Vehicle data analysis method, system, equipment and medium

Abstract

The invention discloses a vehicle data analysis method, system and device and a medium. The method comprises the steps of obtaining vehicle original data and analyzing a relational database of the vehicle original data; matching rule data for analyzing the original data of the vehicle from the relational database; and analyzing the vehicle original data based on the rule data to form analyzed signal data. According to the invention, the data transmission and storage cost is optimized, the data transmission and analysis efficiency is improved, and the demand of rapid increase of the data volume of the new energy vehicle is met.

Description

Technical Field

[0001] The present invention relates to the field of vehicle-side data, and in particular to a vehicle data analysis method, system, device and medium. Background Art

[0002] With the development of new energy vehicles, the amount of vehicle-side data is growing exponentially, and the types of data are increasing. Fast and accurate transmission and analysis of vehicle-side data while reducing storage space has become a key requirement for optimizing the automotive big data link.

[0003] There are multiple problems with current vehicle-side data processing. First, the vehicle-side gateway and data parsing use independent Kafka clusters, which results in high costs. Second, the amount of data after parsing is huge, which puts pressure on the storage and receiving performance of the big data platform and takes up a lot of storage space. In addition, the processing of the parsed data is inefficient, time-consuming, and difficult to adapt to business needs. Different data structures are required for different types of data, and the code reusability is poor. In the process of querying and exporting large amounts of data, there are also major problems with efficiency and timeliness. Summary of the invention

[0004] In response to the above technical problems, the present invention provides a vehicle data analysis method, system, device and medium, which optimizes data transmission and storage costs, improves data transmission and analysis efficiency, and adapts to the rapid growth of new energy vehicle data volume.

[0005] A first aspect of the present invention provides a vehicle data analysis method, comprising: Acquiring vehicle raw data and parsing a relational database of the vehicle raw data; Matching rule data for parsing the original vehicle data from the relational database; The vehicle original data is parsed based on the rule data to form parsed signal data.

[0006] In a possible implementation, the acquiring of original vehicle data includes: The vehicle’s raw data is transmitted to the cloud through the collection service; The cloud directly collects the original vehicle data through Kafka and connects it to the big data platform; The big data platform stores the original vehicle data before analysis.

[0007] In a possible implementation, the matching of rule data from the relational database to parse the original vehicle data includes: Obtain the version ID corresponding to the original data of the vehicle; Searching for all rule data corresponding to the version ID from the relational database; All the rule data found are organized into a complete mapping structure according to the preset data format.

[0008] In a possible implementation, the rule data includes the signal name, signal value, signal value type, signal value length, signal position in the original code, and signal ID of each signal on the vehicle side.

[0009] In a possible implementation, parsing the vehicle raw data based on the rule data includes: According to the position of the signal in the original code and the length of the signal value, the specific position of the signal in the original data of the vehicle is identified, and the original data corresponding to the signal is extracted; According to the signal value type, the extracted raw data is converted into a type; According to the signal name, signal value and signal ID, structured signal data is generated.

[0010] In a possible implementation, the parsing the vehicle raw data based on the rule data further includes: Only the signals of interest are analyzed through preset filtering conditions, while other signals are ignored; The filtering condition is determined by the business demand signal.

[0011] In a possible implementation, the parsing method of parsing the vehicle raw data based on the rule data includes offline parsing and real-time parsing; During offline parsing, the rule data is stored in an entity table for scheduling; During real-time parsing, the rule data is stored in a view table for scheduling.

[0012] A second aspect of the present invention provides a vehicle data analysis system, comprising: A vehicle-side data acquisition module, at least used to acquire vehicle raw data and parse a relational database of the vehicle raw data; A rule data matching module, at least used to match the rule data for parsing the original vehicle data from the relational database; The raw data parsing module is at least used to parse the raw vehicle data based on the rule data to form parsed signal data.

[0013] According to a third aspect of the present invention, there is provided an electronic device, comprising: At least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method described in the first aspect of the embodiment of the present invention.

[0014] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a computer, the method according to the first aspect of the embodiment of the present invention is executed.

[0015] The present invention achieves efficient analysis based on rule data by matching the original vehicle data with the rule data in the relational database. Compared with the prior art, the present invention optimizes the data analysis process, simplifies the redundant Kafka cluster, directly stores the original data and analyzes it on demand, and reduces the data storage and transmission costs. In addition, the timeliness and accuracy of data analysis are improved by flexibly calling rule data, while reducing the analysis pressure and improving the overall analysis efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flowchart of a vehicle data analysis method according to an embodiment of the present invention.

