Self-diagnosis processing method and device for direct current controller

Abstract

The invention discloses a self-diagnosis processing method and device for a direct current controller, belongs to the technical field of computers, and determines a corresponding target self-diagnosis parameter acquisition mode, a target self-diagnosis parameter processing mode and a target self-diagnosis parameter type set under the condition that a target direct current controller meets a target self-diagnosis condition. And based on the target self-diagnosis parameter acquisition mode, acquiring a plurality of first target self-diagnosis parameters of which the parameter types belong to the target self-diagnosis parameter type set, thereby realizing acquisition of basic self-diagnosis parameters. And processing the plurality of first target self-diagnosis parameters by adopting a target self-diagnosis parameter processing mode to obtain a plurality of second target self-diagnosis parameters and a plurality of third target self-diagnosis parameters of different self-diagnosis dimensions. And determining a target self-diagnosis result of the target DC controller based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a self-diagnosis processing method and device for a DC controller. Background Art

[0002] As the core unit of new energy grid connection, electric vehicles and intelligent manufacturing, the reliability of the DC power system's controller directly affects the system safety.

[0003] Currently, self-diagnosis techniques for DC controllers primarily rely on single parameter thresholds, such as determining faults based on voltage or current exceeding a certain limit. However, these methods are significantly less applicable under complex operating conditions, and the accuracy of self-diagnosis is low.

[0004] Therefore, how to improve the accuracy of self-diagnosis of DC controllers is a research hotspot. Summary of the Invention

[0005] The present invention provides a method and apparatus for self-diagnosis of a DC controller, which can improve the accuracy of self-diagnosis of the DC controller. The technical solution is as follows: In one aspect, a self-diagnosis processing method for a DC controller is provided, the method comprising: determining, when a target DC controller of a target DC component satisfies a target self-diagnostic condition among a plurality of candidate self-diagnostic conditions, a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method corresponding to the target self-diagnostic condition, and determining a target self-diagnostic parameter type set corresponding to the target DC controller and the target self-diagnostic condition, the target self-diagnostic condition including a target working environment condition, a target controller condition, and a target DC component condition; Based on the target self-diagnostic parameter acquisition method, acquiring a plurality of first target self-diagnostic parameters whose parameter types belong to the target self-diagnostic parameter type set, the plurality of first target self-diagnostic parameters including a plurality of first self-diagnostic parameters and a plurality of second self-diagnostic parameters, the plurality of first self-diagnostic parameters being self-diagnostic parameters related to the target DC component, and the plurality of second self-diagnostic parameters being self-diagnostic parameters related to the target DC controller; Processing the plurality of first target self-diagnostic parameters using the target self-diagnostic parameter processing method to obtain a plurality of second target self-diagnostic parameters and a plurality of third target self-diagnostic parameters of the target DC controller, wherein the plurality of second target self-diagnostic parameters and the plurality of third target self-diagnostic parameters correspond to different self-diagnostic dimensions; A target self-diagnosis result of the target DC controller is determined based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters, where the target self-diagnosis result is used to indicate whether the target DC controller has a fault.

[0006] In one aspect, a self-diagnosis processing device for a DC controller is provided, the device comprising: a determination module, configured to, when a target DC controller of a target DC component satisfies a target self-diagnostic condition among a plurality of candidate self-diagnostic conditions, determine a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method corresponding to the target self-diagnostic condition, and determine a target self-diagnostic parameter type set corresponding to the target DC controller and the target self-diagnostic condition, the target self-diagnostic condition including a target working environment condition, a target controller condition, and a target DC component condition; an acquisition module, configured to acquire, based on the target self-diagnostic parameter acquisition method, a plurality of first target self-diagnostic parameters whose parameter types belong to the target self-diagnostic parameter type set, the plurality of first target self-diagnostic parameters including a plurality of first self-diagnostic parameters and a plurality of second self-diagnostic parameters, the plurality of first self-diagnostic parameters being self-diagnostic parameters related to the target DC component, and the plurality of second self-diagnostic parameters being self-diagnostic parameters related to the target DC controller; a processing module, configured to process the plurality of first target self-diagnostic parameters using the target self-diagnostic parameter processing method to obtain a plurality of second target self-diagnostic parameters and a plurality of third target self-diagnostic parameters of the target DC controller, wherein the plurality of second target self-diagnostic parameters and the plurality of third target self-diagnostic parameters correspond to different self-diagnostic dimensions; The diagnostic module is configured to determine a target self-diagnosis result of the target DC controller based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters, wherein the target self-diagnosis result is configured to indicate whether the target DC controller has a fault.

[0007] On the one hand, a computer device is provided, comprising one or more processors and one or more memories, wherein at least one computer program is stored in the one or more memories, and the computer program is loaded and executed by the one or more processors to implement the self-diagnosis processing method for a DC controller.

[0008] In one aspect, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, and the computer program is loaded and executed by a processor to implement the self-diagnosis processing method for a DC controller.

[0009] On the one hand, a computer program product or computer program is provided, which includes program code, which is stored in a computer-readable storage medium. A processor of a computer device reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the computer device performs the above-mentioned self-diagnosis processing method for a DC controller.

[0010] Through the technical solution provided by the embodiment of the present application, when the target DC controller meets the target self-diagnosis conditions, the corresponding target self-diagnosis parameter acquisition method, the target self-diagnosis parameter processing method and the target self-diagnosis parameter type set are determined. Based on the target self-diagnosis parameter acquisition method, a plurality of first target self-diagnosis parameters whose parameter types belong to the target self-diagnosis parameter type set are acquired, thereby realizing the acquisition of basic self-diagnosis parameters. The target self-diagnosis parameter processing method is adopted to process the plurality of first target self-diagnosis parameters to obtain a plurality of second target self-diagnosis parameters and a plurality of third target self-diagnosis parameters of different self-diagnosis dimensions. The target self-diagnosis result of the target DC controller is determined based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters. Compared with the self-diagnosis result obtained by the self-diagnosis method in the related art, the target self-diagnosis result is more accurate due to the combination of richer data and the use of an appropriate data processing method. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0012] Figure 1 Schematic diagram of an implementation environment of a self-diagnosis processing method for a DC controller provided in an embodiment of the present application; Figure 2 This is a flow chart of a self-diagnosis processing method for a DC controller provided in an embodiment of the present application; Figure 3 This is a flow chart of another self-diagnosis processing method for a DC controller provided in an embodiment of the present application; Figure 4 1 is a structural diagram of a self-diagnosis processing device for a DC controller provided in an embodiment of the present application; Figure 5 It is a structural diagram of a diagnostic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0014] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with substantially the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on the quantity and execution order.

[0015] A DC controller is a device used to regulate, manage, and protect the transmission and conversion of DC power. Its core functions include voltage and current stability control, system status monitoring, and fault protection. It is widely used in power systems, industrial equipment, electric vehicles, and aerospace.

[0016] Self-diagnosis: Self-diagnosis is an intelligent function that allows a device or system to monitor its operating status in real time through built-in algorithms, identify anomalies, and provide feedback. It is mainly used to improve reliability and safety.

[0017] Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to achieve better results.

[0018] Machine learning (ML) is a multidisciplinary field that encompasses probability theory, statistics, approximation theory, convex analysis, and algorithmic complexity theory. It specifically studies how computers can simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge sub-models to continuously improve their performance. Machine learning is at the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications span all areas of AI.

[0019] Normalization: Mapping sequences of numbers with different value ranges to the interval (0, 1) facilitates data processing. In some cases, the normalized values ​​can be directly implemented as probabilities.

[0020] Embedded Coding: Mathematically, embedded coding represents a correspondence relationship, mapping data in X space to Y space using a function F, where F is an injective function. The mapping result preserves structure. An injective function indicates that the data after mapping uniquely corresponds to the data before mapping. Structural preservation means that the size relationship of the data before mapping is the same as the size relationship of the data after mapping. For example, if there are data X1 and X2 before mapping, the data Y1 corresponding to X1 and Y2 corresponding to X2 will be obtained after mapping. If the data X1 before mapping is greater than X2, then the data Y1 after mapping is greater than Y2. For words, this means mapping the words to another space to facilitate subsequent machine learning and processing.

[0021] Attention weight: This indicates the importance of a piece of data during training or prediction. Importance indicates the impact of the input data on the output data. Highly important data has a higher attention weight, while lowly important data has a lower attention weight. Data importance varies in different scenarios, and the process of training a model's attention weights is also the process of determining data importance.

[0022] Figure 1 This is a schematic diagram of an implementation environment of a self-diagnosis processing method for a DC controller provided by an embodiment of the present application, see Figure 1 The implementation environment may include a diagnostic device 110 , a target DC component 120 , and a target DC controller 140 .

[0023] Diagnostic device 110 is connected to target DC component 120 and target DC controller 140 via a wired connection. Optionally, diagnostic device 110 is a smartphone, tablet computer, laptop computer, desktop computer, or dedicated handheld device, but is not limited thereto. Diagnostic device 110 has an application installed and running that supports self-diagnosis of the DC controller.

[0024] The target DC component 120 is a DC working component. For example, the target DC component 120 is a DC motor, a battery, or a generator, etc., which is not limited in the embodiment of the present application.

[0025] The target DC controller 140 is used to control the target DC component 120 , and the target DC controller 140 is connected to the target DC component via a wire.

[0026] In related technologies, self-diagnosis of a target DC controller can only be achieved through a self-diagnosis program provided by the target DC controller. Due to computing power limitations, the self-diagnosis program is usually relatively simple and cannot meet the needs of special scenarios.

