Automobile signal collection method and system based on edge computing

Abstract

The application relates to an automobile signal collection method and system based on edge computing, and the method comprises the following steps: importing a signal source file to the cloud, and generating configuration information according to the signal source file and a preset configuration template; sending the configuration information to a vehicle-side controller by the cloud; determining a message needing to be collected according to the configuration information by the vehicle-side controller, and acquiring corresponding bus messages from a lower layer; performing multi-stage caching and screening on the bus messages according to the configuration information by the vehicle-side controller, and uploading the screened data to the cloud after compression. The application proposes an edge screening strategy based on signal attributes, the configuration information is sent to the vehicle side according to the cloud big data algorithm, the collected data is simplified according to the configuration information, the data is screened and collected by using the edge computing strategy on the local, and then the data is uploaded after compression, so that the storage and uploading amount of useless data is greatly reduced, and cost is reduced and efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle networking technology, specifically to a vehicle signal acquisition method and system based on edge computing. Background Technology

[0002] With the development of intelligent and connected vehicles, the complexity of vehicles is increasing rapidly, and the amount of data in the vehicle network is also increasing significantly. Therefore, the amount of data collected for remote monitoring and fault data logs will also increase significantly, leading to an increase in system costs and server-side data storage costs.

[0003] In related technologies, traditional recording methods rely on a rudimentary blind recording strategy. This strategy results in a massive amount of recorded automotive bus message data; a single CAN bus can generate 5GB of data per day. Local storage and data upload costs are extremely high, and most vehicle configurations cannot meet this demand, necessitating selective recording. However, selective recording cannot guarantee that necessary signals will not be lost, often resulting in the absence of one or two required signals. This significantly complicates backend fault analysis and leads to a lack of depth in big data analysis. Clearly, this method is inefficient, costly, and ineffective. Intelligent vehicles will handle even larger volumes of bus data transmission, further amplifying the problems in data storage and upload. Summary of the Invention

[0004] To at least partially overcome the problems existing in related technologies, this application provides a vehicle signal acquisition method and system based on edge computing.

[0005] According to a first aspect of the embodiments of this application, a vehicle signal acquisition method based on edge computing is provided, comprising:

[0006] Import signal source files from the cloud and generate configuration information based on the signal source files and preset configuration templates;

[0007] The cloud sends the configuration information to the vehicle controller;

[0008] The vehicle-side controller determines the messages to be collected based on the configuration information and obtains the corresponding bus messages from the lower layer.

[0009] The vehicle-side controller performs multi-level caching and filtering of the bus messages based on the configuration information, and then uploads the filtered data to the cloud after compression.

[0010] Furthermore, the signal source file includes at least one of the following: bus data and diagnostic data;

[0011] The step of generating configuration information based on the signal source file and configuration template includes:

[0012] The imported signal source file is parsed to obtain basic information; the basic information includes at least one of the following: the signal definition of the vehicle bus and the real-time data parameters of the diagnostics.

[0013] Input the basic information into the configuration template, and the output will be the set of signals to be collected;

[0014] Configure the signal set to generate configuration information.

[0015] Further, the configuration based on the signal set includes:

[0016] Based on the attributes of each signal in the signal set, they are classified into two categories: switching signals and numerical signals.

[0017] Switch signals are registered according to events, and numerical signals are configured with maximum, minimum, and typical values.

[0018] Configure the triggering method and the acquisition time period before and after each signal;

[0019] The configuration generation tool generates the data mirror structure;

[0020] Configure the upload cycle to control the frequency of data collection.

[0021] Furthermore, the method also includes:

[0022] The cloud decompresses and parses the uploaded data packets, analyzes the data, and determines the cause of the fault.

[0023] The acquired batch of vehicle data and fault events are matched.

[0024] By analyzing the correlation between data anomalies and faults using algorithms, potential fault data feature points are extracted and added to the parameter configuration to generate a configuration template.

