Internet of vehicles data dynamic management method and system and vehicle-mounted equipment

Abstract

The invention provides an Internet of Vehicles data dynamic management method and system and vehicle-mounted equipment, and relates to the technical field of Internet of Vehicles, and the method comprises the steps: obtaining the current operation data of N different vehicle-mounted terminals; determining the current driving state of the ith vehicle-mounted terminal according to the current operation data of the ith vehicle-mounted terminal; determining the ith target sampling frequency according to the current driving state of the ith vehicle-mounted terminal; obtaining ith time sequence data from the ith vehicle-mounted terminal based on the ith target sampling frequency; determining the ith time sequence data meeting a preset judgment condition as core data, otherwise, determining the ith time sequence data as non-core data; the core data is written into the first storage area according to the first writing mode, the non-core data is written into the second storage area according to the second writing mode, and the query efficiency of the first storage area is higher than that of the second storage area. According to the invention, dynamic downsampling of the vehicle-mounted terminal data and heat distribution of data storage are realized, the utilization rate of storage resources is improved, and the data management cost is reduced.

Description

Technical Field

[0001] This application relates to the field of vehicle networking technology, and more specifically, to a method, system, and in-vehicle equipment for dynamic management of vehicle networking data. Background Technology

[0002] Vehicle networking platforms face the challenge of managing massive amounts of time-series data. Existing vehicle networking platforms generally adopt a data processing method of full reception and unified storage: sampling data reported by vehicles is sampled and stored uniformly based on a fixed frequency sampling mechanism. This processing method has obvious limitations: (1) Data collected based on a fixed sampling frequency is prone to redundancy or insufficient sampling; (2) Undifferentiated data value is not distinguished for unified storage, resulting in a waste of storage resources. Summary of the Invention

[0003] In view of this, the purpose of this application is to overcome the shortcomings of the prior art and provide a method, system, and in-vehicle equipment for dynamic management of vehicle network data. This application provides the following technical solution: Firstly, this application provides a method for dynamic management of vehicle network data, the method comprising: Obtain the current operating data of N different vehicle terminals, where N≥1; Based on the current operating data of the i-th vehicle terminal, determine the current driving status of the i-th vehicle terminal, where 1≤i≤N; Based on the current driving state of the i-th vehicle terminal, the i-th target sampling frequency corresponding to the i-th vehicle terminal is determined from multiple preset sampling frequencies; Based on the i-th target sampling frequency, the i-th time-series data is obtained from the i-th vehicle terminal; Determine whether the i-th time series data meets the preset judgment condition. If it does, the i-th time series data is determined as core data. If it does not, the i-th time series data is determined as non-core data. According to the first writing method, the core data is written to the first storage area, and according to the second writing method, the non-core data is written to the second storage area. The query efficiency of the first storage area is higher than that of the second storage area.

[0004] In one embodiment, the current operating data includes: current speed and current acceleration, and determining the current driving state of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal includes: If the current speed of the i-th vehicle terminal is greater than or equal to the first preset speed, and the current acceleration of the i-th vehicle terminal is within the first preset acceleration range, then the current driving state of the i-th vehicle terminal is determined to be: high-speed constant speed state.

[0005] In one embodiment, the current operating data further includes GPS data, and determining the current driving status of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal further includes: The displacement change rate of the i-th vehicle-mounted terminal is determined based on the GPS data; If the current speed of the i-th vehicle terminal is less than the second preset speed, and the current acceleration of the i-th vehicle terminal meets the preset acceleration condition, then the current driving state of the i-th vehicle terminal is determined to be: congested driving state. If the current speed of the i-th vehicle terminal is less than the second preset speed, and the displacement change rate of the i-th vehicle terminal is greater than or equal to the preset change rate threshold, then the current driving state of the i-th vehicle terminal is determined to be: congested driving state, and the first preset speed is greater than the second preset speed.

[0006] In one embodiment, the current operating data further includes: fault codes and tire pressure status indicators; determining the current driving status of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal further includes: If the fault code of the i-th vehicle terminal is not empty, or the tire pressure status indicator of the i-th vehicle terminal is a preset alarm indicator, or the current acceleration of the i-th vehicle terminal is within the second preset acceleration range, then the current driving state of the i-th vehicle terminal is determined to be: abnormal state.

