Vehicle big data analysis method and device, electronic equipment and medium

Abstract

The invention discloses a vehicle big data analysis method and device, electronic equipment and a medium, and relates to the field of data analysis, and the method comprises the steps: obtaining a CAN message of a vehicle through a CAN network, obtaining the vehicle type data of the vehicle at the same time, carrying out the corresponding analysis of the CAN message according to the vehicle type data, and obtaining the vehicle real-time data of the corresponding vehicle through analysis. The situation that after CAN messages of all vehicles are uploaded to the cloud end together, analysis errors possibly occur due to the fact that the number of vehicle type analysis algorithms of the cloud end is insufficient is avoided, and the accuracy of data processing is improved; in addition, according to the scheme, the vehicle real-time data is analyzed in advance, and the analyzed vehicle real-time data is directly uploaded to the cloud, so that the calculation amount of the cloud is reduced, and the data processing efficiency of the cloud is also improved.

Description

Technical Field

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

[0002] With the rapid development of the automobile industry, especially the popularization of vehicles, a large amount of data is generated during the operation of vehicles. These data not only include the location information, speed, fuel consumption, fault information, etc. of traditional vehicles, but also involve the 32960 national standard data of vehicles, such as battery power, charging status, current, voltage, etc. This information is crucial to improving the performance of vehicles and optimizing operation management. However, the existing data processing method often uploads the collected CAN (Controller Area Network) messages of the vehicle to the cloud, and the cloud performs corresponding parsing to parse out the real-time data of the vehicle. However, because the number of vehicles that need to be collected in practice is large, and the vehicle models are different, the parsing algorithms corresponding to different models are also different. If the number of parsing algorithms in the cloud is less than the number of vehicle models that need to be calculated and analyzed, then this method is prone to parsing errors; similarly, if the parsing algorithm in the cloud is the same as the vehicle model, but the cloud needs to classify the collected vehicle CAN messages first, and then parse the CAN messages according to the corresponding parsing algorithm, but this method is less efficient. Therefore, a new vehicle big data analysis method is urgently needed to improve data processing efficiency and accuracy. Summary of the invention

[0003] The purpose of the present invention is to provide a vehicle big data analysis method, device, electronic device and medium. This solution obtains the vehicle's CAN message through the CAN network while obtaining the vehicle model data of the vehicle, and parses the CAN message accordingly according to the model data, thereby avoiding the parsing errors that may occur after uploading all the vehicle's CAN messages to the cloud together, and improving the accuracy of data processing; in addition, the parsed vehicle real-time data is directly uploaded to the cloud, which not only reduces the computing workload of the cloud, but also improves the data processing efficiency of the cloud.

[0004] The purpose of the present invention is to provide a vehicle big data analysis method, device, electronic device and medium. This solution avoids the situation where parsing errors may occur in the cloud due to insufficient number of vehicle model parsing algorithms after uploading CAN messages of all vehicles to the cloud, thereby improving the accuracy of data processing; in addition, this solution also reduces the computing power of the cloud and improves the data processing efficiency of the cloud.

[0005] In order to solve the above technical problems, the present invention provides a vehicle big data analysis method, comprising:

[0006] Obtain CAN messages corresponding to several vehicles through the CAN network, and obtain the model data of each of the vehicles;

[0007] Parsing the vehicle real-time data corresponding to each of the vehicles from the corresponding CAN messages according to the vehicle model data;

[0008] The real-time data of each vehicle is packaged and transmitted to the cloud, so that the real-time data of each vehicle is analyzed accordingly through the cloud, and the analyzed data is stored accordingly through the cloud.

[0009] Optionally, after parsing the vehicle real-time data corresponding to each of the vehicles from each of the CAN messages according to the vehicle model data, the method further includes:

[0010] Cleaning the real-time data of each vehicle to filter out error frames in the real-time data of each vehicle;

[0011] Accordingly, the real-time data of each vehicle is packaged and transmitted to the cloud, including:

[0012] The real-time data of each vehicle after cleaning is packaged and transmitted to the cloud.

[0013] Optionally, after the real-time data of each vehicle is packaged and transmitted to the cloud, the method further includes:

[0014] Determining whether a response signal transmitted from the cloud is received within a preset period;

[0015] If the response signal transmitted by the cloud is not received within a preset period, the real-time data of each vehicle is repackaged and transmitted to the cloud until the response signal transmitted by the cloud is received;

[0016] If a response signal transmitted from the cloud is received within a preset period, it triggers the steps of analyzing the real-time data of each vehicle through the cloud and storing the analyzed data through the cloud.

