Internet of vehicles V2X service information coordination method based on micro-service optimization

Abstract

The invention provides an Internet of Vehicles V2X service information coordination method based on micro-service optimization. The method comprises the following steps: obtaining service information of the Internet of Vehicles; according to the function category of the obtained service information, constructing a micro-service architecture, and dividing service information processing modules with different functions into independent micro-services; the method comprises the following steps: setting a rule for locally processing service information with high real-time requirement at an edge node of the Internet of Vehicles, and setting a rule for uploading historical data and a non-real-time analysis task to a cloud for deep processing and analysis; and performing priority division on the processed service information based on an event-driven architecture, and distributing the processed service information to corresponding target micro-services or terminal equipment. According to the invention, the service information processing modules with different functions are divided into independent micro-services by constructing the micro-service architecture, so that the micro-services are deployed according to the actual functions of the Internet of Vehicles, and the overall performance and response speed of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Vehicles, and in particular to a method for coordinating V2X service information based on micro-service optimization. BACKGROUND

[0002] In the field of Internet of Vehicles (V2X), traditional service information processing mainly relies on centralized architecture. This architecture collects massive data collected by various sensors distributed on roads and vehicles through communication networks and uniformly transmits the data to cloud data centers. The cloud data centers store, process and analyze the data, and then distribute the processed results, such as traffic condition information, path planning suggestions, safety warning information, etc., to terminal devices such as vehicles and roadside units (RSUs). In the aspect of traffic flow analysis, the centralized system calculates the degree of road congestion and the estimated travel time by using complex traffic flow models based on the data of vehicle speed, density, etc. of each road section, thereby providing travel reference for drivers. In the field of safety warning, the system sends corresponding warning signals to surrounding vehicles based on the abnormal event data such as emergency braking and collision reported by vehicles, thereby reminding drivers to pay attention to safety.

[0003] However, with the rapid development and continuous expansion of the application scale of Internet of Vehicles, the centralized architecture gradually exposes some problems. On the one hand, for service information with extremely high real-time requirements, such as emergency braking warning of vehicles during high-speed driving, real-time perception and feedback of traffic events, etc., due to the long delay of data transmission to the cloud and then returning the processing results, it is difficult to meet the demand for rapid response, thereby possibly affecting the personal safety and travel efficiency of traffic participants; on the other hand, a large amount of real-time data flows to the cloud, which not only brings great pressure to network bandwidth, but also may cause the cloud server to be overloaded, further aggravating the delay of data processing, and also increasing the risk of system failure and operating cost. SUMMARY

[0004] The present application provides a method for coordinating V2X service information of Internet of Vehicles based on micro-service optimization, which divides service information processing modules with different functions into independent micro-services by constructing a micro-service architecture, so that each micro-service is deployed according to the actual functions of Internet of Vehicles, thereby improving the overall performance and response speed of the system.

[0005] The present application provides a method for coordinating V2X service information of Internet of Vehicles based on micro-service optimization, which divides service information processing modules with different functions into independent micro-services by constructing a micro-service architecture, so that each micro-service is deployed according to the actual functions of Internet of Vehicles, thereby improving the overall performance and response speed of the system.

[0006] Obtaining service information of Internet of Vehicles, wherein the service information includes road network static information, road network event information and road network traffic participant information;

[0007] According to the function category of the acquired service information, a micro-service architecture is constructed, and service information processing modules with different functions are divided into independent micro-services; wherein the micro-services communicate with each other through a V2X communication protocol and perform data transmission based on a message publishing / subscription mode;

[0008] Rules for locally processing service information with high real-time requirements at the edge node of the Internet of Vehicles are set, and rules for uploading historical data and non-real-time analysis tasks to the cloud for deep processing and analysis are set;

[0009] The edge node processes service information with high real-time requirements, and uploads historical data and non-real-time analysis tasks to the cloud for processing;

[0010] Based on an event-driven architecture, the processed service information is prioritized and distributed to the corresponding target micro-service or terminal device through a message bus or an event bus.

