A circuit breaker fault diagnosis model training method and a circuit breaker fault diagnosis method

Abstract

The present invention discloses a circuit breaker fault diagnosis model training method and a circuit breaker fault diagnosis method. The method includes: constructing an initial model, the initial model including multiple generators and a discriminator; training, verifying, and testing the initial model in multiple batches based on a training data set to obtain a circuit breaker fault diagnosis model; during the training process of any batch, adjusting the model parameters of the multiple generators and discriminators based on the sample data of the current batch and the fault labels corresponding to the sample data to obtain multiple first generators and discriminators of the current batch; exchanging model parameters of at least some of the multiple first generators in the current batch, and / or performing parameter mutation based on a preset mutation ratio to obtain multiple second generators of the current batch; and training the multiple second generators and discriminators for the next batch. This method can prevent the fault diagnosis model from falling into a local optimum and improve the fault diagnosis accuracy of the fault diagnosis model.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to a fault diagnosis model training method for a circuit breaker and a fault diagnosis method for a circuit breaker. Background Art

[0002] Circuit breakers play a dual role of control and protection in power distribution systems. Their operating conditions directly determine the operation of the entire power system. Therefore, fault diagnosis of circuit breakers is of great significance.

[0003] A variety of diagnostic methods have been proposed, involving various artificial intelligence algorithms, such as neural networks. However, circuit breaker fault diagnosis methods based on neural networks have the problem of being easily trapped in local optimality. Summary of the Invention

[0004] The present invention provides a circuit breaker fault diagnosis model training method and a circuit breaker fault diagnosis method, so as to prevent the fault diagnosis model from falling into a local optimum and improve the fault diagnosis accuracy of the fault diagnosis model.

[0005] According to one aspect of the present invention, a method for training a fault diagnosis model for a circuit breaker is provided, comprising:

[0006] Acquire a training data set, wherein the training data set includes circuit breaker sample data and fault labels corresponding to the circuit breaker sample data;

[0007] Constructing an initial model, wherein the initial model includes a plurality of generators and a discriminator; training the initial model in multiple batches based on the training data set: in the training process of any batch, adjusting the model parameters of the plurality of generators and the discriminator based on the sample data of the current batch and the fault labels corresponding to the sample data, to obtain a plurality of first generators and discriminators of the current batch; exchanging model parameters of at least a portion of the first generators of the multiple first generators of the current batch, and / or performing parameter mutation on the model parameters of at least a portion of the first generators of the multiple first generators of the previous batch based on a preset mutation ratio, to obtain a plurality of second generators of the current batch; training the multiple second generators and the discriminator for the next batch;

[0008] When the training end condition is met, a plurality of candidate generators that have been trained in a plurality of batches are obtained; a verification data set is obtained, and verification processing is performed on the plurality of candidate generators based on the verification data set to obtain verification indicators corresponding to the plurality of candidate generators, and a target generator is determined from the plurality of candidate generators based on the verification indicators corresponding to the plurality of candidate generators;

[0009] A test data set is obtained, and the target generator is tested based on the test data set to obtain a test indicator of the target generator. If the test indicator meets a test condition, the target generator is determined to be a circuit breaker fault diagnosis model.

[0010] According to another aspect of the present invention, a method for diagnosing a fault of a circuit breaker is provided, comprising:

[0011] Acquiring operational data related to a fault of a circuit breaker to be diagnosed;

[0012] The operating data is input into a circuit breaker fault diagnosis model for fault diagnosis to obtain a fault diagnosis result of the circuit breaker to be diagnosed, wherein the circuit breaker fault diagnosis model is trained based on the circuit breaker fault diagnosis model training method according to any embodiment of the present invention.

[0013] According to another aspect of the present invention, a circuit breaker fault diagnosis model training device is provided, comprising:

[0014] A training data set acquisition module, configured to acquire a training data set, wherein the training data set includes circuit breaker sample data and fault labels corresponding to the circuit breaker sample data;

[0015] A training module is used to construct an initial model, wherein the initial model includes multiple generators and a discriminator; the initial model is trained in multiple batches based on the training data set: in the training process of any batch, the model parameters of the multiple generators and the discriminators are adjusted based on the sample data of the current batch and the fault labels corresponding to the sample data, so as to obtain multiple first generators and discriminators of the current batch; the model parameters of at least some of the multiple first generators of the current batch are exchanged, and / or the model parameters of at least some of the multiple first generators of the previous batch are mutated based on a preset mutation ratio, so as to obtain multiple second generators of the current batch; the multiple second generators and the discriminators are trained for the next batch;

[0016] A target generator determination module is configured to obtain a plurality of candidate generators trained in a plurality of batches when a training end condition is satisfied; obtain a verification data set, perform verification processing on the plurality of candidate generators based on the verification data set, obtain verification indicators corresponding to the plurality of candidate generators, and determine a target generator from the plurality of candidate generators based on the verification indicators corresponding to the plurality of candidate generators;

[0017] The target generator test module is used to obtain a test data set, test the target generator based on the test data set, obtain the test index of the target generator, and determine the target generator as a circuit breaker fault diagnosis model when the test index meets the test conditions.