[0007] In one embodiment, determining the current driving state of the i-th vehicle terminal based on its current operating data further includes: Determine whether the current operating data of the i-th vehicle terminal meets the preset driving conditions. If it does, then determine the current driving state of the i-th vehicle terminal as: normal driving state.

[0008] In one embodiment, the plurality of preset sampling frequencies include: a full sampling frequency, a first preset sampling frequency, a second preset sampling frequency, and a third preset sampling frequency, wherein the first preset sampling frequency is less than the second preset sampling frequency, the second preset sampling frequency is less than the third preset sampling frequency, and the step of determining the i-th target sampling frequency corresponding to the i-th vehicle terminal from the plurality of preset sampling frequencies based on the current driving state of the i-th vehicle terminal includes: If the current driving state of the i-th vehicle terminal is the high-speed constant speed state, then the first preset sampling frequency is determined as the i-th target sampling frequency; If the current driving state of the i-th vehicle terminal is the normal driving state, then the second preset sampling frequency is determined as the i-th target sampling frequency; If the current driving state of the i-th vehicle terminal is the congested driving state, then the third preset sampling frequency is determined as the i-th target sampling frequency; If the current driving state of the i-th vehicle terminal is the abnormal state, then the full sampling frequency is determined as the i-th target sampling frequency.

[0009] In one embodiment, the i-th time-series data includes: the i-th fault code, the i-th acceleration, and the i-th tire pressure status indicator. The step of determining whether the i-th time-series data meets a preset judgment condition includes: If the i-th fault code is not empty, or the i-th acceleration belongs to the second preset acceleration range, or the i-th tire pressure status identifier is a preset alarm identifier, then the i-th timing data is determined to meet the preset judgment condition.

[0010] In one embodiment, the method further includes: Within a preset period, the query popularity of each core data in the first storage area is statistically analyzed; The core data whose query popularity is greater than or equal to a preset popularity threshold is identified as hot data, and the core data whose query popularity is less than the preset popularity threshold is identified as cold data. The hot data is synchronized to a third storage area, where the query efficiency of the third storage area is greater than that of the first storage area. The cold data is migrated to a fourth storage area, where the query efficiency is greater than that of the second storage area but less than that of the third storage area.

[0011] Secondly, this application also provides a vehicle-to-everything (V2X) data dynamic management system, the system comprising: The data acquisition module is used to acquire the current operating data of N different vehicle terminals, where N≥1; The state determination module is used to determine the current driving state of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal, where 1≤i≤N; The frequency determination module is used to determine the i-th target sampling frequency corresponding to the i-th vehicle terminal from multiple preset sampling frequencies based on the current driving state of the i-th vehicle terminal. A dynamic downsampling module is used to acquire the i-th time-series data from the i-th vehicle terminal based on the i-th target sampling frequency; The splitting module is used to determine whether the i-th time series data meets the preset judgment conditions. If it does, the i-th time series data is determined as core data; if it does not, the i-th time series data is determined as non-core data. The write storage module is used to write the core data into a first storage area according to a first write method and to write the non-core data into a second storage area according to a second write method. The query efficiency of the first storage area is higher than that of the second storage area.

[0012] Thirdly, this application provides an in-vehicle device, including a memory and a processor, wherein the memory stores a computer program, and the computer program executes the vehicle network data dynamic management method described in the first aspect when it is run on the processor.

