Vehicle-mounted wireless charging system fault identification method, device, equipment and medium

Abstract

The invention provides a vehicle-mounted wireless charging system fault identification method, apparatus and device, and a medium thereof. The method comprises the following steps: step 1, obtaining working parameters of a vehicle-mounted wireless charging system; 2, analyzing the obtained working parameters, and judging whether abnormity exists or not. Comprehensive working parameters are acquired through multiple sensors, the parameters are analyzed in real time to quickly identify faults, fault data are deeply analyzed by using an artificial intelligence algorithm, types are determined, processing is performed according to priorities, and meanwhile, a fault historical database is established to perform trend analysis to predict potential problems. Faults can be reproduced through simulation software, algorithms and processes can be optimized, the maintenance efficiency can be improved by means of remote diagnosis, the complexity of a vehicle-mounted environment, such as electromagnetic interference, temperature change and vibration, and the problems of the wireless charging technology, such as low charging efficiency and compatibility, can be comprehensively solved through a series of measures, and the maintenance efficiency can be improved. And the reliability and the stability of the vehicle-mounted wireless charging system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-mounted wireless charging, and in particular to a vehicle-mounted wireless charging system fault identification method, device, equipment and medium thereof. Background Art

[0002] With the rapid development of electric vehicles and smart electronic devices, in-vehicle wireless charging systems are increasingly used in modern cars. In-vehicle wireless charging systems provide users with a convenient way to charge electronic devices such as mobile phones and tablets without using charging cables. However, due to the complexity of the in-vehicle environment and the characteristics of wireless charging technology itself, various faults may occur in in-vehicle wireless charging systems, affecting the user experience and system reliability.

[0003] On the one hand, there are various interference factors in the vehicle environment, such as electromagnetic interference, temperature changes, vibration, etc., which may affect the normal operation of the vehicle wireless charging system. For example, electromagnetic interference may cause abnormal fluctuations in the electromagnetic field strength of the wireless charging coil, affecting the charging efficiency; temperature changes may cause the system to overheat, affecting the safety and stability of the system.

[0004] On the other hand, wireless charging technology itself also has some technical difficulties, such as low charging efficiency and compatibility issues. Low charging efficiency may cause the charging time to be too long, affecting the user experience; compatibility issues may cause some electronic devices to fail to charge normally.

[0005] To this end, a vehicle-mounted wireless charging system fault identification method, device, equipment and medium thereof are proposed. Summary of the invention

[0006] In view of this, the embodiments of the present invention hope to provide a vehicle-mounted wireless charging system fault identification method, device, equipment and medium thereof to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0007] In order to solve the above technical problems, a technical solution adopted in the present application is: a method for identifying faults of a vehicle-mounted wireless charging system, comprising the following steps: Step 1: Obtain the working parameters of the vehicle wireless charging system; Step 2: Analyze the acquired working parameters to determine whether there is an abnormality. If there is an abnormality, identify the fault data; Step 3: Perform in-depth analysis of fault data based on artificial intelligence algorithms to identify fault types. If there are multiple fault types, sort them by priority and give priority to faults that seriously affect system safety and performance. Step 4: Generate corresponding fault codes and fault alarm information according to the detected fault type, and display the fault codes and fault alarm information to the user through the vehicle display screen; Step 5: Automatically record each fault alarm information and store it in the backend database of the vehicle communication system. Based on big data analysis technology, perform trend analysis on the fault history data to identify potential fault modes or periodic faults. Step 6: Based on the determined fault type, use simulation software to reproduce the fault, verify the accuracy of fault diagnosis, and optimize the fault identification algorithm and processing flow; Step 7: Send the fault alarm information to the maintenance center or manufacturer's server through the vehicle communication system, and professional technicians will conduct remote diagnosis and provide corresponding maintenance suggestions and guidance.

