Merchandise data real-time synchronization system supporting distributed environment

Abstract

The application discloses a kind of commodity data real-time synchronization system and method supporting distributed environment, belong to distributed data processing technical field.The application includes hierarchical consistency protocol module, three-layer fault-tolerant self-healing network module, heterogeneous stream uniform adaptation module, resource-aware dynamic scheduling module.Hierarchical consistency protocol dynamically selects synchronization strategy according to commodity data importance, L1 level key data uses delay optimization Raft protocol.Three-layer fault-tolerant network realizes node level, regional level, three-level fault-tolerant global level.Heterogeneous stream uniform adaptation supports multi-source heterogeneous data access.Resource-aware scheduling realizes cost performance dynamic balance.The application effectively solves the consistency and delay contradiction of commodity data synchronization under the distributed environment of small and medium-sized enterprises, multi-node fault tolerance, heterogeneous data source integration, cost performance balance Four technical problems, key data synchronization delay is controlled within 100ms, system availability reaches 99.99%, with remarkable creativity and practical value.

Description

Technical Field

[0001] This invention relates to the field of distributed data processing technology, and more specifically to a real-time synchronization system for commodity data that supports a distributed environment. Background Technology

[0002] With the rapid development of e-commerce, SMEs are increasingly adopting distributed cluster architectures to deploy e-commerce systems. However, real-time synchronization of product data in a distributed environment faces numerous technical challenges. First, there is an inherent contradiction between data consistency and synchronization latency. According to the CAP theorem, distributed systems cannot simultaneously satisfy strong consistency, high availability, and partition tolerance. In existing technologies, strong consistency schemes such as two-phase commit and the Paxos protocol result in significant synchronization latency, typically exceeding 200ms, impacting user experience. Eventual consistency schemes, on the other hand, have a time window for data inconsistency, potentially leading to serious business problems such as overselling inventory and incorrect pricing. While Google's Photon system implements distributed streaming connections, it is designed specifically for advertising systems and employs a full Paxos consensus mechanism, which is costly and has high latency, making it unsuitable for SME e-commerce scenarios.

[0003] Secondly, the fault tolerance capability of multiple nodes is insufficient. Distributed clusters for small and medium-sized enterprises (SMEs) typically use inexpensive commercial servers, which have a high probability of node failure. Existing master-slave replication architectures have the risk of single point of failure, and data recovery is difficult when multiple nodes fail concurrently. Although existing technologies for distributed e-commerce clusters for SMEs consider basic fault tolerance, they lack multi-level fault tolerance mechanisms for product data synchronization, and a single node failure may cause the entire synchronization link to be interrupted.

[0004] Third, integrating heterogeneous data sources is difficult. Small and medium-sized enterprise (SME) e-commerce systems typically use multiple data sources: MySQL for order storage, MongoDB for product details, and Redis for caching frequently accessed data. Existing synchronization tools often only support a single data source, lacking unified capabilities for accessing and transforming heterogeneous data. The Photon system only supports log data streams and cannot handle product data synchronization from multiple data sources such as relational databases and NoSQL databases.

[0005] Fourth, balancing cost and performance is difficult. Small and medium-sized enterprises (SMEs) have limited IT budgets and cannot afford the high deployment and maintenance costs of large-scale distributed systems like Photon. Existing solutions either lack sufficient performance to handle high concurrency or consume excessive resources, resulting in waste, and lack a dynamic cost-performance balancing mechanism.

[0006] Therefore, a real-time product data synchronization system that supports a distributed environment is needed. Summary of the Invention

[0007] The technical problem to be solved by this invention is how to achieve real-time synchronization of commodity data in a distributed environment for small and medium-sized enterprises, while solving four major technical challenges: the contradiction between consistency and latency, fault tolerance of multiple nodes, integration of heterogeneous data sources, and balance between cost and performance.

