A data interface synchronization method for a supply chain system

Abstract

The present application relates to the technical field of data processing, and more particularly to a data interface synchronization method for a supply chain system, which acquires heterogeneous interface data and task progress of each node, extracts target synchronization source data, collects interface load data within a preset range after an initial link, dynamically re-plans a synchronization link in combination with source data, generates an update scheme to avoid congestion, calculates a task limit synchronization range and determines a target synchronization partition according to a current resource state and business data of the system, optimizes resource matching, and monitors a resource state in real time; if it is determined that a task is about to fail, a target degradation synchronization area is intelligently determined in combination with progress, business data and resource conditions to preferentially ensure core data synchronization. The present application dynamically senses load and resources, realizes adaptive optimization of a synchronization link and intelligent degradation fault tolerance under an exception, and significantly improves a data synchronization success rate, transmission efficiency and operation robustness of the supply chain system in a complex environment.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data interface synchronization method for supply chain systems. Background Technology

[0002] With the continuous development of supply chain systems and the deepening of digital transformation, data interaction among various participants in the supply chain is becoming increasingly frequent, involving multiple stages such as procurement, production, warehousing, logistics, and sales. These stages are typically supported by different information systems, resulting in a large number of heterogeneous data interfaces in the supply chain, such as API interfaces, database interfaces, and file transfer interfaces. Significant differences exist between different systems in data formats, communication protocols, response times, and business update frequencies, posing a significant challenge to cross-node data synchronization.

[0003] Currently, data synchronization in the supply chain typically employs fixed synchronization links and preset synchronization strategies. However, this static synchronization method struggles to adapt to complex and ever-changing network environments and dynamically fluctuating system resource states. When interface load is too high, system resources are strained, or some nodes experience performance bottlenecks, synchronization tasks are prone to delays, failures, or even cascading data inconsistencies. Furthermore, existing technologies lack effective dynamic adjustment mechanisms when facing synchronization anomalies, especially when synchronization tasks are at risk of failure. They often fail to optimize links or downgrade strategies in a timely manner, thus impacting the collaborative efficiency and data reliability of the entire supply chain system.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a data interface synchronization method for supply chain systems. This method aims to solve the technical problem that existing supply chain data synchronization technologies, due to their static strategies, are unable to adapt to dynamically changing load and resource states in heterogeneous interface environments. Consequently, they lack effective adaptive adjustment and degradation fault tolerance mechanisms when the system is abnormal, leading to synchronization failures and data inconsistencies.

[0006] To achieve the above objectives, the present invention provides a data interface synchronization method for a supply chain system, the method comprising: Obtain heterogeneous interface data from each node in the supply chain and the progress status of the current synchronization task. Extract data from the heterogeneous interface data based on the preset synchronization range of the synchronization task to obtain the target synchronization source data. Obtain the initial synchronization link of the synchronization task; collect interface load data within a preset range of the subsequent synchronization task based on the initial synchronization link and the progress status; re-plan the initial synchronization link based on the interface load data and the target synchronization source data to obtain an update synchronization scheme. Obtain the current resource status of the synchronization system and the business data of each node in the supply chain; obtain the limit synchronization range of the synchronization task based on the current resource status and the target synchronization source data; and determine the target synchronization partition based on the limit synchronization range and the business data of each node. Based on the current resource status, determine whether the synchronization task is about to fail. If it is determined that the synchronization task is about to fail, then determine the target downgrade synchronization area based on the progress status, the business data of each node, and the current resource status.

[0007] Optionally, the step of acquiring heterogeneous interface data of each node in the supply chain and the progress status of the current synchronization task, and extracting data from the heterogeneous interface data based on the preset synchronization range of the synchronization task to obtain the target synchronization source data, specifically includes: Obtain heterogeneous interface data of the supply chain and the progress status of the current synchronization task; group the heterogeneous interface data according to the preset synchronization range to obtain grouped interface data. Based on the synchronization task, extract the influencing factors that affect the execution of the synchronization task; Based on the aforementioned influencing factors, the group interface data is filtered to obtain the target synchronization source data.

[0008] Optionally, the step of collecting interface load data within a preset range for the subsequent synchronization task based on the initial synchronization link and the progress status specifically includes: Based on the initial synchronization link and the progress status, determine the progress position of the synchronization task on the initial synchronization link; Based on the progress position, the subsequent synchronization direction of the synchronization task is determined; Based on the subsequent synchronization direction, collect interface response delay data, interface concurrency data, data format difference data, and business update frequency data within a preset range for the subsequent synchronization task; Based on the interface response latency data and the interface concurrency data, the network load within the preset range is obtained, and initial synchronization load data is generated based on the network load. Based on the data format difference data and the business update frequency data, an auxiliary synchronization constraint distribution map is generated, and the auxiliary synchronization constraint distribution map is added to the initial synchronization load data to generate interface load data.

[0009] Optionally, the step of replanning the initial synchronization link based on the interface load data and the target synchronization source data to obtain an updated synchronization scheme specifically includes: Based on the interface load data, a multidimensional load distribution within the preset range is obtained, and a synchronization resource space model is constructed according to the multidimensional load distribution. Obtain the task priority parameters and data volume requirements of the synchronization task, and obtain the maximum concurrency, maximum latency tolerance, maximum error tolerance, and maximum resource consumption limit of the synchronization task based on the task priority parameters and the data volume requirements. The limit constraints for the synchronization task are generated based on the maximum concurrency, the maximum latency tolerance, the maximum error tolerance, and the maximum resource consumption limit. Based on the aforementioned limit constraints, multi-dimensional data matching is performed in the synchronization resource space model to generate a synchronization resource availability corridor. Extract the topology of the available corridors for the synchronization resources, generate a secure synchronization link based on the topology, and replan the initial synchronization link based on the secure synchronization link to obtain an update synchronization scheme.

[0010] Optionally, the step of obtaining the extreme synchronization range of the synchronization task based on the current resource status and the target synchronization source data, and determining the target synchronization partition based on the extreme synchronization range and the service data of each node, specifically includes: Based on the current resource status, the synchronization health value of the synchronization task is obtained, and it is determined whether the synchronization health value is lower than a preset health threshold. If it is determined that the synchronization health value is lower than the health threshold, then it is determined that the synchronization task needs to adjust the synchronization strategy. Based on the current resource status, the extreme synchronization range of the synchronization task is extracted, and the business data of each node is extracted based on the extreme synchronization range to obtain the target synchronization candidate range; A traversal query is performed within the target synchronization candidate range to determine whether a preset standard synchronization partition exists. If the preset standard synchronization partition exists, the target synchronization partition of the preset standard synchronization partition is extracted. Extract the configuration data of the target synchronization partition, generate a synchronization control scheme for the synchronization task based on the configuration data and the progress status, and control the synchronization task to execute on the target synchronization partition according to the synchronization control scheme.

[0011] Optionally, after determining that the preset standard synchronization partition exists, the step of extracting the target synchronization partition of the preset standard synchronization partition further includes: If it is determined that the preset standard synchronization partition does not exist, the minimum availability requirement, minimum bandwidth requirement, and minimum data consistency requirement of the synchronization task are obtained, and the minimum availability requirement, minimum bandwidth requirement, and minimum data consistency requirement are integrated to generate the minimum synchronization conditions. Based on the minimum synchronization condition, the business data of each node, and the target synchronization candidate range, a location query is performed to obtain multiple non-standard candidate synchronization partitions. Extract the synchronization condition data and resource distance data from the current progress position for each of the non-standard candidate synchronization partitions; Based on the current resource status, the urgency value of the synchronization task that needs to be adjusted is obtained; Based on the synchronization condition data, the resource distance data, and the urgency value, multiple non-standard candidate synchronization partitions are filtered to obtain the target non-standard synchronization partition; Based on the target non-standard synchronization partition and the progress status of the synchronization task, a backup synchronization control scheme is generated for the synchronization task, and the synchronization task is controlled to execute in the target non-standard synchronization partition according to the backup synchronization control scheme.

[0012] Optionally, the step of determining the target degradation synchronization region based on the progress status, the business data of each node, and the current resource status specifically includes: Based on the current resource status, the failure trend of the synchronization task is obtained, and the initial degradation direction is obtained based on the failure trend; The data size of the synchronization task is obtained based on the task parameters of the synchronization task, and the impact weight when the synchronization task fails is obtained based on the data size and the task priority. By combining the progress status, the target synchronization source data, and the influence weight, the initial degradation direction is corrected to obtain the final degradation direction; Based on the final degradation direction and the impact weight, a degradation synchronization route for the synchronization task is generated, and the target degradation synchronization area is determined by combining the degradation synchronization route and the business data of each node.