[0018] According to another aspect of the present invention, an electronic device is provided, comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the circuit breaker fault diagnosis model training method described in any embodiment of the present invention and / or the circuit breaker fault diagnosis method described in any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and wherein the computer instructions are configured to cause a processor to implement the circuit breaker fault diagnosis model training method according to any embodiment of the present invention, and / or the circuit breaker fault diagnosis method according to any embodiment of the present invention, when executed.

[0023] The technical solution of the embodiment of the present invention is to train multiple batches of multiple generators and a discriminator in the initial model, and for any batch, exchange model parameters of at least some of the multiple first generators obtained by training in the current batch, and / or perform parameter mutation on the model parameters of at least some of the multiple first generators obtained by training in the previous batch based on a preset mutation ratio, so as to prevent the fault diagnosis model from falling into local optimality and improve the fault diagnosis accuracy of the fault diagnosis model.

[0024] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 This is a flow chart of a method for training a fault diagnosis model for a circuit breaker provided in the first embodiment of the present invention;

[0027] Figure 2 This is a flow chart of a circuit breaker fault diagnosis method provided by the second embodiment of the present invention;

[0028] Figure 3 This is a structural diagram of a circuit breaker fault diagnosis model training device provided by the third embodiment of the present invention;

[0029] Figure 4 This is a schematic structural diagram of a circuit breaker fault diagnosis device provided by a fourth embodiment of the present invention;

[0030] Figure 5 This is a structural diagram of an electronic device provided in Example 5 of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Example 1

[0034] Figure 1This is a flow chart of a method for training a fault diagnosis model for a circuit breaker provided in the first embodiment of the present invention. This embodiment is applicable to the case of training a fault diagnosis model for a circuit breaker. The method can be executed by a fault diagnosis model training device for a circuit breaker. The fault diagnosis model training device for a circuit breaker can be implemented in the form of hardware and / or software. The fault diagnosis model training device for a circuit breaker can be configured in an electronic device such as a computer or server for training a fault diagnosis model. Figure 1 As shown, the method includes:

[0035] S110: Acquire a training data set, wherein the training data set includes circuit breaker sample data and fault labels corresponding to the circuit breaker sample data.

[0036] Among them, circuit breaker sample data refers to the operating data of the circuit breaker in a fault state. Specifically, the circuit breaker sample data includes but is not limited to the voltage and current data in the circuit breaker fault state, the mechanical vibration signal of the circuit breaker, the circuit breaker temperature, etc.; the voltage and current data in the circuit breaker fault state can be used to identify whether the circuit breaker has electrical faults such as overload and short circuit; the circuit breaker mechanical vibration signal can reflect the working status of the internal mechanical components of the circuit breaker, such as loose base screws, shock absorber impact, and detachment of mechanism moving parts; through the temperature sensor installed on the circuit breaker, the circuit breaker temperature can be monitored in real time to determine whether there is an overheating problem, thereby preventing the occurrence of dangerous situations such as fire. The fault label is used to characterize the type of fault corresponding to the circuit breaker sample data.

[0037] In this embodiment, before model training, a data set is pre-constructed and randomly divided into a training data set, a validation data set, and a test data set, wherein the random division ratios of the training data set, the validation data set, and the test data set can be 40%, 40%, and 20%.

[0038] S120. Construct an initial model, which includes multiple generators and a discriminator; train the initial model in multiple batches based on the training data set: in the training process of any batch, adjust the model parameters of the multiple generators and the discriminators based on the sample data of the current batch and the fault labels corresponding to the sample data, to obtain multiple first generators and discriminators of the current batch; exchange model parameters of at least some of the multiple first generators of the current batch, and / or mutate the model parameters of at least some of the multiple first generators of the previous batch based on a preset mutation ratio, to obtain multiple second generators of the current batch; train the multiple second generators and the discriminators for the next batch.

[0039] Among them, the initial model includes multiple generators and a discriminator. The structures of multiple generators are the same, but the initialization processes of different generators are different. The generator can be a neural network model, such as a BP neural network model, a convolutional neural network model, a fully connected neural network model, etc. The discriminator can be a multi-layer perceptron.

