Diagnostic service fault storage method and system for new energy automobile

Abstract

The invention relates to the field of vehicles, and discloses a diagnosis service fault storage method and system for a new energy vehicle. The method comprises the following steps: respectively acquiring fault data from each ECU; the graph model is initialized, fault data of each ECU are mapped into nodes, and attributes of the nodes comprise fault codes, fault occurrence timestamps and / or fault severity; establishing an edge between two corresponding nodes in the graph model for two ECUs which have a communication or data dependency relationship and have faults in the same time period, and calculating the weight of the edge; and identifying an association relationship between faults in the graph model, and analyzing and generating a fault association report according to the association relationship. According to the embodiment of the invention, through an innovative technical means, the storage and management efficiency of the fault data and the real-time performance and accuracy of data processing are remarkably improved, the safety of the data is guaranteed, and powerful support is provided for fault diagnosis and maintenance of the new energy automobile.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a diagnostic service fault storage method and system for new energy vehicles. Background Art

[0002] With the rapid development of new energy vehicle technology, the electronic control system of automobiles is becoming increasingly complex, and the demand for fault diagnosis and data management is also increasing. As an international standard diagnostic protocol, UDS (Unified Diagnostic Services) is widely used in fault detection and data transmission of automotive electronic control units (ECUs). However, existing diagnostic services still have some shortcomings in fault storage and management.

[0003] In the practical application of new energy vehicles, the storage and management of fault information is crucial, especially when multiple ECUs work together, the sharing and synchronization of fault information between control units is particularly complex. Existing fault storage methods often fail to record fault information in a timely and accurate manner, resulting in a decrease in the accuracy and efficiency of fault diagnosis. At the same time, with the upgrade of vehicle electronic systems and the increase in functions, traditional fault storage methods are difficult to meet the storage needs of large amounts of data and diversified fault information.

[0004] In addition, in new energy vehicles, the introduction of new control systems such as electric drive systems and energy management systems has put forward higher requirements for fault storage of UDS diagnostic services. Traditional fault storage systems lack support for these new control units, resulting in insufficient fault diagnosis coverage and increasing the difficulty of repair and maintenance. Summary of the invention

[0005] The purpose of the present invention is to provide a diagnostic service fault storage method and system, and a storage medium for new energy vehicles to overcome the above defects in the prior art.

[0006] To achieve this object, the present invention adopts the following technical solutions: A diagnostic service fault storage method for a new energy vehicle, comprising: Collect fault data from each ECU and record the corresponding fault code and occurrence time; Initialize an empty graph model, map the fault data of each ECU into nodes, and the attributes of the nodes include fault code, timestamp of fault occurrence and / or severity of the fault; For two ECUs that have a communication or data dependency relationship with each other and fail in the same time period, an edge is established between the corresponding two nodes in the graph model, and a weight of the edge is calculated; the weight represents the strength of the failure association between the corresponding two nodes; The shortest path algorithm or connected component analysis in graph theory is used to identify the association relationship between faults in the graph model, and a fault association report is generated based on the analysis.

[0007] Optionally, the weight calculation formula is:

[0008] in, is the edge weight, is the frequency of two node failures, is the time interval between failures, is the severity of the fault, To adjust the parameters.

[0009] Optionally, it also includes: before storing the collected fault data of each ECU, filtering the fault data based on a rule set and a machine learning model, and marking and classifying the fault data according to predetermined rules.

[0010] Optionally, it also includes adopting a tiered storage architecture for data storage; The hierarchical storage architecture includes at least a bottom layer, a middle layer and a top layer; the bottom layer is used to store original fault data, including original sensor data and / or ECU error codes; the middle layer is used to store processed fault information, including data that has been initially analyzed and / or filtered; the top layer is used to store summary information, including comprehensive fault analysis results and / or fault frequency statistics.

[0011] Optionally, it also includes: during the data storage process, using prediction models and historical data analysis to estimate the future data volume, and automatically and dynamically adjusting the space allocation of each storage unit in the tiered storage architecture accordingly.