[0013] Optionally, after determining the target degradation synchronization area by combining the degradation synchronization route and the service data of each node, the method further includes: Based on the current resource status, extract the remaining system resource data that the synchronization task could call before the synchronization failed; Based on the influence weights and the remaining system resource data, the maximum range of adjustments that the synchronization task can generate during the degradation process is obtained; Based on the maximum range adjustment amount and the target degradation synchronization region, a candidate degradation region range is generated, and the region size of the target degradation synchronization region is extracted. The candidate degradation region is divided according to the region size to generate multiple alternative degradation regions, and the business value data of each alternative degradation region is extracted. Select the candidate degradation region with the lowest business value data as the final degradation region, and extract the configuration location data of the final degradation region; Based on the configured location data, the progress status, and the influence weight, a final resource allocation scheme for the synchronization task is generated. The synchronization system allocates resources according to the final resource allocation scheme and adjusts the downgraded synchronization route.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a data interface synchronization device for a supply chain system, the device comprising: a memory, a processor, and a data interface synchronization program for a supply chain system stored in the memory and executable on the processor, the data interface synchronization program for a supply chain system being configured to implement the steps of the data interface synchronization method for a supply chain system as described in any one of the above descriptions.

[0015] In addition, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a data interface synchronization program for a supply chain system, wherein the data interface synchronization program for a supply chain system, when executed by a processor, implements the steps of the data interface synchronization method for a supply chain system as described above.

[0016] This invention provides a data interface synchronization method for supply chain systems. The method achieves dynamic perception and intelligent control of the synchronization process by comprehensively acquiring heterogeneous interface data from various nodes in the supply chain, synchronization task progress status, system resource status, and business data. By replanning the initial synchronization link based on real-time interface load and target source data, the stability and transmission efficiency of the synchronization path are improved. The method enhances the matching degree between the synchronization strategy and the actual operating environment by determining the extreme synchronization range and target synchronization partitions based on the current system resource status and task requirements. When synchronization tasks face failure risks, the method can intelligently determine the target degraded synchronization area based on progress, resources, and business data, effectively ensuring the priority synchronization of critical business data and the overall fault tolerance of the system. In summary, this method significantly improves the data synchronization success rate, resource utilization, and operational robustness of supply chain systems in complex heterogeneous environments. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the data interface synchronization method for a supply chain system according to the present invention; Figure 2 This is a flowchart illustrating another embodiment of the data interface synchronization method for supply chain systems according to the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] Reference Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the data interface synchronization method for a supply chain system according to the present invention.

[0021] In one embodiment, the data interface synchronization method for a supply chain system includes: Step S100: Obtain heterogeneous interface data of each node in the supply chain and the progress status of the current synchronization task. Extract data from the heterogeneous interface data based on the preset synchronization range of the synchronization task to obtain the target synchronization source data.

[0022] The heterogeneous interface data of each node in the supply chain can be the original interface data provided by different participating systems in the supply chain, which differ in data format, communication protocol, or transmission method. This data can serve as the foundation for synchronization tasks, supporting subsequent data extraction and link planning. In this embodiment, the heterogeneous interface data of each node in the supply chain can be uniformly accessed and collected from heterogeneous interfaces such as APIs, databases, and files through adapters or middleware. For example, the heterogeneous interface data of each node in the supply chain can include, but is not limited to, one or more of the following: RESTful API return data, relational database table records, and CSV / XML format file content. The progress status of the current synchronization task can be the real-time status information of the amount of data completed, the nodes processed, and the current stage during the execution of the synchronization task. This information can be used to evaluate the execution efficiency and remaining workload of the synchronization task, supporting link replanning and failure risk assessment. Furthermore, the progress status of the current synchronization task can be maintained by the synchronization scheduling engine and periodically reported to the control module. The preset synchronization range can be the data boundary to be synchronized defined before the synchronization task starts, including time windows, business entity ranges, or data field sets. This can be used to limit the extraction boundary of the target synchronization source data and avoid loading invalid data. The target synchronization source data can be a set of valid data to be synchronized selected from heterogeneous interface data based on a preset synchronization range. It can be used as input for link replanning and synchronization partitioning. In an exemplary embodiment, the target synchronization source data can be extracted from the original heterogeneous data through methods such as field mapping, time filtering, or business rule matching.

[0023] Acquiring heterogeneous interface data from each node in the supply chain and the progress status of the current synchronization task can be achieved by calling a unified data access layer to pull interface data from each node in parallel, while simultaneously reading the progress status from the task scheduler. Furthermore, this operation can utilize a unified access proxy to achieve concurrent acquisition and status synchronization of multi-source heterogeneous data, thereby establishing a technical foundation for real-time awareness of data sources and task execution status. Extracting data from heterogeneous interface data based on the preset synchronization range of the synchronization task to obtain the target synchronization source data can be achieved by applying predefined filtering rules to trim and standardize the original heterogeneous data. In a specific embodiment, this operation can be performed by directly filtering at the database interface layer using SQL queries, or by loading the full data in memory and then filtering by timestamp or business ID range, thereby reducing invalid data transmission and focusing on the actual needs of the synchronization task.

[0024] Step S200: Obtain the initial synchronization link of the synchronization task; collect interface load data within a preset range of the subsequent synchronization task based on the initial synchronization link and progress status; re-plan the initial synchronization link based on the interface load data and target synchronization source data to obtain an update synchronization scheme.

[0025] The initial synchronization link can be a preset data transmission path when the synchronization task starts, including the connection sequence of the source node, target node, and intermediate forwarding nodes. It can be used to provide the initial execution path of the synchronization task and serve as the default transmission channel when there is no dynamic adjustment. In this embodiment, the initial synchronization link can be read from the preset node transmission path in the task configuration metadata. Interface load data can be performance indicators such as the number of requests processed by a specific interface per unit time, response latency, or resource utilization. It can be used to reflect the current service capacity of the interface and evaluate the stability of the link. Furthermore, interface load data can be collected by monitoring agents to collect runtime indicators of the interface service, such as QPS, CPU utilization, and queue length. The update synchronization scheme can be a new transmission path and strategy combination formed by optimizing the initial synchronization link based on real-time load and target synchronization source data. It can be used to replace the initial synchronization link and improve transmission efficiency and link robustness. In an exemplary embodiment, the update synchronization scheme can be generated by combining data dependencies with a path search algorithm (such as the shortest path or the minimum load path). For example, the update synchronization scheme is generated based on interface load data and target synchronization source data and is called by the subsequent synchronization execution module.

[0026] Obtaining the initial synchronization link for the synchronization task can be achieved by reading the preset node transmission path from the task configuration metadata, thus providing a benchmark for dynamic link optimization. Based on the initial synchronization link and progress status, interface load data within a preset range for the subsequent synchronization task is collected. This can be done by selectively collecting interface performance metrics along the incomplete node paths in the initial link. Furthermore, this operation can be achieved by actively sending probe requests to measure response latency or subscribing to performance monitoring topics for each node to obtain real-time load streams, thereby accurately acquiring the load information of key nodes affecting subsequent synchronization. The initial synchronization link is then replanned based on the interface load data and the target synchronization source data to obtain an updated synchronization scheme. This can involve removing high-load nodes from the path or replacing them with backup nodes, and adjusting the transmission order considering data dependencies. In one specific embodiment, this operation can be based on Dijkstra's algorithm to find the path with the minimum accumulated load, or by using a greedy strategy to select the next hop node with the lowest current load hop-by-hop, thereby avoiding performance bottleneck nodes and improving overall transmission stability and efficiency.

[0027] Step S300: Obtain the current resource status of the synchronization system and the business data of each node in the supply chain. Based on the current resource status and the target synchronization source data, obtain the limit synchronization range of the synchronization task. Based on the limit synchronization range and the business data of each node, determine the target synchronization partition.

[0028] The current resource status of the synchronization system can be the total amount and allocation of computing, storage, or network resources currently available to the system executing the synchronization task. This can be used to constrain the data scale and concurrency capabilities that the synchronization task can execute. In this embodiment, the current resource status of the synchronization system can be obtained through the resource monitoring interface of the operating system or container orchestration platform. The business data of each node in the supply chain can be core data directly related to business activities such as procurement, production, warehousing, logistics, and sales in the systems of each participating party. This can be used to identify data priorities and support the determination of the target synchronization partition and degradation area. For example, the business data of each node in the supply chain can include, but is not limited to, one or more of the following: purchase order details, inventory level information, and logistics waybill status. The extreme synchronization range can be the maximum data synchronization boundary that the synchronization task can actually complete under the current system resource constraints. This can be used to dynamically correct the preset synchronization range and prevent task crashes due to resource overruns. Furthermore, the extreme synchronization range can be estimated using a resource consumption model to estimate the resources required for unit data synchronization, and the amount of data that can be processed can be deduced by combining the remaining total resources. In an exemplary embodiment, the extreme synchronization range is jointly determined by the current resource status of the synchronization system and the target synchronization source data, and serves as the input boundary of the target synchronization partition. A target synchronization partition can be a set of logical data subsets divided according to the importance of business data within the extreme synchronization range. It can be used to implement differentiated synchronization strategies, prioritizing the transmission of high-value business data. In one specific embodiment, the target synchronization partition can cluster or group data within the extreme synchronization range using business rules (such as order status or customer level). For example, the target synchronization partition can include, but is not limited to, one or more of the following: high-priority customer order partitions, urgent replenishment demand partitions, and regular inventory synchronization partitions.