[0027] By adopting the technical solution provided in the embodiment of the present application, by using an external high-computing-power diagnostic device and combining it with a unique self-diagnosis method, it is possible to complete accurate self-diagnosis of the target DC controller without replacing the target DC controller.

[0028] The following describes a self-diagnosis processing method for a DC controller provided in an embodiment of the present application. Figure 2 This is a flow chart of a self-diagnosis processing method for a DC controller provided in an embodiment of the present application, see Figure 2 Taking the execution subject as a diagnostic device as an example, the method includes the following steps.

[0029] 201. When the target DC controller of the target DC component satisfies a target self-diagnostic condition among a plurality of candidate self-diagnostic conditions, the diagnostic device determines a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method corresponding to the target self-diagnostic condition, and determines a target self-diagnostic parameter type set corresponding to the target DC controller and the target self-diagnostic condition, where the target self-diagnostic condition includes a target working environment condition, a target controller condition, and a target DC component condition.

[0030] Among them, the target self-diagnosis condition is one of the multiple candidate self-diagnosis conditions. The target DC controller meeting the target self-diagnosis condition indicates that the target DC controller needs to perform self-diagnosis. The target working environment condition is a condition related to the working environment of the target DC controller. The target controller condition refers to a condition related to the target DC controller itself. The target DC component condition refers to a condition related to the target DC component. The target DC component condition is introduced because the target DC controller directly controls the target DC component. Therefore, the condition of the target DC component can actually reflect to a certain extent whether the target DC controller is abnormal. The target DC component is a DC device, such as a DC motor, a battery, and a generator. Under normal circumstances, the target DC controller can perform stable voltage / current control, system status monitoring, and fault protection on the target DC component. The target self-diagnosis parameter acquisition method is a method for obtaining self-diagnosis parameters. The self-diagnosis parameters are relevant parameters used to perform self-diagnosis on the target DC controller in the embodiment of the present application. The target self-diagnostic parameter processing method refers to the method for processing the acquired self-diagnostic parameters. In an embodiment of the present application, the target self-diagnostic parameter acquisition method and the target self-diagnostic parameter processing method are associated with the target self-diagnostic condition. Since there are multiple candidate self-diagnostic conditions, the target self-diagnostic parameter acquisition method and the target self-diagnostic parameter processing method are different when different candidate self-diagnostic conditions are the target self-diagnostic conditions. The target self-diagnostic parameter type set is a set of parameter types of the self-diagnostic parameters to be acquired. The target self-diagnostic parameter type set is associated with the target DC controller and the target self-diagnostic condition. When faced with different DC controllers and self-diagnostic conditions, the corresponding target self-diagnostic parameter type set can be found, thereby achieving more accurate self-diagnosis.

[0031] 202. The diagnostic device obtains a plurality of first target self-diagnostic parameters whose parameter types belong to the target self-diagnostic parameter type set based on the target self-diagnostic parameter acquisition method, the plurality of first target self-diagnostic parameters including a plurality of first self-diagnostic parameters and a plurality of second self-diagnostic parameters, the plurality of first self-diagnostic parameters being self-diagnostic parameters related to the target DC component, and the plurality of second self-diagnostic parameters being self-diagnostic parameters related to the target DC controller.

[0032] The parameter type of the first target self-diagnostic parameter belongs to the target self-diagnostic parameter type set. The first self-diagnostic parameter is related to the target DC component, in other words, a component parameter of the target DC component. The second self-diagnostic parameter is related to the target DC controller, in other words, a controller parameter of the target DC controller. The concept of obtaining multiple first self-diagnostic parameters is based on the fact that the target DC controller directly controls the target DC component. Therefore, the condition of the target DC component can actually reflect whether the target DC controller is abnormal to a certain extent.

[0033] 203. The diagnostic device processes the multiple first target self-diagnostic parameters using the target self-diagnostic parameter processing method to obtain multiple second target self-diagnostic parameters and multiple third target self-diagnostic parameters of the target DC controller, where the multiple second target self-diagnostic parameters and the multiple third target self-diagnostic parameters correspond to different self-diagnostic dimensions.

[0034] The second target self-diagnosis mode and the third target self-diagnosis mode are obtained by processing the plurality of first target self-diagnosis parameters using the target self-diagnosis parameter processing mode, and can be used in subsequent self-diagnosis processes. Different self-diagnosis dimensions refer to different data dimensions.

[0035] 204. The diagnostic device determines a target self-diagnosis result of the target DC controller based on the multiple second target self-diagnosis parameters and the multiple third target self-diagnosis parameters, where the target self-diagnosis result is used to indicate whether the target DC controller has a fault.

[0036] The target self-diagnosis result is the final self-diagnosis result of the target DC controller. Compared with the self-diagnosis result obtained by using the self-diagnosis program of the target DC controller, the target self-diagnosis result has higher accuracy.

[0037] Through the technical solution provided by the embodiment of the present application, when the target DC controller meets the target self-diagnosis conditions, the corresponding target self-diagnosis parameter acquisition method, the target self-diagnosis parameter processing method and the target self-diagnosis parameter type set are determined. Based on the target self-diagnosis parameter acquisition method, a plurality of first target self-diagnosis parameters whose parameter types belong to the target self-diagnosis parameter type set are acquired, thereby realizing the acquisition of basic self-diagnosis parameters. The target self-diagnosis parameter processing method is adopted to process the plurality of first target self-diagnosis parameters to obtain a plurality of second target self-diagnosis parameters and a plurality of third target self-diagnosis parameters of different self-diagnosis dimensions. The target self-diagnosis result of the target DC controller is determined based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters. Compared with the self-diagnosis result obtained by the self-diagnosis method in the related art, the target self-diagnosis result is more accurate due to the combination of richer data and the use of an appropriate data processing method.

[0038] The above steps 201-204 are a brief introduction to the self-diagnosis processing method for the DC controller provided in the embodiment of the present application. The following will combine some examples to more clearly illustrate the self-diagnosis processing method for the DC controller provided in the embodiment of the present application. Figure 3 Taking the execution subject as a diagnostic device as an example, the method includes the following steps.

[0039] 301. A diagnostic device determines whether a target DC controller of a target DC component satisfies any candidate self-diagnosis condition among a plurality of candidate self-diagnosis conditions.

[0040] The target DC component is a DC device, such as a DC motor, battery, or generator. Under normal circumstances, the target DC controller is capable of providing stable voltage and current control, system status monitoring, and fault protection for the target DC component. Determining whether the target DC controller meets multiple candidate self-diagnostic conditions is both a way to determine whether self-diagnosis is necessary and to identify a suitable self-diagnostic method. The candidate self-diagnostic conditions include candidate operating environment conditions, candidate controller conditions, and candidate DC component conditions.

[0041] In one possible implementation, the diagnostic device obtains operating environment parameters of a target DC controller, controller parameters of the target DC controller, and DC component parameters of a target DC component. The diagnostic device conditionally matches the operating environment parameters, the controller parameters, and the third operating parameter with candidate operating environment conditions, candidate controller conditions, and candidate DC component conditions of a plurality of candidate self-diagnostic conditions, respectively, to determine whether the target DC controller satisfies any of the plurality of candidate self-diagnostic conditions.

[0042] Among them, the working environment parameters include ambient temperature and ambient humidity, the controller parameters include the controller temperature, controller current, and controller continuous operation time of the target DC controller, and the DC component parameters include the component current and component voltage of the target DC component. Candidate working environment conditions include whether the ambient temperature is greater than or equal to a candidate ambient temperature threshold and whether the ambient humidity is greater than or equal to a candidate ambient humidity threshold. Different candidate working environment conditions correspond to different candidate ambient temperature thresholds and candidate ambient humidity thresholds. Candidate controller conditions include whether the controller temperature is greater than or equal to a candidate controller temperature threshold, whether the controller current is greater than or equal to a candidate controller current threshold, and whether the controller continuous operation time is greater than or equal to a controller continuous operation time threshold. Different candidate controller conditions correspond to different candidate controller conditions. Candidate DC component conditions include whether the current fluctuation degree of the component current is greater than or equal to a candidate current fluctuation degree threshold, and whether the voltage fluctuation degree of the component voltage is greater than or equal to a candidate voltage fluctuation degree threshold. Different candidate DC component conditions correspond to different candidate current fluctuation degree thresholds and candidate voltage fluctuation degree thresholds. In some embodiments, the current fluctuation degree and voltage fluctuation degree can be represented by current variance and voltage variance, respectively.

[0043] In some embodiments, for any candidate self-diagnostic condition among multiple candidate self-diagnostic conditions, when the working environment parameter satisfies the candidate working environment condition among the candidate self-diagnostic conditions, the controller parameter satisfies the candidate controller condition among the candidate self-diagnostic conditions, and the DC component parameter satisfies the candidate DC component condition among the candidate self-diagnostic conditions, the diagnostic device determines that the target DC controller satisfies the candidate self-diagnostic condition, and the candidate self-diagnostic condition is the target self-diagnostic condition.

[0044] Accordingly, when the working environment parameter does not satisfy the candidate working environment condition in the candidate self-diagnostic condition, the controller parameter does not satisfy the candidate controller condition in the candidate self-diagnostic condition, or the DC component parameter does not satisfy the candidate DC component condition in the candidate self-diagnostic condition, the diagnostic device determines that the target DC controller does not satisfy the candidate self-diagnostic condition.