[0011] Further, the micro-services include a road network static information management micro-service, a road network event information processing micro-service, a traffic participant state tracking micro-service, a path planning micro-service, and a safety warning micro-service.

[0012] Further, the road network static information includes road topology, lane line data, traffic sign position and rules; the road network event information includes traffic accidents, construction areas, sudden weather, congestion states and durations; and the road network traffic participant information includes vehicle position / speed / acceleration, pedestrian movement trajectory, and non-motor vehicle state.

[0013] Further, according to the function category of the acquired service information, a micro-service architecture is constructed, and service information processing modules with different functions are divided into independent micro-services, including the following steps:

[0014] Identify high-cohesion functional units in the service information processing flow;

[0015] Create an independently deployed micro-service instance for each functional unit;

[0016] Define the JSON / Protobuf data format between micro-services based on the V2X protocol;

[0017] Realize dynamic registration and calling routing of micro-services through Consul or Nacos.

[0018] Further, the rules for locally processing service information with high real-time requirements at the edge node of the Internet of Vehicles include the following steps:

[0019] Set a response delay threshold to classify sensor data in real time;

[0020] Filtering redundant data at roadside RSU and forwarding abnormal state;

[0021] Setting a lightweight AI model to analyze traffic events;

[0022] According to the edge node load, dynamically allocate computing resources to high priority tasks.

[0023] Further, the setting uploads historical data and non-real-time analysis tasks to the cloud for deep processing and analysis, including the following steps:

[0024] Compressing historical data through LZ77 algorithm and transmitting through TLS1.3 protocol encryption;

[0025] Splitting large analysis tasks into MapReduce subtasks and uploading in parallel;

[0026] Pushing analysis results to edge nodes for updating local databases.

[0027] Further, the event-driven architecture is based on the message bus or event bus, and the processed service information is prioritized, including the following steps:

[0028] Setting a QoS grading strategy including setting the highest level of security class information, the middle level of efficiency class information and the basic level of information service class;

[0029] Defining event types and publishing events to subscribing microservices through Kafka / RabbitMQ;

[0030] According to the network state, the message bus bandwidth ratio is adjusted according to priority.

[0031] The second aspect of the present application provides a vehicle-to-everything (V2X) service information coordination system based on microservice optimization, comprising a first computing unit for obtaining service information of vehicle-to-everything (V2X), wherein the service information includes road network static information, road network event information and road network traffic participant information;

[0032] A second computing unit is configured to construct a microservice architecture according to the functional categories of the obtained service information, and divide different functional service information processing modules into independent microservices; wherein the microservices communicate with each other through a V2X communication protocol and transmit data based on a message publishing / subscription mode;

[0033] A third computing unit is configured to set rules for local processing of service information with high real-time requirements at the edge node of vehicle-to-everything (V2X), and set rules for uploading historical data and non-real-time analysis tasks to the cloud for deep processing and analysis;

[0034] A fourth computing unit is configured to process service information with high real-time requirements at an edge node, and upload historical data and non-real-time analysis tasks to a cloud for processing.

[0035] A fifth computing unit is configured to perform priority division on the processed service information based on an event-driven architecture through a message bus or an event bus, and distribute the processed service information to corresponding target microservices or terminal devices.

[0036] The third aspect of the present application provides a computer device, comprising:

[0037] a memory, a transceiver, a processor and a bus system;

[0038] The memory is configured to store a program.

[0039] The processor is configured to execute the program in the memory, including executing the above-mentioned micro-service optimized V2X service information coordination method.

[0040] The bus system is configured to connect the memory and the processor to enable the memory and the processor to communicate.

[0041] The fourth aspect of the present application provides a readable storage medium, which stores computer readable instructions, and the computer readable instructions are executed by a processor to implement the above-mentioned micro-service optimized V2X service information coordination method.

