CN116467939A - Electric energy meter service life prediction method, electric energy meter service life prediction device, computer equipment and storage medium - Google Patents

Electric energy meter service life prediction method, electric energy meter service life prediction device, computer equipment and storage medium Download PDF

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CN116467939A
CN116467939A CN202310376281.XA CN202310376281A CN116467939A CN 116467939 A CN116467939 A CN 116467939A CN 202310376281 A CN202310376281 A CN 202310376281A CN 116467939 A CN116467939 A CN 116467939A
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李丹
潘广泽
陈勃琛
王远航
孙立军
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China Electronic Product Reliability and Environmental Testing Research Institute
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Abstract

The application relates to a life prediction method, a life prediction device, computer equipment and a storage medium of an electric energy meter, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring target environment data of a target area at the current moment, and processing the target environment data based on a life prediction model to obtain the predicted life of the electric energy meter in the target area, wherein the life prediction model is obtained by training a fuzzy neural network according to the historical environment data of a reference area before the current moment and the actual life of the electric energy meter in the reference area by adopting a particle swarm algorithm. The service life of the electric energy meter can be accurately predicted by adopting the method.

Description

Electric energy meter service life prediction method, electric energy meter service life prediction device, computer equipment and storage medium
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a method and apparatus for predicting lifetime of an electric energy meter, a computer device, and a storage medium.
Background
The service life condition of the electric energy meter is one of important factors affecting the stability and the safety of the electric power system, and in order to ensure that the electric energy meter can accurately monitor the safe operation of the electric power system, an electric energy meter service life prediction method is provided. The service life of the electric energy meter is predicted by adopting a large amount of historical fault and degradation data of the electric energy meter.
However, in the current electric energy meter life prediction method, the influence of the electric energy meter life caused by the different climatic environments of the electric energy meter is not involved, so that the problems of inaccurate electric energy meter life prediction and the like are easily caused, and improvement is needed.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, and a storage medium for predicting lifetime of an electric energy meter, which can accurately predict lifetime of the electric energy meter.
In a first aspect, the present application provides a method for predicting lifetime of an electric energy meter, the method comprising:
acquiring target environment data of a target area at the current moment;
processing the target environment data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
In one embodiment, the life prediction model comprises at least two parallel fuzzy sub-function layers; based on a life prediction model, processing the target environment data to obtain the predicted life of the electric energy meter in the target area, including:
Selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers; and processing the target environment data based on the target fuzzy sub-function layer to obtain the predicted service life of the electric energy meter in the target area.
In one embodiment, the number of target fuzzy sub-function layers is at least two; processing the target environment data based on the target fuzzy sub-function layer to obtain the predicted life of the electric energy meter in the target area, comprising:
aiming at each target fuzzy subfunction layer, processing target environment data through a central value and a width value of a membership function corresponding to the target fuzzy subfunction layer to obtain a target fuzzy membership value output by the target fuzzy subfunction layer; and obtaining the predicted service life of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer.
In one embodiment, the target environmental data is at least two-dimensional target environmental data; processing the target environment data through the central value and the width value of the membership function corresponding to the target fuzzy subfunction layer to obtain a target fuzzy membership value output by the target fuzzy subfunction layer, wherein the method comprises the following steps:
Processing the target environment data of each dimension through the central value and the width value of the membership function corresponding to the target fuzzy sub-function layer to obtain the target membership corresponding to the target environment data of each dimension output by the target fuzzy sub-function layer; and taking the product of the target membership degrees corresponding to the target environment data of each dimension as a target fuzzy membership degree value output by the target fuzzy subfunction layer.
In one embodiment, obtaining the predicted lifetime of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer includes:
determining a total fuzzy membership value according to the target fuzzy membership value output by each target fuzzy subfunction layer; determining a total fuzzy membership function value according to the target environment data, the target fuzzy membership value output by each target fuzzy subfunction layer and the fuzzy coefficient corresponding to each target fuzzy subfunction layer; and taking the ratio of the total fuzzy membership value to the total fuzzy membership value as the predicted service life of the electric energy meter in the target area.
