Data processing method, device and equipment based on internet of vehicles

Abstract

Embodiments of the present application provide a data processing method, device and equipment based on vehicle networking. The method comprises: obtaining running state data of a target vehicle; determining a target business scenario in which the target vehicle is currently located according to the running state data; determining a reference transmission strategy matched with the target business scenario according to preset configuration information; wherein the configuration information comprises priorities corresponding to each signal; the priority is calculated by a cloud device based on multi-dimensional attribute information of the signal; determining a target signal to be transmitted currently and a priority of the target signal according to the reference transmission strategy and the priority in the configuration information; performing correction processing on the reference transmission strategy according to the priority of the target signal to obtain a target transmission strategy of the target signal; and transmitting the target signal to the cloud device according to the target transmission strategy. The method is used to improve transmission efficiency.

Description

Technical Field

[0001] This application relates to the field of vehicle networking technology, and in particular to a data processing method, apparatus and equipment based on vehicle networking. Background Technology

[0002] In intelligent connected vehicle scenarios, it is necessary to collect vehicle operation data (such as vehicle speed, engine speed, battery status, etc.) and upload the data to the cloud through the vehicle communication module for real-time monitoring of vehicle status, fault diagnosis, training of autonomous driving algorithms, etc.

[0003] In related technologies, vehicle data acquisition and uploading typically employ a unified parsing and transmission strategy. For example, ECU communication methods based on the CAN protocol use a service conversion ECU to determine the type of CAN signals and perform conversion service processing. However, the data uploading granularity in the above method is singular, resulting in low transmission efficiency and wasted resources. Summary of the Invention

[0004] The data processing method, apparatus, and equipment based on the Internet of Vehicles provided in this application are used to improve transmission efficiency.

[0005] In a first aspect, embodiments of this application provide a data processing method based on the Internet of Vehicles (IoV), the method being applied to a target vehicle; including:

[0006] Obtain the operating status data of the target vehicle;

[0007] Based on the operational status data, the target business scenario currently in which the target vehicle is located is determined;

[0008] Based on preset configuration information, a baseline transmission strategy matching the target business scenario is determined; wherein, the configuration information includes the priority of each signal; the priority is calculated by the cloud device based on the multi-dimensional attribute information of the signal;

[0009] Based on the baseline transmission strategy and the priority in the configuration information, determine the target signal to be transmitted and the priority of the target signal;

[0010] Based on the priority of the target signal, the reference transmission strategy is modified to obtain the target transmission strategy of the target signal;

[0011] According to the target transmission strategy, the target signal is transmitted to the cloud device.

[0012] In one possible implementation, the baseline transmission strategy includes the signal to be transmitted, baseline parsing rules, baseline upload frequency, and baseline resource allocation rules corresponding to each service scenario;

[0013] The step of modifying the baseline transmission strategy according to the priority of the target signal to obtain the target transmission strategy for the target signal includes:

[0014] Based on the priority of the target signal, the baseline parsing rules, baseline upload frequency, and baseline resource allocation rules of the target signal are modified to obtain the modified parsing rules, modified upload frequency, and modified resource allocation rules; wherein, the target transmission strategy includes the modified parsing rules, modified upload frequency, and modified resource allocation rules.

[0015] In one possible implementation, the configuration information further includes the correspondence between service scenarios and transmission strategies; the step of determining a baseline transmission strategy matching the target service scenario based on preset configuration information includes:

[0016] Based on the correspondence between business scenarios and transmission strategies in the configuration information, a baseline transmission strategy matching the target business scenario is determined.

[0017] In one possible implementation, the calculation process for the priority of each signal in the configuration information includes the following steps:

[0018] For each signal, obtain multi-dimensional attribute information of the signal; wherein, the multi-dimensional attribute information includes at least two of the following: real-time performance, fault correlation, service importance, and historical fault trigger frequency;

[0019] The multi-dimensional attribute information of the signal is input into a preset classification model to obtain the priority of the signal.

[0020] In one possible implementation, transmitting the target signal to the cloud device according to the target transmission strategy includes:

[0021] Obtain the network status parameter information of the target vehicle;

[0022] Based on the network status parameter information, determine whether the network status of the target vehicle meets the preset conditions; wherein, the preset conditions are network congestion or the remaining bandwidth is less than a preset threshold.

[0023] If the conditions are met, the target transmission strategy of the target signal is optimized according to the priority of the target signal to obtain the optimized transmission strategy of the target signal, and the target signal is transmitted to the cloud device according to the optimized transmission strategy.

[0024] If the conditions are not met, the target signal will be transmitted to the cloud device according to the target transmission strategy.

[0025] In one possible implementation, optimizing the target transmission strategy of the target signal according to its priority to obtain an optimized transmission strategy for the target signal includes:

[0026] If the target signal has the highest priority, then the transmission resources of the target signal are compressed or the transmission of the target signal is suspended to obtain an optimized transmission strategy for the target signal.

[0027] If the priority of the target signal is the second priority, then the target transmission strategy is used as the optimized transmission strategy; wherein, the first priority is lower than the second priority.

[0028] In one possible implementation, the method further includes:

[0029] If it is determined that the target vehicle's network is disconnected, the target signal is stored in the storage area that matches the priority of the target signal.

[0030] After determining that the network connection of the target vehicle has been restored, the target signal with the second priority and the target signal with the first priority stored in the storage area are transmitted to the cloud device in sequence; wherein, the first priority is lower than the second priority.

[0031] Secondly, embodiments of this application provide a data processing device based on the Internet of Vehicles, the device being applied to a target vehicle; comprising:

[0032] The acquisition module is used to acquire the operating status data of the target vehicle;

[0033] The first determining module is used to determine the target business scenario currently in which the target vehicle is located based on the operating status data.

[0034] The second determining module is used to determine a baseline transmission strategy that matches the target business scenario based on preset configuration information; wherein, the configuration information includes the priority of each signal; the priority is calculated by the cloud device based on the multi-dimensional attribute information of the signal;

[0035] The third determining module is used to determine the target signal to be transmitted and the priority of the target signal based on the reference transmission strategy and the priority in the configuration information.

[0036] The correction module is used to correct the reference transmission strategy according to the priority of the target signal to obtain the target transmission strategy of the target signal.

[0037] The transmission module is used to transmit the target signal to the cloud device in accordance with the target transmission strategy.