[0045] 302. When the target DC controller of the target DC component satisfies a target self-diagnostic condition among a plurality of candidate self-diagnostic conditions, the diagnostic device determines a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method corresponding to the target self-diagnostic condition, and determines a target self-diagnostic parameter type set corresponding to the target DC controller and the target self-diagnostic condition, where the target self-diagnostic condition includes a target working environment condition, a target controller condition, and a target DC component condition.

[0046] The target self-diagnostic condition is one of multiple candidate self-diagnostic conditions. If the target DC controller satisfies the target self-diagnostic condition, it indicates that the target DC controller needs to perform self-diagnosis, and a method matching the target diagnostic condition will be used during self-diagnosis. The target operating environment condition is a condition related to the operating environment of the target DC controller. The target controller condition refers to a condition related to the target DC controller itself. The target DC component condition refers to a condition related to the target DC component. The target DC component condition is introduced because the target DC controller directly controls the target DC component. Therefore, the condition of the target DC component can actually reflect whether the target DC controller is abnormal to a certain extent. The target self-diagnostic parameter acquisition method is a method for obtaining self-diagnostic parameters. The self-diagnostic parameters are the relevant parameters used to perform self-diagnosis on the target DC controller in this embodiment of the application. The target self-diagnostic parameter processing method refers to the method for processing the obtained self-diagnostic parameters. In this embodiment of the application, the target self-diagnostic parameter acquisition method and the target self-diagnostic parameter processing method are associated with the target self-diagnostic condition. Since there are multiple candidate self-diagnostic conditions, when different candidate self-diagnostic conditions are the target self-diagnostic conditions, the target self-diagnostic parameter acquisition method and the target self-diagnostic parameter processing method are different. The target self-diagnostic parameter type set is a set of parameter types of the self-diagnostic parameters that need to be obtained. The target self-diagnostic parameter type set is associated with the target DC controller and the target self-diagnostic condition. When faced with different DC controllers and self-diagnostic conditions, the corresponding target self-diagnostic parameter type set can be found, thereby achieving more accurate self-diagnosis.

[0047] In one possible implementation, when a target DC controller of a target DC component satisfies a target self-diagnostic condition from among multiple candidate self-diagnostic conditions, the diagnostic device determines the degree to which the target DC controller satisfies the condition, where the degree to which the target DC controller exceeds the target self-diagnostic condition. Based on the target self-diagnostic condition and the degree to which the condition is satisfied, the diagnostic device determines a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method. Based on the degree to which the condition is satisfied, the controller type of the target DC controller, and the target self-diagnostic condition, the diagnostic device determines a target self-diagnostic parameter type set.

[0048] Among them, the degree of exceeding the target diagnostic condition refers to the degree to which the target DC controller exceeds the parameter threshold corresponding to the target self-diagnostic condition on the basis of satisfying the target self-diagnostic condition. For example, the target self-diagnostic parameters include the target working environment condition, the target controller condition, and the target DC component condition. The parameter threshold corresponding to the target self-diagnostic condition includes the environmental condition parameter threshold corresponding to the target working environment condition, the controller condition parameter threshold corresponding to the target controller condition, and the component condition parameter threshold corresponding to the target DC component condition. In the embodiment of the present application, determining the degree of condition satisfaction is one of the core steps in performing self-diagnosis, and plays a very important role in improving the accuracy of self-diagnosis in the process of adopting ablation experiments.

[0049] In order to explain the above embodiment more clearly, the above embodiment will be described in several parts below.

[0050] Part 1: When a target DC controller of a target DC component satisfies a target self-diagnosis condition among a plurality of candidate self-diagnosis conditions, the diagnostic device determines the degree to which the condition is satisfied by the target DC controller.

[0051] In one possible embodiment, when a target DC controller of a target DC component satisfies a target self-diagnostic condition among multiple candidate self-diagnostic conditions, the diagnostic device determines the degree of satisfaction of the condition based on the working environment parameters of the target DC controller and the environmental condition parameter thresholds corresponding to the target working environment conditions, the controller parameters of the target DC controller and the controller condition parameter thresholds corresponding to the target controller conditions, and the DC component parameters of the target DC component and the component condition parameter thresholds corresponding to the target DC component conditions.

[0052] The working environment parameters are used to match the target working environment conditions, the controller parameters are used to match the target controller conditions, and the DC component parameters are used to match the target DC component conditions. The matching method is described in the relevant description of step 301 above and will not be repeated here.

[0053] For example, if a target DC controller of a target DC component satisfies a target self-diagnostic condition from among multiple candidate self-diagnostic conditions, the diagnostic device determines the degree of environmental parameter excess based on an operating environment parameter of the target DC controller and an environmental condition parameter threshold corresponding to the target operating environment condition. The diagnostic device determines the degree of controller parameter excess based on a controller parameter of the target DC controller and a controller condition parameter threshold corresponding to the target controller condition. The diagnostic device determines the degree of DC component parameter excess based on a DC component parameter of the target DC component and a component condition parameter threshold corresponding to the target DC component condition. The controller determines the degree of condition satisfaction based on the degree of environmental parameter excess, the degree of controller parameter excess, and the degree of DC component parameter excess.

[0054] The environmental parameter exceedance is calculated by subtracting the operating environment parameter from the environmental condition parameter threshold, then dividing the result by the environmental condition parameter threshold. The controller parameter exceedance is calculated by subtracting the controller parameter from the controller condition parameter threshold, then dividing the result by the controller condition parameter threshold. The DC component parameter exceedance is calculated by subtracting the component parameter from the component condition parameter threshold, then dividing the result by the component condition parameter threshold.

[0055] The following is divided into several parts to illustrate the above examples.

[0056] A. The diagnostic device determines the degree to which the environmental parameter exceeds the target operating environment parameter based on the target DC controller's operating environment parameter and the environmental condition parameter threshold corresponding to the target operating environment condition.

[0057] In one possible embodiment, the operating environment parameters include ambient temperature and ambient humidity, and the environmental condition parameter thresholds include an ambient temperature threshold and an ambient humidity threshold. The diagnostic device subtracts the ambient temperature from the ambient temperature threshold and divides the result by the ambient temperature threshold to obtain the degree of ambient temperature excess. The diagnostic device subtracts the ambient humidity from the ambient humidity threshold and divides the result by the ambient humidity threshold to obtain the degree of ambient humidity excess. The diagnostic device performs a weighted fusion of the degree of ambient temperature excess and the degree of ambient humidity excess to obtain the degree of environmental parameter excess.

[0058] The weights of the weighted fusion are associated with the target self-diagnosis conditions, and different target self-diagnosis conditions correspond to different weights.

[0059] B. The diagnostic device determines the degree of controller parameter excess based on the controller parameter of the target DC controller and the controller condition parameter threshold corresponding to the target controller condition.

[0060] In one possible embodiment, the controller parameters include the controller temperature, controller current, and controller continuous operation time of the target DC controller, and the controller condition parameter thresholds include a controller temperature threshold, a controller current threshold, and a controller continuous operation time threshold. The diagnostic device subtracts the controller temperature from the controller temperature threshold and divides the result by the controller temperature threshold to obtain the controller temperature excess degree. The diagnostic device subtracts the controller current from the controller current threshold and divides the result by the controller current threshold to obtain the controller current excess degree. The diagnostic device subtracts the controller continuous operation time from the controller continuous operation time threshold and divides the result by the controller continuous operation time threshold to obtain the controller continuous operation time excess degree. The diagnostic device performs a weighted fusion of the controller temperature excess degree, the controller current excess degree, and the controller continuous operation time excess degree to obtain the controller parameter excess degree.

[0061] The weights of the weighted fusion are associated with the target self-diagnosis conditions, and different target self-diagnosis conditions correspond to different weights.

[0062] C. The diagnostic device determines the degree to which the DC component parameter exceeds the target DC component based on the DC component parameter of the target DC component and the component condition parameter threshold corresponding to the target DC component condition.

[0063] In one possible embodiment, the DC component parameters include the component current and component voltage of the target DC component, and the component condition parameter thresholds include a current fluctuation degree threshold and a voltage fluctuation degree threshold. The diagnostic device determines the current fluctuation degree of the component current based on the component current of the target DC component. The diagnostic device determines the voltage fluctuation degree of the component voltage based on the component voltage of the target DC component. The diagnostic device subtracts the current fluctuation degree from the current fluctuation degree threshold and then divides the result by the current fluctuation degree threshold to obtain the current fluctuation excess degree. The diagnostic device subtracts the voltage fluctuation degree from the voltage fluctuation degree threshold and then divides the result by the voltage fluctuation degree threshold to obtain the voltage fluctuation excess degree. The diagnostic device performs a weighted fusion of the current fluctuation excess degree and the voltage fluctuation excess degree to obtain the DC component parameter excess degree.

[0064] The current fluctuation degree and voltage fluctuation degree are represented by the current variance and voltage variance, respectively. The weights of the weighted fusion are associated with the target self-diagnosis conditions, and different target self-diagnosis conditions have different corresponding weights.

[0065] D. The controller determines the degree to which the condition is satisfied based on the degree to which the environmental parameters exceed, the degree to which the controller parameters exceed, and the degree to which the DC component parameters exceed.

[0066] In a possible implementation, the controller combines the degree to which the environmental parameter exceeds, the degree to which the controller parameter exceeds, and the degree to which the DC component parameter exceeds to obtain the degree to which the condition is satisfied.

[0067] In the second part, the diagnostic device determines the target self-diagnosis parameter acquisition method and the target self-diagnosis parameter processing method based on the target self-diagnosis condition and the degree to which the condition is satisfied.

