CN119397450A

CN119397450A - A method, device, equipment and medium for identifying faults of a vehicle-mounted wireless charging system

Info

Publication number: CN119397450A
Application number: CN202411588128.4A
Authority: CN
Inventors: 张韶芳; 陈翔; 麦翠萍; 麦贵平; 朱杲
Original assignee: Shenzhen Meskey Technology Co ltd
Current assignee: Shenzhen Meskey Technology Co ltd
Priority date: 2024-11-08
Filing date: 2024-11-08
Publication date: 2025-02-07

Abstract

The present invention provides a method, device, equipment and medium for identifying faults of an on-board wireless charging system, and the method includes the following steps: Step 1, obtaining the working parameters of the on-board wireless charging system; Step 2, analyzing the obtained working parameters to determine whether there is an abnormality. The present invention obtains comprehensive working parameters through multiple sensors, analyzes the parameters in real time to quickly identify faults, uses artificial intelligence algorithms to deeply analyze fault data to determine the type and process them according to priority. At the same time, a fault history database is established for trend analysis to predict potential problems. It can also reproduce faults, optimize algorithms and processes through simulation software, and improve maintenance efficiency with the help of remote diagnosis. This series of measures comprehensively responds to the complexity of the on-board 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, ensuring the reliability and stability of the on-board wireless charging system.

Description

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

Technical Field

The present invention relates to the field of vehicle wireless charging technologies, and in particular, to a method, an apparatus, a device, and a medium for identifying a vehicle wireless charging system fault.

Background

With the rapid development of electric automobiles and intelligent electronic devices, the application of vehicle-mounted wireless charging systems in modern automobiles is becoming more and more widespread. The vehicle-mounted wireless charging system provides a convenient charging mode for users, and can charge electronic equipment such as mobile phones, tablet computers and the like without using a charging wire. However, due to the complexity of the vehicle-mounted environment and the characteristics of the wireless charging technology, various faults may occur in the vehicle-mounted wireless charging system, which affect the use experience of the user and the reliability of the system.

On the one hand, various interference factors exist in the vehicle-mounted environment, such as electromagnetic interference, temperature variation, vibration, etc., and these factors may affect the normal operation of the vehicle-mounted wireless charging system. For example, electromagnetic interference may cause abnormal electromagnetic field intensity fluctuation of a wireless charging coil, which affects charging efficiency, and temperature variation may cause overheating of a system, which affects safety and stability of the system.

On the other hand, the wireless charging technology has some technical problems, such as low charging efficiency, compatibility problem and the like. The low charging efficiency may result in too long charging time, affecting the user experience, and the compatibility problem may result in some electronic devices not being charged normally.

For this purpose, a method, a device, equipment and a medium for identifying faults of a vehicle-mounted wireless charging system are provided.

Disclosure of Invention

In view of the foregoing, embodiments of the present invention desire to provide a method, an apparatus, a device, and a medium for identifying a failure of a vehicle-mounted wireless charging system, so as to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial choice.

In order to solve the technical problems, the technical scheme adopted by the application is that the vehicle-mounted wireless charging system fault identification method comprises the following steps:

step one, acquiring working parameters of a vehicle-mounted wireless charging system;

Analyzing the acquired working parameters, judging whether the working parameters are abnormal, and if so, identifying fault data;

Step three, carrying out deep analysis on fault data based on an artificial intelligence algorithm, identifying fault types, and if multiple fault types exist, sequencing according to priority, and preferentially treating faults seriously affecting the safety and performance of the system;

Generating corresponding fault codes and fault alarm information according to the detected fault types, and displaying the fault codes and the fault alarm information to a user through a vehicle-mounted display screen;

Step five, automatically recording fault alarm information of each time, storing the fault alarm information into a background database of the vehicle-mounted communication system, carrying out trend analysis on fault historical data based on a big data analysis technology, and identifying a potential fault mode or periodic fault;

Step six, based on the determined fault type, the fault is reproduced by using simulation software, the accuracy of fault diagnosis is verified, and a fault recognition algorithm and a processing flow are optimized;

and step seven, fault alarm information is sent to a maintenance center or a manufacturer server through an on-board communication system, and a professional technician carries out remote diagnosis to provide corresponding maintenance advice and guidance.