[0038] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0039] The memory stores computer-executed instructions;

[0040] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0041] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0042] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0043] The data processing method, apparatus, and equipment based on the Internet of Vehicles (IoV) provided in this application embodiment first collect vehicle operating status data and determine the current business scenario of the vehicle based on the data. Then, it retrieves the preset configuration information stored locally in the cloud to match the baseline transmission strategy adapted to the scenario. Based on the priority calculated in advance by the cloud based on the multi-dimensional attributes of the signal within the configuration information, it filters out the target signal that needs to be transmitted and matches the corresponding priority. Then, it modifies the baseline transmission strategy based on the priority of each signal to generate a refined target transmission strategy for each target signal. Finally, it completes the data transmission to the cloud according to the strategy. In this way, the uniform and indiscriminate data transmission mode is abandoned. It can define the basic data transmission range based on the real-time business scenario of the vehicle. At the same time, it can adjust the transmission rules according to the business value of the signal itself, and adopt differentiated transmission strategies for signals of different values. It realizes dynamic transmission control based on scenario and signal value, which greatly reduces the waste of bandwidth and storage resources caused by invalid data. It realizes refined hierarchical scheduling and transmission of data collected by the vehicle, and improves the resource utilization, data transmission efficiency, and reliability of the overall vehicle-cloud data interaction. Attached Figure Description

[0044] 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.

[0045] Figure 1 A flowchart illustrating the data processing method based on the Internet of Vehicles provided in this application. Figure 1 ;

[0046] Figure 2A flowchart illustrating the data processing method based on the Internet of Vehicles provided in this application. Figure 2 ;

[0047] Figure 3 This is a schematic diagram of the structure of the data processing device based on the Internet of Vehicles provided in this application;

[0048] Figure 4 A schematic diagram of the structure of the electronic device provided in this application.

[0049] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0050] 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 denote 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 apparatuses and methods consistent with some aspects of this application.

[0051] In digital scenarios such as intelligent connected vehicles, industrial IoT, and smart cities, devices or systems need to collect massive amounts of raw data in real time (such as CAN bus messages, sensor signals, vehicle status information, etc.) through onboard controllers, sensors, or industrial terminals, and upload the data to a cloud platform for analysis and processing according to business needs. For example, in the intelligent connected vehicle scenario, the onboard electronic control unit (ECU) needs to collect vehicle operating data (such as vehicle speed, engine speed, battery status, etc.) and upload the data to the cloud through the onboard communication module for real-time monitoring of vehicle status, fault diagnosis, and training of autonomous driving algorithms.

[0052] However, different business scenarios have significantly different data processing requirements: some scenarios require real-time uploading of critical signals at the second level to support emergency decision-making (such as obstacle detection in autonomous driving), while other scenarios (such as energy consumption analysis) allow uploading at the minute level or in batches to reduce transmission costs.

[0053] Current vehicle data acquisition and uploading methods generally employ a unified parsing and transmission strategy with a single data upload granularity. This fails to adapt to the differentiated data transmission needs of various business scenarios, resulting in unnecessary waste of bandwidth and storage resources and making it difficult to guarantee the transmission priority and integrity of high-value signals, posing a risk of critical data loss. Therefore, the inventors recognize that the transmission value of vehicle data is not homogeneous. Its business importance and real-time requirements are deeply tied to the vehicle's operating scenario and directly related to the fault correlation and business support function of the signals themselves. A single transmission strategy essentially ignores the dual differences in scenario and signal value dimensions, failing to achieve precise allocation of transmission resources. Based on this, the inventors propose first identifying the vehicle's current business scenario, matching the corresponding baseline transmission strategy and the range of signals to be transmitted according to a preset configuration file, and then combining the priority calculated in the configuration file based on the multi-dimensional attributes of the signals to modify and adapt the baseline transmission strategy signal by signal. This ensures that each signal to be transmitted receives transmission rules matching its value, ultimately reducing the transmission overhead of unnecessary data while ensuring reliable and timely transmission of high-value signals, achieving simultaneous optimization of transmission efficiency and resource utilization efficiency.

[0054] The execution entity of this application embodiment may be an on-board controller, on-board gateway, or on-board terminal with data processing capabilities deployed in the target vehicle; this application does not limit this to any particular type.

[0055] It should be noted that the user information and user data involved in the embodiments of this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant laws, regulations and standards, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0056] The following describes the technical solution of this application and how it solves the aforementioned technical problems, using an in-vehicle terminal as the execution subject. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0057] Figure 1 A flowchart illustrating the data processing method based on the Internet of Vehicles provided in this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0058] S101. Obtain the operating status data of the target vehicle.

[0059] For example, the target vehicle is the vehicle to which the signal is to be transmitted. This target vehicle is an intelligent connected vehicle equipped with an on-board terminal, capable of collecting various message signals from the entire vehicle and communicating with cloud devices. It should be noted that this application embodiment does not limit the type or number of target vehicles.

[0060] Operational status data is used to characterize the current operating status of the target vehicle. This application embodiment does not limit the content included in the operational status data. For example, it may include Controller Area Network (CAN) bus messages, mode trigger identifiers, fault alarm information, driving condition parameters, and other status information related to business processing.

[0061] In one example, the vehicle terminal receives data generated during vehicle operation and forms operational status data arranged in chronological order.

[0062] S102. Based on the operational status data, determine the target business scenario in which the target vehicle is currently located.

[0063] For example, the target business scenario is a business classification label based on the vehicle's operating conditions and corresponding differentiated data transmission requirements. For example, it may include intelligent driving scenario, fault diagnosis scenario, energy consumption analysis scenario, etc. Different target business scenarios correspond to different data collection and transmission requirements, and at any given time, the vehicle corresponds to only a single main target business scenario.

[0064] In one example, the in-vehicle terminal extracts scene recognition features from operational status data and matches these features with preset scene determination rules to output the current target business scenario. These scene recognition features can include state variable values, state change trends, state combination relationships, and trigger event identifiers, such as changes in vehicle speed, braking state triggering, fault code changes, changes in vehicle power-on status, and changes in location information. The preset scene determination rules are stored in the in-vehicle terminal's configuration area or distributed from the cloud and saved locally on the in-vehicle terminal. These rules define the correspondence between operational status data and business scenarios.

[0065] For example, the vehicle terminal parses the mode trigger identifier in the operating status data. If it recognizes the autonomous driving start command, it determines that the current scenario is intelligent driving. If it reads the fault code message, it determines that the current scenario is fault diagnosis. If there is no special working condition trigger, it determines that the current scenario is energy consumption analysis.