[0025] Furthermore, the method also includes:

[0026] After configuration, perform data validation and delete duplicates and invalid configurations;

[0027] After verification is complete, a configuration structure file and a corresponding unique ID will be generated;

[0028] The configuration structure file is sent to the designated vehicle controller based on the VIN of the specified vehicle.

[0029] Furthermore, obtaining the corresponding bus message from the lower layer includes:

[0030] Obtain the required messages from lower layers via API; and / or,

[0031] Send a request to the diagnostic service to obtain a response, and extract the valid data from the response;

[0032] The messages that need to be collected are obtained by querying configuration information.

[0033] Furthermore, the vehicle-side controller performs multi-level caching and filtering of the bus messages based on the configuration information, including:

[0034] The latest bus message obtained is stored in the first-level cache;

[0035] According to the configuration information, the bus messages in the first-level cache are converted into recognizable real signal values ​​and stored in the second-level cache;

[0036] The change difference data is extracted from the signal in the second-level cache, stored in the third-level cache, and saved to flash as a file.

[0037] Furthermore, the method also includes:

[0038] The edge model accesses data in the second-level cache, performs timeline-based event discrimination according to the configured logic, and executes corresponding operations when the conditions are met.

[0039] The edge model is an algorithm model pre-stored in the vehicle controller; the corresponding operations include: triggering data upload, and / or executing a troubleshooting process.

[0040] Furthermore, the method also includes:

[0041] Based on signal values ​​and fault codes, a judgment is made according to preset logic;

[0042] When the triggering condition is met, continue collecting data until the specified time, compress and save the data, and upload event information and logs. If the logs fail to upload successfully, resend the data after a certain period of time.

[0043] According to a second aspect of the embodiments of this application, an edge computing-based vehicle signal acquisition system is provided, comprising: a cloud-based controller and a vehicle-side controller;

[0044] The cloud is used to import signal source files, generate configuration information based on the signal source files and preset configuration templates, and then send the configuration information to the vehicle-side controller.

[0045] The vehicle-side controller is used to determine the messages to be collected based on the configuration information and obtain the corresponding bus messages from the lower layer; it is also used to perform multi-level caching and filtering of the bus messages based on the configuration information, and to compress the filtered data and upload it to the cloud.

[0046] The technical solutions provided by the embodiments of this application have the following beneficial effects:

[0047] This invention proposes a strategy for edge filtering based on signal attributes. The cloud-based big data algorithm is used to send configuration information to the vehicle. The vehicle then simplifies the collected data based on the configuration information, refining the data into those that are changing and those that are not, and those that are normal and those that are not. By focusing on these two dimensions, the edge computing strategy is used locally to filter and aggregate the data before compressing and uploading it. This significantly reduces the amount of useless data storage and uploading, thereby reducing costs and improving efficiency.

[0048] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0050] Figure 1 This is a schematic diagram illustrating an application environment for a vehicle signal acquisition method based on configurable lightweight edge computing, according to an exemplary embodiment.

[0051] Figure 2 This is a flowchart illustrating an edge computing-based vehicle signal acquisition method according to an exemplary embodiment.

[0052] Figure 3 It is an execution flow for automotive signal acquisition based on configurable lightweight edge computing. Detailed Implementation

[0053] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0054] The edge computing-based vehicle signal acquisition method provided in this application can be applied to, for example... Figure 1 The application environment shown includes both cloud and local (vehicle-side controller).

[0055] Figure 2 This is a flowchart illustrating an edge computing-based vehicle signal acquisition method according to an exemplary embodiment. The method may include the following steps:

[0056] Step S1: Import the signal source file from the cloud and generate configuration information based on the signal source file and the preset configuration template;

[0057] Step S2: The cloud sends the configuration information to the vehicle controller;

[0058] Step S3: The vehicle-side controller determines the messages to be collected based on the configuration information and obtains the corresponding bus messages from the lower layer.