It is further preferable to provide the technical scheme, in the first step, the operation parameters include input voltage, input current, output voltage, output current, temperature, electromagnetic field strength of the wireless charging coil and connection state of the charging device, wherein,

The input voltage and the output voltage are acquired in real time through a voltage sensor;

the input current and the output current are acquired in real time through a current sensor;

The temperature is obtained through measurement of a temperature sensor;

The electromagnetic field intensity of the wireless charging coil is obtained through detection of an electromagnetic field intensity sensor;

And the connection state of the charging equipment is obtained through judgment by a communication protocol.

In a second step, the method for identifying fault data includes the following steps:

step 1, comparing input voltage with output voltage, calculating voltage conversion efficiency, and judging that the charging efficiency is low if the voltage conversion efficiency is lower than a set threshold value;

Step 2, monitoring input current and output current, and judging that the charging failure is impossible if the input current is normal and the output current is zero;

Step 3, monitoring the temperature in real time, and judging that the temperature is overheat fault if the temperature exceeds a set safe temperature range;

and 4, analyzing the electromagnetic field intensity of the wireless charging coil, and judging that the wireless charging coil fails or has external interference if the electromagnetic field intensity fluctuates abnormally or changes irregularly.

In the third step, the artificial intelligence algorithm performs feature extraction and classification on the input fault data by using a deep learning neural network model to determine the fault type.

In the fourth step, the fault alarm information comprises fault type, occurrence time, fault parameters, fault reason speculation and temporary processing suggestions, and the generation of the fault codes utilizes specific coding rules to map different fault types into different digital codes.

In the fifth step, the big data analysis technology classifies the fault history data by using a clustering algorithm to identify fault modes with similar characteristics, the periodic faults are identified by calculating time intervals of occurrence of two adjacent similar faults, and if the time intervals are stable, the periodic faults are judged.

In the step six, the optimized fault recognition algorithm and the optimized fault recognition process flow are updated through an online upgrading function of the vehicle-mounted system.

In order to solve the technical problems, the application adopts another technical scheme that the vehicle-mounted wireless charging system fault identification device comprises a data acquisition module, a data analysis module, a fault type determination module, an alarm information generation module, a history database module, a simulation verification module and a communication module;

The data acquisition module is used for acquiring 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 state of charging equipment;

The data analysis module is used for analyzing the collected working parameters and judging whether the working parameters are abnormal, and if the working parameters are abnormal, the data analysis module identifies fault data;

The fault type determining module is used for carrying out deep analysis on fault data by utilizing the deep learning neural network model, determining the fault type, and if multiple fault types exist, sequencing according to priority, and preferentially processing faults seriously affecting the safety and performance of the system;

The alarm information generation module is used for generating corresponding fault codes and fault alarm information according to the detected fault types, and displaying the fault codes and the fault alarm information to a user through the vehicle-mounted display screen;

the historical database module is used for automatically recording fault alarm information of each time, establishing a fault historical database, carrying out trend analysis on fault historical data based on a big data analysis technology, and identifying potential fault modes or periodic faults;

The simulation verification module is used for reproducing faults by using simulation software according to the determined fault types, verifying the accuracy of fault diagnosis and optimizing a fault recognition algorithm and a processing flow;

The communication module is used for sending the fault alarm information to a maintenance center or a manufacturer server through the vehicle-mounted communication system, and the professional technician carries out remote diagnosis to provide corresponding maintenance advice and guidance.

In order to solve the technical problem, the application adopts another technical scheme that the electronic equipment comprises a processor and a memory, wherein,

The memory is used for storing programs;

the processor is coupled to the memory, and is configured to execute the program stored in the memory, so as to implement the steps in a method for identifying a fault of an on-vehicle wireless charging system according to any one of the above.

In order to solve the technical problem, another technical scheme adopted by the application is a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program realizes the fault identification method of the vehicle-mounted wireless charging system when being executed by a processor.