[0013] The vehicle-to-everything (V2X) data dynamic management method, system, and vehicle-mounted device provided in this application acquire current operating data from N different vehicle-mounted terminals, where N≥1; determine the current driving state of the i-th vehicle-mounted terminal based on its current operating data, where 1≤i≤N; determine the i-th target sampling frequency corresponding to the i-th vehicle-mounted terminal from multiple preset sampling frequencies based on its current driving state; acquire i-th time-series data from the i-th vehicle-mounted terminal based on the i-th target sampling frequency; determine whether the i-th time-series data meets preset judgment conditions; if it does, the i-th time-series data is determined as core data; if it does not, the i-th time-series data is determined as non-core data; write the core data to a first storage area according to a first writing method, and write the non-core data to a second storage area according to a second writing method. The query efficiency of the first storage area is higher than that of the second storage area, thereby realizing dynamic downsampling of vehicle-mounted terminal data and hot-spot distribution of data storage. While ensuring data integrity, this improves storage resource utilization and reduces data management costs.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This paper illustrates a flowchart of a method for dynamic management of vehicle network data provided in an embodiment of this application. Figure 2 This paper shows a schematic diagram of the structure of a vehicle network data dynamic management system provided in an embodiment of this application; Figure 3 A schematic diagram of the structure of the vehicle-mounted device provided in an embodiment of this application is shown.

[0017] Explanation of key component symbols: 200-Vehicle Internet of Things Data Dynamic Management System; 210-Data Acquisition Module; 220-Status Determination Module; 230-Frequency Determination Module; 240-Dynamic Downsampling Module; 250-Shunting Module; 260-Write Storage Module; 300-Vehicle Equipment; 301-Transceiver; 302-Processor; 303-Memory. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0019] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the template description is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] Example 1 With the rapid development of vehicle-to-everything (V2X) technology, the penetration rate of in-vehicle terminals (e.g., Telematics-Boxes) has increased significantly. Each different in-vehicle terminal reports a large amount of time-series data in real time, including GPS data, driving speed, acceleration, fault codes, and Adaptive Cruise Control (ACC) data. The daily data volume reported by a single in-vehicle terminal can reach GB levels, posing a significant challenge to V2X platforms receiving this data in managing massive amounts of time-series data. Currently, V2X platforms process this time-series data by receiving all data and storing it uniformly. This approach results in excessively high storage costs and a serious waste of storage resources. For further information, please refer to [link to relevant documentation / reference]. Figure 1 This application provides a method for dynamic management of vehicle network data, including steps S110 to S160.

[0022] Step S110: Obtain the current operating data of N different vehicle terminals, where N≥1.

[0023] In this embodiment, the vehicle network data management platform obtains current operating data from various vehicle terminals through Message Queuing Telemetry Transport (MQTT), a low-power, highly reliable data transmission protocol adapted to vehicle network wireless communication scenarios.

[0024] Specifically, each vehicle terminal reports data to the "vehicle_data_topic" specified in the Kafka message queue of the vehicle network data management platform via the MQTT protocol. The Flink streaming data processing engine subscribes to the Kafka topic used to store vehicle data through the consumer component and reads data from Kafka for subsequent processing.

[0025] It should be noted that keeping the number of partitions in the Kafka topic consistent with the parallelism of Flink enables distributed consumption and processing of data reported by massive vehicle terminals, thereby ensuring the independence of data collection for each vehicle terminal, the efficiency of overall data acquisition, and the load balance.

[0026] Step S120: Determine the current driving status of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal, where 1≤i≤N.

[0027] The current operating data of the vehicle terminal can be understood as the vehicle status data, including the timestamp, reported by the vehicle terminal at the current moment. It is usually in JSON format. Below is an example of JSON data from a vehicle terminal: { "vin": "LVVDB21B0NF000001", / / Unique Vehicle Identifier "timestamp": 1699876543210, / / Reported timestamp (milliseconds) "gpsLng": 120.123456, / / GPS longitude "gpsLat": 30.654321, / / GPS latitude "speed": 90.5, / / Driving speed (km / h) "acceleration": 0.3, / / Acceleration (m / s²) "faultCode": "", / / Fault code (empty indicates no fault) "tpmsStatus": "NORMAL" / / Tire pressure status indicator (NORMAL / ALARM) }

[0028] Before determining the current driving status of the i-th vehicle terminal based on its current operating data, it is necessary to standardize the current operating data of the i-th vehicle terminal, which is implemented based on Flink's Map operator. Specifically, this includes: (1) format verification, verifying the integrity of the fields in the current operating data (e.g., verifying whether the required fields vin, timestamp, speed, etc. exist); (2) removing abnormal data values, for example, removing the abnormal value when the driving speed is greater than 250km / h; (3) field completion, completing the default value (e.g., "UNKNOWM") for the actual optional fields, such as tpmsStatus.

