A vehicle operation data collection method, system, computer device and medium

Abstract

The application provides a vehicle operation data collection method and system, computer equipment and medium, and belongs to the field of vehicle data processing. The method comprises the following steps: obtaining vehicle model information and user demand of a vehicle to be collected; determining data analysis service project content options based on a data analysis service project content information table and vehicle model information corresponding information table, and screening data analysis service project content according to the user demand of the vehicle to be collected; generating a natural language text for the data analysis service project content to obtain a vehicle data collection strategy; collecting environmental data and judging based on data collection conditions; when the environmental data falls within the range of the data collection conditions, collecting operation data of the vehicle to be collected according to the data collection project, and obtaining operation data of the vehicle to be collected. The method can dynamically collect data based on the actual operation state, driving environment and user demand of the vehicle, and ensure the effectiveness of operation data collection.

Description

Technical Field

[0001] This invention belongs to the field of vehicle data processing, and specifically relates to a method, system, computer equipment, and medium for collecting vehicle operation data. Background Technology

[0002] In the field of vehicle operation data extraction, traditional data collection methods mainly rely on preset fixed rules and manually configured parameters. Specifically, technicians pre-set data collection items such as speed, acceleration, engine speed, and fuel consumption, as well as corresponding time intervals and collection conditions such as specific driving scenario triggers, for various sensors and data acquisition devices on the vehicle, based on experience or specific needs. During vehicle operation, the data acquisition devices strictly record data according to these preset rules and store the collected data in local storage or transmit it to a remote server via network for subsequent analysis.

[0003] In recent years, with the widespread application of artificial intelligence and machine learning technologies in various fields, some vehicle data extraction systems have begun to introduce simple automation techniques to assist data collection. For example, rule-based expert systems are used to identify some common driving scenarios and dynamically adjust data collection items and conditions based on the identification results. However, these methods still have significant limitations because rule formulation relies on expert knowledge, making it difficult to fully cover the complex and ever-changing real-world driving environment. When vehicles enter special driving scenarios such as complex road conditions in mountainous areas or severe weather, the preset data collection items and conditions may not accurately reflect the vehicle's operating characteristics in that scenario, resulting in low data validity. Summary of the Invention

[0004] To address the problem that existing rules, which rely on expert knowledge, cannot accurately reflect the operational characteristics of vehicles in the given scenario, and that the collected data has low validity, this invention provides a vehicle operation data acquisition method, system, computer equipment, and medium.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for collecting vehicle operation data, comprising:

[0007] Obtain vehicle model information and user requirements for the vehicles to be collected;

[0008] Based on the pre-set data analysis service item content and vehicle model information correspondence table, determine the data analysis service item content options for the vehicle to be collected, and select the data analysis service item content from the data analysis service item content options according to the user needs of the vehicle to be collected.

[0009] Natural language text is generated from the content of the data analysis service project to obtain the vehicle data collection strategy for the vehicle to be collected. The collection strategy includes data collection conditions and corresponding data collection items.

[0010] The system collects environmental data of the vehicle to be collected and makes judgments based on the data collection conditions. When the environmental data falls within the range of the data collection conditions, the system collects the operating data of the vehicle to be collected according to the data collection items, and obtains the operating data of the vehicle to be collected.

[0011] Optionally, the vehicle data acquisition strategy also includes the priority of the data acquisition items. The vehicle operation data acquisition method provided by this invention further includes:

[0012] The data analysis service project content is input into a pre-trained large language model to obtain the data collection projects and their corresponding priorities.

[0013] Based on the environmental conditions and network load of the vehicle to be collected, data collection conditions are generated for the corresponding data collection items. Based on the data collection items, their priorities, and the data collection conditions, the vehicle data collection strategy is determined.

[0014] Optionally, the vehicle operation data acquisition method provided by the present invention further includes:

[0015] The signal category, frame identifier, signal location, and signal format of the data collection items are determined based on the vehicle model information of the vehicle to be collected;

[0016] A location information index table is constructed based on signal type, frame identifier, signal location, and signal format. The location information index table is used to locate vehicle operation data from signal frames in the vehicular network and associate it with the corresponding project name.

[0017] Optionally, the vehicle operation data acquisition method provided by the present invention further includes:

[0018] Assign acquisition tokens to data acquisition projects, whereby the acquisition tokens include the project name and the signal location index determined based on the location information index table;

[0019] The signal frame is decoded according to the signal location index to obtain the vehicle data corresponding to the project name, and a timestamp is added to obtain the running data of the vehicle to be collected. The signal frame is obtained through the vehicle network.

[0020] Optionally, the data analysis service includes a driving range estimation project. The environmental conditions of the vehicle to be collected include ambient temperature, and the collection items include battery operating status collection related to ambient temperature. The vehicle operation data collection method provided by this invention also includes:

[0021] When the temperature of the vehicle to be collected is greater than the temperature threshold corresponding to the data collection conditions, the battery operating status of the vehicle to be collected is collected to obtain the driving range operating data of the vehicle to be collected.