[0008] As a further preferred embodiment of the present technical solution, in step 1, the operating parameters include input voltage, input current, output voltage, output current, temperature, electromagnetic field strength of the wireless charging coil and connection status of the charging device; wherein, The input voltage and output voltage are acquired in real time by a voltage sensor; The input current and the output current are acquired in real time by a current sensor; The temperature is obtained by measuring with a temperature sensor; The electromagnetic field strength of the wireless charging coil is acquired by detecting with an electromagnetic field strength sensor; The connection status of the charging device is determined and obtained through a communication protocol.

[0009] As a further preferred embodiment of the present technical solution, in step 2, the method for identifying fault data comprises the following steps: Step 1: Compare the input voltage and the output voltage, calculate the voltage conversion efficiency, and if the voltage conversion efficiency is lower than the set threshold, it is judged as a low charging efficiency fault; Step 2: Monitor the input current and output current. If the input current is normal but the output current is zero, it is determined to be a charging failure. Step 3: Monitor the temperature in real time. If the temperature exceeds the set safe temperature range, it is judged as an overheating fault. Step 4: Analyze the electromagnetic field strength of the wireless charging coil. If the electromagnetic field strength fluctuates abnormally or changes irregularly, it is determined that the wireless charging coil is faulty or has external interference.

[0010] As a further preferred embodiment of the present technical solution, in step three, the artificial intelligence algorithm utilizes a deep learning neural network model to perform feature extraction and classification on the input fault data to determine the fault type.

[0011] As a further preferred embodiment of the present technical solution, in step four, the fault alarm information includes fault type, occurrence time, fault parameters, fault cause speculation and temporary processing suggestions; the generation of the fault code utilizes specific coding rules to map different fault types into different digital codes.

[0012] As a further preferred embodiment of the present technical solution, in step five, the big data analysis technology utilizes a clustering algorithm to classify the fault history data and identifies fault modes with similar characteristics. The periodic fault is identified by calculating the time interval between two adjacent similar faults. If the time interval is relatively stable, it is determined to be a periodic fault.

[0013] As a further preferred embodiment of the present technical solution, in step six, the optimized fault identification algorithm and processing flow are updated through an online upgrade function of the vehicle-mounted system.

[0014] To solve the above technical problems, another technical solution adopted by the present application is: a vehicle-mounted wireless charging system fault identification device, comprising: a data acquisition module, a data analysis module, a fault type determination module, an alarm information generation module, a historical database module, a simulation verification module and a communication module; The data acquisition module is used to collect the working parameters of the vehicle-mounted wireless charging system, including input voltage, input current, output voltage, output current, temperature, electromagnetic field strength of the wireless charging coil and connection status of the charging device; The data analysis module is used to analyze the collected working parameters to determine whether there is an abnormality, and if there is an abnormality, identify the fault data; The fault type determination module is used to perform in-depth analysis on fault data using a deep learning neural network model to determine the fault type. If there are multiple fault types, they are sorted according to priority, giving priority to faults that seriously affect system safety and performance; The alarm information generation module is used to generate corresponding fault codes and fault alarm information according to the detected fault type, and display the fault code and fault alarm information to the user through the vehicle display screen; The historical database module is used to automatically record each fault alarm information, establish a fault history database, perform trend analysis on the fault history data based on big data analysis technology, and identify potential fault modes or periodic faults; The simulation verification module is used to reproduce the fault using simulation software according to the determined fault type, verify the accuracy of fault diagnosis, and optimize the fault identification algorithm and processing flow; The communication module is used to send fault alarm information to a maintenance center or manufacturer's server through the vehicle-mounted communication system, so that professional technicians can perform remote diagnosis and provide corresponding maintenance suggestions and guidance.

[0015] To solve the above technical problems, another technical solution adopted by the present application is: an electronic device, including a processor and a memory, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the vehicle wireless charging system fault identification method as described in any one of the above.

[0016] To solve the above technical problems, another technical solution adopted in the present application is: a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, a vehicle-mounted wireless charging system fault identification method as described in any one of the above items is implemented.