[0017] Optionally, the corresponding analysis of the real-time data of each vehicle through the cloud includes:

[0018] The real-time data of each vehicle are stored separately, and the brake opening, throttle opening, vehicle speed, vehicle wiper information, vehicle ambient temperature, and vehicle position in the real-time data of each vehicle are determined;

[0019] determining a driving habit of a driver of the corresponding vehicle based on the brake opening, the throttle opening and the vehicle speed;

[0020] The vehicle operating condition of the corresponding vehicle is determined based on the vehicle wiper information, the vehicle ambient temperature and the vehicle position.

[0021] Optionally, the corresponding analysis of the real-time data of each vehicle through the cloud further includes:

[0022] Determine the latitude and longitude information, mileage data, current value, and voltage value in the real-time data of each vehicle;

[0023] The running track of the corresponding vehicle is calculated based on the longitude and latitude information, the mileage of the corresponding vehicle is determined based on the mileage data, and the energy consumption per kilometer of the corresponding vehicle is determined based on the current value and the voltage value.

[0024] Optionally, the corresponding analysis of the real-time data of each vehicle through the cloud further includes:

[0025] Determining the total average energy consumption of each of the vehicles according to the energy consumption per kilometer corresponding to each of the vehicles;

[0026] Determine the total mileage of each of the vehicles using the mileage corresponding to each of the vehicles;

[0027] Determining the distribution position of the corresponding vehicle according to the running trajectory;

[0028] The overall operating conditions of each of the vehicles are determined based on the total average energy consumption, the total mileage and the distribution positions corresponding to each of the vehicles.

[0029] Optionally, after the analyzed data is stored accordingly in the cloud, the method further includes:

[0030] Determining the acquisition time corresponding to each of the vehicle real-time data through the cloud, and determining the target data that meets the preset privacy conditions in the analyzed data;

[0031] A plurality of groups of virtual data tables are generated through the cloud according to the data type corresponding to the analyzed data, the corresponding acquisition time and the analyzed data, and the target virtual data tables corresponding to the target data in each of the virtual data tables are fuzzy processed, so that the virtual data tables other than the target virtual data tables in each of the virtual data tables can be displayed accordingly using a display device.

[0032] In order to solve the above technical problems, the present invention also provides a vehicle big data analysis device, comprising:

[0033] An acquisition unit, used to acquire CAN messages corresponding to a plurality of vehicles through a CAN network, and acquire vehicle model data of each of the vehicles;

[0034] A parsing unit, used for parsing the vehicle real-time data corresponding to each of the vehicles from the corresponding CAN messages according to the vehicle model data;

[0035] The transmission unit is used to package and transmit the real-time data of each vehicle to the cloud, so that the real-time data of each vehicle can be analyzed accordingly through the cloud, and the analyzed data can be stored accordingly through the cloud.

[0036] In order to solve the above technical problems, the present invention further provides an electronic device, comprising:

[0037] Memory for storing computer programs;

[0038] A processor is used to implement the steps of the vehicle big data analysis method as described above when executing the computer program.

[0039] In order to solve the above technical problems, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the vehicle big data analysis method as described above are implemented.

[0040] The purpose of the present invention is to provide a vehicle big data analysis method, device, electronic device and medium. This solution obtains the vehicle model data while obtaining the vehicle CAN message of the vehicle through the CAN network, and performs corresponding analysis on the CAN message according to the model data to parse out the vehicle real-time data of the corresponding vehicle, thereby avoiding the parsing errors that may occur in the cloud due to insufficient number of model parsing algorithms after uploading all vehicles' CAN messages to the cloud, thereby improving the accuracy of data processing; in addition, this solution parses out the vehicle real-time data in advance and uploads the parsed vehicle real-time data directly to the cloud, thereby reducing the amount of calculation in the cloud and improving the data processing efficiency in the cloud. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0042] Figure 1 A process flow chart of a vehicle big data analysis method provided by the present invention;

[0043] Figure 2 A working schematic diagram of a vehicle big data analysis system provided by the present invention;

[0044] Figure 3 A schematic diagram of the structure of a vehicle big data analysis device provided by the present invention;

[0045] Figure 4The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0046] The core of the present invention is to provide a vehicle big data analysis method, device, electronic device and medium. This solution avoids the situation where the CAN messages of all vehicles are uploaded to the cloud together, and the parsing errors that may occur in the cloud due to the insufficient number of vehicle model parsing algorithms, thereby improving the accuracy of data processing; in addition, this solution also reduces the computing power of the cloud and improves the data processing efficiency of the cloud.