[0066] In another example, the vehicle terminal has a pre-trained detection model. The acquired operational status data is converted into feature vectors, input into the detection model, and the target business scenario is output. It should be noted that this application embodiment does not limit the type of detection model; for example, it can be a neural network model.

[0067] S103. Based on the preset configuration information, determine the baseline transmission strategy that matches the target business scenario.

[0068] For example, the configuration information is a rule file pre-generated by the cloud device and distributed to the local persistent storage of the vehicle terminal. This configuration information includes the mapping relationship between scenarios and baseline transmission strategies, and the priority of each signal. The priority is a value level calculated by the cloud device based on the multi-dimensional attribute information of the signal, used to distinguish the urgency of signal transmission. The signal refers to the valid service parameters collected by the vehicle after message parsing. The multi-dimensional attribute information is used to evaluate the service value of the signal, and may include at least two of the following: real-time performance, fault relevance, service importance, and historical fault trigger frequency. It should be noted that this application embodiment does not limit the priority setting hierarchy; for example, it can set a first priority and a second priority, with the first priority being lower than the second priority; it can also set multiple priority levels.

[0069] The baseline transmission strategy is a standardized set of basic transmission rules adapted to corresponding business scenarios. This strategy includes the signals to be transmitted, baseline parsing rules, baseline upload frequency, and baseline resource allocation rules for each business scenario. Specifically, the signals to be transmitted are the set of signals that need to be collected and reported in that scenario; the baseline parsing rules specify the default parsing timing of messages; the baseline upload frequency specifies the default signal reporting interval; and the baseline resource allocation rules define the basic allocation ratio of communication bandwidth and local buffer in that scenario.

[0070] In one example, the cloud device acts as the primary entity for priority calculation and configuration generation. It pre-collects multi-dimensional attribute information for each signal, processes each signal to obtain its corresponding priority, forms configuration information, and then sends this configuration information to the target vehicle. After identifying the target service scenario, the in-vehicle terminal determines a baseline transmission strategy matching that scenario based on the pre-defined configuration information. By pre-calculating signal priorities and writing them into the configuration information, and then selecting a baseline transmission strategy according to the target service scenario, strategy determination can be completed quickly under the limited computing resources of the in-vehicle terminal, while simultaneously allowing signal importance and scenario requirements to jointly participate in subsequent decision-making.

[0071] Specifically, for each signal, multi-dimensional attribute information of the signal is obtained; among which, the multi-dimensional attribute information includes at least two of the following: real-time performance, fault relevance, business importance, and historical fault trigger frequency; the multi-dimensional attribute information of the signal is input into a preset classification model to obtain the signal priority.

[0072] The signal refers to vehicle operating parameter data obtained by the on-board terminal through CAN bus message parsing. Multi-dimensional attribute information consists of various evaluation indicators used to comprehensively assess the signal's service value and differentiate transmission priorities. Real-time performance characterizes the sensitivity of the corresponding service to data reporting latency; fault correlation characterizes the close correlation between abnormal signal values ​​and vehicle malfunctions; service importance characterizes the necessity of the signal in supporting core services such as intelligent driving, fault diagnosis, and energy consumption analysis; historical fault trigger frequency characterizes the statistical number of times the signal has exhibited anomalies in historical vehicle malfunction events. Any two or more of these four indicators can be selected to complete the value assessment.

[0073] The preset classification model is a machine learning classification model that has been pre-trained offline in the cloud using massive vehicle signal samples and attribute labels of various dimensions, without requiring the vehicle terminal to participate in inference calculations. It should be noted that the embodiments of this application do not limit the type of classification model, for example, it can be a neural network model.

[0074] In this embodiment, the cloud device first extracts corresponding attribute information from a pre-stored signal dictionary, scene configuration table, or historical operation records for the signal. It then organizes at least two of the following factors—real-time performance, fault relevance, business importance, and historical fault trigger frequency—into a unified feature vector, which is then input into a preset classification model. The preset classification model can be a pre-trained multi-class classifier, where the model's output class result is mapped to the signal's priority level, or it can output continuous values ​​and obtain the priority through hierarchical mapping. The preset classification model can be trained based on historical labeled samples, assigning higher priority to signals with high real-time performance, high fault relevance, high business importance, or high historical fault trigger frequency.

[0075] By uniformly inputting multi-dimensional attribute information into a pre-defined classification model, cloud devices can generate consistent priority determination results for different signals and write these results into the configuration information. Therefore, the priority generation process is more closely linked to the signal value in the business scenario, enabling critical signals to achieve higher transmission guarantees under resource-constrained conditions, while also improving the dynamic adaptability of configuration information and the consistency of subsequent transmission strategy execution.

[0076] S104. Determine the target signal to be transmitted and its priority based on the reference transmission strategy and the priority in the configuration information.

[0077] For example, the target signal is any type of service signal that needs to be parsed and transmitted to the cloud within the scope defined by the baseline transmission strategy, and each target signal can be matched with a unique priority level in the configuration information.

[0078] In one example, the vehicle terminal extracts the set of signals to be transmitted recorded in the baseline transmission strategy, traverses the priority lookup table in the configuration information, and binds the corresponding priority to each target signal in the set.

[0079] S105. Based on the priority of the target signal, the reference transmission strategy is modified to obtain the target transmission strategy of the target signal.

[0080] For example, the correction process refers to making differentiated adjustments to the three basic parameters—the benchmark parsing rules, benchmark upload frequency, and benchmark resource allocation rules—based on the benchmark transmission strategy and in conjunction with the priority of the corresponding target signal. The target transmission strategy is a refined and executable transmission rule specific to a single target signal and adapted for priority.

[0081] In one example, the vehicle-mounted terminal reads the priority of each target signal and adjusts the corresponding transmission parameters in the baseline transmission strategy according to preset correction rules. Different transmission processing methods can be used for target signals of different priorities to ensure higher transmission guarantees for high-priority target signals, while transmitting low-priority target signals with lower transmission strength. After correction, the vehicle-mounted terminal generates a corresponding target transmission strategy for each target signal. For example, for high-priority target signals, the baseline upload interval is shortened, real-time parsing rules are enabled, and dedicated bandwidth and independent buffer resources are allocated; for low-priority target signals, the baseline upload interval is lengthened, batch delayed parsing is used, and communication resource quotas are reduced. After parameter adjustment, an independent target transmission strategy is generated for each signal.

[0082] This approach allows the transmission strategy to be adjusted in a more refined manner based on the importance of the current target signal, thereby enabling different signals to receive differentiated transmission processing under the same target service scenario.