In one embodiment, selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers includes:
And comparing the target environment data of each dimension with a corresponding environment data threshold value, and selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers according to a comparison result.
In a second aspect, the present application further provides an apparatus for predicting lifetime of an electric energy meter, the apparatus comprising:
the data acquisition module is used for acquiring target environment data of a target area at the current moment;
the life prediction module is used for processing the target environment data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring target environment data of a target area at the current moment;
processing the target environment data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring target environment data of a target area at the current moment;
processing the target environment data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
acquiring target environment data of a target area at the current moment;
processing the target environment data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
According to the electric energy meter life prediction method, the device, the computer equipment and the storage medium, the life prediction model which is obtained by training the fuzzy neural network based on the historical environment data of the reference area before the current moment and the actual life of the electric energy meter in the reference area is introduced and adopted, and the predicted life of the electric energy meter in the target area can be obtained by acquiring the target environment data of the target area at the current moment and processing the target environment data based on the life prediction model. Compared with the related art, in the process of predicting the service life of the electric energy meter, the scheme fully considers the influence of the regional environment of the electric energy meter on the service life of the electric energy meter, and can ensure the accuracy of the service life prediction of the electric energy meter.
Drawings
FIG. 1 is an application environment diagram of a method for predicting lifetime of an electric energy meter in one embodiment;
FIG. 2 is a flow chart of a method for predicting lifetime of an electric energy meter according to an embodiment;
FIG. 3 is a flow chart of determining a predicted lifetime of an electric energy meter in one embodiment;
FIG. 4 is a flow chart of determining predicted lifetime of an electric energy meter according to another embodiment;
FIG. 5 is a flowchart of a method for predicting lifetime of an electric energy meter according to another embodiment;
FIG. 6 is a block diagram of a device for predicting lifetime of an electric energy meter according to an embodiment;
FIG. 7 is a block diagram of a device for predicting lifetime of an electric energy meter according to another embodiment;
fig. 8 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The method for predicting the service life of the electric energy meter, provided by the embodiment of the application, can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. For example, data such as target environment data of a target area. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. For example, the target environment data of the target area at the current moment can be collected by the terminal 102 deployed in the target area and then sent to the server 104; further, the server 104 obtains the target environmental data of the target area at the current moment, and processes the target environmental data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area. Wherein the terminal 102 may be, but is not limited to, various environmental data collection devices, and the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
The service life of the electric energy meter is one of important factors affecting the stability and safety of the electric power system, and in order to ensure that the electric energy meter can accurately monitor the safe operation of the electric power system, an electric energy meter service life prediction method for predicting the service life of the electric energy meter is provided. The service life of the electric energy meter is predicted by adopting a large amount of historical fault and degradation data of the electric energy meter.
The service life of the electric energy meter can be influenced to a certain extent by different climatic environments of the electric energy meter, however, in the current electric energy meter service life prediction method, the influence of the different climatic environments of the electric energy meter on the service life of the electric energy meter is not involved, and the problems of inaccurate electric energy meter service life prediction and the like are easily caused.
Based on this, in one embodiment, as shown in fig. 2, there is provided a method for predicting lifetime of an electric energy meter, which is described by taking an example that the method is applied to the server in fig. 1, and includes the following steps:
s201, acquiring target environment data of a target area at the current moment.
The target area may be any area where an electric energy meter exists. The target environment data may be related data of the environment within the target area; alternatively, the target environmental data may be environmental data of one, two or more dimensions, for example, data of one or more dimensions including temperature data, humidity data, barometric pressure data, illumination data, salt spray data, or wind speed data.
Alternatively, one or more environmental data collection devices for collecting environmental data may be deployed within the target area; and then acquiring target environment data of the target area at the current moment by using an environment data collecting device.
For example, a temperature data collection device for collecting temperature data can be deployed in the target area to obtain the temperature data of the target area at the current moment; alternatively, a temperature data collecting device for collecting temperature data and a humidity data collecting device for acquiring humidity data may be disposed in the target area, so that the temperature data and the humidity data of the target area at the current moment may be acquired.