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Please refer to Figure 1 , Figure 1 A process flow chart of a vehicle big data analysis method provided by the present invention. The vehicle big data analysis method includes:

[0049] S11: Obtain CAN messages corresponding to several vehicles through the CAN network, and obtain the model data of each vehicle;

[0050] S12: parsing the corresponding vehicle real-time data of each vehicle from the corresponding CAN messages according to the data of each vehicle model;

[0051] S13: Packaging and transmitting the real-time data of each vehicle to the cloud, so as to analyze the real-time data of each vehicle accordingly through the cloud, and store the analyzed data accordingly through the cloud.

[0052] In the present invention, considering that the existing technology must use the cloud to analyze vehicle data, and the analysis algorithms corresponding to different vehicle models are also different, if it is necessary to analyze data of multiple vehicles, it is necessary to ensure that the cloud has the analysis algorithms corresponding to all vehicles at the same time. If the cloud has too few analysis algorithms, it will cause analysis errors, which will affect the accuracy of data processing. Therefore, this solution obtains CAN messages corresponding to several vehicles through the CAN network, and obtains the model data of each vehicle, and then parses the real-time vehicle data corresponding to each vehicle from the corresponding CAN messages according to the obtained model data, so as to ensure that the CAN messages corresponding to each vehicle can be accurately analyzed, so as to obtain to obtain accurate real-time vehicle data; in addition, considering that when the number of vehicles that need data processing and analysis is too large, the cloud computing workload is too large, and even if the number of parsing algorithms available on the cloud is equal to the parsing algorithms corresponding to each vehicle, when the cloud actually processes the CAN message, it is still necessary to first determine the model of each vehicle based on the CAN message, and then select the corresponding parsing algorithm to parse the CAN message based on the model of the vehicle. The efficiency of the entire data processing process is too low, and the cloud computing workload is too high. Therefore, this solution will parse the real-time vehicle data from the CAN message in advance based on the model data, and there is no need for the cloud to parse the CAN message, which greatly reduces the cloud computing workload and improves the cloud data processing efficiency.

[0053] The present embodiment provides a vehicle big data analysis method. The present scheme obtains the vehicle model data while obtaining the vehicle CAN message of the vehicle through the CAN network, and performs corresponding analysis on the CAN message according to the model data to parse out the vehicle real-time data of the corresponding vehicle, thereby avoiding the analysis errors that may occur in the cloud due to insufficient number of model analysis algorithms after uploading all the vehicle CAN messages to the cloud, thereby improving the accuracy of data processing; in addition, the present scheme parses out the vehicle real-time data in advance and uploads the analyzed vehicle real-time data directly to the cloud, thereby reducing the computing workload on the cloud and improving the data processing efficiency on the cloud.

[0054] Based on the above embodiments:

[0055] As an optional embodiment, after parsing the vehicle real-time data corresponding to each vehicle from each CAN message according to each vehicle model data, it also includes:

[0056] Clean the real-time data of each vehicle to filter out the error frames in the real-time data of each vehicle;

[0057] Accordingly, the real-time data of each vehicle is packaged and transmitted to the cloud, including:

[0058] The real-time data of each vehicle after cleaning is packaged and transmitted to the cloud.

[0059] In the present invention, considering that CAN messages are easily affected by factors such as the environment and vehicles during the actual collection process, error frames are easily present in the collected CAN messages. In addition, during the parsing process of the CAN messages, error frames may also be present in the generated vehicle real-time data due to the influence of actual operations. Therefore, in order to ensure the accuracy of the vehicle real-time data processing and analysis process, this solution will clean the real-time data of each vehicle after parsing the vehicle real-time data corresponding to each vehicle from each CAN message according to the data of each vehicle model, so as to filter out the error frames in the real-time data of each vehicle, and then package the cleaned real-time data of each vehicle for transmission to the cloud, which greatly improves the accuracy of the solution.

