On-board charger fault self-diagnosis system, method and device based on edge computing

Abstract

The present invention provides an on-board charger fault self-diagnosis system, method, and device based on edge computing, relating to the technical field of new energy vehicles. The system includes: a data acquisition module, an edge computing module, and a fault prediction module. The data acquisition module is used to collect on-board charger data during operation; the edge computing module is used to schedule and manage the on-board charger data, and allocate computing tasks for the on-board charger data based on data priority and computing resource usage, so as to determine the fault diagnosis results locally at the on-board charger; the fault prediction module includes: a gray prediction model, which is used to combine the gray prediction model with a Markov chain to perform fault prediction processing on the on-board charger data and determine the fault prediction results. The present invention can significantly improve the accuracy of fault self-diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to an on-board charger fault self-diagnosis system, method, and device based on edge computing. Background Art

[0002] With the rapid development of new energy vehicles, on-board chargers, as key components, are of vital importance for their reliability and stability. At present, relevant technologies have proposed that the traditional on-board charger fault diagnosis method mainly relies on a centralized data processing center, and the data needs to be uploaded to the cloud for analysis and diagnosis. This solution has problems such as high data transmission delay, strong network dependence, and high privacy and security risks. Therefore, it is difficult to meet the needs of real-time fault diagnosis of on-board chargers. At the same time, the existing diagnostic methods have low accuracy in identifying complex faults, and thus cannot effectively adapt to the operating status of on-board chargers under different working conditions. Summary of the Invention

[0003] In view of this, the object of the present invention is to provide a vehicle charger fault self-diagnosis system, method and device based on edge computing, which can significantly improve the accuracy of fault self-diagnosis.

[0004] In a first aspect, an embodiment of the present invention provides an on-board charger fault self-diagnosis system based on edge computing, the system comprising: a data acquisition module, an edge computing module and a fault prediction module; wherein the data acquisition module is used to collect a set of on-board charger data during the operation of the on-board charger, wherein the on-board charger data set comprises: input voltage, input current, output voltage, output current, charging temperature, charging time, switching device status information and insulation resistance information; the edge computing module is used to schedule and manage the on-board charger data, and allocate computing tasks for the on-board charger data according to the priority of the data and the usage of computing resources, so as to determine the fault diagnosis result locally at the on-board charger; the fault prediction module comprises: a gray prediction model, and the fault prediction module is used to combine the gray prediction model with the Markov chain to perform fault prediction processing on the on-board charger data and determine the fault prediction result.

[0005] In one embodiment, the system also includes: a data preprocessing module; wherein the data preprocessing module includes: an adaptive filtering model, the data preprocessing module is used to perform data cleaning processing on the on-board charger data set to remove noise and outliers in the data, and based on a preset adaptive filtering model, perform data filtering processing and normalization processing on the cleaned on-board charger data set, determine the target input data, and send the target input data to the edge computing module.

[0006] In one embodiment, the edge computing module also includes: a fault diagnosis module; wherein the fault diagnosis module includes: a convolutional neural network and a long short-term memory network based on deep learning, and the fault diagnosis module is used to perform real-time data analysis and processing on the on-board charger data based on the convolutional neural network and the long short-term memory network based on deep learning to determine the fault diagnosis result, wherein the fault diagnosis result includes: the fault type, fault location and fault severity of the charger identified locally.

[0007] In one embodiment, the system also includes: a communication module; wherein the communication module is used to transmit and communicate data with the cloud server and the remote monitoring terminal, upload the fault diagnosis results and fault prediction results to the cloud server and the remote monitoring terminal, and receive model update instructions sent by the cloud server and control instructions sent by the remote monitoring terminal.

[0008] In one embodiment, the system also includes: a human-computer interaction module; wherein the human-computer interaction module includes a display screen and an operation button, which is used to display the operating status, fault diagnosis results and fault prediction results of the on-board charger to the user terminal through the display screen, and determine the operation instructions based on the interaction information between the user terminal and the operation button.

[0009] In the second aspect, an embodiment of the present invention provides an on-board charger fault self-diagnosis method based on edge computing, which is applied to an on-board charger fault self-diagnosis system based on edge computing. The method includes: obtaining an on-board charger data set, and performing data preprocessing on the on-board charger data set based on an adaptive filtering model to determine target input data; assigning computing tasks to the target input data through edge computing, and performing real-time data analysis and processing on the target input data based on deep learning convolutional neural networks and long short-term memory networks to determine fault diagnosis results, wherein the fault diagnosis results include: the fault type, fault location and fault severity of the charger identified locally; by combining the gray prediction model with the Markov chain, the on-board charger data is subjected to fault prediction processing to determine the fault prediction results, and the fault diagnosis results and fault prediction results are respectively fed back to the cloud server, the remote monitoring terminal and the user end.