[0017] Figure 2 The present invention is a flowchart of another embodiment of a vehicle data analysis method.

[0018] Figure 3 The present invention is a flowchart of another embodiment of a vehicle data analysis method.

[0019] Figure 4 The present invention is a flow chart of a vehicle data analysis method according to an embodiment of the present invention.

[0020] Figure 5 The figure is a schematic structural diagram of an embodiment of a vehicle data analysis system of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] It should be understood that the terms "first", "second", and "third" etc. in the claims, specifications, and drawings of the present disclosure are used to distinguish different objects rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of the present disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections. It should also be understood that the terms used in this disclosure specification are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0023] Reference Figure 1 , an embodiment of the present invention provides a vehicle data analysis method, comprising the following steps.

[0024] S1, obtaining vehicle raw data and a relational database for parsing the vehicle raw data; specifically, collecting vehicle raw data in real time through vehicle-mounted sensors or vehicle-mounted gateways, or extracting vehicle raw data from vehicle historical data records; when parsing the vehicle raw data, the relational database can be a local database, a cloud database, or a distributed database, storing parsing rules, and obtaining relevant parsing rules from the database through an API or a data synchronization mechanism to support the parsing process of the raw data.

[0025] S2, matching the rule data for parsing the original vehicle data from the relational database; specifically, the parsing rules matching the original vehicle data can be retrieved from the relational database by version ID, data type or other identifiers. Database retrieval can be implemented through SQL query statements, or by interacting with the database through API requests to obtain corresponding rule data. In addition, the cache mechanism can be used to load commonly used rules in advance to reduce the delay of real-time matching and improve matching efficiency. In a distributed database environment, the matching process of rule data can also be accelerated through sharding or indexing technology.

[0026] S3, parsing the original vehicle data based on the rule data to form parsed signal data; specifically, parsing the original vehicle data field by field based on the rule data obtained from the relational database, and the parsing method may include batch parsing, real-time parsing or segmented parsing. The parsing tool can use a variety of technologies, such as parallel parsing of large-scale data through a big data processing framework (such as Spark or Flink), or writing parsing logic through traditional programming languages ​​(such as Python, Java, etc.). The parsed signal data can be directly stored in a data warehouse, display platform, or further filtered, converted or processed to meet specific business needs. In addition, the parsing process can also optimize performance by filtering unnecessary signal data.

[0027] The embodiment of the present invention achieves efficient parsing of vehicle raw data and simplifies the complex parsing process by obtaining matching parsing rules from a relational database. By accurately matching rule data, redundant calculations during parsing are reduced, and the accuracy and timeliness of parsing are improved. In addition, the use of various technical means such as batch processing and real-time parsing significantly reduces the cost and pressure of data processing, enhances the flexibility and scalability of the system, and improves the utilization efficiency of data and the response speed of business applications.

[0028] Furthermore, as an embodiment of the present invention, the acquisition of vehicle raw data includes: the vehicle raw data is transmitted to the cloud through a collection service; the cloud directly collects the vehicle raw data through Kafka and connects it to the big data platform; the big data platform stores the vehicle raw data before parsing.

[0029] Specifically, refer to Figure 2 First, the vehicle-side data collection and upload is performed. The sensors or on-board equipment on the vehicle are responsible for collecting various vehicle data. After the data is collected, it forms raw data. These raw data are usually unprocessed or unparsed coded data, generally binary code or other low-level coding forms (such as 01 code). The collected raw data is uploaded to the cloud through the on-board equipment. At this stage, the data is not processed or filtered in any way and remains in its original form. The main task of this part is to ensure that the vehicle-side data can be collected stably and accurately, and transmitted to the remote server through the wireless communication network.

[0030] Next, the original data of the remote gateway is transmitted. The original data uploaded to the cloud first passes through the remote gateway. The main function of the gateway is to be responsible for data access and preliminary processing to ensure that data from multiple vehicle ends can be effectively transmitted to the big data platform. Data transmission can be achieved through the Kafka message queue. Kafka is a distributed messaging system that can efficiently and reliably process large-scale data streams. The original data of the vehicle is transmitted and consumed through the Kafka queue, ensuring that a large amount of data can be quickly and losslessly transmitted to the back-end big data platform.

[0031] Then, the original data is stored on the big data platform and transferred through Kafka, and then enters the big data platform. At this stage, the original data of the vehicle has not been parsed, but is stored in its original form in the database of the big data platform. This method of direct storage without parsing reduces the pressure of real-time parsing on the system, because the original data may flow in at a high speed. If real-time parsing is performed during the transmission process, a large amount of computing resources will be consumed, affecting the efficiency of the system. Direct storage can effectively reduce the system load during parsing. At the same time, the integrity of the data is retained, and the original data has not been processed in any way, which means that it can be flexibly processed according to different business needs in the subsequent parsing process, avoiding the loss of information that may be caused by early parsing of the data.