[0029] The current operating data of the vehicle-mounted terminal includes: unique vehicle identifier, timestamp, GPS longitude, GPS latitude, driving speed, acceleration, fault codes, and tire pressure status. Based on the current operating data of the i-th vehicle-mounted terminal, the current driving status of the i-th vehicle-mounted terminal can be determined.

[0030] In one embodiment, the current operating data includes: current speed, i.e., the driving speed at the current moment, and current acceleration, i.e., the acceleration at the current moment. Determining the current driving state of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal includes: if the current speed of the i-th vehicle terminal is greater than or equal to a first preset speed, and the current acceleration of the i-th vehicle terminal is within the range of the first preset acceleration, then the current driving state of the i-th vehicle terminal is determined to be: high-speed constant speed state.

[0031] For example, if the current speed of the i-th vehicle terminal is speed≥80km / h and the current acceleration|acceleration|≤0.5m / s², then it can be determined that the current speed of the i-th vehicle terminal is relatively fast and the speed change is gradual. In this case, the driving state of the i-th vehicle terminal can be determined as: high-speed constant speed state.

[0032] In one embodiment, the current operating data further includes: GPS data, namely the GPS longitude and GPS latitude of the i-th vehicle terminal; determining the current driving state of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal further includes: determining the displacement change rate of the i-th vehicle terminal based on the GPS data. If the current speed of the i-th vehicle terminal is less than the second preset speed, and the current acceleration of the i-th vehicle terminal meets the preset acceleration condition, then the current driving state of the i-th vehicle terminal is determined to be: congested driving state. If the current speed of the i-th vehicle terminal is less than the second preset speed, and the displacement change rate of the i-th vehicle terminal is greater than or equal to the preset change rate threshold, then the current driving state of the i-th vehicle terminal is determined to be: congested driving state, and the first preset speed is greater than the second preset speed.

[0033] When the i-th vehicle terminal accelerates and decelerates repeatedly, the current acceleration of the i-th vehicle terminal can reflect this. The value of the current acceleration of the i-th vehicle terminal alternates between positive and negative, that is, the current acceleration of the i-th vehicle terminal meets the preset acceleration condition.

[0034] In this embodiment, if the current speed of the i-th vehicle terminal is less than 30 km / h, and the current acceleration value of the i-th vehicle terminal alternates between positive and negative, it can be determined that the i-th vehicle terminal is traveling at a slow speed and is repeatedly accelerating and decelerating. At this time, the driving state of the i-th vehicle terminal can be determined as: congested driving state.

[0035] In addition to determining the driving status based on driving speed and acceleration, the driving status of the i-th vehicle terminal can be further determined by combining the GPS data of the i-th vehicle terminal. The displacement change rate of the i-th vehicle terminal can be determined based on the GPS data. If the displacement change rate is greater than or equal to a preset change rate threshold, it can be determined that the displacement data of the i-th vehicle terminal changes frequently. Similarly, combined with the driving speed, the driving status of the i-th vehicle terminal can be determined as: congested driving status.

[0036] In one embodiment, the current operating data further includes: fault codes and tire pressure status indicators. Determining the current driving state of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal further includes: if the fault code of the i-th vehicle terminal is not empty, or the tire pressure status indicator of the i-th vehicle terminal is a preset alarm indicator, or the current acceleration of the i-th vehicle terminal is within a second preset acceleration range, then the current driving state of the i-th vehicle terminal is determined to be: abnormal state.

[0037] If the fault code is not empty in the current operating data of the i-th vehicle terminal, or the acceleration is within the second preset acceleration range (e.g., acceleration|acceleration|≥3m / s²), or the tire pressure status indicator tpmsStatus is the preset alarm indicator ALARM, then the current driving status of the i-th vehicle terminal is determined to be abnormal.

[0038] In one embodiment, determining the current driving state of the i-th vehicle terminal based on its current operating data further includes: determining whether the current operating data of the i-th vehicle terminal meets preset driving conditions; if so, determining the current driving state of the i-th vehicle terminal as: normal driving state.

