CN110940875A - Equipment abnormality detection method and device, storage medium and electronic equipment - Google Patents
Equipment abnormality detection method and device, storage medium and electronic equipment Download PDFInfo
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- CN110940875A CN110940875A CN201911141841.3A CN201911141841A CN110940875A CN 110940875 A CN110940875 A CN 110940875A CN 201911141841 A CN201911141841 A CN 201911141841A CN 110940875 A CN110940875 A CN 110940875A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/003—Measuring mean values of current or voltage during a given time interval
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/10—Measuring sum, difference or ratio
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
- G01R19/16566—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533
- G01R19/16571—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533 comparing AC or DC current with one threshold, e.g. load current, over-current, surge current or fault current
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Abstract
The embodiment of the application provides a device abnormality detection method and device, a storage medium and an electronic device. In the embodiment of the application, when equipment starts to work, a process parameter set of the equipment in a preset time period is obtained, wherein the process parameter set comprises process parameters of each target product produced by the equipment in the preset time period; acquiring a residual set of the equipment according to the process parameter set, wherein the residual set comprises a residual value of each target product generated by the equipment in a preset time period; judging whether to acquire the current parameter average value of the equipment or not according to the residual set; and when the current parameter average value of the equipment is obtained, judging whether the equipment is abnormal or not according to the current parameter average value. By the scheme, the equipment can be monitored in real time, and the abnormality of the equipment can be found in time, so that the capacity loss is reduced, and the production efficiency is improved.
Description
Technical Field
The present disclosure relates to the field of fault detection, and in particular, to a method and an apparatus for detecting device abnormality, a storage medium, and an electronic device.
Background
In the manufacturing process of the liquid crystal display panel, the film coating process is an important part.
However, in the process of coating the liquid crystal display panel by the coating process equipment, the service life of the electrode consumables is limited. When the service life of the electrode consumables reaches the limit, the electrode consumables can fall off, the fallen electrode consumables can scratch the liquid crystal display panel easily, the liquid crystal display panel is scrapped, meanwhile, the coating process equipment needs to be stopped for detection, and the production efficiency of the liquid crystal display panel is influenced.
At present, the abnormity detection of the coating process equipment is realized on the basis of manual work, namely, an equipment operator judges whether the coating process equipment is abnormal or not through experience in the coating process. The manual method is difficult to find early tiny abnormality of the equipment in time, thereby causing serious failure of the equipment and influencing the production efficiency of the liquid crystal display panel.
Disclosure of Invention
The embodiment of the application provides a method and a device for detecting equipment abnormity, a storage medium and electronic equipment, which can improve the timeliness of equipment abnormity detection.
In a first aspect, an embodiment of the present application provides an apparatus anomaly detection method, including:
when equipment starts to work, acquiring a process parameter set of the equipment in a preset time period, wherein the process parameter set comprises process parameters of the equipment for producing each target product in the preset time period;
acquiring a residual set of the equipment according to the process parameter set, wherein the residual set comprises a residual value of each target product generated by the equipment in a preset time period;
judging whether to acquire the current parameter average value of the equipment or not according to the residual set;
and when the current parameter average value of the equipment is obtained, judging whether the equipment is abnormal or not according to the current parameter average value.
In the method for detecting an abnormality of a device provided in an embodiment of the present application, the determining whether the device is abnormal according to the average value of the current parameter includes:
acquiring a difference value between the current parameter average value and a preset current parameter average value;
and comparing the difference value with a first threshold value, and judging whether the equipment is abnormal or not according to a comparison result.
In the method for detecting device abnormality provided in the embodiment of the present application, the determining whether the device is abnormal according to the comparison result includes:
judging whether the average current value exceeds the standard or not according to the comparison result;
if so, counting the result that the current average value exceeds the standard into the total number of exceeding current, and judging whether the equipment is abnormal or not according to the total number of exceeding current;
if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In the method for detecting an abnormality of a device provided in an embodiment of the present application, the determining whether the device is abnormal according to the total number of the current exceeding includes:
if the total number of the current exceeding the standard is larger than or equal to a second threshold value, determining that the equipment is abnormal;
and if the total number of the current overproof is smaller than the second threshold, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In the method for detecting an abnormality of a device provided in an embodiment of the present application, the determining whether to obtain a current parameter average value of the device according to the residual set includes:
judging whether the residual value of the equipment exceeds the standard or not according to the residual set;
if so, acquiring the current parameter average value of the equipment;
if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In the method for detecting an abnormality of a device provided in an embodiment of the present application, the determining whether a residual value of the device exceeds a standard according to the residual set includes:
comparing each residual value in the set of residual values to a third threshold;
if the number of the residual values in the residual set larger than the third threshold is larger than or equal to a fourth threshold, determining that the residual value of the equipment exceeds the standard;
if the number of the residual values in the residual set larger than the third threshold is smaller than the fourth threshold, determining that the residual value of the equipment does not exceed the standard.
In the method for detecting device anomaly provided in the embodiment of the present application, when the step of obtaining the process parameter set of the device in the preset time period is executed again, the method further includes:
and clearing the total number of the current exceeding the standard.
In a second aspect, an embodiment of the present application provides an apparatus for detecting device abnormality, including:
the parameter acquisition unit is used for acquiring a process parameter set of the equipment in a preset time period when the equipment starts to work, wherein the process parameter set comprises process parameters of each target product produced by the equipment in the preset time period;
a residual error obtaining unit, configured to obtain a residual error set of the device according to the process parameter set, where the residual error set includes a residual error value of each target product generated by the device within a preset time period;
the current acquisition unit is used for judging whether to acquire the current parameter average value of the equipment according to the residual difference set;
and the abnormity judging unit is used for judging whether the equipment is abnormal or not according to the current parameter average value when the current parameter average value of the equipment is acquired.