[0010] In one embodiment, based on the adaptive filtering model, the on-board charger data set is preprocessed to determine the target input data, including: performing data cleaning processing on the on-board charger data set to remove noise and outliers in the data to determine the input data; through the adaptive filtering model, based on the adaptive filter weight coefficient, error signal and filter order preset in the model, the input data is filtered, and the filtered input data is mapped to a preset interval through normalization processing to determine the target input data.

[0011] In one embodiment, a convolutional neural network and a long short-term memory network based on deep learning are used to perform real-time data analysis and processing on target input data to determine the fault diagnosis result, including: extracting the spatial features of the target input data through a convolutional neural network based on deep learning to determine the spatial features of the data, and extracting the time features of the target input data through a long short-term memory network to determine the time series features of the data; and training the fault data based on the spatial features and the time series features through a preset cross-entropy loss function to determine the fault diagnosis result.

[0012] In one embodiment, the steps of combining the gray prediction model with the Markov chain to perform fault prediction processing on the on-board charger data and determining the fault prediction result include: determining a prediction value based on the on-board charger data through the gray prediction model, and using the Markov chain to predict the possibility of a fault occurring according to the state transition probability of the prediction value to determine the fault prediction result.

[0013] On the third aspect, an embodiment of the present invention also provides an on-board charger fault self-diagnosis device based on edge computing, which is applied to an on-board charger fault self-diagnosis system based on edge computing. The device includes: a data processing unit, which obtains an on-board charger data set, and pre-processes the on-board charger data set based on an adaptive filtering model to determine target input data; a fault diagnosis unit, which assigns computing tasks to the target input data through edge computing, and performs real-time data analysis and processing on the target input data based on deep learning convolutional neural networks and long short-term memory networks to determine fault diagnosis results, wherein the fault diagnosis results include: the fault type, fault location and fault severity of the charger identified locally; a fault prediction unit, which combines the gray prediction model with the Markov chain to perform fault prediction processing on the on-board charger data, determine the fault prediction results, and feed back the fault diagnosis results and fault prediction results to the cloud server, remote monitoring terminal and user end respectively.

[0014] In a fourth aspect, an embodiment of the present invention further provides a server, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the second aspect.

[0015] In a fifth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the second aspect.

[0016] The embodiments of the present invention bring the following beneficial effects:

[0017] The embodiment of the present invention provides a vehicle charger fault self-diagnosis system, method and device based on edge computing, the system includes: a data acquisition module, an edge computing module and a fault prediction module; wherein the data acquisition module is used to collect the vehicle charger data set during the operation of the vehicle charger, wherein the vehicle charger data set includes: input voltage, input current, output voltage, output current, charging temperature, charging time, switch device status information and insulation resistance information; the edge computing module is used to schedule and manage the vehicle charger data, and allocate the computing tasks of the vehicle charger data according to the priority of the data and the usage of computing resources, so as to determine the fault locally at the vehicle charger. Fault diagnosis results; the fault prediction module includes: a gray prediction model. The fault prediction module is used to combine the gray prediction model with the Markov chain, perform fault prediction processing on the on-board charger data, and determine the fault prediction results. The embodiment of the present invention can realize local processing and fault diagnosis of data by deploying an edge computing module, thereby reducing data transmission delay, improving the real-time performance of fault diagnosis, and meeting the real-time operation requirements of the on-board charger. In addition, by adopting a fault diagnosis model that integrates CNN and LSTM, and a fault prediction model that combines a gray prediction model with a Markov chain, the accuracy of fault diagnosis and prediction can be significantly improved, and various complex faults and potential faults can be effectively identified.