S202, processing the target environment data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area.
The life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm. The reference area may be any area where historical environmental data exists and where a power meter is provided.
Alternatively, a fuzzy neural network may be initialized first, and the fuzzy neural network may be optimized by using a particle swarm algorithm. Specifically, parameters of each fuzzy sub-function layer in the fuzzy neural network can be used as parameters in position vectors of particles in the particle swarm, and historical environment data of a reference area before the current moment and actual service life of an electric energy meter in the reference area are obtained to be used as training sample data; and initializing a plurality of particles at different positions, wherein all the particles can randomly move within a preset range, and obtaining the adaptability between the positions of the particles and the fuzzy neural network according to the positions of the particles and training sample data. Furthermore, after multiple iterations of the particle swarm, the particle position with the highest fitness with the fuzzy neural network can be selected, and the optimized fuzzy neural network, namely the life prediction model, can be obtained according to the parameters in the particle position vector.
Optionally, after the life prediction model is obtained, the obtained target environmental data may be directly input into the life prediction model, and then the life prediction model may directly output the predicted life of the electric energy meter in the target area based on the input target environmental data and the parameters of the life prediction model.
According to the electric energy meter life prediction method, a life prediction model obtained by training the fuzzy neural network based on historical environment data of a reference area before the current moment and the actual life of the electric energy meter in the reference area is introduced and adopted, and the predicted life of the electric energy meter in the target area can be obtained by acquiring target environment data of the target area at the current moment and processing the target environment data based on the life prediction model. Compared with the related art, in the process of predicting the service life of the electric energy meter, the scheme fully considers the influence of the regional environment of the electric energy meter on the service life of the electric energy meter, and can ensure the accuracy of the service life prediction of the electric energy meter.
In order to improve accuracy of life prediction of the electric energy meter, in the case that the life prediction model includes at least two parallel fuzzy sub-function layers, in this embodiment, an alternative manner of predicting the life of the electric energy meter is provided, as shown in fig. 3, and specifically includes the following steps:
S301, selecting a target fuzzy subfunction layer from at least two parallel fuzzy subfunction layers.
Specifically, at least two parallel fuzzy sub-function layers exist in the life prediction model and are used for extracting data characteristics of target environment data so as to predict the life of the electric energy meter. The number of the fuzzy sub-function layers can be directly set according to historical experience, and can also be determined according to the data types contained in the target environment data. For example, if the target environment data includes 6 dimensions of target environment data, 12 parallel fuzzy sub-function layers may be set in the life prediction model.
It is understood that all fuzzy sub-function layers can be used once when the life prediction of the ammeter is performed, and part of fuzzy sub-function layers can be selected for use.
Optionally, after the target environment data is acquired, one or more fuzzy sub-function layers may be selected as the target fuzzy sub-function layers according to the target environment data of each dimension. For example, the target environment data of each dimension is compared with a corresponding environment data threshold, and a target fuzzy sub-function layer is selected from at least two parallel fuzzy sub-function layers according to the comparison result.
Specifically, the obtained target environment data of each dimension may be compared with the corresponding environment data threshold value, and then the target fuzzy sub-function layer is selected from at least two parallel fuzzy sub-function layers according to the comparison result. For example, the target environment data is temperature data and humidity data, wherein the temperature data is greater than a temperature data threshold and the humidity data is greater than a humidity data threshold, and therefore, a fuzzy sub-function layer corresponding to high temperature and high humidity may be selected as the target fuzzy sub-function layer.
S302, processing the target environment data based on the target fuzzy sub-function layer to obtain the predicted service life of the electric energy meter in the target area.
Optionally, if there is only one objective sub-function layer, the objective sub-function layer may be directly adopted to process the objective environment data, so as to obtain the predicted lifetime of the electric energy meter in the objective area.