[0022] Optionally, the data analysis service item includes vehicle maintenance items, and the environmental conditions of the vehicle to be collected include maintenance time. The data collection strategy for the vehicle to be collected also includes collection frequency. The vehicle operation data collection method provided by this invention further includes:

[0023] When the maintenance time of the vehicle to be collected exceeds the maintenance time threshold corresponding to the data collection conditions, vehicle data is collected from the vehicle to be collected according to the collection frequency, where the collection frequency is negatively correlated with the maintenance time.

[0024] Optionally, the vehicle operation data acquisition method provided by the present invention further includes:

[0025] Obtain user demand information and user vehicle model information;

[0026] Determine the data that can be collected based on the user's vehicle model information;

[0027] The content of data analysis services is determined based on user needs and available data collection items.

[0028] A table mapping data analysis service content to vehicle model information is constructed based on the content of the data analysis service and the corresponding user vehicle model information.

[0029] The present invention also provides a vehicle operation data acquisition system, comprising:

[0030] The requirement acquisition module is used to acquire vehicle model information and user requirements for the vehicles to be collected.

[0031] The project determination module is used to determine the data analysis service content options for the vehicle to be collected based on the pre-set data analysis service content and vehicle model information correspondence table, and to select the data analysis service content from the data analysis service content options according to the user needs of the vehicle to be collected.

[0032] The data collection strategy generation module is used to generate natural language text from the content of the data analysis service project to obtain the vehicle data collection strategy for the vehicle to be collected. The data collection strategy includes data collection conditions and corresponding data collection items.

[0033] The data collection and execution module is used to collect environmental data of the vehicle to be collected and make judgments based on the data collection conditions. When the environmental data falls within the range of the data collection conditions, the module collects the operating data of the vehicle to be collected according to the data collection items, and obtains the operating data of the vehicle to be collected.

[0034] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in a vehicle operation data acquisition method.

[0035] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute any step of a vehicle operation data acquisition method.

[0036] The vehicle operation data acquisition method provided by this invention has the following beneficial effects:

[0037] Because the vehicle operation data acquisition method provided by this invention can make judgments based on real-time detected driving environment, and trigger corresponding operation data acquisition actions when the corresponding data acquisition conditions are met, it can change the operation data acquisition content in the face of special driving scenarios, realize dynamic data acquisition based on the actual operating status of the vehicle, driving environment and user needs, ensure the effectiveness of operation data acquisition, and provide data support for subsequent vehicle performance analysis, fault diagnosis and other tasks.

[0038] Furthermore, the vehicle operation data collection method provided by this invention generates the collection strategy through natural language text, which can fully take into account the differences in data distribution of different vehicle models and the data collection needs of different users, realize intelligent collection of vehicle data, reduce data collection costs, and ensure the relevance of the collected data. Attached Figure Description

[0039] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is one of the schematic diagrams of a vehicle operation data acquisition method provided in an embodiment of the present invention;

[0041] Figure 2 This is a second schematic diagram of a vehicle operation data acquisition method provided in an embodiment of the present invention;

[0042] Figure 3 This is a third schematic diagram of a vehicle operation data acquisition method provided in an embodiment of the present invention;

[0043] Figure 4 This is a fourth schematic diagram of a vehicle operation data acquisition method provided in an embodiment of the present invention;

[0044] Figure 5This is an example of a vehicle data acquisition condition generation process provided in an embodiment of the present invention;

[0045] Figure 6 This is one example of the acquisition conditions for generating an LLM model provided in an embodiment of the present invention;

[0046] Figure 7 This is the second example of LLM model generation acquisition conditions provided in the embodiments of the present invention;

[0047] Figure 8 This is an example of a data acquisition rule generation process provided in an embodiment of the present invention;

[0048] Figure 9 This is the fifth schematic diagram of a vehicle operation data acquisition method provided in an embodiment of the present invention;

[0049] Figure 10 Example of vehicle data collection process provided in this embodiment of the invention;

[0050] Figure 11 This is a sixth schematic diagram of a vehicle operation data acquisition method provided in an embodiment of the present invention;

[0051] Figure 12 This is an example of data location index information for a specific vehicle model provided in an embodiment of the present invention;

[0052] Figure 13 This is the seventh schematic diagram of a vehicle operation data acquisition method provided in an embodiment of the present invention;

[0053] Figure 14 This is an example of user-data service item corresponding information provided in an embodiment of the present invention;

[0054] Figure 15 This is an example of vehicle data corresponding information provided in an embodiment of the present invention;

[0055] Figure 16 This is an example of the vehicle model-vehicle data information correspondence provided in an embodiment of the present invention;

[0056] Figure 17 This is an example of a vehicle data collection preparation process provided in an embodiment of the present invention;

[0057] Figure 18 This is an example of an existing vehicle network architecture provided in the embodiments of the present invention;