[0018] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A schematic diagram of the structure of an on-board charger fault self-diagnosis system based on edge computing provided by an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of the specific structure of an on-board charger fault self-diagnosis system based on edge computing provided by an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of a flow chart of a vehicle charger fault self-diagnosis method based on edge computing provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of the structure of an on-board charger fault self-diagnosis device based on edge computing provided by an embodiment of the present invention;

[0025] Figure 5 A schematic diagram of the structure of a server provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] At present, with the rapid development of new energy vehicles, on-board chargers are key components, and their reliability and stability are of vital importance. Related technologies have proposed that traditional on-board charger fault diagnosis methods mainly rely on centralized data processing centers, and data needs to be uploaded to the cloud for analysis and diagnosis. This solution has problems such as high data transmission delay, strong network dependence, and high privacy and security risks. Therefore, it is difficult to meet the needs of real-time fault diagnosis of on-board chargers. At the same time, the existing diagnostic methods have low accuracy in identifying complex faults, and thus cannot effectively adapt to the operating status of on-board chargers under different working conditions. Based on this, the on-board charger fault self-diagnosis system, method, and device based on edge computing provided by the present invention can realize local data processing and fault diagnosis by deploying edge computing modules, thereby reducing data transmission delay, improving the real-time nature of fault diagnosis, and meeting the real-time operation requirements of on-board chargers. In addition, by adopting a fault diagnosis model that integrates CNN and LSTM, and a fault prediction model that combines a gray prediction model and a Markov chain, the accuracy of fault diagnosis and prediction can be significantly improved, and various complex faults and potential faults can be effectively identified.

[0028] To facilitate understanding of this embodiment, first, a vehicle charger fault self-diagnosis method based on edge computing disclosed in an embodiment of the present invention is introduced in detail. This method is applied to a vehicle charger fault self-diagnosis system based on edge computing. To facilitate understanding of the vehicle charger fault self-diagnosis system based on edge computing, an embodiment of the present invention provides a structural diagram of a vehicle charger fault self-diagnosis based on edge computing, as shown in FIG. Figure 1As shown, the system includes: a data acquisition module, an edge computing module and a fault prediction module.

[0029] The data acquisition module is used to collect the on-board charger data set during the operation of the on-board charger. The on-board charger data set includes: input voltage, input current, output voltage, output current, charging temperature, charging time, switch device status information and insulation resistance information. Specifically, the sensors selected for the data acquisition module are as follows:

[0030] Current detection: Adopts Honeywell SL353 series Hall effect sensor (accuracy ±0.5%FS, response time <1μs), supports differential current measurement, and is suitable for current monitoring of high-frequency switching devices.

[0031] Temperature detection: Deploy a PT100 temperature sensor (accuracy ±0.1°C) in conjunction with the LM-PT100 acquisition module (RS-485 interface, supporting three-wire / four-wire compensation) to implement multi-point temperature monitoring of the charger's power components and heat sink.

[0032] Insulation test: Integrated Hipot HIOKI 3193 insulation resistance tester (test voltage 500 VDC, range 0.1 MΩ-1 GΩ) to periodically test the insulation resistance of high-voltage circuits to ground.

[0033] Signal conditioning: The AD7606 16-bit synchronous sampling ADC chip (8 channels, 200kSPS) is used to achieve high-precision digital conversion of analog signals.

[0034] The edge computing module is used to schedule and manage on-board charger data. It allocates computing tasks based on data priority and computing resource usage, thereby determining fault diagnosis results locally at the on-board charger. Specifically, the edge computing module can be built on an embedded processor equipped with a lightweight operating system and computing framework. It receives data from the data preprocessing module and uses pre-trained fault diagnosis and fault prediction models for data processing and analysis. It has task scheduling and resource management capabilities, and can rationally allocate computing tasks based on data priority and computing resource usage. The hardware platform of the edge computing module is based on the NVIDIA Jetson Nano development kit (4GB of memory, 128-core GPU), equipped with the Ubuntu 18.04 operating system and the TensorFlow Lite framework, supporting real-time inference. Resource management of the edge computing module uses control groups to limit memory usage of the fault diagnosis model (approximately 500MB of memory) and the prediction model (approximately 200MB of memory). In addition, it uses dynamic voltage and frequency scaling (DVFS) technology to automatically adjust the CPU frequency (528MHz-1.43GHz) according to load.

[0035] The edge computing module also includes: a fault diagnosis module, which includes: a convolutional neural network and a long short-term memory network based on deep learning. The fault diagnosis module is used to perform real-time analysis and processing of the on-board charger data based on the convolutional neural network and the long short-term memory network based on deep learning, and determine the fault diagnosis results, wherein the fault diagnosis results include: the locally identified charger fault type, fault location and fault severity. In other words, the fault diagnosis module is responsible for analyzing and processing the data generated during the operation of the on-board charger, and identifying the possible fault type, location and severity of the charger through specific algorithms and rules.