[0059] Step S4: The vehicle controller performs multi-level caching and filtering of the bus messages according to the configuration information, and uploads the filtered data to the cloud after compression.

[0060] This invention proposes a strategy for edge filtering based on signal attributes. The cloud-based big data algorithm is used to send configuration information to the vehicle. The vehicle then simplifies the collected data based on the configuration information, refining the data into those that are changing and those that are not, and those that are normal and those that are not. By focusing on these two dimensions, the edge computing strategy is used locally to filter and aggregate the data before compressing and uploading it. This significantly reduces the amount of useless data storage and uploading, thereby reducing costs and improving efficiency.

[0061] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0063] like Figure 1 As shown in the figure, the main architecture of a configurable, lightweight edge computing-based automotive signal acquisition system is illustrated. This architecture mainly consists of two parts: a cloud-based system and a local system (in-vehicle controller).

[0064] The main tasks in the cloud consist of two parts: configuration and editing, and data visualization and model training.

[0065] In some embodiments, the signal source file includes at least one of the following: bus data and diagnostic data. Step S1, generating configuration information based on the signal source file and configuration template, includes the following steps: parsing the imported signal source file to obtain basic information; the basic information includes at least one of the following: signal definitions of the automotive bus and real-time diagnostic data parameters; inputting the basic information into the configuration template and outputting the set of signals to be collected; configuring according to the signal set to generate configuration information.

[0066] In a more specific embodiment, the configuration based on the signal set includes the following steps: classifying each signal in the signal set according to its attributes into two categories: switch signals and numerical signals; registering switch signals according to events, and configuring maximum, minimum, and typical values ​​for numerical signals; setting the triggering method and the collection time period before and after triggering for each signal; generating a data mirror structure using a configuration generation tool; and configuring the upload cycle to control the frequency of collection.

[0067] The main tasks of configuration and model editing are parsing the input signal source files, such as the signal definitions of an automotive bus and real-time diagnostic data parameters, and then inputting them uniformly into the configuration interface. The system then outputs the set of signals to be collected, either manually or through the model. Once the signal set is acquired, it is categorized according to signal attributes into two types: switching signals and numerical signals. Switching signals are registered according to events, while numerical signals require configuration of maximum, minimum, and typical values. Based on these signals, the logical settings and combinations of trigger conditions are defined. Then, the configuration generation tool generates a data mirror structure, which will directly map a memory block (signal pool) in memory. The upload period is configured to control the frequency of data acquisition.

[0068] The model editor provides a visual representation of the model calculation formulas, which can be used to set the calculation logic for combined conditions.

[0069] The data display and model training section mainly involves the parsing and processing of results. This includes decompressing and parsing the uploaded data packets, restoring the data frames, and displaying them on the UI for data playback and analysis to determine the cause of the fault.

[0070] Model training involves matching data from a batch of vehicles with fault events, analyzing the correlation between data anomalies and faults through algorithms, extracting data feature points of potential faults, adding them to the parameter configuration, and storing them in the form of cases as templates for subsequent distribution.

[0071] The vehicle-side component is the execution end. Its main task is to run according to the configuration issued by the cloud, extracting and refining information layer by layer to obtain the information that meets the collection requirements, and then compressing and uploading the data. The goal is to perfectly reproduce the useful data in the cloud for display and big data analysis.

[0072] In some embodiments, the method further includes: decompressing and parsing the uploaded data packets in the cloud, analyzing the data, and determining the cause of the fault; matching the acquired batch of vehicle data with fault events; analyzing the correlation between data anomalies and faults through algorithms, extracting data feature points of potential faults, adding them to the parameter configuration, and generating a configuration template.

[0073] In some embodiments, the method further includes: performing data verification after configuration, deleting duplicates and invalid configurations; generating a configuration structure file and a corresponding unique ID after verification; and sending the configuration structure file to the designated vehicle controller according to the VIN of the specified vehicle.