If the number of the target fuzzy sub-function layers is at least two, a plurality of target sub-function layers can be adopted to process the target environment data at the same time, and then the predicted service life of the electric energy meter in the target area can be obtained according to the processing results of all the target sub-function layers.
In the embodiment, by introducing a plurality of parallel fuzzy sub-function layers, the data characteristics of the target environment data can be extracted more comprehensively, and further the accuracy of life prediction of the electric energy meter is improved.
In order to further improve accuracy of life prediction of the electric energy meter, in the above embodiment, in the case that the number of objective fuzzy sub-function layers is at least two, an alternative manner of life prediction of the electric energy meter is provided, as shown in fig. 4, and specifically includes the following steps:
s401, processing the target environment data according to the central value and the width value of the membership function corresponding to each target fuzzy subfunction layer to obtain the target fuzzy membership value output by the target fuzzy subfunction layer.
Wherein each fuzzy sub-function layer can correspond to one or more membership functions; optionally, the number of membership functions corresponding to each fuzzy sub-function layer is the same as the dimension of the target environment data. And for the membership function corresponding to the target environment data of each dimension, the membership function is a function for calculating the influence degree of the target environment data of the dimension on the service life of the electric energy meter.
Optionally, if the target environment data is only one dimension of target environment data, for each target fuzzy sub-function layer, the target environment data may be directly input to the target fuzzy sub-function layer, the target fuzzy sub-function layer processes the target environment data based on the central value and the width value of the corresponding membership function, and outputs a target fuzzy membership value, thereby obtaining a target fuzzy membership value output by the target fuzzy sub-function layer.
If the target environment data is the target environment data with at least two dimensions, the target fuzzy membership value output by the target fuzzy subfunction layer can be determined through the following steps.
The first step is to process the target environment data of each dimension through the central value and the width value of the membership function corresponding to the target fuzzy sub-function layer, and obtain the target membership corresponding to the target environment data of each dimension output by the target fuzzy sub-function layer.
Specifically, referring to the formula (1), according to the input target environment data of each dimension and the central value and the width value of the membership function corresponding to the target fuzzy sub-function layer, the target membership of the target environment data of each dimension is calculated.
Wherein the target environment data is target environment data of k dimensions i=1, 2,3 i Is the input target environment data in the ith dimension; the life prediction model contains n target blur subfunction layers, j=1, 2, 3.For the target ring in the ith dimension corresponding to the jth target fuzzy subfunction layerA central value of a membership function of the context data; />The width value of the membership function of the target environment data in the ith dimension corresponds to the jth target fuzzy subfunction layer; />And the target membership degree of the target environment data in the ith dimension corresponds to the jth target fuzzy subfunction layer.
And step two, taking the product of the target membership degrees corresponding to the target environment data of each dimension as a target fuzzy membership degree value output by the target fuzzy subfunction layer.
Specifically, referring to the formula (2), for each target fuzzy subfunction layer, multiplying the target membership corresponding to the target environment data of each dimension to obtain the target fuzzy membership value output by the target fuzzy subfunction layer.
Wherein,,the target membership degree of the target environment data under the ith dimension corresponds to the jth target fuzzy sub-function layer; omega j Is the target fuzzy membership value output by the jth target fuzzy subfunction layer.
S402, obtaining the predicted service life of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer.
Optionally, after obtaining the target fuzzy membership value output by each target fuzzy subfunction layer, the following steps may be adopted to obtain the predicted lifetime of the electric energy meter in the target area.
And a first step of determining a total fuzzy membership value according to the target fuzzy membership value output by each target fuzzy subfunction layer.
Specifically, adding the target fuzzy membership values output by each target fuzzy subfunction layer to obtain the total fuzzy membership value.
And a second step of determining a total fuzzy membership function value according to the target environment data, the target fuzzy membership value output by each target fuzzy subfunction layer and the fuzzy coefficient corresponding to each target fuzzy subfunction layer.
