Database data real-time synchronization method and device

Abstract

The invention discloses a database data real-time synchronization method and device, and relates to the technical field of data synchronization, the method comprises the following specific steps: S100, task receiving and preprocessing: receiving a synchronization task request from a source database, including a to-be-synchronized data set and target database information, and performing data format verification and duplicate removal processing; s200, performing priority evaluation and sorting: performing priority evaluation on the synchronization tasks according to a preset rule, and performing sorting according to priorities; according to the method, the priority and the data volume of the synchronization task and the load condition of the target database are dynamically evaluated through an intelligent scheduling and load balancing mechanism, so that the execution sequence and the speed of the synchronization task are intelligently adjusted, and the task with high priority can be preferentially processed; and meanwhile, resource waste on low-load nodes or overload on high-load nodes are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of data synchronization, and in particular to a real-time synchronization method for database data. Background Art

[0002] In today's era of rapid information development, databases are key infrastructure for storing, processing and analyzing data. Their performance and stability directly affect the business operation efficiency and decision-making quality of enterprises. With the rapid increase in the amount of enterprise data and the increasing complexity of business needs, real-time synchronization of data between databases has become a crucial technical challenge. Real-time data synchronization requires that data can be transmitted quickly and accurately between different database systems to support real-time response of business systems and immediacy of data analysis.

[0003] Traditional database synchronization methods often seem to be unable to cope with large amounts of data and complex business scenarios. On the one hand, these methods lack flexibility and cannot dynamically adjust the synchronization strategy according to the priority of data tasks, the size of data, and the load of the target database. As a result, during peak hours and in specific business scenarios, the synchronization of key data will be delayed, affecting the timeliness of business decisions. On the other hand, the load balancing mechanism in traditional technologies is often based on simple polling and static allocation strategies, which cannot effectively cope with the dynamic changes in node status in a distributed environment, resulting in uneven allocation of system resources, some nodes are overloaded, and other nodes are idle, reducing the overall performance and stability of the system.

[0004] In view of the above problems, it is necessary to optimize the existing database synchronization method and realize the priority-driven synchronization of data between databases through dynamic scheduling strategies and efficient load balancing mechanisms. Therefore, it is of great significance to develop a real-time database data synchronization method that can comprehensively realize the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a real-time database data synchronization method, which can achieve efficient, real-time, priority-driven synchronization of data between databases through the collaborative work of intelligent scheduling and load balancing, dynamically adjust the execution order and rate of synchronization tasks according to preset priority rules and data volume and load analysis results, and ensure that system resources are fully utilized and single-point overload is avoided by real-time monitoring of node status and using intelligent algorithms for task allocation. In addition, the system also has a real-time monitoring and feedback mechanism, which can promptly detect and handle abnormal situations in the synchronization process to ensure the stability and reliability of the system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a method for real-time synchronization of database data comprises the following specific steps:

[0007] S100, task reception and preprocessing: receiving a synchronization task request from a source database, including the data set to be synchronized and the target database information, and performing data format verification and deduplication processing;

[0008] S200, priority evaluation and sorting: according to preset rules, priority evaluation is performed on synchronization tasks, and they are sorted according to priority;

[0009] S300, data analysis and load assessment: real-time analysis of the amount of data to be synchronized, monitoring the current load of the target database, and assessing the system resources required for task execution;

[0010] S400, dynamic scheduling and load balancing: intelligently schedules the execution order and rate of synchronization tasks based on priority sorting, data volume and load evaluation results, and automatically distributes tasks to multiple nodes in a distributed environment to achieve load balancing;

[0011] S500, concurrent synchronization and monitoring: uses efficient data transmission protocols and concurrent processing mechanisms to execute synchronization tasks in parallel on multiple nodes, while monitoring the synchronization process in real time and collecting synchronization performance data;

[0012] S600, exception handling and performance optimization: abnormal situations in the synchronization process are promptly fed back to system administrators and users, and corresponding handling measures are taken. At the same time, according to the collected synchronization performance data, the synchronization strategy and load balancing scheme are dynamically adjusted and optimized.