[0068] In one possible implementation, the diagnostic device performs conditional conversion on the target operating environment conditions, target controller conditions, and target DC component conditions to obtain a conditional description text for the target self-diagnostic condition. Based on the conditional description text and the degree of satisfaction of the condition, the diagnostic device determines the controller state and target self-diagnostic mode of the target DC controller. Based on the controller state and the target self-diagnostic mode, the diagnostic device determines a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method.

[0069] For example, the diagnostic device concatenates the target operating environment conditions, target controller conditions, and target DC component conditions, then performs feature extraction to obtain a self-diagnostic condition feature for the target self-diagnostic condition. Based on the self-diagnostic condition feature, the diagnostic device generates a condition description text for the target self-diagnostic condition. The diagnostic device concatenates the condition description text and the degree of condition satisfaction, then performs feature extraction to obtain a state prediction feature and a diagnostic mode prediction feature. The diagnostic device fully connects and normalizes the state prediction feature to obtain a probability set, which includes multiple probabilities, each corresponding to a candidate controller state. The diagnostic device determines the candidate controller state corresponding to the highest probability in the probability set as the controller state of the target DC controller. The diagnostic device uses the diagnostic mode prediction feature to match the candidate self-diagnostic mode features of multiple candidate self-diagnostic modes to obtain a target self-diagnostic mode. The target self-diagnostic mode is the candidate self-diagnostic mode with the highest feature similarity between the candidate self-diagnostic mode features and the diagnostic mode prediction feature. The diagnostic device uses the controller state and the target self-diagnostic mode to query a first relational table to obtain the target self-diagnostic parameter acquisition method and the target self-diagnostic parameter processing method.

[0070] Among them, feature extraction refers to encoding based on the attention mechanism, and generating condition description text based on the self-diagnosis condition features is obtained by multiple rounds of iterative decoding based on the attention mechanism. The number of candidate controller states is multiple, for example, multiple candidate controller states include abnormal, slightly abnormal, abnormal, significantly abnormal and normal. The above-mentioned full connection refers to multiplying the features with the full connection matrix, and the full connection matrix is ​​obtained through multiple rounds of training. Normalization refers to processing using a normalization function, and the normalization function includes a SoftMax function, a Relu function, etc., which is not limited in the embodiments of the present application. The first relationship table stores multiple controller states, multiple self-diagnosis modes, multiple self-diagnosis parameter acquisition methods, multiple self-diagnosis parameter processing methods and corresponding relationships. A self-diagnosis parameter acquisition method corresponds to a controller state and a self-diagnosis mode, and a self-diagnosis parameter processing method corresponds to a controller state and a self-diagnosis mode. The first relationship table is configured by technical personnel according to actual conditions, and is not limited in the embodiments of the present application.

[0071] Part 3: The diagnostic device determines the target self-diagnosis parameter type set based on the degree of satisfaction of the condition, the controller type of the target DC controller, and the target self-diagnosis condition.

[0072] The controller type is the result of classifying the target DC controller. For example, in the embodiment of the present application, the controller type includes a motor controller and a battery controller.

[0073] In one possible implementation, the diagnostic device inputs the degree of condition satisfaction, the controller type of the target DC controller, and the target self-diagnostic condition into a type prediction model. The type prediction model encodes the degree of condition satisfaction, the controller type of the target DC controller, and the target self-diagnostic condition to obtain a type prediction feature. The diagnostic device uses the type prediction model to perform multiple rounds of iterative decoding on the type prediction feature to obtain the target self-diagnostic parameter type set.

[0074] Encoding refers to an encoding process based on an attention mechanism, and multi-round iterative decoding refers to a multi-round iterative decoding process based on an attention mechanism. Multiple rounds of iterative decoding yield multiple diagnostic parameter types, which together form the target self-diagnostic parameter type set. Optional diagnostic parameter types include the target DC controller's controller temperature, controller input current, controller input voltage, controller output current, controller output voltage, and controller continuous run time, as well as the target DC component's component temperature, component current, component voltage, component vibration frequency, component vibration intensity, component power, and component remaining capacity. Depending on the degree of condition satisfaction, the target DC controller's controller type, and the target self-diagnostic conditions, the target self-diagnostic parameter type set differs, enabling personalized self-diagnosis of the target DC component. This type of prediction model includes an encoder for encoding and a decoder for multi-round iterative decoding. The encoder is an attention-based encoder, such as the encoder of the BERT model, and the decoder is an attention-based decoder, such as the decoder of the BERT model. When training this type prediction model, a supervised learning approach is used. Specifically, the degree of sample condition satisfaction, the sample controller type, and the sample self-diagnostic condition are input into the type prediction model. After processing by the type prediction model, a predicted self-diagnostic parameter type set is obtained. The type prediction model is trained based on the difference between the predicted self-diagnostic parameter type set and the annotated self-diagnostic parameter type set. The annotated self-diagnostic parameter type set is the set of self-diagnostic parameter types corresponding to the degree of sample condition satisfaction, the sample controller type, and the sample self-diagnostic condition.

[0075] 303. The diagnostic device obtains a plurality of first target self-diagnostic parameters whose parameter types belong to the target self-diagnostic parameter type set based on the target self-diagnostic parameter acquisition method, the plurality of first target self-diagnostic parameters including a plurality of first self-diagnostic parameters and a plurality of second self-diagnostic parameters, the plurality of first self-diagnostic parameters being self-diagnostic parameters related to the target DC component, and the plurality of second self-diagnostic parameters being self-diagnostic parameters related to the target DC controller.

[0076] The parameter type of the first target self-diagnostic parameter belongs to the target self-diagnostic parameter type set. The first self-diagnostic parameter is related to the target DC component, in other words, a component parameter of the target DC component. The second self-diagnostic parameter is related to the target DC controller, in other words, a controller parameter of the target DC controller. The concept of obtaining multiple first self-diagnostic parameters is based on the fact that the target DC controller directly controls the target DC component. Therefore, the condition of the target DC component can actually reflect whether the target DC controller is abnormal to a certain extent.

[0077] In one possible implementation, the diagnostic device determines a target parameter source and a parameter acquisition frequency indicated by the target self-diagnostic parameter acquisition method. The diagnostic device acquires multiple initial self-diagnostic parameters from the target parameter source according to the parameter acquisition frequency. The diagnostic device determines, among the multiple initial self-diagnostic parameters, output diagnostic parameters whose parameter types fall within the target self-diagnostic parameter type set as target self-diagnostic parameters, to obtain the multiple first target self-diagnostic parameters.

[0078] Among them, the target parameter source refers to the source of obtaining the parameter. For the same parameter, there may be at least two parameter sources to obtain the parameter. For example, when the target DC component is a motor, the input current of the motor can be obtained directly through the motor current sensor or through the target DC controller. The target parameter source is to clarify the source of the parameter. The target parameter source is a collection of a group of parameter sources, that is, the target parameter source includes the parameter source corresponding to each parameter in the optional diagnostic parameter type. For example, the optional diagnostic parameter type includes controller temperature and controller input current, then the target parameter source includes the parameter source corresponding to the controller temperature and the parameter source corresponding to the controller input current. The parameter acquisition frequency is used to indicate the frequency of parameter acquisition. The higher the parameter acquisition frequency, the higher the frequency; the lower the parameter acquisition frequency, the lower the frequency.

[0079] 304. The diagnostic device processes the multiple first target self-diagnostic parameters using the target self-diagnostic parameter processing method to obtain multiple second target self-diagnostic parameters and multiple third target self-diagnostic parameters of the target DC controller, where the multiple second target self-diagnostic parameters and the multiple third target self-diagnostic parameters correspond to different self-diagnostic dimensions.

[0080] The second target self-diagnosis mode and the third target self-diagnosis mode are obtained by processing the plurality of first target self-diagnosis parameters using the target self-diagnosis parameter processing mode, and can be used in subsequent self-diagnosis processes. Different self-diagnosis dimensions refer to different data dimensions.

[0081] In one possible embodiment, the target self-diagnostic parameter processing method includes a first self-diagnostic parameter processing method, a second self-diagnostic parameter processing method, and a third self-diagnostic parameter processing method. The diagnostic device uses the first self-diagnostic parameter processing method to process multiple first self-diagnostic parameters among the multiple first target self-diagnostic parameters to obtain multiple component self-diagnostic processing parameters of the target DC component. The diagnostic device uses the second self-diagnostic parameter processing method to process multiple second self-diagnostic parameters among the multiple first target self-diagnostic parameters to obtain multiple controller self-diagnostic processing parameters of the target DC controller. The diagnostic device uses the third self-diagnostic parameter processing method to process the multiple first self-diagnostic parameters and the multiple second self-diagnostic parameters to determine multiple fused self-diagnostic processing parameters. The diagnostic device determines multiple second target self-diagnostic parameters of the target DC controller based on the multiple component self-diagnostic processing parameters and the multiple fused self-diagnostic processing parameters. The diagnostic device determines multiple third target self-diagnostic parameters of the target DC controller based on the multiple controller self-diagnostic processing parameters and the multiple fused self-diagnostic processing parameters.

[0082] Among them, the first self-diagnostic parameter processing method, the second self-diagnostic parameter processing method and the third self-diagnostic parameter processing method are different parameter processing methods. Using the multiple second target self-diagnostic parameters and multiple third target self-diagnostic parameters obtained by the first self-diagnostic parameter processing method, the second self-diagnostic parameter processing method and the third self-diagnostic parameter processing method can obtain more accurate self-diagnostic results.

[0083] In order to explain the above embodiment more clearly, the above embodiment will be described in several parts below.