[0060] As an optional embodiment, after the real-time data of each vehicle is packaged and transmitted to the cloud, the method further includes:

[0061] Determine whether a response signal transmitted from the cloud is received within a preset period;

[0062] If the response signal transmitted from the cloud is not received within the preset period, the real-time data of each vehicle is repackaged and transmitted to the cloud until the response signal transmitted from the cloud is received;

[0063] If a response signal transmitted from the cloud is received within a preset period, it triggers the steps of analyzing the real-time data of each vehicle through the cloud and storing the analyzed data through the cloud.

[0064] In the present invention, considering that during the process of uploading vehicle real-time data to the cloud, if a failure occurs in the cloud, resulting in the failure to receive vehicle real-time data within a certain period of time, it will have a great impact on the subsequent data analysis process. Therefore, in order to ensure that the parsed vehicle real-time data can be fully transmitted to the cloud for corresponding storage and analysis, this solution will determine whether a response signal transmitted from the cloud is received within a preset period after the real-time data of each vehicle is packaged and transmitted to the cloud. If the response signal transmitted from the cloud is not received within the preset period, it proves that the vehicle real-time data has not been successfully transmitted to the cloud. In this way, the real-time data of each vehicle will be repackaged and transmitted to the cloud until a response signal transmitted from the cloud is received, so as to ensure that the parsed vehicle real-time data can be fully transmitted to the cloud, thereby improving the accuracy of the data analysis process.

[0065] As an optional embodiment, the real-time data of each vehicle is analyzed accordingly, including:

[0066] The real-time data of each vehicle is stored separately, and the brake opening, throttle opening, vehicle speed, vehicle wiper information, vehicle ambient temperature, and vehicle position in the real-time data of each vehicle are determined;

[0067] Determine the driving habits of the driver of the corresponding vehicle based on the brake opening, throttle opening and vehicle speed;

[0068] The vehicle operating condition of the corresponding vehicle is determined based on the vehicle wiper information, vehicle ambient temperature and vehicle position.

[0069] In the present invention, considering that the real-time data of vehicles can reflect the driving habits of the driver and the operating conditions of the vehicle, this solution first uses the cloud to store the real-time data of each vehicle separately, and determines the brake opening, throttle opening, vehicle speed, vehicle wiper information, vehicle ambient temperature, and vehicle position in the real-time data of each vehicle, and then determines the driving habits of the driver of the corresponding vehicle based on the determined brake opening, throttle opening and vehicle speed, and determines the vehicle operating conditions of the corresponding vehicle based on the determined vehicle wiper information, vehicle ambient temperature and vehicle position, thereby ensuring the integrity of the data analysis process.

[0070] As an optional embodiment, the real-time data of each vehicle is analyzed accordingly, further comprising:

[0071] Determine the latitude and longitude information, mileage data, current value, and voltage value in the real-time data of each vehicle;

[0072] The running track of the corresponding vehicle is calculated based on the longitude and latitude information, the mileage of the corresponding vehicle is determined based on the mileage data, and the energy consumption per kilometer of the corresponding vehicle is determined based on the current value and the voltage value.

[0073] In the present invention, considering that the real-time data of the vehicle can not only reflect the driving habits of the driver and the operating conditions of the vehicle, but also reflect the operating trajectory of the vehicle, the mileage of the vehicle and the energy consumption per kilometer of the vehicle, the present solution will also use the cloud to determine the longitude and latitude information, mileage data, current value, and voltage value in the real-time data of each vehicle, and then calculate the operating trajectory of the corresponding vehicle according to the longitude and latitude information, determine the mileage of the corresponding vehicle according to the mileage data, and determine the energy consumption per kilometer of the corresponding vehicle according to the current value and voltage value, so as to conduct a more in-depth analysis of the actual situation of the vehicle, so as to facilitate users to understand the situation of the vehicle.

[0074] As an optional embodiment, the real-time data of each vehicle is analyzed accordingly, further comprising:

[0075] Determine the total average energy consumption of each vehicle based on the energy consumption per kilometer corresponding to each vehicle;

[0076] Determine the total mileage of each vehicle using the mileage corresponding to each vehicle;

[0077] Determine the distribution position of the corresponding vehicle according to the running trajectory;

[0078] The overall operation status of each vehicle is determined based on the total average energy consumption, total mileage and the corresponding distribution position of each vehicle.