[0017] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution: The present invention obtains comprehensive working parameters through multiple sensors, analyzes the parameters in real time to quickly identify faults, and uses artificial intelligence algorithms to deeply analyze fault data to determine the type and process it according to priority. At the same time, a fault history database is established for trend analysis to predict potential problems, and faults can be reproduced, algorithms and processes can be optimized through simulation software, and maintenance efficiency can be improved with the help of remote diagnosis. This series of measures comprehensively responds to the complexity of the vehicle environment, such as electromagnetic interference, temperature changes, vibration, etc., as well as the difficulties of wireless charging technology itself, such as low charging efficiency and compatibility issues, to ensure the reliability and stability of the vehicle-mounted wireless charging system; the present invention generates detailed fault alarm information and displays it to the user through the vehicle display screen, so that the user can understand the fault situation in time and take corresponding measures, thereby improving the user experience. At the same time, by giving priority to handling faults that seriously affect the safety and performance of the system, the safety of the system is guaranteed, and a safer and more reliable charging environment is provided for the user. The above overview is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be easily understood by referring to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. 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 paying creative work.

[0019] Figure 1 A schematic diagram of a flow chart of a method for identifying a fault of a vehicle-mounted wireless charging system according to the present invention; Figure 2 A schematic diagram of a flow chart of a method for identifying fault data of the present invention; Figure 3 It is a structural schematic diagram of a vehicle-mounted wireless charging system fault identification device of the present invention; Figure 4 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0021] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0022] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0023] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0024] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0025] Figure 1 FIG. 1 is a flow chart of a method for identifying a fault in a vehicle wireless charging system according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not limited to the method of FIG. Figure 1 The process sequence shown is limited. Figure 1-Figure 2 As shown: A method for identifying faults in a vehicle wireless charging system, comprising the following steps: Step 1: Obtain the working parameters of the vehicle-mounted wireless charging system. Specifically, deploy voltage sensors, current sensors, temperature sensors, electromagnetic field strength sensors, etc. in the wireless charging system, and connect them to the central processing unit (CPU) through a communication interface (such as CAN bus). Through the communication protocol between the vehicle's internal network (such as LIN, CAN) and the charging device, monitor the connection status of the charging device in real time; Step 2: Analyze the acquired working parameters to determine whether there are any abnormalities. If there are any abnormalities, identify the fault data. Specifically, the CPU receives the data from the sensor, performs preliminary data cleaning and verification to ensure the accuracy and integrity of the data, and identifies abnormal data in the working parameters according to the preset thresholds and logic rules, such as voltage fluctuations, current interruptions, temperature exceeding the limit, etc. Step 3: Perform in-depth analysis of fault data based on artificial intelligence algorithms to identify fault types. If there are multiple fault types, sort them according to priority and give priority to faults that seriously affect system safety and performance. Specifically, First, the fault data is cleaned to remove noise and outliers to ensure the quality of the input data. At the same time, the data is normalized so that data of different dimensions can be compared and analyzed on the same scale. Then, artificial intelligence algorithms (such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in deep learning models) are used to automatically extract key features from fault data. These features can characterize the essential attributes of the fault and are crucial for subsequent fault classification. Next, the extracted features are input into a trained classifier (such as support vector machine SVM, random forest RF, neural network NN, etc.) for classification judgment. The classifier will classify the fault data into predefined fault types based on the learned knowledge. If the system uses a deep learning model, such as a neural network, the model will automatically learn the inherent laws and patterns of the fault data, thereby achieving high-precision fault classification. Finally, the system sorts the identified fault types according to preset priority rules. These rules are usually formulated based on the impact of the fault on system safety and performance. For example, an overheating fault may directly cause system damage or even fire, so its priority should be higher than other types of faults. The sorting algorithm can consider multiple factors such as the severity of the fault type, the frequency of occurrence, and the repair time in historical data to ensure that those faults with the greatest impact on the system are handled first; Step 4: Generate corresponding fault codes and fault alarm information according to the detected fault type, and display the fault codes and fault alarm information to the user through the vehicle display screen. Specifically, First, the system maps the identified fault type to a specific numeric or letter code based on predefined fault code encoding rules; Then, according to the fault type and fault code, the system compiles detailed fault alarm information, which should include the description of the fault type, the time of occurrence, the possible cause, the impact on the system and the recommended temporary treatment measures; Finally, according to the fault type and fault code, the system compiles detailed fault alarm information, which should include the description of the fault type, occurrence time, possible causes, impact on the system, and recommended temporary treatment measures; Step 5: Automatically record each fault alarm information and store it in the backend database of the vehicle communication system. Based on big data analysis technology, perform trend analysis on the fault history data to identify potential fault modes or periodic faults. Specifically, First, the system captures and records each fault alarm information in real time, and stores the recorded fault alarm information in the background database stored in the vehicle communication system; Then, according to the type and characteristics of the fault alarm information, a reasonable database structure is designed. The database should contain multiple tables, such as fault information table, fault code table, fault type table, etc., to support complex data query and analysis requirements; Finally, big data analysis technology is used to perform trend analysis on historical fault data to identify potential failure modes or periodic failures; Step 6: Based on the determined fault type, use simulation software to reproduce the fault, verify the accuracy of fault diagnosis, and optimize the fault identification algorithm and processing flow. Specifically, First, based on the identified fault type, use simulation software to build a corresponding fault model. The model should contain key information such as the cause of the fault, its manifestation, and its impact on the system, so as to accurately reproduce the fault. Then, according to the fault model and the actual system operating environment, set appropriate simulation parameters, which should include system status, environmental conditions, input signals, etc., to ensure that the simulation results can truly reflect the actual fault situation; Next, start the simulation software and perform simulation experiments. Use the simulation software to reproduce the failure process, observe and record the system's response and performance for subsequent analysis and verification. Finally, the simulation results are compared with the actual fault conditions to verify the accuracy of fault diagnosis by comparing the differences in fault phenomena, impact range, system response, etc. Step 7: Send the fault alarm information to the maintenance center or manufacturer's server through the vehicle communication system, and professional technicians will conduct remote diagnosis and provide corresponding maintenance suggestions and guidance. Specifically, First, the system establishes a communication connection with the maintenance center or manufacturer's server through the vehicle communication system (such as mobile communication network, satellite communication, etc.); Then, the fault alarm information (including fault code, fault type description, occurrence time, possible cause, impact on the system and recommended temporary treatment measures, etc.) is packaged into a standard format and sent to the maintenance center or manufacturer's server through the established communication connection; Next, after the maintenance center or the manufacturer's server receives the fault alarm information, professional technicians will immediately parse and analyze the information; Finally, based on the fault information and analysis results, professional technicians use remote diagnostic tools (such as remote desktop control, video calls, etc.) to remotely diagnose the system, and based on the remote diagnosis results, professional technicians will provide users with detailed maintenance suggestions and guidance.