[0012] Optionally, the method for automatically and dynamically adjusting space allocation of each storage unit in the tiered storage architecture includes: Assume that the data volume of the real-time data stream is , the total storage space of the system is , the space allocated to the i-th storage unit is , the storage space is adjusted using the following formula:

[0013] in, is the importance weight of the i-th storage unit, is the importance weight of the jth storage unit, and n is the total number of storage units.

[0014] Optionally, it also includes: when storing data, encrypting the fault data using the Advanced Encryption Standard AES; when transmitting data, encrypting the fault data using the RSA encryption algorithm.

[0015] Optionally, the method further includes: generating a unique fingerprint for the fault data before storing the data, and identifying and compressing repeated fault data based on the fingerprint.

[0016] A diagnostic service fault storage system for new energy vehicles, comprising a memory and a processor; The memory is used to store instructions; The processor is used to execute the instructions in the memory to implement any one of the above-mentioned diagnostic service fault storage methods for new energy vehicles.

[0017] A computer-readable storage medium includes instructions, which, when executed on a computer, enables the computer to execute any one of the above-mentioned diagnostic service fault storage methods for new energy vehicles.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The embodiments of the present invention not only significantly improve the storage and management efficiency of fault data, as well as the real-time and accuracy of data processing, but also ensure the security of data through innovative technical means, providing strong support for fault diagnosis and maintenance of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A diagram of a fault storage framework provided in an embodiment of the present invention.

[0021] Figure 2 A system architecture diagram provided for an embodiment of the present invention.

[0022] Figure 3 A diagram of a graphical model framework provided for an embodiment of the present invention.

[0023] Figure 4 A flowchart of multi-ECU fault correlation analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In order to solve some deficiencies in the prior art, the embodiment of the present invention proposes a diagnostic service fault storage method and system suitable for new energy vehicles (its storage framework is as follows Figure 1 As shown in the figure, it aims to improve the storage efficiency and accuracy of fault information, enhance the system's ability to handle multi-ECU coordinated faults, and provide more powerful support for the maintenance and fault diagnosis of new energy vehicles, including the following aspects: 1. Technical implementation Most traditional fault storage methods usually directly store fault codes and their related data in the local storage of the ECU. Each ECU independently records its own faults without further analysis or association.

[0026] The present invention introduces a multi-ECU fault correlation analysis method based on graph theory. Specifically, the system first collects the fault data of different ECUs and maps them into a graph model. In the graph model, each node represents a fault, and the edges represent the potential correlation between different faults. The system calculates the weights of these edges to quantify the strength of the correlation between faults. This method can reveal systemic problems that a single ECU cannot identify independently.

[0027] 2. Data processing flow Traditional data processing usually involves only basic fault recording and retrieval without complex data security measures. Fault data may be stored in simple flat files without any form of encryption or compression.

[0028] In the present invention, the data processing process is greatly improved. First, the fault data collected by the system will be immediately encrypted by AES to ensure the security of the data during storage. Then, the encrypted data will be stored in a redundant distributed storage system. The system adopts a multi-copy mechanism to ensure that the data will not be lost even if some storage nodes fail. In addition, during the data transmission stage, the system uses the RSA encryption algorithm to encrypt the data to ensure the confidentiality and integrity of the data during transmission.

[0029] 3. Fault Tolerance The fault tolerance mechanism of traditional systems is relatively simple, usually relying only on hardware redundancy design, such as increasing fault tolerance by adding backup servers or storage devices. However, this approach is both costly and difficult to effectively deal with all types of failures.

[0030] The present invention greatly improves the reliability of the system by introducing a software-based fault-tolerant mechanism. Specifically, when the system stores data, in addition to encryption, it also generates checksum and error correction codes, which will be stored in different nodes. When the system detects data corruption, these error correction codes can be used to automatically repair the error. In addition, the system also introduces an automated fault detection and repair mechanism. When a fault is detected, the system automatically generates and executes a repair plan, thereby reducing the need for human intervention.

[0031] 4. Fault correlation analysis In traditional systems, each ECU is only responsible for recording its own fault information, and the relationship between faults is usually analyzed based on manual experience, which lacks systematicity and comprehensiveness.

