CN118376974B - Fault monitoring method, device and equipment of electric energy meter and storage medium - Google Patents
Fault monitoring method, device and equipment of electric energy meter and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of power data analysis, and provides a fault monitoring method, device and equipment of an electric energy meter and a storage medium. The method comprises the steps of obtaining a target operation parameter information set of a target electric energy meter in a preset time period, and obtaining total power information of each electric equipment on a circuit corresponding to the target electric energy meter in the preset time period; inputting the total power information into a standard operation parameter generation model to obtain a standard operation parameter information set corresponding to the total power information; and judging whether the target electric energy meter has a fault or not based on the target operation parameter information set and the standard operation parameter information set. The method can realize automatic monitoring of faults of the electric energy meter, reduces the monitoring cost and improves the monitoring efficiency.
Description
Technical Field
The present application relates to the field of power data analysis technologies, and in particular, to a fault monitoring method, apparatus, device and storage medium for an electric energy meter.
Background
The electric energy meter is equipment for measuring electric energy consumption, and is widely applied to various industrial and daily life scenes. In the use process of the electric energy meter, various faults, such as mechanical faults, circuit faults and the like, may exist, so that the problems of increased metering error, inaccurate test and the like of the electric energy meter are caused, and further, the accuracy of electricity charging is affected, and the existing method for monitoring faults of the electric energy meter mainly depends on manual inspection, and has the problems of long period, high cost, low efficiency and the like.
Disclosure of Invention
The application provides a fault monitoring method, device and equipment of an electric energy meter and a storage medium, which are used for solving the problems set forth in the background technology.
In a first aspect, the present application provides a fault monitoring method for an electric energy meter, including:
acquiring a target operation parameter information set of a target electric energy meter in a preset time period, and acquiring total power information of each electric equipment on a circuit corresponding to the target electric energy meter in the preset time period; the target operation parameter information sets comprise a plurality of target operation parameter information subsets, and each target operation parameter information subset corresponds to different types of operation parameters respectively;
Inputting the total power information into a standard operation parameter generation model to obtain a standard operation parameter information set corresponding to the total power information; the standard operation parameter information sets comprise a plurality of standard operation parameter information subsets, and each standard operation parameter information subset corresponds to different types of operation parameters respectively;
Determining an operation deviation index of the operation parameters corresponding to the target operation parameter information subsets based on the standard operation parameter information subsets corresponding to the target operation parameter information subsets for each target operation parameter information subset;
And judging whether the electric energy meter has faults or not based on each operation deviation index.
In one possible implementation manner, the determining the operation deviation index of the operation parameter corresponding to the target operation parameter information subset based on the standard operation parameter information subset corresponding to the target operation parameter information subset includes:
Drawing a target operation parameter change curve corresponding to the target operation parameter information subset in the preset time period based on each target operation parameter value in the target operation parameter information subset, and drawing a standard operation parameter change curve corresponding to the standard operation parameter information subset in the preset time period based on each standard operation parameter value in the standard operation parameter information subset;
The operating deviation index is determined based on the target operating parameter profile and the standard operating parameter profile.
In one possible implementation, the determining the operating deviation index based on the target operating parameter profile and the standard operating parameter profile includes:
Acquiring a first similarity of the target operation parameter change curve and the standard operation parameter change curve;
dividing the target operation parameter change curve based on a preset curve dividing rule to obtain a plurality of first curve segments, and dividing the standard operation parameter change curve based on the curve dividing rule to obtain a plurality of second curve segments;
For each first curve segment, obtaining a second similarity between the first curve segment and a second curve segment corresponding to the first curve segment;
obtaining standard deviation among the second similarity;
determining a ratio between the standard deviation and the first similarity as the running deviation index.