[0026] In one embodiment, specifically, in step 1, the operating parameters include input voltage, input current, output voltage, output current, temperature, electromagnetic field strength of the wireless charging coil, and connection status of the charging device; wherein, The input voltage and output voltage are acquired in real time through voltage sensors. The voltage sensors are installed at the input and output ends of the system and can accurately measure and convert voltage signals into electrical signals for subsequent processing and recording. The voltage sensors have the characteristics of high precision, high stability and strong anti-interference ability, which can ensure that the collected voltage data is accurate. The input current and output current are acquired in real time through current sensors. Current sensors are usually connected in series in the input and output loops of the circuit. Data is acquired by measuring the current in the loop. Current sensors can directly reflect the current changes in the circuit, which is of great significance for judging the working status of the circuit and fault detection. The temperature is measured and obtained through temperature sensors. Temperature sensors are placed in key parts of the system, such as radiators and circuit boards, to monitor the temperature changes of these parts in real time. Temperature is one of the important indicators reflecting the working status of the system. Through temperature monitoring, potential problems such as overheating can be discovered in time to prevent the system from being damaged due to excessive temperature. The electromagnetic field strength of the wireless charging coil is detected and acquired by the electromagnetic field strength sensor. The electromagnetic field strength sensor is placed near the wireless charging coil and can accurately measure and convert the electromagnetic field strength into a processable electrical signal. The accurate measurement of the electromagnetic field strength is of great significance for evaluating the efficiency of wireless charging, ensuring charging safety, and detecting potential electromagnetic interference. The connection status of the charging device is determined and obtained through the communication protocol. Through specific communication protocols and instructions, the system can query the connection status of the charging device in real time (such as connected, disconnected, abnormal connection, etc.). The way the communication protocol determines the connection status is efficient, accurate and reliable. It can promptly reflect the connection status between the charging device and the system, providing an important basis for subsequent charging operations and troubleshooting.