[0032] The embodiments of the present invention achieve efficient storage and management of complex fault information through innovative technical means, ensuring the real-time, accuracy and security of fault data, which will be described in detail below.

[0033] (1) System architecture design: The core technology design of this system is based on multi-layer storage architecture, dynamic space management, intelligent identification and processing of redundant data, multi-ECU synchronization and security encryption mechanism. The overall design goal of the system architecture is to improve the storage efficiency of fault data, reduce the waste of storage space, and enhance the real-time and security of data processing.

[0034] Hierarchical storage architecture: The system adopts a hierarchical storage architecture, in which each level corresponds to fault information of different granularity. The bottom layer is used to store raw fault data, including raw sensor data and ECU error codes; the middle layer stores processed fault information, including data that has been initially analyzed and filtered; the top layer stores summary information, such as comprehensive fault analysis results, fault frequency statistics, etc. This hierarchical structure can not only improve the efficiency of data storage and retrieval, but also effectively support complex fault analysis.

[0035] Dynamic Space Management Module: The system's dynamic space management module can automatically adjust the space allocation of each storage unit according to the changes in real-time data flow. The module uses predictive models and historical data analysis to estimate future data volume and adjust storage space in advance to avoid insufficient or wasted storage space. In addition, the module is also equipped with an automatic expansion function of storage space to ensure that the system can quickly respond and adjust storage strategies in abnormal situations.

[0036] Formula: Assume that the data volume of the real-time data stream is , the total storage space of the system is ,The space allocated to a specific storage unit is The dynamic space management module will adjust the storage space using the following formula:

[0037] in, is the importance weight of the ith storage unit, and n is the total number of storage units.

[0038] See also Figure 2 ,The architecture diagram of this system includes the following parts: Input layer: Get fault data from different ECUs.

[0039] Data preprocessing module: performs preliminary filtering and preprocessing on data.

[0040] Storage management layer: dynamically adjusts storage space and performs data compression and redundancy elimination.

[0041] Data analysis layer: perform failure mode analysis and multi-ECU failure correlation analysis.

[0042] Output layer: Outputs the final fault report and analysis results.

[0043] (2) Data processing flow and key algorithms: The present invention adopts a stream processing architecture in the data processing flow to ensure that the whole process from data collection to storage is efficient and real-time. The design of the key algorithm is based on the following points: Data preprocessing and screening algorithms: Before data storage, the system preprocesses the collected data, filters out irrelevant or useless data, and marks and classifies key data. The data preprocessing algorithm is based on rule sets and machine learning models, which can automatically identify and process abnormal data, and classify and store fault data according to predetermined rules.

[0044] Data compression and redundancy elimination algorithm: For the processing of redundant data, the system adopts an intelligent recognition algorithm based on data fingerprint. By generating a unique fingerprint for the fault data, the system can quickly identify and compress repeated fault information to ensure that redundant data does not occupy too much storage space. The data compression algorithm selects the appropriate compression method according to the type and importance of the data to minimize the storage space.

[0045] formula: Given two fault data and , whose fingerprints are and .if , then it is believed that and To de-duplicate data, the system performs the following compression operations:

[0046] (3) Multi-ECU fault correlation analysis algorithm: In order to better deal with the complex fault correlation between multiple ECUs (electronic control units) in new energy vehicles, this paper designs a multi-ECU fault correlation analysis algorithm based on graph theory. The algorithm constructs a graph model of fault information and uses the analysis method of graph theory to perform correlation analysis on the fault information between different ECUs, thereby identifying potential fault relationships and assisting the diagnostic system in fault location and troubleshooting.

[0047] 3.1. Construction of the graph model In this algorithm, fault information is represented as nodes in the graph model, and the communication and dependency relationships between different ECUs are represented as edges in the graph model. The specific steps are as follows: Node definition: The fault information of each ECU is mapped to a node. The attributes of the node include fault code, timestamp of fault occurrence, severity of fault, etc.

[0048] Edge definition: If there is a communication or data dependency between two ECUs and they fail in the same time period, an edge is established in the graph, indicating that there may be a failure correlation between them.