[0083] S106. Transmit the target signal to the cloud device according to the target transmission strategy.

[0084] For example, the cloud device is a cloud platform used to receive, store, standardize and process vehicle-uploaded data and connect to downstream business systems.

[0085] In one example, the vehicle-mounted terminal encodes and encapsulates the target signal according to the target transmission strategy and sends it to the cloud device through the vehicle communication module. The transmission link can be a cellular mobile communication link, a vehicle-mounted wireless wide-area communication link, or other vehicle-to-everything (V2X) data link. By directly applying the target transmission strategy to the vehicle-mounted terminal's transmission process, signals of different value levels in the current target business scenario can be transmitted using differentiated transmission strategies. For example, important signals can receive higher real-time performance and stronger transmission guarantees, while general signals can be uploaded with relatively lower intensity.

[0086] Optionally, after receiving the target signal, the cloud device can perform secondary analysis on the signal as needed, or forward the signal to different downstream business systems; at the same time, data of the same vehicle model and matrix version can be uniformly stored and reused to avoid duplicate storage and processing and improve overall data utilization efficiency.

[0087] In related technologies, the data formats and storage structures of different vehicle models or matrix versions vary significantly, leading to high complexity in cloud data management. Optionally, after completing this step, the target signal for the current processing cycle is uploaded to the cloud device, which can then receive and manage it according to a unified format. For example, the cloud pre-builds a cross-vehicle signal mapping table. Based on this mapping table, the original message signals with differentiated definitions for each vehicle model are uniformly converted into standardized business signals. The standardized business signals include common business identifiers such as vehicle speed and engine speed. Combined with the vehicle model matrix version, unified data governance is achieved to realize cross-vehicle data reuse. For example, the cloud is equipped with a signal parsing engine to read the heterogeneous original CAN messages uploaded by the vehicle, completes the standardized conversion of message signals based on the cross-vehicle signal mapping table, and generates a unified and standardized data storage structure according to the vehicle model matrix version. The unified data storage structure includes a standardized database table structure. In one example, the CAN message identified as 0x123 in vehicle model A corresponds to vehicle speed data, and the CAN message identified as 0x456 in vehicle model B is also mapped to the unified standardized signal "vehicle speed" through a cross-vehicle signal mapping table. This eliminates the data barriers caused by the differences in message definitions between different vehicle models and enables the sharing and reuse of data collected from multiple vehicle models.

[0088] By using a mapping table to standardize and convert the original CAN message signals that are different for each vehicle model, a unified storage structure adapted to all vehicle models is generated to store the standardized signals. This eliminates the data fragmentation problem caused by inconsistent message IDs and signal definitions for different vehicle models. Data collected from various vehicle models for the same business can be aggregated, stored, and analyzed together, which greatly reduces the computing power and storage overhead of repeated table creation and parsing in the cloud, and effectively improves the governance efficiency and data reuse capability of multi-vehicle network data.

[0089] The vehicle-to-everything (V2X) data processing method provided in this application involves the vehicle terminal first collecting vehicle operating status data and determining the current business scenario based on the data. Then, it retrieves pre-configured cloud information stored locally to match a baseline transmission strategy adapted to the scenario. Based on the priority calculated in advance by the cloud based on multi-dimensional signal attributes within the configuration information, it filters out the target signals that need to be transmitted and matches their corresponding priorities. Finally, it modifies the baseline transmission strategy based on the priorities of each signal, generating a refined target transmission strategy specific to each target signal. Data is then transmitted to the cloud according to this strategy. This approach abandons the uniform and indiscriminate data transmission mode, defining the basic data transmission range based on the vehicle's real-time business scenario. Simultaneously, it adjusts transmission rules based on the service value of the signals themselves, employing differentiated transmission strategies for signals of different values. This achieves dynamic transmission control oriented towards scenarios and signal value, significantly reducing bandwidth and storage resource waste caused by invalid data. It enables refined, layered scheduling and transmission of vehicle-collected data, improving the overall resource utilization, data transmission efficiency, and reliability of vehicle-to-cloud data interaction.

[0090] Figure 2 A flowchart illustrating the data processing method based on the Internet of Vehicles provided in this application. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the data processing method based on the Internet of Vehicles is described in detail, and the method includes:

[0091] S201. Obtain the operating status data of the target vehicle.

[0092] It should be noted that this step is similar to the aforementioned step S101, and will not be repeated here.

[0093] S202. Based on the operational status data, determine the target business scenario in which the target vehicle is currently located.

[0094] It should be noted that this step is similar to the aforementioned step S102, and will not be repeated here.

[0095] S203. Based on the correspondence between business scenarios and transmission strategies in the configuration information, determine the baseline transmission strategy that matches the target business scenario.

[0096] For example, the configuration information is a standardized rule file that is generated offline by the cloud device and sent to the vehicle's local storage, with a built-in one-to-one mapping relationship between fixed business scenarios and transmission strategies.

[0097] In one example, after identifying the target service scenario, the vehicle terminal performs a search and matching based on the corresponding relationship in the configuration information to obtain the baseline transmission strategy corresponding to the target service scenario.

[0098] By pre-establishing a correspondence between business scenarios and transmission strategies, vehicles can quickly determine the matching baseline transmission strategy after obtaining the target business scenario, reducing the overhead of strategy generation after scenario identification.

[0099] S204. Determine the target signal to be transmitted and its priority based on the reference transmission strategy and the priority in the configuration information.

[0100] It should be noted that this step is similar to the aforementioned step S104, and will not be repeated here.

[0101] S205. Based on the priority of the target signal, the baseline parsing rules, baseline upload frequency, and baseline resource allocation rules of the target signal are modified to obtain the modified parsing rules, modified upload frequency, and modified resource allocation rules.

[0102] For example, the baseline parsing rule is the preset message parsing timing and parsing logic in the baseline transmission strategy; the baseline upload frequency is the preset signal-based reporting time interval; and the baseline resource allocation rule is the preset bandwidth and local cache basic allocation standard. The correction operation uses the baseline parameters corresponding to the scenario as a fixed base, combined with signal priority, to perform differentiated fine-tuning and optimization of the transmission parameters of a single signal. The target transmission strategy is a dedicated transmission rule adapted to the value of a single signal, refined and directly executable. The target transmission strategy includes the corrected parsing rule, the corrected upload frequency, and the corrected resource allocation rule.