Specifically, referring to formula (3), for each target fuzzy sub-function layer, the fuzzy coefficient corresponding to the target fuzzy sub-function layer and the target environment data in each corresponding dimension are multiplied and added, and then the added and target fuzzy membership value of the target fuzzy sub-function layer is multiplied to obtain the fuzzy membership function value of the target fuzzy sub-function layer; and finally, adding the fuzzy membership function values of all the target fuzzy subfunction layers to obtain the total fuzzy membership function value.
Wherein,,the fuzzy coefficient of the target environment data in the kth dimension corresponds to the jth target fuzzy sub-function layer; s is the total fuzzy membership function value.
And thirdly, taking the ratio of the total fuzzy membership function value to the total fuzzy membership value as the predicted service life of the electric energy meter in the target area.
Specifically, referring to formula (4), dividing the total fuzzy membership function value by the total fuzzy membership value to obtain the predicted life of the electric energy meter in the target area. And y is the predicted life of the electric energy meter in the target area.
In the embodiment, the target environment data are respectively processed by introducing a plurality of target fuzzy subfunction layers and adopting the target fuzzy subfunction layers, so that the accuracy of life prediction of the electric energy meter is improved.
Fig. 5 is a schematic flow chart of a lifetime prediction method of an electric energy meter in another embodiment, and on the basis of the foregoing embodiment, this embodiment provides an alternative example of a lifetime prediction method of an electric energy meter. With reference to fig. 5, the specific implementation procedure is as follows:
s501, acquiring target environment data of a target area at the current moment.
S502, selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers in the life prediction model.
The life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
Specifically, comparing the target environment data of each dimension with a corresponding environment data threshold value, and selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers according to a comparison result.
S503, processing the target environment data according to the central value and the width value of the membership function corresponding to each target fuzzy subfunction layer to obtain the target fuzzy membership value output by the target fuzzy subfunction layer.
Specifically, processing the target environment data of each dimension through the central value and the width value of the membership function corresponding to the target fuzzy sub-function layer to obtain the target membership corresponding to the target environment data of each dimension output by the target fuzzy sub-function layer; and taking the product of the target membership degrees corresponding to the target environment data of each dimension as a target fuzzy membership degree value output by the target fuzzy subfunction layer.
S504, obtaining the predicted service life of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer.
Specifically, determining a total fuzzy membership value according to the target fuzzy membership value output by each target fuzzy subfunction layer; determining a total fuzzy membership function value according to the target environment data, the target fuzzy membership value output by each target fuzzy subfunction layer and the fuzzy coefficient corresponding to each target fuzzy subfunction layer; and taking the ratio of the total fuzzy membership value to the total fuzzy membership value as the predicted service life of the electric energy meter in the target area.
The specific process of S501 to S504 may refer to the description of the foregoing method embodiment, and the implementation principle and technical effects are similar, and are not repeated herein.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an electric energy meter service life prediction device for realizing the electric energy meter service life prediction method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the lifetime prediction device for an electric energy meter provided below may be referred to the limitation of the lifetime prediction method for an electric energy meter hereinabove, and will not be described herein.
In one embodiment, as shown in fig. 6, there is provided an electric energy meter lifetime prediction device 1, comprising: a data acquisition module 10 and a life prediction module 20, wherein:
a data acquisition module 10, configured to acquire target environment data of a target area at a current time;
the life prediction module 20 is configured to process the target environmental data based on the life prediction model to obtain a predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
In one embodiment, where at least two parallel fuzzy sub-function layers are included in the life prediction model, as shown in FIG. 7, the life prediction module 20 includes:
A function layer determining unit 21 for selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers;
and the service life prediction unit 22 is used for processing the target environment data based on the target fuzzy sub-function layer to obtain the predicted service life of the electric energy meter in the target area.
In one embodiment, in the case where the number of target blur subfunction layers is at least two, the lifetime prediction unit 22 includes:
the first prediction subunit is used for processing the target environment data according to the central value and the width value of the membership function corresponding to each target fuzzy subfunction layer to obtain a target fuzzy membership value output by the target fuzzy subfunction layer;
and the second prediction subunit obtains the predicted service life of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer.