[0042] From the above technical solutions, the present application has the following advantages:

[0043] The present application divides different function service information processing modules into independent microservices by constructing a microservice architecture, and deploys each microservice according to the actual function of the Internet of Vehicles, thereby improving the overall performance and response speed of the system. The data transmission between each microservice is based on the V2X communication protocol and the message publishing / subscription mode, which realizes efficient and reliable information interaction and enhances the real-time performance and stability of the system.

[0044] Meanwhile, the rules for processing service information with high real-time requirements are set at the edge node of the Internet of Vehicles, and the advantages of edge computing are utilized to reduce the delay of data transmission to the cloud and return, thereby ensuring that traffic events with high real-time requirements can be quickly responded, such as emergency braking warning, real-time traffic congestion sensing, etc., and effectively improving the traffic safety.

[0045] In addition, the historical data and non-real-time analysis tasks are uploaded to the cloud for deep processing and analysis, the computing resources and storage resources of the edge nodes and the cloud are reasonably allocated, and waste of resources is avoided. Meanwhile, the computing resources are dynamically allocated to high-priority tasks according to the load of the edge nodes, the utilization efficiency of the resources is further improved, and the stable operation of the system under high load is ensured.

[0046] Other advantages, objects, and features of the application will be apparent to those skilled in the art from the following specification, which is to be taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 A method flowchart is provided. DETAILED DESCRIPTION

[0048] The terms "first", "second", "third", "fourth" and the like in the description of the application and in the claims, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of these terms herein is to be construed to cover a general order and / or structure where more than one step is performed in between steps described. The description and drawings merely schematically illustrate the implementation of the application. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the application and are included within its spirit and scope. Furthermore, any arrangement of the applications that embodies essential characteristics of the application falls within the scope of the application. The application is to be limited only by the claims.

[0049] Embodiment one

[0050] The method implemented in this embodiment can be implemented in a system, and can be implemented in a server or a terminal, and the specific implementation is not limited. The method in this application will be introduced from the perspective of system implementation. As shown in the figure, a V2X service information coordination method based on micro-service optimization for Internet of Vehicles, comprising the following steps:

[0051] Obtain service information of the Internet of Vehicles, the service information comprising road network static information, road network event information and road network traffic participant information;

[0052] According to the function category of the obtained service information, a micro-service architecture is constructed, and service information processing modules with different functions are divided into independent micro-services; wherein the micro-services communicate with each other through a V2X communication protocol, and data transmission is carried out based on a message publishing / subscription mode;

[0053] Rules for local processing of service information with high real-time requirements at the edge node of the Internet of Vehicles, and rules for uploading historical data and non-real-time analysis tasks to the cloud for deep processing and analysis;

[0054] The edge node processes service information with high real-time requirements, and uploads historical data and non-real-time analysis tasks to the cloud for processing;

[0055] Based on the event-driven architecture, the processed service information is prioritized and distributed to the corresponding target microservice or terminal device through the message bus or event bus.

[0056] According to the functional categories of the obtained service information, a microservice architecture is built, and service information processing modules with different functions are divided into independent microservices. Each microservice communicates with each other through the V2X communication protocol and transmits data based on the message publishing / subscription mode. Such architecture design helps to improve the flexibility, scalability and maintainability of the system, and facilitates the separate development, deployment and optimization of different functions. On the one hand, rules for local processing of service information with high real-time requirements at the edge node of the Internet of Vehicles are set to achieve fast response; on the other hand, rules for uploading historical data and non-real-time analysis tasks to the cloud for deep processing and analysis are set to fully utilize the powerful computing and storage capabilities of the cloud and reasonably allocate system resources. The edge node processes service information with high real-time requirements according to the established rules, and sends historical data and non-real-time analysis tasks to the cloud for processing. Then, based on the event-driven architecture, the processed service information is prioritized and distributed to the corresponding target microservice or terminal device through the message bus or event bus, ensuring that the information can be efficiently and orderly circulated in the system to meet the needs of different users and business scenarios.