[0029] Obtaining the current resource status of the synchronization system and business data from each node in the supply chain can be achieved by pulling two types of data from the resource monitoring system and the business database respectively, thereby achieving the technical effect of constructing a joint resource-business decision-making context. Determining the extreme synchronization range of the synchronization task based on the current resource status and the target synchronization source data can be achieved by establishing a resource consumption prediction model to calculate the maximum amount of data that can be processed with remaining resources. Furthermore, this operation can be performed by linear extrapolation based on the resource consumption ratio of historical synchronization tasks, or by using machine learning models to predict the resource consumption of different types of data, thereby preventing task interruptions due to insufficient resources and achieving the technical effect of adaptive resource synchronization. Determining the target synchronization partition based on the extreme synchronization range and the business data of each node can be achieved by tagging and grouping data according to business rules within the extreme synchronization range. In a specific embodiment, this operation can divide the system into urgent and non-urgent partitions based on order delivery deadlines, or into high / medium / low priority partitions based on customer SLA levels, thereby achieving the technical effect of implementing a business-oriented differentiated synchronization strategy.

[0030] Step S400: Based on the current resource status, determine whether the synchronization task is about to fail. If it is determined that the synchronization task is about to fail, determine the target downgrade synchronization area based on the progress status, business data of each node, and the current resource status.

[0031] The target degradation synchronization region can be a subset of critical data that the system intelligently selects when a synchronization task faces the risk of failure, ensuring synchronization. It can be used to implement minimal but critical data synchronization when resources are limited or links are abnormal, maintaining basic system coordination capabilities. In an exemplary embodiment, the target degradation synchronization region is determined jointly by the progress status, business data of each node, and the current resource status, and is the output of the synchronization fault tolerance mechanism. Based on the current resource status, determining whether a synchronization task is about to fail can be achieved by setting resource thresholds or progress deviation thresholds. The judgment is triggered when the remaining resource is below a critical value or the progress lag exceeds the tolerance window. Furthermore, this operation can calculate the resource consumption rate based on a sliding window to predict future resource exhaustion time, or compare the deviation between the current progress and the expected progress curve, thereby achieving the technical effect of identifying failure risks in advance and gaining a time window for degradation decisions. If it is determined that the synchronization task is about to fail, the target degradation synchronization region is determined based on the progress status, business data of each node, and the current resource status. This can involve prioritizing the retention of a subset of data with high business value and low resource consumption from the incomplete data. In one specific embodiment, this operation can construct a multi-dimensional scoring function (business weight × reciprocal of resource cost), select Top-K data, or use a rule engine to match a preset degradation strategy template (such as "only synchronize paid orders"), thereby achieving the technical effect of ensuring the synchronization of core business data under abnormal conditions and improving the system's fault tolerance.

[0032] Taking cross-enterprise order and inventory synchronization as an example, the data interface synchronization method for the supply chain system in this embodiment can be as follows: A manufacturing enterprise needs to synchronize thousands of purchase orders, production plans, and inventory change data with its upstream suppliers and downstream distributors every day. The initial synchronization link is set to direct transmission, but on a certain day, the supplier's ERP system experienced a surge in API response delays due to promotional activities. In step S100, this method obtains heterogeneous interface data (including the supplier's JSON API, the distributor's database view, and the message queue of the internal MES system) and the current synchronization progress of each node; in step S200, it detects that the supplier's interface is overloaded, automatically switches the node's traffic to its backup FTP file interface, and adjusts the transmission order; in step S300, it finds that the system memory resources are tight on that day, and identifies "confirmed orders that need to be shipped within 72 hours" as high priority based on business data, and delineates the extreme synchronization range and divides it into "emergency fulfillment partitions" accordingly; in step S400, it monitors that the network bandwidth continues to decline, predicts that the synchronization may time out, and then activates the degradation mechanism, synchronizing only the key fields (such as material code, quantity, and delivery time) in the emergency partition to ensure that the production schedule is not affected.

[0033] In one embodiment, the steps of acquiring heterogeneous interface data from each node of the supply chain and the progress status of the current synchronization task, and extracting data from the heterogeneous interface data based on the preset synchronization range of the synchronization task to obtain the target synchronization source data are as follows: Obtain heterogeneous interface data from the supply chain and the progress status of the current synchronization task. Group the heterogeneous interface data according to the preset synchronization range to obtain grouped interface data. Based on synchronous tasks, extract the influencing factors that affect the execution of synchronous tasks; Based on the influencing factors, the group interface data is filtered to obtain the target synchronization source data.

[0034] The grouped interface data can be a subset of interfaces formed by logically classifying heterogeneous supply chain interface data according to task relevance within a preset synchronization range. This can be used to narrow down the data domain for subsequent processing and improve the structure and targeting of interface data processing. In this embodiment, the grouped interface data can map the original heterogeneous interfaces to several logical groups based on dimensions such as business entities, time windows, or data types defined within the preset synchronization range. For example, the grouped interface data can include, but is not limited to, one or more of the following: procurement-related interface groups, warehousing status interface groups, and logistics trajectory interface groups. Grouping heterogeneous interface data according to the preset synchronization range to obtain grouped interface data can be achieved by parsing business or structural constraints within the preset synchronization range and clustering heterogeneous interfaces according to the data theme they serve or the node they belong to. Furthermore, this operation can be achieved by rule matching grouping based on interface metadata tags (such as the business domain to which they belong, data update cycle), or by aggregating interfaces by entity type after associating them with their supporting business entities through graph relationships. This enables the structured organization of heterogeneous interfaces and reduces the participation of irrelevant interfaces in subsequent processing.

[0035] Influencing factors can be dynamic or static attribute variables that significantly affect the efficiency, success rate, or resource consumption of the current synchronization task. They can be used as a basis for data filtering decisions to identify data that is truly effective for the synchronization task. In an exemplary embodiment, influencing factors can be extracted from system monitoring indicators, historical task logs, or business rule bases, focusing on feature parameters strongly correlated with task execution. In a specific embodiment, influencing factors can include, but are not limited to, one or more of the following: average interface response latency, data change frequency, and business entity priority level. Extracting influencing factors that affect the execution of the synchronization task based on the synchronization task can be achieved by analyzing the historical execution records and current context of the synchronization task to identify variables significantly related to task performance or results. Furthermore, this operation can be implemented by reading a predefined list of key influencing factors from the task configuration or by automatically mining influencing factors from historical data using a feature importance assessment model (such as SHAP value), thereby establishing task-oriented filtering criteria and avoiding insufficient adaptability due to reliance on fixed rules. Filtering grouped interface data based on influencing factors to obtain target synchronization source data can be achieved by converting influencing factors into filtering thresholds or weight coefficients, and then selecting an effective subset after pruning or sorting the grouped interface data. Furthermore, this operation can be achieved by setting a response latency limit to eliminate data returned by timeout interfaces, or by merging data from multiple interfaces according to business priority and retaining only records with a comprehensive score higher than the threshold. This can improve the relevance and effectiveness of the target synchronization source data and reduce the overhead of invalid transmission and processing.

[0036] Taking multi-supplier order collaborative synchronization optimization as an example, the data interface synchronization method for the supply chain system in this embodiment can be as follows: A retail enterprise needs to synchronize product inventory and price data from more than 50 suppliers daily. Traditional methods directly pull all the full data from all interfaces, resulting in a large amount of low-frequency updates or non-core product category data occupying bandwidth. In this embodiment, the supplier interfaces are first divided into "home appliance group", "fast-moving consumer goods group", etc., according to the product categories involved in the daily promotional activities (preset synchronization range); then, influencing factors are extracted, such as "number of price changes in the past 24 hours", "interface P95 response time", and "whether it is a core supplier"; finally, only the core supplier data in the home appliance group with a response time of less than 2 seconds and price changes in the past 6 hours are retained as the target synchronization source data, so that subsequent link replanning and partitioning strategies focus on high-value, high-activity data, significantly reducing redundant synchronization.

[0037] In one embodiment, the step of collecting interface load data within a preset range for the subsequent synchronization task based on the initial synchronization link and progress status specifically includes: Based on the initial synchronization link and progress status, determine the progress position of the synchronization task on the initial synchronization link; Based on the progress position, determine the subsequent synchronization direction of the synchronization task; Based on the subsequent synchronization direction, collect interface response delay data, interface concurrency data, data format difference data, and business update frequency data within the preset range of the subsequent synchronization task; Based on the interface response latency data and interface concurrency data, the network load within a preset range is obtained, and initial synchronization load data is generated based on the network load. Based on the differences in data format and the business update frequency data, an auxiliary synchronization constraint distribution map is generated, and the auxiliary synchronization constraint distribution map is added to the initial synchronization load data to generate interface load data.