[0084] In the first part, the diagnostic device processes a plurality of first self-diagnostic parameters among the plurality of first target self-diagnostic parameters using the first self-diagnostic parameter processing method to obtain a plurality of component self-diagnostic processing parameters of the target DC component.

[0085] The first self-diagnostic parameter is a self-diagnostic parameter related to the target DC component, and the first self-diagnostic parameter processing method is a parameter processing method that matches the target DC component and the target diagnostic condition.

[0086] In one possible implementation, the diagnostic device preprocesses the plurality of first self-diagnostic parameters using the preprocessing method indicated by the first self-diagnostic parameter processing method to obtain the plurality of preprocessed first self-diagnostic parameters. The diagnostic device inputs the plurality of preprocessed first self-diagnostic parameters into a first parameter processing model indicated by the first self-diagnostic parameter processing method, encodes and decodes the plurality of preprocessed first self-diagnostic parameters, and obtains the plurality of component self-diagnostic processing parameters.

[0087] The preprocessing is intended to remove abnormal parameters from the first self-diagnostic parameters. The preprocessing method indicated by the first self-diagnostic parameter processing mode is one of multiple candidate preprocessing modes, and different candidate preprocessing modes have different preprocessing accuracy. The function of the first parameter processing model is to convert one parameter sequence into another parameter sequence. In the above method, this means converting the sequence of multiple preprocessed first self-diagnostic parameters into a sequence of multiple component self-diagnostic processing parameters. Sequence conversion is to convert the parameters into a specified format. In this embodiment of the present application, there are multiple candidate first parameter processing models, and the first parameter processing model indicated by the first self-diagnostic parameter processing mode is one of these multiple candidate first parameter processing models. Different candidate first parameter processing models have different parameter processing effects. In other words, in the above embodiment, the first self-diagnostic parameter processing mode can be used to select a first parameter processing model from the multiple candidate first parameter processing models, and this first parameter processing model can be used to implement parameter processing to meet the parameter processing requirements of the current scenario. The first parameter processing model is a sequence encoding / decoding model, such as an encoding / decoding model based on a long short-term memory (LSTM) network or an encoding / decoding model based on an attention mechanism, but this embodiment is not limited to this.

[0088] In the second part, the diagnostic device processes a plurality of second self-diagnostic parameters in the plurality of first target self-diagnostic parameters using the second self-diagnostic parameter processing method to obtain a plurality of controller self-diagnostic processing parameters of the target DC controller.

[0089] The second self-diagnosis parameter is a self-diagnosis parameter related to the target DC controller, and the second self-diagnosis parameter processing method is a parameter processing method that matches the target DC controller and the target diagnostic condition.

[0090] In one possible implementation, the diagnostic device preprocesses the plurality of second self-diagnostic parameters using a preprocessing method indicated by the second self-diagnostic parameter processing method to obtain the plurality of preprocessed second self-diagnostic parameters. The diagnostic device inputs the plurality of preprocessed second self-diagnostic parameters into a second parameter processing model indicated by the second self-diagnostic parameter processing method, encodes and decodes the plurality of preprocessed second self-diagnostic parameters, and obtains the plurality of controller self-diagnostic processing parameters.

[0091] Among them, the preprocessing is to remove abnormal parameters in the second self-diagnosis parameter. The preprocessing method indicated by the second self-diagnosis parameter processing method is one of a plurality of candidate preprocessing methods, and the preprocessing accuracy of different candidate preprocessing methods is different. The role of the second parameter processing model is to convert a parameter sequence into another parameter sequence. In the above method, that is, to convert a sequence consisting of a plurality of second self-diagnosis parameters after preprocessing into a sequence consisting of a plurality of controller self-diagnosis processing parameters. The sequence conversion is to convert the parameters into a specified form. In an embodiment of the present application, there are a plurality of candidate second parameter processing models, and the second parameter processing model indicated by the second self-diagnosis parameter processing method is one of the plurality of candidate second parameter processing models, and the parameter processing effects of different candidate second parameter processing models are different. That is, in the above embodiment, the second self-diagnosis parameter processing method can be used to screen out a second parameter processing model from a plurality of candidate second parameter processing models, and the second parameter processing model can be used to implement parameter processing to meet the parameter processing requirements in the current scenario. Similar to the first parameter processing model, the second parameter processing model is a sequence encoding and decoding model, such as an encoding and decoding model based on a long short-term memory (LSTM) network, or an encoding and decoding model based on an attention mechanism, which is not limited in the embodiments of the present application.

[0092] In the third part, the diagnostic device processes the multiple first self-diagnostic parameters and the multiple second self-diagnostic parameters using the third self-diagnostic parameter processing method to determine multiple fusion self-diagnostic processing parameters.

[0093] In one possible embodiment, the diagnostic device preprocesses the multiple first self-diagnostic parameters and the multiple second self-diagnostic parameters using the preprocessing method indicated by the third self-diagnostic parameter processing method to obtain the preprocessed multiple first self-diagnostic parameters and the preprocessed multiple second self-diagnostic parameters. The diagnostic device determines multiple self-diagnostic parameter pairs based on the parameter types of the preprocessed multiple first self-diagnostic parameters and the parameter types of the preprocessed multiple second self-diagnostic parameters, wherein a self-diagnostic parameter pair includes a first self-diagnostic parameter and a second self-diagnostic parameter, and the type similarity between the parameter type of the first self-diagnostic parameter and the parameter type of the second self-diagnostic parameter is greater than or equal to the similarity threshold indicated by the third self-diagnostic parameter processing method. The diagnostic device inputs the multiple self-diagnostic parameter pairs into the third parameter processing model indicated by the third self-diagnostic parameter processing method, encodes and decodes the multiple self-diagnostic parameter pairs, and obtains the multiple fused self-diagnostic processing parameters.

[0094] Among them, the preprocessing is to remove abnormal parameters in the first self-diagnostic parameter and the second self-diagnostic parameter. The preprocessing method indicated by the third self-diagnostic parameter processing method is one of multiple candidate preprocessing methods, and the preprocessing accuracy of different candidate preprocessing methods is different. The role of the third parameter processing model is to convert a parameter sequence into another parameter sequence. In the above method, that is, to convert the sequence composed of multiple self-diagnostic parameter pairs after preprocessing into a sequence composed of multiple fused self-diagnostic processing parameters. The sequence conversion is to convert the parameters into a specified form. In an embodiment of the present application, there are multiple candidate third parameter processing models, and the third parameter processing model indicated by the third self-diagnostic parameter processing method is one of the multiple candidate third parameter processing models. The parameter processing effects of different candidate third parameter processing models are different. That is, in the above embodiment, the third self-diagnostic parameter processing method can be used to screen out a third parameter processing model from multiple candidate third parameter processing models, and the third parameter processing model can be used to implement parameter processing to meet the parameter processing requirements in the current scenario. Similar to the first parameter processing model, the third parameter processing model is a sequence encoding and decoding model, such as an encoding and decoding model based on a long short-term memory (LSTM) network, or an encoding and decoding model based on an attention mechanism, which is not limited in the embodiments of the present application.

[0095] Part 4: The diagnostic device determines a plurality of second target self-diagnostic parameters of the target DC controller based on the plurality of component self-diagnostic processing parameters and the plurality of fusion self-diagnostic processing parameters.

[0096] Among them, the above-mentioned component self-diagnosis processing parameters and fusion self-diagnosis processing parameters are intermediate variables in the data processing process and do not have specific physical meanings.

[0097] In one possible implementation, the diagnostic device determines a plurality of first parameter correction coefficients based on the plurality of component self-diagnostic processing parameters, where each first parameter correction coefficient corresponds to each component self-diagnostic processing parameter. The diagnostic device obtains the plurality of second target self-diagnostic parameters based on the plurality of first parameter correction coefficients and the plurality of fused self-diagnostic processing parameters, where the number of the plurality of first parameter correction coefficients is the same as the number of the plurality of fused self-diagnostic processing parameters.

[0098] The first parameter correction coefficient is used to correct the fused self-diagnosis parameter, thereby obtaining the corresponding second target self-diagnosis parameter.

[0099] For example, the diagnostic device performs full connection on the plurality of component self-diagnosis processing parameters to obtain a plurality of first parameter correction coefficients, and then multiplies the plurality of first parameter correction coefficients by the plurality of fusion self-diagnosis processing parameters to obtain the plurality of second target self-diagnosis parameters.

[0100] Among them, the full connection is to convert multiple component self-diagnosis processing parameters into a matrix composed of multiple first parameter correction coefficients, and multiply the multiple first parameter correction coefficients with the multiple fusion self-diagnosis processing parameters, that is, to multiply the matrix composed of the multiple first parameter correction coefficients with the matrix composed of the multiple fusion self-diagnosis processing parameters.

[0101] Part 5: The diagnostic device determines a plurality of third target self-diagnostic parameters of the target DC controller based on the plurality of controller self-diagnostic processing parameters and the plurality of fusion self-diagnostic processing parameters.

[0102] Among them, the above-mentioned controller self-diagnosis processing parameters and fusion self-diagnosis processing parameters are intermediate variables in the data processing process and do not have specific physical meanings.

[0103] In one possible implementation, the diagnostic device determines a plurality of second parameter correction coefficients based on the plurality of controller self-diagnostic processing parameters, with one second parameter correction coefficient corresponding to one controller self-diagnostic processing parameter. The diagnostic device obtains the plurality of third target self-diagnostic parameters based on the plurality of second parameter correction coefficients and the plurality of fused self-diagnostic processing parameters, with the number of the plurality of second parameter correction coefficients being the same as the number of the plurality of fused self-diagnostic processing parameters.