[0057] Embodiment Two

[0058] The difference between this embodiment and Embodiment One is that the microservices include road network static information management microservice, road network event information processing microservice, traffic participant state tracking microservice, path planning microservice and safety warning microservice.

[0059] The microservice architecture includes a road network static information management microservice, a road network event information processing microservice, a traffic participant state tracking microservice, a path planning microservice, and a safety warning microservice. Each microservice has its specific responsibilities and functions, for example, the road network static information management microservice is specifically responsible for managing static information such as road topology, lane line data, traffic sign location and rules; the road network event information processing microservice processes road network event information such as traffic accidents, construction areas, and sudden weather; the traffic participant state tracking microservice focuses on the state information of traffic participants such as vehicles, pedestrians, and non-motor vehicles, including their positions, speeds, accelerations, and movement trajectories; the path planning microservice plans the optimal path for traffic participants based on relevant data; the safety warning microservice analyzes various information and issues safety warning information in a timely manner, and the entire system works cooperatively through these different microservices.

[0060] Embodiment Three

[0061] The difference between this embodiment and embodiment two is that the road network static information includes road topology, lane line data, traffic sign location and rules; the road network event information includes traffic accidents, construction areas, sudden weather, congestion state and duration; and the road network traffic participant information includes vehicle position / speed / acceleration, pedestrian movement trajectory, and non-motor vehicle state.

[0062] The specific contents of road network static information, road network event information, and road network traffic participant information make the information scope handled by the system more clear and specific. The road network static information is specifically defined as road topology, lane line data, traffic sign location and rules; the road network event information covers traffic accidents, construction areas, sudden weather, congestion state and duration; and the road network traffic participant information includes vehicle position / speed / acceleration, pedestrian movement trajectory, and non-motor vehicle state, providing a more explicit data basis for subsequent accurate processing, analysis and coordination of various information by the system, which helps each microservice work more efficiently, for example, enabling the path planning microservice to plan more accurate and reasonable driving paths for traffic participants based on more detailed traffic sign location and rules, real-time traffic accident and congestion state information, etc.

[0063] Embodiment Four

[0064] The difference between this embodiment and embodiment three is that according to the functional categories of the acquired service information, a microservice architecture is constructed, and service information processing modules with different functions are divided into independent microservices, including the following steps:

[0065] Identify the high cohesion functional units in the service information processing flow;

[0066] Create an independently deployed microservice instance for each functional unit;

[0067] Define the JSON / Protobuf data format for inter-microservice communication based on V2X protocols.

[0068] Implement dynamic registration and call routing of microservices through Consul or Nacos.

[0069] In the process of identifying service information, identify the functional units with high cohesion characteristics, such as road network static information management, road event information processing, and traffic participant state tracking, and identify them as independent functional units. For each identified functional unit, create a corresponding and independently deployable microservice instance, and each microservice can focus on its specific function, achieving high modularity and flexibility.

[0070] Microservices communicate with each other based on V2X protocols using JSON or Protobuf data formats. Both formats have good cross-platform and scalability, ensuring efficient and accurate data interaction between different microservices, whether in message transmission or data parsing. They can meet the needs of different microservices working together in the Internet of Vehicles scenario.

[0071] With the help of service registration and discovery tools such as Consul or Nacos, dynamic registration of microservices is achieved. When a new microservice instance is created and deployed, it can automatically register its relevant information, including service address and port, in the registration center. At the same time, dynamic call routing of microservices is also supported. Based on service registration information and service health status, requests are reasonably routed to appropriate target microservice instances, ensuring high availability and dynamic expansion capability of the microservice architecture.