By adopting the technical scheme, the embodiment of the invention has the following advantages:

According to the invention, comprehensive working parameters are obtained through multiple sensors, the parameters are analyzed in real time to quickly identify faults, and fault data are analyzed deeply by utilizing an artificial intelligent algorithm to determine types and are processed according to priorities. Meanwhile, a fault history database is established for trend analysis so as to predict potential problems, faults can be reappeared through simulation software, an algorithm and a flow are optimized, and maintenance efficiency is improved by means of remote diagnosis. The invention ensures the reliability and stability of the vehicle-mounted wireless charging system by generating detailed fault alarm information and displaying the detailed fault alarm information to a user through the vehicle-mounted display screen, so that the user can know the fault condition in time and take corresponding measures, the use experience of the user is improved, and meanwhile, the safety of the system is ensured by preferentially treating the faults which seriously affect the safety and performance of the system, and the safer and more reliable charging environment is provided for the user. The foregoing summary 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 become apparent by reference to the drawings and the following detailed description.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are necessary for the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a method for identifying faults of a vehicle-mounted wireless charging system;

FIG. 2 is a flow chart of a method for identifying fault data according to the present invention;

FIG. 3 is a schematic diagram of a failure recognition device of a vehicle-mounted wireless charging system according to the present invention;

fig. 4 is a schematic structural diagram of an electronic device according to the present invention.

Detailed Description

Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

It should be appreciated that the following specific embodiments of the disclosure are described in order to provide a better understanding of the present disclosure, and that other advantages and effects will be apparent to those skilled in the art from the present disclosure. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.

It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.

It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.

In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.

Fig. 1 is a flow chart of a fault identification method of a vehicle-mounted wireless charging system according to an embodiment of the application. It should be noted that, if there are substantially the same results, the method of the present application is not limited to the flow sequence shown in fig. 1. 1-2, the fault identification method of the vehicle-mounted wireless charging system comprises the following steps:

Step one, acquiring working parameters of a vehicle-mounted wireless charging system, specifically, arranging a voltage sensor, a current sensor, a temperature sensor, an electromagnetic field intensity sensor and the like in the wireless charging system, connecting with a Central Processing Unit (CPU) through a communication interface (such as a CAN bus), and monitoring the connection state of charging equipment in real time through a communication protocol between a vehicle internal network (such as LIN and CAN) and the charging equipment;

Analyzing the acquired working parameters to judge whether the working parameters are abnormal, if so, identifying fault data, specifically, receiving data from a sensor by a CPU, performing preliminary data cleaning and checking to ensure the accuracy and the integrity of the data, and identifying abnormal data in the working parameters, such as voltage fluctuation, current interruption, temperature overrun and the like according to a preset threshold value and a logic rule;

Step three, carrying out deep analysis on fault data based on an artificial intelligence algorithm, identifying fault types, if a plurality of fault types exist, sequencing according to priority, preferentially treating faults seriously affecting the safety and performance of the system, specifically,

Firstly, cleaning fault data, removing noise and abnormal values, ensuring the quality of input data, and simultaneously, carrying out normalization processing on the data, so that data with different dimensions can be compared and analyzed on the same scale;

then, automatically extracting key features from fault data by using an artificial intelligence algorithm (such as a convolutional neural network CNN, a cyclic neural network RNN and the like in a deep learning model), wherein the features can represent essential attributes of faults and are vital to subsequent fault classification;

then, the extracted features are input into a trained classifier (such as a Support Vector Machine (SVM), a Random Forest (RF), a Neural Network (NN) and the like) for classification judgment, the classifier classifies fault data into predefined fault types according to learned knowledge, and if a deep learning model such as the neural network is adopted by the system, the model automatically learns the internal rules and modes of the fault data, so that high-precision fault classification is realized;

Finally, the system ranks the identified multiple fault types according to preset priority rules, wherein the rules are usually formulated based on the influence degree of the faults on the safety and performance of the system, for example, overheat faults can directly cause damage to the system and even fire disaster, so that the priority of the faults is higher than that of other types of faults, and the ranking algorithm can consider multiple factors such as the severity degree, the occurrence frequency, the repair time in historical data and the like of the fault types so as to ensure that the faults with the greatest influence on the system are preferentially processed;

generating corresponding fault codes and fault alarm information according to the detected fault types, displaying the fault codes and the fault alarm information to a user through a vehicle-mounted display screen, specifically,

Firstly, the system maps the identified fault type into a specific number or letter code according to a predefined fault code coding rule;

Then, according to the fault type and fault code, the system compiles detailed fault alarm information, wherein the information comprises fault type description, occurrence time, possible reasons, influence on the system, suggested temporary treatment measures and the like;