[0039] In this embodiment, if the current driving state of the i-th vehicle terminal is determined to be neither a high-speed constant speed state, nor a congested driving state, nor an abnormal state based on the current operating data of the i-th vehicle terminal, then the current operating data of the i-th vehicle terminal is determined to meet the preset driving conditions, and the current driving state of the i-th vehicle terminal is further determined to be: normal driving state.

[0040] Step S130: Based on the current driving state of the i-th vehicle terminal, determine the i-th target sampling frequency corresponding to the i-th vehicle terminal from multiple preset sampling frequencies.

[0041] In this embodiment, the sampling frequency is dynamically adjusted according to different driving states of the vehicle terminal to avoid data sampling redundancy or loss caused by sampling at a fixed sampling frequency. Multiple sampling frequencies are pre-defined according to different driving states, specifically including: a full sampling frequency, a first preset sampling frequency, a second preset sampling frequency, and a third preset sampling frequency. The first preset sampling frequency is lower than the second preset sampling frequency, and the second preset sampling frequency is lower than the third preset sampling frequency.

[0042] If the current driving state of the i-th vehicle terminal is the high-speed constant speed state, then the first preset sampling frequency is determined as the i-th target sampling frequency; If the current driving state of the i-th vehicle terminal is the normal driving state, then the second preset sampling frequency is determined as the i-th target sampling frequency; If the current driving state of the i-th vehicle terminal is the congested driving state, then the third preset sampling frequency is determined as the i-th target sampling frequency; If the current driving state of the i-th vehicle terminal is the abnormal state, then the full sampling frequency is determined as the i-th target sampling frequency.

[0043] It is understandable that when the i-th vehicle terminal is in a high-speed, constant-speed state, data changes are infrequent, leading to a large amount of redundant data. Therefore, in this driving state, the sampling frequency can be appropriately reduced to avoid data redundancy. When the i-th vehicle terminal is in a normal driving state, data changes are moderate, so a medium sampling frequency, i.e., the second preset sampling frequency, is used for sampling. When the i-th vehicle terminal is in a congested driving state, data changes frequently, requiring an appropriate increase in the sampling frequency to retain key information and avoid data loss. When the i-th vehicle terminal is in an abnormal state, to avoid data loss, the full sampling frequency is used. At the full sampling frequency, all data reported by the vehicle terminal will be saved.

[0044] This embodiment distinguishes the driving scenarios of the vehicle terminal and dynamically adjusts the sampling frequency of the vehicle terminal, avoiding data redundancy and loss of key data, reducing data management costs, and improving scenario adaptability.

[0045] Step S140: Based on the i-th target sampling frequency, obtain the i-th time-series data from the i-th vehicle terminal.

[0046] After adjusting the sampling frequency, based on the adjusted sampling frequency, i.e. the i-th target sampling frequency, the data reported by the i-th vehicle terminal is continued to be acquired to obtain the i-th time series data.

[0047] During data acquisition, the Flink Checkpoint mechanism is enabled. Every preset time interval, the previous time-series data is backed up. RockDB is used as the backend to store the backup data. When the Flink task fails and restarts, it can restore the state from the most recent checkpoint, thereby avoiding data anomalies caused by sampling logic termination.

[0048] Step S150: Determine whether the i-th time series data meets the preset judgment condition. If it does, the i-th time series data is determined as core data. If it does not, the i-th time series data is determined as non-core data.

[0049] In one embodiment, the i-th time-series data includes: the i-th fault code, the i-th acceleration, and the i-th tire pressure status indicator. The step of determining whether the i-th time-series data meets the preset determination conditions includes: if the i-th fault code is not empty, or the i-th acceleration belongs to a second preset acceleration range, or the i-th tire pressure status indicator is a preset alarm indicator, then the i-th time-series data is determined to meet the preset determination conditions.

[0050] If the i-th time-series data meets any of the following conditions, then the i-th time-series data is determined to be core data: the fault code is not empty, or the acceleration is within the second preset acceleration range (e.g., |acceleration|≥3m / s²), or the tire pressure status indicator tpmsStatus is the preset alarm indicator ALARM, or it includes other key alarm data. The other key alarm data can be set according to actual needs and are not restricted here.