[0104] The second parameter correction coefficient is used to correct the fused self-diagnosis parameter, thereby obtaining the corresponding third target self-diagnosis parameter.

[0105] For example, the diagnostic device performs full connection and normalization on the plurality of controller self-diagnosis processing parameters to obtain a plurality of second parameter correction coefficients, and then multiplies the plurality of second parameter correction coefficients by the plurality of fusion self-diagnosis processing parameters to obtain the plurality of third target self-diagnosis parameters.

[0106] Among them, the full connection is to convert multiple controller self-diagnosis processing parameters into a matrix composed of multiple second parameter correction coefficients, and multiply the multiple second parameter correction coefficients with the multiple fusion self-diagnosis processing parameters, that is, to multiply the matrix composed of the multiple second parameter correction coefficients with the matrix composed of the multiple fusion self-diagnosis processing parameters.

[0107] 305. The diagnostic device determines a target self-diagnosis result of the target DC controller based on the multiple second target self-diagnosis parameters and the multiple third target self-diagnosis parameters, where the target self-diagnosis result is used to indicate whether the target DC controller has a fault.

[0108] The target self-diagnosis result is the final self-diagnosis result of the target DC controller. Compared with the self-diagnosis result obtained by using the self-diagnosis program of the target DC controller, the target self-diagnosis result has higher accuracy.

[0109] In one possible implementation, the diagnostic device determines an auxiliary diagnostic result for the target DC controller based on the plurality of second target self-diagnostic parameters. The diagnostic device determines an initial diagnostic result based on the plurality of third target self-diagnostic parameters. The diagnostic device determines a target self-diagnostic result for the target DC controller based on the auxiliary diagnostic result and the initial diagnostic result.

[0110] For example, the diagnostic device performs feature extraction on the multiple second target self-diagnosis parameters to obtain auxiliary diagnosis result prediction features. The diagnostic device performs full connection and normalization on the auxiliary diagnosis result prediction features to obtain auxiliary result classification values ​​corresponding to the auxiliary diagnosis results. The diagnostic device performs feature extraction on the multiple third target self-diagnosis parameters to obtain initial diagnosis result prediction features. The diagnostic device performs full connection and normalization on the initial diagnosis result prediction features to obtain initial result classification values ​​corresponding to the initial diagnosis results. The diagnostic device performs weighted summation on the auxiliary result classification value and the initial result classification value to obtain a target classification value. When the target classification value is greater than or equal to the classification value threshold, the diagnostic device determines the target diagnosis result as a first diagnosis result, and the first diagnosis result is used to indicate that the target DC controller has a fault. When the target classification value is less than the classification value threshold, the diagnostic device determines the target diagnosis result as a second diagnosis result, and the second diagnosis result is used to indicate that the target DC controller does not have a fault.

[0111] The weight corresponding to the auxiliary result classification value is smaller than the weight corresponding to the initial result classification value. Preferably, the weight corresponding to the auxiliary result classification value is one-fourth of the weight corresponding to the initial result classification value, that is, the weight corresponding to the auxiliary result classification value is 0.2, and the weight corresponding to the initial result classification value is 0.8. In the experiment, the target diagnosis result obtained with such weights has the highest accuracy. The classification value threshold is the candidate classification value threshold that matches the target self-diagnosis condition among multiple candidate classification value thresholds.

[0112] Optionally, in addition to the above embodiments, the diagnostic device can also perform the following steps to determine the target self-diagnosis result.

[0113] In one possible implementation, a diagnostic device obtains an original self-diagnostic result of the target DC controller, first abnormality description information of the target DC component, and second abnormality description information of the target DC controller. The original self-diagnostic result is a self-diagnostic result obtained through a self-diagnostic program of the target DC controller. Based on the original self-diagnostic result, the first abnormality description information, and the second abnormality description information, the diagnostic device determines a reference self-diagnostic result of the target DC controller. Based on the auxiliary diagnostic result, the initial diagnostic result, and the reference self-diagnostic result, the diagnostic device determines a target self-diagnostic result of the target DC controller.

[0114] The first exception description information and the second exception description information are both filled in by technical personnel, and the first exception description information and the second exception description information are both in the form of natural language.

[0115] The following describes a manner in which the diagnostic device in the above embodiment determines the reference self-diagnosis result of the target DC controller based on the original self-diagnosis result, the first abnormality description information, and the second abnormality description information.

[0116] In one possible implementation, the diagnostic device inputs the initial self-diagnosis result, the first abnormal description information, and the second abnormal description information into a diagnostic result prediction model, and performs feature extraction on the first abnormal description information and the second abnormal description information respectively through the diagnostic result prediction model to obtain a first description information feature of the first abnormal description information and a second description information feature of the second abnormal description information. The diagnostic device fuses the initial self-diagnosis result, the first description information feature, and the second description information feature through the diagnostic result prediction model to obtain a diagnostic result prediction feature. The diagnostic device fully connects and normalizes the diagnostic result prediction feature through the diagnostic result prediction model to obtain a reference result classification value corresponding to the reference self-diagnosis result.

[0117] The diagnostic result prediction model is a large language model with natural language processing capabilities. The natural language processing capabilities of the diagnostic result prediction model can be used to encode abnormality description information into descriptive information features, thereby subsequently obtaining a reference result classification value. The diagnostic result prediction model can be any type of large language model, and the embodiments of the present application are not limited thereto.

[0118] For example, the diagnostic device inputs the initial self-diagnosis result, the first abnormal description information, and the second abnormal description information into a diagnostic result prediction model. Through the diagnostic result prediction model, the first abnormal description information and the second abnormal description information are respectively encoded based on the attention mechanism to obtain the first description information feature of the first abnormal description information and the second description information feature of the second abnormal description information. The diagnostic device fuses the initial self-diagnosis result, the first description information feature, and the second description information feature through the diagnostic result prediction model to obtain the diagnostic result prediction feature. The diagnostic device fully connects and normalizes the diagnostic result prediction feature through the diagnostic result prediction model to obtain the reference result classification value corresponding to the reference self-diagnosis result.

[0119] The following describes how the diagnostic device in the above embodiment determines the target self-diagnosis result of the target DC controller based on the auxiliary diagnosis result, the initial diagnosis result, and the reference self-diagnosis result.

[0120] In one possible implementation, the diagnostic device performs a weighted summation of the auxiliary result classification value, the reference result classification value, and the initial result classification value to obtain a target classification value. If the target classification value is greater than or equal to a classification value threshold, the diagnostic device determines the target diagnostic result as a first diagnostic result, which indicates that the target DC controller has a fault. If the target classification value is less than the classification value threshold, the diagnostic device determines the target diagnostic result as a second diagnostic result, which indicates that the target DC controller does not have a fault.

[0121] The weights corresponding to the auxiliary result classification value and the reference result classification value are smaller than the weight corresponding to the initial result classification value. Preferably, the weight corresponding to the auxiliary result classification value is 0.1, the weight corresponding to the reference result classification value is 0.2, and the weight corresponding to the initial result classification value is 0.7. In the experiment, the target diagnosis result obtained using such weights has the highest accuracy. The classification value threshold is the candidate classification value threshold that matches the target self-diagnosis condition among multiple candidate classification value thresholds.

[0122] 306. When the target self-diagnosis result indicates that the target DC controller has a fault, the diagnostic device determines a cause of the fault of the target DC controller based on the multiple second target self-diagnosis parameters and the multiple third target self-diagnosis parameters.

[0123] In one possible implementation, if the target self-diagnosis result indicates a fault in the target DC controller, the diagnostic device inputs the second target self-diagnosis parameter and the plurality of third target self-diagnosis parameters into a fault cause prediction model. The fault cause prediction model then encodes the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters using an attention mechanism to obtain a fault cause prediction feature. The diagnostic device then performs multiple rounds of iterative decoding on the fault cause prediction feature using the attention mechanism using the fault cause prediction model to obtain the fault cause.

[0124] Among them, the fault cause prediction model is an encoding and decoding model based on the attention mechanism. For example, it can be a model fine-tuned based on the BERT model, or a model fine-tuned based on other encoding and decoding models based on the attention mechanism. The embodiments of the present application are not limited to this.

[0125] All of the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here.

[0126] Through the technical solution provided by the embodiment of the present application, when the target DC controller meets the target self-diagnosis conditions, the corresponding target self-diagnosis parameter acquisition method, the target self-diagnosis parameter processing method and the target self-diagnosis parameter type set are determined. Based on the target self-diagnosis parameter acquisition method, a plurality of first target self-diagnosis parameters whose parameter types belong to the target self-diagnosis parameter type set are acquired, thereby realizing the acquisition of basic self-diagnosis parameters. The target self-diagnosis parameter processing method is adopted to process the plurality of first target self-diagnosis parameters to obtain a plurality of second target self-diagnosis parameters and a plurality of third target self-diagnosis parameters of different self-diagnosis dimensions. The target self-diagnosis result of the target DC controller is determined based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters. Compared with the self-diagnosis result obtained by the self-diagnosis method in the related art, the target self-diagnosis result is more accurate due to the combination of richer data and the use of an appropriate data processing method.

[0127] Figure 4 This is a structural diagram of a self-diagnosis processing device for a DC controller provided in an embodiment of the present application, see Figure 4 The device includes: a determination module 401, an acquisition module 402, a processing module 403 and a diagnosis module 404.