[0040] In this embodiment, the sample data in the training data set can be divided into batches to obtain different batches of sample data and fault labels corresponding to the sample data. The training process of any batch is as follows:

[0041] 1) Adjusting model parameters of multiple generators and discriminators based on the sample data of the current batch and the fault labels corresponding to the sample data to obtain multiple first generators and discriminators of the current batch;

[0042] 2) Exchanging model parameters of at least some of the multiple first generators in the current batch, and / or performing parameter mutation on the model parameters of at least some of the multiple first generators in the previous batch based on a preset mutation ratio to obtain multiple second generators in the current batch.

[0043] Among them, the first generator refers to the generator obtained after training in the current batch, the second generator refers to the generator after parameter exchange and / or parameter variation of at least a part of the first generator obtained by training in the current batch, and the at least part of the first generator refers to the first generator obtained by training in the current batch that undergoes parameter exchange and / or parameter variation. It should be noted that if the current batch is the first batch, the generator trained in the current batch is the initial generator in the initial model; if the current batch is not the first batch, the multiple generators trained in the current batch include the first generator and / or the second generator obtained in the previous batch, wherein the first generator is the first generator in the previous batch that has not undergone parameter exchange and / or parameter variation, and the second generator is the second generator obtained after parameter exchange and / or parameter variation of at least a part of the first generator in the previous batch.

[0044] During the training of multiple batches of the initial model, the generator's loss function includes the adversarial loss variable function and the cross entropy loss function. The specific expressions are as follows:

[0045]

[0046] Among them, loss G Represents the total loss function of the generator, loss G1 Represents the adversarial loss variable function, loss G2 Cross entropy loss function, batch represents the total training batch, W represents the number of generators, b imrepresents the fault label of the mth generator in the i-th training batch, G im represents the fault sample data of the mth generator in the i-th training batch, Indicates the fault sample data accepted by the i-th training batch discriminator After the output,

[0047] The loss function of the discriminator includes the true fault label loss function and the generated fault label loss function. The specific expression is as follows:

[0048]

[0049]

[0050] Among them, loss D Represents the total loss function of the discriminator, loss D1 Represents the true fault label loss function, loss D2 represents the loss function for generating fault labels, Indicates the fault label of the input received by the discriminator in the i-th batch After the output,

[0051] On the basis of the above embodiment, optionally, the model parameter exchange of at least a local first generator among the multiple first generators of the current batch includes: determining at least a local first generator among the multiple first generators of the current batch; determining a generator pair among the at least local first generators, the generator pair including the two first generators that perform model parameter exchange; and correspondingly exchanging the local layer model parameters of the two first generators in the generator pair.

[0052] A generator pair refers to a pair of first generators that exchange model parameters. Specifically, each generator pair includes two generators, which can be randomly matched in pairs in a local first generator, and the two successfully matched first generators are regarded as a generator pair. For example, two adjacent first generators can be regarded as a generator pair, or matching can be performed based on preset matching rules in at least a local first generator to obtain a generator pair.

[0053] It should be noted that in the process of model parameter exchange, the parameters of the local network layers of the two first generators in the generator pair are exchanged accordingly, but the parameters of the first network layer in the first generator are not exchanged.

[0054] Among them, the local layer refers to the local network layer in the first generator, and the model parameters of the local layer are exchanged. It can be understood that the number of layers of the local layer corresponding to different generator pairs is different; optionally, the method of determining the number of layers of the local layer is: for any generator pair, the number of layers of the local layer corresponding to the generator pair is determined based on the number of the first generators and a first preset parameter; wherein the first preset parameter is a random number, or the first preset parameter is determined based on the processing accuracy of the two first generators in the generator pair; or the number of layers of the local layer is determined based on the order of the first generators in the generator pair.

[0055] Specifically, for any generator pair, the number of local layers corresponding to the generator pair can be determined based on the number of first generators and a random number, where the first preset parameter can be a random number m, where 1<m<the number of network layers of the first generator. For example, assuming that the number of network layers of the first generator is M, m (1<m<M) network layers can be randomly selected as local layers.

[0056] For any generator pair, the first preset parameter can also be determined based on the processing accuracy of the two first generators in the generator pair. Furthermore, the number of layers of the local layer corresponding to the generator pair can be determined based on the number of first generators and the first preset parameter. The processing accuracy of the first generator refers to the accuracy of the fault classification of the first generators in the current batch, and the first preset parameter is negatively correlated with the processing accuracy of the first generator. In other words, the smaller the processing accuracy of the first generator, the larger the first preset parameter, and correspondingly, the larger the number of layers of the local layer.