In a third aspect, the present application provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the above-mentioned device abnormality detection method.
In a fourth aspect, an embodiment of the present application provides an electronic device, which includes 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 device abnormality detection method when executing the program.
In the embodiment of the application, when equipment starts to work, a process parameter set of the equipment in a preset time period is obtained, wherein the process parameter set comprises process parameters of each target product produced by the equipment in the preset time period; acquiring a residual set of the equipment according to the process parameter set, wherein the residual set comprises a residual value of each target product generated by the equipment in a preset time period; judging whether to acquire the current parameter average value of the equipment or not according to the residual set; and when the current parameter average value of the equipment is obtained, judging whether the equipment is abnormal or not according to the current parameter average value. By the scheme, the equipment can be monitored in real time, and the abnormality of the equipment can be found in time, so that the capacity loss is reduced, and the production efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of an apparatus anomaly detection method according to an embodiment of the present application.
Fig. 2 is another schematic flow chart of the device abnormality detection method according to the embodiment of the present application.
Fig. 3 is a schematic structural diagram of an apparatus anomaly detection device according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a device abnormality detection method and device, a storage medium and an electronic device, which will be described in detail respectively.
In the present embodiment, a description will be made from the perspective of an apparatus abnormality detection apparatus, which may be specifically integrated in an electronic apparatus such as a notebook computer or the like.
Referring to fig. 1, fig. 1 is a schematic flow chart of a device anomaly detection method according to an embodiment of the present application. The specific flow of the equipment abnormality detection method can be as follows:
101. when the equipment starts to work, acquiring a process parameter set of the equipment in a preset time period, wherein the process parameter set comprises process parameters of the equipment for producing each target product in the preset time period.
The equipment can be any processing equipment. Such as a coating process equipment, a semiconductor process equipment, or a Printed Circuit Board (PCB) process equipment.
The process parameter may be basic data or index of the equipment when the equipment is producing the target product. Such as current parameters, temperature parameters, and resistance parameters.
It should be noted that, when the production program is set in the apparatus, the time period for the apparatus to produce each target product is fixed. For example, the time period for producing each target product may be 5 minutes, 10 minutes, 20 minutes, 30 minutes, or the like. Thus, the amount of target product that can be produced by the apparatus within a preset period of time is also fixed. It will be appreciated that the amount of target product that can be produced by the apparatus within a predetermined period of time is related to the length of time that the apparatus produces each target product.
Wherein, the preset time interval can be set according to the actual situation. For example, 1 hour, 2 hours, 3 hours, etc.
102. And acquiring a residual set of the equipment according to the process parameter set, wherein the residual set comprises a residual value of each target product generated by the equipment in a preset time period.
Specifically, the process parameter set may be input into a Principal Component Analysis (PCA) model to calculate a residual Error (SPE) set of the equipment. That is, the process parameters of the apparatus for producing each target product within the predetermined time period may be respectively input into the PCA model, so as to calculate the residual value of each target product within the predetermined time period.
The PCA model is a multivariate statistical method model. The original more indexes can be replaced by a few comprehensive indexes. The original data are mapped to a low-dimensional principal component subspace by solving a covariance matrix of the process data, and meanwhile most of variance information of the original data is reserved and used for removing noise and redundant information in the original data.
The SPE is a statistic, can depict the deviation degree of the measured value of the input variable to the principal component model, and carries out fault detection through the PCA model. After passing through the PCA model, the principal component space and the residual space of the original data are available, and the SPE is built in the residual space.
It should be noted that, the process parameters of the apparatus during the production of the target product within a period of time may be collected, and the PCA model may be established by selecting the relevant process parameters through parameter screening. And calculating upper and lower triple Sigma (Sigma) values of the SPE value of the equipment through the PCA model, and using the upper and lower triple Sigma values of the SPE value as a control line of the SPE value of the equipment.
It should be noted that the process parameters can be uploaded to the Kafka message middleware in real time (the Kafka message middleware can process a large amount of data in real time to meet various demand scenarios). The Kafka message middleware analyzes the process parameters in real time so as to obtain the PCA model in real time.
103. And judging whether to acquire the current parameter average value of the equipment or not according to the residual difference set.
The average value of the current parameter of the device may be an average value of the current parameter when the device produces each target product within a preset time period.
Specifically, whether the residual value of the equipment exceeds the standard or not can be judged according to the residual set; if so, acquiring the current parameter average value of the equipment; if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
It is understood that when the residual value of the device is not over-calibrated, it may be determined that the device is not anomalous. Therefore, the step of obtaining the process parameter set of the equipment within the preset time period may be returned to continue monitoring the equipment. When the residual value of the equipment exceeds the standard, the equipment is determined to be possible to be abnormal. Therefore, the step of obtaining the average value of the current parameter of the equipment can be executed to further judge the abnormal condition of the equipment, so that the accuracy of judging the abnormal condition of the equipment is improved, and the phenomenon that the production efficiency of the equipment is influenced by misjudgment is avoided.
When the number of the residual values in the residual set exceeding the third threshold is greater than or equal to the fourth threshold, the residual value of the device can be considered as exceeding.
In some embodiments, the step of determining whether a residual value of the device is out of bounds based on the set of residuals may comprise:
comparing each residual value in the set of residual values to a third threshold;
if the number of the residual values in the residual set larger than the third threshold is larger than or equal to a fourth threshold, determining that the residual value of the equipment exceeds the standard;
if the number of the residual values in the residual set larger than the third threshold is smaller than the fourth threshold, determining that the residual value of the equipment does not exceed the standard.