[0032] Finally, the big data platform is connected to the parsing platform, and the raw data stored in the big data platform is then transmitted to the parsing platform. The parsing platform is the core part used to convert raw binary or encoded data into structured data.

[0033] Based on the above embodiment, the entire process starts with data collection on the vehicle side, and transmits the original data to the big data platform through the remote gateway and Kafka message queue in the cloud, and directly stores it before parsing. This processing method effectively reduces the system load during the data inflow process, while ensuring that the original data is preserved intact, which facilitates flexible parsing and processing according to business needs.

[0034] Furthermore, as an embodiment of the present invention, the rule data matched from the relational database to parse the vehicle original data includes: obtaining a version ID corresponding to the vehicle original data; searching for all rule data corresponding to the version ID from the relational database; and organizing all the rule data found into a complete mapping structure according to a preset data format.

[0035] The rule data includes the signal name, signal value, signal value type, signal value length, signal position in the original code, and signal ID of each signal on the vehicle side.

[0036] Specifically, a relational database is a database management system that stores and organizes data in a tabular form, where data is represented by a table consisting of rows and columns. Each table consists of multiple columns (fields) that define the attributes of the data, and each row (record) represents an instance of the data. The core concept of a relational database is "relationship", and tables are associated with each other through primary keys and foreign keys, so that efficient data query, insertion, update, and deletion operations can be performed through SQL (Structured Query Language). Common relational database management systems include MySQL, PostgreSQL, Oracle, and SQL Server, which provide data consistency, integrity, and security, and are suitable for processing structured data and complex query operations.

[0037] The initially defined rule data (such as the signal name, signal location, signal type, etc. used to parse the original data) is stored in a relational database, which allows the management of this data in a tabular manner. Upstream maintenance personnel can directly add, delete, and modify these rule data through SQL or database management tools to ensure that the rule data used in the parsing process is always up to date. This structured storage method of the relational database makes the management and update of rule data more flexible and efficient, which helps the accuracy and timeliness of the parsing process.

[0038] Reference Figure 3 , the rule data is synchronized from the relational database to the big data platform in real time through the FLINK CDC component (i.e., change data capture component). The FLINK CDC component can capture updates to the rule data in the database and transmit these updates to the big data platform in real time. After receiving these rule data, the big data platform processes the original data and converts it into a format suitable for downstream parsing tools (such as data parsing platforms) so that the original data can be processed efficiently and accurately during the parsing process. This real-time synchronization and data processing mechanism ensures that the analysis of rule data and original data remains consistent and timely.

[0039] When parsing the original data, all rule data (such as signal name, location, type, etc.) corresponding to the version ID are found according to the version ID of the data. In order to facilitate efficient search and application of rule data, the relevant rule data can be organized according to the version ID to form a mapping structure. In this structure, the version ID is used as the mapping key, and all rule data associated with the version ID is used as the mapping value. This structure allows the corresponding parsing rules to be quickly found according to the version ID during parsing, ensuring that the parsing process is accurate.

[0040] Furthermore, as an embodiment of the present invention, the parsing of the vehicle original data based on the rule data includes: identifying the specific position of the signal in the vehicle original data according to the position of the signal in the original code and the length of the signal value, and extracting the original data corresponding to the signal; performing type conversion on the extracted original data according to the signal value type; generating structured signal data according to the signal name, signal value and signal ID.

[0041] Specifically, the exact position of the signal in the original data is determined based on the position of the signal in the original code and the length of the signal value defined in the rule data. For example, if the rule defines that a vehicle speed signal starts from the 5th byte of the original data and is 2 bytes long, the corresponding code is extracted from the 5th and 6th bytes of the original data. Next, according to the signal value type defined in the rule, the extracted data is type-converted, such as converting binary data to decimal integers. Finally, based on the signal name (such as "vehicle speed"), signal value (such as "80km / h"), and signal ID (such as "001"), structured signal data is generated for subsequent processing and display. This ensures accurate data parsing and flexibility in business applications.

[0042] Furthermore, as an embodiment of the present invention, the parsing of the vehicle raw data based on the rule data also includes: only parsing the signal of interest and ignoring other signals through preset filtering conditions; the filtering conditions are determined by the business demand signal.