[0057] Exemplarily, the calculation formula for the number of layers of a local layer is as follows:

[0058] m n =(M-1)*η n ,

[0059] Among them, m n Represents the number of local layers corresponding to the nth first generator, n∈[1,N], N represents the number of first generators, M represents the total number of network layers of the first generator, η n Represents the first preset parameter corresponding to the nth first generator, η∈(0,1]. The smaller the processing accuracy of the first generator, the larger the first preset parameter. It should be noted that since the structure of the generator is the same, the total number of layers of each first generator is M.

[0060] For any generator pair, the number of layers in the local layer can also be determined based on the order of the first generator in the generator pair. Exemplarily, the number of layers in the local layer is calculated as follows:

[0061]

[0062] Among them, m n Indicates the number of local layers corresponding to the nth first generator, m n is an integer, n∈[1,N], N represents the number of the first generator, M represents the total number of layers of the first generator, k1 is a hyperparameter, for example, k1=3 or 4; γ is bound to the order of the first generator and is evenly distributed in Wherein, k2 is the ranking position of the first generator. For example, a first generator is at the first position in the ranking, and the k2 corresponding to the first generator is 1. It should be noted that if If the result of the calculation is not an integer, The calculation results of the approximate processing are approximated. The calculation result is m n Specifically, the approximate method based on rounding can be used to The calculation results of m are approximated and m is obtained. n ; For example: If The calculated result is 3.4, so the m obtained by approximate processing is n =3, if The calculated result is 4.7, so the m obtained by approximate processing is n =5. You can also use the preset rules to The calculation results of m are approximated and m is obtained. n For example, the preset rule can be rounding up or rounding down. Taking rounding up as an example, regardless of The calculated result is 3.1 or 3.7, and the approximate m n Both are 4.

[0063] In this embodiment, the model parameter exchange method is: determine at least a local first generator among the multiple first generators obtained from the current batch training. Specifically, the first generators can be sorted, and the local first generator can be selected according to the sorting result, or the local first generator can be randomly extracted from the first generator; further, random matching is performed between at least the local first generators to obtain a generator pair; further, the local layer of the first generator in the generator pair is determined based on the number of layers of the local layer, and the model parameters of the local layers in the two first generators of the generator pair are correspondingly exchanged. Exemplarily, it can be done according to Any scheme extracts m from any first generator in the generator pair n The parameters of the layers are the same as the parameters of the other generator in the generator pair m n The layers are exchanged accordingly.

[0064] On the basis of the above embodiment, optionally, the determining of at least a local first generator among the multiple first generators of the current batch includes: sorting the multiple first generators based on the processing accuracy of the multiple first generators of the current batch; dividing the multiple first generators into a first generator group and a second generator group based on the sorting of the multiple first generators; wherein the processing accuracy of at least one of the first generators in the first generator group is higher than the processing accuracy of at least one of the first generators in the second generator group; and determining at least one of the first generators in the second generator group as the at least local first generator that performs the model parameter exchange.

[0065] In which, the first generator group is a generator group composed of generators with higher processing accuracy, and the second generator group is a generator group composed of generators with lower processing accuracy. It is understandable that, since the first generators in the first generator group have higher processing accuracy, the parameters of the generators in the first generator group do not need to be exchanged, and the parameters remain unchanged. In this embodiment, the first generators can be sorted according to the processing accuracy of multiple first generators obtained from the current batch training to obtain a sorting result of the first generators. The first generators are divided according to the sorting result of the first generators according to a preset division ratio to obtain a first generator group and a second generator group, and at least one first generator in the second generator group is determined as at least a partial first generator for performing model parameter exchange. The preset division ratio is used to divide the sorted first generators into the first generator group and the second generator group. Exemplarily, the preset division ratio can be 30%. The first generators are sorted according to their processing accuracy, and the first generators ranked in the top 30% of the processing accuracy are divided into the first generator group, and the first generators ranked in the bottom 60% of the processing accuracy are divided into the second generator group.

[0066] On the basis of the above embodiment, optionally, the model parameters of at least some first generators among the multiple first generators of the current batch are mutated based on a preset variation ratio, including: determining at least some first generators among the multiple first generators of the current batch; determining the preset variation ratio corresponding to each first generator, and mutating the model parameters of the first generator based on the preset variation ratio.

[0067] The preset mutation ratio refers to the ratio of parameter mutation of the first generator. Specifically, the preset mutation ratio can be a random ratio, such as 5%. The preset mutation ratio of the first generator can also be determined based on the batch. The preset mutation ratio of the first generator is negatively correlated with the batch. Exemplarily, the calculation formula of the preset mutation ratio is as follows:

[0068]

[0069] Among them, q n represents the preset mutation ratio of the nth first generator, s represents the current batch, S represents the maximum batch, and y is a hyperparameter.

[0070] The preset variation ratio of the first generator can also be determined according to the processing accuracy of the first generator. The preset variation ratio of the first generator is negatively correlated with the processing accuracy based on the first generator.