[0072] Embodiment Five

[0073] The difference between this embodiment and Embodiment Four is that the rule for locally processing service information with high real-time requirements at the edge node of the Internet of Vehicles includes the following steps:

[0074] Set a response delay threshold to classify sensor data by real-time performance;

[0075] Filter redundant data at the roadside RSU and forward abnormal states;

[0076] Set up a lightweight AI model to analyze traffic events;

[0077] Dynamically allocate computing resources to high-priority tasks based on edge node load.

[0078] By setting the response delay threshold, the data collected by the sensor is classified according to its real-time requirement, and for those data with high real-time requirement, it can be processed and responded in priority. At the roadside rsu location, the sensor data is preliminarily processed, and the redundant data is filtered out, only the key and valuable information is retained for forwarding, which can not only reduce the burden of network transmission, but also improve the processing efficiency of the whole system.

[0079] By deploying lightweight AI models in edge nodes, these models can quickly analyze traffic events while ensuring a certain level of analysis accuracy. According to the current load of the edge node, the computing resources are dynamically allocated to those high-priority tasks to ensure that high-priority tasks can obtain sufficient computing resources for fast execution.

[0080] Embodiment six

[0081] The difference between this embodiment and embodiment five is that the rule of uploading historical data and non-real-time analysis tasks to the cloud for deep processing and analysis includes the following steps:

[0082] Compress the historical data by LZ77 algorithm and transmit it by TLS1.3 protocol encryption;

[0083] Split large analysis tasks into MapReduce subtasks and upload them in parallel;

[0084] Push the analysis results to the edge node for updating the local database.

[0085] LZ77 algorithm is used to compress historical data, which reduces data redundancy and volume, reduces the space and bandwidth required for data storage and transmission, and uses TLS1.3 protocol to encrypt the compressed data for transmission, ensuring the security of historical data during transmission, preventing data from being stolen or tampered with, and ensuring that data can be safely and reliably transmitted from edge nodes to the cloud for deep processing and analysis. Large analysis tasks are divided into multiple MapReduce subtasks, which can use the parallel processing capability of the MapReduce model to upload these subtasks to the cloud in parallel.

[0086] After the cloud completes the deep processing and analysis of historical data and non-real-time analysis tasks, the analysis results are pushed back to the edge node in a timely manner, and the edge node uses these analysis results to update the local database, so that the database of the edge node can keep up-to-date.

[0087] Embodiment seven

[0088] The embodiment differs from embodiment six in that the event-driven architecture is used to prioritize processed service information through a message bus or event bus, including the following steps:

[0089] Setting a QoS grading policy including setting the highest level of security class information, setting the medium level of efficiency class information, and setting the basic level of information service class;

[0090] Defining event types and publishing events to subscribing microservices through Kafka / RabbitMQ;

[0091] Adjusting the bandwidth proportion of the message bus according to network status by priority.

[0092] According to the importance and urgency of service information, different levels of QoS (Quality of Service) grading policy are set, the security class information is set to the highest level, this kind of key information can be processed and transmitted with the highest priority, and is transmitted to related microservices or terminal devices in time to ensure traffic safety; the efficiency class information is set to the medium level, balancing processing efficiency and transmission guarantee; the information service class information is set to the basic level, while ensuring basic services, resources are reasonably allocated.

[0093] Detailed event types are defined so that microservices can accurately identify and process different types of service information. By using message middleware such as Kafka or RabbitMQ, processed service information is published to corresponding topics or queues according to defined event types, so that microservices subscribing to corresponding event types can receive and process related information in time.

[0094] According to the current network status, such as network congestion degree, bandwidth utilization, etc., the bandwidth proportion of the message bus is dynamically adjusted according to the pre-set priority rules. For high-priority messages, the bandwidth proportion on the message bus is appropriately increased to ensure that they can be transmitted quickly and stably, while for relatively low-priority messages, the bandwidth proportion is appropriately reduced.

[0095] Embodiment eight

[0096] A vehicle-to-everything (V2X) service information coordination system based on microservice optimization includes a first computing unit configured to obtain service information of a vehicle-to-everything network, wherein the service information includes road network static information, road network event information, and road network traffic participant information.