[0008] To address the aforementioned technical problems, this invention provides a real-time product data synchronization system supporting distributed environments, comprising a hierarchical consistency protocol module, a three-layer fault-tolerant self-healing network module, a heterogeneous stream unified adaptation module, and a resource-aware dynamic scheduling module. The hierarchical consistency protocol module dynamically selects the appropriate consistency synchronization strategy based on the importance level of the product data. Product data is divided into three levels: L1 strongly consistent data, L2 causally consistent data, and L3 eventually consistent data. For L1 data, a latency-optimized Raft consensus protocol is used, separating log replication and commit confirmation, with critical data receiving priority confirmation. For L2 data, a vector clock is used to ensure the causal order of operations. For L3 data, an asynchronous message queue is used to achieve eventual consistency.

[0009] A three-layer fault-tolerant and self-healing network module is used to achieve fault tolerance at the node, region, and global levels. At the node level, heartbeat detection enables rapid failover in less than 3 seconds. At the region level, chained replication and parallel acknowledgment achieve multi-replica redundancy. At the global level, cross-regional fault tolerance is achieved through cross-regional state synchronization and vector clock conflict resolution. A differentiated replication strategy is adopted, with multiple active-active replication for hot data and single active-active replication for cold data.

[0010] The heterogeneous stream unified adaptation module enables unified access and real-time streaming connections for multi-source heterogeneous product data sources. It includes a unified change capture layer, a semantic transformation layer, and a stream connection processor. The unified change capture layer supports multiple change capture methods, including CDC, triggers, polling, and message queues. The semantic transformation layer performs automatic semantic mapping and format conversion based on the product data ontology. The stream connection processor supports the unique semantic relationships specific to product data.

[0011] The resource-aware dynamic scheduling module is used to achieve adaptive resource scheduling based on a cost-performance model. It includes a resource usage prediction unit, a cost-performance trade-off unit, and an elastic scaling unit. The resource usage prediction unit predicts synchronization load based on historical data. The cost-performance trade-off unit minimizes resource costs while meeting SLAs. The elastic scaling unit enables second-level node addition / removal and hierarchical resource pool management.

[0012] The hierarchical consistency protocol module also includes a dynamic consistency level adjustment unit, which is used to adjust the data consistency level online according to system load and business scenarios, and automatically increase the synchronization priority of L1 level data during peak sales periods.

[0013] The three-layer fault-tolerant self-healing network module also includes a fault impact range prediction unit, which automatically activates the corresponding level of fault-tolerant recovery mechanism based on the importance of the faulty node and the data type it carries. This enables gradual recovery, prioritizing the restoration of critical data synchronization.

[0014] The heterogeneous stream unified adaptation module also includes a schema auto-evolution unit, used to detect changes in the source data structure and automatically synchronize them to the target. It enables automatic incremental and full data switching, and automatically determines the synchronization strategy when resuming interrupted downloads.

[0015] The resource-aware dynamic scheduling module also includes an idle-time resource reclamation unit, used to automatically release redundant resources during off-peak hours at night. A tiered resource pool strategy is adopted, with critical data synchronously using a high-performance resource pool, and non-critical data using a low-cost resource pool.

[0016] Accordingly, this invention also provides a method for real-time synchronization of product data in a distributed environment. First, product data is classified into three levels: L1, L2, and L3, based on its importance. Then, an appropriate consistency synchronization strategy is selected according to the data level. Next, changes to product data are captured from heterogeneous data sources. Then, the multi-source data streams are connected and processed. Afterward, a three-level fault-tolerant network ensures synchronization reliability. Finally, synchronization resources are dynamically adjusted to achieve a balance between cost and performance.

[0017] In summary, this invention provides a real-time product data synchronization system supporting a distributed environment. Compared with existing technologies, this invention has the following advantages: It effectively resolves the contradiction between consistency and latency. Through a hierarchical consistency protocol, the latency of L1-level critical data, such as inventory and price, is controlled within 100ms, while ensuring quasi-strong consistency. L2 and L3-level data pursue even higher performance. Compared with traditional strong consistency schemes, latency is reduced by more than 60%, and compared with eventual consistency schemes, critical data consistency is improved by 100%.