[0079] In the present invention, considering that in actual applications, the real-time data of the vehicle will be parsed and uploaded to the cloud in real time within a day, the cloud will also conduct an overall analysis of the real-time vehicle data corresponding to the single vehicle received within a day. Therefore, this solution will also determine the total average energy consumption of each vehicle according to the energy consumption per kilometer corresponding to each vehicle, determine the total mileage of each vehicle according to the mileage corresponding to each vehicle, and determine the distribution position of the corresponding vehicle according to the operation trajectory. Finally, the overall operation status of each vehicle is determined based on the determined total average energy consumption, total mileage and the distribution position corresponding to each vehicle, which is convenient for users to understand the operation status of a single vehicle in a day and the overall operation status of all vehicles in a day, thereby improving the depth of data analysis.

[0080] As an optional embodiment, after the analyzed data is stored accordingly in the cloud, the method further includes:

[0081] Determine the acquisition time corresponding to each vehicle's real-time data through the cloud, and determine the target data in the analyzed data that meets the preset privacy conditions;

[0082] Several groups of virtual data tables are generated through the cloud according to the data type corresponding to the analyzed data, the corresponding acquisition time and the analyzed data, and the target virtual data table corresponding to the target data in each virtual data table is fuzzy processed, so that the virtual data tables except the target virtual data table in each virtual data table can be displayed accordingly using the display device.

[0083] In the present invention, considering that users and maintenance personnel need to view the acquired vehicle real-time data in real time, in order to make it more convenient for users and maintenance personnel to use, this solution can first use the cloud to determine the acquisition time corresponding to each vehicle real-time data, and then use the vehicle real-time data, the data type of the vehicle real-time data and the corresponding acquisition time to generate a virtual data table. However, considering that some types of data in the vehicle real-time data have extremely high privacy, in order to maintain confidentiality, this solution will also use the cloud to determine the target data that meets the preset privacy conditions in the analyzed data, and finally use the cloud to generate several groups of virtual data tables according to the data type corresponding to the analyzed data, the corresponding acquisition time and the analyzed data, and use the cloud to perform fuzzy processing on the target virtual data table corresponding to the target data in each virtual data table, so as to use the display device through the cloud to display the virtual data table except the target virtual data table in each virtual data table accordingly, that is, the vehicle real-time data with high privacy is not displayed, but the vehicle real-time data with low privacy can be displayed through the virtual data table, thereby ensuring the privacy of the solution.

[0084] It should be noted that if Figure 2As shown, the system corresponding to this solution includes: data collection and transmission: mainly through T-BOX (Telematics-BOX, telematics processor), hereinafter referred to as the terminal. The terminal obtains the vehicle's message through the CAN network, parses the vehicle's real-time data from it, and cleans the data, that is, removes invalid values ​​and abnormal values. Then the real-time data is sorted at a certain frequency, packaged and transmitted to the cloud through the wireless network. The terminal parses the data and then uploads it, which has two advantages over directly uploading CAN data. First, it reduces the computing pressure of parsing in the cloud; second, the terminal can parse according to its own situation, avoiding data parsing errors caused by different parsing rules for different vehicles in the cloud or increasing the steps of judging the parsing rules. The terminal needs to design a missed transmission retransmission mechanism. When the terminal communicates with the cloud, a response mechanism should be designed. When the missed transmission is caused by network fluctuations or server abnormalities, there should be a retransmission mechanism, that is, the data that has not been successfully sent will be sent again when normal communication is restored.

[0085] Data reception and storage: Due to the large number of vehicles and the large number of data packets per vehicle, the data reception and storage part must be highly efficient to handle high-concurrency data. The system uses Kafka for real-time data stream processing and analysis, which can handle large amounts of data streams and provide high throughput and low latency. Hbase (a distributed database for column-based storage) is used for data storage. After receiving the data, it should be stored in the database immediately and a reply should be given to the data sending module. When data is uploaded repeatedly, the function of not repeatedly storing the same content should be available to avoid affecting the calculation and save space.