[0097] A second computing unit is configured to construct a microservice architecture based on the functional categories of the obtained service information, and divide service information processing modules with different functions into independent microservices; wherein the microservices communicate with each other through a V2X communication protocol and transmit data based on a message publishing / subscription mode.

[0098] A third computing unit is configured to set rules for local processing of service information with high real-time requirements at the edge node of the Internet of Vehicles, and set rules for uploading historical data and non-real-time analysis tasks to the cloud for deep processing and analysis.

[0099] A fourth computing unit is configured to process service information with high real-time requirements at the edge node, and upload historical data and non-real-time analysis tasks to the cloud for processing.

[0100] A fifth computing unit is configured to perform priority division on the processed service information based on an event-driven architecture through a message bus or an event bus, and distribute the processed service information to corresponding target microservices or terminal devices.

[0101] Embodiment nine

[0102] A computer device comprises:

[0103] a memory, a transceiver, a processor, and a bus system;

[0104] The memory is configured to store a program.

[0105] The processor is configured to execute the program in the memory, including executing the above-mentioned micro-service optimized vehicle-to-everything (V2X) service information coordination method.

[0106] The bus system is configured to connect the memory and the processor to enable the memory and the processor to communicate.

[0107] Embodiment ten

[0108] A readable storage medium stores computer readable instructions, which are executed by a processor to implement the above-mentioned micro-service optimized vehicle-to-everything (V2X) service information coordination method.

[0109] In summary, the present application divides different functional service information processing modules into independent microservices by constructing a microservice architecture, and deploys each microservice according to the actual function of the Internet of Vehicles, thereby improving the overall performance and response speed of the system. The data transmission between each microservice is based on the V2X communication protocol and the message publishing / subscription mode, which realizes efficient and reliable information interaction and enhances the real-time performance and stability of the system.

[0110] Meanwhile, rules for local processing of service information with high real-time requirements are set at the edge node of the Internet of Vehicles, and the advantages of edge computing are utilized to reduce the delay of data transmission to the cloud and return, thereby ensuring that traffic events with high real-time requirements can be quickly responded, such as emergency braking warning and real-time traffic congestion perception, thereby effectively improving the traffic safety.

[0111] In addition, the historical data and non-real-time analysis tasks are uploaded to the cloud for deep processing and analysis, and the computing resources and storage resources of the edge nodes and the cloud are reasonably allocated to avoid waste of resources. At the same time, according to the dynamic allocation of computing resources to high-priority tasks according to the load of the edge node, the utilization efficiency of resources is further improved, and the stable operation of the system under high load is ensured.

[0112] It can be understood that those skilled in the art can combine various embodiments in each of the above embodiments according to the teachings of the above embodiments to obtain various embodiments of the technical solutions.

[0113] Those skilled in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components of each example have been described in the above description in general terms. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0114] In the embodiments provided by the present application, it should be understood that the division of units is only a logical functional division, and actual implementation can have another division manner. For example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0115] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0116] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-0nly Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0117] The above merely describes preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for coordinating V2X service information in vehicle-to-everything (V2X) networks based on microservice optimization, characterized in that, Includes the following steps: Obtain service information from the Internet of Vehicles, including road network static information, road network event information, and road network traffic participant information; Based on the functional categories of the acquired service information, a microservice architecture is constructed, dividing service information processing modules with different functions into independent microservices; wherein, the microservices communicate with each other through the V2X communication protocol and transmit data based on the message publish / subscribe model; Rules are set up to process service information with high real-time requirements locally at the edge nodes of the vehicle network, and rules are set up to upload historical data and non-real-time analysis tasks to the cloud for in-depth processing and analysis; Process service information with high real-time requirements at edge nodes, and upload historical data and non-real-time analysis tasks to the cloud for processing; Based on an event-driven architecture, processed service information is prioritized and distributed to the corresponding target microservices or terminal devices via a message bus or event bus.