In one possible implementation manner, the obtaining the total power information of each electric device on the line corresponding to the target electric energy meter in the preset time period includes:
For each test time point in the preset time period, respectively determining an electric power consumption value corresponding to each electric equipment on the line at the test time point, and adding the electric power consumption values to obtain a total electric power consumption value corresponding to the test time point;
and generating the total electric power information based on each total electric power value.
In one possible implementation, before inputting the total power information into the standard operating parameter generation model, the method further includes:
Acquiring an electric energy meter identifier of the target electric energy meter;
Acquiring a target model parameter set from a database based on the electric energy meter identifier; wherein the target model parameter set comprises a plurality of model parameters;
Acquiring a preset deep learning model;
And correspondingly updating each model parameter in the target model parameter set to the deep learning model to obtain the standard operation parameter generation model.
In one possible implementation manner, the acquiring the target model parameter set in the database based on the electric energy meter identifier includes:
Acquiring an activation text from a text database based on the electric energy meter identifier; the text database stores a corresponding relation between an electric energy meter identifier and an activated text, wherein the activated text comprises a character table, a first objective function and a second objective function;
Drawing a first function curve and a second function curve in a preset plane rectangular coordinate system, and determining an intersection point between the first function curve and the second function curve; the first function curve is a function curve corresponding to the first objective function, and the second function curve is a function curve corresponding to the second objective function;
determining the intersection point closest to the origin of coordinates as a target mapping point; the coordinate origin is the coordinate origin of the plane rectangular coordinate system;
mapping the character table to the plane rectangular coordinate system; wherein, the diagonal intersection point of the character table coincides with the target mapping point;
determining a first target character and a second target character in the character table; the first target character and the second target character are arranged in a matrix, wherein a cell where the first target character is located is broken down by the first function curve, a cell where the second target character is located is broken down by the second function curve, and the first target character and the second target character both comprise at least one;
Sequentially arranging the first target characters based on the positions of the first target characters in the character table to obtain a first character sequence, and sequentially arranging the second target characters based on the positions of the second target characters in the character table to obtain a second character sequence;
performing coding processing on the first character sequence based on a preset coding rule to obtain a first coding sequence, and performing coding processing on the second character sequence based on the coding rule to obtain a second coding sequence;
and placing the second coding sequence below the first coding sequence to obtain an activation matrix, respectively carrying out activation processing on each model parameter set in a database based on the activation matrix, and determining the successfully activated model parameter set as the target model parameter set.
In one possible implementation manner, the determining whether the electric energy meter has a fault based on each of the running deviation indexes includes:
Comparing each operation deviation index with a first preset operation deviation index respectively;
if any operation deviation index is larger than the first preset operation deviation index, judging that the target electric energy meter has a fault;
If all the running deviation indexes are not larger than the first preset running deviation index, comparing all the running deviation indexes with a second preset running deviation index respectively, and determining the running parameters corresponding to the running deviation indexes as abnormal parameters when the running deviation indexes are larger than the second preset running deviation index; wherein the first preset operational deviation index is greater than the second preset operational deviation index;
and counting the total number of the abnormal parameters, judging whether the total number reaches a preset threshold, and if so, judging that the target electric energy meter has faults.
In a second aspect, the present application provides a fault monitoring device for an electric energy meter, including:
the device comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a target operation parameter information set of a target electric energy meter in a preset time period and acquiring total power utilization information of each electric equipment on a circuit corresponding to the target electric energy meter in the preset time period; the target operation parameter information sets comprise a plurality of target operation parameter information subsets, and each target operation parameter information subset corresponds to different types of operation parameters respectively;
The input module is used for inputting the total power information into a standard operation parameter generation model to obtain a standard operation parameter information set corresponding to the total power information; the standard operation parameter information sets comprise a plurality of standard operation parameter information subsets, and each standard operation parameter information subset corresponds to different types of operation parameters respectively;
The determining module is used for determining an operation deviation index of the operation parameters corresponding to the target operation parameter information subsets based on the standard operation parameter information subsets corresponding to the target operation parameter information subsets for each target operation parameter information subset;
And the judging module is used for judging whether the electric energy meter has faults or not based on the running deviation indexes.