[0103] In one example, for high-priority target signals, the vehicle terminal modifies the baseline parsing rules to real-time instant parsing, the baseline upload frequency to second-level high-frequency upload, and allocates more transmission bandwidth and independent buffer resources; for low-priority target signals, the baseline parsing rules are modified to batch delayed parsing, the baseline upload frequency to minute-level batch upload, and the transmission resource quota is reduced, ultimately generating the target transmission strategy. It should be noted that this application embodiment does not limit the specific parameters of the transmission strategy; they can be set according to actual needs.

[0104] For example, the vehicle-mounted terminal can reconstruct the aforementioned baseline parameters based on the priority of the target signal. For higher-priority target signals, the revised parsing rules can employ more complete field parsing methods or lower parsing thresholds to retain more critical state information; the revised upload frequency can increase the number of uploads per unit time or shorten the cycle duration; and the revised resource allocation rules can increase the transmission bandwidth, cache priority, or scheduling weight occupied by the target signal. For lower-priority target signals, the baseline configuration can be maintained or its resource usage ratio can be reduced, thereby forming a target transmission strategy that matches the importance of the signal.

[0105] The revised parsing rules, revised upload frequency, and revised resource allocation rules together constitute the target transmission strategy, which is executed by the vehicle-mounted terminal in the target service scenario. This target transmission strategy enables different signals in the same target service scenario to receive differentiated processing according to priority, thereby ensuring a higher level of protection for critical data in the parsing, transmission, and resource utilization stages.

[0106] By adopting the above method, the vehicle terminal can adaptively correct the signals to be transmitted according to priority without changing the basic configuration of the business scenario. This enables important signals to obtain more suitable parsing methods, higher upload strength, and more reasonable resource allocation, thereby improving the upload integrity and transmission adaptability of key data.

[0107] S206. Transmit the target signal to the cloud device according to the target transmission strategy.

[0108] In some possible implementations, network status parameter information of the target vehicle is obtained; based on the network status parameter information, it is determined whether the network status of the target vehicle meets preset conditions; wherein, the preset conditions are network congestion or the remaining bandwidth is less than a preset threshold.

[0109] If the conditions are met, the target signal transmission strategy is optimized according to the priority of the target signal to obtain an optimized transmission strategy, and the target signal is transmitted to the cloud device according to the optimized transmission strategy; if the conditions are not met, the target signal is transmitted to the cloud device according to the target transmission strategy.

[0110] Among them, network status parameters are quantitative indicators that reflect the current vehicle-to-cloud communication link quality, collected in real time by the vehicle-mounted communication module. These include various monitoring values ​​such as remaining available bandwidth, data transmission latency, and packet loss rate, serving as the basis for judging the communication link load. This embodiment does not limit the collection period or communication standard for network parameters. The target vehicle's network status is the communication environment status obtained based on a comprehensive judgment of the network status parameters, used to determine whether the current link is suitable for directly executing the predetermined transmission strategy. Preset conditions are used to trigger transmission strategy switching. When network congestion is detected or the remaining bandwidth is lower than a preset bandwidth threshold, it is considered that the current link is not suitable for maintaining the original transmission strength.

[0111] Network congestion refers to a congested state in a communication link where the backlog of pending messages exceeds the capacity of the communication module, and message transmission delays continuously increase. Remaining bandwidth is the idle transmission rate of the current communication link available for transmitting vehicle signal data. The preset threshold is a bandwidth critical value pre-stored in the vehicle's configuration file, used to distinguish between states of ample communication links and resource scarcity. Optimization processing involves adjusting transmission resource allocation and transmission timing based on signal priority when network resources are scarce. The optimized transmission strategy is a lightweight, adaptive transmission rule generated for network-constrained scenarios.

[0112] For example, the vehicle-mounted communication module periodically acquires network status parameter information and sends it to the vehicle-mounted terminal for judgment. The vehicle-mounted terminal can calculate the remaining bandwidth based on the bandwidth estimation module, which can determine the real-time available bandwidth based on the amount of data sent, the link feedback rate, and the current number of connections. When the calculation result is less than a preset threshold, or the network congestion index exceeds a set threshold, the vehicle-mounted terminal determines that the preset conditions are met. If the judgment is met, the vehicle-mounted terminal calls the strategy optimization unit to modify the original target transmission strategy according to the priority of the target signal. For example, it may reduce the transmission frequency, compress the payload, or postpone the transmission of low-priority signals, while maintaining the original transmission frequency or performing only lightweight encoding processing on high-priority signals, thereby generating an optimized transmission strategy and executing the upload. If the judgment is not met, the vehicle-mounted terminal directly encapsulates the target signal according to the original target transmission strategy and sends it to the cloud device. By adopting the above method, transmission resources can be preferentially allocated to critical target signals when the link is abnormal, and the established transmission behavior can be maintained in accordance with the network when it is normal, thereby improving the adaptability of the transmission strategy to changes in network status and the stability of critical data upload.

[0113] Specifically, if the target signal has the highest priority, the transmission resources of the target signal are compressed or the transmission of the target signal is suspended to obtain an optimized transmission strategy for the target signal; if the target signal has the highest priority, the target transmission strategy is used as the optimized transmission strategy; wherein, the first priority is lower than the second priority.

[0114] In this system, the first priority represents low-value signals, corresponding to non-core data such as in-vehicle entertainment and seat adjustment. The second priority represents high-value signals, corresponding to critical data such as autonomous driving perception and fault codes. The transmission guarantee level for the first priority is lower than that for the second priority. Transmission resources include in-vehicle communication bandwidth, local cache usage, and transmission time slot allocation. Compressing transmission resources refers to reducing the bandwidth quota available for the signal, extending the upload interval, and enabling data compression and packetization for transmission. Suspending transmission refers to temporarily stopping the data parsing and reporting of the signal. Optimizing transmission strategies are temporary transmission execution rules formed after adjusting link resource limitations in scenarios of network congestion and insufficient bandwidth.

[0115] For example, after the network condition meets the preset conditions of congestion or insufficient bandwidth, the vehicle terminal identifies the priority identifier corresponding to each target signal one by one. For signals marked as first priority, it chooses to reduce the allocated bandwidth or directly suspend reporting and generates an optimized transmission strategy adapted to the network load. For signals marked as second priority, it does not make any changes to the original transmission rules and directly uses the target transmission strategy as the currently effective optimized transmission strategy.