2. The method for coordinating V2X service information in a vehicle-to-everything (V2X) network based on microservice optimization according to claim 1, characterized in that, The microservices include road network static information management microservice, road network event information processing microservice, traffic participant status tracking microservice, route planning microservice, and safety early warning microservice.

3. The method for coordinating V2X service information in a vehicle-to-everything (V2X) network based on microservice optimization according to claim 1, characterized in that, The static information of the road network includes road topology, lane line data, and traffic sign locations and rules; the event information of the road network includes traffic accidents, construction areas, sudden weather events, congestion status and duration; the information of road network traffic participants includes vehicle location / speed / acceleration, pedestrian movement trajectory, and non-motorized vehicle status.

4. The method for coordinating V2X service information in a vehicle-to-everything (V2X) network based on microservice optimization according to claim 1, characterized in that, Based on the functional categories of the acquired service information, a microservice architecture is constructed, dividing service information processing modules with different functions into independent microservices, including the following steps: Identify highly cohesive functional units within the service information processing flow; Create independently deployed microservice instances for each functional unit; Define the JSON / Protobuf data format for inter-service communication based on the V2X protocol; Implement dynamic registration and invocation routing of microservices using Consul or Nacos.

5. The method for coordinating V2X service information in a vehicle-to-everything (V2X) network based on microservice optimization according to claim 1, characterized in that, The rules for local processing of service information with high real-time requirements at the edge node of the vehicle network include the following steps: Set a response delay threshold to classify the real-time performance of sensor data; Redundant data is filtered by the roadside RSU and abnormal statuses are forwarded. Set up a lightweight AI model to analyze traffic events; Dynamically allocate computing resources to high-priority tasks based on the load of edge nodes.

6. The method for coordinating V2X service information in a vehicle-to-everything (V2X) network based on microservice optimization according to claim 1, characterized in that, The rules for uploading historical data and non-real-time analysis tasks to the cloud for in-depth processing and analysis include the following steps: Historical data is compressed using the LZ77 algorithm and transmitted encrypted using the TLS 1.3 protocol. Break down large analysis tasks into MapReduce subtasks and upload them in parallel; The analysis results are pushed to edge nodes to update the local database.

7. The method for coordinating V2X service information in a vehicle-to-everything (V2X) network based on microservice optimization according to claim 1, characterized in that, The event-driven architecture prioritizes processed service information via a message bus or event bus, including the following steps: The settings include QoS classification policies for security information at the highest level, efficiency information at the medium level, and information service information at the basic level. Define event types and publish events to subscribing microservices via Kafka / RabbitMQ; Adjust the message bus bandwidth allocation according to network status and priority.

8. A V2X service information coordination system for vehicle networking based on microservice optimization, characterized in that, It includes a first computing unit for acquiring service information of the vehicle network, the service information including road network static information, road network event information and road network traffic participant information; The second computing unit is used to construct a microservice architecture based on the functional categories of the acquired service information, and to divide the service information processing modules with different functions into independent microservices; wherein, the microservices communicate with each other through the V2X communication protocol and transmit data based on the message publish / subscribe model; The third computing unit is used to set rules for local processing of service information with high real-time requirements at the edge nodes of the vehicle network, and to set rules for uploading historical data and non-real-time analysis tasks to the cloud for in-depth processing and analysis. The fourth computing unit is used to process service information with high real-time requirements at the edge nodes and upload historical data and non-real-time analysis tasks to the cloud for processing. The fifth computing unit is used to prioritize processed service information and distribute it to the corresponding target microservices or terminal devices based on an event-driven architecture, through a message bus or event bus.

9. A computer device, characterized in that, include: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is configured to execute a program in the memory, including performing the method as described in any one of claims 1 to 7; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

10. A readable storage medium storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by a processor, they implement the steps of the method as described in any one of claims 1 to 7.

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