[0058] Figure 19 This is an example of a vehicle operation data acquisition system provided in an embodiment of the present invention. Detailed Implementation

[0059] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0060] Existing methods for extracting vehicle operation data suffer from several drawbacks. Firstly, they lack flexibility and adaptability in setting data collection items and conditions. Secondly, the development of data collection rules relies heavily on expert knowledge, leading to high costs and low efficiency. Specifically, traditional techniques require domain experts to develop data collection rules. Experts need a deep understanding of various vehicle systems, sensors, and different driving scenarios to design appropriate data collection items and conditions. However, this manual rule-making approach is not only time-consuming and labor-intensive, but also prone to blind spots or redundancy due to limitations in expert knowledge. Furthermore, the continuous development of vehicle technology and the emergence of new driving scenarios necessitate constant rule updates and adjustments, further increasing the complexity and cost of rule development. Moreover, existing vehicle operation data extraction methods have limited data processing and analysis capabilities, making it difficult to uncover the potential value of the data. Traditional techniques primarily focus on simple statistical calculations and visualizations of vehicle operation data, lacking the ability to mine deeper information and patterns behind the data. For example, they cannot automatically identify potential fault modes, driving behavior characteristics, or vehicle performance optimization points from massive amounts of vehicle data. While some machine learning algorithms are applied to vehicle data analysis, these algorithms typically require large amounts of labeled data for training, and the models often exhibit poor versatility and adaptability. In practical applications, due to the significant differences in data characteristics among different types of vehicles and the complexity of driving environments, well-trained models often struggle to achieve good results in new scenarios, failing to fully realize the potential value of vehicle operation data. Furthermore, existing vehicle operation data extraction methods lack intelligent guidance, resulting in a highly arbitrary data collection process. In traditional vehicle data extraction, technicians often lack effective intelligent guidance tools and cannot dynamically adjust data collection strategies based on real-time data and vehicle status. This leads to a degree of randomness in the data collection process, potentially collecting large amounts of useless or duplicate data, increasing the burden of data storage and processing, and possibly overlooking crucial data information, affecting subsequent accurate analysis and judgment of vehicle operation. In summary, existing vehicle operation data extraction technologies suffer from numerous technical problems in areas such as data collection project and condition settings, rule formulation, data processing and analysis capabilities, and intelligent guidance, making it difficult to meet the demands of modern vehicle technology development and practical applications for efficient, accurate, and intelligent data extraction.

[0061] To address these needs, Amazon Web Services (AWS) offers a vehicle data collection, management, and edge computing service called AWS IoT Fleetwise. Designed specifically for automakers and fleet operators, it simplifies the processes of collecting, processing, and analyzing vehicle data, helping to build smart car applications and optimize fleet management. Through AWS IoT Fleetwise's intelligent data collection control capabilities, automakers can precisely select the data needed for their use cases and limit the amount of data transmitted to the cloud by creating conditional rules, such as transmitting only sensor data related to emergency braking for a specific vehicle model. After uploading data to the cloud using AWS IoT Fleetwise, automakers can perform in-depth data analysis and machine learning services to extract value from vehicle data. For example, once the temperature drops below zero, automakers can collect data from electric vehicle batteries, perform data analysis and simulations in the cloud, and improve battery performance in cold weather. Simply put, AWS IoT Fleetwise supports conditional and precise collection of vehicle data, meaning that vehicle collection events are triggered when the vehicle's operating environment or state meets pre-defined data collection conditions. However, these data collection trigger conditions are often fixed in advance and cannot be dynamically adjusted in real time according to the vehicle's actual operating status, driving environment, and user needs. Moreover, the rules for data collection rely on expert knowledge, making it difficult to comprehensively cover the complex and ever-changing real-world driving environment. For example, AWS IoT Fleetwise supports automakers collecting data from electric vehicle batteries once the temperature drops below zero. However, vehicles should also consider collecting data from other vehicles, such as air conditioning operation, in-vehicle multimedia operation, and road traffic conditions, which may collectively affect vehicle energy consumption. Furthermore, the in-vehicle network architecture and sensor configurations of different vehicle models or even different configurations of the same model generally vary, resulting in different types of data that can be collected. This may further amplify the inflexibility and adaptability of pre-defined vehicle data collection conditions that rely on expert knowledge. Therefore, designing a method that can intelligently guide the flexible and accurate collection of vehicle data is essential.

[0062] To address the aforementioned needs, the vehicle operation data acquisition solution provided by this invention utilizes a Large Language Model (LLM) to assist in adjusting vehicle data acquisition conditions. This fully considers the differences in data distribution among different vehicle models and the data acquisition needs of different users, dynamically adjusting the vehicle data acquisition conditions to achieve low-cost and intelligent vehicle data acquisition, thus providing a solution for flexible and low-cost vehicle operation data collection.

[0063] Example 1

[0064] This invention provides a method for collecting vehicle operation data, specifically as follows: Figure 1 As shown, it includes the following steps:

[0065] Step 11: Obtain the vehicle model information and user requirements for the vehicle to be collected.