[0128] Determination module 401 is used to determine a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method corresponding to the target self-diagnostic condition when the target DC controller of the target DC component satisfies the target self-diagnostic condition among multiple candidate self-diagnostic conditions, and determine a target self-diagnostic parameter type set corresponding to the target DC controller and the target self-diagnostic condition, where the target self-diagnostic condition includes a target working environment condition, a target controller condition, and a target DC component condition.

[0129] An acquisition module 402 is configured to acquire, based on the target self-diagnostic parameter acquisition method, a plurality of first target self-diagnostic parameters whose parameter types belong to the target self-diagnostic parameter type set, the plurality of first target self-diagnostic parameters including a plurality of first self-diagnostic parameters and a plurality of second self-diagnostic parameters, the plurality of first self-diagnostic parameters being self-diagnostic parameters related to the target DC component, and the plurality of second self-diagnostic parameters being self-diagnostic parameters related to the target DC controller.

[0130] The processing module 403 is configured to process the multiple first target self-diagnostic parameters using the target self-diagnostic parameter processing method to obtain multiple second target self-diagnostic parameters and multiple third target self-diagnostic parameters of the target DC controller, where the multiple second target self-diagnostic parameters and the multiple third target self-diagnostic parameters correspond to different self-diagnostic dimensions.

[0131] The diagnostic module 404 is configured to determine a target self-diagnosis result of the target DC controller based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters, wherein the target self-diagnosis result is configured to indicate whether the target DC controller has a fault.

[0132] In one possible implementation, the determination module 401 is configured to, when a target DC controller of a target DC component satisfies a target self-diagnostic condition from among multiple candidate self-diagnostic conditions, determine a condition satisfaction level of the target DC controller, where the condition satisfaction level indicates the extent to which the target DC controller exceeds the target self-diagnostic condition. Based on the target self-diagnostic condition and the condition satisfaction level, determine a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method. A target self-diagnostic parameter type set is determined based on the condition satisfaction level, the controller type of the target DC controller, and the target self-diagnostic condition.

[0133] In one possible implementation, the determination module 401 is configured to, when a target DC controller of a target DC component satisfies a target self-diagnostic condition from among multiple candidate self-diagnostic conditions, determine the degree of satisfaction of the condition based on an operating environment parameter of the target DC controller and an environmental condition parameter threshold corresponding to the target operating environment condition, a controller parameter of the target DC controller and a controller condition parameter threshold corresponding to the target controller condition, and a DC component parameter of the target DC component and a component condition parameter threshold corresponding to the target DC component condition. The operating environment parameter is used to match the target operating environment condition, the controller parameter is used to match the target controller condition, and the DC component parameter is used to match the target DC component condition.

[0134] In one possible implementation, the determination module 401 is configured to perform conditional conversion on the target operating environment condition, the target controller condition, and the target DC component condition to obtain a condition description text of the target self-diagnostic condition. Based on the condition description text and the degree of satisfaction of the condition, the determination module 401 determines a controller state and a target self-diagnostic mode of the target DC controller. Based on the controller state and the target self-diagnostic mode, the determination module 401 determines a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method.

[0135] In one possible implementation, the acquisition module 402 is configured to determine a target parameter source and a parameter acquisition frequency indicated by the target self-diagnostic parameter acquisition method. A plurality of initial self-diagnostic parameters are acquired from the target parameter source according to the parameter acquisition frequency. Output diagnostic parameters of the plurality of initial self-diagnostic parameters whose parameter types fall within the target self-diagnostic parameter type set are determined as target self-diagnostic parameters, thereby obtaining the plurality of first target self-diagnostic parameters.

[0136] In one possible embodiment, the target self-diagnostic parameter processing method includes a first self-diagnostic parameter processing method, a second self-diagnostic parameter processing method, and a third self-diagnostic parameter processing method. The processing module 403 is configured to process multiple first self-diagnostic parameters among the multiple first target self-diagnostic parameters using the first self-diagnostic parameter processing method to obtain multiple component self-diagnostic processing parameters of the target DC component. The processing module 403 is configured to process multiple second self-diagnostic parameters among the multiple first target self-diagnostic parameters using the second self-diagnostic parameter processing method to obtain multiple controller self-diagnostic processing parameters of the target DC controller. The processing module 403 is configured to process the multiple first self-diagnostic parameters and the multiple second self-diagnostic parameters using the third self-diagnostic parameter processing method to determine multiple fused self-diagnostic processing parameters. Multiple second target self-diagnostic parameters of the target DC controller are determined based on the multiple component self-diagnostic processing parameters and the multiple fused self-diagnostic processing parameters. Multiple third target self-diagnostic parameters of the target DC controller are determined based on the multiple controller self-diagnostic processing parameters and the multiple fused self-diagnostic processing parameters.

[0137] In one possible implementation, the processing module 403 is configured to determine a plurality of first parameter correction coefficients based on the plurality of component self-diagnosis processing parameters, where one first parameter correction coefficient corresponds to one component self-diagnosis processing parameter. Based on the plurality of first parameter correction coefficients and the plurality of fused self-diagnosis processing parameters, the plurality of second target self-diagnosis parameters are obtained, where the number of the plurality of first parameter correction coefficients is the same as the number of the plurality of fused self-diagnosis processing parameters.

[0138] The processing module 403 is configured to determine a plurality of second parameter correction coefficients based on the plurality of controller self-diagnosis processing parameters, wherein each second parameter correction coefficient corresponds to each controller self-diagnosis processing parameter. The processing module 403 is configured to obtain the plurality of third target self-diagnosis parameters based on the plurality of second parameter correction coefficients and the plurality of fused self-diagnosis processing parameters, wherein the number of the plurality of second parameter correction coefficients is the same as the number of the plurality of fused self-diagnosis processing parameters.

[0139] In one possible implementation, the diagnostic module 404 is configured to determine an auxiliary diagnostic result of the target DC controller based on the plurality of second target self-diagnostic parameters, determine an initial diagnostic result based on the plurality of third target self-diagnostic parameters, and determine a target self-diagnostic result of the target DC controller based on the auxiliary diagnostic result and the initial diagnostic result.

[0140] In one possible implementation, the acquisition module 402 is further configured to acquire an original self-diagnosis result of the target DC controller, first abnormality description information of the target DC component, and second abnormality description information of the target DC controller, wherein the original self-diagnosis result is a self-diagnosis result obtained by a self-diagnosis program of the target DC controller. A reference self-diagnosis result of the target DC controller is determined based on the original self-diagnosis result, the first abnormality description information, and the second abnormality description information.

[0141] The diagnostic module 404 is further configured to determine a target self-diagnosis result of the target DC controller based on the auxiliary diagnostic result, the initial diagnostic result, and the reference self-diagnosis result.

[0142] It should be noted that the self-diagnosis processing device for a DC controller provided in the above embodiment is only illustrated by the division of the above-mentioned functional modules when performing self-diagnosis. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the self-diagnosis processing device for a DC controller provided in the above embodiment and the self-diagnosis processing method embodiment for a DC controller are based on the same concept. The specific implementation process is detailed in the method embodiment and is not further described here.

[0143] Through the technical solution provided by the embodiment of the present application, when the target DC controller meets the target self-diagnosis conditions, the corresponding target self-diagnosis parameter acquisition method, the target self-diagnosis parameter processing method and the target self-diagnosis parameter type set are determined. Based on the target self-diagnosis parameter acquisition method, a plurality of first target self-diagnosis parameters whose parameter types belong to the target self-diagnosis parameter type set are acquired, thereby realizing the acquisition of basic self-diagnosis parameters. The target self-diagnosis parameter processing method is adopted to process the plurality of first target self-diagnosis parameters to obtain a plurality of second target self-diagnosis parameters and a plurality of third target self-diagnosis parameters of different self-diagnosis dimensions. The target self-diagnosis result of the target DC controller is determined based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters. Compared with the self-diagnosis result obtained by the self-diagnosis method in the related art, the target self-diagnosis result is more accurate due to the combination of richer data and the use of an appropriate data processing method.

[0144] Figure 5This is a schematic diagram of the structure of a diagnostic device provided in an embodiment of the present application. The diagnostic device 500 may vary significantly due to different configurations or performance, and may include one or more processors (Central Processing Units, CPUs) 501 and one or more memories 502, wherein the one or more memories 502 store at least one computer program, and the at least one computer program is loaded and executed by the one or more processors 501 to implement the methods provided in the above-mentioned various method embodiments. Of course, the diagnostic device 500 may also have components such as a wired or wireless network interface, a keyboard, and an input / output interface for input and output. The diagnostic device 500 may also include other components for implementing device functions, which will not be described in detail here.

[0145] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including a computer program. The computer program can be executed by a processor to implement the self-diagnosis processing method for a DC controller in the above-described embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0146] In an exemplary embodiment, a computer program product or computer program is also provided, which includes a program code, which is stored in a computer-readable storage medium. A processor of a computer device reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the computer device performs the above-mentioned self-diagnosis processing method for a DC controller.

[0147] In some embodiments, the computer program involved in the embodiments of the present application may be deployed and executed on a computer device, or on multiple computer devices located at one location, or on multiple computer devices distributed at multiple locations and interconnected through a communication network. Multiple computer devices distributed at multiple locations and interconnected through a communication network may constitute a blockchain system.