[0116] After adopting this priority optimization mechanism, the transmission resources occupied by low-level signals under network-limited conditions are compressed or temporarily released, while high-level signals continue to execute the original transmission strategy. Therefore, more limited bandwidth can be allocated to important signals, and the transmission of critical data can be kept continuous and stable, thereby improving the transmission scheduling adaptation capability and critical data protection capability in the vehicle-to-everything (V2X) scenario.

[0117] In some embodiments, if it is determined that the target vehicle's network is disconnected, the target signal is stored in a storage area that matches the priority of the target signal; after it is determined that the target vehicle's network is restored, the target signal with the second priority and the target signal with the first priority stored in the storage area are transmitted to the cloud device in sequence; wherein, the first priority is lower than the second priority.

[0118] Network disconnection refers to a complete interruption of the communication link between the vehicle terminal and the cloud device, making it impossible to establish a data interaction channel. Priority is divided into first priority and second priority. First priority corresponds to low-value signals, and second priority corresponds to high-value signals. The transmission guarantee level of first priority is lower than that of second priority. The storage area is an independent storage space allocated locally by the vehicle terminal. Different priority signals correspond to mutually isolated dedicated storage partitions, and the partition capacity and read / write priority are pre-configured according to the value of the corresponding signal. Storage operation refers to caching target signals that have not been uploaded during the network outage to prevent data from being directly discarded. Network reconnection means that the vehicle terminal detects that the communication link with the cloud has been re-established and that normal data transmission conditions are available. Sequential transmission means that cached data is pushed in batches according to a fixed order from high to low priority.

[0119] In practical implementation, the vehicle-mounted terminal continuously monitors link status parameters through the vehicle-mounted communication module. When it determines that the current link is unreachable, the session is interrupted, or the heartbeat timeout reaches a preset threshold, it confirms that the network is disconnected and writes the target signal to be uploaded into the storage area corresponding to its priority. This storage area can be set in an independent buffer partition in the vehicle's memory or in a dedicated cache directory in the file system. Signals of different priorities are written to their respective partitions for subsequent retrieval and retransmission according to priority. When it is detected that the link is restored, the connection is rebuilt, or the network status parameters meet the connectivity conditions again, the control unit reads data from the storage area, sends the target signal of the second priority first, then sends the target signal of the first priority, and marks or clears the sent data after successful transmission.

[0120] In this implementation, the second priority is higher than the first priority. Therefore, after network recovery, the second priority data is transmitted back first, followed by the first priority data. By partitioning and caching by priority and transmitting data in sequence, the uplink data organization after connectivity is restored can be kept consistent, making it easier for cloud devices to complete parsing, storage, and business processing in the order of reception.

[0121] By adopting the above method, the target signal during the network interruption can be reliably stored and re-uploaded according to priority after the network is restored, thereby improving the continuity of the return transmission of critical target signals and the integrity of data, and enabling the cloud to prioritize the acquisition of higher priority data after communication is restored, avoiding the loss of critical information and improving reliability.

[0122] The vehicle-to-everything (V2X) data processing method provided in this application first collects real-time vehicle operating status data and identifies and matches the current target business scenario. Based on the correspondence between scenarios and strategies in the configuration information distributed by the cloud, it matches a baseline transmission strategy adapted to the scenario. Combining the baseline transmission strategy, it determines the target signal to be transmitted and retrieves pre-stored signal priorities. Based on the signal priorities, it specifically modifies the baseline parsing rules, baseline upload frequency, and baseline resource allocation rules for each target signal, generating a dedicated target transmission strategy adapted to the service value of a single signal. Finally, it completes vehicle-to-cloud data transmission based on the refined target transmission strategy. This method abandons the traditional fixed and unified data transmission mode, defining the transmission baseline based on the business scenario and performing refined and differentiated optimization based on signal value priority. It achieves dual dynamic scheduling of scenario adaptation and signal value adaptation, accurately matching the basic data transmission needs of different business scenarios, ensuring the real-time performance and integrity of high-priority core signals, and reducing the frequency and burden of low-priority non-core signals. This effectively reduces bandwidth occupation and storage resource waste caused by invalid data transmission, greatly improving the accuracy, rationality, and overall resource utilization efficiency of V2X data transmission.

[0123] In one possible implementation, the target vehicle is an intelligent connected vehicle traveling on a highway in autonomous driving mode. The vehicle collects and uploads obstacle detection signals in real time during the highway autonomous driving scenario, and dynamically adjusts the upload strategy for non-critical signals based on network conditions. The vehicle uses onboard cameras, radar, and other sensors to detect obstacles ahead in real time, including vehicles and pedestrians. It also collects signals such as engine temperature, vehicle speed, and battery status through the onboard ECU, and acquires operational status data for the target vehicle, including vehicle speed, gear position, braking status, steering status, powertrain status, fault codes, location information, and business status information output by the onboard controller. The onboard terminal identifies the current business scenario through an event detection module, which includes lane line detection by cameras and obstacle detection by radar. This event detection module, combined with the autonomous driving mode triggering a "smart driving" scenario tag, identifies the target vehicle's current business scenario as a "smart driving" scenario.

[0124] After identifying the "intelligent driving" scenario, the system reads preset configuration information, which includes the correspondence between business scenarios and transmission strategies, as well as the priority of each signal. It then calls a preset scenario-strategy mapping table in the cloud to determine a baseline transmission strategy matching the "intelligent driving" scenario based on the correspondence between business scenarios and transmission strategies. The cloud generates a structured configuration file based on the autonomous driving scenario and distributes it to the in-vehicle terminal. The configuration file defines that obstacle detection signals must be uploaded in real-time at the second level, while seat status signals must be uploaded at a reduced frequency at the minute level. In the "intelligent driving" scenario, obstacle detection signals are marked as high priority, and seat status signals are marked as low priority, with upload rules defined as real-time upload and batch compression. The baseline transmission strategy includes the signals to be transmitted corresponding to this business scenario, baseline parsing rules, baseline upload frequency, and baseline resource allocation rules. The signals to be transmitted include at least obstacle detection signals, engine temperature, vehicle speed, battery status, seat status signals, and entertainment system logs.

[0125] The priority of each signal in the configuration information is pre-calculated by the cloud device. The cloud device acquires multi-dimensional attribute information for each signal, including at least two of the following: real-time performance, fault relevance, business importance, and historical fault trigger frequency. This multi-dimensional attribute information is then input into a pre-defined classification model, outputting the priority of each signal. The priority is then written into the configuration information and sent to the target vehicle. After determining the baseline transmission strategy, the target vehicle identifies the target signal from the currently transmitted signals and reads its priority from the configuration information. It then associates the target signal's priority with the baseline transmission strategy to form the input data for the current transmission decision. In this highway autonomous driving scenario, obstacle detection signals, as data supporting autonomous driving decisions, are identified as high-priority target signals, while seat status signals and entertainment system logs, as non-critical signals, are identified as low-priority target signals.

