Cross-regional synchronization method for distributed database of intelligent networked automobile cloud control platform

Abstract

The invention discloses a cross-regional synchronization method for a distributed database of an intelligent networked automobile cloud control platform, and belongs to the technical field of intelligent networked automobiles and distributed databases, and the method comprises the steps: a cloud control platform obtains the vehicle data information of a plurality of vehicles in real time, carries out the incremental recognition of the vehicle data information, obtains a plurality of pieces of incremental data, and transmits the incremental data to the cloud control platform; performing priority marking on each piece of incremental data by using a preset priority strategy to obtain a plurality of pieces of marked data, and sending each piece of marked data to message-oriented middleware through a first gateway based on a priority sequence; the message-oriented middleware distributes the marked data into the corresponding queues based on the priority sequence; and the synchronous database reads the marked data in each queue of the message-oriented middleware through the second gateway, analyzes and processes the marked data, and synchronizes the analyzed and processed data to the target distributed database. According to the method, when cross-region data synchronization is carried out, efficient data synchronization can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent connected vehicles and distributed databases, and specifically relates to a cross-regional synchronization method for a distributed database of an intelligent connected vehicle cloud control platform. Background Art

[0002] With the rapid development of artificial intelligence, big data, the Internet of Things, and 5G communication technologies, intelligent connected vehicles (ICVs) have become a significant force driving transformation in the global automotive industry. ICVs have achieved a significant leap from "single-vehicle intelligence" to "collaborative intelligence." As technology continues to advance, efficiently managing and processing massive amounts of data distributed across regions to ensure stable and efficient system operation has become a core challenge that needs to be addressed. To this end, ICV cloud control platforms and distributed database systems have emerged. In distributed database systems, cross-regional data synchronization is a core component supporting the operation of ICV cloud control platforms, especially in global deployment scenarios. ICVs require real-time collection and processing of vehicle status and environmental data from different regions, requiring distributed databases to have efficient data synchronization mechanisms. However, existing cloud control platforms and distributed databases struggle to achieve efficient data synchronization across regions.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0004] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0005] The embodiments of the present disclosure provide a cross-region synchronization method for a distributed database of a cloud control platform for intelligent connected vehicles, so as to achieve efficient data synchronization when facing cross-region data synchronization.

[0006] In some embodiments, the cross-regional synchronization method of the distributed database of the intelligent connected vehicle cloud control platform includes: the cloud control platform obtains vehicle data information of multiple vehicles in real time, and performs incremental identification on each of the vehicle data information to obtain multiple incremental data, uses a preset priority strategy to prioritize each of the incremental data to obtain multiple marked data, and sends each of the marked data to the message middleware via the first gateway based on the priority order; the message middleware assigns each of the marked data to the corresponding queue based on the priority order; the synchronization database reads the marked data in each queue of the message middleware via the second gateway and performs parsing processing, and synchronizes the parsed data to the target distributed database; wherein, the first gateway and the second gateway are gateways for different regions.

[0007] The beneficial effects of the present invention are:

[0008] The cloud control platform acquires vehicle data and performs incremental identification to obtain multiple incremental data, which significantly reduces the transmission and storage overhead of synchronization; then it uses the preset priority strategy to prioritize the incremental data, determine the priority of each tagged data, and send each tagged data to the message middleware through the first gateway in order of priority to give priority to the transmission of high-importance data. The message middleware then allocates the tagged data to the corresponding queues in order of priority so that it can be read by the synchronous database. The synchronous database reads the tagged data in each queue of the message middleware through the second gateway and parses the data to obtain data for cross-regional transmission, and then synchronizes the parsed data to the target distributed database, thereby achieving cross-regional synchronization.

[0009] In this way, when cross-regional data synchronization is performed between the cloud control platform and the distributed database, high-importance data is transmitted first and only incremental data is transmitted through a combination of priority and incremental synchronization to reduce transmission overhead. The message middleware is then used to transfer and decouple cross-regional data, ensuring reliable data transmission between gateways in different regions, thereby achieving efficient data synchronization.