Finally, according to the fault type and fault code, the system compiles detailed fault alarm information, wherein the information comprises fault type description, occurrence time, possible reasons, influence on the system, suggested temporary treatment measures and the like;

Step five, automatically recording fault alarm information of each time, storing the fault alarm information into a background database of the vehicle-mounted communication system, carrying out trend analysis on fault history data based on a big data analysis technology, identifying potential fault modes or periodic faults, specifically,

Firstly, capturing and recording fault alarm information of each time in real time by a system, and storing the recorded fault alarm information into a background database stored in a vehicle-mounted communication system;

then, according to the type and characteristics of the fault alarm information, a reasonable database structure is designed, and the database comprises a plurality of tables, such as a fault information table, a fault code table, a fault type table and the like, so as to support complex data query and analysis requirements;

finally, carrying out trend analysis on the fault history data by utilizing a big data analysis technology, and identifying potential fault modes or periodic faults;

Step six, based on the determined fault type, the simulation software is utilized to reproduce the fault, verify the accuracy of fault diagnosis, optimize the fault recognition algorithm and the processing flow, in particular,

Firstly, constructing a corresponding fault model by using simulation software based on the determined fault type, wherein the model contains key information such as reasons, manifestations, influences on a system and the like of fault occurrence so as to accurately reproduce the fault;

then, setting proper simulation parameters according to the fault model and the running environment of an actual system, wherein the parameters comprise system states, environment conditions, input signals and the like so as to ensure that a simulation result can truly reflect the actual fault condition;

Then, starting simulation software, executing a simulation experiment, reproducing a fault process through the simulation software, and observing and recording the response and the performance of the system so as to facilitate subsequent analysis and verification;

Finally, comparing and analyzing the results of the simulation experiment with the actual fault conditions, and verifying the accuracy of fault diagnosis by comparing differences in aspects of fault phenomenon, influence range, system response and the like;

Step seven, the fault alarm information is sent to a maintenance center or a manufacturer server through an on-board communication system, the remote diagnosis is carried out by professional technicians, corresponding maintenance suggestions and guidance are provided, in particular,

Firstly, the system establishes communication connection with a maintenance center or a manufacturer server through a vehicle-mounted communication system (such as a mobile communication network, satellite communication and the like);

Then, the fault alarm information (including fault codes, fault type descriptions, occurrence time, possible reasons, influence on the system, suggested temporary treatment measures and the like) is packed into a standard format and sent to a maintenance center or manufacturer server through an established communication connection;

Then, after the maintenance center or the manufacturer server receives the fault alarm information, the professional technician immediately analyzes and analyzes the information;

Finally, based on the fault information and the analysis result, the professional technician uses a remote diagnosis tool (such as remote desktop control, video call, etc.) to remotely diagnose the system, and based on the remote diagnosis result, the professional technician provides detailed maintenance advice and guidance to the user.

In one embodiment, in particular, in step one, the operating parameters include an input voltage, an input current, an output voltage, an output current, a temperature, an electromagnetic field strength of the wireless charging coil, and a connection state of the charging device, wherein,

The input voltage and the output voltage are acquired in real time through the voltage sensor, the voltage sensor is arranged at the input end and the output end of the system, voltage signals can be accurately measured and converted into electric signals, subsequent processing and recording are convenient, and the voltage sensor has the characteristics of high precision, high stability, strong anti-interference capability and the like, and the acquired voltage data can be ensured to be accurate;

The input current and the output current are acquired in real time through the current sensor, the current sensor is usually connected in series in the input loop and the output loop of the circuit, the data is acquired through measuring the current in the loop, the current sensor can directly reflect the current change in the circuit, and the method has important significance for judging the working state and fault detection of the circuit;

The temperature is obtained through measurement of a temperature sensor, the temperature sensor is arranged at key parts of the system, such as a radiator, a circuit board and the like, so that the temperature change of the parts is monitored in real time, the temperature is one of important indexes reflecting the working state of the system, potential problems such as overheat and the like can be found in time through temperature monitoring, and the system is prevented from being damaged due to overhigh temperature;

The electromagnetic field intensity of the wireless charging coil is obtained through detection of an electromagnetic field intensity sensor, the electromagnetic field intensity sensor is placed near the wireless charging coil, the electromagnetic field intensity can be accurately measured and converted into a processable electric signal, and the accurate measurement of the electromagnetic field intensity has important significance for evaluating the wireless charging efficiency, ensuring the charging safety, detecting potential electromagnetic interference and other problems;

The connection state of the charging equipment is judged and obtained through a communication protocol, and the system can inquire the connection state (such as connected, unconnected, abnormal connection and the like) of the charging equipment in real time through a specific communication protocol and an instruction.