[0027] In one embodiment, specifically, in step 2, the method for identifying fault data includes the following steps: Step 1: Compare the input voltage and the output voltage, calculate the voltage conversion efficiency, and if the voltage conversion efficiency is lower than the set threshold, it is judged as a low charging efficiency fault; Step 2: Monitor the input current and output current. If the input current is normal but the output current is zero, it is determined to be a charging failure. Step 3: Monitor the temperature in real time. If the temperature exceeds the set safe temperature range, it is judged as an overheating fault. Step 4: Analyze the electromagnetic field strength of the wireless charging coil. If the electromagnetic field strength fluctuates abnormally or changes irregularly, it is determined that the wireless charging coil is faulty or has external interference.

[0028] Through in-depth analysis and comparison of different parameters, various fault types in the charging system can be comprehensively and accurately identified, providing a reliable basis for subsequent fault handling.

[0029] In one embodiment, specifically, in step three, the artificial intelligence algorithm uses a deep learning neural network model to extract features and classify the input fault data to determine the fault type. Specifically, a deep learning neural network model suitable for processing complex data classification tasks is selected, such as a convolutional neural network (CNN), a recurrent neural network (RNN) or its variants (such as LSTM, GRU), etc. The specific selection depends on the nature of the fault data (such as time series data, image data, etc.) and the complexity of the classification task; the model is trained using sample data of known fault types, and these sample data should include instances of multiple fault types and be annotated with corresponding fault labels. Through training, the model can learn the feature representations of different fault types; the deep learning neural network model has a strong feature extraction capability. In the first few layers of the model (such as convolutional layers, pooling layers, etc.), the model automatically extracts useful feature information from the input fault data. These feature information can be numerical, image or other forms, which can reflect the essential attributes of the fault data; in the subsequent layers of the model (such as fully connected layers, softmax layers, etc.), the extracted feature information is used to make classification decisions. The model calculates the probability value corresponding to each fault type and outputs the category with the highest probability as the final fault type.

[0030] In one embodiment, specifically, in step 4, the fault alarm information includes the fault type, occurrence time, fault parameters, fault cause speculation and temporary processing suggestions, wherein: Fault type: clearly indicates the type of fault that occurred, such as low charging efficiency, failure to charge, overheating, wireless charging coil failure, etc. This helps maintenance personnel quickly locate the problem; Occurrence time: Record the specific time when the fault occurs, including date and time, which helps to analyze the pattern and cause of the fault and subsequent fault tracing; Fault parameters: List the specific parameter values ​​related to the fault, such as input voltage, output voltage, current, temperature, electromagnetic field strength, etc. These parameter values ​​can provide important basis for fault analysis; Fault cause speculation: Based on the fault type and parameter value, a preliminary speculation on the fault cause is made, which helps maintenance personnel prepare corresponding maintenance tools and spare parts in advance and improve maintenance efficiency; Temporary processing suggestions: Before the cause of the fault is clear, some temporary processing suggestions are given, such as cutting off power, reducing load, increasing heat dissipation, etc. These suggestions can help reduce the impact of the fault on the system and prevent the fault from further expanding.

[0031] The generation of fault codes uses specific encoding rules to map different fault types to different digital codes. The generation of fault codes should follow the following principles: Uniqueness: Each fault code should uniquely correspond to a fault type to avoid confusion and misunderstanding; Scalability: The coding rules should be scalable to a certain extent to adapt to new types of faults that may appear in the future; Readability: Fault codes should be as concise and clear as possible, easy to remember and query.