[0010] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] One or more embodiments are exemplarily described by corresponding drawings. These exemplary descriptions and drawings do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation. In addition,

[0012] Figure 1 This is a schematic diagram of a cross-region synchronization architecture of a distributed database of a cloud control platform for intelligent connected vehicles provided by the present invention;

[0013] Figure 2 This is a flow chart of a cross-region synchronization method for a distributed database of a cloud control platform for intelligent connected vehicles provided by the present invention;

[0014] Figure 3 This is a schematic diagram of a synchronization process based on a gateway and a message queue provided by the present invention;

[0015] Figure 4 This is a process sequence diagram of a synchronization architecture based on message middleware provided by the present invention. DETAILED DESCRIPTION

[0016] In order to be able to understand the features and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The accompanying drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the sake of convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0017] In the description and claims of the embodiments of the present disclosure, as well as in the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate to describe the embodiments of the present disclosure herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0018] Unless otherwise stated, the term "plurality" means two or more.

[0019] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0020] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0021] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0022] The cloud-based control platform for intelligent connected vehicles (ICVs) is a critical system for real-time collection, processing, and analysis of massive amounts of heterogeneous data, encompassing multiple sources such as vehicle status, sensor information, traffic conditions, and user behavior. The high frequency, large scale, and diverse nature of this data place extremely high demands on the storage performance, scalability, and data processing capabilities of database systems. Traditional single-node database architectures are already struggling to cope with such large-scale data demands. In contrast, distributed database systems, through multi-node collaborative storage and parallel access, not only meet the high-performance and fault-tolerance requirements of ICV cloud-based control platforms but also provide strong technical support for cross-regional data sharing and processing. In distributed database systems, cross-regional data synchronization is a core component supporting the operation of ICV cloud-based control platforms, especially in global deployment scenarios. ICVs require real-time collection and processing of vehicle status and environmental data from diverse regions, requiring distributed databases to possess efficient data synchronization mechanisms. However, cross-regional synchronization presents a range of technical challenges, including network latency, high bandwidth consumption, data conflicts, and node failures. Furthermore, in a distributed environment, cross-regional synchronization also raises the issue of data transmission efficiency across multiple time zones, high-latency networks, and complex topologies. How to optimize synchronization performance, reduce data transmission delay, and effectively handle failure recovery of cross-regional nodes is an important direction of current research.

[0023] Current research on database synchronization technology primarily focuses on distributed heterogeneous systems, aiming to improve the efficiency and reliability of data synchronization. Zhang Zhen et al. proposed an event-driven database synchronization system based on transactional implementation. Ding Jianli et al., through driver research, proposed a method for extracting and validating SQL statements through the driver to achieve data synchronization. Wang Jue et al. proposed a serial synchronization method to address data consistency issues in distributed heterogeneous systems. Yang Peng et al. used triggers to monitor database changes and synchronize them through log capture. Xiao Xiao et al. proposed a message-based database synchronization method that communicates via XML encapsulation. Xiong Xian et al. proposed a database synchronization system based on message queues that integrates temporal, spatial, and process attributes. Liao G et al. used middleware to transform database synchronization into a front-end and back-end separated operation, ensuring the validity of data synchronization and retransmitting failed data. Lu et al. proposed a middleware-based database synchronization method that transmits database change information in XML format. Cui et al., targeting the data forwarding characteristics of gateway deployments, proposed a relational database synchronization method based on SQL data-level triggering.