In a third aspect, the present application provides a terminal device comprising a processor, a memory and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the fault monitoring method of an electric energy meter as described above.
In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the fault monitoring method of an electric energy meter as described above.
The application provides a fault monitoring method, device, equipment and storage medium of an electric energy meter, wherein the method comprises the steps of obtaining a target operation parameter information set of a target electric energy meter in a preset time period, and obtaining total power information of each electric equipment on a circuit corresponding to the target electric energy meter in the preset time period; the target operation parameter information sets comprise a plurality of target operation parameter information subsets, and each target operation parameter information subset corresponds to different types of operation parameters respectively; inputting the total power information into a standard operation parameter generation model to obtain a standard operation parameter information set corresponding to the total power information; the standard operation parameter information sets comprise a plurality of standard operation parameter information subsets, and each standard operation parameter information subset corresponds to different types of operation parameters respectively; determining an operation deviation index of the operation parameters corresponding to the target operation parameter information subsets based on the standard operation parameter information subsets corresponding to the target operation parameter information subsets for each target operation parameter information subset;
And judging whether the electric energy meter has faults or not based on each operation deviation index. According to the method, on one hand, the faults of the electric energy meter can be automatically monitored, the monitoring cost is reduced, the monitoring efficiency is improved, on the other hand, the actual running states of the running parameters of the electric energy meter in the preset time period are respectively compared with the standard running states, whether the electric energy meter has faults or not is judged according to the comparison result, and the accuracy of monitoring the faults of the electric energy meter is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a fault monitoring method of an electric energy meter according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of a fault monitoring device of an electric energy meter according to an embodiment of the present application;
fig. 3 is a schematic block diagram of a structure of a terminal device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The electric energy meter is equipment for measuring electric energy consumption, and is widely applied to various industrial and daily life scenes. In the use process of the electric energy meter, various faults, such as mechanical faults, circuit faults and the like, may exist, so that the problems of increased metering error, inaccurate test and the like of the electric energy meter are caused, and further, the accuracy of electricity charging is affected, and the existing method for monitoring faults of the electric energy meter mainly depends on manual inspection, and has the problems of long period, high cost, low efficiency and the like. Therefore, the application provides a fault monitoring method, device and equipment for an electric energy meter and a storage medium, so as to solve the problems.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flowchart of a fault monitoring method of an electric energy meter according to an embodiment of the present application, and as shown in fig. 1, the fault monitoring method of an electric energy meter according to an embodiment of the present application includes steps S100 to S400.
Step S100, acquiring a target operation parameter information set of a target electric energy meter in a preset time period, and acquiring total power information of each electric equipment on a circuit corresponding to the target electric energy meter in the preset time period; the target operation parameter information set comprises a plurality of target operation parameter information subsets, and each target operation parameter information subset corresponds to different types of operation parameters respectively.
The operation parameters comprise voltages of each phase, currents of each phase, active power, reactive power, apparent power and the like of the electric energy meter. And acquiring a target operation parameter information subset corresponding to each operation parameter through a corresponding sensor in the preset time period.
Specifically, the preset time period is provided with a plurality of test time points, and the specific method for acquiring the target operation parameter information set of the target electric energy meter in the preset time period comprises the following steps: and aiming at any operation parameter, acquiring a test value corresponding to the operation parameter at each test point through a sensor corresponding to the operation parameter to obtain a target operation parameter information subset corresponding to the operation parameter, wherein each test value in the target operation parameter information subset is provided with a corresponding test time point. The specific method for obtaining the total power information of each electric equipment on the line corresponding to the target electric energy meter in the preset time period comprises the following steps: firstly, for each test time point in the preset time period, determining an electric power consumption value corresponding to each electric equipment on the line at the test time point, adding the electric power consumption values to obtain a total electric power value corresponding to the test time point, and then generating total electric power information based on the total electric power value, wherein each total electric power value in the total electric power information is provided with a corresponding test time.