[0066] Step 12: Based on the pre-set data analysis service item content and vehicle model information correspondence table, determine the data analysis service item content options for the vehicle to be collected, and select the data analysis service item content from the data analysis service item content options according to the user needs of the vehicle to be collected.

[0067] Step 13: Generate natural language text from the data analysis service project content to obtain the vehicle data collection strategy for the vehicle to be collected. The collection strategy includes data collection conditions and corresponding data collection items.

[0068] Step 14: Collect environmental data of the vehicle to be collected and make a judgment based on the data collection conditions. When the environmental data falls within the range of the data collection conditions, collect the operating data of the vehicle to be collected according to the data collection items to obtain the operating data of the vehicle to be collected.

[0069] Specifically, the present invention provides a method for collecting vehicle operation data. First, the vehicle model information and user needs of the vehicle to be collected are determined. Then, the data analysis service items that may be needed are determined from the vehicle model information through a pre-set data analysis service item content and vehicle model information correspondence table. Finally, the data analysis service items that the user wants are determined in combination with the user needs as the final data analysis service items for the vehicle to be collected.

[0070] Next, the final data analysis service items of the vehicles to be collected are input into the pre-trained LLM model to generate natural language text, so as to obtain the collection conditions and corresponding collection items of the vehicles to be collected, and to construct the collection strategy of the vehicles to be collected.

[0071] Finally, based on the data collection strategy of the vehicle to be collected, data is collected from the vehicle to be collected, and the environmental conditions around the vehicle to be collected are judged in real time. When the environmental conditions meet the collection conditions generated based on the LLM model, the data collection action is performed on the vehicle to be collected according to the new data collection items, collection frequency and other information corresponding to the collection conditions, so as to obtain the operating data of the vehicle to be collected for subsequent analysis and service.

[0072] Because the vehicle operation data acquisition method provided by this invention can make judgments based on real-time detected driving environment, and trigger corresponding operation data acquisition actions when the corresponding data acquisition conditions are met, it can change the operation data acquisition content in the face of special driving scenarios, realize dynamic data acquisition based on the actual operating status of the vehicle, driving environment and user needs, ensure the effectiveness of operation data acquisition, and provide data support for subsequent vehicle performance analysis, fault diagnosis and other tasks.

[0073] Furthermore, the vehicle operation data collection method provided by this invention generates the collection strategy through natural language text, which can fully take into account the differences in data distribution of different vehicle models and the data collection needs of different users, realize intelligent collection of vehicle data, reduce data collection costs, and ensure the relevance of the collected data.

[0074] Based on the above implementation methods, the vehicle data acquisition strategy also includes the priority of data acquisition items, such as... Figure 2 As shown, in the vehicle operation data acquisition method provided by the present invention, step 13 includes:

[0075] Step 131: Input the data analysis service project content into the pre-trained large language model to obtain the data collection projects and their corresponding priorities.

[0076] Step 132: Generate data collection conditions corresponding to the data collection items based on the environmental conditions and network load of the vehicle to be collected. Determine the vehicle data collection strategy based on the data collection items, their priorities, and the data collection conditions.

[0077] And, as Figure 3 As shown, in the vehicle operation data collection method provided by the present invention, the data analysis service item is a driving range estimation item, the environmental conditions of the vehicle to be collected include ambient temperature, the collection item includes battery operating status collection related to ambient temperature, and step 14 includes:

[0078] Step 143: When the temperature of the vehicle to be collected is greater than the temperature threshold corresponding to the data collection condition, the battery operating status of the vehicle to be collected is collected to obtain the driving range operating data of the vehicle to be collected.

[0079] And, as Figure 4 As shown, in the vehicle operation data collection method provided by the present invention, the data analysis service item content is vehicle maintenance item, the environmental conditions of the vehicle to be collected include maintenance time, and the collection strategy of the vehicle to be collected also includes collection frequency. Step 14 includes:

[0080] Step 144: When the maintenance time of the vehicle to be collected is greater than the maintenance time threshold corresponding to the data collection conditions, vehicle data is collected for the vehicle to be collected according to the collection frequency, where the collection frequency is negatively correlated with the maintenance time.

[0081] Specifically, considering that in traditional methods, data collection items and conditions are often pre-set and cannot be dynamically adjusted in real time according to the actual operating status of the vehicle, driving environment, and user needs, the vehicle operation data collection method provided by this invention uses a pre-trained LLM model to assist in generating data collection objects and data collection conditions. The generated data collection objects and data collection conditions are stored in a data collection condition information module, and the collection unit collects data according to the content of the data collection condition information module. Furthermore, the LLM model can also be used to assist in adjusting vehicle data collection conditions, thereby enabling the generation of specific vehicle model signal content and collection conditions after generating data collection items based on the data analysis service item content and vehicle data correspondence information table, thus achieving a flexible and reasonable vehicle data collection process. For example, the vehicle data collection condition generation process is as follows: Figure 5 As shown, firstly, data collection projects are generated based on the data analysis service project content and the vehicle data corresponding information table. Secondly, based on the vehicle model and vehicle data corresponding information, the corresponding collection signal content for specific vehicle models is generated using the LLM model. Finally, the data collection condition prompt text is generated using the LLM model and stored in the data collection condition information.