[0018] Significantly improves fault tolerance capabilities; the three-layer fault-tolerant network achieves node-level failover in less than 3 seconds, seamless regional-level fault handling, and automatic recovery from global-level faults. Compared to master-slave replication architecture, system availability increases from 99.9% to 99.99%, and annual downtime decreases from 8.76 hours to 52 minutes. It fully supports heterogeneous data sources, with a unified adaptation layer supporting over 10 data sources including MySQL, PostgreSQL, MongoDB, and Redis, automatically handling semantic mapping and format conversion. Compared to single-data-source synchronization tools, it improves access efficiency by 80% and reduces development costs by 60%. Attached Figure Description

[0019] Figure 1 This is a diagram of the overall system architecture of the present invention; Figure 2 This is a schematic diagram of the commodity data hierarchical consistency protocol (CGCP) of the present invention; Figure 3 This is a diagram of the three-layer fault-tolerant self-healing network structure of the present invention; Figure 4 This is a flowchart of the heterogeneous flow unified adaptation engine workflow of the present invention; Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0021] like Figure 1 As shown, the real-time product data synchronization system supporting distributed environments of this invention adopts a five-layer architecture. The access layer deploys a multi-source data access module, supporting access from over ten data sources, including MySQL Binlog, MongoDBopLog, Redis keyspace notification, PostgreSQL logical replication, Elasticsearch change API, HTTP interface polling and push, and RocketMQ message subscription. The data synchronization engine layer deploys the core data synchronization module, responsible for scheduling, distributing, and monitoring synchronization tasks, prioritizing tasks based on data type and consistency level. The consistency protocol layer deploys the CGCP protocol module, executing a hierarchical consistency synchronization strategy for product data, dynamically selecting the appropriate consistency synchronization algorithm based on data importance. The fault-tolerant network layer deploys a three-layer fault-tolerant network module, providing node-level, region-level, and global-level fault tolerance guarantees, enabling automatic fault detection and self-healing. The storage layer deploys a distributed storage module, using a distributed file system to store synchronized product data, supporting multi-replica redundant storage and fast retrieval.

[0022] like Figure 2 As shown, the hierarchical consistency protocol module divides product data into three levels based on their importance. Level L1 strongly consistent data includes product inventory, product price, order status, and payment status. Changes to this type of data directly affect the correctness of transactions and must be guaranteed to be quasi-strong consistency. The specific judgment rule for L1 level data is that fields involving fund settlement or inventory deduction are included. Any data inconsistency may lead to serious business problems such as overselling and price errors. The synchronization trigger condition is that the data change exceeds the threshold or a real-time event is triggered. Synchronization is immediately triggered when the inventory quantity changes by more than 1 item or the price changes by more than 0.01 yuan. Level L1 adopts an optimized Raft protocol, with an election timeout of 150 milliseconds, a heartbeat interval of 50 milliseconds, and a threshold for the number of acknowledgment nodes of N / 2+1. Log replication and commit confirmation are separated. The Leader returns success immediately after receiving the log replication ACK from the Follower. Commit confirmation is executed asynchronously. Critical data is placed in a high-priority queue for priority processing. Logs within a 10-millisecond window are batch committed to reduce the number of RPCs. The L1 level latency threshold is set to 85 milliseconds to ensure the real-time synchronization of critical data.

[0023] Level 2 causal consistency data includes product descriptions, product categories, product images, and user reviews. This type of data requires ensuring the causal order of operations but allows for brief windows of inconsistency. The classification criteria are information that affects the user browsing experience but does not directly affect transactions. Synchronization trigger conditions are time-based intervals or data change events, with data changes checked every 100 milliseconds. Level 2 uses a vector clock algorithm with millisecond precision and a conflict detection window of 5 seconds. A vector clock is attached to each product data item, and the causal relationship of operations is determined by comparing the vector clocks to ensure the correctness of the operation order. The Level 2 latency threshold is set to 500 milliseconds to improve synchronization performance while ensuring causal consistency. Level 3 eventually consistent data includes pageviews, sales statistics, product ratings, and log data. This type of data has lower real-time requirements and covers statistical analysis and log recording data. Synchronization trigger conditions are timed batch synchronization, with batch synchronization performed every 5 seconds.