[0038] The progress position can be the logical location identifier corresponding to the node or data segment endpoint that has been completed in the initial synchronization link of the synchronization task. It can be used as the starting point for determining the subsequent synchronization direction, ensuring that load collection focuses on the incomplete path. In this embodiment, the progress position can be located by comparing the progress status of the current synchronization task with the node sequence of the initial synchronization link to pinpoint the index or data offset of the last successfully synchronized node. The subsequent synchronization direction can be the data transmission path that has not yet been executed in the initial synchronization link, starting from the progress position. It can be used to limit the collection range of interface load data and avoid repeated monitoring of completed nodes. For example, the subsequent synchronization direction can extract the remaining node sequence after the progress position along the topology of the initial synchronization link. The subsequent preset range can be the boundary of the data to be synchronized defined by the original task configuration in the subsequent synchronization direction. It can be used to constrain the spatiotemporal granularity of load data collection and prevent over-probing of irrelevant interfaces.

[0039] Interface response latency data represents the average or percentile response time required for a target interface to process a single request, reflecting the processing capacity and network transmission efficiency of the interface server. Interface concurrency data represents the number of requests being processed or queued by the interface per unit time, characterizing the current load and resource contention level. Data format difference data quantifies the degree of inconsistency between the source and target systems in data structure, encoding methods, or field semantics, measuring the additional overhead required for data conversion between heterogeneous systems and impacting synchronization stability. In an exemplary embodiment, data format difference data can be calculated using metadata comparison or schema mapping analysis, such as field missing rate and type conversion complexity. Business update frequency data represents the number of changes or activity levels of specific business data within the source system per unit time, reflecting data timeliness requirements; high-frequency updates pose a greater challenge to real-time synchronization.

[0040] Based on the initial synchronization link and progress status, the progress position of the synchronization task on the initial synchronization link is determined. This can be achieved by parsing the list of completed nodes in the progress status, matching them against the initial synchronization link sequence, and locating the last successful node. Furthermore, this operation can be implemented through backtracking of operation and maintenance logs or comparison of task status snapshots, thereby achieving precise anchoring of the task execution position and providing a starting point for subsequent directional deduction. Based on the progress position, the subsequent synchronization direction of the synchronization task is determined, which can be achieved by extracting a subsequence of nodes after the progress position in the initial synchronization link. In a specific embodiment, this operation can be implemented through a link topology traversal algorithm, clearly defining the target path for load collection and avoiding invalid probes. Based on the subsequent synchronization direction, interface response latency data, interface concurrency data, data format difference data, and business update frequency data within a preset range of the subsequent synchronization task are collected. This can be achieved by calling monitoring probes, metadata comparison tools, and business log analyzers to obtain four types of indicators for each interface in the subsequent synchronization direction. Furthermore, this operation can obtain response latency and concurrency through proactive heartbeat detection, obtain format differences through the schema registry center, and count update frequency through CDC logs; or subscribe to the operation and maintenance metrics topics and business event streams of each node to aggregate the four types of data in real time, thereby building a multi-dimensional interface status profile that transcends the limitations of a single network metric.

[0041] Network load can be a real-time performance stress state of link nodes, characterized by both interface response latency and concurrency. It can serve as a core component of initial synchronized load data to identify performance bottleneck nodes. In an exemplary embodiment, network load can normalize and weight the latency and concurrency to form a comprehensive load score. Initial synchronized load data can be a preliminary representation of interface load based solely on network and system performance metrics (response latency, concurrency), providing a traditional view of resource bottlenecks and supporting basic link evaluation. For example, initial synchronized load data can be a structured load vector organized by nodes based on network load. Based on interface response latency data and interface concurrency data, network load within a preset range is obtained. This can be achieved by linearly or non-linearly combining latency and concurrency after standardization to generate a comprehensive load score. Further, this operation can employ a weighted average (e.g., latency weight 0.6, concurrency weight 0.4), or use a clustering algorithm to map both to high / medium / low load categories, thereby quantifying node performance stress and identifying potential transmission bottlenecks.

[0042] Initial synchronization load data is generated based on network load conditions. This can be achieved by organizing the network load scores of each node into structured data objects according to link order, forming a basic load view for subsequent fusion. The auxiliary synchronization constraint distribution map can be a two-dimensional mapping structure reflecting heterogeneous compatibility and timeliness constraints, constructed from data format differences and business update frequency. It can be used to embed synchronization difficulty information at the business semantic level, supplementing the deficiencies of pure performance indicators. In one specific embodiment, the auxiliary synchronization constraint distribution map can use format difference degree and update frequency as coordinate axes to locate and assign weights to each interface node. For example, the auxiliary synchronization constraint distribution map can include, but is not limited to, one or more of the following: high difference-low frequency constraint region, low difference-high frequency constraint region, and high difference-high frequency constraint region.

[0043] Based on data format differences and business update frequency data, an auxiliary synchronization constraint distribution map is generated. This can be achieved by constructing a two-dimensional coordinate system with format differences as the X-axis and update frequency as the Y-axis, mapping each interface to weighted points. Furthermore, this operation can use a heatmap to represent the constraint strength distribution or construct an edge attribute matrix in a graph neural network to express the constraint relationships, thereby explicitly modeling the impact of heterogeneity and timeliness on synchronization. The auxiliary synchronization constraint distribution map is added to the initial synchronization load data to generate interface load data. This can be done by injecting node attributes (such as constraint strength) from the auxiliary constraint map as additional fields into the corresponding node records of the initial synchronization load data. In an exemplary embodiment, this operation can extend the JSON load object by adding "format difference score" and "update frequency score" fields; or construct a composite vector where the first two dimensions represent performance load and the last two dimensions represent constraint indicators, thereby generating a multi-dimensional load representation that integrates performance and business semantics, improving the decision-making quality of link replanning. Interface load data can be a multi-dimensional comprehensive representation of interface status formed by fusing initial synchronization load data and auxiliary synchronization constraint distribution map. It can be used to provide decision-making basis for link replanning, encompassing performance, compatibility, and timeliness. In this embodiment, interface load data can be obtained by embedding the auxiliary synchronization constraint distribution map as an additional dimension into the node attributes of the initial synchronization load data.

[0044] For example, in a scenario of multi-system collaboration and synchronization in a cross-border supply chain, the data interface synchronization method for the supply chain system in this embodiment could be as follows: A global manufacturing company needs to synchronize production, inventory, and customer order data from systems in countries A, B, and C. The initial synchronization link is A→B→C. When the task has completed the A to B segment, the system locates the progress position as "node B," and the subsequent synchronization direction is "B→C." The system then collects the response latency, concurrency, data format differences, and business update frequency of this interface segment. The system first generates a high network load score based on latency and concurrency, then generates a strong constraint label based on format differences and high-frequency updates, ultimately merging them into interface load data containing four labels: "high latency + high concurrency + high differences + high frequency." In link replanning, the system avoids direct paths and instead selects an alternative link via Singapore (although the latency is slightly higher, the format compatibility is good and the update frequency matches), significantly improving the synchronization success rate.

[0045] In one embodiment, the step of replanning the initial synchronization link based on interface load data and target synchronization source data to obtain an updated synchronization scheme is as follows: Based on the interface load data, a multi-dimensional load distribution within a preset range is obtained, and a synchronization resource space model is constructed based on the multi-dimensional load distribution. Obtain the task priority parameters and data volume requirements of the synchronization task, and based on the task priority parameters and data volume requirements, obtain the maximum concurrency, maximum latency tolerance, maximum error tolerance, and maximum resource consumption limit of the synchronization task. Generate the limit constraints for synchronous tasks based on the maximum concurrency, maximum latency tolerance, maximum error tolerance, and maximum resource consumption limit; Based on limit constraints, multi-dimensional data matching is performed in the synchronization resource space model to generate a synchronization resource availability corridor. Extract the topology of available corridors for synchronization resources, generate secure synchronization links based on the topology, and replan the initial synchronization links based on the secure synchronization links to obtain an updated synchronization scheme.

[0046] The multidimensional load distribution can be a joint distribution of resource states within a preset range, presented by interface load data, encompassing multiple dimensions such as network performance, data heterogeneity, and business dynamics. It can provide structured input for constructing a synchronous resource space model, representing the position of each node in the multidimensional resource space. In this embodiment, the multidimensional load distribution can normalize indicators such as response latency, concurrency, format differences, and update frequency in the interface load data and map them to a unified coordinate system. For example, the multidimensional load distribution can collaborate with other objects as the input basis for the synchronous resource space model. The synchronous resource space model can be a high-dimensional spatial representation built based on the multidimensional load distribution, used to describe the availability status of each node in the supply chain under multidimensional resource dimensions. It can provide a unified framework to evaluate whether nodes meet the comprehensive constraints of a specific synchronous task. In an exemplary embodiment, the synchronous resource space model can use each load dimension as a coordinate axis, representing each interface node as an attributed point or region in the space. Further, the synchronous resource space model can include, but is not limited to, one or more of the following: a network-format two-dimensional subspace, a latency-concurrency-update frequency three-dimensional subspace, and a full-dimensional resource hypercube.