[0036] The fault prediction module includes: a gray prediction model. The fault prediction module is used to combine the gray prediction model with the Markov chain, perform fault prediction processing on the on-board charger data, and determine the fault prediction results. The gray prediction model is a prediction method based on gray system theory. It is suitable for situations where data is incomplete or uncertainty is high. Its basic principle is to accumulate and generate the original data, convert the non-stationary sequence into a stationary sequence, and then use the differential equation model for prediction. The gray prediction model has strong adaptability and is especially suitable for the prediction of small sample data. The fault prediction model is used to predict the future operating status of the on-board charger, determine whether there is a potential fault, and output the fault prediction result.

[0037] See also Figure 2 The specific structural diagram of a vehicle charger fault self-diagnosis system based on edge computing is shown. The system also includes: a data preprocessing module, a communication module and a human-computer interaction module.

[0038] The data preprocessing module includes: an adaptive filtering model. The data preprocessing module is used to perform data cleaning processing on the on-board charger data set to remove noise and outliers in the data, and based on the preset adaptive filtering model, perform data filtering processing and normalization processing on the cleaned on-board charger data set to determine the target input data to send the target input data to the edge computing module. In other words, the data preprocessing module cleans, filters and normalizes the collected data to remove noise and outliers, improve data quality, and then transmits the processed data to the edge computing module.

[0039] The communication module is used to transmit and communicate data with the cloud server and remote monitoring terminal, upload fault diagnosis results and fault prediction results to the cloud server and remote monitoring terminal, and receive model update instructions sent by the cloud server and control instructions sent by the remote monitoring terminal. Among them, the communication module supports multiple communication protocols, including on-board Ethernet, CAN bus, and 4G / 5G mobile communication; upload fault diagnosis results and fault prediction information to the cloud server and remote monitoring terminal, and at the same time receive model update instructions from the cloud server and control instructions from the remote monitoring terminal. Furthermore, the on-board network of the communication module: interacts with BMS, VCU and other systems through the CAN bus (baud rate 500kbps) to transmit charging status and control instructions. The communication module remote communication: integrates the Quectel EC200U5G module, supports NSA / SA dual-mode, and interacts with the cloud server (EMQX cluster) through the MQTT protocol (QoS1) to transmit fault data and receive model updates.

[0040] The human-computer interaction module includes a display screen and operation buttons, which are used to display the operating status, fault diagnosis results and fault prediction results of the on-board charger to the user end through the display screen, and determine the operation instructions based on the interaction information between the user end and the operation buttons. The human-computer interaction module provides the user end with an intuitive and convenient operation interface, making it easier for the driver to understand the operating status and fault information of the on-board charger, thereby enhancing the user experience.

[0041] In addition, the system also includes a data management module. The data management module is based on the integrated function of the graphic model library and complies with the IEC61970CIM standard to achieve unified input, storage and management of power grid models, parameters and graphic data.

[0042] based on Figure 1 The structural diagram of the vehicle charger fault self-diagnosis system based on edge computing is shown in the figure. Figure 2 The specific structural diagram of the vehicle charger fault self-diagnosis system based on edge computing is shown in FIG. The embodiment of the present invention introduces a detailed description of the vehicle charger fault self-diagnosis method based on edge computing, see FIG. Figure 3 The flowchart of a vehicle charger fault self-diagnosis method based on edge computing is shown, and the method mainly includes the following steps S302 to S306:

[0043] Step S302: Obtain a vehicle charger data set, and perform data preprocessing on the vehicle charger data set based on an adaptive filtering model to determine target input data. In one embodiment, data cleaning can be performed on the vehicle charger data set to remove noise and outliers in the data to determine the input data. Then, the input data is filtered based on the adaptive filter weight coefficient, error signal, and filter order preset in the model using the adaptive filtering model. The filtered input data is mapped to a preset interval through normalization to determine the target input data. The adaptive filtering model is:

[0044]

[0045] in, is the output signal after filtering, is the input signal, is the weight coefficient of the adaptive filter, is the error signal, is the filter order.

[0046] Normalization maps the data to Range:

[0047]

[0048] in, is the normalized data, is the original data, and are the maximum and minimum values ​​of the original data respectively; the processed data is transmitted to the edge computing module.