[0024] Mainstream database vendors such as Oracle and IBM have also launched their own database synchronization systems. For example, IBM's CDC synchronization software enables cross-database data synchronization regardless of database type. Chen Guangxun et al. proposed a distributed intermittent synchronization database system that supports connecting heterogeneous and heterogeneous data sources and supports horizontal and vertical partitioning and replication of database tables. Wang Yubiao et al. analyzed the issues of change data capture and incremental data synchronization in heterogeneous databases in enterprise data integration and proposed a change data capture method that combines triggers and log tables. Currently, data synchronization among heterogeneous database systems is primarily used in network interconnection scenarios, and common ETL tools such as Kettle can meet most of these requirements. However, these tools struggle in cross-region and cross-security zone scenarios requiring security partitioning, dedicated networks, horizontal isolation, and vertical authentication. Data synchronization becomes even more challenging when different security zones are separated by physical air gaps. Data synchronization for distributed databases has long been a hot topic in the distributed field. Therefore, achieving efficient data synchronization between cloud control platforms and distributed databases is an urgent challenge when faced with cross-region data synchronization.

[0025] Combine Figure 1 As shown, the disclosed embodiment provides a cross-regional synchronization architecture of a distributed database of a cloud control platform for intelligent connected vehicles, targeting the characteristics of data storage and access in intelligent connected vehicles, to meet the challenges of large-scale data processing and real-time collaboration. This system includes an intelligent connected vehicle layer, a cloud control platform (i.e., a monitoring platform), a distributed database layer, a message middleware service, a synchronization node, and a gateway module. Intelligent connected vehicles upload real-time data to the cloud control platform through the on-board system, and the cloud control platform stores the data in a distributed database through a load balancing algorithm. Cross-regional data synchronization adopts a priority-based incremental synchronization strategy and a message middleware service to reduce transmission load and latency, ensuring fast consistency of data between regions in mission-critical scenarios. In Figure 1 In the example, region A is where the cloud control platform is located, and region B is where the target distributed database is located. The target distributed database is used to store incremental data for the cloud control platform.

[0026] Combine Figure 2 As shown, the embodiment of the present disclosure provides a cross-region synchronization method for a distributed database of a cloud control platform for intelligent connected vehicles, including:

[0027] In step S101, the cloud control platform obtains vehicle data information of multiple vehicles in real time, and performs incremental identification on each vehicle data information respectively to obtain multiple incremental data, and uses a preset priority strategy to prioritize each incremental data to obtain multiple marked data, and sends each marked data to the message middleware via the first gateway based on the priority order.

[0028] It is understandable that the first gateway is the gateway for the area where the cloud control platform (i.e., monitoring platform) is located. The gateway establishes a secure connection with the relay message queue (i.e., message middleware) through a public network encrypted tunnel to achieve secure data transmission between different areas. In large-scale data scenarios, the traditional full data synchronization method requires frequent transmission and storage of large amounts of unchanged data, resulting in excessive transmission bandwidth usage, waste of storage resources, and low synchronization efficiency. It is difficult to meet high efficiency requirements in real-time and resource-sensitive scenarios. In contrast, incremental synchronization technology can significantly reduce the transmission and storage overhead of synchronization by only transmitting changed data (i.e., incremental data).

[0029] In step S102 , the message middleware allocates each tagged data to a corresponding queue based on the priority order.

[0030] Step S103: The synchronization database reads the marked data in each queue of the message middleware through the second gateway and performs parsing processing, and synchronizes the parsed data to the target distributed database. The first gateway and the second gateway are gateways in different regions.

[0031] It is understandable that the second gateway is a gateway for the area where the target distributed database is located. The target distributed database is a database for storing incremental data of the cloud control platform.

[0032] Using the cross-region synchronization method for a distributed database on a cloud-based intelligent connected vehicle platform provided by the present disclosure, the cloud-based control platform acquires vehicle data and performs incremental identification to obtain multiple incremental data, significantly reducing synchronization transmission and storage overhead. The cloud-based control platform then prioritizes the incremental data using a preset priority strategy, determines the priority of each tagged data, and then sends each tagged data to the message middleware via a first gateway in order of priority, prioritizing the transmission of high-importance data. The message middleware then assigns the tagged data to corresponding queues in order of priority for retrieval by the synchronization database. The synchronization database then reads the tagged data in each queue of the message middleware via a second gateway and parses the data to obtain cross-region data for transmission. The parsed data is then synchronized to the target distributed database, thereby achieving cross-region synchronization. In this way, when performing cross-region data synchronization between the cloud-based control platform and the distributed database, a combination of priority and incremental synchronization prioritizes the transmission of high-importance data and only transmits the incremental data portion to reduce transmission overhead. The message middleware then facilitates the transfer and decoupling of cross-region data, ensuring reliable data transmission between gateways in different regions, thereby achieving efficient data synchronization.