In one embodiment, in the case that the target environment data is target environment data of at least two dimensions, the first prediction subunit is specifically configured to:
processing the target environment data of each dimension through the central value and the width value of the membership function corresponding to the target fuzzy sub-function layer to obtain the target membership corresponding to the target environment data of each dimension output by the target fuzzy sub-function layer; and taking the product of the target membership degrees corresponding to the target environment data of each dimension as a target fuzzy membership degree value output by the target fuzzy subfunction layer.
In one embodiment, the second prediction subunit is specifically configured to:
determining a total fuzzy membership value according to the target fuzzy membership value output by each target fuzzy subfunction layer; determining a total fuzzy membership function value according to the target environment data, the target fuzzy membership value output by each target fuzzy subfunction layer and the fuzzy coefficient corresponding to each target fuzzy subfunction layer; and taking the ratio of the total fuzzy membership value to the total fuzzy membership value as the predicted service life of the electric energy meter in the target area.
In one embodiment, the function layer determining unit 21 is specifically configured to:
and comparing the target environment data of each dimension with a corresponding environment data threshold value, and selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers according to a comparison result.
The modules in the electric energy meter life prediction device can be realized in whole or in part through software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 8. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data such as target environment data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a method for predicting lifetime of an electric energy meter.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring target environment data of a target area at the current moment;
processing the target environment data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
In one embodiment, the life prediction model comprises at least two parallel fuzzy sub-function layers; the processor executes the logic of the predicted service life of the electric energy meter in the target area, which is based on the service life prediction model and processes the target environment data in the computer program, and the following steps are specifically realized:
Selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers; and processing the target environment data based on the target fuzzy sub-function layer to obtain the predicted service life of the electric energy meter in the target area.
In one embodiment, the number of target fuzzy sub-function layers is at least two; the processor executes logic based on a target fuzzy subfunction layer in a computer program for processing target environment data to obtain the predicted service life of the electric energy meter in a target area, and specifically realizes the following steps:
aiming at each target fuzzy subfunction layer, processing target environment data through a central value and a width value of a membership function corresponding to the target fuzzy subfunction layer to obtain a target fuzzy membership value output by the target fuzzy subfunction layer; and obtaining the predicted service life of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer.
In one embodiment, the target environmental data is at least two-dimensional target environmental data; the processor executes the logic of the target fuzzy membership value output by the target fuzzy subfunction layer by processing the target environment data through the central value and the width value of the membership function corresponding to the target fuzzy subfunction layer in the computer program, and specifically realizes the following steps:
Processing the target environment data of each dimension through the central value and the width value of the membership function corresponding to the target fuzzy sub-function layer to obtain the target membership corresponding to the target environment data of each dimension output by the target fuzzy sub-function layer; and taking the product of the target membership degrees corresponding to the target environment data of each dimension as a target fuzzy membership degree value output by the target fuzzy subfunction layer.
In one embodiment, when the processor executes logic for obtaining the predicted lifetime of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer in the computer program, the following steps are specifically implemented:
determining a total fuzzy membership value according to the target fuzzy membership value output by each target fuzzy subfunction layer; determining a total fuzzy membership function value according to the target environment data, the target fuzzy membership value output by each target fuzzy subfunction layer and the fuzzy coefficient corresponding to each target fuzzy subfunction layer; and taking the ratio of the total fuzzy membership value to the total fuzzy membership value as the predicted service life of the electric energy meter in the target area.
In one embodiment, when the processor executes logic in the computer program to select a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers, the following steps are specifically implemented:
And comparing the target environment data of each dimension with a corresponding environment data threshold value, and selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers according to a comparison result.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring target environment data of a target area at the current moment;
processing the target environment data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
In one embodiment, the life prediction model comprises at least two parallel fuzzy sub-function layers; based on a life prediction model, processing target environment data in the computer program, and when the code logic for obtaining the predicted life of the electric energy meter in the target area is executed by the processor, the following steps are specifically realized:
selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers; and processing the target environment data based on the target fuzzy sub-function layer to obtain the predicted service life of the electric energy meter in the target area.