[0074] In some embodiments, obtaining the corresponding bus messages from the lower layer includes: obtaining the messages to be collected from the lower layer via an API; and / or, sending a request via a diagnostic service to obtain a response and extracting valid data from the response; wherein the messages to be collected are obtained by querying configuration information.

[0075] In some embodiments, the vehicle-side controller performs multi-level caching and filtering of the bus messages according to the configuration information, including: storing the latest bus messages obtained into a first-level cache; converting the bus messages in the first-level cache into recognizable real signal values ​​according to the configuration information and storing them in a second-level cache; extracting change difference data from the signals in the second-level cache, storing the change difference data in a third-level cache, and saving it to flash as a file.

[0076] In some embodiments, the method further includes: the edge model accesses data in the secondary cache, performs timeline-based event discrimination according to configured logic, and executes corresponding operations when conditions are met; wherein the edge model is an algorithm model pre-stored in the vehicle controller; the corresponding operations include: triggering data upload, and / or executing a troubleshooting process.

[0077] In some embodiments, the method further includes: judging based on signal values ​​and fault codes according to preset logic; when the triggering condition is met, continuing to collect data for a specified time, compressing and saving the data, and uploading event information and logs at the same time; if the logs are not successfully uploaded, retransmitting them after a period of time.

[0078] The vehicle-side module comprises seven modules, which are described below:

[0079] (1) The task management module is the main control module on the vehicle side. It is the main manager of this SDK (Software Development Kit). Its main responsibilities are to connect to the cloud to obtain configuration and local initialization, schedule and monitor the operation of SDK package sub-modules, collect and upload data, and complete the self-upgrade work.

[0080] (2) The acquisition configuration is a configuration issued by the cloud. This configuration includes the set of signals to be acquired, the triggering method, the acquisition time period before and after the trigger, and defines the signal attributes and the MAP rules of the signal. This is corresponding to the cloud configuration. The model algorithm is also stored in the form of a script array.

[0081] (3) The signal pool is a module used to buffer signals. The signal pool is stored in three levels. The first level buffer is used to store the latest received bus message, the second level buffer is used to extract the signal in the message, and the third level buffer is used to store the signal changes and save them to flash as a file. It is stored once every 5 seconds.

[0082] (4) Bus acquisition and diagnostic acquisition is an interface layer. It obtains the required messages from the lower layer through API (Application Programming Interface) or sends a request through the diagnostic service to obtain the response and extract the valid data in the response. The message ID to be acquired is obtained through configuration query.

[0083] (5) Signal extraction is to delete and parse the useless information in the message according to the configuration, and convert it into a recognizable real signal value, which is then stored in signal pool 2.

[0084] (6) The edge model accesses the data in signal pool 2 and performs event discrimination according to the timeline based on the configured logic (logic conditions loaded from the cloud). When the conditions are met, it performs the corresponding operation, such as triggering data upload or executing a troubleshooting process, and then performs further troubleshooting data collection.

[0085] (7) Trigger monitoring is based on the logic of signal value and fault code. When the trigger condition is met, log data is saved locally and the upper layer is notified that an event has been triggered. This step involves sending an event message first.

[0086] (8) Data upload is based on the data in signal pool 3, which is stored and compressed, and then the compressed data is uploaded to the cloud.

[0087] like Figure 3 As shown, the entire process is a closed loop.

[0088] First, when data collection is required, bus data and diagnostic data need to be imported. Bus information can typically be directly imported using a DBC file, which contains message and signal parameters. Diagnostic data can be obtained using Excel or ODX files. After importing, parameters can be selected and configured using existing templates, or manually added data to be collected and the collection frequency can be configured. Trigger conditions and the duration of pre- and post-collection data can then be configured. Trigger conditions can be configured using signal values ​​or fault codes, supporting combinations of AND, OR, and NOT conditions. After module configuration, data validation is performed to remove duplicates and invalid configurations (such as overlapping signal positions or incorrect lengths, primarily addressing manual input errors). After validation, a configuration structure file and a unique configuration ID are generated, used for task matching with subsequently uploaded LOG data. Once configuration is complete, the required vehicle's VIN (Vehicle Identification Number) can be selected and sent to the vehicle-side controller.