[0033] In addition, the embodiment of the present disclosure provides a cross-regional synchronization method for a distributed database of a cloud control platform for intelligent connected vehicles, which is applied to a system of a cross-regional synchronization architecture of a distributed database of a cloud control platform for intelligent connected vehicles, focusing on solving the problem of efficient synchronization of massive data in a multi-regional environment. This method is based on distributed message middleware and incremental synchronization strategy. By introducing the message middleware communication mechanism, the system realizes efficient data transmission between edge nodes, regional nodes and central nodes, significantly reduces synchronization delay and resource consumption, and uses the message middleware to build an asynchronous data transmission architecture. Through dynamic priority adjustment, batch transmission, edge collaboration and other optimization strategies, the real-time synchronization of high-priority data is improved, and the stability and fault tolerance of the cloud control platform and the distributed database during data synchronization are guaranteed. This method provides solid data support for real-time monitoring, intelligent scheduling and safety management of intelligent connected vehicles, and promotes the application and development of the platform in complex distributed environments.

[0034] It is understandable that in cross-region distributed database synchronization, since each regional database is in an independent intranet environment, traditional direct communication is difficult to implement. The data gateway is used to bridge the internal and external network data, and the message queue is used to achieve efficient cross-region transmission and asynchronous decoupling. Figure 3 As shown, the embodiment of the present disclosure provides a synchronization process diagram based on a gateway and a message queue. The system architecture for cross-regional data synchronization includes an intranet data gateway, a relay transmission channel, a target data gateway, and a fault-tolerant and monitoring module. As the entrance and exit of the regional intranet, the intranet data gateway is responsible for capturing the incremental synchronization data of the distributed database, pre-processing the data and pushing it to the message queue (i.e., message middleware), and decrypting and writing the pulled data. The gateway establishes a secure connection with the relay message queue through a public network encrypted tunnel to achieve secure data transmission between different intranets. As the middle layer, the message queue is responsible for the transit and decoupling of cross-regional data, supports a high-throughput, low-latency distributed data transmission mechanism, and ensures the reliable transmission of data between gateways in different regions. Among them, Figure 3 In this example, the source database gateway is the gateway for the region where the cloud control platform is located, the source database is the cloud control platform, the target debugging gateway is the gateway for the region where the target distributed database is located, and the target database is the target distributed database. Specifically, after the source database (cloud control platform) generates incremental data (data processed by combining incremental identification and priority), it transmits the incremental data to the source database gateway for preprocessing. The source database gateway then transmits the preprocessed data to the message middleware. The message middleware acts as a transit station, transmitting the data to the target debugging gateway, which then writes the data to the target database. Feedback steps such as data reception completion are not detailed here.

[0035] Preferably, incremental identification is performed on each vehicle data information respectively, including: using a change detection algorithm, performing incremental identification based on the update timestamp of each data in each vehicle data information, and screening out the newly added or updated data as incremental data.

[0036] In this way, a change detection algorithm based on data characteristics is adopted to perform incremental identification according to the update timestamp of the intelligent connected vehicle data, so as to quickly determine the data changes between the source and target ends, and quickly filter out the newly added or updated data as incremental data.