In one embodiment, in particular, in the second step, the method for identifying fault data includes the steps of:

Through deep analysis and comparison of different parameters, various fault types in the charging system can be comprehensively and accurately identified, and a reliable basis is provided for subsequent fault processing.

In a third embodiment, specifically, in the step three, the artificial intelligence algorithm performs feature extraction and classification on the input fault data by using a deep learning neural network model, determines the fault type, specifically, selects a deep learning neural network model suitable for processing a complex data classification task, such as a Convolutional Neural Network (CNN), a cyclic neural network (RNN), or variants thereof (such as LSTM, GRU), specifically selects a complexity of a classification task and properties (such as time series data, image data, etc.) of the fault data, trains the model by using sample data of known fault types, the sample data should include examples of multiple fault types, and is labeled with corresponding fault labels, the model can learn feature representations of different fault types through training, the deep learning neural network model has strong feature extraction capability, and can automatically extract useful feature information from the input fault data in the previous layers (such as a convolutional layer, a pooling layer, etc.) of the model, the feature information can be of a numerical type, an image type or other form, can reflect the properties of the fault data, and can calculate the probability value of the fault type at the subsequent layer (such as a full-layer, a probability layer, a final class is calculated, and the final class is calculated by using the probability value of the full-layer-class fault type, and the final class is calculated.

In one embodiment, in particular, in step four, the fault alert information includes fault type, time of occurrence, fault parameters, fault cause speculation, and temporary handling recommendations, wherein,

The fault type is explicitly pointed out that the fault type occurs, such as low charging efficiency, incapacity of charging, overheat, wireless charging coil fault and the like, which is helpful for maintenance personnel to quickly locate the problem;

Recording specific time of occurrence of faults, including date and specific time, which is helpful for analyzing rules and reasons of occurrence of faults and subsequent fault tracing;

fault parameters, namely listing specific parameter values related to faults, such as input voltage, output voltage, current, temperature, electromagnetic field intensity and the like, wherein the parameter values can provide important basis for fault analysis;

the fault cause is presumed, namely, based on the fault type and the parameter value, the fault cause is primarily presumed, which is helpful for maintenance personnel to prepare corresponding maintenance tools and spare parts in advance, and the maintenance efficiency is improved;

temporary handling advice, which may help mitigate the impact of the fault on the system, preventing further expansion of the fault, by giving some temporary handling advice, such as cutting off power, reducing load, increasing heat dissipation, etc., before the cause of the fault is clear.

The generation of fault codes maps different fault types into different digital codes by using specific coding rules, wherein the generation of the fault codes is based on the following principle:

Each fault code should uniquely correspond to one fault type to avoid confusion and misunderstanding;

Scalability: the coding rules should have a certain scalability to accommodate new fault types that may occur in the future;

Legibility the fault code should be as simple and clear as possible, so that it is convenient for memorizing and inquiring.

In one embodiment, in step five, the big data analysis technique classifies the fault history data by using a clustering algorithm to identify a fault mode with similar characteristics, the periodic fault is identified by calculating a time interval between two adjacent similar faults, if the time interval is relatively stable, the periodic fault is determined, wherein the clustering algorithm is an unsupervised learning algorithm, which can divide samples in a data set into a plurality of clusters (or called classes) so that the similarity of samples in the same cluster is relatively high, and the similarity of samples in different clusters is relatively low, and in the analysis of the fault history data, the clustering algorithm can be used for identifying the fault mode with similar characteristics, and the specific steps are as follows:

Firstly, cleaning, converting and standardizing fault history data to ensure the quality and consistency of the data;

Then, extracting features which have important influence on fault classification, such as fault type, fault parameters, occurrence time and the like, from the fault history data;

Then, selecting a proper clustering algorithm (such as K-means, DBSCAN, hierarchical clustering and the like), dividing the fault history data into a plurality of clusters, wherein each cluster represents a fault mode with similar characteristics;

Finally, the fault data in each cluster is analyzed to understand the characteristics, frequency of occurrence and possible cause of the fault pattern.