[0032] In one embodiment, specifically, in step five, the big data analysis technology uses a clustering algorithm to classify the fault history data, identifies fault modes with similar characteristics, and identifies periodic faults by calculating the time interval between two adjacent similar faults. If the time interval is relatively stable, it is determined to be a periodic fault. The clustering algorithm is an unsupervised learning algorithm that can divide samples in a data set into several clusters (or classes), so that the samples in the same cluster have a high similarity, while the samples in different clusters have a low similarity. In the analysis of fault history data, the clustering algorithm can be used to identify fault modes with similar characteristics; the specific steps are as follows: First, the fault history data is cleaned, converted, and standardized to ensure data quality and consistency; Then, features that have an important impact on fault classification are extracted from the fault history data, such as fault type, fault parameters, occurrence time, etc. Next, select a suitable clustering algorithm (such as K-means, DBSCAN, hierarchical clustering, etc.) to divide the fault history data into several clusters, each cluster representing a fault mode with similar characteristics; Finally, the failure data in each cluster is analyzed to understand the characteristics, frequency and possible causes of the failure mode.

[0033] Periodic faults are those that occur repeatedly within a certain time interval. Periodic faults can be identified by calculating the time interval between two consecutive similar faults. The specific steps are as follows: First, clustering algorithms or other methods are used to classify faults in the fault history data to ensure that similar faults are grouped together; Then, for each type of fault, the time interval between two adjacent faults is calculated. This can be achieved by traversing all records of this type of fault and calculating the time difference between adjacent records. Next, analyze the calculated time interval data. If the time interval is relatively stable (that is, most time intervals are close to a fixed value), it can be determined that this type of fault is a periodic fault. The stability can be evaluated by calculating the standard deviation of the time interval, the coefficient of variation and other indicators; Finally, for the identified periodic faults, their occurrence patterns, causes and influencing factors can be further analyzed in order to formulate corresponding preventive measures and maintenance plans.

[0034] In one embodiment, specifically, in step six, the optimized fault identification algorithm and processing flow are updated through the online upgrade function of the vehicle system. Updating the optimized fault identification algorithm and processing flow through the online upgrade function (OTA) of the vehicle system is an efficient, convenient and safe process. This process helps to improve the accuracy and efficiency of vehicle fault identification and provide car owners with a safer and more reliable driving experience.

[0035] Figure 3 is a structural diagram of a vehicle-mounted wireless charging system fault identification device according to an embodiment of the present application, such as Figure 3 As shown, a vehicle-mounted wireless charging system fault identification device includes: a data acquisition module, a data analysis module, a fault type determination module, an alarm information generation module, a historical database module, a simulation verification module and a communication module; The data acquisition module is used to collect the working parameters of the vehicle-mounted wireless charging system, including input voltage, input current, output voltage, output current, temperature, electromagnetic field strength of the wireless charging coil and connection status of the charging device. Comprehensive data acquisition provides a solid foundation for subsequent fault analysis; The data analysis module is used to analyze the collected working parameters to determine whether there are any abnormalities. If there are any abnormalities, the fault data is identified to provide clues for the subsequent determination of the fault type; The fault type determination module is used to use the deep learning neural network model to conduct in-depth analysis of fault data and determine the fault type. The advantage of the deep learning model lies in its powerful feature extraction and classification capabilities, which can handle complex nonlinear relationships, thereby improving the accuracy and efficiency of fault identification. If there are multiple fault types, they are sorted according to priority, giving priority to faults that seriously affect system safety and performance, ensuring that the most urgent faults are handled first; The alarm information generation module is used to generate the corresponding fault code and fault alarm information according to the detected fault type, and display the fault code and fault alarm information to the user through the vehicle display screen, which not only improves the user's awareness of the fault situation, but also guides the user to take preliminary response measures, such as disconnecting the charging connection; The historical database module is used to automatically record each fault alarm information, establish a fault history database, and perform trend analysis on the fault history data based on big data analysis technology to identify potential fault modes or periodic faults. This predictive maintenance strategy helps to prevent the occurrence of faults in advance and reduce losses caused by faults. The simulation verification module is used to reproduce the fault using simulation software according to the determined fault type and verify the accuracy of fault diagnosis. This process not only helps to optimize the fault identification algorithm and processing flow, but also provides a fault reproduction environment for technicians to conduct in-depth research on the cause of the fault and the solution. The communication module is used to send fault alarm information to the maintenance center or manufacturer's server through the vehicle-mounted communication system, so that professional technicians can perform remote diagnosis and provide corresponding maintenance suggestions and guidance to improve the efficiency and accuracy of fault resolution.