[0049] In step S304, computing tasks are assigned to the target input data through edge computing, and the target input data is analyzed and processed in real time based on the convolutional neural network and long short-term memory network of deep learning to determine the fault diagnosis result, wherein the fault diagnosis result includes: the fault type, fault location and fault severity of the charger identified locally. In one embodiment, the spatial features of the target input data can be extracted through the convolutional neural network based on deep learning to determine the spatial features of the data, and the time features of the target input data can be extracted through the long short-term memory network to determine the time series features of the data. Then, the fault data is trained based on the spatial features and time series features through a preset cross-entropy loss function to determine the fault diagnosis result.

[0050] Specifically, the fault diagnosis module adopts a fault diagnosis model that integrates a convolutional neural network (CNN) and a long short-term memory (LSTM) network based on deep learning. The convolutional neural network (CNN) is used to extract the spatial features of the data, and the long short-term memory (LSTM) is used to process the time series features of the data. The spatial features and time series features are then mapped to fault categories, the number of fault categories C is determined, and fault training is performed using a cross-entropy loss function. The cross-entropy loss function is:

[0051]

[0052] in, is the sample size, is the number of fault categories, For samples Belong to category The true label, For samples Belong to category The cross entropy loss function is trained on a large amount of historical fault data to enable the model to accurately identify various fault types of the on-board charger and output the fault diagnosis results.

[0053] Step S306 combines the grey prediction model with the Markov chain to perform fault prediction processing on the on-board charger data, determine the fault prediction result, and feed back the fault diagnosis result and the fault prediction result to the cloud server, the remote monitoring terminal, and the user end, respectively. In one embodiment, the grey prediction model can be used to determine the predicted value based on the on-board charger data, and the Markov chain can be used to predict the possibility of fault occurrence based on the state transition probability of the predicted value to determine the fault prediction result. The grey prediction model is used to predict the trend of the on-board charger operating parameters. The formula is:

[0054]

[0055] in, is the predicted value, and are model parameters, is the first value of the original data; the Markov chain is used to predict the possibility of a fault based on the state transition probability of the predicted value.

[0056] Furthermore, the functionality can be verified through the following test cases:

[0057] Overvoltage fault: When the analog input voltage rises to 420V, the system triggers protection within 200ms and sends fault code 0x123 via the CAN bus.

[0058] Overheat warning: When the power device is heated to 85°C, the system predicts that a failure may occur within the next 5 minutes and displays a red warning on the display.

[0059] Performance indicators: Diagnostic latency: Local processing is performed when it is less than 100ms, and cloud collaboration is required when it is less than 500ms; Prediction accuracy: The accuracy of predicting faults 10 minutes in advance is 92%.

[0060] In actual applications, Scenario 1: When the insulation resistance drops to 0.5MΩ during charging, the system immediately cuts off the high-voltage circuit and uploads the fault log to the cloud via 5G; Scenario 2: When the fan speed is abnormal when the vehicle is started, the system receives the updated diagnostic model via OTA and optimizes the fan fault identification logic.

[0061] In summary, the present invention can realize local data processing and fault diagnosis by deploying an edge computing module on the vehicle side, thereby reducing data transmission delay, improving the real-time performance of fault diagnosis, and meeting the real-time operation requirements of the on-board charger. In addition, by adopting a fault diagnosis model that integrates CNN and LSTM, and a fault prediction model that combines a grey prediction model and a Markov chain, the accuracy of fault diagnosis and prediction can be significantly improved, and various complex faults and potential faults can be effectively identified.

[0062] Furthermore, the system also has multiple communication methods, which can realize the interconnection between the on-board charger and other systems, cloud servers and remote monitoring terminals, facilitate remote monitoring and management, and improve the intelligence level of new energy vehicles. In addition, the human-computer interaction module provides the driver with an intuitive and convenient operation interface, making it easier for the driver to understand the operating status and fault information of the on-board charger, thereby enhancing the user experience.