The periodic faults are faults which repeatedly occur within a certain time interval, and can be identified by calculating the time interval of occurrence of two adjacent similar faults, and the specific steps are as follows:

firstly, classifying faults in fault history data by using a clustering algorithm or other methods to ensure that similar faults are classified into a group;

then, for each type of fault, calculating the time interval between occurrence of two adjacent faults, which can be achieved by traversing all records of the type of fault and calculating the time difference between the adjacent records;

then, analyzing the calculated time interval data, if the time interval is stable (namely, most of the time intervals are close to a certain fixed value), judging that the faults are periodic faults, and evaluating the stability by calculating indexes such as standard deviation, variation coefficient and the like of the time interval;

finally, for the identified periodic faults, the occurrence rules, reasons and influencing factors thereof can be further analyzed so as to formulate corresponding preventive measures and maintenance plans.

In an embodiment, specifically, in step six, the optimized fault identification algorithm and the processing flow are updated through an online upgrade function of the vehicle-mounted system, and updating the optimized fault identification algorithm and the processing flow through an online upgrade function (OTA) of the vehicle-mounted system is an efficient, convenient and safe process, which is helpful for improving accuracy and efficiency of vehicle fault identification and providing safer and more reliable driving experience for vehicle owners.

FIG. 3 is a schematic structural diagram of a failure recognition device of a vehicle-mounted wireless charging system according to an embodiment of the present application, as shown in FIG. 3, the failure recognition device of a vehicle-mounted wireless charging system includes a data acquisition module, a data analysis module, a failure type determination module, an alarm information generation module, a history database module, a simulation verification module and a communication module;

The data acquisition module is used for acquiring 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 state of the charging equipment, and provides a solid foundation for subsequent fault analysis through comprehensive data acquisition;

the data analysis module is used for analyzing the collected working parameters, judging whether the collected working parameters are abnormal, if so, identifying fault data and providing clues for the subsequent fault type determination;

The fault type determining module is used for carrying out deep analysis on fault data by utilizing the deep learning neural network model to determine the fault type, and the deep learning model has the advantages of strong feature extraction and classification capability and capability of processing complex nonlinear relations so as to improve the accuracy and efficiency of fault identification;

the alarm information generation module is used for generating corresponding fault codes and fault alarm information according to the detected fault types, and displaying the fault codes and the fault alarm information to a user through the vehicle-mounted display screen, so that the awareness of the user on fault conditions is improved, and the user can be guided to take preliminary response measures such as disconnection of charging connection and the like;

The historical database module is used for automatically recording fault alarm information of each time, establishing a fault historical database, carrying out trend analysis on fault historical data based on a big data analysis technology, and identifying potential fault modes or periodic faults, wherein the predictive maintenance strategy is beneficial to preventing faults in advance and reducing losses caused by the faults;

The simulation verification module is used for reproducing the faults by using simulation software according to the determined fault types and verifying the accuracy of fault diagnosis, and the process is not only beneficial to optimizing a fault recognition algorithm and a processing flow, but also provides a fault reproduction environment for technicians, so that the fault reasons and solutions can be studied deeply;

And the communication module is used for sending the fault alarm information to a maintenance center or a manufacturer server through the vehicle-mounted communication system, carrying out remote diagnosis by professional technicians, providing corresponding maintenance advice and guidance, and improving the efficiency and accuracy of fault resolution.

In summary, the fault identification device for the vehicle-mounted wireless charging system provided by the embodiment of the application can monitor and accurately identify possible faults of the wireless charging system in real time by integrating a plurality of functional modules, so that the comprehensive, real-time and accurate fault monitoring and identification of the wireless charging system are realized, and a powerful guarantee is provided for safe charging of vehicles.

It should be noted that, in the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other. For the apparatus class embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference is made to the description of the method embodiments for relevant points.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. A schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure is shown. The electronic device shown in fig. 4 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.