In one embodiment, the number of target fuzzy sub-function layers is at least two; the code logic for processing the target environment data based on the target fuzzy sub-function layer in the computer program to obtain the predicted service life of the electric energy meter in the target area is executed by the processor, and the following steps are specifically realized:
aiming at each target fuzzy subfunction layer, processing target environment data through a central value and a width value of a membership function corresponding to the target fuzzy subfunction layer to obtain a target fuzzy membership value output by the target fuzzy subfunction layer; and obtaining the predicted service life of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer.
In one embodiment, the target environmental data is at least two-dimensional target environmental data; processing the target environment data through the central value and the width value of the membership function corresponding to the target fuzzy subfunction layer in the computer program, and when the code logic for obtaining the target fuzzy membership value output by the target fuzzy subfunction layer is executed by a processor, specifically realizing the following steps:
processing the target environment data of each dimension through the central value and the width value of the membership function corresponding to the target fuzzy sub-function layer to obtain the target membership corresponding to the target environment data of each dimension output by the target fuzzy sub-function layer; and taking the product of the target membership degrees corresponding to the target environment data of each dimension as a target fuzzy membership degree value output by the target fuzzy subfunction layer.
In one embodiment, the code logic for obtaining the predicted lifetime of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer in the computer program is executed by the processor, and specifically implements the following steps:
determining a total fuzzy membership value according to the target fuzzy membership value output by each target fuzzy subfunction layer; determining a total fuzzy membership function value according to the target environment data, the target fuzzy membership value output by each target fuzzy subfunction layer and the fuzzy coefficient corresponding to each target fuzzy subfunction layer; and taking the ratio of the total fuzzy membership value to the total fuzzy membership value as the predicted service life of the electric energy meter in the target area.
In one embodiment, this code logic in the computer program for selecting a target fuzzy subfunction layer from at least two parallel fuzzy subfunction layers, when executed by a processor, performs the steps of:
and comparing the target environment data of each dimension with a corresponding environment data threshold value, and selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers according to a comparison result.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
Acquiring target environment data of a target area at the current moment;
processing the target environment data based on the life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
In one embodiment, the life prediction model comprises at least two parallel fuzzy sub-function layers; when the computer program is executed by a processor and processes the target environment data based on the life prediction model to obtain the operation of predicting the life of the electric energy meter in the target area, the following steps are specifically realized:
selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers; and processing the target environment data based on the target fuzzy sub-function layer to obtain the predicted service life of the electric energy meter in the target area.
In one embodiment, the number of target fuzzy sub-function layers is at least two; the computer program is executed by a processor and is based on the target fuzzy subfunction layer, the target environment data are processed, and when the operation of the predicted service life of the electric energy meter in the target area is obtained, the following steps are specifically realized:
Aiming at each target fuzzy subfunction layer, processing target environment data through a central value and a width value of a membership function corresponding to the target fuzzy subfunction layer to obtain a target fuzzy membership value output by the target fuzzy subfunction layer; and obtaining the predicted service life of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer.
In one embodiment, the target environmental data is at least two-dimensional target environmental data; the computer program is executed by the processor to process the target environment data through the central value and the width value of the membership function corresponding to the target fuzzy subfunction layer, and when the operation of obtaining the target fuzzy membership value output by the target fuzzy subfunction layer is carried out, the following steps are specifically realized:
processing the target environment data of each dimension through the central value and the width value of the membership function corresponding to the target fuzzy sub-function layer to obtain the target membership corresponding to the target environment data of each dimension output by the target fuzzy sub-function layer; and taking the product of the target membership degrees corresponding to the target environment data of each dimension as a target fuzzy membership degree value output by the target fuzzy subfunction layer.