[0037] Preferably, each incremental data is prioritized using a preset priority strategy to obtain a plurality of tagged data, including: determining the importance, timeliness and transmission cost of each incremental data; for each incremental data, based on importance, timeliness and transmission cost, the priority value is calculated by the following formula: P = W1I + W2T + W3C, W1 + W2 + W3 = 1. Wherein: P is the priority value, I is the importance, T is the timeliness, C is the transmission cost, W1 is the weight of importance, W2 is the weight of timeliness, and W3 is the weight of transmission cost; based on the priority value of each incremental data, each incremental data is prioritized to obtain a plurality of tagged data. Wherein, importance represents the criticality of data to system functions or decisions; the timeliness of data represents the time sensitivity of data, such as the effective time window or delay tolerance of data; the transmission cost represents the resource overhead required to transmit the data, including bandwidth, energy consumption and computing cost.

[0038] In this way, the priority value of the incremental data is calculated based on the importance, timeliness and transmission cost of the incremental data to determine the importance of each incremental data, and then the incremental data is prioritized based on the priority value of each incremental data. In this way, the priority of each incremental data in the data synchronization process can be determined based on the three aspects of importance, timeliness and transmission cost.

[0039] It is understood that in actual operation, the weights (W1, W2, W3) can be dynamically adjusted based on changes in system load and application scenarios. For example, when handling an emergency, the weight of timeliness W2 can be increased to ensure timely transmission of critical data; when there is network congestion, the weight of transmission cost W3 can be increased to prioritize the transmission of low-cost data.

[0040] In this embodiment, since the dimensions of I, T, and C may be different, in order to ensure the rationality of the priority score, the original values need to be normalized. The normalization formula is: Among them: X is the original indicator value, X min is the minimum value of the indicator and X max is the maximum value of the indicator, and X′ is the standardized value.

[0041] Preferably, the importance, timeliness and transmission cost of each incremental data are determined, including: for each incremental data, determining the importance based on the data type of the incremental data, determining the timeliness based on the sensitivity of the incremental data to time, and determining the transmission cost based on the resource overhead of transmitting the incremental data.

[0042] In this way, the importance, timeliness and transmission cost of incremental data can be reasonably determined based on the data type, time sensitivity and resource overhead required for data transmission.

[0043] In some embodiments, different scores can be assigned in advance based on data types (such as collision warnings, vehicle status, etc.). When calculating the priority value, the corresponding score is found based on the data type, and the score represents the importance of the data. Similarly, the timeliness of incremental data and the transmission cost of incremental data can also be quantified in this way. I will not elaborate on this here.

[0044] In some embodiments, data types include vehicle status data (e.g., speed, direction, location), collision warning information, emergency braking signals, environmental perception data (e.g., information about surrounding vehicles and obstacles captured by radar and cameras), road traffic status information, vehicle usage records, driving behavior analysis data, etc. Different scores can be assigned based on the data type. During calculations, the corresponding score is found based on the data type, and the score represents the importance of the data.

[0045] Preferably, the marked data includes incremental data marked with high priority, incremental data marked with medium priority and incremental data marked with low priority; each incremental data is prioritized based on the priority value of each incremental data, including: marking incremental data with a priority value greater than or equal to a first preset value as incremental data marked with high priority; marking incremental data with a priority value greater than or equal to a second preset value and less than the first preset value as incremental data marked with medium priority; wherein the second preset value is less than the first preset value; marking incremental data with a priority value less than the second preset value as incremental data marked with low priority.

[0046] When the priority value is greater than or equal to the first preset value, it means that after considering the importance, timeliness and transmission cost, the incremental data is extremely important to driving safety and millisecond-level synchronization is required, so it is marked as a high priority. When the priority value is less than the first preset value and greater than or equal to the second preset value, it means that after considering the importance, timeliness and transmission cost, the incremental data is relatively important to driving safety, so it is marked as a medium priority. When the priority value is less than the second preset value, it means that after considering the importance, timeliness and transmission cost, the incremental data is generally important to driving safety, so it is marked as a low priority. In this way, the priority is determined based on the priority value, so that the priority marking of the incremental data can be completed quickly.