[0047] Task priority parameters can be quantified as the importance of a synchronous task at the business level. They can be used as one of the inputs for deriving limit constraints, influencing the setting of tolerances for latency, errors, etc. Data volume requirements can be the total amount of data or the rate requirement for synchronous task transmission. They can be used to determine the task's basic requirements for concurrency and resource consumption. The maximum concurrency can be the maximum number of parallel transmission channels the system can allocate to the task under the current task priority and data volume requirements. It can be used to constrain the number of allowed parallel paths in link planning. In a specific embodiment, the maximum concurrency can be calculated based on the system's total concurrency capacity, task priority weights, and historical consumption models of similar tasks. The maximum latency tolerance can be the maximum acceptable end-to-end transmission latency threshold for a synchronous task. It can be used to filter potential paths with excessive latency. In this embodiment, the maximum latency tolerance can be determined by mapping task priority parameters (e.g., low tolerance for high-priority tasks) and combining them with the business SLA. The maximum error tolerance can be the upper limit of the allowable data loss rate, field misalignment, or semantic deviation for a synchronous task. It can be used to exclude links with a high risk of conversion errors due to format incompatibility. For example, the tolerance for extreme errors can be set based on historical statistics of data format differences and business rules (such as extremely low tolerance for errors in financial data).

[0048] The upper limit of resource consumption can be the maximum amount of system resources, such as CPU, memory, or network bandwidth, that a synchronous task is allowed to consume. This can be used to prevent excessive resource consumption by a task from affecting other critical processes. In an exemplary embodiment, the upper limit of resource consumption can be determined by allocating the total system resource reserve according to task priority. Limit constraints can be a set of task execution boundary conditions composed of limit concurrency, limit latency tolerance, limit error tolerance, and the upper limit of resource consumption. These can be used to define the search boundary of feasible solutions for the task, ensuring that the generated links meet service quality requirements. Furthermore, limit constraints can encode four types of limit indicators as hyperplane or region constraints in the synchronous resource space. A synchronous resource availability corridor can be a feasible node connectivity region in the synchronous resource space model that simultaneously satisfies multidimensional load availability and limit constraint conditions. This can be used to identify a set of safe resources that can theoretically support the current task. In a specific embodiment, the synchronous resource availability corridor can be used to filter out a subset of nodes that meet all limit constraints in the resource space through multidimensional range queries or spatial index matching.

[0049] The topology can be a graph of nodes in a synchronous resource available corridor, including attributes such as edge directionality, bandwidth capacity, and conversion overhead, which can be used to provide a structural basis for path generation. In this embodiment, the topology can extract node adjacency relationships from the available corridor to construct a directed weighted graph. The secure synchronization link can be a transmission path generated based on the topology of the synchronous resource available corridor, satisfying task limit constraints and possessing redundancy or fault tolerance capabilities. It can be used as an optimized alternative to the initial synchronization link to ensure synchronization stability and service quality. In an exemplary embodiment, the secure synchronization link can run constrained shortest path or maximum reliable path algorithms (e.g., considering spare edges) on the topology. Based on interface load data, a multidimensional load distribution within a preset range is obtained. This can be achieved by aggregating the interface load data according to nodes within the preset range and standardizing its various dimensional indicators to form a joint distribution. Furthermore, this operation can be achieved by converting the original indicators into a structured resource state expression, thereby achieving the technical effect of converting from the original indicators to a structured resource state expression.

[0050] Constructing a synchronous resource space model based on multidimensional load distribution can be achieved by establishing a coordinate system with each load dimension as an axis, mapping each node to a point or region in space. Furthermore, this operation can be implemented using a vector space model (each node represented as a feature vector) or a graph database storing nodes and their multidimensional attributes (supporting efficient range queries), thereby achieving the technical effect of establishing a unified resource evaluation framework and supporting multidimensional constraint matching. Obtaining the task priority parameters and data volume requirements of synchronous tasks can be achieved by reading predefined priority levels and data scale parameters from the task metadata configuration, thus achieving the technical effect of obtaining key inputs from the task requirement side. Based on the task priority parameters and data volume requirements, the maximum concurrency, maximum latency tolerance, maximum error tolerance, and maximum resource consumption limit of the synchronous task can be obtained. This can be transformed into four types of limit indicators through preset mapping rules or resource allocation strategies. Furthermore, this operation can be implemented using a lookup table method (directly obtained from a priority-tolerance lookup table) or a dynamic calculation method (derived by combining the system's real-time resource pool and task data volume model), thereby achieving the technical effect of transforming business requirements into executable technical constraint boundaries.

[0051] Based on the limits of concurrency, latency tolerance, error tolerance, and resource consumption, limit constraints for synchronization tasks are generated. These four limit indicators can be encoded as filtering conditions or feasible region definitions in the synchronization resource space, thus achieving the technical effect of forming task-oriented resource selection criteria. Based on these limit constraints, multi-dimensional data matching is performed in the synchronization resource space model to generate available synchronization resource corridors. This can be achieved by performing multi-dimensional range queries and retaining nodes that simultaneously satisfy all limit constraints. Furthermore, this operation can use R-trees or KD-trees to accelerate high-dimensional space matching, or by using Boolean logic to combine constraints of each dimension for layer-by-layer filtering, thereby achieving the technical effect of identifying feasible resource subspaces for the task. Extracting the topology of the available synchronization resource corridors can be achieved by traversing the nodes in the available corridors and recording their adjacency relationships and edge attributes (such as latency and format compatibility), thus providing a graph structure basis for path planning. Based on the topology, a secure synchronization link is generated. This can be achieved by running a constrained path search algorithm on the topology graph, prioritizing low-latency, highly compatible, and redundant paths. Furthermore, this operation can be implemented using a constrained Dijkstra's algorithm (edge ​​weights fusion of latency and error risk) or a k-shortest path algorithm (generating primary and backup link pairs), thereby achieving the technical effect of outputting optimized paths that meet QoS requirements and have fault tolerance capabilities. Based on the secure synchronization link, the initial synchronization link is replanned to obtain an updated synchronization scheme. This can involve replacing corresponding segments in the initial synchronization link with secure synchronization links to form a new end-to-end synchronization scheme, thus achieving the technical effect of link upgrades from static presets to dynamic adaptation.

[0052] Taking the synchronization of high-priority emergency replenishment orders as an example, the data interface synchronization method for the supply chain system in this embodiment can be as follows: An e-commerce platform needs to synchronize a high-priority emergency replenishment order during a major promotion (task priority parameter = high, data volume requirement = 500 SKUs). The system first constructs a four-dimensional synchronization resource space model based on interface load data, including latency, concurrency, format differences, and update frequency; then, based on high priority, it derives the limit constraints: maximum concurrency = 8, maximum latency tolerance = 2 seconds, maximum error tolerance = 0.1%, and maximum resource usage limit = 30% CPU. After matching in the resource space, it is found that the path directly connecting to the supplier's ERP has low latency but large format differences (error risk exceeds the limit), while the path via the intermediate data lake has one more hop but a high degree of format standardization. Based on this, the system generates a synchronization resource available corridor that only includes the transit path and its backup link; after extracting its topology, it generates a secure synchronization link that includes the main path and the hot standby path. The final update synchronization scheme adopts this secure link to ensure that the order is accurately synchronized within 2 seconds, avoiding the risk of stockouts.

[0053] In one embodiment, reference Figure 2 The steps for determining the target synchronization partition based on the current resource status and the target synchronization source data are as follows: Step S301: Based on the current resource status, obtain the synchronization health value of the synchronization task, and determine whether the synchronization health value is lower than the preset health threshold.

[0054] The synchronization health value can be a comprehensive evaluation indicator used to quantify the matching degree between the current system resource status and the synchronization task requirements. It can be used as a basis for determining whether to trigger synchronization strategy adjustments, thus achieving a preliminary assessment of the feasibility of task execution. In this embodiment, the synchronization health value can be derived through weighted calculation or model prediction based on the remaining resources such as CPU, memory, and network bandwidth, and their critical weight to the synchronization task. For example, the synchronization health value can include, but is not limited to, one or more of the following: resource health score, storage capacity health score, and network throughput health score. The health threshold can be a preset synchronization health value critical point used to determine whether the current synchronization task is in a stable execution state. It can be used to provide a trigger boundary for strategy adjustments, avoiding the continued execution of full synchronization when resources are about to be exhausted.

[0055] Based on the current resource status, the synchronization health value of the synchronization task is obtained. This can be achieved by collecting system resource indicators and substituting them into a preset evaluation function to calculate the health value. Furthermore, this operation can be implemented using a linear weighted formula (i.e., the health value equals the sum of CPU surplus, memory surplus, and bandwidth surplus multiplied by their respective weights) or by using a lightweight neural network model to input multidimensional resource status and output a health score. This allows the multidimensional resource status to be abstracted into a single discriminant indicator, simplifying the decision-making logic. Determining whether the synchronization health value is lower than a preset health threshold can be done by comparing the synchronization health value with the health threshold, thus deciding whether to initiate the synchronization strategy adjustment process.

[0056] Step S302: If it is determined that the synchronization health value is lower than the health threshold, then it is determined that the synchronization task needs to adjust the synchronization strategy.

[0057] If the synchronization health value is determined to be lower than the health threshold, the synchronization task needs to adjust the synchronization strategy. This can be done by setting a strategy adjustment flag to trigger the subsequent extreme synchronization range calculation process, thereby enabling a switch from regular synchronization to resource adaptive synchronization.