Step 200, inputting the total power information into a standard operation parameter generation model to obtain a standard operation parameter information set corresponding to the total power information; the standard operation parameter information set comprises a plurality of standard operation parameter information subsets, and each standard operation parameter information subset corresponds to different types of operation parameters.
The standard operation parameter generation model is obtained through training of a neural network model and comprises an input layer, a feature extraction layer, a deep network learning layer, a generation layer and an output layer.
And each standard operation parameter in the standard operation parameter information subset is provided with a test time corresponding to the total power consumption value corresponding to the standard operation parameter.
Step S300, determining, for each of the target operating parameter information subsets, an operating deviation index of an operating parameter corresponding to the target operating parameter information subset based on a standard operating parameter information subset corresponding to the target operating parameter information subset.
Specifically, the determining the operation deviation index of the operation parameter corresponding to the target operation parameter information subset based on the standard operation parameter information subset corresponding to the target operation parameter information subset includes the following steps:
Drawing a target operation parameter change curve corresponding to the target operation parameter information subset in the preset time period based on each target operation parameter value in the target operation parameter information subset, and drawing a standard operation parameter change curve corresponding to the standard operation parameter information subset in the preset time period based on each standard operation parameter value in the standard operation parameter information subset;
The operating deviation index is determined based on the target operating parameter profile and the standard operating parameter profile.
Wherein said determining said operating deviation index based on said target operating parameter profile and said standard operating parameter profile comprises the steps of:
Acquiring a first similarity of the target operation parameter change curve and the standard operation parameter change curve; specifically, inputting the target operation parameter change curve into a preset curve feature extraction model to obtain a first feature vector, inputting the standard operation parameter change curve into the preset curve feature extraction model to obtain a second feature vector, and determining a cosine value between the first feature vector and the second feature vector as the first similarity; the curve characteristic extraction model comprises an input layer, a characteristic extraction layer, a characteristic vector generation layer and an output layer;
dividing the target operation parameter change curve based on a preset curve dividing rule to obtain a plurality of first curve segments, and dividing the standard operation parameter change curve based on the curve dividing rule to obtain a plurality of second curve segments;
For each first curve segment, obtaining a second similarity between the first curve segment and a second curve segment corresponding to the first curve segment; specifically, inputting the first curve segment into the curve feature extraction model to obtain a third feature vector, inputting a second curve segment corresponding to the first curve segment into the curve feature extraction model to obtain a fourth feature vector, and determining a cosine value between the third feature vector and the fourth feature vector as the second similarity;
obtaining standard deviation among the second similarity;
determining a ratio between the standard deviation and the first similarity as the running deviation index.
It can be appreciated that the standard deviation can reflect the stability of the target operating parameter variation curve relative to the standard operating parameter variation curve, the first similarity can reflect the similarity degree of the target operating parameter variation curve and the standard operating parameter variation curve, and the ratio between the standard deviation and the first similarity is used as the operating deviation index, so that the reliability of the operating deviation index is improved, and further, the reliability of the fault monitoring method of the electric energy meter is improved.
And step 400, judging whether the electric energy meter has faults or not based on each operation deviation index.
Specifically, step S400 includes the steps of:
Comparing each operation deviation index with a first preset operation deviation index respectively;
if any operation deviation index is larger than the first preset operation deviation index, judging that the target electric energy meter has a fault;
If all the running deviation indexes are not larger than the first preset running deviation index, comparing all the running deviation indexes with a second preset running deviation index respectively, and determining the running parameters corresponding to the running deviation indexes as abnormal parameters when the running deviation indexes are larger than the second preset running deviation index; wherein the first preset operational deviation index is greater than the second preset operational deviation index;
and counting the total number of the abnormal parameters, judging whether the total number reaches a preset threshold, and if so, judging that the target electric energy meter has faults.