[0071] For example, the calculation formula of the preset variation ratio is as follows:

[0072] q n =y%*λ n

[0073] Among them, λ n is the coefficient of the preset mutation ratio of the nth first generator, λ n Negatively correlated with the processing accuracy of the first generator.

[0074] The preset variation ratio of the first generator can also be determined based on the batch and the processing accuracy of the first generator. For example, by combining the above two methods for calculating the preset variation ratio, the calculation formula for the preset variation ratio can be obtained as follows:

[0075]

[0076] It can be understood that the larger the training batch is, the higher the processing accuracy of the first generator obtained by the current batch training is, and correspondingly, the smaller the proportion of model parameters that undergo parameter variation in the first generator is.

[0077] In this embodiment, at least some of the first generators in the current batch are determined; the above-mentioned method for determining the preset variation ratio determines the preset variation ratio corresponding to each first generator, and the model parameters of the first generator are mutated based on the preset variation ratio. Any scheme extracts the model parameters in the nth first generator for mutation processing, where W represents the total number of parameters of the first generator.

[0078] S130. When the training end conditions are met, multiple candidate generators that have been trained in multiple batches are obtained; a verification data set is obtained, and verification processing is performed on the multiple candidate generators based on the verification data set to obtain verification indicators corresponding to the multiple candidate generators, and a target generator is determined among the multiple candidate generators based on the verification indicators corresponding to the multiple candidate generators.

[0079] Among them, the training end condition refers to the condition for the end of training. Specifically, the training end condition may be that the processing accuracy of the generator reaches a preset accuracy, and the training end condition may also be that the training batch reaches a preset batch. In this embodiment, the judgment is made based on the preset training end condition. When the training end condition is met, the multiple first generators obtained through multiple batches of training are determined as candidate generators. Further, a verification data set is obtained, and verification processing is performed on the multiple candidate generators based on the verification data set to obtain verification indicators corresponding to the multiple candidate generators; further, the verification indicators corresponding to the multiple candidate generators can be judged based on the determination condition of the target generator, and the target generator is determined among the multiple candidate generators. Among them, the verification indicator is an indicator used to evaluate the performance of multiple candidate generators. Specifically, the verification indicator includes but is not limited to accuracy, recall rate, F1 score, etc. The determination condition of the target generator can be a threshold corresponding to the verification indicator, such as an accuracy threshold, an F1 score threshold, etc., which is not limited here.

[0080] S140: Acquire a test data set, perform test processing on the target generator based on the test data set, obtain test indicators of the target generator, and determine the target generator as a circuit breaker fault diagnosis model if the test indicators meet test conditions.

[0081] Among them, the test indicators are used to further evaluate the performance of the target generator to determine whether they meet the test conditions. The test indicators include but are not limited to accuracy, recall rate, F1 score, etc. It should be noted that the test indicators can be the same as the verification indicators or different from the verification indicators, and can be set by those skilled in the art according to needs. In this embodiment, a test data set is obtained, and the target generator is tested based on the test data set to obtain the test indicators of the target generator. When the test indicators meet the test conditions, the target generator is determined to be a circuit breaker fault diagnosis model. Among them, the test conditions can be thresholds corresponding to the test indicators, such as accuracy thresholds, recall thresholds, etc.

[0082] The technical solution of this embodiment is to train multiple batches of multiple generators and a discriminator in the initial model, and for any batch, exchange model parameters of at least some of the multiple first generators obtained by training in the current batch, and / or perform parameter mutation on the model parameters of at least some of the multiple first generators obtained by training in the previous batch based on a preset mutation ratio, so as to prevent the fault diagnosis model from falling into local optimality and improve the fault diagnosis accuracy of the fault diagnosis model.

[0083] Example 2

[0084] Figure 2This is a flow chart of a circuit breaker fault diagnosis method provided by the second embodiment of the present invention. This embodiment uses the circuit breaker fault diagnosis model trained by the circuit breaker fault diagnosis model training method of the above embodiment to perform fault diagnosis on the circuit breaker to be diagnosed. Figure 2 As shown, the method includes:

[0085] S210: Acquire operation data related to the fault of the circuit breaker to be diagnosed.

[0086] S220: Input the operating data into a circuit breaker fault diagnosis model to perform fault diagnosis, and obtain a fault diagnosis result of the circuit breaker to be diagnosed, wherein the circuit breaker fault diagnosis model is trained based on the circuit breaker fault diagnosis model training method according to any embodiment of the present invention.