It should be noted that the third threshold refers to a control line of the residual value of the device. The fourth threshold value may be set according to actual conditions. For example, when there are 5 residual values in the residual set, if there are 3 residual values greater than the third threshold, it may be considered that the residual value of the device exceeds the standard; when there are 10 residual values in the residual set, if there are 6 residual values greater than the third threshold, it can be considered that the residual values of the device exceed the standard, and the like.
It should be noted that the terms "first", "second" and "third" in the description of the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", "third" may explicitly or implicitly include one or more of the described features.
104. And when the current parameter average value of the equipment is obtained, judging whether the equipment is abnormal or not according to the current parameter average value.
Specifically, a difference value between the current parameter average value and a preset current parameter average value can be obtained; and comparing the difference value with a first threshold value, and judging whether the equipment is abnormal or not according to a comparison result.
The preset current parameter average value can be obtained by counting and calculating the current parameters in the collected process parameters through the PCA model after the PCA model is established. The first threshold value can be set according to actual conditions.
It will be appreciated that when the difference is greater than the first threshold, then it may be determined that the current average current of the device is out of compliance. When the difference is less than or equal to the first threshold, then it may be determined that the current average current of the device is not out of compliance.
It will be appreciated that when the current average current value of the device exceeds the standard, it may be determined that the device is likely to be abnormal. Therefore, the abnormal condition of the equipment can be further judged, so that the accuracy of judging the abnormal condition of the equipment is improved, and the phenomenon that the production efficiency of the equipment is influenced due to misjudgment is avoided. When the current average current value of the device does not exceed the standard, the device can be determined not to be abnormal. Therefore, the step of obtaining the process parameter set of the equipment within the preset time period may be returned to continue monitoring the equipment. In some embodiments, the step of determining whether the device is abnormal according to the comparison result may include:
judging whether the average current value exceeds the standard or not according to the comparison result;
if so, counting the result that the current average value exceeds the standard into the total number of exceeding current, and judging whether the equipment is abnormal or not according to the total number of exceeding current;
if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In some embodiments, a counter may be provided in the device abnormality detection apparatus, and the total number of current exceedances may be counted by the counter. It will be appreciated that when the current average current value of the device exceeds the standard, it may be determined that the device is likely to be abnormal. The current excess total may be monitored for further determination of the abnormal condition of the device. For example, the step of determining whether the device is abnormal according to the total number of the current exceeds the standard may include:
if the total number of the current exceeding the standard is larger than or equal to a second threshold value, determining that the equipment is abnormal;
and if the total number of the current overproof is smaller than the second threshold, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
Wherein, the second threshold value can be set according to the actual situation. For example, 3 times, 4 times, 5 times, 6 times, 10 times, etc.
In some embodiments, when the current average value of the current parameter of the device or the residual value of the device is not exceeded, the current exceeding total counted by the counter can be cleared. It can be understood that when the current parameter average value of the device or the residual value of the device does not exceed the standard, it can be determined that the current parameter average value of the device does not continuously occur. Generally, the device can be determined to be abnormal only when the continuous occurrence frequency of the condition that the average value of the current parameter of the device exceeds the standard reaches the second threshold value. Therefore, when the current parameter average value of the equipment or the residual value of the equipment does not exceed the standard, the condition that the current parameter average value of the equipment exceeds the standard does not continuously occur, and the equipment can be determined not to be abnormal. Therefore, in order to avoid the situation of misjudgment, when the current parameter average value of the equipment or the residual value of the equipment does not exceed the standard, the current exceeding total number counted by the counter needs to be cleared.
In some embodiments, a reminder device, such as a ringer device, a notification light, or a vibration device, may be provided on the device. When the equipment is abnormal, the reminding device can remind engineers in time. And moreover, related process parameters of the equipment can be sent to corresponding engineers through mails, and the engineers can further judge the abnormality of the equipment according to the mail content, so as to accurately judge whether the equipment is abnormal or not. When the abnormality is determined, the engineer can perform shutdown detection on the equipment and detect the currently produced target product so as to reduce capacity loss.
It is understood that in order to improve the accuracy of the device anomaly detection, maintenance updates to the PCA model are required. In some embodiments, the residual value of the device may be counted up by the counter. When the residual error value of the equipment exceeds the standard, the result that the residual error value of the equipment exceeds the standard can be counted as the total number of the residual errors exceeding the standard; when the residual error value of the equipment is not over-standard, the total over-standard residual error value can be cleared.
It can be understood that when the residual value of the device is not exceeded, it can be determined that the situation in which the residual value of the device is exceeded does not continuously occur. Generally, the PCA model can be determined to be abnormal only when the number of consecutive occurrences of the device whose residual value exceeds the standard reaches a fifth threshold. Therefore, when the residual error value of the equipment is not over the standard, the condition that the average value of the current parameter of the equipment is over the standard does not continuously occur, and the PCA model can be determined to be not abnormal. Therefore, in order to avoid the situation of misjudgment, when the residual error value of the equipment does not exceed the standard, the residual error exceeding total number counted by the counter needs to be cleared.
Wherein, the fifth threshold value can be set according to the actual situation. For example, 3 times, 4 times, 5 times, 6 times, 10 times, etc.
In some embodiments, when it is determined that the PCA model is abnormal, the PCA model may be retrained by the process parameters acquired by the apparatus within a preset time period to ensure the accuracy of the PCA model. Meanwhile, the PCA model can be prevented from being manually updated by personnel, and the manpower resource is saved.