[0051] Step S160: Write the core data to the first storage area according to the first writing method, and write the non-core data to the second storage area according to the second writing method. The query efficiency of the first storage area is higher than that of the second storage area.

[0052] In this embodiment, core data is written to ClickHouse via Flink JDBC Sink to ensure low latency for real-time queries; batch submission is used during writing to improve throughput. For non-core data, data is grouped by vin as the key, and a rolling window aggregation with a preset time (e.g., minutes) is used to package multiple data entries into a JSON array, which is then written to HDFS / MinIO (a low-cost file system) via FlinkFileSink to reduce storage costs.

[0053] In one embodiment, the method further includes: within a preset period, calculating the query popularity of each core data in the first storage area; determining the core data whose query popularity is greater than or equal to a preset popularity threshold as hot data, and determining the core data whose query popularity is less than the preset popularity threshold as cold data; The hot data is synchronized to the third storage area, where the query efficiency is greater than that of the first storage area; the cold data is migrated to the fourth storage area, where the query efficiency is greater than that of the second storage area but less than that of the third storage area.

[0054] In this embodiment, the data reported by the vehicle terminal is classified by popularity and stored in layers. The hot data that is frequently queried and the cold data that is queried infrequently are stored in different storage areas to avoid inefficient use of high-performance storage resources.

[0055] For example, using a one-hour timeframe, data with ≥10 queries for the same vehicle identifier is classified as hot data, while data with fewer than 10 queries is classified as cold data. Differentiated storage processing is then achieved through scheduled tasks: every 5 minutes, hot data from the past 24 hours that meets the criteria in ClickHouse is synchronized to a Redis in-memory database, stored using a hash structure with an expiration time to prevent dirty data and meet millisecond-level query requirements; every day at midnight, cold data stored in ClickHouse for more than 7 days is migrated to a low-cost MongoDB document database using a "write-then-delete" strategy to ensure data consistency. Ultimately, a hierarchical storage system is built: Redis (hot data, millisecond-level) → ClickHouse (core data, second-level) → MongoDB (cold data, minute-level) → HDFS / MinIO (non-core data, batch processing). This allows high-performance storage resources to be concentrated on frequently queried hot data, while infrequently queried cold data and non-core data are stored in low-cost storage media. This avoids inefficient use of high-performance storage resources and matches the query needs of different types and frequencies of data, achieving a balance between storage cost and query performance.

[0056] When data needs to be queried, the query proceeds in the order of Redis→ClickHouse→MongoDB→HDFS / MinIO. Once the data is found, the result is returned immediately; otherwise, the query continues to the next level.

[0057] The vehicle network data dynamic management method provided in this application embodiment acquires the current operating data of N different vehicle terminals, where N≥1; determines the current driving state of the i-th vehicle terminal based on its current operating data, where 1≤i≤N; determines the i-th target sampling frequency corresponding to the i-th vehicle terminal from multiple preset sampling frequencies based on its current driving state; acquires i-th time-series data from the i-th vehicle terminal based on the i-th target sampling frequency; determines whether the i-th time-series data meets preset judgment conditions; if it does, the i-th time-series data is determined as core data; if it does not, the i-th time-series data is determined as non-core data; writes the core data to a first storage area according to a first writing method, and writes the non-core data to a second storage area according to a second writing method. The query efficiency of the first storage area is higher than that of the second storage area, realizing dynamic downsampling of vehicle terminal data and hot-spot distribution of data storage. While ensuring data integrity, it improves storage resource utilization and reduces data management costs.

[0058] Example 2 In addition, please see Figure 2 This application also provides a vehicle network data dynamic management system 200, including: The data acquisition module 210 is used to acquire the current operating data of N different vehicle terminals, where N≥1; The state determination module 220 is used to determine the current driving state of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal, where 1≤i≤N; The frequency determination module 230 is used to determine the i-th target sampling frequency corresponding to the i-th vehicle terminal from a plurality of preset sampling frequencies based on the current driving state of the i-th vehicle terminal. The dynamic downsampling module 240 is used to acquire the i-th time-series data from the i-th vehicle terminal based on the i-th target sampling frequency; The splitting module 250 is used to determine whether the i-th time-series data meets the preset judgment conditions. If it does, the i-th time-series data is determined as core data; if it does not, the i-th time-series data is determined as non-core data. The write storage module 260 is used to write the core data into a first storage area according to a first write method and to write the non-core data into a second storage area according to a second write method. The query efficiency of the first storage area is higher than that of the second storage area.