[0086] Data calculation and analysis: Spark is used to retrieve data from Hbase for calculation, and the calculation results are stored in MySQL (relational database management system). Create a Spark session and configure the connection information of HBase and MySQL. Then, we use Spark's DataFrame API (Application Programming Interface) to read the data in HBase and calculate the data. This includes calculating the vehicle's daily running trajectory, daily mileage, and daily energy consumption per kilometer based on the real-time data uploaded by the single vehicle. The driver's driving habits are analyzed based on the brake opening, throttle opening, vehicle speed and other information in the vehicle's real-time data. The vehicle's operating conditions can be inferred based on the vehicle's wiper information, vehicle ambient temperature, vehicle location and other information. The calculation service then calculates the overall vehicle's operating conditions based on the single vehicle's operating conditions.

[0087] Virtual data table: By building and configuring virtual data tables, you can avoid the storage overhead of building data tables in real MySQL. In addition, virtual data tables are more flexible and convenient to modify. In virtual data tables, you can also perform fuzzy processing on data to ensure data security. Virtual data tables are directly facing the user end.

[0088] Low-code data display module: The low-code data display module allows users to build an interface to display analysis results. Through the combination of different components, the required data can be quickly associated to achieve instant visualization. For example, through the line chart component, the bicycle mileage meter in the virtual database is associated, and then the corresponding date is selected to see the daily mileage trend of the bicycle.

[0089] It is also necessary to explain the relevant terms of the above system:

[0090] Telecommunication terminal TBOX: It is an automotive electronic module that integrates intelligent information processing and communication technologies. Its main function is to realize wireless communication between the vehicle and the outside world, providing various conveniences and safety guarantees for the driver and the vehicle. It is one of the important controllers for the communication and interaction between the vehicle and the outside world.

[0091] CAN bus and CAN communication: CAN bus is a high-speed communication protocol used in vehicle electronic systems. It supports real-time and reliable data exchange and is mainly used for communication between in-vehicle controllers and sensors. The interactive information is usually called a message, which can be parsed into data.

[0092] Kafka: Kafka is an open source distributed stream processing platform originally developed by LinkedIn and now maintained by the Apache Software Foundation. It is mainly used for real-time data stream processing and analysis, capable of handling large amounts of data streams and providing high throughput and low latency. Kafka can process millions of messages per second, suitable for large-scale data processing, and can easily expand the cluster to handle larger loads by increasing the number of partitions and nodes.

[0093] MySQL: MySQL is an open source relational database management system known for its high performance, reliability, and ease of use. It uses Structured Query Language (SQL) for data operations and management, supports multiple operating systems, and can handle large amounts of data and high concurrent requests. MySQL uses a client-server architecture that allows multiple users to access and manage data over the network. Its features include transaction support, data integrity, a rich selection of storage engines, and strong security, making it suitable for a variety of application scenarios, from small websites to large enterprise-level applications. Due to its mature ecosystem and broad community support, MySQL has become one of the most popular databases in the world.

[0094] Hbase: HBase is an open source distributed, columnar storage database built on the Hadoop ecosystem and designed for processing large-scale data. It can store and retrieve massive structured data in an efficient way, supports real-time read and write operations, and is suitable for application scenarios that require fast access and large-scale data storage. HBase adopts a flexible architecture that can be horizontally expanded and supports distributed storage, allowing users to easily add nodes to cope with the growing amount of data. Its data model provides the concepts of column families and version control, making data storage more flexible. HBase is widely used in big data analysis, real-time data processing, and online applications, and is an important part of enterprises building big data solutions.

[0095] VMDM: VMDM (Virtual Master Data Management) is a data management strategy that aims to integrate and manage the scattered master data (such as customers, products, etc.) in an enterprise, and achieve a unified view and management of these data through virtualization technology. VMDM does not require all master data to be stored in a centralized manner, but integrates the master data in various data sources by establishing a data virtualization layer to ensure data consistency and accuracy. This method is highly flexible and can quickly adapt to business changes, reduce the cost and complexity of data management, and improve data utilization efficiency.

[0096] Hadoop: Hadoop is an open source distributed computing framework for storing and processing large-scale data sets. It uses HDFS (Hadoop Distributed File System) for data storage and supports parallel data processing in a cluster. Hadoop's core components include MapReduce (for distributed data processing), YARN (Yet Another Resource Negotiator), and HDFS. By distributing data across multiple nodes, Hadoop is able to process PB (storage capacity)-level data, has high fault tolerance and scalability, and is widely used in data analysis, data mining, and big data application scenarios.