[0036] In summary, the vehicle-mounted wireless charging system fault identification device provided in the embodiment of the present application integrates multiple functional modules, can monitor in real time and accurately identify possible faults in the wireless charging system, realizes comprehensive, real-time and accurate fault monitoring and identification of the wireless charging system, and provides a strong guarantee for the safe charging of the vehicle.

[0037] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0038] like Figure 4 The present invention provides a schematic diagram of the structure of an electronic device according to an embodiment of the present invention, which is suitable for implementing the electronic device in the embodiment of the present invention. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0039] like Figure 4As shown, the electronic device may include a processor, a memory, a communication bus, a sensor interface, a display interface and a communication module; wherein the processor, the memory, the sensor interface, the display interface and the communication module communicate with each other through the communication bus; the processor may be a high-performance vehicle-mounted processor with multi-core processing capability, capable of quickly executing complex computing tasks; the memory includes a random access memory (RAM) and a read-only memory (ROM), the RAM is used to temporarily store data during program operation, and the ROM is used to store solidified program code and system parameters. In addition, a large-capacity flash memory may be provided for storing fault history data, etc.; the sensor interface is used to connect various sensors, such as voltage sensors, current sensors, temperature sensors and electromagnetic field strength sensors, etc., and the interface types may include analog input interfaces, digital input interfaces and communication interfaces (such as I2C, SPI, etc.); the display interface is used to connect to the vehicle-mounted display screen to display fault codes and alarm information to users, and the interface type may be LVDS, HDMI, etc.; the communication module includes a vehicle-mounted communication system, such as Bluetooth, Wi-Fi, 4G / 5G modules, etc., for sending fault alarm information to a maintenance center or a manufacturer's server.

[0040] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of a vehicle-mounted wireless charging system fault identification method of an embodiment of the present disclosure are executed.

[0041] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0042] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the vehicle-mounted wireless charging system fault identification method of each embodiment of the present disclosure are executed.

[0043] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0044] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0045] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0046] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0047] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0048] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0049] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0050] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0051] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for identifying faults in a vehicle-mounted wireless charging system, characterized in that: The following steps are involved: Step 1: Obtain the working parameters of the vehicle wireless charging system; Step 2: Analyze the acquired working parameters to determine whether there is an abnormality. If there is an abnormality, identify the fault data; Step 3: Perform in-depth analysis of fault data based on artificial intelligence algorithms to identify fault types. If there are multiple fault types, sort them by priority and give priority to faults that seriously affect system safety and performance. Step 4: Generate corresponding fault codes and fault alarm information according to the detected fault type, and display the fault codes and fault alarm information to the user through the vehicle display screen; Step 5: Automatically record each fault alarm information and store it in the backend database of the vehicle communication system. Based on big data analysis technology, perform trend analysis on the fault history data to identify potential fault modes or periodic faults. Step 6: Based on the determined fault type, use simulation software to reproduce the fault, verify the accuracy of fault diagnosis, and optimize the fault identification algorithm and processing flow; Step 7: Send the fault alarm information to the maintenance center or manufacturer's server through the vehicle communication system, and professional technicians will conduct remote diagnosis and provide corresponding maintenance suggestions and guidance.