According to the method provided by the embodiment, on one hand, the faults of the electric energy meter can be automatically monitored, the monitoring cost is reduced, and the monitoring efficiency is improved, on the other hand, the actual running states of the running parameters of the electric energy meter in the preset time period are respectively compared with the standard running states, whether the electric energy meter has the faults or not is judged according to the comparison result, and the accuracy of monitoring the faults of the electric energy meter is improved.
In some embodiments, before inputting the total power information into the standard operating parameter generation model, the method further comprises the steps of:
Acquiring an electric energy meter identifier of the target electric energy meter;
Acquiring a target model parameter set from a database based on the electric energy meter identifier; wherein the target model parameter set comprises a plurality of model parameters;
Acquiring a preset deep learning model;
And correspondingly updating each model parameter in the target model parameter set to the deep learning model to obtain the standard operation parameter generation model.
In this embodiment, for different electric energy meters, a model parameter set corresponding to a corresponding standard operation parameter generation model is preconfigured; i.e. different electric energy meters, different model parameters are adopted. Therefore, before the total power information is input into a standard operation parameter generation model, a target model parameter set corresponding to the target electric energy meter needs to be acquired, and each model parameter in the target model parameter set is correspondingly updated to the deep learning model to obtain the standard operation parameter generation model. It can be understood that, for different electric energy meters, the corresponding standard operation parameter generation model is preconfigured with the corresponding model parameter set instead of directly configuring the corresponding standard operation parameter generation model, which is helpful for saving the memory of the database.
In some embodiments, the acquiring the target model parameter set in the database based on the electric energy meter identification includes the steps of:
Acquiring an activation text from a text database based on the electric energy meter identifier; the text database stores a corresponding relation between an electric energy meter identifier and an activated text, wherein the activated text comprises a character table, a first objective function and a second objective function;
Drawing a first function curve and a second function curve in a preset plane rectangular coordinate system, and determining an intersection point between the first function curve and the second function curve; the first function curve is a function curve corresponding to the first objective function, and the second function curve is a function curve corresponding to the second objective function;
determining the intersection point closest to the origin of coordinates as a target mapping point; the coordinate origin is the coordinate origin of the plane rectangular coordinate system;
mapping the character table to the plane rectangular coordinate system; wherein, the diagonal intersection point of the character table coincides with the target mapping point;
determining a first target character and a second target character in the character table; the first target character and the second target character are arranged in a matrix, wherein a cell where the first target character is located is broken down by the first function curve, a cell where the second target character is located is broken down by the second function curve, and the first target character and the second target character both comprise at least one;
Sequentially arranging the first target characters based on the positions of the first target characters in the character table to obtain a first character sequence, and sequentially arranging the second target characters based on the positions of the second target characters in the character table to obtain a second character sequence;
performing coding processing on the first character sequence based on a preset coding rule to obtain a first coding sequence, and performing coding processing on the second character sequence based on the coding rule to obtain a second coding sequence;
and placing the second coding sequence below the first coding sequence to obtain an activation matrix, respectively carrying out activation processing on each model parameter set in a database based on the activation matrix, and determining the successfully activated model parameter set as the target model parameter set.
In this embodiment, in order to ensure security of each model parameter set, prevent model parameters in each model parameter set from being tampered, lock each model parameter set, when a target model parameter set corresponding to the target electric energy meter is obtained, obtain a corresponding activation text through an electric energy meter identifier of the target electric energy meter, further generate an activation matrix based on the activation text, and respectively perform activation processing on each model parameter set in a database based on the activation matrix, and determine that a successfully activated model parameter set is the target model parameter set. The method improves the activation difficulty of each model parameter set, further improves the safety of each model parameter set, and further improves the reliability of the fault monitoring method of the electric energy meter.