[0089] After receiving the configuration task, the controller performs configuration verification to check if the configuration is complete and matches the vehicle model. Then, it begins the initialization task and starts data reception. During vehicle operation, the vehicle-side controller transmits the necessary messages to the SDK via API. The SDK caches the messages in cache pool 1, and then the signal processing module extracts and parses the signals into signal pool 2, thus reducing the amount of data. It's worth noting that in signal pool 2, the triggering module simultaneously reads the signals to determine if the triggering conditions are met. If they are, it continues to collect data for a specified time, compresses and saves the data, and uploads event information and logs. If the log fails to upload (e.g., due to server busyness or weak 4G signal), it retransmits the data after a certain interval.

[0090] While triggering the judgment, the edge model module is also performing calculations and identifications. When the model conditions are met, it can also trigger the data upload and correlation process to perform in-depth troubleshooting and detection, thereby obtaining more detailed and richer fault scene data.

[0091] After the data is uploaded, the bus DBC and diagnostic configuration data are obtained based on the configuration ID. Data playback is then performed. The playback interface is user-friendly and restores the scene according to the timeline, helping to solve problems. It can also identify the characteristics of the running data when the fault occurred, providing case templates for detecting similar problems in the future.

[0092] By linking uploaded data, fault codes, warning lights, and repair cases to perform big data analysis on a large number of vehicles, we can identify data characteristics of impending faults and iterate on fault monitoring and data collection. This allows us to predict the probability of potential faults before they occur through data analysis and remind vehicle owners to bring their vehicles in for repairs.

[0093] The core of these operations is business. The platform provided by this invention can realize various flexible and intelligent monitoring methods while reducing operating costs.

[0094] In summary, this invention represents a crucial component of proactive automotive after-sales service, addressing the current pain points faced by OEMs regarding data acquisition difficulties, excessive redundant data, and significant cost waste. The algorithm and strategies employed in this invention can substantially reduce the amount of stored and uploaded data by over 80% while still meeting after-sales maintenance requirements, thus saving costs and increasing efficiency for OEMs. It also significantly reduces server-side configuration requirements and procurement costs, resulting in a lightweight and practical system.

[0095] Embodiments of this application also provide an edge computing-based vehicle signal acquisition system, which includes: a cloud-based controller and a vehicle-side controller;

[0096] The cloud is used to import signal source files, generate configuration information based on the signal source files and preset configuration templates, and then send the configuration information to the vehicle-side controller.

[0097] The vehicle-side controller is used to determine the messages to be collected based on the configuration information and obtain the corresponding bus messages from the lower layer; it is also used to perform multi-level caching and filtering of the bus messages based on the configuration information, and to compress the filtered data and upload it to the cloud.

[0098] Regarding the system in the above embodiments, the specific steps for each module to perform operations have been described in detail in the embodiments related to the method, and will not be elaborated further here. Each module in the above-described vehicle signal acquisition system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0099] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0100] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means at least two.

[0101] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0102] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0103] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0104] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0105] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0106] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0107] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An edge-computing-based automobile signal acquisition method, characterized in that, The method comprises the following steps: The cloud imports a signal source file and generates configuration information according to the signal source file and a preset configuration template; The cloud sends the configuration information to a vehicle-side controller; The vehicle-side controller determines messages that need to be collected according to the configuration information and obtains corresponding bus messages from a lower layer; The vehicle-side controller performs multi-level caching and screening on the bus messages according to the configuration information and uploads the screened data to the cloud after compression; The vehicle-side controller performs multi-level caching and screening on the bus messages according to the configuration information, which comprises the following steps: The latest bus messages obtained are stored in a first-level cache; The bus messages in the first-level cache are converted into identifiable real signal values according to the configuration information and are stored in a second-level cache; Difference data of changes are extracted according to the signals in the second-level cache, the difference data of changes are stored in a third-level cache, and are saved in a flash memory in the form of a file; An edge model accesses the data in the second-level cache, performs event discrimination according to a timeline according to a configured logic, and performs corresponding operations when a condition is met. The edge model is a pre-stored algorithm model of the vehicle-side controller, and the corresponding operations include triggering data uploading and / or executing a certain troubleshooting process.