In some embodiments, when the total number of residual errors of the apparatus that exceeds the standard does not reach the fifth threshold within the preset time period, the PCA model may be updated according to the process parameters collected by the apparatus within the preset time period, so as to ensure the accuracy of the PCA model. Meanwhile, the PCA model can be prevented from being manually updated by personnel, and the manpower resource is saved.
The preset time length can be set according to the actual situation. For example, 1 hour, 2 hours, 3 hours, 5 hours, 10 hours, etc. It should be noted that the preset duration is greater than the preset time period.
In this way, in the embodiment of the present application, when an apparatus starts to operate, a process parameter set of the apparatus in a preset time period is obtained, where the process parameter set includes process parameters of the apparatus for producing each target product in the preset time period; acquiring a residual set of the equipment according to the process parameter set, wherein the residual set comprises a residual value of each target product generated by the equipment in a preset time period; judging whether to acquire the current parameter average value of the equipment or not according to the residual set; and when the current parameter average value of the equipment is obtained, judging whether the equipment is abnormal or not according to the current parameter average value. By the scheme, the equipment can be monitored in real time, and the abnormality of the equipment can be found in time, so that the capacity loss is reduced, and the production efficiency is improved.
The methods described in the above examples are further illustrated in detail by way of example. In this embodiment, an example in which the apparatus abnormality detection device is specifically integrated in an electronic apparatus is described, and the apparatus in this embodiment is a coating process apparatus, and a target product is a liquid crystal display panel, specifically referring to the following description.
As shown in fig. 2, fig. 2 is another schematic flow chart of the device abnormality detection method according to the embodiment of the present application. The specific flow of the equipment abnormality detection method can be as follows:
201. when the electronic equipment detects that the coating process equipment starts to work, a process parameter set of the coating process equipment in a preset time period is obtained, wherein the process parameter set comprises process parameters of the coating process equipment for producing each liquid crystal display panel in the preset time period.
The process parameters may be basic data or indexes of the coating process equipment when the coating process equipment is used for producing the liquid crystal display panel. Such as current parameters, temperature parameters, and resistance parameters.
It should be noted that, after the coating process equipment sets the production procedure, the time period for the coating process equipment to produce each lcd panel is fixed. For example, the time period for producing each liquid crystal display panel may be 5 minutes, 10 minutes, 20 minutes, 30 minutes, or the like. Therefore, the number of the liquid crystal display panels which can be produced by the coating process equipment in a preset time period is also fixed. It can be understood that the number of the liquid crystal display panels that can be produced by the coating process equipment within a preset time period is related to the time period that each liquid crystal display panel is produced by the coating process equipment.
Wherein, the preset time interval can be set according to the actual situation. For example, 1 hour, 2 hours, 3 hours, etc.
202. The electronic equipment obtains a residual set of the film coating process equipment according to the process parameter set, wherein the residual set comprises a residual value of each liquid crystal display panel produced by the film coating process equipment within a preset time period.
Specifically, the electronic device may input the process parameter set into a Principal Component Analysis (PCA) model to calculate a residual error (SPE) set of the coating process equipment. That is, the electronic device may input the process parameters of each lcd panel produced by the film coating apparatus within the predetermined time period into the PCA model, so as to calculate the residual error value of each lcd panel produced by the film coating apparatus within the predetermined time period.
203. And the electronic equipment judges whether the residual value of the coating process equipment exceeds the standard or not according to the residual set.
When the electronic equipment determines that the residual error value of the coating processing equipment does not exceed the standard, the electronic equipment can determine that the coating processing equipment is not abnormal. Therefore, the electronic device can return to step 201 to continue monitoring the coating process equipment. When the electronic equipment determines that the residual error value of the coating processing equipment exceeds the standard, the electronic equipment can determine that the coating processing equipment is possible to be abnormal. Therefore, the electronic device can execute step 204 to further determine the abnormal condition of the coating process equipment.
204. The electronic equipment obtains the average value of the current parameters of the coating process equipment.
The current average value of the current of the coating process equipment can be the average value of the current parameters of the coating process equipment during the production of each liquid crystal display panel within a preset time period.
205. The electronic equipment obtains a difference value between the current parameter average value and a preset current parameter average value, and compares the difference value with a first threshold value.
After the PCA model is established, the electronic device may perform statistics and calculation on the current parameters in the collected process parameters through the PCA model to obtain the preset current parameter average. The first threshold value can be set according to actual conditions.
206. And the electronic equipment judges whether the average value of the current of the coating processing equipment exceeds the standard or not according to the comparison result.
When the difference value is larger than the first threshold value, the electronic equipment can determine that the current average current value of the coating process equipment exceeds the standard. When the difference is less than or equal to the first threshold, the electronic equipment can determine that the current average current value of the coating process equipment does not exceed the standard.
It can be understood that when the current average current value of the coating process equipment exceeds the standard, the electronic equipment can determine that the coating process equipment is possibly abnormal. Therefore, the electronic device can execute step 207 to further determine the abnormal condition of the coating process equipment, so as to improve the accuracy of determining the abnormal condition of the equipment and avoid the occurrence of misdetermination to influence the production efficiency of the coating process equipment. When the current average current value of the coating processing equipment does not exceed the standard, the electronic equipment can determine that the coating processing equipment is not abnormal. Therefore, the electronic device can return to step 201 to continue monitoring the coating process equipment.
207. The electronic equipment counts the result of the current average value exceeding the standard into the total number of the current exceeding the standard, and judges whether the coating processing equipment is abnormal or not according to the total number of the current exceeding the standard.