[0049] Weight assignment: The weight of each edge represents the strength of the fault association. The weight can be calculated based on historical data, the frequency of faults, time intervals, and other factors.

[0050] 3.2. Algorithm Flow 1. Data collection: Collect fault data from each ECU and record the fault code and occurrence time.

[0051] 2. Graph model initialization: Initialize an empty graph model and map all collected fault data into nodes.

[0052] 3. Edge generation and weight calculation: Based on the communication relationship between ECUs, generate edges between nodes and calculate edge weights. The weight calculation formula is:

[0053] in, is the frequency of two node failures, is the time interval between failures, is the severity of the fault, To adjust the parameters.

[0054] 4. Fault correlation analysis: Use the shortest path algorithm or connected component analysis in graph theory to identify the correlation between faults. If multiple strongly correlated node clusters are found in the graph, it can be preliminarily determined that there may be systemic problems between these ECUs.

[0055] 5. Fault report generation: Generate a detailed fault correlation report based on the analysis results, including possible root causes of the fault and recommended troubleshooting paths.

[0056] 3.3. Algorithm Example Analysis Assume that in a new energy vehicle, ECU1 is responsible for the battery management system (BMS), ECU2 is responsible for the motor control system (MCU), and ECU3 is responsible for the braking system (ABS). In a certain failure event, these three ECUs all failed in almost the same time period. The following information was obtained through data collection: ECU1: Fault code P0A80, ​​timestamp 12:01, severity 8.

[0057] ECU2: Fault code P0A7F, timestamp 12:02, severity 7.

[0058] ECU3: Fault code C1234, timestamp 12:01, severity 9.

[0059] Step 1: Initialize the graph model The three ECUs are mapped as three nodes in the graph.

[0060] Step 2: Edge generation and weight calculation Considering that there is a data exchange relationship between ECU1 and ECU2, and there is a control logic dependency relationship between ECU2 and ECU3, edges are established between these nodes and weights are calculated. Assume that the weight calculation results are as follows: Edge (ECU1, ECU2): weight w12=5w_{12} = 5w12=5 Edge (ECU2, ECU3): weight w23=4w_{23} = 4w23=4 Edge (ECU1, ECU3): weight w13=7w_{13} = 7w13=7 Step 3: Fault correlation analysis Using the shortest path algorithm to analyze, it is found that there is a high degree of correlation between ECU1, ECU2 and ECU3, indicating that there may be a common fault source among these three ECUs.

[0061] Step 4: Fault Report Generation The generated report shows that the failures of these three ECUs may be caused by abnormalities in the battery management system. It is recommended to give priority to checking the battery management system and its interfaces with other systems.

[0062] 3.4. Multi-ECU fault analysis flow chart See also Figure 4 ,The flowchart shows the whole process of multi-ECU fault correlation ,analysis, including the steps of data collection, graph model construction, ,weight calculation, correlation analysis and final report generation.

[0063] (4) Security fault tolerance and data encryption technology In the fault storage system of new energy vehicles, data security and system fault tolerance are crucial. The present invention specially designs a multi-level data protection and fault tolerance mechanism to ensure data integrity and system stability under extreme conditions.

[0064] 4.1. Data encryption mechanism In order to prevent fault data from being maliciously tampered with or leaked during storage and transmission, the system adopts a data protection mechanism based on a combination of symmetric encryption and asymmetric encryption. The specific implementation is as follows: • Symmetric encryption: Fault data is encrypted using the Advanced Encryption Standard (AES) when the data is stored.

[0065] • Asymmetric encryption: When transmitting data, the RSA encryption algorithm is used to ensure the security of data during transmission.

[0066] 4.2. Fault Tolerance Mechanism The system uses a multi-layer fault tolerance mechanism to cope with possible hardware failures or data corruption: Redundant data storage: Critical failure data will be stored in multiple storage units to prevent single point failure.

[0067] Error detection and correction: Detect and correct errors in stored data through hash checksums and error correction codes (such as Hamming codes).