[0013] Furthermore, the step S200 includes:

[0014] S201, scoring the synchronization tasks according to preset priority rules, including business criticality, data timeliness, and user request urgency, and using the scoring results to determine the priority order of the tasks;

[0015] S202, sorting the synchronization tasks according to the priority scores so that high-priority tasks can be processed first.

[0016] Furthermore, the step S202 scores the synchronization task using a weighted algorithm, and the calculation formula is: Where T represents the data task to be synchronized, n is the number of evaluation factors, and w i is the weight of the i-th evaluation factor, f i (T) is the calculation function of the i-th evaluation factor for task T.

[0017] Furthermore, the step S300 includes:

[0018] S301, calculating the size of the data set to be synchronized, evaluating its demand for system resources, and analyzing the characteristics and structure of the data set;

[0019] S302, real-time monitoring of the CPU usage, memory usage, and IO waiting time indicators of the target database;

[0020] S303, evaluating the current load of the target database to determine whether it can bear the new synchronization task.

[0021] Furthermore, the step S400 includes:

[0022] S401, dynamically adjusting the execution order and rate of synchronization tasks according to task priority, data volume, and target database load;

[0023] S402, in a distributed environment, automatically allocating synchronization tasks to multiple nodes according to the processing capacity of each node and the status information of the current load;

[0024] S403, using genetic algorithm to ensure balanced task allocation, monitor node status, and dynamically adjust task allocation strategy to adapt to changes in system load.

[0025] Furthermore, the specific steps of dynamically adjusting the execution order and rate of synchronization tasks in step S401 are as follows:

[0026] (1) Continuously monitor the execution status of all synchronization tasks, evaluate the current load of the system, and re-evaluate the priority of all current synchronization tasks according to the preset priority rules;

[0027] (2) Based on the results of system load analysis and task priority evaluation, determine the system resources to be allocated to each synchronization task;

[0028] (3) Dynamically adjust the execution order and rate of synchronization tasks according to real-time changes in system load and task execution status;

[0029] (4) Establish a feedback mechanism to adjust the scheduling strategy based on the actual results of task execution.

[0030] Furthermore, the step S403 uses a genetic algorithm to dynamically adjust the task allocation strategy. Specifically, the task allocation scheme is encoded as a population, and an initial population is randomly generated. Each individual in the population represents a task allocation scheme. A fitness function F(x) is defined, where x is a task allocation scheme. The function evaluates the quality of scheme x. Its fitness function is expressed as: F(x) = α·LoadBalance(x)+β·ResourceUtilization(x)+γ·CompletionTime(x), where LoadBalance(x), ResourceUtilization(x)+γ·CompletionTime(x) n(x) and CompletionTime(x) are evaluation functions of load balancing, resource utilization and task completion time respectively. α, β and γ are weight coefficients. Based on the fitness value, a part of excellent individuals are selected from the current population as parents to generate the next generation. The selected parent individuals are crossovered to generate new offspring individuals. Some genes of the offspring individuals are mutated with a certain probability to increase the diversity of the population. The selection, crossover and mutation steps are repeated until the fitness value is no longer significantly improved. The output is a near-optimal task allocation scheme that can maximize load balancing, minimize task completion time and optimize resource utilization.

[0031] Furthermore, the step S500 includes:

[0032] S501, execute synchronization tasks in parallel on multiple nodes, using efficient data transmission protocols and concurrent processing mechanisms;

[0033] S502, monitor the synchronization progress in real time, and collect performance data during the synchronization process, including synchronization speed, data transmission volume and error rate, and monitor the load of the target database.

[0034] Furthermore, the step S600 includes:

[0035] S601, identifying abnormal situations in the synchronization process, including data inconsistency and synchronization failure, and taking corresponding processing measures;

[0036] S602, recording the abnormal situation in a log and notifying the system administrator and user;

[0037] S603: Analyze the bottlenecks and problems existing in the synchronization process according to the collected synchronization performance data, and dynamically adjust and optimize the synchronization strategy and load balancing solution.