[0082] For example, such as Figure 6 As shown, when the data analysis service project content is determined to be range estimation service based on user needs, the project content is input into a pre-trained LLM model. The LLM model helps determine the vehicle's operating data to be collected, such as the motor model code, current battery level, and historical battery level-range records. Furthermore, it generates a data collection strategy for incrementally collecting battery health status data and air conditioner status data when the vehicle's ambient temperature is above 30℃ or below 0℃. While the vehicle is in motion, if the ambient temperature is detected to be above 30℃, exceeding the temperature threshold corresponding to the data collection conditions, battery operating status data and air conditioner status data are collected, resulting in new vehicle operating data.

[0083] like Figure 7As shown, when the data analysis service project content is determined to be vehicle maintenance service based on user needs, the project content is input into a pre-trained LLM model. The LLM model assists in determining the vehicle's operating data to be collected, such as mileage data, tire pressure data, and battery health status data, and determines the collection frequency for each. For example, mileage data is collected once every 3 days, tire pressure data once every 3 days, and battery health status data once every 3 days. Furthermore, a collection strategy is generated to adjust the collection frequency when the vehicle maintenance time exceeds 180 days. During the vehicle's operation, if it is detected that the vehicle maintenance time is too long, exceeding 180 days without maintenance, which is greater than the maintenance time threshold corresponding to the data collection conditions, the collection frequency of mileage data, tire pressure data, and battery health status is adjusted to once per hour, resulting in new vehicle operating data.

[0084] The specific data collection strategy process for the vehicles to be collected is as follows: Figure 8 As shown, firstly, vehicle model-to-vehicle data information is input into a pre-trained LLM model to obtain the data items that can be collected for each vehicle model and their suggested priorities. For each vehicle data category i, there is a priority Pi={a1,a2,a3,…,an} for the specific vehicle data within that category. Then, user-to-data service item correspondence information and data service item-to-vehicle data correspondence information are input into the LLM model to obtain the set S={d1,d2,d3,…,dn} of data items that the current user needs to collect and that the current vehicle model can collect. Next, the LLM model generates vehicle data collection rules R based on environmental conditions, vehicle network load status, and data item priorities. Here, R is a set of triplets Tj={Sj,Cj,Pj}, where S is the data collection item, C is the collection trigger condition, P is the data priority, and j is the data service item count.

[0085] Because the vehicle operation data acquisition method provided by this invention can continuously update the operation data acquisition strategy by using an LLM model and combining it with the vehicle's real-time driving environment and other conditions, it ensures that the acquired vehicle operation data can meet the vehicle's current needs, guarantees the availability of the operation data, and provides a basic guarantee for subsequent vehicle analysis and other services.

[0086] Based on the above implementation methods, such as Figure 9 As shown, in the vehicle operation data acquisition method provided by the present invention, step 14 includes:

[0087] Step 141: Assign a data acquisition token to the data acquisition project, wherein the acquisition token includes the project name and the signal location index determined based on the location information index table.

[0088] Step 142: Decode the signal frame according to the signal location index to obtain the vehicle data corresponding to the project name, and add a timestamp to obtain the running data of the vehicle to be collected. The signal frame is obtained through the vehicle network.

[0089] Specifically, in the vehicle operation data acquisition method provided by this invention, such as... Figure 10 As shown, once the data collection strategy for the vehicle to be collected is determined, for example, when the environmental conditions of the vehicle trigger the data collection conditions, vehicle data is retrieved from the specified location based on the data location index information corresponding to the vehicle model, and the collection action begins. The data collection process needs to consider the data collection strategy determined by the LLM model, such as formatted data collection range and condition suggestions. When the environmental conditions of the vehicle to be collected meet the data collection conditions, the data service items are updated, for example, by adding new data collection items or adjusting the collection frequency corresponding to the collection items. A collection token is set for each data service according to the data collection conditions, including information such as the data collection item, collection frequency, and signal location index, and data collection begins. Furthermore, after decoding, the same batch of collected data is synchronized with a timestamp and stored in the corresponding module for subsequent analysis based on timestamp differentiation.

[0090] Since the vehicle operation data acquisition method provided by this invention can also take into account that there are too many signal frames acquired in real time and it is difficult to distinguish them, it can distinguish them by batch through decoding, timestamp synchronization and other methods, which facilitates further analysis.

[0091] Based on the above implementation methods, such as Figure 11 As shown, in a vehicle operation data acquisition method provided by the present invention, before step 14, the method further includes:

[0092] Step 15: Determine the signal category, frame identifier, signal location, and signal format of the data acquisition item based on the vehicle model information to be collected.