[0126] After determining the target signal and its priority, the baseline transmission strategy is modified according to the priority of the target signal. The baseline parsing rules, baseline upload frequency, and baseline resource allocation rules for the target signal are adjusted to obtain the modified parsing rules, modified upload frequency, and modified resource allocation rules. These modified rules are then used as the target transmission strategy for the target signal. The vehicle terminal performs layered processing on the original messages. Obstacle detection signals are parsed in real time and uploaded within seconds. Seat status signals are batch compressed and uploaded within minutes. In the "intelligent driving" scenario, radar point cloud data from obstacle detection signals is uploaded to the cloud in real time. Simultaneously, seat position and temperature data from the seat status message are compressed and uploaded with a delay. Entertainment system logs are uploaded using a frequency reduction or batch compression method that matches non-critical signals. The modification of parsing rules includes adjusting the range of signal field extraction, data granularity, encapsulation format, or parsing period. The modification of upload frequency includes increasing or decreasing the number of reports per unit time. The modification of resource allocation rules includes adjusting the bandwidth, buffer, transmission time slot, or transmission queue weight occupied by the target signal. As a result, resources that meet the requirements of second-level real-time upload are allocated to obstacle detection signals, and resources that meet the requirements of minute-level down-frequency upload and batch compression are allocated to seat status signals.

[0127] Before sending the target signal, the target vehicle acquires network status parameters, including link bandwidth, remaining bandwidth, latency, packet loss rate, queue occupancy rate, or congestion flags. The vehicle terminal network monitoring module collects network indicators in real time, including bandwidth and latency, and determines whether the current network status meets preset conditions based on the network status parameters. The preset conditions are network congestion or remaining bandwidth less than a preset threshold. The upload mode is dynamically switched according to preset network-mode mapping rules. When the vehicle enters a tunnel causing network congestion, if the preset conditions are met and the target signal has the highest priority (first priority), the target transmission strategy is optimized based on the target signal's priority, either by compressing transmission resources or suspending transmission. In this scenario, seat status signals and entertainment system logs are low-priority signals and their transmission resources are compressed or suspended. When the target signal has the highest priority (second priority), the target transmission strategy remains unchanged and is used as the optimized transmission strategy. The first priority is lower than the second priority. In this scenario, obstacle detection signals are high-priority signals, and the second-level real-time related target transmission strategy remains unchanged. Then, the target signal is transmitted to the cloud device according to the optimized transmission strategy; if the preset conditions are not met, the target signal is transmitted to the cloud device directly according to the target transmission strategy.

[0128] When a network disconnection is detected in a target vehicle, real-time uploading is not performed. Instead, the target signal is stored in a storage area matching its priority, with different priorities corresponding to different storage areas. When a vehicle enters a tunnel, causing network anomalies or disconnections, the onboard terminal automatically switches to caching mode, caching obstacle detection signals in the onboard storage unit and storing other target signals in storage areas matching their priorities. After network connectivity is restored, the cached signals in the storage areas are read in priority order, transmitting the second-priority target signals first, followed by the first-priority target signals. During transmission, the corresponding sending strategy is selected based on the restored network status parameters, and the cached signals are sent to the cloud device. Therefore, after network restoration, the cached obstacle detection signals are uploaded first, followed by the seat status signals and the cached data corresponding to the entertainment system logs.

[0129] After receiving data uploaded by the target vehicle, the cloud device constructs a cross-vehicle signal mapping table based on the vehicle model matrix version. This unifies the original signals from different vehicle models into standardized signals and generates a unified storage structure. For example, CAN message 0x123 for vehicle A and CAN message 0x456 for vehicle B are unified as "vehicle speed" and "engine speed," respectively. The cloud then performs standardized parsing of obstacle detection signals and stores them uniformly in a database table structure. Obstacle detection signals, engine temperature, vehicle speed, battery status, seat status signals, and other signals uploaded by onboard terminals from different vehicle models are stored according to this unified storage structure. This ensures coordination with the onboard terminal's upload strategy, which is based on the "intelligent driving" scenario, signal priority, corrected parsing rules, corrected upload frequency, corrected resource allocation rules, and adaptive transmission strategy according to network status.

[0130] Figure 3 This is a schematic diagram of the structure of the data processing device based on the Internet of Vehicles provided in this application, as shown below. Figure 3 As shown, the data processing device 300 based on the Internet of Vehicles provided in this embodiment includes:

[0131] The acquisition module 301 is used to acquire the operating status data of the target vehicle;

[0132] The first determining module 302 is used to determine the target business scenario currently in which the target vehicle is located based on the operating status data.

[0133] The second determining module 303 is used to determine a baseline transmission strategy that matches the target business scenario based on preset configuration information; wherein, the configuration information includes the priority of each signal; the priority is calculated by the cloud device based on the multi-dimensional attribute information of the signal;

[0134] The third determining module 304 is used to determine the target signal to be transmitted and the priority of the target signal based on the reference transmission strategy and the priority in the configuration information.

[0135] The correction module 305 is used to correct the reference transmission strategy according to the priority of the target signal to obtain the target transmission strategy of the target signal.

[0136] The transmission module 306 is used to transmit the target signal to the cloud device according to the target transmission strategy.

[0137] In one possible implementation, the baseline transmission strategy includes the signal to be transmitted, baseline parsing rules, baseline upload frequency, and baseline resource allocation rules corresponding to each service scenario.

[0138] Correction module 305 is used for:

[0139] Based on the priority of the target signal, the baseline parsing rules, baseline upload frequency, and baseline resource allocation rules of the target signal are modified to obtain the modified parsing rules, modified upload frequency, and modified resource allocation rules; wherein, the target transmission strategy includes the modified parsing rules, modified upload frequency, and modified resource allocation rules.

[0140] In one possible implementation, the configuration information further includes the correspondence between service scenarios and transmission strategies; the second determining module 303 is used for:

[0141] Based on the correspondence between business scenarios and transmission strategies in the configuration information, determine the baseline transmission strategy that matches the target business scenario.