2. The method of claim 1, wherein, The signal source file comprises at least one of the following: bus data, diagnostic data; The configuration information is generated according to the signal source file and the configuration template, which comprises the following steps: The imported signal source file is parsed to obtain basic information, and the basic information comprises at least one of the following: signal definition of an automobile bus, real-time data parameters of diagnosis; The basic information is input into the configuration template to output a signal set that needs to be collected; The signal set is configured to generate configuration information.

3. The method of claim 2, wherein, The signal set is configured, which comprises the following steps: Each signal in the signal set is classified according to its attributes into two categories: switch signals and numerical value signals; Switch signals are registered according to events, and numerical value signals are configured with maximum values, minimum values and typical values; The triggering mode of each signal and the collection time period before and after triggering are set; A data mirror structure is generated by a configuration generation tool; An upload period is configured to control the frequency of collection.

4. The method of claim 1, wherein, The method further comprises the following steps: The cloud decompresses and parses the uploaded data packet, analyzes the data, and judges the fault cause; Batch vehicle data and fault events are matched; Data features of potential faults are extracted by analyzing the correlation between data anomalies and faults through an algorithm, and are added to the parameter configuration to generate a configuration template.

5. The method of claim 4, wherein, The method further comprises the following steps: Data verification is performed after configuration, and repeated and illegal configurations are deleted; A configuration structure file and a corresponding unique ID are generated after verification is completed; The configuration structure file is distributed to a specified vehicle-side controller according to the VIN of the specified vehicle.

6. The method according to any one of claims 1 to 5, characterized in that, The bus messages from the lower layer are obtained, which comprises the following steps: The messages that need to be collected are obtained from the lower layer through an API; and / or A request is sent to the lower layer through a diagnostic service to obtain a response and extract valid data in the response; The messages that need to be collected are obtained by querying the configuration information.

7. The method of claim 6, wherein, The method further comprises the following steps: Signal values and fault codes are discriminated according to a preset logic. When the trigger condition is met, continue to collect to the specified time, and save the data compression, at the same time upload event information and log, if the log is not successfully uploaded, then resend after a period of time.

8. An edge computing based automobile signal acquisition system, characterized in that, It comprises: a cloud controller and a vehicle controller; the cloud controller is configured to import a signal source file, generate configuration information according to the signal source file and a preset configuration template, and send the configuration information to the vehicle controller; the vehicle controller is configured to determine a message to be collected according to the configuration information, and acquire a corresponding bus message from a lower layer; the vehicle controller is further configured to perform multi-level caching and screening on the bus message according to the configuration information, and upload the screened data to the cloud controller after compression; the vehicle controller performs multi-level caching and screening on the bus message according to the configuration information, specifically including: storing the latest acquired bus message in a first-level cache; converting the bus message in the first-level cache into identifiable real signal values according to the configuration information, and storing the real signal values in a second-level cache; extracting change difference data according to the signals in the second-level cache, storing the change difference data in a third-level cache, and storing the change difference data in a flash in a file form; an edge model accesses the data in the second-level cache, performs event judgment according to a timeline according to a preset logic, and executes a corresponding operation when a condition is met; the edge model is an algorithm model pre-stored in the vehicle controller; the corresponding operation includes triggering data uploading and / or executing a certain troubleshooting process.

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