[0063] Regarding the vehicle charger fault self-diagnosis method based on edge computing provided in the aforementioned embodiment, an embodiment of the present invention provides a vehicle charger fault self-diagnosis device based on edge computing, which is applied to the vehicle charger fault self-diagnosis system based on edge computing, see Figure 4 The schematic diagram of the structure of a vehicle charger fault self-diagnosis device based on edge computing is shown in the figure. The device includes the following parts:

[0064] The data processing unit 402 obtains the on-board charger data set and performs data preprocessing on the on-board charger data set based on the adaptive filtering model to determine the target input data;

[0065] The fault diagnosis unit 404 allocates computing tasks to the target input data through edge computing, and performs real-time data analysis and processing on the target input data based on deep learning convolutional neural networks and long short-term memory networks to determine the fault diagnosis results, where the fault diagnosis results include: the locally identified charger fault type, fault location, and fault severity;

[0066] The fault prediction unit 406 performs fault prediction processing on the on-board charger data by combining the grey prediction model with the Markov chain, determines the fault prediction result, and feeds back the fault diagnosis result and the fault prediction result to the cloud server, the remote monitoring terminal and the user end respectively.

[0067] The above-mentioned on-board charger fault self-diagnosis device based on edge computing provided in the embodiment of the present application can significantly improve the accuracy of fault self-diagnosis.

[0068] In one embodiment, when performing data preprocessing on the on-board charger data set based on the adaptive filtering model to determine the target input data, the above-mentioned data processing unit 402 is also used to: perform data cleaning processing on the on-board charger data set to remove noise and outliers in the data and determine the input data; through the adaptive filtering model, based on the adaptive filter weight coefficient, error signal and filter order preset in the model, perform data filtering processing on the input data, and map the filtered input data to a preset interval through normalization processing to determine the target input data.

[0069] In one embodiment, when performing real-time data analysis and processing on the target input data using a deep learning-based convolutional neural network and a long short-term memory network to determine the fault diagnosis result, the above-mentioned fault diagnosis unit 404 is also used to: extract the spatial features of the target input data through a deep learning-based convolutional neural network to determine the spatial features of the data, and extract the time features of the target input data through a long short-term memory network to determine the time series features of the data; and train the fault data based on the spatial features and time series features through a preset cross-entropy loss function to determine the fault diagnosis result.

[0070] In one embodiment, when performing the step of combining the gray prediction model with the Markov chain to perform fault prediction processing on the on-board charger data and determining the fault prediction result, the above-mentioned fault prediction unit 406 is also used to: determine the prediction value based on the on-board charger data through the gray prediction model, and use the Markov chain to predict the possibility of fault occurrence according to the state transition probability of the prediction value to determine the fault prediction result.

[0071] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0072] An embodiment of the present invention provides a server. Specifically, the server includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned embodiments.

[0073] Figure 5 A structural diagram of a server provided in an embodiment of the present invention, wherein the server 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected via the bus 52; the processor 50 is used to execute an executable module stored in the memory 51, such as a computer program.

[0074] The memory 51 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element communicates with at least one other network element via at least one communication interface 53 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0075] The bus 52 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0076] Among them, the memory 51 is used to store programs, and the processor 50 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0077] The processor 50 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method may be completed by hardware integrated logic circuits or software instructions in the processor 50. The processor 50 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It may implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention may be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or the like. The storage medium is located in the memory 51 , and the processor 50 reads the information in the memory 51 and completes the steps of the above method in combination with its hardware.

[0078] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be referred to the previous method embodiment and will not be repeated here.

[0079] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0080] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A vehicle charger fault self-diagnosis system based on edge computing, characterized in that: The system includes: a data acquisition module, an edge computing module and a fault prediction module; wherein, The data acquisition module is used to collect the on-board charger data set during the operation of the on-board charger, wherein the on-board charger data set includes: input voltage, input current, output voltage, output current, charging temperature, charging time, switch device status information and insulation resistance information; The edge computing module is used to schedule and manage the on-board charger data and allocate computing tasks for the on-board charger data based on the data priority and computing resource usage, so as to determine the fault diagnosis results locally at the on-board charger; The fault prediction module includes: a grey prediction model, and the fault prediction module is used to combine the grey prediction model with a Markov chain, perform fault prediction processing on the on-board charger data, and determine a fault prediction result.

2. The vehicle charger fault self-diagnosis system based on edge computing according to claim 1 is characterized in that: The system further includes: a data preprocessing module; wherein, The data preprocessing module includes: an adaptive filtering model, which is used to perform data cleaning processing on the on-board charger data set to remove noise and outliers in the data, and based on a preset adaptive filtering model, perform data filtering processing and normalization processing on the cleaned on-board charger data set to determine the target input data so as to send the target input data to the edge computing module.