[0087] The circuit breaker to be diagnosed refers to a circuit breaker with a fault to be diagnosed. Operational data related to the fault of the circuit breaker to be diagnosed includes, but is not limited to, voltage and current data in the faulty state of the circuit breaker, mechanical vibration signals of the circuit breaker, and temperature of the circuit breaker. In this embodiment, operational data related to the fault of the circuit breaker to be diagnosed is obtained; the operational data is input into a circuit breaker fault diagnosis model for fault diagnosis, thereby obtaining a fault diagnosis result for the circuit breaker to be diagnosed. The fault diagnosis result for the circuit breaker to be diagnosed includes the fault type of the circuit breaker to be diagnosed.

[0088] The technical solution of this embodiment uses a circuit breaker fault diagnosis model to perform fault diagnosis on the circuit breaker to be diagnosed, thereby improving the accuracy of fault diagnosis.

[0089] Example 3

[0090] Figure 3 This is a schematic diagram of the structure of a circuit breaker fault diagnosis model training device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes:

[0091] A training data set acquisition module 310 is configured to acquire a training data set, wherein the training data set includes circuit breaker sample data and fault labels corresponding to the circuit breaker sample data;

[0092] The training module 320 is used to construct an initial model, which includes multiple generators and a discriminator; the initial model is trained in multiple batches based on the training data set: in the training process of any batch, the model parameters of the multiple generators and the discriminator are adjusted based on the sample data of the current batch and the fault labels corresponding to the sample data, so as to obtain multiple first generators and discriminators of the current batch; the model parameters of at least some of the multiple first generators of the current batch are exchanged, and / or the model parameters of at least some of the multiple first generators of the previous batch are mutated based on a preset mutation ratio, so as to obtain multiple second generators of the current batch; the multiple second generators and the discriminator are trained for the next batch;

[0093] The target generator determination module 330 is configured to obtain a plurality of candidate generators that have been trained in a plurality of batches when a training end condition is satisfied; obtain a verification data set, perform verification processing on the plurality of candidate generators based on the verification data set, obtain verification indicators corresponding to the plurality of candidate generators, and determine a target generator from the plurality of candidate generators based on the verification indicators corresponding to the plurality of candidate generators;

[0094] The target generator test module 340 is used to obtain a test data set, test the target generator based on the test data set, obtain the test index of the target generator, and determine the target generator as a circuit breaker fault diagnosis model when the test index meets the test conditions.

[0095] The technical solution of this embodiment is to train multiple batches of multiple generators and a discriminator in the initial model, and for any batch, exchange model parameters of at least some of the multiple first generators obtained by training in the current batch, and / or perform parameter mutation on the model parameters of at least some of the multiple first generators obtained by training in the previous batch based on a preset mutation ratio, so as to prevent the fault diagnosis model from falling into local optimality and improve the fault diagnosis accuracy of the fault diagnosis model.

[0096] Based on the above embodiment, optionally, the training module 320 includes a parameter exchange unit for determining at least a local first generator among the multiple first generators of the current batch; determining a generator pair among the at least local first generators, the generator pair including two first generators for model parameter exchange; and correspondingly exchanging local layer model parameters in the two first generators in the generator pair.

[0097] Based on the above embodiment, optionally, the parameter exchange unit includes a local first generator determination subunit which sorts the multiple first generators based on the processing accuracy of the multiple first generators of the current batch; divides the multiple first generators into a first generator group and a second generator group based on the sorting of the multiple first generators; wherein the processing accuracy of at least one of the first generators in the first generator group is higher than the processing accuracy of at least one of the first generators in the second generator group; and determines at least one of the first generators in the second generator group as the at least local first generator that performs the model parameter exchange.

[0098] Based on the above embodiment, optionally, the number of layers of the local layer corresponding to different generator pairs is different; the training module 320 also includes a local layer number determination unit, and the method for determining the number of layers of the local layer is: for any generator pair, the number of layers of the local layer corresponding to the generator pair is determined based on the number of the first generators and a first preset parameter; wherein the first preset parameter is a random number, or the first preset parameter is determined based on the processing accuracy of the two first generators in the generator pair; or the number of layers of the local layer is determined based on the order of the first generators in the generator pair.

[0099] Based on the above embodiment, optionally, the training module 320 also includes a parameter variation unit for determining at least a local first generator among the multiple first generators of the current batch; determining a preset variation ratio corresponding to each first generator, and performing variation processing on the model parameters of the first generator based on the preset variation ratio.

[0100] Based on the above embodiment, optionally, the preset variation ratio of the first generator is negatively correlated with the batch; or, the preset variation ratio of the first generator is negatively correlated based on the processing accuracy of the first generator.

[0101] The circuit breaker fault diagnosis model training device provided by the embodiment of the present invention can execute the circuit breaker fault diagnosis model training device provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0102] Example 4

[0103] Figure 4 This is a schematic diagram of the structure of a circuit breaker fault diagnosis device provided by the fourth embodiment of the present invention. Figure 4 As shown, the device includes:

[0104] The operating data acquisition module 410 is configured to acquire operating data related to the fault of the circuit breaker to be diagnosed.