[0024] Level 3 employs an asynchronous eventual consistency strategy with a retry interval of 1 second and a maximum number of retries of 10. Asynchronous message delivery is implemented using RocketMQ, with consumers using idempotent processing to ensure at least one delivery. A dead-letter queue handles consecutively failed messages, and the Level 3 latency threshold is set to 5 seconds, aiming for maximum throughput and minimum resource consumption. The hierarchical consistency protocol module also includes a dynamic consistency level adjustment unit, which adjusts the data consistency level online based on system load and business scenarios. During peak business periods such as promotional events, it automatically increases the synchronization priority of Level 1 data and dynamically adjusts the resource allocation ratio of each level to ensure that critical data still meets latency requirements under high load.

[0025] like Figure 3 As shown, the three-layer fault-tolerant self-healing network module implements three levels of fault tolerance: node-level, region-level, and global-level. Node-level fault tolerance employs a heartbeat detection mechanism, with a heartbeat frequency set to once per second. Three consecutive no-response heartbeats are considered a node failure, and the failover time is controlled within 3 seconds. The backup node synchronizes the master node's status information in real time. Upon detecting a fault, it immediately takes over the work of the failed node and restores synchronization from the nearest checkpoint, achieving seamless single-node failure detection. Specific thresholds for node failure determination are as follows: CPU utilization exceeding 95% for 5 seconds is considered overload; memory utilization exceeding 90% for 10 seconds is considered insufficient memory; and network packet loss exceeding 5% for 3 seconds is considered a network failure. Meeting any one of these conditions triggers the failover process.

[0026] Regional fault tolerance employs a chained replication mechanism with a replication chain length of 3, a parallel confirmation node count of 2, and a data synchronization timeout of 1 second. The master node sends data to only one slave node, which then continues replication downwards. Synchronization is considered successful upon receiving confirmations from two replicas. Each region operates independently with loose coupling between regions, ensuring that a single region failure does not affect other regions, achieving an RPO of 0. Global fault tolerance utilizes a cross-regional conflict resolution mechanism with a conflict detection time window of 10 seconds. The conflict resolution strategy combines last-write-winner with business rule arbitration. Data consistency verification uses hash verification, and the core state is synchronized every 10 seconds, with concurrent modifications automatically merged via a vector clock.

[0027] The three-layer fault-tolerant self-healing network module also includes a fault impact range prediction unit. Based on the importance of the faulty node and the data type it carries, it automatically activates the corresponding level of fault-tolerant recovery mechanism to achieve a progressive recovery process. It prioritizes the recovery of L1-level critical data synchronization, then L2-level data, and finally L3-level data. The complete self-healing process includes five stages: fault detection, fault isolation, route switching, data completion, and service recovery. In the fault detection stage, the node status is monitored in real time through multi-dimensional health checks. In the fault isolation stage, the faulty node is automatically removed from the cluster and traffic distribution is stopped. In the route switching stage, the cluster routing table is updated to redirect traffic to healthy nodes. In the data completion stage, missing data increments are synchronized from adjacent healthy nodes. In the service recovery stage, normal service is restored after verifying data consistency.

[0028] like Figure 4 As shown, the heterogeneous stream unified adaptation engine module enables unified access and real-time streaming connection of multi-source heterogeneous product data sources. The unified change capture layer supports adaptation methods for more than ten data sources. MySQL uses binlog parsing, based on the Canal protocol to parse MySQL binary logs with a latency of less than 50 milliseconds. MongoDB uses oplog listening to monitor MongoDB operation logs in real time. Redis uses key space notification to subscribe to Redis key change events. PostgreSQL uses logical replication to obtain data changes through a decoding plugin. Elasticsearch uses a change API to periodically call the index change interface to obtain incremental data. The HTTP interface supports both polling and push methods, with configurable polling intervals. The message queue uses a subscription method and supports mainstream message queues such as RocketMQ and Kafka.