3. The on-board charger fault self-diagnosis system based on edge computing according to claim 1 is characterized in that: The edge computing module also includes: a fault diagnosis module; wherein, The fault diagnosis module includes: a convolutional neural network based on deep learning and a long short-term memory network. The fault diagnosis module is used to perform real-time data analysis and processing on the on-board charger data based on the convolutional neural network based on deep learning and the long short-term memory network to determine the fault diagnosis result, wherein the fault diagnosis result includes: the locally identified charger fault type, fault location and fault severity.

4. The vehicle charger fault self-diagnosis system based on edge computing according to claim 1 is characterized in that: The system further includes: a communication module; wherein, The communication module is used to transmit and communicate data with the cloud server and the remote monitoring terminal, upload the fault diagnosis results and the fault prediction results to the cloud server and the remote monitoring terminal, and receive model update instructions sent by the cloud server and control instructions sent by the remote monitoring terminal.

5. The vehicle charger fault self-diagnosis system based on edge computing according to claim 1 is characterized in that: The system also includes: a human-computer interaction module; wherein, The human-computer interaction module includes a display screen and an operation button, which is used to display the operating status of the on-board charger, the fault diagnosis result and the fault prediction result to the user terminal through the display screen, and determine the operation instruction based on the interaction information between the user terminal and the operation button.

6. A vehicle charger fault self-diagnosis method based on edge computing, characterized in that: The method is applied to an on-board charger fault self-diagnosis system based on edge computing, and the method includes: Acquire a vehicle charger data set, and perform data preprocessing on the vehicle charger data set based on an adaptive filtering model to determine target input data; The target input data is assigned computing tasks through edge computing, and the target input data is analyzed and processed in real time based on deep learning convolutional neural networks and long short-term memory networks to determine a fault diagnosis result, wherein the fault diagnosis result includes: the locally identified charger fault type, fault location, and fault severity; By combining the grey prediction model with the Markov chain, the on-board charger data is subjected to fault prediction processing to determine the fault prediction result, and the fault diagnosis result and the fault prediction result are fed back to the cloud server, the remote monitoring terminal and the user end respectively.

7. The on-board charger fault self-diagnosis method based on edge computing according to claim 6 is characterized in that: The step of performing data preprocessing on the onboard charger data set based on the adaptive filtering model to determine target input data includes: Performing data cleaning on the on-board charger data set to remove noise and outliers in the data and determine input data; Through the adaptive filtering model, based on the adaptive filter weight coefficient, error signal and filter order preset by the model, the input data is filtered, and the filtered input data is mapped to the preset interval through normalization processing to determine the target input data.

8. The on-board charger fault self-diagnosis method based on edge computing according to claim 6 is characterized in that: The steps of performing real-time data analysis and processing on the target input data based on a deep learning convolutional neural network and a long short-term memory network to determine a fault diagnosis result include: The spatial features of the target input data are extracted through a convolutional neural network based on deep learning to determine the spatial features of the data, and the temporal features of the target input data are extracted through a long short-term memory network to determine the time series features of the data; By presetting a cross entropy loss function, fault data training is performed based on the spatial features and the time series features to determine the fault diagnosis result.

9. The on-board charger fault self-diagnosis method based on edge computing according to claim 6 is characterized in that: The step of combining the grey prediction model with the Markov chain to perform fault prediction processing on the on-board charger data and determine the fault prediction result includes: A grey prediction model is used to determine a prediction value based on the on-board charger data, and a Markov chain is used to predict the possibility of a fault occurring according to the state transition probability of the prediction value to determine the fault prediction result.

10. A vehicle charger fault self-diagnosis device based on edge computing, characterized in that: The device is applied to an on-board charger fault self-diagnosis system based on edge computing, and the device includes: A data processing unit, which obtains a data set of an on-board charger and performs data preprocessing on the data set of the on-board charger based on an adaptive filtering model to determine target input data; a fault diagnosis unit that allocates computing tasks to the target input data through edge computing, and performs real-time data analysis and processing on the target input data based on deep learning convolutional neural networks and long short-term memory networks to determine a fault diagnosis result, wherein the fault diagnosis result includes: the locally identified charger fault type, fault location, and fault severity; The fault prediction unit combines the grey prediction model with the Markov chain to perform fault prediction processing on the on-board charger data, determines the fault prediction result, and feeds back the fault diagnosis result and the fault prediction result to the cloud server, the remote monitoring terminal and the user end respectively.

11. A server, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the method according to any one of claims 6 to 9.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 6 to 9.

Images