[0093] Step 16: Construct a location information index table based on signal type, frame identifier, signal location, and signal format. The location information index table is used to locate vehicle operation data from signal frames in the vehicular network and associate it with the corresponding project name.

[0094] Specifically, in the vehicle operation data collection method provided by this invention, a formula can be developed based on the target vehicle model for data collection, such as... Figure 12The data location index information table shown can include multiple signal categories, frame identifiers (IDs), signal locations, and signal formats. For example, signal 001 is a CAN signal with a frame ID of "[aaa]", located at positions X1 to X2, and a data format of hexadecimal "[Hex]"; signal 002 is a CAN signal with a frame ID of "[bbb]", located at positions X3 to X4, and a data format of hexadecimal "[Hex]"; signal 003 is an Ethernet signal with a frame ID of "[ccc]", located at positions X5 to X6, and a data format of "[Dec]"; signal 004 is an Ethernet signal with a frame ID of "[ddd]", located at positions X7 to X8, and a data format of "[Dec]". Once the data location index information table is generated, the acquisition unit in the vehicle operation data acquisition method system can extract target data from the corresponding signal frame based on these data location index information, perform binary conversion, add a timestamp, and store it in the storage unit of the data acquisition system in the form of a structured JSON file, thereby achieving accurate data acquisition for different vehicle models.

[0095] Based on the above implementation methods, such as Figure 13 As shown, in a vehicle operation data acquisition method provided by the present invention, before step 12, the method further includes:

[0096] Step 17: Obtain user demand information and user vehicle model information.

[0097] Step 18: Determine the data to be collected based on the user's vehicle model information.

[0098] Step 19: Determine the content of the data analysis service project based on user needs and collectable items.

[0099] Step 20: Construct a data analysis service item content and vehicle model information correspondence table based on the data analysis service item content and the corresponding user vehicle model information.

[0100] Specifically, in the vehicle operation data collection method provided by this invention, the information table corresponding to the data analysis service items and vehicle model information can be constructed in the following manner: First, based on the user's customized data service items, data service item information is formulated for the user, and corresponding data service item information is formulated for the data service item needs of different vehicle models. For example... Figure 14As shown, User 1 customized two data services: "Personalized Car Insurance" and "Vehicle Maintenance"; User 2 customized one data service: "Safe Driving Monitoring"; User 3 customized two data services: "Driving Range Estimation" and "Safe Driving Monitoring"; and User 4 customized one data service: "Personalized Car Insurance". The system corresponding to the vehicle operation data collection method assigns corresponding user identity information (ID) to the four users for subsequent differentiation of collection strategies.

[0101] And, as Figure 15 As shown, the system also specifies the vehicle operation data that needs to be collected for each data service item. For example, "Personalized Car Insurance" requires vehicle speed data, braking data, steering data, and mileage data; "Driving Range Estimation" requires battery charge data, battery health status data, and air conditioner status data; "Vehicle Maintenance" requires mileage data, tire pressure data, and battery health status data; and "Safe Driving Monitoring" requires vehicle speed data, braking data, and steering data.

[0102] Finally, as Figure 16As shown, a data content information table is created for the target vehicle models for data collection, resulting in a data analysis service item content and corresponding vehicle model information table. For example, for vehicle model 1, the unique vehicle identifier (ID) is "0001," and the collectable speed-related data includes instantaneous vehicle speed signals and engine speed signals; the collectable braking data includes brake signals. Therefore, to implement personalized car insurance data services for users driving vehicle model 1, it is necessary to collect vehicle speed data and braking data to obtain the user's driving style in order to determine the user's car insurance type. The vehicle data that should be collected includes instantaneous vehicle speed signals, engine speed signals, and brake signals. For vehicle model 2, the unique vehicle identifier (ID) is "0002," and the collectable speed-related data includes instantaneous vehicle speed signals, engine speed signals, and pulse vehicle speed signals; the collectable braking data includes brake signals, accelerator signals, and inertial measurement unit signals. Therefore, the vehicle data to be collected when driving vehicle type 2 includes instantaneous vehicle speed signal, engine speed signal, pulse vehicle speed signal, braking signal, accelerometer signal, and inertial measurement unit (IMU) signal. For vehicle type 3, the unique vehicle identifier (ID) is "0003", and the speed-related data that can be collected includes instantaneous vehicle speed signal, engine speed signal, and pulse vehicle speed signal; the braking data that can be collected includes braking signal and IMU signal. Therefore, the vehicle data to be collected when driving vehicle type 3 includes instantaneous vehicle speed signal, engine speed signal, pulse vehicle speed signal, braking signal, and IMU signal. For vehicle type 4, the unique vehicle identifier (ID) is "0004", and the speed-related data that can be collected includes instantaneous vehicle speed signal and engine speed signal; the braking data that can be collected includes braking signal and IMU signal. Therefore, the vehicle data to be collected when driving vehicle type 2 includes instantaneous vehicle speed signal, engine speed signal, braking signal, and IMU signal.