In one embodiment, when the computer program is executed by the processor to obtain the operation of predicting the lifetime of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer, the following steps are specifically implemented:
determining a total fuzzy membership value according to the target fuzzy membership value output by each target fuzzy subfunction layer; determining a total fuzzy membership function value according to the target environment data, the target fuzzy membership value output by each target fuzzy subfunction layer and the fuzzy coefficient corresponding to each target fuzzy subfunction layer; and taking the ratio of the total fuzzy membership value to the total fuzzy membership value as the predicted service life of the electric energy meter in the target area.
In one embodiment, the computer program is executed by a processor to select a target fuzzy subfunction layer from at least two parallel fuzzy subfunction layers, specifically implementing the steps of:
and comparing the target environment data of each dimension with a corresponding environment data threshold value, and selecting a target fuzzy sub-function layer from at least two parallel fuzzy sub-function layers according to a comparison result.
It should be noted that, the data (including, but not limited to, the historical environmental data, the target environmental data, etc.) related to the present application are all data fully authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims (10)

1. A method for predicting lifetime of an electric energy meter, the method comprising:
acquiring target environment data of a target area at the current moment;
processing the target environment data based on a life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
2. The method of claim 1, wherein the life prediction model comprises at least two parallel fuzzy sub-function layers; the life prediction model is based on processing the target environment data to obtain the predicted life of the electric energy meter in the target area, and the method comprises the following steps:
selecting a target fuzzy sub-function layer from the at least two parallel fuzzy sub-function layers;
and processing the target environment data based on the target fuzzy sub-function layer to obtain the predicted service life of the electric energy meter in the target area.
3. The method of claim 2, wherein the number of target blur sub-function layers is at least two; the processing the target environment data based on the target fuzzy sub-function layer to obtain the predicted life of the electric energy meter in the target area comprises the following steps:
aiming at each target fuzzy subfunction layer, processing the target environment data through the central value and the width value of the membership function corresponding to the target fuzzy subfunction layer to obtain a target fuzzy membership value output by the target fuzzy subfunction layer;
and obtaining the predicted service life of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer.
4. A method according to claim 3, wherein the target environmental data is at least two-dimensional target environmental data; the processing the target environment data through the center value and the width value of the membership function corresponding to the target fuzzy subfunction layer to obtain the target fuzzy membership value output by the target fuzzy subfunction layer comprises the following steps:
processing the target environment data of each dimension through the central value and the width value of the membership function corresponding to the target fuzzy sub-function layer to obtain the target membership corresponding to the target environment data of each dimension output by the target fuzzy sub-function layer;
and taking the product of the target membership degrees corresponding to the target environment data of each dimension as a target fuzzy membership degree value output by the target fuzzy subfunction layer.
5. The method of claim 3, wherein the obtaining the predicted lifetime of the electric energy meter in the target area according to the target environment data and the target fuzzy membership value output by each target fuzzy subfunction layer comprises:
determining a total fuzzy membership value according to the target fuzzy membership value output by each target fuzzy subfunction layer;
Determining a total fuzzy membership function value according to the target environment data, the target fuzzy membership value output by each target fuzzy subfunction layer and the fuzzy coefficient corresponding to each target fuzzy subfunction layer;
and taking the ratio between the total fuzzy membership function value and the total fuzzy membership value as the predicted service life of the electric energy meter in the target area.
6. The method of claim 2, wherein selecting the target fuzzy subfunction layer from the at least two parallel fuzzy subfunction layers comprises:
and comparing the target environment data of each dimension with a corresponding environment data threshold value, and selecting a target fuzzy sub-function layer from the at least two parallel fuzzy sub-function layers according to a comparison result.
7. An electrical energy meter life prediction device, the device comprising:
the data acquisition module is used for acquiring target environment data of a target area at the current moment;
the life prediction module is used for processing the target environment data based on a life prediction model to obtain the predicted life of the electric energy meter in the target area; the life prediction model is obtained by training a fuzzy neural network according to historical environment data of a reference area before the current moment and the actual life of an electric energy meter in the reference area by adopting a particle swarm algorithm.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
CN202310376281.XA 2023-04-10 2023-04-10 Electric energy meter service life prediction method, electric energy meter service life prediction device, computer equipment and storage medium Pending CN116467939A (en)

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