Referring to fig. 2, fig. 2 is a schematic block diagram of a fault monitoring device 100 of an electric energy meter according to an embodiment of the present application, where, as shown in fig. 2, the fault monitoring device 100 of an electric energy meter includes:
The acquiring module 110 is configured to acquire a target operation parameter information set of a target electric energy meter in a preset time period, and acquire total power information of each electric equipment on a line corresponding to the target electric energy meter in the preset time period; the target operation parameter information set comprises a plurality of target operation parameter information subsets, and each target operation parameter information subset corresponds to different types of operation parameters respectively.
The input module 120 is configured to input the total power information into a standard operation parameter generation model, so as to obtain a standard operation parameter information set corresponding to the total power information; the standard operation parameter information set comprises a plurality of standard operation parameter information subsets, and each standard operation parameter information subset corresponds to different types of operation parameters.
A determining module 130, configured to determine, for each of the target operating parameter information subsets, an operating deviation index of an operating parameter corresponding to the target operating parameter information subset based on a standard operating parameter information subset corresponding to the target operating parameter information subset.
And the judging module 140 is used for judging whether the electric energy meter has faults or not based on each operation deviation index.
It should be noted that, for convenience and brevity of description, specific working processes of the above-described system and each module may refer to corresponding processes in the foregoing embodiments of the fault monitoring method of the electric energy meter, which are not described herein again.
The fault monitoring device 100 of the electric energy meter provided in the above embodiment may be implemented in the form of a computer program that can be run on the terminal apparatus 200 as shown in fig. 3.
Referring to fig. 3, fig. 3 is a schematic block diagram of a structure of a terminal device 200 according to an embodiment of the present application, where the terminal device 200 includes a processor 201 and a memory 202, and the processor 201 and the memory 202 are connected through a device bus 203, and the memory 202 may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store a computer program. The computer program comprises program instructions which, when executed by the processor 201, cause the processor 201 to perform any of the above-described fault monitoring methods of the electric energy meter.
The processor 201 is used to provide computing and control capabilities supporting the operation of the overall terminal device 200.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium, which when executed by the processor 201, causes the processor 201 to perform any of the fault monitoring methods of the power meter described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation of the terminal device 200 related to the present application, and that a specific terminal device 200 may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
It should be appreciated that the Processor 201 may be a central processing unit (Central Processing Unit, CPU), and the Processor 201 may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSPs), application SPECIFIC INTEGRATED Circuits (ASICs), field-Programmable gate arrays (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In some embodiments, the processor 201 is configured to execute a computer program stored in the memory to implement the following steps:
acquiring a target operation parameter information set of a target electric energy meter in a preset time period, and acquiring total power information of each electric equipment on a circuit corresponding to the target electric energy meter in the preset time period; the target operation parameter information sets comprise a plurality of target operation parameter information subsets, and each target operation parameter information subset corresponds to different types of operation parameters respectively;
Inputting the total power information into a standard operation parameter generation model to obtain a standard operation parameter information set corresponding to the total power information; the standard operation parameter information sets comprise a plurality of standard operation parameter information subsets, and each standard operation parameter information subset corresponds to different types of operation parameters respectively;
Determining an operation deviation index of the operation parameters corresponding to the target operation parameter information subsets based on the standard operation parameter information subsets corresponding to the target operation parameter information subsets for each target operation parameter information subset;
And judging whether the electric energy meter has faults or not based on each operation deviation index.
It should be noted that, for convenience and brevity of description, the specific working process of the terminal device 200 described above may refer to the corresponding process of the fault monitoring method of the electric energy meter, and will not be described herein.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program when executed by one or more processors causes the one or more processors to realize the fault monitoring method of the electric energy meter.