[0043] Specifically, filtering conditions can be set according to business needs, so that only the signals of interest are analyzed and other irrelevant signals are ignored. Before analysis, which signals are "interesting" signals will be defined according to business needs, for example, only "vehicle speed" and "battery power" signals will be analyzed, while other signals such as "engine status" will be ignored. Through this filtering, the analysis process will be more efficient, reducing the amount of calculation and processing time, and avoiding unnecessary data processing. This flexible filtering mechanism is dynamically adjusted according to business needs to ensure that the system only processes signals related to the current business and optimizes resource usage.

[0044] Furthermore, as an embodiment of the present invention, the parsing method of the vehicle original data based on the rule data includes offline parsing and real-time parsing; during offline parsing, the rule data is stored in an entity table for scheduling; during real-time parsing, the rule data is stored in a view table for scheduling.

[0045] Specifically, there are two ways to parse the original vehicle data: offline parsing and real-time parsing. In offline parsing, the rule data will be stored in the entity table in advance, and scheduled and processed regularly. The entity table is persistently stored, suitable for batch processing and regularly updated data parsing tasks, such as processing large amounts of historical data. In contrast, in real-time parsing, the rule data will be stored in the view table. The view table is not persistently stored, but dynamically generated, allowing the latest rule data to be obtained in real time each time it is called, which is suitable for parsing the latest data with high real-time requirements. Through these two methods, the appropriate parsing mode can be selected according to different needs. Offline parsing processes large-scale historical data, while real-time parsing ensures timely processing of the latest data.

[0046] Since the rule data is constantly updated, offline rule data can only update the data before a certain period of time (such as 1 hour), and the rule data within 1 hour needs to be obtained in real time. In the offline platform, the rule data is stored in the entity table and scheduled regularly to reduce the parsing time. Due to the timeliness requirements of the rule data, the rule data in the real-time platform cannot be stored in the entity table for scheduled scheduling. It needs to be stored in the view table and the rule data is updated in real time each time it is called. When parsing the original data, the original data and the rule data are associated through the version ID, and all the rule data and the original data of a version ID need to be input into the parsing platform at the same time. According to business needs, select offline or real-time rule data. If only data before 1 hour is needed, the offline rule table data is used. If data within 1 hour is needed, the real-time rule table data needs to be called. In the parsing platform (such as Trino parsing platform, Spark parsing platform, Flink parsing platform), the original data and the rule data are associated and passed into the parsing platform as input parameters. The corresponding parsing code is developed in the parsing platform to obtain the signal name, signal value, signal type and time according to the parsing rules of the original data to form the parsed signal data.

[0047] Reference Figure 4, a specific embodiment of a vehicle data parsing method of the present invention includes: first, the vehicle side of the car will report various data to the cloud, and the data uploaded to the cloud will be pushed to the gateway kafka. The big data platform directly accesses the original data of the vehicle side from the gateway kafka through the flink CDC component. Secondly, while accessing the original data of the vehicle side, the parsing rule data of the original data is synchronously accessed to the big data platform, and the rule data is processed to form an adaptable data format. Thirdly, for different parsing platforms, different parsing tools are used in combination with the vehicle side data parsing rules to parse the original data of the vehicle side. During the parsing process, signal filtering is performed in combination with different business needs to optimize the parsing efficiency, and the parsed data is accessed to the data warehouse. Finally, the parsed data is processed and displayed according to business needs.

[0048] The embodiment of the present invention also discloses a vehicle data analysis system.

[0049] Reference Figure 5 A vehicle data analysis system includes a vehicle-side data acquisition module 1, a rule data matching module 2 and a raw data analysis module 3.

[0050] The vehicle-side data acquisition module 1 is at least used to acquire the original data of the vehicle and parse the relational database of the original data of the vehicle; specifically, the vehicle-side data acquisition module 1 collects the original data of the vehicle in real time through the vehicle-mounted sensor, OBD interface or other vehicle-mounted acquisition equipment, and these data may include information such as the vehicle's speed, engine status, battery power, etc. The data is uploaded to the cloud or local server through the vehicle communication system (such as 4G / 5G or V2X). In addition, the vehicle-side data acquisition module 1 can also obtain historical vehicle original data from the storage system for processing. For the rules for parsing vehicle data, the vehicle-side data acquisition module 1 can be connected to a relational database to obtain the rule data required for parsing, such as signal name, signal value type, signal location, etc. These rule data can be accessed in real time from a remote database through the network, or the rule data can be cached locally to improve the acquisition speed and parsing efficiency.