[0068] In summary, the diagnostic service fault storage method and system provided by the present invention significantly improves the storage and management efficiency of fault data through multi-layer storage architecture, dynamic space management, data preprocessing, compression and redundancy elimination, fault mode analysis, multi-ECU fault correlation analysis and other technologies. Through innovative technical means, not only the real-time and accuracy of data processing are improved, but also the security of data is guaranteed, providing strong support for fault diagnosis and maintenance of new energy vehicles.

[0069] Based on the same concept, an embodiment of the present invention provides a diagnostic service fault storage system for new energy vehicles, the system comprising a memory and a processor. The memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the diagnostic service fault storage method for new energy vehicles provided in the embodiment of the present invention.

[0070] Based on the same concept, an embodiment of the present invention provides a computer-readable storage medium, in which at least one instruction is stored. The instruction is loaded and executed by a processor to implement the diagnostic service fault storage method for new energy vehicles provided in an embodiment of the present invention.

[0071] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0072] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A diagnostic service fault storage method for new energy vehicles, characterized in that: include: Collect fault data from each ECU and record the corresponding fault code and occurrence time; Initialize an empty graph model, map the fault data of each ECU into nodes, and the attributes of the nodes include fault code, timestamp of fault occurrence and / or severity of the fault; For two ECUs that have a communication or data dependency relationship with each other and fail in the same time period, an edge is established between the corresponding two nodes in the graph model, and a weight of the edge is calculated; the weight represents the strength of the failure association between the corresponding two nodes; The shortest path algorithm or connected component analysis in graph theory is used to identify the association relationship between faults in the graph model, and a fault association report is generated based on the analysis.

2. The diagnostic service fault storage method for new energy vehicles according to claim 1, characterized in that: The calculation formula of the weight is: ， in, is the edge weight, is the frequency of two node failures, is the time interval between failures, is the severity of the fault, To adjust the parameters.

3. The diagnostic service fault storage method for new energy vehicles according to claim 1, characterized in that: Also includes: Before storing the collected fault data of each ECU, the fault data is filtered based on the rule set and machine learning model, and the fault data is marked and classified according to predetermined rules.

4. The diagnostic service fault storage method for new energy vehicles according to claim 1, characterized in that: It also includes adopting a tiered storage architecture for data storage; The layered storage architecture includes at least a bottom layer, a middle layer and a top layer; The bottom layer is used to store original fault data, including original sensor data and / or ECU error codes; The middle layer is used to store processed fault information, including data that has been initially analyzed and / or filtered; The top layer is used to store summary information, including comprehensive fault analysis results and / or fault frequency statistics.

5. The diagnostic service fault storage method for new energy vehicles according to claim 4, characterized in that: Also includes: During the data storage process, predictive models and historical data analysis are used to estimate the future data volume, and the space allocation of each storage unit in the tiered storage architecture is automatically and dynamically adjusted accordingly.

6. The diagnostic service fault storage method for new energy vehicles according to claim 5, characterized in that: The method for automatically and dynamically adjusting the space allocation of each storage unit in the tiered storage architecture includes: Assume that the data volume of the real-time data stream is , the total storage space of the system is , the space allocated to the i-th storage unit is , the storage space is adjusted using the following formula: ， in, is the importance weight of the i-th storage unit, is the importance weight of the jth storage unit, and n is the total number of storage units.

7. The diagnostic service fault storage method for new energy vehicles according to claim 1, characterized in that: Also includes: When storing data, the fault data is encrypted using the Advanced Encryption Standard AES; when transmitting data, the fault data is encrypted using the RSA encryption algorithm.

8. The diagnostic service fault storage method for new energy vehicles according to claim 1, characterized in that: Also includes: Before data is stored, a unique fingerprint is generated for the fault data, and repeated fault data is identified and compressed based on the fingerprint.

9. A diagnostic service fault storage system for new energy vehicles, comprising a memory and a processor; The memory is used to store instructions; The processor is used to execute the instructions in the memory to implement the diagnostic service fault storage method for new energy vehicles as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The method comprises instructions which, when executed on a computer, enable the computer to execute the diagnostic service fault storage method for new energy vehicles as described in any one of claims 1 to 8 above.

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