[0029] The semantic transformation layer implements automatic semantic mapping and format conversion based on the product data ontology. The schema auto-evolution rules include an automatic compatibility strategy for newly added fields, where new fields are automatically added to the target schema and set default values; a retention strategy for deleted fields, where deleted fields retain 30 days of historical data before being automatically cleaned up; and automatic conversion rules for field type changes, supporting automatic conversion between strings and numbers, and dates and timestamps. The schema version number uses an incrementing mechanism, incrementing by 1 with each structural change and recording the change log. The stream connection processor uses an improved Photon algorithm, with a stream connection window size of 10 seconds, an out-of-order tolerance time of 2 seconds, a watermark update frequency of once per second, and a state backend refresh interval of 100 milliseconds. It supports three-source stream connections: product, inventory, and order. A sharding strategy based on product ID ensures that related data is routed to the same processing node.

[0030] The streaming connection window size supports a dynamic adjustment mechanism, adjusting between 5 and 30 seconds based on data traffic and latency requirements. During peak traffic periods, the window size increases throughput, while during off-peak periods, it decreases to reduce latency. The heterogeneous stream unified adaptation module also includes a schema auto-evolution unit, which detects changes in the source data structure and automatically synchronizes them to the target, enabling automatic switching between incremental and full synchronization. When resuming interrupted downloads, it automatically determines whether full or incremental synchronization is needed based on the breakpoint timestamp. The unified change capture layer outputs change events in a standardized format, including a globally unique UUID event ID, a millisecond-level Unix timestamp, data source type, data type, consistency level, and a JSON payload carrying the specific data content, achieving standardized output across different data sources.

[0031] The cost-aware dynamic scheduler module implements adaptive resource scheduling based on a cost-performance model. The resource usage prediction unit uses an LSTM prediction model. The input features include a 128-dimensional input vector consisting of historical 15-minute load data, time features, business cycle features, and holiday features. The hidden layer is set to 2 layers with 64 neurons, and the output layer is the load prediction value for the next 5 minutes. The model is trained using historical data from the past 30 days, with a batch size of 32, a learning rate of 0.001, and 100 training epochs. The Adam optimizer is used for gradient descent, and the prediction accuracy reaches over 90%.

[0032] The hardware configuration standards for the three-tier resource pools are as follows: the high-performance pool uses an Intel Xeon Gold 6248 processor, 384GB of memory, NVMe SSD storage, and a 25Gbps network for L1-level critical data synchronization; the standard pool uses an Intel Xeon Silver 4210 processor, 128GB of memory, SAS SSD storage, and a 10Gbps network for L2-level data synchronization; and the low-cost pool uses an Intel Xeon E5-2670 processor, 64GB of memory, SATA HDD storage, and a 1Gbps network for L3-level data synchronization. The scheduling algorithm employs a cost-weighted least squares optimization algorithm, with weight coefficients dynamically adjusted according to the service level. The weight coefficient for L1-level data is set to 10, for L2 to 3, and for L3 to 1, minimizing the overall resource cost while meeting the SLA.

[0033] Resource reclamation is triggered when CPU utilization is below 20% for 10 minutes and memory utilization is below 30% for 15 minutes; redundant resources are automatically released when these conditions are met. The cost-performance trade-off unit comprehensively considers server cost, performance metrics, and SLA requirements to generate the optimal resource scheduling plan. The elastic scaling unit enables second-level node addition and reduction; it automatically scales up when CPU utilization is above 70% for 5 minutes and automatically scales down when CPU utilization is below 30% for 30 minutes. The cost-aware dynamic scheduling module also includes an idle-time resource reclamation unit, which automatically releases redundant resources during off-peak hours at night. It employs a tiered resource pool strategy, dynamically allocating resource pools of corresponding levels based on data consistency levels, achieving refined resource management.

[0034] The real-time synchronization method for product data in a distributed environment according to this invention is executed according to the following process: First, the product data is classified into importance levels, and based on the business impact of data fields, it is divided into three levels: L1 level strongly consistent data, L2 level causally consistent data, and L3 level eventually consistent data. The classification execution condition is that the classification rules are loaded when the system starts, and the field type is automatically identified and classified when data is accessed. Then, the corresponding consistency synchronization strategy is selected according to the data level. L1 level uses the delay-optimized Raft protocol, L2 level uses vector clock, and L3 level uses asynchronous message queue. The judgment criterion for strategy selection is the data classification result, and different algorithm parameters are applied to different levels.