[0142] In one possible implementation, the calculation process for the priority of each signal in the configuration information includes the following steps:

[0143] For each signal, acquire multi-dimensional attribute information of the signal; the multi-dimensional attribute information includes at least two of the following: real-time performance, fault relevance, business importance, and historical fault trigger frequency.

[0144] The multi-dimensional attribute information of the signal is input into a preset classification model to obtain the signal priority.

[0145] In one possible implementation, the transmission module 306 is configured to:

[0146] Obtain network status parameter information of the target vehicle;

[0147] Based on network status parameter information, determine whether the network status of the target vehicle meets the preset conditions; the preset conditions are network congestion or remaining bandwidth less than a preset threshold.

[0148] If the conditions are met, the target transmission strategy of the target signal is optimized according to the priority of the target signal to obtain the optimized transmission strategy of the target signal, and the target signal is transmitted to the cloud device according to the optimized transmission strategy.

[0149] If the target signal is not met, the target signal will be transmitted to the cloud device according to the target transmission strategy.

[0150] In one possible implementation, the transmission module 306 is configured to:

[0151] If the target signal has the highest priority, then the transmission resources of the target signal are compressed or the transmission of the target signal is suspended to obtain the optimized transmission strategy of the target signal.

[0152] If the target signal has the second priority, then the target transmission strategy will be used as the optimized transmission strategy; where the first priority is lower than the second priority.

[0153] In one possible implementation, the transmission module 306 is configured to:

[0154] If it is determined that the target vehicle's network is disconnected, the target signal is stored in the storage area that matches the priority of the target signal.

[0155] After confirming that the target vehicle's network connection has been restored, the target signal with the second priority and the target signal with the first priority stored in the storage area are transmitted to the cloud device in sequence; wherein, the first priority is lower than the second priority.

[0156] The data processing device based on the Internet of Vehicles provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0157] Figure 4 A schematic diagram of the structure of the electronic device provided in this application. Figure 4 As shown, the electronic device 400 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the electronic device 400 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus. This electronic device can be the aforementioned vehicle-mounted terminal.

[0158] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0159] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0160] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0161] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0162] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0163] This application also provides a vehicle, including a vehicle body and an electronic device for implementing the above-described method.

[0164] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0165] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0166] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0167] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0168] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0169] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0171] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0173] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A data processing method based on the Internet of Vehicles, characterized in that, The method is applied to the target vehicle; including: Obtain the operating status data of the target vehicle; Based on the operational status data, the target business scenario currently in which the target vehicle is located is determined; Based on preset configuration information, a baseline transmission strategy matching the target business scenario is determined; wherein, the configuration information includes the priority of each signal; the priority is calculated by the cloud device based on the multi-dimensional attribute information of the signal; Based on the baseline transmission strategy and the priority in the configuration information, determine the target signal to be transmitted and the priority of the target signal; Based on the priority of the target signal, the reference transmission strategy is modified to obtain the target transmission strategy of the target signal; According to the target transmission strategy, the target signal is transmitted to the cloud device.

2. The method according to claim 1, characterized in that, The baseline transmission strategy includes the signal to be transmitted, baseline parsing rules, baseline upload frequency, and baseline resource allocation rules corresponding to each business scenario. The step of modifying the baseline transmission strategy according to the priority of the target signal to obtain the target transmission strategy for the target signal includes: Based on the priority of the target signal, the baseline parsing rules, baseline upload frequency, and baseline resource allocation rules of the target signal are modified to obtain the modified parsing rules, modified upload frequency, and modified resource allocation rules; wherein, the target transmission strategy includes the modified parsing rules, modified upload frequency, and modified resource allocation rules.

3. The method according to claim 1, characterized in that, The configuration information also includes the correspondence between business scenarios and transmission strategies; determining the baseline transmission strategy matching the target business scenario based on the preset configuration information includes: Based on the correspondence between business scenarios and transmission strategies in the configuration information, a baseline transmission strategy matching the target business scenario is determined.

4. The method according to claim 1, characterized in that, The calculation process for the priority of each signal in the configuration information includes the following steps: For each signal, obtain multi-dimensional attribute information of the signal; wherein, the multi-dimensional attribute information includes at least two of the following: real-time performance, fault correlation, service importance, and historical fault trigger frequency; The multi-dimensional attribute information of the signal is input into a preset classification model to obtain the priority of the signal.

5. The method according to claim 1, characterized in that, The step of transmitting the target signal to the cloud device according to the target transmission strategy includes: Obtain the network status parameter information of the target vehicle; Based on the network status parameter information, determine whether the network status of the target vehicle meets the preset conditions; wherein, the preset conditions are network congestion or the remaining bandwidth is less than a preset threshold. If the conditions are met, the target transmission strategy of the target signal is optimized according to the priority of the target signal to obtain the optimized transmission strategy of the target signal, and the target signal is transmitted to the cloud device according to the optimized transmission strategy. If the conditions are not met, the target signal will be transmitted to the cloud device according to the target transmission strategy.

6. The method according to claim 5, characterized in that, The step of optimizing the target transmission strategy of the target signal according to its priority to obtain an optimized transmission strategy for the target signal includes: If the target signal has the highest priority, then the transmission resources of the target signal are compressed or the transmission of the target signal is suspended to obtain an optimized transmission strategy for the target signal. If the priority of the target signal is the second priority, then the target transmission strategy is used as the optimized transmission strategy; wherein, the first priority is lower than the second priority.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: If it is determined that the target vehicle is disconnected from the network, the target signal is stored in the storage area that matches the priority of the target signal. After determining that the network connection of the target vehicle has been restored, the target signal with the second priority and the target signal with the first priority stored in the storage area are transmitted to the cloud device in sequence; wherein, the first priority is lower than the second priority.

8. A data processing device based on the Internet of Vehicles, characterized in that, The device is applied to the target vehicle; and includes: The acquisition module is used to acquire the operating status data of the target vehicle; The first determining module is used to determine the target business scenario currently in which the target vehicle is located based on the operating status data. The second determining module is used to determine a baseline transmission strategy that matches the target business scenario based on preset configuration information; wherein, the configuration information includes the priority of each signal; the priority is calculated by the cloud device based on the multi-dimensional attribute information of the signal; The third determining module is used to determine the target signal to be transmitted and the priority of the target signal based on the reference transmission strategy and the priority in the configuration information. The correction module is used to correct the reference transmission strategy according to the priority of the target signal to obtain the target transmission strategy of the target signal. The transmission module is used to transmit the target signal to the cloud device in accordance with the target transmission strategy.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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