[0038] On the other hand, a real-time database data synchronization device is characterized in that the device comprises: an intelligent scheduling module, a load balancing module and a synchronization execution and monitoring module;

[0039] The intelligent scheduling module includes a priority evaluation unit, a data volume and load analysis unit and a dynamic scheduling strategy generation unit, wherein the priority evaluation unit performs priority evaluation on the data tasks to be synchronized according to the preset priority rules, the data volume and load analysis unit analyzes the size of the data volume to be synchronized and the current load of the target database in real time, and the dynamic scheduling strategy generation unit dynamically generates and adjusts the execution order and rate of the synchronization tasks based on the priority evaluation results and the data volume and load analysis results;

[0040] The load balancing module includes a node status monitoring unit, a task allocation algorithm unit and a dynamic adjustment mechanism unit, wherein the node status monitoring unit monitors the status information of each node in the distributed environment in real time, the task allocation algorithm unit uses a genetic algorithm to automatically allocate synchronization tasks to multiple nodes according to the node status and task requirements to achieve load balancing, and the dynamic adjustment mechanism unit dynamically adjusts the task allocation according to the real-time changes of the node status during the task execution process;

[0041] The synchronous execution and monitoring module includes a real-time synchronization unit and a monitoring and feedback unit. The real-time synchronization unit adopts an efficient data transmission protocol and concurrent processing mechanism to achieve real-time synchronization of data. The monitoring and feedback unit monitors the synchronization process in real time, collects synchronization performance data, and feeds back abnormal situations to the intelligent scheduling module and load balancing module.

[0042] Compared with the prior art, the real-time database data synchronization method has the following beneficial effects:

[0043] 1. The present invention dynamically evaluates the priority of synchronization tasks, the size of data, and the load of the target database through intelligent scheduling and load balancing mechanisms, thereby intelligently adjusting the execution order and rate of synchronization tasks, ensuring that high-priority tasks can be processed first, while avoiding wasting resources on low-load nodes or causing overloads on high-load nodes.

[0044] 2. The present invention can timely discover and prevent potential system overload problems by real-time monitoring of node status and system load, avoiding system crash or performance degradation caused by excessive load. In addition, for abnormal situations occurring during the synchronization process, the present invention can quickly identify and take corresponding processing measures, while recording abnormal logs and notifying system administrators and users to ensure that problems can be responded to and resolved in a timely manner.

[0045] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] 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 prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 This is a flowchart of the real-time synchronization method for database data.

[0048] Figure 2 This is a structural diagram of a real-time database data synchronization device. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0050] Example 1

[0051] In this embodiment, an implementation process of the database data real-time synchronization method according to the present invention is described in detail.

[0052] In the task receiving and preprocessing stage (S100), the system receives synchronization task requests from the source database through an API interface, a message queue or other communication mechanisms. These requests usually contain a task ID, a data source identifier, target database information, a synchronized data table name, synchronization conditions (such as a time range, a primary key range), etc. The system verifies the data format specified in the request to confirm that it meets the format requirements of the target database, for example, checking whether the data type matches and whether the field exists. In order to prevent duplicate data synchronization, the system can adopt a variety of deduplication strategies, such as deduplication based on primary keys, deduplication based on timestamps and unique identifiers, etc. The system will query the target database to confirm whether the data to be synchronized already exists. If so, the corresponding record will be skipped or updated.

[0053] In the priority evaluation and sorting stage (S200), the system maintains a set of priority scoring rules. These rules are based on factors such as business criticality, data timeliness, and user request urgency. For example, business core data may have a higher business criticality score, while an urgent request just submitted by a user may have a higher urgency score. The system calculates a comprehensive score for each synchronization task based on the scoring rules. This score is the weighted result of multiple evaluation factors and reflects the priority of the task. The calculation formula is: Where T represents the data task to be synchronized, n is the number of evaluation factors, and w i is the weight of the i-th evaluation factor, f i (T) is the calculation function for the i-th evaluation factor of task T. The system sorts the synchronization tasks according to the calculated scores and maintains a priority queue. High-priority tasks are placed at the front of the queue for priority processing.

[0054] In the data analysis and load assessment phase (S300), the system estimates the size of the data set to be synchronized by querying the source database or analyzing the data change log. This helps predict the demand for system resources during the synchronization process. The system monitors key performance indicators such as CPU usage, memory usage, and IO wait time of the target database in real time. These indicators reflect the current load of the database. Based on the size of the data set and the current load of the target database, the system evaluates the system resources required to perform the synchronization task and determines whether the target database can withstand the new synchronization task. If the evaluation result shows that the target database load is too high, the system may delay the synchronization task or adjust the synchronization strategy.