[0035] Next, product data changes are captured from multiple heterogeneous data sources, supporting various capture methods such as CDC, triggers, polling, and message queues. The specific operation selects the appropriate capture mechanism based on the data source type, with parameters ranging from a configurable polling interval of 1 to 60 seconds. Then, the multi-source product data streams are connected, supporting product-specific semantic relationships. Stream connections are established based on business primary keys such as product ID and order ID, with the window size dynamically adjusted according to traffic. Finally, a three-layer fault-tolerant network at the node, region, and global levels ensures synchronization reliability. A differentiated replication strategy is adopted, with multiple active replicas for hot data and single active replicas for cold data. Hot data maintains active replicas simultaneously in multiple regions, while cold data maintains an active replica only in the primary region.

[0036] Finally, based on a cost-performance model, synchronization resources are dynamically adjusted to achieve hierarchical resource pool management and elastic scaling. Resource scheduling is performed in advance based on load prediction results to avoid performance bottlenecks. In a specific implementation on a medium-sized e-commerce platform with an average daily order volume of 100,000 and a peak QPS of 5,000, the test results after deploying this system show that the average latency of L1-level data synchronization is 85 milliseconds, the failover time is less than 3 seconds, the load prediction accuracy is greater than 90%, the overall operating cost is reduced by 40%, and the resource utilization rate is increased from 30% to 72%. For optional implementations for clusters of different sizes, small clusters can use simplified configurations, adjusting the node-level fault-tolerant heartbeat interval to 1 second and the replication chain length to 2; large clusters can use enhanced configurations, adding global-level fault-tolerant conflict detection nodes to improve cross-region synchronization performance. Optimization strategies for different business scenarios: in flash sale scenarios, the priority of L1-level data is further increased, and the Raft confirmation node threshold is adjusted to N / 2 to reduce latency; in ordinary e-commerce scenarios, the default configuration is used to balance performance and cost.

[0037] The embodiments described in this invention are for illustrative purposes only and do not constitute a limitation on the scope of the claims. Other substantially equivalent substitutions that can be conceived by those skilled in the art are all within the scope of protection of this invention.

Claims

1. A real-time synchronization system for commodity data supporting a distributed environment, characterized in that, The system includes a hierarchical consistency protocol module, a three-layer fault-tolerant self-healing network module, a heterogeneous stream unified adaptation module, and a resource-aware dynamic scheduling module. The hierarchical consistency protocol module dynamically selects the appropriate consistency synchronization strategy based on the importance level of the product data, classifying product data into three levels: L1 (strongly consistent), L2 (causally consistent), and L3 (eventally consistent). For L1 data, it uses the latency-optimized Raft consensus protocol, separating log replication and commit confirmation, with critical data receiving priority confirmation. For L2 data, it uses vector clocks to ensure causal order of operations. For L3 data, it uses asynchronous message queues to achieve eventual consistency. The three-layer fault-tolerant self-healing network module implements three levels of fault tolerance: node-level, region-level, and global-level. At the node level, it achieves rapid failover in less than 3 seconds through heartbeat detection. At the region level, it achieves multi-replica redundancy through chained replication and parallel confirmation. At the global level, it achieves fault tolerance through cross-regional state synchronization and... The system resolves clock conflicts to achieve cross-regional fault tolerance, employing a differentiated replication strategy of active-active for hot data and active-active for cold data. A heterogeneous stream unified adaptation module enables unified access and real-time streaming connections for multi-source heterogeneous commodity data sources. This module includes a unified change capture layer, a semantic conversion layer, and a stream connection processor. The unified change capture layer supports multiple change capture methods such as CDC, triggers, polling, and message queues. The semantic conversion layer performs automatic semantic mapping and format conversion based on the commodity data ontology. The stream connection processor supports the unique semantic associations of commodity data. A resource-aware dynamic scheduling module enables adaptive resource scheduling based on a cost-performance model. This module includes a resource usage prediction unit, a cost-performance trade-off unit, and an elastic scaling unit. The resource usage prediction unit predicts synchronous load based on historical data. The cost-performance trade-off unit minimizes resource costs while meeting SLAs. The elastic scaling unit enables second-level node addition / reduction and hierarchical resource pool management.