In some embodiments, a counter may be provided in the electronic device, through which the electronic device may count the total number of current exceedances. It can be understood that when the current average current value of the coating process equipment exceeds the standard, it can be determined that the coating process equipment is possibly abnormal. The current excess total may be monitored for further determination of the abnormal condition of the device.
When the electronic device determines that the total number of the current exceeding the standard is greater than or equal to the second threshold, it may be determined that the coating process equipment is abnormal, and step 208 is executed. When the electronic device determines that the total exceeding current is smaller than the second threshold, it may be determined that the coating process equipment is not abnormal, so the electronic device may return to step 201 to continue monitoring the coating process equipment.
In some embodiments, when the electronic device determines that the average value of the current parameter of the coating process equipment or the residual value of the coating process equipment does not exceed the standard, the electronic device may clear the total number of current exceeding counted by the counter. It can be understood that, when the average value of the current parameters of the coating process equipment or the residual value of the coating process equipment does not exceed the standard, the electronic equipment can determine that the average value of the current parameters of the coating process equipment does not exceed the standard continuously. Generally, the abnormality of the coating process equipment can be determined only when the continuous occurrence frequency of the condition that the average value of the current parameters of the coating process equipment exceeds the standard reaches a second threshold value. Therefore, when the current parameter average value of the coating process equipment or the residual value of the coating process equipment does not exceed the standard, the condition that the current parameter average value of the coating process equipment exceeds the standard does not continuously occur, and the electronic equipment can determine that the coating process equipment is not abnormal. Therefore, in order to avoid the occurrence of misjudgment, when the average value of the current parameters of the coating process equipment or the residual value of the equipment does not exceed the standard, the electronic equipment needs to clear the total number of the current exceeding standard counted by the counter.
208. The electronic equipment judges that the coating process equipment is abnormal.
In some embodiments, a warning device, such as a bell device, a warning light, or a vibration device, may be disposed on the coating process equipment. When the coating process equipment is abnormal, the electronic equipment can remind engineers in time through the reminding device. And the electronic equipment can also send the relevant process parameters of the coating process equipment to a corresponding engineer through a mail, and the engineer can further judge the abnormality of the equipment according to the mail content to accurately judge whether the equipment is abnormal or not. When the abnormality is determined, the engineer can perform shutdown detection on the coating process equipment and detect the currently produced liquid crystal display panel so as to reduce the capacity loss.
In view of the above, in the embodiment of the present application, when the electronic device detects that the film-coating process apparatus starts to operate, the process parameter set of the film-coating process apparatus in a preset time period is obtained, where the process parameter set includes process parameters of the film-coating process apparatus for producing each liquid crystal display panel in the preset time period, the electronic device obtains a residual set of the film-coating process apparatus according to the process parameter set, the residual set includes residual values of the film-coating process apparatus for producing each liquid crystal display panel in the preset time period, the electronic device determines whether a residual value of the film-coating process apparatus exceeds a standard according to the residual set, the electronic device obtains a current parameter average of the film-coating process apparatus, the electronic device obtains a difference between the current parameter average and the preset current parameter average, and compares the difference with a first threshold, and the electronic device determines whether the current average of the film-coating process apparatus exceeds the standard according to the comparison result, and the electronic equipment counts the result of the current average value exceeding the standard into the total number of the current exceeding the standard and judges whether the coating processing equipment is abnormal or not according to the total number of the current exceeding the standard. The scheme can be used for monitoring the film coating process equipment in real time and finding out the abnormality of the film coating process equipment in time, thereby reducing the productivity loss and improving the production efficiency.
In order to better implement the device abnormality detection method provided by the embodiment of the present application, the embodiment of the present application further provides a device based on the device abnormality detection method. The meaning of the noun is the same as that in the above-mentioned device abnormality detection method, and specific implementation details may refer to the description in the method embodiment.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an apparatus abnormality detection device according to an embodiment of the present disclosure, where the apparatus abnormality detection device may include a parameter obtaining unit 301, a residual obtaining unit 302, a current obtaining unit 303, an abnormality determining unit 304, and the like. The method comprises the following specific steps:
the parameter obtaining unit 301 is configured to, when the equipment starts to work, obtain a process parameter set of the equipment in a preset time period, where the process parameter set includes process parameters of the equipment for producing each target product in the preset time period.
A residual obtaining unit 302, configured to obtain a residual set of the apparatus according to the process parameter set, where the residual set includes a residual value of each target product generated by the apparatus within a preset time period.
And the current obtaining unit 303 is configured to determine whether to obtain a current parameter average value of the device according to the residual set.
In some embodiments, the current obtaining unit 303 may be further configured to:
judging whether the residual value of the equipment exceeds the standard or not according to the residual set;
if so, acquiring the current parameter average value of the equipment;
if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In some embodiments, the current obtaining unit 303 may be further configured to:
comparing each residual value in the set of residual values to a third threshold;
if the number of the residual values in the residual set larger than the third threshold is larger than or equal to a fourth threshold, determining that the residual value of the equipment exceeds the standard;
if the number of the residual values in the residual set larger than the third threshold is smaller than the fourth threshold, determining that the residual value of the equipment does not exceed the standard.
In some embodiments, the current obtaining unit 303 may be further configured to: and clearing the total number of the current exceeding the standard.
And the abnormality judgment unit 304 is configured to, when the current parameter average value of the device is obtained, judge whether the device is abnormal according to the current parameter average value.
In some embodiments, the anomaly determination unit 304 may be further configured to:
acquiring a difference value between the current parameter average value and a preset current parameter average value;
and comparing the difference value with a first threshold value, and judging whether the equipment is abnormal or not according to a comparison result.