[0047] In some embodiments, based on the data characteristics of intelligent connected vehicles and the application requirements of the cloud control platform, data is divided into the following categories according to priority: Incremental data marked with high priority include: vehicle status data (such as speed, direction, position), collision warning information, emergency braking signals, etc. These data are critical to driving safety and require millisecond synchronization. Incremental data marked with medium priority include: environmental perception data (such as information on surrounding vehicles and obstacles captured by radar and cameras), road traffic status information, etc., which require sub-second synchronization. Incremental data marked with low priority include: vehicle usage records, driving behavior analysis data, etc. These data have lower real-time requirements and can be synchronized within a longer time interval.

[0048] Preferably, each tagged data is sent to the message middleware via the first gateway based on the priority order, including: packaging the incremental data marked with high priority, the incremental data marked with medium priority, and the incremental data marked with low priority in the order of high priority, medium priority and low priority, and transmitting them to the message middleware via the first gateway.

[0049] In this way, the incremental data marked with high priority will be packaged and transmitted first, so that the incremental data marked with priority can be transmitted to the target end (i.e., the target distributed database) as soon as possible. Incremental packaging can reduce data processing delays and improve transmission priority and efficiency.

[0050] In some embodiments, when packaging incremental data, it is packaged in batches and a lightweight compression algorithm is used to reduce the size of the data packets, thereby further optimizing the transmission overhead. Finally, the transmission mode (such as batch transmission or streaming transmission) is dynamically adjusted according to network conditions (such as bandwidth and latency) to improve the timeliness of data reaching the target end. In this way, a priority-based data tiering strategy is adopted to prioritize the transmission of data with higher real-time requirements and batch process non-critical data, further improving the overall efficiency of cross-region synchronization.

[0051] Preferably, the message middleware allocates each tagged data to the corresponding queue based on the priority order, including: the message middleware allocates each tagged data to the corresponding queue based on the priority order; based on the partition strategy, for each tagged data in the same queue, similar types of tagged data are allocated to the same Topic partition.

[0052] In this way, the message middleware assigns each tagged data to the corresponding queue based on priority, processing incremental data with high-priority tags first, then incremental data with medium-priority tags, and finally incremental data with low-priority tags. Using partitioning strategies to divide queues and send similar data types to the same partition can further optimize transmission and processing performance.

[0053] In some embodiments, the messaging middleware is Kafka. To achieve efficient data transmission and decoupling, connected vehicles rely on numerous data sources distributed across multiple regions. Therefore, the messaging middleware must support distributed messaging, high concurrent throughput, elastic scalability, recoverability, and reliable transmission. A comparison of the most widely used messaging middleware is shown in Table 1.

[0054] Table 1

[0055] Comparison Item ActiveMQ RabbitMQ Kafka Throughput Low high Very high distributed support support support Message Delay Second millisecond millisecond Load Balancing support support support Reliable transmission better good good

[0056] Considering the distributed data synchronization requirements of intelligent connected vehicles and comparing the aforementioned mainstream message middleware, Kafka was selected as the message middleware for cross-region synchronization of distributed databases. Different topic partitions were established within the Kafka cluster. The Kafka cluster supports load balancing to ensure data balance across nodes, preventing uneven data distribution from causing some nodes to be overloaded, leading to severe performance degradation or downtime.

[0057] For ease of understanding, combined Figure 4As shown, an embodiment of the present disclosure provides a process sequence diagram of a synchronization architecture based on a message middleware. The architecture includes: a producer module (Producer), a middleware module (Middleware) and a consumer module (Consumer). Among them, the producer module (Producer): the main distributed database of the intelligent connected vehicle (i.e., the cloud control platform, also known as the monitoring platform) serves as the data producer. The data stored in the database is prioritized according to business needs (for example, emergency data and vehicle alarm information are prioritized), and the data is divided into high, medium and low priorities to optimize the processing efficiency of the message middleware. Middleware module (Middleware): Assign different queues or topics (Topic) to the data according to priority, and give priority to high-priority data. Use partitioning strategies to divide the queues and send similar types of data to the same partition to optimize transmission and processing performance. Through the multi-node architecture of the middleware, data traffic is automatically balanced to avoid single-point performance bottlenecks. Consumer module (Consumer): Synchronize the database (i.e. Figure 4 The consumer module in the ) acts as a data consumer, and synchronizes the changed data to the target distributed database (i.e. Figure 4 Target database module in .