2. The real-time synchronization system for commodity data supporting a distributed environment according to claim 1, characterized in that, The hierarchical consistency protocol module also includes a dynamic consistency level adjustment unit, which is used to adjust the data consistency level online according to system load and business scenarios, and automatically increase the synchronization priority of L1 level data during peak business periods.

3. A real-time synchronization system for commodity data supporting a distributed environment according to claim 1, characterized in that, Level 1 strongly consistent data includes product inventory, product price, order status, and payment status; Level 2 causally consistent data includes product description, product category, product image, and user reviews; and Level 3 eventually consistent data includes page views, sales statistics, product ratings, and log data.

4. A real-time synchronization system for commodity data supporting a distributed environment according to claim 1, characterized in that, The three-layer fault-tolerant self-healing network module also includes a fault impact range prediction unit, which is used to automatically activate the corresponding level of fault-tolerant recovery mechanism according to the importance of the fault node and the data type it carries, to achieve gradual recovery, prioritizing the recovery of L1 level key data synchronization, and then recovering L2 and L3 level data.

5. A real-time synchronization system for commodity data supporting a distributed environment according to claim 1, characterized in that, The three-layer fault-tolerant self-healing network module achieves node-level fault tolerance through heartbeat detection every 500ms. If no heartbeat is received for three consecutive times, it is considered a fault. The regional fault tolerance adopts a chain replication architecture, where the master node sends data to only one slave node. If two replicas confirm the data, the synchronization is considered successful. The global fault tolerance synchronizes the core state every 10 seconds.

6. A real-time synchronization system for commodity data supporting a distributed environment according to claim 1, characterized in that, The heterogeneous stream unified adaptation module also includes a schema automatic evolution unit, which is used to detect changes in the source data structure and automatically synchronize them to the target end, realizing automatic switching between incremental and full data, and automatically determining whether full synchronization or incremental synchronization is needed when resuming interrupted transmissions.

7. A real-time synchronization system for commodity data supporting a distributed environment according to claim 1, characterized in that, The unified change capture layer outputs change events in a unified format, including event ID, timestamp, data source type, data type, consistency level, and payload field.

8. A real-time synchronization system for commodity data supporting a distributed environment according to claim 1, characterized in that, The resource-aware dynamic scheduling module also includes an idle-time resource reclamation unit, which is used to automatically release redundant resources during off-peak hours at night. It adopts a hierarchical resource pool strategy: L1 level data synchronization uses a high-performance resource pool, L2 level uses a standard resource pool, and L3 level uses a low-cost resource pool.

9. A real-time synchronization system for commodity data supporting a distributed environment according to claim 1, characterized in that, The resource usage prediction unit uses an LSTM time series model to predict the synchronous load for the next hour based on 7 days of historical data, with a prediction accuracy of over 90%. The elastic scaling unit automatically scales up when the CPU utilization is over 70% for 5 minutes and automatically scales down when the CPU utilization is under 30% for 30 minutes.

10. A method for real-time synchronization of commodity data in a distributed environment, characterized in that, A real-time synchronization system for commodity data supporting a distributed environment, as described in claims 1 to 9, includes the following steps: First, product data is classified into three levels based on importance: L1 (strongly consistent), L2 (causally consistent), and L3 (eventically consistent). Then, a corresponding consistency synchronization strategy is selected based on the data level: L1 uses the latency-optimized Raft protocol, L2 uses vector clocks, and L3 uses asynchronous message queues. Next, product data changes are captured from multiple heterogeneous data sources, supporting various capture methods such as CDC, triggers, polling, and message queues. The multi-source product data streams are then connected, supporting the unique semantic relationships of product data. Next, a three-layer fault-tolerant network (node-level, region-level, and global-level) ensures synchronization reliability, employing a multi-active strategy for hot data and a single-active strategy for cold data. Finally, synchronization resources are dynamically adjusted based on a cost-performance model, achieving hierarchical resource pool management and elastic scaling.

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