In some embodiments, the anomaly determination unit 304 may be further configured to:
judging whether the average current value exceeds the standard or not according to the comparison result;
if so, counting the result that the current average value exceeds the standard into the total number of exceeding current, and judging whether the equipment is abnormal or not according to the total number of exceeding current;
if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In some embodiments, the anomaly determination unit 304 may be further configured to:
if the total number of the current exceeding the standard is larger than or equal to a second threshold value, determining that the equipment is abnormal;
and if the total number of the current overproof is smaller than the second threshold, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In some embodiments, the anomaly determination unit 304 may be further configured to: and clearing the total number of the current exceeding the standard.
It should be noted that, when the device abnormality detection apparatus provided in the foregoing embodiment performs device abnormality detection, the division of each functional module is merely illustrated, and in practical applications, the above functions may be distributed to different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. In addition, the device abnormality detection apparatus and the device abnormality detection method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
The device for detecting equipment anomaly provided by the embodiment of the application can acquire a process parameter set of equipment in a preset time period through a parameter acquisition unit 301 when the equipment starts to work, wherein the process parameter set comprises process parameters of each target product produced by the equipment in the preset time period; obtaining, by a residual obtaining unit 302, a residual set of the equipment according to the process parameter set, where the residual set includes a residual value of each target product generated by the equipment within a preset time period; judging whether to acquire the current parameter average value of the equipment or not by a current acquisition unit 303 according to the residual difference set; when the current parameter average value of the equipment is obtained, the abnormity judgment unit 304 judges whether the equipment is abnormal according to the current parameter average value. The equipment can be monitored in real time through the scheme, and the abnormality of the equipment can be found in time, so that the capacity loss is reduced, and the production efficiency is improved.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to implement the device abnormality detection method provided by the method embodiment.
In another embodiment of the present application, an electronic device is further provided, and as shown in fig. 4, the electronic device 400 may include a processor 401 and a memory 402, where the processor 401 and the memory 402 are electrically connected.
The processor 401 is a control center of the electronic device 400, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device 400 and processes data by running or loading an application program stored in the memory 402 and calling data stored in the memory 402, thereby performing overall monitoring of the electronic device 400.
The memory 402 may be used to store applications and data. The memory 402 stores applications containing executable code. The application programs may constitute various functional modules. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
In this embodiment, the processor 401 in the electronic device 400 loads instructions corresponding to processes of one or more application programs into the memory 402 according to the following steps, and the processor 401 runs the application programs stored in the memory 402, thereby implementing various functions:
when equipment starts to work, acquiring a process parameter set of the equipment in a preset time period, wherein the process parameter set comprises process parameters of the equipment for producing each target product in the preset time period;
acquiring a residual set of the equipment according to the process parameter set, wherein the residual set comprises a residual value of each target product generated by the equipment in a preset time period;
judging whether to acquire the current parameter average value of the equipment or not according to the residual set;
and when the current parameter average value of the equipment is obtained, judging whether the equipment is abnormal or not according to the current parameter average value.
In some embodiments, when determining whether the device has an abnormality according to the average value of the current parameter, the processor 401 may be configured to:
acquiring a difference value between the current parameter average value and a preset current parameter average value;
and comparing the difference value with a first threshold value, and judging whether the equipment is abnormal or not according to a comparison result.
In some embodiments, when determining whether the device is abnormal according to the comparison result, the processor 401 may be configured to:
judging whether the average current value exceeds the standard or not according to the comparison result;
if so, counting the result that the current average value exceeds the standard into the total number of exceeding current, and judging whether the equipment is abnormal or not according to the total number of exceeding current;
if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In some embodiments, when determining whether the device is abnormal according to the current superscalar total, the processor 401 may be configured to:
if the total number of the current exceeding the standard is larger than or equal to a second threshold value, determining that the equipment is abnormal;
and if the total number of the current overproof is smaller than the second threshold, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In some embodiments, when determining whether to obtain the current parameter average value of the device according to the set of residual differences, the processor 401 may be configured to:
judging whether the residual value of the equipment exceeds the standard or not according to the residual set;
if so, acquiring the current parameter average value of the equipment;
if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
In some embodiments, when determining from the set of residuals whether the residual values of the device exceed a threshold, processor 401 may be configured to:
comparing each residual value in the set of residual values to a third threshold;
if the number of the residual values in the residual set larger than the third threshold is larger than or equal to a fourth threshold, determining that the residual value of the equipment exceeds the standard;
if the number of the residual values in the residual set larger than the third threshold is smaller than the fourth threshold, determining that the residual value of the equipment does not exceed the standard.
In some embodiments, in returning to the step of obtaining the set of process parameters of the equipment within the preset time period, the processor 401 may be configured to: and clearing the total number of the current exceeding the standard.
As can be seen from the above, the electronic device 400 provided in the embodiment of the present application obtains, when the device starts to operate, a process parameter set of the device in a preset time period, where the process parameter set includes process parameters of the device for producing each target product in the preset time period; acquiring a residual set of the equipment according to the process parameter set, wherein the residual set comprises a residual value of each target product generated by the equipment in a preset time period; judging whether to acquire the current parameter average value of the equipment or not according to the residual set; and when the current parameter average value of the equipment is obtained, judging whether the equipment is abnormal or not according to the current parameter average value. By the scheme, the equipment can be monitored in real time, and the abnormality of the equipment can be found in time, so that the capacity loss is reduced, and the production efficiency is improved.
An embodiment of the present application further provides a storage medium, where a computer program is stored in the storage medium, and when the computer program runs on a computer, the computer executes the method for detecting an apparatus anomaly according to any one of the above embodiments.