[0058] Preferably, a cross-regional synchronization method for a distributed database of a cloud control platform for intelligent connected vehicles further includes: the target distributed database applies the parsed data based on an incremental update method.

[0059] In this way, the target distributed database applies the received incremental data (ie, the parsed data) to the system using an incremental update method, thereby being able to determine the integrity and consistency of the target end data (ie, the target distributed database).

[0060] Specifically, the target distributed database (distributed database system) prioritizes updating the incremental data corresponding to frequently accessed data to the in-memory cache, Redis, to ensure real-time query performance. Furthermore, upon receiving the incremental data, the target distributed database triggers a data consistency check based on the synchronization strategy to ensure eventual data consistency.

[0061] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A cross-region synchronization method for a distributed database of an intelligent connected vehicle cloud control platform, characterized in that: include: The cloud control platform obtains vehicle data information of multiple vehicles in real time, performs incremental identification on each of the vehicle data information to obtain multiple incremental data, marks each of the incremental data with a priority using a preset priority strategy to obtain multiple marked data, and sends each of the marked data to the message middleware via the first gateway based on the priority order; The message middleware allocates each of the marked data to a corresponding queue based on the priority order; The synchronization database reads the marked data in each queue of the message middleware through the second gateway and parses and processes it, and synchronizes the parsed data to the target distributed database; wherein the first gateway and the second gateway are gateways of different regions.

2. The method according to claim 1, characterized in that The incremental identification of each vehicle data information includes: Using a change detection algorithm, incremental identification is performed based on the update timestamp of each data in the vehicle data information, and newly added or updated data is screened out as incremental data.

3. The method according to claim 1, characterized in that The step of using a preset priority strategy to perform priority marking on each incremental data to obtain a plurality of marked data includes: Determining the importance, timeliness, and transmission cost of each incremental data; For each incremental data, the priority value is calculated based on the importance, timeliness, and transmission cost using the following formula: P=W1I+W2T+W3C W1+W2+W3=1 Where: P is the priority value, I is the importance, T is the timeliness, C is the transmission cost, W1 is the weight of importance, W2 is the weight of timeliness, and W3 is the weight of transmission cost; Priority marking is performed on each incremental data based on the priority value of each incremental data to obtain multiple marked data.

4. The method according to claim 3, characterized in that Determining the importance, timeliness, and transmission cost of each incremental data includes: For each incremental data, the importance is determined based on the data type of the incremental data, the timeliness is determined based on the sensitivity of the incremental data to time, and the transmission cost is determined based on the resource overhead of transmitting the incremental data.

5. The method according to claim 3, characterized in that The marked data includes incremental data of high priority marks, incremental data of medium priority marks and incremental data of low priority marks; The step of marking the priority of each incremental data based on the priority value of each incremental data includes: Marking incremental data with a priority value greater than or equal to a first preset value as incremental data with a high priority mark; Marking incremental data whose priority value is greater than or equal to a second preset value and less than a first preset value as incremental data with a medium priority mark; wherein the second preset value is less than the first preset value; The incremental data having a priority value less than a second preset value is marked as low-priority marked incremental data.

6. The method according to any one of claims 1 to 5, characterized in that The message middleware distributes each of the marked data to a corresponding queue based on a priority order, including: The message middleware allocates each of the marked data to a corresponding queue based on the priority order; Based on the partitioning strategy, for each tagged data in the same queue, similar types of tagged data are assigned to the same Topic partition.

7. The method according to any one of claims 1 to 5, characterized in that Also includes: The target distributed database applies the parsed data based on an incremental update method.

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