[0097] Spark: Apache Spark is an open source big data processing framework designed to provide fast and general cluster computing capabilities. It supports batch processing, real-time stream processing, and interactive queries, and can quickly process large-scale data sets in memory, significantly improving data processing speed. The core components of Spark include Resilient Distributed Datasets (RDD), DataFrame, and Dataset, allowing developers to use multiple languages ​​​​such as Scala, Java, Python, and R for data analysis. In addition, Spark also integrates a machine learning library (MLlib), a graph computing library (GraphX), and SQL query capabilities (Spark SQL), which is suitable for various big data application scenarios, such as data analysis, machine learning, and real-time data processing.

[0098] It should also be noted that this solution provides a new type of vehicle big data analysis and display system to improve data processing efficiency, meet relevant requirements, and ensure the accuracy and real-time nature of data. The system can not only effectively integrate and analyze various types of vehicle data, but also realize real-time monitoring of unique indicators of new energy vehicles, such as battery performance, charging efficiency, energy consumption analysis, and driving habit analysis. This will enhance the management and decision-making capabilities of users, help enterprises and car owners to timely identify potential problems, optimize vehicle use and maintenance strategies, thereby improving overall operational efficiency and reducing resource waste. By adopting advanced data analysis technology and visual display methods, this system will provide users with intuitive vehicle status monitoring, fault warning and historical data trend analysis, ensuring that users can make more scientific and reasonable decisions in a rapidly changing market environment. In addition, this solution also has the following advantages: 1. The terminal directly uploads the parsed data, which is conducive to reducing cloud computing pressure and operating costs. 2. A virtual data table is formed for use, which is convenient for use and subsequent modification. 3. Spark can take big data from Hbase for calculation and store the calculation results in MySQL, and can also take the results from MySQL for recalculation.

[0099] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of a vehicle big data analysis device provided by the present invention. The vehicle big data analysis device includes:

[0100] The acquisition unit 11 is used to acquire CAN messages corresponding to a number of vehicles through the CAN network, and acquire the model data of each vehicle;

[0101] The parsing unit 12 is used to parse the corresponding CAN messages according to the data of each vehicle model to obtain the real-time vehicle data corresponding to each vehicle;

[0102] The transmission unit 13 is used to package and transmit the real-time data of each vehicle to the cloud, so as to analyze the real-time data of each vehicle accordingly through the cloud, and store the analyzed data accordingly through the cloud.

[0103] The vehicle big data analysis device provided in this embodiment corresponds to the above-mentioned method, and therefore has the same beneficial effects as the above-mentioned method. Therefore, for the embodiments of the vehicle big data analysis device part, please refer to the description of the embodiments of the method part, which will not be repeated here.

[0104] Please refer to Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided by the present invention. The electronic device comprises:

[0105] A memory 20, for storing computer programs;

[0106] The processor 21 is used to implement the steps of the above-mentioned vehicle big data analysis method when executing the computer program.

[0107] The electronic device provided in this embodiment may include but is not limited to a smart phone, a tablet computer, a laptop computer or a desktop computer.

[0108] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of a digital signal processor (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0109] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201, wherein, after the computer program is loaded and executed by the processor 21, it can implement the relevant steps of the vehicle big data analysis method disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. Data 203 may include but is not limited to vehicle big data analysis methods, etc.

[0110] In some embodiments, the electronic device may further include a display screen 22 , an input / output interface 23 , a communication interface 24 , a power source 25 , and a communication bus 26 .

[0111] Those skilled in the art will understand that Figure 4 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure.

[0112] The purpose of this embodiment is to provide an electronic device, in which the memory 20 is used to store computer programs, and the processor 21 is used to implement the steps of the above-mentioned vehicle big data analysis method when executing the computer program, so as to make the analysis process more efficient and accurate.

[0113] In order to solve the above technical problems, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned vehicle big data analysis method are implemented.

[0114] It is understandable that if the method in the above embodiment is implemented in the form of 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 the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0115] The computer-readable storage medium provided in this embodiment corresponds to the above method, and therefore has the same beneficial effects as the above method. Therefore, for the embodiments of the computer-readable storage medium part, please refer to the description of the embodiments of the method part, which will not be repeated here.