[0058] Step S303: Based on the current resource status, extract the limit synchronization range of the synchronization task, and extract the business data of each node according to the limit synchronization range to obtain the target synchronization candidate range.

[0059] The target synchronization candidate range can be a set of potential data suitable for partition matching, filtered from the business data of each node within the limit synchronization range. This can be used to narrow the search space for subsequent partition matching and improve the identification efficiency of preset standard synchronization partitions. In an exemplary embodiment, the target synchronization candidate range can use the limit synchronization range as a filtering condition to perform field-level or record-level pruning on the business data of each node. Extracting the limit synchronization range of the synchronization task based on the current resource status can be done by using the method of back-calculating the amount of data that can be processed based on the resource consumption model in the previous steps. Extracting data from the business data of each node based on the limit synchronization range to obtain the target synchronization candidate range can be done by using the limit synchronization range as a filtering condition to prune the original business data. Furthermore, this operation can be implemented by limiting the time range and entity ID set through the WHERE clause at the database layer, or by indexing and slicing according to the limit range after loading the full amount of business data at the application layer. This allows focusing on the data subset that resources can support, providing input for partition matching.

[0060] Step S304: Perform a traversal query within the target synchronization candidate range to determine whether a preset standard synchronization partition exists. If a preset standard synchronization partition exists, extract the target synchronization partition of the preset standard synchronization partition.

[0061] The preset standard synchronization partition can be a pre-configured logical data partition template with clear business semantics and synchronization priorities. It can be used to provide directly reusable high-value synchronization units when resources are limited, avoiding real-time calculation of partitioning rules. In one specific embodiment, the preset standard synchronization partition can be defined and stored in the configuration center by operations personnel or the business system based on historical experience or SLA requirements. For example, the preset standard synchronization partition can include, but is not limited to, one or more of the following: core customer order partition, safety stock early warning partition, and cross-border customs clearance key document partition. Traversing and querying within the target synchronization candidate range can involve scanning candidate data one by one or in batches to match the definition rules of the preset standard synchronization partition, thereby identifying whether a directly reusable standard partition exists. Determining whether a preset standard synchronization partition exists can involve checking whether the traversal results match the business characteristics of any preset partition template, thus deciding whether to adopt a templated partitioning strategy. If a preset standard synchronization partition is determined to exist, the target synchronization partition of the preset standard synchronization partition is extracted. This can involve returning the successfully matched preset partition as the final target synchronization partition, thereby avoiding real-time calculation of partitioning logic and improving decision-making efficiency and consistency.

[0062] Step S305: Extract the configuration data of the target synchronization partition, generate a synchronization control scheme for the synchronization task based on the configuration data and progress status, and control the synchronization task to execute in the target synchronization partition according to the synchronization control scheme.

[0063] The configuration data can be metadata describing the execution parameters of the target synchronization partition, including concurrency, retry strategy, field mapping rules, etc., which can be used to provide the basis for the execution strategy of generating the synchronization control scheme. The synchronization control scheme can be a specific set of instructions generated by integrating partition configuration and task progress status, guiding the execution of synchronization tasks within the target synchronization partition, and can be used to ensure that critical data synchronization can still be completed in an orderly and controllable manner in degraded or restricted modes. In an exemplary embodiment, the synchronization control scheme can combine configuration data with the current progress (such as the synchronized record ID) to dynamically generate a queue of remaining subtasks. Furthermore, the synchronization control scheme can be generated based on configuration data and progress status and called by the synchronization execution engine to control the actual data transmission. Extracting the configuration data of the target synchronization partition can be done by reading the execution parameters associated with the partition from the configuration storage, thereby obtaining the partition-specific synchronization strategy metadata.

[0064] Based on configuration data and progress status, a synchronization control scheme is generated for the synchronization tasks. This can be achieved by combining parameters such as concurrency and retry from the configuration data with progress status (e.g., synchronized offset) to generate a list of executable subtasks. Furthermore, this operation can be implemented by generating a paginated synchronization task queue with a starting cursor, or by constructing a partitioned synchronization instruction stream based on a message queue. This allows for dynamic coupling of strategy and execution status, ensuring continuous and controllable synchronization. Controlling the execution of synchronization tasks within the target synchronization partition according to the synchronization control scheme can be achieved by distributing the synchronization control scheme to the execution engine and scheduling data transmission according to instructions. This allows for precise execution of critical data synchronization under resource-constrained conditions.

[0065] Taking cross-warehouse inventory synchronization during a major promotional event as an example, the data interface synchronization method for the supply chain system in this embodiment can be as follows: During the Double Eleven promotion, an e-commerce platform needs to synchronize the real-time inventory of 20 regional warehouses across the country to the central fulfillment system. The system detects that the current memory resources are tight, calculates the synchronization health value to be 0.35, which is lower than the preset health threshold of 0.5, and triggers a strategy adjustment. Based on the remaining resources, the system calculates the limit synchronization range as the inventory change records in the most recent 4 hours, and extracts the target synchronization candidate range from the business data of each warehouse accordingly. It finds that it includes a preset standard synchronization partition "high turnover product inventory partition" (covering the top 10% of SKU sales). The system extracts the configuration data of this partition (such as 100 concurrent transactions per second, automatic retry 3 times on failure), and combines it with the current progress status that has been synchronized up to 2 hours ago to generate a synchronization control scheme: within the time window from 2 hours ago to the present, only the inventory increase of high turnover products is synchronized. The execution engine accurately pulls key data accordingly, ensuring the fulfillment accuracy of best-selling products, while avoiding system crashes caused by full synchronization.

[0066] In one embodiment, after determining that a preset standard synchronization partition exists, the step of extracting the target synchronization partition of the preset standard synchronization partition further includes: If it is determined that there is no preset standard synchronization partition, the minimum availability requirement, minimum bandwidth requirement, and minimum data consistency requirement of the synchronization task are obtained, and the minimum availability requirement, minimum bandwidth requirement, and minimum data consistency requirement are integrated to generate the minimum synchronization conditions. Based on the minimum synchronization conditions, business data of each node, and the target synchronization candidate range, a location query is performed to obtain multiple non-standard candidate synchronization partitions. Extract the synchronization condition data and resource distance data from the current progress position for each non-standard candidate synchronization partition; Based on the current resource status, obtain the urgency value of the synchronization task that needs to be adjusted; Based on synchronization condition data, resource distance data, and urgency value, multiple non-standard candidate synchronization partitions are filtered to obtain the target non-standard synchronization partition; Based on the target non-standard synchronization partition and the progress status of the synchronization task, a backup synchronization control scheme is generated for the synchronization task, and the synchronization task is controlled to execute in the target non-standard synchronization partition according to the backup synchronization control scheme.

[0067] The minimum availability requirement can be the minimum system availability time ratio or service responsiveness required for the synchronization task to maintain basic business continuity. It can serve as one of the bottom-line constraints for degraded synchronization, ensuring that the synchronization process is not completely interrupted. The minimum bandwidth requirement can be the minimum network transmission rate required to complete specific data synchronization. It can be used to constrain the data volume and transmission feasibility of optional partitions. The minimum data consistency requirement can be the minimum data integrity or timeliness standards that the synchronization results must meet, such as the maximum allowable latency or field missing tolerance. It can be used to define the acceptable lower limit of data quality for degraded synchronization. In this embodiment, if it is determined that there is no preset standard synchronization partition, the minimum availability requirement, minimum bandwidth requirement, and minimum data consistency requirement of the synchronization task can be obtained by reading these three types of minimum requirements from the task metadata or SLA configuration. This achieves the technical effect of establishing the basic constraint boundaries for degraded synchronization.

[0068] The minimum synchronization conditions can be a comprehensive set of constraints formed by integrating minimum availability, bandwidth, and data consistency requirements. These can be used as an entry threshold for screening non-standard candidate synchronization partitions. Furthermore, generating minimum synchronization conditions by integrating minimum availability, minimum bandwidth, and minimum data consistency requirements can be achieved by converting these three types of requirements into structured query conditions or Boolean expressions. For example, this operation can be performed by constructing a JSON-formatted constraint object containing thresholds for each dimension, or by generating SQLWHERE clause fragments for subsequent data filtering, thereby achieving the technical effect of forming unified entry rules and supporting subsequent partition screening. Non-standard candidate synchronization partitions can be logical subsets of data within the target synchronization candidate range that meet the minimum synchronization conditions but are not preset as standard templates. They can be used to provide feasible alternative synchronization units when no standard partitions are available. In an exemplary embodiment, non-standard candidate synchronization partitions can include, but are not limited to, one or more of the following: regional temporary promotional order partitions, single-supplier emergency replenishment partitions, and cross-warehouse transfer in-transit document partitions. Based on the minimum synchronization conditions, business data of each node, and the target synchronization candidate range, a location query is performed to obtain multiple non-standard candidate synchronization partitions. The minimum synchronization conditions can be applied to filter within the target synchronization candidate range, and the results can be clustered according to business semantics. Furthermore, this operation can be achieved by aggregating candidate records by business entity relationships using a graph database, or by forming partitions through two-dimensional slicing based on time windows and customer grouping. This achieves the technical effect of identifying feasible synchronization units even without a standard template.