[0055] In the dynamic scheduling and load balancing stage (S400), the system dynamically adjusts the execution order and rate of synchronization tasks according to task priority, data volume and target database load. For example, high-priority tasks may be scheduled first, while tasks with small data volume may be executed during low-load periods. In a distributed environment, the system automatically allocates synchronization tasks to multiple nodes according to the processing capacity and current load status of each node, which helps to achieve load balancing and improve synchronization efficiency. The system uses a genetic algorithm to dynamically adjust the task allocation strategy by encoding the task allocation scheme as individuals in the population and defining a fitness function to evaluate the quality of the scheme. Specifically, the task allocation scheme is encoded as a population, and an initial population is randomly generated. Each individual in the population represents a task allocation scheme. The fitness function F(x) is defined, where x is a task allocation scheme. The function evaluates the quality of scheme x. Its fitness function is expressed as: F(x) = α·Lo adBalance(x)+β·ResourceUtilization(x)+γ·CompletionTime(x), where LoadBalance(x), ResourceUtilization(x) and CompletionTime(x) are evaluation functions of load balance, resource utilization and task completion time respectively, α, β and γ are weight coefficients. Based on the fitness value, a part of excellent individuals are selected from the current population as parents to generate the next generation. The selected parent individuals are crossovered to generate new offspring individuals. Some genes of the offspring individuals are mutated with a certain probability to increase the diversity of the population. The selection, crossover and mutation steps are repeated until the fitness value is no longer significantly improved. The system can gradually optimize the task allocation plan to maximize load balance, minimize task completion time and optimize resource utilization.

[0056] In the concurrent synchronization and monitoring stage (S500), the system uses the computing power of multiple nodes to execute synchronization tasks in parallel on multiple nodes. Through efficient data transmission protocols and concurrent processing mechanisms, the system can significantly improve the synchronization speed. The system monitors the synchronization progress and performance data in real time, including synchronization speed, data transmission volume, error rate, and load of the target database. These data help the system to detect problems in a timely manner and take corresponding treatment measures.

[0057] In the exception handling and performance optimization stage (S600), the system can automatically identify abnormal situations in the synchronization process, such as data inconsistency, synchronization failure, etc. For these abnormal situations, the system will take corresponding processing measures, such as retrying synchronization, recording error information and notifying relevant personnel. The system will record the abnormal situation in the log for subsequent analysis and troubleshooting. At the same time, the system will also notify system administrators and users through emails, SMS or message push. Based on the collected synchronization performance data, the system analyzes the bottlenecks and problems in the synchronization process and dynamically adjusts the synchronization strategy and load balancing solution. For example, the system may adjust the task scheduling strategy, optimize the data transmission protocol or increase computing resources to improve synchronization efficiency.

[0058] Example 2

[0059] This embodiment describes in detail the steps and methods for using the real-time database data synchronization device, covering the specific implementation of the intelligent scheduling module, the load balancing module, and the synchronous execution and monitoring module.

[0060] First, users need to set priority rules (such as business criticality weights and data timeliness thresholds), target database load thresholds, node performance parameters, etc. through the configuration interface or API interface. The system automatically performs initialization self-checks, including network connections, node status, database connections, etc., to ensure the normal operation of all components.

[0061] Secondly, it receives synchronization task requests, including data source, target database, data table and synchronization condition information. The priority evaluation unit scores the task according to preset rules (such as business criticality scoring model, data timeliness check, etc.) to determine the priority. The data volume and load analysis unit queries the source database to estimate the amount of data to be synchronized. At the same time, it obtains real-time load information (such as CPU usage, IO waiting time, memory occupancy, etc.) through the monitoring interface of the target database. Based on the priority evaluation results and the data volume and load analysis results, the dynamic scheduling strategy generation unit calculates and determines the execution order and rate of the synchronization tasks. If it is found that the target database load is too high, the strategy is adjusted, such as delaying non-critical tasks, reducing the synchronization rate, or reallocating tasks to other nodes.