It should be noted that, all or part of the steps in the methods of the above embodiments may be implemented by relevant hardware instructed by a program, which may be stored in a computer readable storage medium, such as a memory of the terminal, and executed by at least one processor in the terminal, and during the execution, the flow of the embodiments such as the application program starting method may be included. Among others, the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
In the description above, particular embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise indicated. Accordingly, these steps and operations will be referred to, several times, as being performed by a computer, the computer performing operations involving a processing unit of the computer in electronic signals representing data in a structured form. This operation transforms the data or maintains it at locations in the computer's memory system, which may be reconfigured or otherwise altered in a manner well known to those skilled in the art. The data maintains a data structure that is a physical location of the memory that has particular characteristics defined by the data format. However, while the principles of the application have been described in language specific to above, it is not intended to be limited to the specific form set forth herein, and it will be recognized by those of ordinary skill in the art that various of the steps and operations described below may be implemented in hardware.
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.
The method, the apparatus, the storage medium, and the electronic device for detecting device abnormality provided in the embodiments of the present application are described in detail above, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiments is only used to help understand the technical solution and the core idea of the present application; those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the present disclosure as defined by the appended claims.
Claims (10)
1. An apparatus abnormality detection method characterized by comprising:
when equipment starts to work, acquiring a process parameter set of the equipment in a preset time period, wherein the process parameter set comprises process parameters of the equipment for producing each target product in the preset time period;
acquiring a residual set of the equipment according to the process parameter set, wherein the residual set comprises a residual value of each target product generated by the equipment in a preset time period;
judging whether to acquire the current parameter average value of the equipment or not according to the residual set;
and when the current parameter average value of the equipment is obtained, judging whether the equipment is abnormal or not according to the current parameter average value.
2. The device abnormality detection method according to claim 1, wherein said judging whether the device is abnormal or not based on the current parameter average value includes:
acquiring a difference value between the current parameter average value and a preset current parameter average value;
and comparing the difference value with a first threshold value, and judging whether the equipment is abnormal or not according to a comparison result.
3. The apparatus abnormality detection method according to claim 2, wherein said judging whether the apparatus is abnormal or not based on the comparison result includes:
judging whether the average current value exceeds the standard or not according to the comparison result;
if so, counting the result that the current average value exceeds the standard into the total number of exceeding current, and judging whether the equipment is abnormal or not according to the total number of exceeding current;
if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
4. The apparatus abnormality detection method according to claim 3, wherein said judging whether an abnormality occurs in said apparatus based on said current excess total includes:
if the total number of the current exceeding the standard is larger than or equal to a second threshold value, determining that the equipment is abnormal;
and if the total number of the current overproof is smaller than the second threshold, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
5. The apparatus abnormality detection method according to claim 1, wherein said determining whether to obtain a current parameter average value of the apparatus based on the residual set includes:
judging whether the residual value of the equipment exceeds the standard or not according to the residual set;
if so, acquiring the current parameter average value of the equipment;
if not, returning to the step of acquiring the process parameter set of the equipment in the preset time period.
6. The apparatus anomaly detection method according to claim 5, wherein said determining whether the residual values of said apparatus exceed said residual set comprises:
comparing each residual value in the set of residual values to a third threshold;
if the number of the residual values in the residual set larger than the third threshold is larger than or equal to a fourth threshold, determining that the residual value of the equipment exceeds the standard;
if the number of the residual values in the residual set larger than the third threshold is smaller than the fourth threshold, determining that the residual value of the equipment does not exceed the standard.
7. The apparatus anomaly detection method according to claim 3 or 5, wherein, when returning to the step of obtaining the set of process parameters of said apparatus within a preset time period, further comprising:
and clearing the total number of the current exceeding the standard.
8. An apparatus abnormality detection device characterized by comprising:
the parameter acquisition unit is used for acquiring a process parameter set of the equipment in a preset time period when the equipment starts to work, wherein the process parameter set comprises process parameters of each target product produced by the equipment in the preset time period;
a residual error obtaining unit, configured to obtain a residual error set of the device according to the process parameter set, where the residual error set includes a residual error value of each target product generated by the device within a preset time period;
the current acquisition unit is used for judging whether to acquire the current parameter average value of the equipment according to the residual difference set;
and the abnormity judging unit is used for judging whether the equipment is abnormal or not according to the current parameter average value when the current parameter average value of the equipment is acquired.
9. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the method according to any one of claims 1-7.