[0103] The preparation process for vehicle data collection is as follows: Figure 17 As shown, the process begins by setting up data analysis service items for the target user or vehicle, clarifying the personalized data services required by a particular user or vehicle model. Next, a data service item and vehicle data mapping table is created, specifying which onboard data each personalized service requires. Then, a vehicle model and data mapping table is developed. Different vehicle models have different ECUs and sensors, resulting in varying data collection capabilities. For example, an X signal present on model A may not exist on model B. This mapping table effectively addresses this issue, enabling data collection across different vehicle models. Finally, a vehicle data location index table is created, specifying the locations for each data collection item.

[0104] Example 2

[0105] The present invention also provides a vehicle operation data acquisition system, comprising:

[0106] The requirements acquisition module is used to acquire vehicle model information and user requirements for the vehicles to be collected.

[0107] The project determination module is used to determine the data analysis service content options for the vehicle to be collected based on a pre-set data analysis service content and vehicle model information correspondence table, and to select the data analysis service content from the data analysis service content options according to the user needs of the vehicle to be collected.

[0108] The data collection strategy generation module is used to generate natural language text from the data analysis service project content to obtain the vehicle data collection strategy for the vehicle to be collected. The data collection strategy includes data collection conditions and corresponding data collection items.

[0109] The data collection and execution module is used to collect environmental data of the vehicle to be collected and make judgments based on the data collection conditions. When the environmental data falls within the range of the data collection conditions, the module collects the operating data of the vehicle to be collected according to the data collection items, and obtains the operating data of the vehicle to be collected.

[0110] Specifically, such as Figure 18 As shown, existing vehicle data networks include multiple sensors, a domain ECU that controls multiple sensors simultaneously, and a central ECU that connects multiple domain ECUs. The vehicle data collected by different domain ECUs includes various categories such as CAN signals, Ethernet data, LIN signals, and MOST data. The vehicle operation data acquisition system provided by this invention can identify these different types of signal frames and determine the corresponding acquisition items, thereby determining the operation data of the vehicle to be acquired.

[0111] The vehicle operation data acquisition system provided by this invention is as follows: Figure 19 As shown, it includes an LLM language model, a domain ECU, and software and data modules. The software modules are a collection of executable computer programs that enable the acquisition and processing of vehicle data, while the data modules are a collection of non-executable computer data used to collect and store vehicle data. The vehicle operation data acquisition system can be deployed on... Figure 18 In the central ECU.

[0112] Specifically, the software modules include an acquisition unit, a control unit, a storage unit, a data transmission unit, a clock synchronization unit, and a data decoding unit. The acquisition unit assigns an acquisition token to each data service, containing information such as the data acquisition item, acquisition frequency, and signal location index, and then initiates data acquisition. The data decoding unit decodes the signal frames corresponding to the acquired data, and the storage unit stores the decoded results after synchronizing a timestamp. The control unit executes the vehicle data acquisition program, controlling parameters such as the timing, frequency, and data volume of data acquisition. The data transmission unit transmits vehicle data from one designated space to another, for example, transmitting vehicle data from the central ECU to a cloud server when the vehicle is connected to Wi-Fi. The clock synchronization unit assigns the same timestamp to vehicle data collected simultaneously from different sensors or different ECUs, facilitating later data utilization and analysis.

[0113] The data module includes data service item information, vehicle model-vehicle data information, data location index information, data collection condition information, vehicle data, and data collection logs. The entire vehicle data collection process is recorded in a log, including but not limited to data types, data collection conditions, and data volume. The data collection logs are used to analyze and improve the behavior of this vehicle data collection system. Data service item information is formulated by the vehicle operation data collection system provided by this invention based on the data service item requirements of different vehicle models; vehicle model-vehicle data information is determined according to the user's vehicle model and is used to input into the LLM model to obtain the data items that can be collected for the vehicle model and the suggested priority of these data items; data location index information is used to specify the location of each data collection item; data collection condition information is generated by the LLM model based on the input data analysis service item content; vehicle data is collected through multiple sensors connected to the vehicle network.

[0114] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in an embodiment of a vehicle operation data acquisition method. Specific implementation methods can be found in the method embodiments, and will not be repeated here.

[0115] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions on which a computer program is stored. For example, a memory containing instructions that can be executed by a processor of a computer device to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in an embodiment of a vehicle operation data acquisition method. Specific implementation methods can be found in the method embodiments, which will not be repeated here.