[0105] The fault diagnosis module 420 is configured to input the operating data into a circuit breaker fault diagnosis model for fault diagnosis to obtain a fault diagnosis result of the circuit breaker to be diagnosed, wherein the circuit breaker fault diagnosis model is trained based on the circuit breaker fault diagnosis model training method according to any embodiment of the present invention.

[0106] The technical solution of this embodiment uses a circuit breaker fault diagnosis model to perform fault diagnosis on the circuit breaker to be diagnosed, thereby improving the accuracy of fault diagnosis.

[0107] The circuit breaker fault diagnosis device provided in the embodiment of the present invention can execute the circuit breaker fault diagnosis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0108] Example 5

[0109] Figure 5 1 is a structural diagram of an electronic device provided in Example 5 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0110] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0111] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0112] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the circuit breaker fault diagnosis model training method and / or the circuit breaker fault diagnosis method.

[0113] In some embodiments, the circuit breaker fault diagnosis model training method and / or the circuit breaker fault diagnosis method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the circuit breaker fault diagnosis model training method and / or the circuit breaker fault diagnosis method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the circuit breaker fault diagnosis model training method and / or the circuit breaker fault diagnosis method by any other appropriate means (for example, by means of firmware).

[0114] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] The computer programs for implementing the circuit breaker fault diagnosis model training method and / or the circuit breaker fault diagnosis method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that, when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] Example 6

[0117] Embodiment 6 of the present invention further provides a computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute a method for training a fault diagnosis model for a circuit breaker, the method comprising:

[0118] Acquire a training data set, wherein the training data set includes circuit breaker sample data and fault labels corresponding to the circuit breaker sample data;

[0119] Constructing an initial model, wherein the initial model includes a plurality of generators and a discriminator; training the initial model in multiple batches based on the training data set: in the training process of any batch, adjusting the model parameters of the plurality of generators and the discriminator based on the sample data of the current batch and the fault labels corresponding to the sample data, to obtain a plurality of first generators and discriminators of the current batch; exchanging model parameters of at least a portion of the first generators of the multiple first generators of the current batch, and / or performing parameter mutation on the model parameters of at least a portion of the first generators of the multiple first generators of the previous batch based on a preset mutation ratio, to obtain a plurality of second generators of the current batch; training the multiple second generators and the discriminator for the next batch;

[0120] When the training end condition is met, a plurality of candidate generators that have been trained in a plurality of batches are obtained; a verification data set is obtained, and verification processing is performed on the plurality of candidate generators based on the verification data set to obtain verification indicators corresponding to the plurality of candidate generators, and a target generator is determined from the plurality of candidate generators based on the verification indicators corresponding to the plurality of candidate generators;

[0121] A test data set is obtained, and the target generator is tested based on the test data set to obtain a test indicator of the target generator. If the test indicator meets a test condition, the target generator is determined to be a circuit breaker fault diagnosis model.

[0122] And / or, executing a circuit breaker fault diagnosis method, the method comprising:

[0123] Acquiring operational data related to a fault of a circuit breaker to be diagnosed;

[0124] The operating data is input into a circuit breaker fault diagnosis model for fault diagnosis to obtain a fault diagnosis result of the circuit breaker to be diagnosed, wherein the circuit breaker fault diagnosis model is trained based on the circuit breaker fault diagnosis model training method according to any embodiment of the present invention.

[0125] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0126] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0127] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0128] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0129] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0130] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A circuit breaker fault diagnosis model training method, characterized in that: include: Acquire a training data set, wherein the training data set includes circuit breaker sample data and fault labels corresponding to the circuit breaker sample data; Constructing an initial model, the initial model including a plurality of generators and a discriminator; training the initial model in multiple batches based on the training data set: in the training process of any batch, adjusting model parameters of the plurality of generators and the discriminators based on sample data of the current batch and fault labels corresponding to the sample data, to obtain a plurality of first generators and discriminators of the current batch; Performing model parameter exchange on at least some of the multiple first generators of the current batch, and, Performing parameter mutation on model parameters of at least some of the multiple first generators of the previous batch based on a preset mutation ratio to obtain multiple second generators of the current batch; and performing training on the multiple second generators and the discriminator for the next batch; When the training end condition is met, a plurality of candidate generators that have been trained in a plurality of batches are obtained; a verification data set is obtained, and verification processing is performed on the plurality of candidate generators based on the verification data set to obtain verification indicators corresponding to the plurality of candidate generators, and a target generator is determined from the plurality of candidate generators based on the verification indicators corresponding to the plurality of candidate generators; Acquiring a test data set, testing the target generator based on the test data set to obtain a test indicator of the target generator, and determining the target generator as a circuit breaker fault diagnosis model if the test indicator meets a test condition; The step of exchanging model parameters of at least some of the multiple first generators in the current batch includes: determining at least a partial first generator among the plurality of first generators of the current batch; determining a generator pair among the at least local first generators, the generator pair including two of the first generators that exchange model parameters; correspondingly exchanging local layer model parameters in two first generators in the generator pair; The step of performing parameter variation on model parameters of at least some of the multiple first generators in the current batch based on a preset variation ratio includes: determining at least a partial first generator among the plurality of first generators of the current batch; Determine a preset variation ratio corresponding to each of the first generators, and perform variation processing on the model parameters of the first generator based on the preset variation ratio.