[0062] Then, the node status monitoring unit collects the status information of each node in the distributed environment regularly or in real time, including CPU usage, memory usage, network bandwidth, etc. The task allocation algorithm unit uses genetic algorithm to optimize the task allocation plan according to the node status, task requirements and priority, and automatically allocates synchronization tasks to multiple nodes. During the task execution process, if the node status changes (such as sudden increase in load, node failure, etc.), the dynamic adjustment mechanism unit will automatically re-evaluate the task allocation to ensure load balancing and stable system operation.

[0063] Next, the real-time synchronization unit starts the synchronization task according to the scheduling and allocation results, and adopts efficient data transmission protocols (such as TCP / IP optimization, compressed transmission, etc.) and concurrent processing mechanisms (such as multi-threading, asynchronous IO, etc.) to achieve fast data synchronization. The monitoring and feedback unit monitors the synchronization process in real time and collects performance indicators (such as synchronization rate, data transmission volume, error rate, etc.). If an abnormal situation is found (such as synchronization failure, data inconsistency, etc.), it will immediately record the log and notify the relevant modules (such as intelligent scheduling module, load balancing module) to make adjustments. At the same time, the synchronization progress and results are fed back to the user or upper-level application to ensure transparency and traceability.

[0064] Finally, the system regularly records and saves synchronization logs, error logs, and performance data for subsequent analysis and optimization. Based on historical data and user feedback, it continuously optimizes scheduling strategies, load balancing algorithms, and synchronization mechanisms to improve the overall performance and stability of the system.

[0065] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for real-time synchronization of database data, characterized in that: The method comprises the following specific steps: S100, task reception and preprocessing: receiving a synchronization task request from a source database, including the data set to be synchronized and the target database information, and performing data format verification and deduplication processing; S200, priority evaluation and sorting: according to preset rules, priority evaluation is performed on synchronization tasks, and they are sorted according to priority; S300, data analysis and load assessment: real-time analysis of the amount of data to be synchronized, monitoring the current load of the target database, and assessing the system resources required for task execution; S400, dynamic scheduling and load balancing: intelligently schedules the execution order and rate of synchronization tasks based on priority sorting, data volume and load evaluation results, and automatically distributes tasks to multiple nodes in a distributed environment to achieve load balancing; S500, concurrent synchronization and monitoring: uses efficient data transmission protocols and concurrent processing mechanisms to execute synchronization tasks in parallel on multiple nodes, while monitoring the synchronization process in real time and collecting synchronization performance data; S600, exception handling and performance optimization: abnormal situations in the synchronization process are promptly fed back to system administrators and users, and corresponding handling measures are taken. At the same time, according to the collected synchronization performance data, the synchronization strategy and load balancing scheme are dynamically adjusted and optimized.

2. The method for real-time synchronization of database data according to claim 1, characterized in that: The step S200 includes: S201, scoring the synchronization tasks according to preset priority rules, including business criticality, data timeliness, and user request urgency, and using the scoring results to determine the priority order of the tasks; S202, sorting the synchronization tasks according to the priority scores so that high-priority tasks can be processed first.

3. The method for real-time synchronization of database data according to claim 2, characterized in that: The step S202 scores the synchronization task using a weighted algorithm, and the calculation formula is: Where T represents the data task to be synchronized, n is the number of evaluation factors, and w i is the weight of the i-th evaluation factor, f i (T) is the calculation function of the i-th evaluation factor for task T.

4. The method for real-time synchronization of database data according to claim 1, characterized in that: The step S300 includes: S301, calculating the size of the data set to be synchronized, evaluating its demand for system resources, and analyzing the characteristics and structure of the data set; S302, real-time monitoring of the CPU usage, memory usage, and IO waiting time indicators of the target database; S303, evaluating the current load of the target database to determine whether it can bear the new synchronization task.

5. The method for real-time synchronization of database data according to claim 1, characterized in that: The step S400 includes: S401, dynamically adjusting the execution order and rate of synchronization tasks according to task priority, data volume, and target database load; S402, in a distributed environment, automatically allocating synchronization tasks to multiple nodes according to the processing capacity of each node and the status information of the current load; S403, using genetic algorithm to ensure balanced task allocation, monitor node status, and dynamically adjust task allocation strategy to adapt to changes in system load.