[0116] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] It should be noted that the above specific embodiments enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the present invention patent. No reference numerals in the claims should be construed as limiting the scope of the claims. Any simple variations or equivalent substitutions of technical solutions that can be readily obtained by those skilled in the art within the scope of the technology disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A method for collecting vehicle operation data, characterized in that, include: Obtain vehicle model information and user requirements for the vehicles to be collected; Based on a pre-set data analysis service item content and vehicle model information correspondence table, the data analysis service item content options for the vehicle to be collected are determined, and the data analysis service item content is selected from the data analysis service item content options according to the user needs of the vehicle to be collected. Natural language text generation is performed on the content of the data analysis service items to obtain a vehicle data collection strategy for the vehicle to be collected. The collection strategy includes data collection conditions, corresponding data collection items, and priorities for the data collection items. Specifically, the data collection strategy includes inputting the content of the data analysis service items into a pre-trained large language model to obtain the data collection items and their corresponding priorities; generating data collection conditions for the data collection items based on the environmental conditions and network load of the vehicle to be collected; and determining the vehicle data collection strategy based on the data collection items, their priorities, and the data collection conditions. Based on the vehicle model information of the vehicle to be collected, the signal category, frame identifier, signal location, and signal format of the data collection item are determined. A location information index table is constructed based on the signal category, frame identifier, signal location, and signal format. The location information index table is used to locate vehicle operation data from signal frames of the vehicular network and associate it with the corresponding item name. Environmental data of the vehicle to be collected is collected, and a judgment is made based on the data collection conditions. When the environmental data falls within the range of the data collection conditions, the operation data of the vehicle to be collected is collected according to the data collection item to obtain the operation data of the vehicle to be collected. Specifically, this includes assigning a collection token to the data collection item, wherein the collection token includes the item name and the signal location index determined based on the location information index table. The signal frame is decoded according to the signal location index to obtain the vehicle data corresponding to the item name, and a timestamp is added to obtain the operation data of the vehicle to be collected. The signal frame is obtained through the vehicular network.

2. The vehicle operation data acquisition method according to claim 1, characterized in that, The data analysis service project includes a range estimation project. The environmental conditions of the vehicle to be collected include ambient temperature, and the collection project includes battery operating status collection related to ambient temperature. When the temperature of the vehicle to be collected is greater than the temperature threshold corresponding to the data collection condition, the battery operating status of the vehicle to be collected is collected to obtain the driving range operating data of the vehicle to be collected.

3. The vehicle operation data acquisition method according to claim 1, characterized in that, The data analysis service project is a vehicle maintenance project. The environmental conditions of the vehicle to be collected include the maintenance time, and the collection strategy of the vehicle to be collected also includes the collection frequency. When the maintenance time of the vehicle to be collected is greater than the maintenance time threshold corresponding to the data collection condition, vehicle data is collected from the vehicle to be collected according to the collection frequency, wherein the collection frequency is negatively correlated with the maintenance time.

4. The vehicle operation data acquisition method according to claim 1, characterized in that, The steps for setting up the information table corresponding to the data analysis service items and vehicle model information include: Obtain user demand information and user vehicle model information; The collectable items are determined based on the user's vehicle model information; The content of the data analysis service items is determined based on the user needs information and the collectable items. Based on the data analysis service items and the corresponding user vehicle model information, construct the data analysis service items content and vehicle model information correspondence information table.

5. A vehicle operation data acquisition system, characterized in that, include: The requirement acquisition module is used to acquire vehicle model information and user requirements for the vehicles to be collected. The project determination module is used to determine the data analysis service item content options for the vehicle to be collected based on a pre-set data analysis service item content and vehicle model information correspondence table, and to select the data analysis service item content from the data analysis service item content options according to the user needs of the vehicle to be collected. The data collection strategy generation module is used to generate natural language text from the content of the data analysis service project to obtain a vehicle data collection strategy for the vehicle to be collected. The collection strategy includes data collection conditions, corresponding data collection items, and priorities for the data collection items. Specifically, it includes inputting the content of the data analysis service project into a pre-trained large language model to obtain the data collection items and their corresponding priorities; generating data collection conditions for the data collection items based on the environmental conditions and network load of the vehicle to be collected; and determining the vehicle data collection strategy based on the data collection items, their priorities, and the data collection conditions. The data acquisition execution module is used to determine the signal category, frame identifier, signal location, and signal format of the data acquisition item based on the vehicle model information of the vehicle to be acquired; construct a location information index table based on the signal category, frame identifier, signal location, and signal format; wherein the location information index table is used to locate vehicle operation data from signal frames of the vehicular network and associate it with the corresponding item name; acquire environmental data of the vehicle to be acquired and make judgments based on the data acquisition conditions; when the environmental data falls within the range of the data acquisition conditions, acquire operation data of the vehicle to be acquired according to the data acquisition item to obtain the operation data of the vehicle to be acquired; specifically, it includes allocating acquisition tokens for the data acquisition item, wherein the acquisition token includes the item name and the signal location index determined based on the location information index table; decode the signal frame according to the signal location index to obtain the vehicle data corresponding to the item name; add a timestamp to obtain the operation data of the vehicle to be acquired; wherein the signal frame is obtained through the vehicular network.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the vehicle operation data acquisition method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded by the processor, it is able to execute the steps of the vehicle operation data acquisition method according to any one of claims 1 to 4.

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