[0116] It should be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0117] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle big data analysis method, characterized in that: include: Obtain CAN messages corresponding to several vehicles through the CAN network, and obtain the model data of each of the vehicles; Parsing the vehicle real-time data corresponding to each of the vehicles from the corresponding CAN messages according to the vehicle model data; The real-time data of each vehicle is packaged and transmitted to the cloud, so that the real-time data of each vehicle is analyzed accordingly through the cloud, and the analyzed data is stored accordingly through the cloud.

2. The vehicle big data analysis method according to claim 1, characterized in that: After parsing the vehicle real-time data corresponding to each of the vehicles from each of the CAN messages according to the vehicle model data, the method further includes: Cleaning the real-time data of each vehicle to filter out error frames in the real-time data of each vehicle; Accordingly, the real-time data of each vehicle is packaged and transmitted to the cloud, including: The real-time data of each vehicle after cleaning is packaged and transmitted to the cloud.

3. The vehicle big data analysis method according to claim 1, characterized in that: After the real-time data of each vehicle is packaged and transmitted to the cloud, the method further includes: Determining whether a response signal transmitted from the cloud is received within a preset period; If the response signal transmitted by the cloud is not received within a preset period, the real-time data of each vehicle is repackaged and transmitted to the cloud until the response signal transmitted by the cloud is received; If a response signal transmitted by the cloud is received within a preset period, it triggers the steps of correspondingly analyzing the real-time data of each vehicle through the cloud and correspondingly storing the analyzed data through the cloud.

4. The vehicle big data analysis method according to claim 1, characterized in that: The corresponding analysis of the real-time data of each vehicle includes: The real-time data of each vehicle are stored separately, and the brake opening, throttle opening, vehicle speed, vehicle wiper information, vehicle ambient temperature, and vehicle position in the real-time data of each vehicle are determined; determining a driving habit of a driver of the corresponding vehicle based on the brake opening, the throttle opening and the vehicle speed; The vehicle operating condition of the corresponding vehicle is determined based on the vehicle wiper information, the vehicle ambient temperature and the vehicle position.

5. The vehicle big data analysis method according to claim 4, characterized in that: The corresponding analysis of the real-time data of each vehicle also includes: Determine the latitude and longitude information, mileage data, current value, and voltage value in the real-time data of each vehicle; The running track of the corresponding vehicle is calculated based on the longitude and latitude information, the mileage of the corresponding vehicle is determined based on the mileage data, and the energy consumption per kilometer of the corresponding vehicle is determined based on the current value and the voltage value.

6. The vehicle big data analysis method according to claim 5, characterized in that: The corresponding analysis of the real-time data of each vehicle also includes: Determining the total average energy consumption of each of the vehicles according to the energy consumption per kilometer corresponding to each of the vehicles; Determining the total mileage of each of the vehicles using the mileage corresponding to each of the vehicles; Determining the distribution position of the corresponding vehicle according to the running trajectory; The overall operating conditions of each of the vehicles are determined based on the total average energy consumption, the total mileage and the distribution positions corresponding to each of the vehicles.

7. The vehicle big data analysis method according to any one of claims 1 to 6, characterized in that: After the analyzed data is stored accordingly through the cloud, the method further includes: Determining the acquisition time corresponding to each of the vehicle real-time data through the cloud, and determining the target data that meets the preset privacy conditions in the analyzed data; A plurality of groups of virtual data tables are generated through the cloud according to the data type corresponding to the analyzed data, the corresponding acquisition time and the analyzed data, and the target virtual data tables corresponding to the target data in each of the virtual data tables are fuzzy processed, so that the virtual data tables other than the target virtual data tables in each of the virtual data tables can be displayed accordingly using a display device.

8. A vehicle big data analysis device, characterized in that: include: An acquisition unit, used to acquire CAN messages corresponding to a plurality of vehicles through a CAN network, and acquire vehicle model data of each of the vehicles; A parsing unit, used for parsing the vehicle real-time data corresponding to each of the vehicles from the corresponding CAN messages according to the vehicle model data; The transmission unit is used to package and transmit the real-time data of each vehicle to the cloud, so that the real-time data of each vehicle can be analyzed accordingly through the cloud, and the analyzed data can be stored accordingly through the cloud.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, used to implement the steps of the vehicle big data analysis method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the vehicle big data analysis method as described in any one of claims 1 to 7.

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