[0148] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or may be accomplished by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0149] The above are only optional embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A self-diagnosis processing method for a DC controller, characterized in that: The method comprises: determining, when a target DC controller of a target DC component satisfies a target self-diagnostic condition among a plurality of candidate self-diagnostic conditions, a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method corresponding to the target self-diagnostic condition, and determining a target self-diagnostic parameter type set corresponding to the target DC controller and the target self-diagnostic condition, the target self-diagnostic condition including a target working environment condition, a target controller condition, and a target DC component condition; Based on the target self-diagnostic parameter acquisition method, acquiring a plurality of first target self-diagnostic parameters whose parameter types belong to the target self-diagnostic parameter type set, the plurality of first target self-diagnostic parameters including a plurality of first self-diagnostic parameters and a plurality of second self-diagnostic parameters, the plurality of first self-diagnostic parameters being self-diagnostic parameters related to the target DC component, and the plurality of second self-diagnostic parameters being self-diagnostic parameters related to the target DC controller; Processing the plurality of first target self-diagnostic parameters using the target self-diagnostic parameter processing method to obtain a plurality of second target self-diagnostic parameters and a plurality of third target self-diagnostic parameters of the target DC controller, wherein the plurality of second target self-diagnostic parameters and the plurality of third target self-diagnostic parameters correspond to different self-diagnostic dimensions; A target self-diagnosis result of the target DC controller is determined based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters, where the target self-diagnosis result is used to indicate whether the target DC controller has a fault.

2. The method according to claim 1, characterized in that The method further comprises: determining, when a target DC controller of a target DC component satisfies a target self-diagnosis condition among a plurality of candidate self-diagnosis conditions, a target self-diagnosis parameter acquisition method and a target self-diagnosis parameter processing method corresponding to the target self-diagnosis condition, and determining a target self-diagnosis parameter type set corresponding to the target DC controller and the target self-diagnosis condition. determining, when a target DC controller of a target DC component satisfies a target self-diagnosis condition among a plurality of candidate self-diagnosis conditions, a condition satisfaction degree of the target DC controller, the condition satisfaction degree being used to indicate a degree to which the target DC controller exceeds the target self-diagnosis condition; determining, based on the target self-diagnosis condition and the degree to which the condition is satisfied, a target self-diagnosis parameter acquisition method and a target self-diagnosis parameter processing method; The target self-diagnosis parameter type set is determined based on the degree of satisfaction of the condition, the controller type of the target DC controller, and the target self-diagnosis condition.

3. The method according to claim 2, characterized in that The step of determining the degree to which the target DC controller of the target DC component satisfies a target self-diagnosis condition among a plurality of candidate self-diagnosis conditions includes: determining, when a target DC controller of a target DC component satisfies a target self-diagnosis condition among a plurality of candidate self-diagnosis conditions, a degree of satisfaction of the condition based on an operating environment parameter of the target DC controller and an environmental condition parameter threshold corresponding to the target operating environment condition, a controller parameter of the target DC controller and a controller condition parameter threshold corresponding to the target controller condition, and a DC component parameter of the target DC component and a component condition parameter threshold corresponding to the target DC component condition; The working environment parameters are used to match the target working environment conditions, the controller parameters are used to match the target controller conditions, and the DC component parameters are used to match the target DC component conditions.

4. The method according to claim 2, characterized in that The determining, based on the target self-diagnosis condition and the degree to which the condition is satisfied, the target self-diagnosis parameter acquisition method and the target self-diagnosis parameter processing method includes: Performing condition conversion on the target working environment condition, the target controller condition, and the target DC component condition to obtain a condition description text of the target self-diagnosis condition; determining a controller state and a target self-diagnosis mode of the target DC controller based on the condition description text and the degree to which the condition is satisfied; The target self-diagnosis parameter acquisition method and the target self-diagnosis parameter processing method are determined based on the controller state and the target self-diagnosis mode.

5. The method according to claim 1, characterized in that The acquiring, based on the target self-diagnosis parameter acquiring method, a plurality of first target self-diagnosis parameters whose parameter types belong to the target self-diagnosis parameter type set, includes: Determining a target parameter source and a parameter acquisition frequency indicated by the target self-diagnosis parameter acquisition method; acquiring a plurality of initial self-diagnostic parameters from the target parameter source according to the parameter acquisition frequency; The output diagnostic parameters of the multiple initial self-diagnostic parameters whose parameter types belong to the target self-diagnostic parameter type set are determined as target self-diagnostic parameters to obtain the multiple first target self-diagnostic parameters.

6. The method according to claim 1, characterized in that The target self-diagnostic parameter processing mode includes a first self-diagnostic parameter processing mode, a second self-diagnostic parameter processing mode, and a third self-diagnostic parameter processing mode. The target self-diagnostic parameter processing mode is used to process the plurality of first target self-diagnostic parameters to obtain a plurality of second target self-diagnostic parameters and a plurality of third target self-diagnostic parameters of the target DC controller, including: Processing a plurality of first self-diagnostic parameters among the plurality of first target self-diagnostic parameters using the first self-diagnostic parameter processing method to obtain a plurality of component self-diagnostic processing parameters of the target DC component; Processing a plurality of second self-diagnostic parameters in the plurality of first target self-diagnostic parameters using the second self-diagnostic parameter processing method to obtain a plurality of controller self-diagnostic processing parameters of the target DC controller; Processing the plurality of first self-diagnostic parameters and the plurality of second self-diagnostic parameters using the third self-diagnostic parameter processing method to determine a plurality of fused self-diagnostic processing parameters; determining a plurality of second target self-diagnostic parameters of the target DC controller based on the plurality of component self-diagnostic processing parameters and the plurality of fused self-diagnostic processing parameters; A plurality of third target self-diagnosis parameters of the target DC controller are determined based on the plurality of controller self-diagnosis processing parameters and the plurality of fused self-diagnosis processing parameters.

7. The method according to claim 6, characterized in that The determining of a plurality of second target self-diagnosis parameters of the target DC controller based on the plurality of component self-diagnosis processing parameters and the plurality of fusion self-diagnosis processing parameters includes: determining a plurality of first parameter correction coefficients based on the plurality of component self-diagnosis processing parameters, one first parameter correction coefficient corresponding to one component self-diagnosis processing parameter; obtaining the plurality of second target self-diagnostic parameters based on the plurality of first parameter correction coefficients and the plurality of fused self-diagnostic processing parameters, wherein the number of the plurality of first parameter correction coefficients is the same as the number of the plurality of fused self-diagnostic processing parameters; The determining of a plurality of third target self-diagnosis parameters of the target DC controller based on the plurality of controller self-diagnosis processing parameters and the plurality of fusion self-diagnosis processing parameters includes: determining a plurality of second parameter correction coefficients based on the plurality of controller self-diagnosis processing parameters, one second parameter correction coefficient corresponding to one controller self-diagnosis processing parameter; The plurality of third target self-diagnosis parameters are obtained based on the plurality of second parameter correction coefficients and the plurality of fused self-diagnosis processing parameters, and the number of the plurality of second parameter correction coefficients is the same as the number of the plurality of fused self-diagnosis processing parameters.

8. The method according to any one of claims 1 to 7, characterized in that The step of determining a target self-diagnosis result of the target DC controller based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters includes: determining an auxiliary diagnosis result of the target DC controller based on the plurality of second target self-diagnosis parameters; determining an initial diagnosis result based on the plurality of third target self-diagnostic parameters; A target self-diagnosis result of the target DC controller is determined based on the auxiliary diagnosis result and the initial diagnosis result.

9. The method according to claim 8, characterized in that Before determining the target self-diagnosis result of the target DC controller based on the auxiliary diagnosis result and the initial diagnosis result, the method further includes: Obtaining an original self-diagnosis result of the target DC controller, first abnormality description information of the target DC component, and second abnormality description information of the target DC controller, wherein the original self-diagnosis result is a self-diagnosis result obtained through a self-diagnosis program of the target DC controller; determining a reference self-diagnosis result of the target DC controller based on the original self-diagnosis result, the first abnormality description information, and the second abnormality description information; The step of determining a target self-diagnosis result of the target DC controller based on the auxiliary diagnosis result and the initial diagnosis result includes: A target self-diagnosis result of the target DC controller is determined based on the auxiliary diagnosis result, the initial diagnosis result, and the reference self-diagnosis result.

10. A self-diagnosis processing device for a DC controller, characterized in that: The device comprises: a determination module, configured to, when a target DC controller of a target DC component satisfies a target self-diagnostic condition among a plurality of candidate self-diagnostic conditions, determine a target self-diagnostic parameter acquisition method and a target self-diagnostic parameter processing method corresponding to the target self-diagnostic condition, and determine a target self-diagnostic parameter type set corresponding to the target DC controller and the target self-diagnostic condition, the target self-diagnostic condition including a target working environment condition, a target controller condition, and a target DC component condition; an acquisition module, configured to acquire, based on the target self-diagnostic parameter acquisition method, a plurality of first target self-diagnostic parameters whose parameter types belong to the target self-diagnostic parameter type set, the plurality of first target self-diagnostic parameters including a plurality of first self-diagnostic parameters and a plurality of second self-diagnostic parameters, the plurality of first self-diagnostic parameters being self-diagnostic parameters related to the target DC component, and the plurality of second self-diagnostic parameters being self-diagnostic parameters related to the target DC controller; a processing module, configured to process the plurality of first target self-diagnostic parameters using the target self-diagnostic parameter processing method to obtain a plurality of second target self-diagnostic parameters and a plurality of third target self-diagnostic parameters of the target DC controller, wherein the plurality of second target self-diagnostic parameters and the plurality of third target self-diagnostic parameters correspond to different self-diagnostic dimensions; The diagnostic module is configured to determine a target self-diagnosis result of the target DC controller based on the plurality of second target self-diagnosis parameters and the plurality of third target self-diagnosis parameters, wherein the target self-diagnosis result is configured to indicate whether the target DC controller has a fault.

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