The computer readable storage medium may be an internal storage unit of the terminal device 200 of the foregoing embodiment, for example, a hard disk or a memory of the terminal device 200. The computer readable storage medium may also be an external storage device of the terminal device 200, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like, which the terminal device 200 is equipped with.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (7)
1. A fault monitoring method for an electric energy meter, comprising:
acquiring a target operation parameter information set of a target electric energy meter in a preset time period, and acquiring total power information of each electric equipment on a circuit corresponding to the target electric energy meter in the preset time period; the target operation parameter information sets comprise a plurality of target operation parameter information subsets, and each target operation parameter information subset corresponds to different types of operation parameters respectively;
Inputting the total power information into a standard operation parameter generation model to obtain a standard operation parameter information set corresponding to the total power information; the standard operation parameter information sets comprise a plurality of standard operation parameter information subsets, and each standard operation parameter information subset corresponds to different types of operation parameters respectively;
Determining an operation deviation index of the operation parameters corresponding to the target operation parameter information subsets based on the standard operation parameter information subsets corresponding to the target operation parameter information subsets for each target operation parameter information subset; comprising the following steps:
Drawing a target operation parameter change curve corresponding to the target operation parameter information subset in the preset time period based on each target operation parameter value in the target operation parameter information subset, and drawing a standard operation parameter change curve corresponding to the standard operation parameter information subset in the preset time period based on each standard operation parameter value in the standard operation parameter information subset;
Acquiring a first similarity of the target operation parameter change curve and the standard operation parameter change curve;
dividing the target operation parameter change curve based on a preset curve dividing rule to obtain a plurality of first curve segments, and dividing the standard operation parameter change curve based on the curve dividing rule to obtain a plurality of second curve segments;
For each first curve segment, obtaining a second similarity between the first curve segment and a second curve segment corresponding to the first curve segment;
obtaining standard deviation among the second similarity;
determining a ratio between the standard deviation and the first similarity as the running deviation index;
Judging whether the electric energy meter has faults or not based on each operation deviation index;
before inputting the total power usage information into the standard operating parameter generation model, the method further includes:
Acquiring an electric energy meter identifier of the target electric energy meter;
Acquiring a target model parameter set from a database based on the electric energy meter identifier; wherein the target model parameter set comprises a plurality of model parameters;
Acquiring a preset deep learning model;
And correspondingly updating each model parameter in the target model parameter set to the deep learning model to obtain the standard operation parameter generation model.
2. The fault monitoring method of the electric energy meter according to claim 1, wherein the obtaining the total power information of each electric equipment on the line corresponding to the target electric energy meter in the preset time period includes:
For each test time point in the preset time period, respectively determining an electric power consumption value corresponding to each electric equipment on the line at the test time point, and adding the electric power consumption values to obtain a total electric power consumption value corresponding to the test time point;
and generating the total electric power information based on each total electric power value.
3. The method for fault monitoring of an electric energy meter according to claim 1, wherein the obtaining a target model parameter set in a database based on the electric energy meter identification comprises:
Acquiring an activation text from a text database based on the electric energy meter identifier; the text database stores a corresponding relation between an electric energy meter identifier and an activated text, wherein the activated text comprises a character table, a first objective function and a second objective function;
Drawing a first function curve and a second function curve in a preset plane rectangular coordinate system, and determining an intersection point between the first function curve and the second function curve; the first function curve is a function curve corresponding to the first objective function, and the second function curve is a function curve corresponding to the second objective function;
determining the intersection point closest to the origin of coordinates as a target mapping point; the coordinate origin is the coordinate origin of the plane rectangular coordinate system;
mapping the character table to the plane rectangular coordinate system; wherein, the diagonal intersection point of the character table coincides with the target mapping point;
determining a first target character and a second target character in the character table; the first target character and the second target character are arranged in a matrix, wherein a cell where the first target character is located is broken down by the first function curve, a cell where the second target character is located is broken down by the second function curve, and the first target character and the second target character both comprise at least one;
Sequentially arranging the first target characters based on the positions of the first target characters in the character table to obtain a first character sequence, and sequentially arranging the second target characters based on the positions of the second target characters in the character table to obtain a second character sequence;
performing coding processing on the first character sequence based on a preset coding rule to obtain a first coding sequence, and performing coding processing on the second character sequence based on the coding rule to obtain a second coding sequence;
and placing the second coding sequence below the first coding sequence to obtain an activation matrix, respectively carrying out activation processing on each model parameter set in a database based on the activation matrix, and determining the successfully activated model parameter set as the target model parameter set.