2. A vehicle-mounted wireless charging system fault identification method according to claim 1, characterized in that: In step 1, the operating parameters include input voltage, input current, output voltage, output current, temperature, electromagnetic field strength of the wireless charging coil and connection status of the charging device; wherein, The input voltage and output voltage are acquired in real time by a voltage sensor; The input current and the output current are acquired in real time by a current sensor; The temperature is obtained by measuring with a temperature sensor; The electromagnetic field strength of the wireless charging coil is acquired by detecting with an electromagnetic field strength sensor; The connection status of the charging device is determined and obtained through a communication protocol.

3. The method for identifying faults in a vehicle-mounted wireless charging system according to claim 1, characterized in that: In step 2, the method for identifying fault data includes the following steps: Step 1: Compare the input voltage and the output voltage, calculate the voltage conversion efficiency, and if the voltage conversion efficiency is lower than the set threshold, it is judged as a low charging efficiency fault; Step 2: Monitor the input current and output current. If the input current is normal but the output current is zero, it is determined to be a charging failure. Step 3: Monitor the temperature in real time. If the temperature exceeds the set safe temperature range, it is judged as an overheating fault. Step 4: Analyze the electromagnetic field strength of the wireless charging coil. If the electromagnetic field strength fluctuates abnormally or changes irregularly, it is determined that the wireless charging coil is faulty or has external interference.

4. The method for identifying faults of a vehicle-mounted wireless charging system according to claim 1, characterized in that: In step three, the artificial intelligence algorithm uses a deep learning neural network model to extract features and classify the input fault data to determine the fault type.

5. The method for identifying faults of a vehicle-mounted wireless charging system according to claim 1, characterized in that: In step 4, the fault alarm information includes the fault type, occurrence time, fault parameters, fault cause speculation and temporary processing suggestions; The fault code is generated by using specific coding rules to map different fault types into different digital codes.

6. The method for identifying faults of a vehicle-mounted wireless charging system according to claim 1, characterized in that: In step five, the big data analysis technology uses a clustering algorithm to classify the fault history data and identify fault modes with similar characteristics. The periodic fault is identified by calculating the time interval between two adjacent similar faults. If the time interval is stable, it is judged to be a periodic fault.

7. The method for identifying faults in a vehicle-mounted wireless charging system according to claim 1, characterized in that: In step six, the optimized fault identification algorithm and processing flow are updated through the online upgrade function of the vehicle system.

8. A vehicle-mounted wireless charging system fault identification device, characterized in that: include: Data acquisition module, data analysis module, fault type determination module, alarm information generation module, historical database module, simulation verification module and communication module; The data acquisition module is used to collect the working parameters of the vehicle-mounted wireless charging system, including input voltage, input current, output voltage, output current, temperature, electromagnetic field strength of the wireless charging coil and connection status of the charging device; The data analysis module is used to analyze the collected working parameters to determine whether there is an abnormality, and if there is an abnormality, identify fault data; The fault type determination module is used to perform in-depth analysis on fault data using a deep learning neural network model to determine the fault type. If there are multiple fault types, they are sorted according to priority, giving priority to faults that seriously affect system safety and performance; The alarm information generation module is used to generate corresponding fault codes and fault alarm information according to the detected fault type, and display the fault code and fault alarm information to the user through the vehicle display screen; The historical database module is used to automatically record each fault alarm information, establish a fault history database, perform trend analysis on the fault history data based on big data analysis technology, and identify potential fault modes or periodic faults; The simulation verification module is used to reproduce the fault using simulation software according to the determined fault type, verify the accuracy of fault diagnosis, and optimize the fault identification algorithm and processing flow; The communication module is used to send fault alarm information to a maintenance center or manufacturer's server through the vehicle-mounted communication system, so that professional technicians can perform remote diagnosis and provide corresponding maintenance suggestions and guidance.

9. An electronic device, characterized in that: comprising a processor and a memory, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the vehicle-mounted wireless charging system fault identification method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying faults of a vehicle-mounted wireless charging system as claimed in any one of claims 1 to 7 is implemented.

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