6. The method for real-time synchronization of database data according to claim 5, characterized in that: The specific steps of step S401 to dynamically adjust the execution order and rate of synchronization tasks are as follows: (1) Continuously monitor the execution status of all synchronization tasks, evaluate the current load of the system, and re-evaluate the priority of all current synchronization tasks according to the preset priority rules; (2) Based on the results of system load analysis and task priority evaluation, determine the system resources to be allocated to each synchronization task; (3) Dynamically adjust the execution order and rate of synchronization tasks according to real-time changes in system load and task execution status; (4) Establish a feedback mechanism to adjust the scheduling strategy based on the actual results of task execution.

7. The method for real-time synchronization of database data according to claim 5, characterized in that: The step S403 uses a genetic algorithm to dynamically adjust the task allocation strategy. Specifically, the task allocation scheme is encoded as a population, and an initial population is randomly generated. Each individual in the population represents a task allocation scheme. A fitness function F(x) is defined, where x is a task allocation scheme. The function evaluates the quality of the scheme x. The fitness function is expressed as: F(x) = α·LoadBalance(x) + β·ResourceUtilization(x) + γ·CompletionTime(x), where LoadBalance(x), ResourceUtilization(x) ) and CompletionTime(x) are evaluation functions of load balancing, resource utilization and task completion time, respectively. α, β and γ are weight coefficients. Based on the fitness value, a part of excellent individuals are selected from the current population as parents to generate the next generation. The selected parent individuals are crossovered to generate new offspring individuals. Some genes of the offspring individuals are mutated with a certain probability to increase the diversity of the population. The selection, crossover and mutation steps are repeated until the fitness value is no longer significantly improved. The optimal task allocation scheme is output, which can maximize load balancing, minimize task completion time and optimize resource utilization.

8. The method for real-time synchronization of database data according to claim 1, characterized in that: The step S500 includes: S501, execute synchronization tasks in parallel on multiple nodes, using efficient data transmission protocols and concurrent processing mechanisms; S502, monitor the synchronization progress in real time, and collect performance data during the synchronization process, including synchronization speed, data transmission volume and error rate, and monitor the load of the target database.

9. The method for real-time synchronization of database data according to claim 1, characterized in that: The step S600 includes: S601, identifying abnormal situations in the synchronization process, including data inconsistency and synchronization failure, and taking corresponding processing measures; S602, recording the abnormal situation in a log and notifying the system administrator and user; S603: Analyze the bottlenecks and problems existing in the synchronization process according to the collected synchronization performance data, and dynamically adjust and optimize the synchronization strategy and load balancing solution.

10. Database data real-time synchronization equipment, characterized in that, The device comprises: an intelligent scheduling module, a load balancing module and a synchronous execution and monitoring module; The intelligent scheduling module includes a priority evaluation unit, a data volume and load analysis unit and a dynamic scheduling strategy generation unit, wherein the priority evaluation unit performs priority evaluation on the data tasks to be synchronized according to the preset priority rules, the data volume and load analysis unit analyzes the size of the data volume to be synchronized and the current load of the target database in real time, and the dynamic scheduling strategy generation unit dynamically generates and adjusts the execution order and rate of the synchronization tasks based on the priority evaluation results and the data volume and load analysis results; The load balancing module includes a node status monitoring unit, a task allocation algorithm unit and a dynamic adjustment mechanism unit, wherein the node status monitoring unit monitors the status information of each node in the distributed environment in real time, the task allocation algorithm unit uses a genetic algorithm to automatically allocate synchronization tasks to multiple nodes according to the node status and task requirements to achieve load balancing, and the dynamic adjustment mechanism unit dynamically adjusts the task allocation according to the real-time changes of the node status during the task execution process; The synchronous execution and monitoring module includes a real-time synchronization unit and a monitoring and feedback unit. The real-time synchronization unit adopts an efficient data transmission protocol and concurrent processing mechanism to achieve real-time synchronization of data. The monitoring and feedback unit monitors the synchronization process in real time, collects synchronization performance data, and feeds back abnormal situations to the intelligent scheduling module and load balancing module.

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