[0059] The vehicle network data dynamic management system 200 provided in this application embodiment can execute the vehicle network data dynamic management method provided in the above method embodiment 1. To avoid repetition, it will not be described again here.

[0060] Example 3 Furthermore, embodiments of the present invention provide an in-vehicle device, including a memory and a processor. The memory stores a computer program, and the computer program executes the vehicle network data dynamic management method provided in Embodiment 1 when it runs on the processor.

[0061] For details, please see Figure 3 The vehicle-mounted device 300 includes a transceiver 301, a bus interface, and a processor 302. The processor 302 is used to acquire current operating data of N different vehicle-mounted terminals, where N≥1; determine the current driving state of the i-th vehicle-mounted terminal based on its current operating data, where 1≤i≤N; determine the i-th target sampling frequency corresponding to the i-th vehicle-mounted terminal from multiple preset sampling frequencies based on its current driving state; acquire i-th time-series data from the i-th vehicle-mounted terminal based on the i-th target sampling frequency; determine whether the i-th time-series data meets preset judgment conditions; if it does, the i-th time-series data is determined as core data; if it does not, the i-th time-series data is determined as non-core data; write the core data to a first storage area according to a first writing method, and write the non-core data to a second storage area according to a second writing method, wherein the query efficiency of the first storage area is higher than that of the second storage area.

[0062] In this embodiment of the invention, the vehicle-mounted device 300 further includes a memory 303. Figure 3 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 302) and memory (memory 303). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 301 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. The processor 302 is responsible for managing the bus architecture and general processing, and the memory 303 can store data used by the processor 302 during operation.

[0063] The vehicle-mounted device 300 provided in this application embodiment can execute the vehicle network data dynamic management method provided in the above method embodiment 1. To avoid repetition, it will not be described again here.

[0064] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0065] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0066] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for dynamic management of vehicle network data, characterized in that, The method includes: Obtain the current operating data of N different vehicle terminals, where N≥1; Based on the current operating data of the i-th vehicle terminal, determine the current driving status of the i-th vehicle terminal, where 1≤i≤N; Based on the current driving state of the i-th vehicle terminal, the i-th target sampling frequency corresponding to the i-th vehicle terminal is determined from multiple preset sampling frequencies; Based on the i-th target sampling frequency, the i-th time-series data is obtained from the i-th vehicle terminal; Determine whether the i-th time series data meets the preset judgment condition. If it does, the i-th time series data is determined as core data. If it does not, the i-th time series data is determined as non-core data. According to the first writing method, the core data is written to the first storage area, and according to the second writing method, the non-core data is written to the second storage area. The query efficiency of the first storage area is higher than that of the second storage area.

2. The method for dynamic management of vehicle network data according to claim 1, characterized in that, The current operating data includes: current speed and current acceleration. Determining the current driving state of the i-th vehicle terminal based on its current operating data includes: If the current speed of the i-th vehicle terminal is greater than or equal to the first preset speed, and the current acceleration of the i-th vehicle terminal is within the first preset acceleration range, then the current driving state of the i-th vehicle terminal is determined to be: high-speed constant speed state.

3. The method for dynamic management of vehicle network data according to claim 2, characterized in that, The current operating data also includes: GPS data. Determining the current driving status of the i-th vehicle terminal based on its current operating data further includes: The displacement change rate of the i-th vehicle-mounted terminal is determined based on the GPS data; If the current speed of the i-th vehicle terminal is less than the second preset speed, and the current acceleration of the i-th vehicle terminal meets the preset acceleration condition, then the current driving state of the i-th vehicle terminal is determined to be: congested driving state. If the current speed of the i-th vehicle terminal is less than the second preset speed, and the displacement change rate of the i-th vehicle terminal is greater than or equal to the preset change rate threshold, then the current driving state of the i-th vehicle terminal is determined to be: congested driving state, and the first preset speed is greater than the second preset speed.