4. The fault monitoring method of an electric energy meter according to claim 1, wherein the determining whether the electric energy meter has a fault based on each of the running deviation indexes includes:
Comparing each operation deviation index with a first preset operation deviation index respectively;
if any operation deviation index is larger than the first preset operation deviation index, judging that the target electric energy meter has a fault;
If all the running deviation indexes are not larger than the first preset running deviation index, comparing all the running deviation indexes with a second preset running deviation index respectively, and determining the running parameters corresponding to the running deviation indexes as abnormal parameters when the running deviation indexes are larger than the second preset running deviation index; wherein the first preset operational deviation index is greater than the second preset operational deviation index;
and counting the total number of the abnormal parameters, judging whether the total number reaches a preset threshold, and if so, judging that the target electric energy meter has faults.
5. A fault monitoring device for an electric energy meter, comprising:
the device comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a target operation parameter information set of a target electric energy meter in a preset time period and acquiring total power utilization information of each electric equipment on a circuit corresponding to the target electric energy meter in the preset time period; the target operation parameter information sets comprise a plurality of target operation parameter information subsets, and each target operation parameter information subset corresponds to different types of operation parameters respectively;
The input module is used for inputting the total power information into a standard operation parameter generation model to obtain a standard operation parameter information set corresponding to the total power information; the standard operation parameter information sets comprise a plurality of standard operation parameter information subsets, and each standard operation parameter information subset corresponds to different types of operation parameters respectively;
The determining module is used for determining an operation deviation index of the operation parameters corresponding to the target operation parameter information subsets based on the standard operation parameter information subsets corresponding to the target operation parameter information subsets for each target operation parameter information subset;
the judging module is used for judging whether the electric energy meter has faults or not based on the running deviation indexes;
Wherein the determining an operation deviation index of the operation parameter corresponding to the target operation parameter information subset based on the standard operation parameter information subset corresponding to the target operation parameter information subset includes:
Drawing a target operation parameter change curve corresponding to the target operation parameter information subset in the preset time period based on each target operation parameter value in the target operation parameter information subset, and drawing a standard operation parameter change curve corresponding to the standard operation parameter information subset in the preset time period based on each standard operation parameter value in the standard operation parameter information subset;
Acquiring a first similarity of the target operation parameter change curve and the standard operation parameter change curve;
dividing the target operation parameter change curve based on a preset curve dividing rule to obtain a plurality of first curve segments, and dividing the standard operation parameter change curve based on the curve dividing rule to obtain a plurality of second curve segments;
For each first curve segment, obtaining a second similarity between the first curve segment and a second curve segment corresponding to the first curve segment;
obtaining standard deviation among the second similarity;
determining a ratio between the standard deviation and the first similarity as the running deviation index;
Before inputting the total electric power information into the standard operation parameter generation model, the method further comprises:
Acquiring an electric energy meter identifier of the target electric energy meter;
Acquiring a target model parameter set from a database based on the electric energy meter identifier; wherein the target model parameter set comprises a plurality of model parameters;
Acquiring a preset deep learning model;
And correspondingly updating each model parameter in the target model parameter set to the deep learning model to obtain the standard operation parameter generation model.
6. A terminal device comprising a processor, a memory and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the fault monitoring method of the electric energy meter according to any one of claims 1 to 4.
7. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, wherein the computer program, when executed by a processor, implements the fault monitoring method of the electric energy meter according to any one of claims 1 to 4.
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