[0051] The rule data matching module 2 is at least used to match the rule data for parsing the original vehicle data from the relational database; specifically, the rule data matching module 2 accurately matches the corresponding parsing rule data from the relational database through the version ID or other identifiers in the original vehicle data. The matching process can obtain rule data from the relational database through SQL queries, or interact with the database through an API interface. The rule data matching module 2 can realize automatic matching of rules for different vehicle models and different data types, and can also use a cache mechanism to store commonly used rule data to improve matching speed and response efficiency. In addition, the rule data matching module 2 can monitor the update of rule data in real time to ensure that the matched rules are always the latest, and supports rule data matching in both offline and real-time modes.

[0052] The raw data parsing module 3 is at least used to parse the raw data of the vehicle based on the rule data to form parsed signal data. The raw data parsing module 3 parses the signals in the raw data of the vehicle one by one according to the matched rule data. The parsing method can extract and convert the signal value according to the position, length and type of the signal in the raw data, such as converting binary data into integers, floating points or strings. The raw data parsing module 3 can realize batch offline parsing, process a large amount of historical data, or perform real-time parsing to meet the real-time processing requirements of the latest data. During the parsing process, the raw data parsing module 3 also supports signal filtering according to business needs, and only parses specific signals. In addition, the parsed signal data can be stored in a database or data warehouse for subsequent business applications or visual display. The raw data parsing module 3 can also optimize the parsing logic, reduce computing overhead, and improve system performance.

[0053] The embodiment of the present invention also discloses an electronic device.

[0054] An electronic device comprises: at least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute a vehicle data parsing method as described in any one of the above embodiments.

[0055] The embodiment of the present invention also discloses a readable storage medium.

[0056] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the vehicle data parsing method described in any one of the above embodiments.

[0057] It can be understood that computer-readable storage media may include: any entity or device capable of carrying a computer program, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc. A computer program includes a computer program code. The computer program code may be in source code form, object code form, an executable file, or some intermediate form, etc. A computer-readable storage medium may include: any entity or device capable of carrying a computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc.

[0058] In certain embodiments of the present invention, the electronic device may include a controller or a processor, and the controller is a single-chip microcomputer chip that integrates a processor, a memory, a communication module, etc. The processor may refer to a processor included in the controller. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.

[0059] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0060] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0061] 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 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 vehicle data analysis method, characterized in that: include: Acquiring vehicle raw data and parsing a relational database of the vehicle raw data; Matching rule data for parsing the original vehicle data from the relational database; The vehicle original data is parsed based on the rule data to form parsed signal data.

2. The vehicle data analysis method according to claim 1, characterized in that: The obtaining of vehicle original data comprises: The vehicle’s raw data is transmitted to the cloud through the collection service; The cloud directly collects the original vehicle data through Kafka and connects it to the big data platform; The big data platform stores the original vehicle data before analysis.

3. The vehicle data analysis method according to claim 1, characterized in that: The rule data matched from the relational database to parse the original vehicle data includes: Obtain the version ID corresponding to the original data of the vehicle; Searching for all rule data corresponding to the version ID from the relational database; All the rule data found are organized into a complete mapping structure according to the preset data format.

4. The vehicle data analysis method according to claim 1, characterized in that: The rule data includes the signal name, signal value, signal value type, signal value length, signal position in the original code, and signal ID of each signal on the vehicle side.

5. The vehicle data analysis method according to claim 4, characterized in that: The parsing of the vehicle original data based on the rule data includes: According to the position of the signal in the original code and the length of the signal value, the specific position of the signal in the original data of the vehicle is identified, and the original data corresponding to the signal is extracted; According to the signal value type, the extracted raw data is converted into a type; According to the signal name, signal value and signal ID, structured signal data is generated.

6. The vehicle data analysis method according to claim 5, characterized in that: The parsing of the vehicle original data based on the rule data further includes: Only the signals of interest are analyzed through preset filtering conditions, while other signals are ignored; The filtering condition is determined by the business demand signal.

7. The vehicle data analysis method according to claim 4, characterized in that: The parsing method of parsing the vehicle raw data based on the rule data includes offline parsing and real-time parsing; During offline parsing, the rule data is stored in an entity table for scheduling; During real-time parsing, the rule data is stored in a view table for scheduling.

8. A vehicle data analysis system, characterized in that: include: A vehicle-side data acquisition module, at least used to acquire vehicle raw data and parse a relational database of the vehicle raw data; A rule data matching module, at least used to match the rule data for parsing the original vehicle data from the relational database; The raw data parsing module is at least used to parse the raw vehicle data based on the rule data to form parsed signal data.

9. An electronic device, characterized in that: include: at least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the vehicle data parsing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a computer, the vehicle data analysis method according to any one of claims 1 to 7 is executed.

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