning result is abnormal data and normal data;
if the abnormal data appears, the alarm generates alarm information, and meanwhile, the checking equipment acquires the working environment information of the sensor;
and if the normal data appear, continuing to clean the data.
2. The method as claimed in claim 1, wherein the operation data of the sensor includes model sub-information and operation sub-information.
3. The method for determining the alarm to prevent the sensor from misjudging according to claim 1, wherein before reading the alarm preset condition comparison table, the method further comprises:
the method comprises the steps of collecting working data of a plurality of types of sensors, wherein the working data of the plurality of types of sensors comprise model sub information and worker sub information;
screening integrated data from the working data of the plurality of types of sensors, wherein the integrated data are the model sub information and the working sub information;
and inputting the integrated data corresponding to the working data of the plurality of types of sensors into a deep neural network for learning training to obtain the preset condition comparison table.
4. The method for determining an alarm for preventing a sensor from misjudging according to claim 1, wherein before the step of establishing the data cleaning pool corresponding to the working environment, the method further comprises:
constructing a basic frame of a data cleaning pool, wherein the basic frame of the data cleaning pool comprises a plurality of corresponding grooves, abnormal grooves and outflow grooves;
filling experience barrier strips into the basic frame of the data cleaning pool, wherein the experience barrier strips are arranged in corresponding grooves and are provided with a plurality of experience barrier strips;
the abnormal groove and the outflow groove are both connected with the corresponding groove, but the abnormal groove and the outflow groove are not communicated with each other.
5. The method for judging an alarm for preventing the misjudgment of the sensor according to claim 1, wherein the abnormal data is data of working data flowing into an abnormal groove;
the normal data is data of working data flowing into the outflow slot.
6. The method for judging the alarm for preventing the sensor from misjudging as claimed in claim 1, wherein the verification device comprises one or more of a camera, a standby sensor, an internet of things device and an intelligent device.
7. An alarm for preventing erroneous judgment of a sensor, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any one of claims 1-6.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102135995A (en) * | 2011-03-17 | 2011-07-27 | 新太科技股份有限公司 | Extract transform and load (ETL) data cleaning design method |
| CN102923538A (en) * | 2012-07-06 | 2013-02-13 | 天津大学 | Elevator health management and maintenance system based on Internet of things and collection and assessment method |
| US20130218518A1 (en) * | 2012-02-21 | 2013-08-22 | International Business Machines Corporation | Automated, three dimensional mappable environmental sampling system and methods of use |
| CN103729384A (en) * | 2012-10-16 | 2014-04-16 | ***通信集团公司 | Information filtering method, system and device |
| WO2014112909A1 (en) * | 2013-01-21 | 2014-07-24 | Saab Ab | Method and system for geo-referencing at least one sensor image |
| CN104348667A (en) * | 2014-11-11 | 2015-02-11 | 上海新炬网络技术有限公司 | Fault positioning method based on warning information |
| CN105279811A (en) * | 2014-07-24 | 2016-01-27 | 沈阳博林特电梯集团股份有限公司 | Independent elevator data analysis recorder and method thereof |
| CN105668374A (en) * | 2016-04-14 | 2016-06-15 | 天津天为电梯有限公司 | Safe escape system during elevator fault |
| CN107025750A (en) * | 2016-02-02 | 2017-08-08 | 橙远科技股份有限公司 | Abnormality monitoring system |
| CN109189807A (en) * | 2018-09-13 | 2019-01-11 | 北京奇虎科技有限公司 | A kind of filter method and device of alert data |
| CN110286656A (en) * | 2019-05-07 | 2019-09-27 | 清华大学 | A kind of the false-alarm filter method and device of wrong data tolerance |
| CN110493348A (en) * | 2019-08-26 | 2019-11-22 | 山东融为信息科技有限公司 | A kind of intelligent monitoring and alarming system based on Internet of Things |
| US20200135342A1 (en) * | 2018-10-30 | 2020-04-30 | LogicMatter, Inc. | Immutable System of Records for IoT / Control Systems for Compliance |
| CN111382146A (en) * | 2018-12-29 | 2020-07-07 | 浙江宇视科技有限公司 | Data storage control method, device, equipment and storage medium |
-
2021
- 2021-04-07 CN CN202110370672.1A patent/CN113077613A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102135995A (en) * | 2011-03-17 | 2011-07-27 | 新太科技股份有限公司 | Extract transform and load (ETL) data cleaning design method |
| US20130218518A1 (en) * | 2012-02-21 | 2013-08-22 | International Business Machines Corporation | Automated, three dimensional mappable environmental sampling system and methods of use |
| CN102923538A (en) * | 2012-07-06 | 2013-02-13 | 天津大学 | Elevator health management and maintenance system based on Internet of things and collection and assessment method |
| CN103729384A (en) * | 2012-10-16 | 2014-04-16 | ***通信集团公司 | Information filtering method, system and device |
| WO2014112909A1 (en) * | 2013-01-21 | 2014-07-24 | Saab Ab | Method and system for geo-referencing at least one sensor image |
| CN105279811A (en) * | 2014-07-24 | 2016-01-27 | 沈阳博林特电梯集团股份有限公司 | Independent elevator data analysis recorder and method thereof |
| CN104348667A (en) * | 2014-11-11 | 2015-02-11 | 上海新炬网络技术有限公司 | Fault positioning method based on warning information |
| CN107025750A (en) * | 2016-02-02 | 2017-08-08 | 橙远科技股份有限公司 | Abnormality monitoring system |
| CN105668374A (en) * | 2016-04-14 | 2016-06-15 | 天津天为电梯有限公司 | Safe escape system during elevator fault |
| CN109189807A (en) * | 2018-09-13 | 2019-01-11 | 北京奇虎科技有限公司 | A kind of filter method and device of alert data |
| US20200135342A1 (en) * | 2018-10-30 | 2020-04-30 | LogicMatter, Inc. | Immutable System of Records for IoT / Control Systems for Compliance |
| CN111382146A (en) * | 2018-12-29 | 2020-07-07 | 浙江宇视科技有限公司 | Data storage control method, device, equipment and storage medium |
| CN110286656A (en) * | 2019-05-07 | 2019-09-27 | 清华大学 | A kind of the false-alarm filter method and device of wrong data tolerance |
| CN110493348A (en) * | 2019-08-26 | 2019-11-22 | 山东融为信息科技有限公司 | A kind of intelligent monitoring and alarming system based on Internet of Things |
Non-Patent Citations (1)
| Title |
|---|
| 孙天成: "一种基于时空相关性的感知数据清洗研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
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