10. 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 according to any of claims 1-7 are implemented when the processor executes the program.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112784217A (en) * | 2021-01-28 | 2021-05-11 | 上海西码智能科技股份有限公司 | Industrial equipment utilization rate statistical method and device, computer equipment and storage medium |
| CN114265390A (en) * | 2021-12-22 | 2022-04-01 | 苏州华星光电技术有限公司 | Equipment data acquisition diagnosis method and device, server and storage medium |
| CN115307669A (en) * | 2022-10-11 | 2022-11-08 | 蘑菇物联技术（深圳）有限公司 | Method, apparatus, and medium for detecting abnormal sensor of system under test |
| CN115543729A (en) * | 2022-09-08 | 2022-12-30 | 华能信息技术有限公司 | Data acquisition method and system |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050131573A1 (en) * | 2003-12-12 | 2005-06-16 | Huei-Shyang You | System and method for manufacturing control |
| CN201063928Y (en) * | 2007-06-27 | 2008-05-21 | 志圣工业股份有限公司 | Manufacturing equipment abnormality automatic detection real-time reporting device |
| TW201017447A (en) * | 2008-10-31 | 2010-05-01 | Tong Dean Tech Co Ltd | An early stage abnormality prediction device for fabrication process and its prediction method |
| CN103811367A (en) * | 2012-11-07 | 2014-05-21 | 中芯国际集成电路制造(上海)有限公司 | Product defect detection method |
| US20150127595A1 (en) * | 2013-11-01 | 2015-05-07 | Numenta, Inc. | Modeling and detection of anomaly based on prediction |
| CN105470163A (en) * | 2014-09-03 | 2016-04-06 | 中芯国际集成电路制造(上海)有限公司 | Semiconductor manufacturing process control system and analysis method |
| TW201629919A (en) * | 2015-02-09 | 2016-08-16 | 臺灣塑膠工業股份有限公司 | An alert method of predicting nonstandard |
| CN106841854A (en) * | 2016-12-30 | 2017-06-13 | 国网山东省电力公司鄄城县供电公司 | Power equipment safety monitoring method and system |
| CN206584224U (en) * | 2016-12-23 | 2017-10-24 | 联策科技股份有限公司 | Process parameter monitoring system |
| TW201836031A (en) * | 2017-03-27 | 2018-10-01 | 聯華電子股份有限公司 | Process control method for use in a semiconductor manufacturing apparatus control system |
| CN109521725A (en) * | 2017-09-20 | 2019-03-26 | 西门子公司 | The method, apparatus and equipment and machine readable media of detection abnormal data |
| CN110050125A (en) * | 2017-03-17 | 2019-07-23 | 株式会社荏原制作所 | Information processing unit, information processing system, information processing method, program, substrate board treatment, reference data determination device and reference data determining method |
| CN110191017A (en) * | 2019-05-28 | 2019-08-30 | 上海连尚网络科技有限公司 | It is a kind of for monitoring the monitoring system and method for routing device exception |
| CN110399996A (en) * | 2018-04-25 | 2019-11-01 | 深圳富桂精密工业有限公司 | Processing procedure abnormality pre-judging method and anticipation system |
-
2019
- 2019-11-20 CN CN201911141841.3A patent/CN110940875B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050131573A1 (en) * | 2003-12-12 | 2005-06-16 | Huei-Shyang You | System and method for manufacturing control |
| CN201063928Y (en) * | 2007-06-27 | 2008-05-21 | 志圣工业股份有限公司 | Manufacturing equipment abnormality automatic detection real-time reporting device |
| TW201017447A (en) * | 2008-10-31 | 2010-05-01 | Tong Dean Tech Co Ltd | An early stage abnormality prediction device for fabrication process and its prediction method |
| CN103811367A (en) * | 2012-11-07 | 2014-05-21 | 中芯国际集成电路制造(上海)有限公司 | Product defect detection method |
| US20150127595A1 (en) * | 2013-11-01 | 2015-05-07 | Numenta, Inc. | Modeling and detection of anomaly based on prediction |
| CN105470163A (en) * | 2014-09-03 | 2016-04-06 | 中芯国际集成电路制造(上海)有限公司 | Semiconductor manufacturing process control system and analysis method |
| TW201629919A (en) * | 2015-02-09 | 2016-08-16 | 臺灣塑膠工業股份有限公司 | An alert method of predicting nonstandard |
| CN206584224U (en) * | 2016-12-23 | 2017-10-24 | 联策科技股份有限公司 | Process parameter monitoring system |
| CN106841854A (en) * | 2016-12-30 | 2017-06-13 | 国网山东省电力公司鄄城县供电公司 | Power equipment safety monitoring method and system |
| CN110050125A (en) * | 2017-03-17 | 2019-07-23 | 株式会社荏原制作所 | Information processing unit, information processing system, information processing method, program, substrate board treatment, reference data determination device and reference data determining method |
| TW201836031A (en) * | 2017-03-27 | 2018-10-01 | 聯華電子股份有限公司 | Process control method for use in a semiconductor manufacturing apparatus control system |
| CN109521725A (en) * | 2017-09-20 | 2019-03-26 | 西门子公司 | The method, apparatus and equipment and machine readable media of detection abnormal data |
| CN110399996A (en) * | 2018-04-25 | 2019-11-01 | 深圳富桂精密工业有限公司 | Processing procedure abnormality pre-judging method and anticipation system |
| CN110191017A (en) * | 2019-05-28 | 2019-08-30 | 上海连尚网络科技有限公司 | It is a kind of for monitoring the monitoring system and method for routing device exception |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112784217A (en) * | 2021-01-28 | 2021-05-11 | 上海西码智能科技股份有限公司 | Industrial equipment utilization rate statistical method and device, computer equipment and storage medium |
| CN114265390A (en) * | 2021-12-22 | 2022-04-01 | 苏州华星光电技术有限公司 | Equipment data acquisition diagnosis method and device, server and storage medium |
| CN114265390B (en) * | 2021-12-22 | 2024-02-20 | 苏州华星光电技术有限公司 | Equipment data acquisition diagnosis method, device, server and storage medium |
| CN115543729A (en) * | 2022-09-08 | 2022-12-30 | 华能信息技术有限公司 | Data acquisition method and system |
| CN115543729B (en) * | 2022-09-08 | 2023-07-07 | 华能信息技术有限公司 | Data acquisition method and system |
| CN115307669A (en) * | 2022-10-11 | 2022-11-08 | 蘑菇物联技术（深圳）有限公司 | Method, apparatus, and medium for detecting abnormal sensor of system under test |
| CN115307669B (en) * | 2022-10-11 | 2023-01-10 | 蘑菇物联技术（深圳）有限公司 | Method, apparatus, and medium for detecting abnormal sensor of system under test |
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