The electronic device may include a processor, a memory, a communication bus, a sensor interface, a display screen interface and a communication module, where the processor, the memory, the sensor interface, the display screen interface, the communication module complete communication with each other through the communication bus, the processor may be a high-performance vehicle-mounted processor with multi-core processing capability and 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 in a program running process, the ROM is used to store solidified program codes and system parameters, and in addition, a large-capacity flash memory may be further configured to store fault history data, etc., the sensor interface is used to connect various sensors, such as a voltage sensor, a current sensor, a temperature sensor, an electromagnetic field intensity sensor, etc., the interface type may include an analog input interface, a digital input interface, a communication interface (such as I2C, SPI, etc.), the display screen interface is used to connect a vehicle-mounted display screen, display screen display fault codes and alarm information to a user, the interface type may be LVDS, HDMI, etc., the communication module includes a bluetooth communication module, a Wi-Fi, a vehicle-mounted service center, a Wi-G5/G service module, etc., and the service center is used to send fault information to a service center, etc.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from ROM. All or part of the steps of a vehicle-mounted wireless charging system fault identification method of an embodiment of the present disclosure are performed when the computer program is executed by a processor.

The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.

A computer-readable storage medium according to an embodiment of the present disclosure has stored thereon non-transitory computer-readable instructions. All or part of the steps of a vehicle-mounted wireless charging system fault identification method of the embodiments of the present disclosure described above are performed when the non-transitory computer readable instructions are executed by a processor.

Such 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., tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

The basic principles of the present disclosure have been described above in connection with specific embodiments, but it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present disclosure. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, since the disclosure is not necessarily limited to practice with the specific details described.

In this disclosure, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and the block diagrams of devices, apparatuses, devices, systems involved in this disclosure are merely illustrative examples and are not intended to require or implicate that connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.

In addition, as used herein, the use of "or" in the recitation of items beginning with "at least one" indicates a separate recitation, such that recitation 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), for example. Furthermore, the term "exemplary" does not mean that the described example is preferred or better than other examples.

It is also noted that in the systems and methods of the present disclosure, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered equivalent to the present disclosure.

Various changes, substitutions, and alterations are possible to the techniques described herein without departing from the teachings of the techniques defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the particular aspects of the process, machine, manufacture, composition of matter, means, methods and acts described above. The processes, machines, manufacture, compositions of matter, means, methods, or acts, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein may be utilized. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or acts.

The previous 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 generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the disclosure to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.

Claims

1. The fault identification method for the vehicle-mounted wireless charging system is characterized by comprising the following steps of:

2. The method for recognizing a malfunction of a vehicle-mounted wireless charging system according to claim 1, wherein in the first step, the operation parameters include an input voltage, an input current, an output voltage, an output current, a temperature, an electromagnetic field strength of the wireless charging coil, and a connection state of the charging device,

The temperature is obtained through measurement of a temperature sensor;

3. The method for identifying a fault of an on-vehicle wireless charging system according to claim 1, wherein in the second step, the method for identifying fault data comprises the steps of:

4. The method for identifying faults of a vehicle-mounted wireless charging system according to claim 1, wherein in the third step, the artificial intelligence algorithm utilizes a deep learning neural network model to conduct feature extraction and classification on input fault data and determine fault types.

5. The method for identifying faults of a vehicle-mounted wireless charging system according to claim 1, wherein in the fourth step, the fault alarm information comprises fault types, occurrence time, fault parameters, fault reason speculation and temporary processing suggestions, and the generation of the fault codes maps different fault types into different digital codes by using specific coding rules.

6. The method for identifying a fault of a vehicle-mounted wireless charging system according to claim 1, wherein in the fifth step, the big data analysis technology classifies fault history data by using a clustering algorithm to identify fault modes with similar characteristics, the periodic faults are identified by calculating time intervals of occurrence of two adjacent similar faults, and if the time intervals are stable, the periodic faults are determined.

7. The method for identifying a fault of a vehicle-mounted wireless charging system according to claim 1, wherein in the sixth step, the optimized fault identification algorithm and the processing flow are updated through an online upgrade function of the vehicle-mounted system.

8. The fault identification device of the vehicle-mounted wireless charging system is characterized by comprising a data acquisition module, a data analysis module, a fault type determination module, an alarm information generation module, a history database module, a simulation verification module and a communication module;

9. An electronic device comprising a processor and a memory, wherein,

The memory is used for storing programs;

the processor is coupled to the memory, and is configured to execute the program stored in the memory, so as to implement the steps in a vehicle-mounted wireless charging system fault identification method as claimed in any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements a vehicle-mounted wireless charging system fault identification method according to any one of claims 1 to 7.