[0069] Synchronization condition data can be the resource and performance parameters required to describe the synchronization feasibility of a non-standard candidate synchronization partition, and can be used to evaluate the execution cost and success rate of the partition in the current environment. Resource distance data from the current progress position can be the total amount of additional system resources required to switch to a non-standard candidate synchronization partition from the current synchronization progress, and can be used to measure the resource cost of switching to that partition, avoiding exacerbating resource strain due to frequent jumps. In a specific embodiment, the resource distance data from the current progress position can be estimated based on historical synchronization logs or a resource prediction model to estimate the CPU, memory, and I / O overhead of jumping to that partition. Extracting the synchronization condition data and resource distance data from the current progress position for each non-standard candidate synchronization partition can be achieved by querying the partition metadata database to obtain the synchronization condition data and calculating the resource distance using a resource prediction model, thereby providing quantitative input for multi-factor screening. The urgency value can be a quantitative indicator reflecting the urgency of the synchronization task requiring immediate strategy adjustments due to insufficient resources or progress delays, and can be used to adjust the risk preference in the screening of non-standard candidate partitions, favoring low-overhead partitions when urgency is high. In an exemplary embodiment, the urgency value may include, but is not limited to, one or more of the following: resource depletion urgency, schedule lag urgency, and business deadline approaching urgency. Based on the current resource status, the urgency value for adjusting the synchronous task is obtained by substituting indicators such as the rate of change of resource reserves and the degree of schedule lag into the urgency function. Furthermore, this operation can be achieved by employing an exponential decay model (urgency equals one minus the natural exponential negative alpha multiplied by the resource consumption rate) or by matching composite conditions such as "resources less than 20% and schedule lag greater than 30%" based on a rule engine, thereby achieving the technical effect of dynamically adjusting the risk tolerance of the degradation strategy.

[0070] The target non-standard synchronization partition can be the optimal non-standard candidate synchronization partition selected after multi-dimensional screening for performing degraded synchronization. It can be used to ensure minimal synchronization of critical data in scenarios without standard partitions. In one specific embodiment, the target non-standard synchronization partition can be selected based on a weighted score of synchronization condition data, resource distance data, and urgency value among the non-standard candidate synchronization partitions. The target non-standard synchronization partition is obtained by screening multiple non-standard candidate synchronization partitions according to the synchronization condition data, resource distance data, and urgency value. This can be achieved by constructing a multi-dimensional scoring function (such as a weighted sum) and selecting the partition with the highest score. For example, this operation can assign higher weight to resource distance and prioritize low-overhead partitions during high urgency, or focus on synchronization condition data and prioritize partitions with high data integrity during low urgency, thereby achieving the technical effect of intelligent partition selection for resource-business-risk collaborative optimization.

[0071] A backup synchronization control scheme can be a set of instructions generated based on the target non-standard synchronization partition and the current progress state, used to perform degraded synchronization. This can be used to achieve controllable and orderly synchronization execution in the absence of a standard template. Furthermore, the backup synchronization control scheme can be generated based on the target non-standard synchronization partition and the progress state, and invoked by the synchronization engine to execute the actual transmission. Generating a backup synchronization control scheme for a synchronization task based on the target non-standard synchronization partition and the progress state of the synchronization task can combine the partition configuration with the current progress offset to generate a sub-task queue with a starting point. In an exemplary embodiment, this operation can be achieved by generating cursor-based incremental synchronization instructions or constructing partition-specific synchronization events in a message queue, thereby ensuring the continuity and traceability of the degraded synchronization process. Controlling the execution of the synchronization task in the target non-standard synchronization partition according to the backup synchronization control scheme can be achieved by distributing the backup synchronization control scheme to the execution engine and monitoring its execution, thereby maintaining the ability to synchronize critical data under extreme conditions.

[0072] Taking a sudden network outage in a cross-border supply chain as an example, the data interface synchronization method for the supply chain system in this embodiment can be as follows: When a multinational manufacturing company synchronizes production reporting data from its overseas factories, a local network failure prevents it from matching any preset standard synchronization partitions. The system then obtains the minimum synchronization conditions for this task: availability ≥ 60%, bandwidth ≥ 50KB / s, and data latency ≤ 24 hours. Within the target synchronization candidate range (reporting records from the past 48 hours), the system identifies three non-standard candidate synchronization partitions: Zone A (containing only the completion count of key production lines), Zone B (containing all reporting records but requiring high bandwidth), and Zone C (containing some fields but with a small file size). After extracting the synchronization condition data and resource distance data for each partition, and combining the high urgency value (0.92) under the current extremely low bandwidth, the system assigns a higher weight to resource distance and ultimately selects Zone A as the target non-standard synchronization partition. The backup synchronization control scheme only retrieves the completion count and timestamp of key production lines to ensure that headquarters can maintain basic capacity monitoring and avoid a complete shutdown.

[0073] In one embodiment, the step of determining the target degradation synchronization region based on the progress status, business data of each node, and current resource status is as follows: Based on the current resource status, the failure trend of the synchronization task is obtained, and the initial degradation direction is determined based on the failure trend; The data size of the synchronization task is obtained based on the task parameters of the synchronization task. The impact weight when the synchronization task fails is obtained based on the data size and task priority. By combining the progress status, target synchronization source data, and impact weights, the initial degradation direction is revised to obtain the final degradation direction; Based on the final degradation direction and the impact weight, a degradation synchronization route for the synchronization task is generated, and the target degradation synchronization area is determined by combining the degradation synchronization route and the business data of each node.

[0074] The failure trend of a synchronous task can be a prediction of the probability or tendency for the synchronous task to be interrupted or timed out in the future execution process based on the current resource status. This can be used as a preliminary judgment basis for triggering a degradation mechanism, enabling early risk identification. In this embodiment, the failure trend of a synchronous task can be inferred by dynamically comparing the resource consumption rate, remaining resource quantity, and remaining workload of the task, combined with historical failure patterns. Furthermore, the failure trend of a synchronous task can be an input variable used to drive the generation of the initial degradation direction. For example, the failure trend of a synchronous task can include, but is not limited to, one or more of resource exhaustion failure trends, progress lag failure trends, and link interruption failure trends. The initial degradation direction can be a preliminary strategy direction for reducing or simplifying the execution of the synchronous task, derived solely from the failure trend before considering business semantics. It can be used to provide an initial anchor point for degradation decisions and will be subsequently corrected by business weights. In an exemplary embodiment, the initial degradation direction can select the corresponding degradation dimension (such as reducing concurrency, shortening the scope, or reducing the frequency) according to the failure trend type (such as insufficient CPU or bandwidth bottleneck). Furthermore, the initial degradation direction can be a strategy output that has a direct mapping relationship with the failure trend of the synchronous task.

[0075] Based on the current resource status, the failure trend of the synchronization task can be obtained. This can be achieved by analyzing the resource consumption curve and remaining capacity to determine whether critical resources will be exhausted before the task is completed. Furthermore, this operation can be implemented by using a sliding window to calculate the resource consumption slope and extrapolating it to the resource threshold time point, or by using a classification model (such as a random forest) to input multi-dimensional resource indicators and output the failure probability, thereby enabling early and quantitative perception of synchronization failure risks. The initial degradation direction can be obtained based on the failure trend, which can be achieved by matching the failure trend type to a preset degradation strategy template (e.g., insufficient memory → reducing batch size). In a specific embodiment, this operation can be achieved through table lookup mapping (failure trend category → degradation action set) or rule engine reasoning (IF trend = 'bandwidth exhaustion' THEN direction = 'compressed data + single-channel transmission'), enabling the rapid generation of preliminary response strategies and providing a foundation for subsequent refined adjustments.

[0076] Data scale data can be quantifiable metrics such as the total amount of data to be processed, the number of fields, or the number of records, derived from the task parameters of the synchronization task. It can be used as one of the basic inputs for calculating impact weights, reflecting the breadth of the impact of task failure on the system. In this embodiment, data scale data can be extracted from the task configuration to define the data range and combined with the target synchronization source data to estimate the actual scale. Impact weight can be the quantified degree of potential impact of synchronization task failure on overall business continuity, derived by combining data scale and task priority. It can be used to distinguish the retention priority of different data subsets during the degradation process. For example, impact weights can be generated numerically using a weighting function (such as priority × data scale) or a rule engine mapping. The data scale data of the synchronization task is obtained from the task parameters. This can be achieved by parsing the time range, entity ID list, or field list in the task parameters to estimate the data volume, thereby quantifying the task load and supporting impact assessment.

[0077] Based on the data size and task priority, the impact weight of a failed synchronization task is obtained. This can be achieved by non-linearly combining the data size and task priority (e.g., through product or piecewise function) to generate the weight value. Further, this operation can be implemented by mapping priority levels (high / medium / low) to coefficients of 1.0 / 0.6 / 0.3 and then multiplying by the data size, or by using a business impact matrix to determine the weight. This allows for the establishment of a business semantic-driven degradation priority ranking basis. The final degradation direction can be a precise degradation strategy guide formed by optimizing the initial degradation direction after integrating progress status, target synchronization source data, and impact weights. This can be used to ensure that degradation behavior avoids redundant work while protecting critical business operations. In a specific embodiment, the final degradation direction can be based on the initial direction, eliminating redundant operations based on completed progress, and focusing on high-value data segments based on impact weights.