2. The method according to claim 1, characterized in that The determining of at least a portion of the first generators of the plurality of first generators of the current batch comprises: Sorting the plurality of first generators based on the processing accuracy of the plurality of first generators of the current batch; dividing the plurality of first generators into a first generator group and a second generator group based on the sorting of the plurality of first generators; wherein a processing accuracy of at least one of the first generators in the first generator group is higher than a processing accuracy of at least one of the first generators in the second generator group; At least one of said first generators in said second group of generators is determined as said at least local first generator performing said model parameter exchange.

3. The method according to claim 1, characterized in that Different generator pairs have different numbers of corresponding local layers; The number of layers of the local layer is determined by: for any generator pair, determining the number of layers of the local layer corresponding to the generator pair based on the number of the first generators and a first preset parameter; wherein the first preset parameter is a random number, or the first preset parameter is determined based on the processing accuracy of the two first generators in the generator pair; Alternatively, the number of layers of the local layer is determined based on the ordering of the first generator in the generator pair.

4. The method according to claim 1, wherein The preset variation ratio of the first generator is negatively correlated with the batch; or, The preset variation ratio of the first generator is negatively correlated with the processing accuracy of the first generator.

5. A circuit breaker fault diagnosis method, characterized in that: include: Acquiring operational data related to a fault of a circuit breaker to be diagnosed; The operating data is input into a circuit breaker fault diagnosis model for fault diagnosis to obtain a fault diagnosis result of the circuit breaker to be diagnosed, wherein the circuit breaker fault diagnosis model is trained based on the circuit breaker fault diagnosis model training method according to any one of claims 1 to 4.

6. A circuit breaker fault diagnosis model training device, characterized in that: include: A training data set acquisition module, configured to acquire a training data set, wherein the training data set includes circuit breaker sample data and fault labels corresponding to the circuit breaker sample data; A training module is used to construct an initial model, which includes multiple generators and a discriminator; the initial model is trained in multiple batches based on the training data set: in the training process of any batch, the model parameters of the multiple generators and the discriminators are adjusted based on the sample data of the current batch and the fault labels corresponding to the sample data, so as to obtain multiple first generators and discriminators of the current batch; the model parameters of at least some of the multiple first generators of the current batch are exchanged, and the model parameters of at least some of the multiple first generators of the previous batch are mutated based on a preset mutation ratio, so as to obtain multiple second generators of the current batch; the multiple second generators and the discriminators are exchanged. The discriminator performs training for the next batch; wherein the training module includes: a parameter exchange unit and a parameter variation unit; the parameter exchange unit is used to determine at least a local first generator among the multiple first generators of the current batch; determine a generator pair among the at least local first generators, the generator pair including two first generators for model parameter exchange; perform corresponding exchange on the local layer model parameters of the two first generators in the generator pair; the parameter variation unit is used to determine at least a local first generator among the multiple first generators of the current batch; determine a preset variation ratio corresponding to each first generator, and perform variation processing on the model parameters of the first generator based on the preset variation ratio; A target generator determination module is configured to obtain a plurality of candidate generators trained in a plurality of batches when a training end condition is satisfied; obtain a verification data set, perform verification processing on the plurality of candidate generators based on the verification data set, obtain verification indicators corresponding to the plurality of candidate generators, and determine a target generator from the plurality of candidate generators based on the verification indicators corresponding to the plurality of candidate generators; The target generator test module is used to obtain a test data set, test the target generator based on the test data set, obtain the test index of the target generator, and determine the target generator as a circuit breaker fault diagnosis model when the test index meets the test conditions.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the circuit breaker fault diagnosis model training method according to any one of claims 1 to 4, and / or the circuit breaker fault diagnosis method according to claim 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the circuit breaker fault diagnosis model training method according to any one of claims 1 to 4, and / or the circuit breaker fault diagnosis method according to claim 5 when executed.

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