4. The method for dynamic management of vehicle network data according to claim 3, characterized in that, The current operating data also includes: fault codes and tire pressure status indicators. Determining the current driving status of the i-th vehicle terminal based on its current operating data further includes: If the fault code of the i-th vehicle terminal is not empty, or the tire pressure status indicator of the i-th vehicle terminal is a preset alarm indicator, or the current acceleration of the i-th vehicle terminal is within the second preset acceleration range, then the current driving state of the i-th vehicle terminal is determined to be: abnormal state.

5. The method for dynamic management of vehicle network data according to claim 4, characterized in that, Determining the current driving status of the i-th vehicle terminal based on its current operating data further includes: Determine whether the current operating data of the i-th vehicle terminal meets the preset driving conditions. If it does, then determine the current driving state of the i-th vehicle terminal as: normal driving state.

6. The method for dynamic management of vehicle network data according to claim 5, characterized in that, The plurality of preset sampling frequencies include: a full sampling frequency, a first preset sampling frequency, a second preset sampling frequency, and a third preset sampling frequency, wherein the first preset sampling frequency is less than the second preset sampling frequency, the second preset sampling frequency is less than the third preset sampling frequency, and the step of determining the i-th target sampling frequency corresponding to the i-th vehicle terminal from the plurality of preset sampling frequencies based on the current driving state of the i-th vehicle terminal includes: If the current driving state of the i-th vehicle terminal is the high-speed constant speed state, then the first preset sampling frequency is determined as the i-th target sampling frequency; If the current driving state of the i-th vehicle terminal is the normal driving state, then the second preset sampling frequency is determined as the i-th target sampling frequency; If the current driving state of the i-th vehicle terminal is the congested driving state, then the third preset sampling frequency is determined as the i-th target sampling frequency; If the current driving state of the i-th vehicle terminal is the abnormal state, then the full sampling frequency is determined as the i-th target sampling frequency.

7. The method for dynamic management of vehicle network data according to claim 1, characterized in that, The i-th time-series data includes: the i-th fault code, the i-th acceleration, and the i-th tire pressure status indicator. The step of determining whether the i-th time-series data meets preset judgment conditions includes: If the i-th fault code is not empty, or the i-th acceleration belongs to the second preset acceleration range, or the i-th tire pressure status identifier is a preset alarm identifier, then the i-th timing data is determined to meet the preset judgment condition.

8. The method for dynamic management of vehicle network data according to claim 1, characterized in that, The method further includes: Within a preset period, the query popularity of each core data in the first storage area is statistically analyzed; The core data whose query popularity is greater than or equal to a preset popularity threshold is identified as hot data, and the core data whose query popularity is less than the preset popularity threshold is identified as cold data. The hot data is synchronized to a third storage area, where the query efficiency of the third storage area is greater than that of the first storage area. The cold data is migrated to a fourth storage area, where the query efficiency is greater than that of the second storage area but less than that of the third storage area.

9. A vehicle-to-everything (V2X) data dynamic management system, characterized in that, The system includes: The data acquisition module is used to acquire the current operating data of N different vehicle terminals, where N≥1; The state determination module is used to determine the current driving state of the i-th vehicle terminal based on the current operating data of the i-th vehicle terminal, where 1≤i≤N; The frequency determination module is used to determine the i-th target sampling frequency corresponding to the i-th vehicle terminal from multiple preset sampling frequencies based on the current driving state of the i-th vehicle terminal. A dynamic downsampling module is used to acquire the i-th time-series data from the i-th vehicle terminal based on the i-th target sampling frequency; The splitting module is used to determine whether the i-th time series data meets the preset judgment conditions. If it does, the i-th time series data is determined as core data; if it does not, the i-th time series data is determined as non-core data. The write storage module is used to write the core data into a first storage area according to a first write method and to write the non-core data into a second storage area according to a second write method. The query efficiency of the first storage area is higher than that of the second storage area.

10. A vehicle-mounted device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed on the processor, performs the vehicle network data dynamic management method according to any one of claims 1-8.

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