[0078] By combining the progress status, target synchronization source data, and impact weights, the initial degradation direction is revised to obtain the final degradation direction. This can be achieved by removing operations corresponding to the already synchronized parts and retaining the data subset with impact weights higher than a threshold as the degradation focus. Furthermore, this operation can be implemented by sorting the target synchronization source data by impact weight and selecting the Top-N as the retain set, or by converting the progress status into a completed mask and finding the intersection with high-weight regions to determine the final direction. This allows the degradation strategy to be both progress-aware and business-value-oriented. The degradation synchronization route can be a simplified transmission path and execution sequence planned based on the final degradation direction and impact weights, covering only key data. It can be used to provide an executable, minimal synchronization solution for resource-constrained scenarios. In this embodiment, the degradation synchronization route can prune low-weight branches in the original synchronization topology, retaining the simplest connectivity path between high-weight nodes.

[0079] Based on the final degradation direction and its impact weight, a degradation synchronization route for the synchronization task is generated. This can be achieved by retaining node paths in the original link topology that match the final degradation direction and have high impact weights. For example, this operation can be implemented by running a minimum subgraph extraction algorithm with weight constraints, or by manually constructing a backbone path (containing only interface nodes corresponding to high-weight business entities), thus outputting a resource-feasible and business-critical minimum synchronization path. The target degradation synchronization area is determined by combining the degradation synchronization route and the business data of each node. This can be achieved by mapping the degradation synchronization route back to the business data of each node and extracting the corresponding key fields or record sets. Furthermore, this operation can be achieved by filtering relevant data by reverse-looking up the supporting business entities (such as order IDs) of each route node, or by executing a route-driven SQL query in the business database to retrieve only the data within the route coverage area, thus clearly defining the specific data content that must be synchronized under abnormal conditions.

[0080] Taking the critical shortage of core order synchronization resources during a major promotional event as an example, the data interface synchronization method for the supply chain system in this embodiment can be as follows: During the Double Eleven promotional peak, an e-commerce platform executes a high-priority order synchronization task. The system detects a continuous decrease in memory resources, predicting that they will be exhausted in 30 seconds (failure trend is "resource exhaustion type"). The initial degradation direction is set to "narrow the synchronization scope". The task parameters show that it involves 100,000 orders (data scale), with a priority of "urgent", and the calculated impact weight is extremely high. Combining the progress status (60% of ordinary orders have been completed), the system excludes the synchronized part and focuses on the high-weight subset (about 8,000 orders) of the remaining 40% that are "paid and need to be shipped within 2 hours". Based on this, the final degradation direction is corrected to "synchronize only the high-weight subset", generating a degradation synchronization route that only includes the core order service and the fulfillment system. The final target degradation synchronization area is the key fields (product, quantity, address, time requirement) of these 8,000 orders, ensuring that even if resources fail, core fulfillment is not affected.

[0081] In one embodiment, after determining the target degraded synchronization area by combining the degraded synchronization route and the service data of each node, the method further includes: Based on the current resource status, extract the remaining system resource data that the synchronization task could call before synchronization failed; The remaining system resource data can be the amount of computing, storage, or network resources that the system can still call for the task before the synchronization task is about to fail. This can be used to limit the resource ceiling for executable operations during the degradation process, preventing secondary failures. In an exemplary embodiment, the remaining system resource data can predict the cumulative available resources up to the failure threshold based on the current resource status and the task's resource consumption rate. Further, the remaining system resource data can include, but is not limited to, one or more of the following: remaining CPU time slices, available memory buffers, and schedulable network bandwidth. Extracting the remaining system resource data that the synchronization task can call before synchronization failure based on the current resource status can be done by extrapolating the resource consumption trend to the failure threshold time point and integrating to obtain the total remaining available resources. For example, this operation can be achieved by using linear regression to predict resource exhaustion time and calculating the resource integral within the remaining window, or by estimating the instantaneous availability based on the difference between the container resource limit and the current utilization rate, thereby quantifying the resource feasibility boundary of the degradation operation.

[0082] Based on the impact weights and remaining system resource data, the maximum range of adjustments that the synchronization task can generate during the degradation process is obtained. The maximum range adjustment amount can be the maximum data size or range that the target downgrade synchronization region can be further reduced or pruned under the constraint of remaining system resources. It can be used to provide a quantitative pruning boundary for candidate region generation. In a specific embodiment, the maximum range adjustment amount can be obtained by converting the remaining system resource data into a maximum amount of data that can be processed through a resource-data consumption model, and comparing it with the original region to obtain the adjustment margin. Based on the influence weight and the remaining system resource data, the maximum range adjustment amount that the synchronization task can generate during the downgrade process can be obtained. This can be achieved by mapping the remaining resources into a amount of data that can be processed, and comparing it with the target region size to obtain the pruning ratio. Furthermore, this operation can be achieved by using a unit data synchronization resource consumption model to back-calculate the maximum number of records that can be deleted, or by setting the adjustment amount to the smaller value between the resource allowable pruning amount and the total region size multiplied by a preset upper limit, thereby transforming resource constraints into data operation constraints.

[0083] Based on the maximum range adjustment amount and the target degradation synchronization area, the candidate degradation area range is generated, and the area size of the target degradation synchronization area is extracted. The candidate degradation region can be a set of feasible regions formed by combining the target degradation synchronization region with the allowable pruning space based on the maximum adjustment range. This can provide multiple optional degradation implementation intervals, supporting refined selection. In this embodiment, the candidate degradation region can be centered on the target region, expanding outwards or contracting inwards according to the maximum adjustment amount to form a flexible boundary. The region size can be a quantified value of the number of data records, fields, or logical units contained in the target degradation synchronization region, and can be used as the granularity basis for dividing candidate regions. In an exemplary embodiment, the region size can be obtained by counting the number of data records, fields, or logical units within the region. Generating the candidate degradation region based on the maximum adjustment range and the target degradation synchronization region can be achieved by defining a pruning flexible subset space within the target region, whose size does not exceed the maximum adjustment amount, thereby constructing a feasible solution space for degradation decisions. Extracting the region size of the target degradation synchronization region can be achieved by counting the number of data records, fields, or logical units within the region, thereby providing a benchmark scale for region division.

[0084] The candidate degradation areas are divided according to the size of the area, generating multiple alternative degradation areas, and the business value data of each alternative degradation area is extracted. The alternative degradation regions can be several mutually exclusive or overlapping sub-region options divided from the candidate degradation region range according to the region size, which can be used to provide multiple degradation execution units that can be evaluated and selected. In a specific embodiment, the alternative degradation regions can be segmented by methods such as equal division, sliding windows, or business semantic segmentation to divide the candidate range. Further, the alternative degradation regions can include, but are not limited to, one or more of the following: sub-regions divided by customer level, sub-regions divided by timeliness and urgency, and sub-regions divided by supply chain links. Business value data can be a comprehensive score that measures the degree of impact of the alternative degradation regions on overall business continuity, fulfillment capability, or upstream and downstream collaboration, and can be used as the core criterion for selecting the final degradation region to ensure that losses are minimized. In this embodiment, the business value data can be calculated by integrating multi-dimensional indicators such as impact weight, data timeliness, number of dependent nodes, and historical failure impact.

[0085] The candidate downgrade region is divided based on its size, generating multiple alternative downgrade regions. This can be achieved by dividing the candidate range into several sub-regions according to fixed granularity or business rules. For example, this operation can be implemented by dividing the region into equal blocks, ensuring each block contains the same number of records, or by semantic segmentation based on business dimensions such as order status or customer type, thus generating downgrade options that can be independently evaluated and selected. The business value data of each alternative downgrade region is extracted. This can be done by calling a business value assessment model, inputting the region content and context parameters, and outputting a score. Furthermore, this operation can be achieved by weighting the value of each data item within the region based on its impact weight, or by querying a pre-computed business impact knowledge graph to obtain the region score, thereby enabling a quantitative mapping from data to business impact.

[0086] Select the candidate degradation region with the lowest business value data as the final degradation region, and extract the configuration location data of the final degradation region; The final degradation region can be the discarded region among all candidate degradation regions that has the lowest business value data and meets resource constraints. This clarifies the range of data that can be safely abandoned under resource constraints. In an exemplary embodiment, the final degradation region can be selected as the degradation target by sorting or threshold filtering to identify the region with the least business impact. Configuration location data can be the specific location information of the final degradation region within the system, including its node, interface identifier, data table name, field path, or timestamp range, providing precise operational anchors for resource allocation and link adjustment. In this embodiment, the configuration location data can be extracted from the metadata registry or task context to obtain the physical / logical address corresponding to the region. Selecting the candidate degradation region with the lowest business value data as the final degradation region can be achieved by sorting all candidate regions in ascending order of business value data and selecting the first one, thus ensuring minimal business loss while meeting resource constraints. Extracting the configuration location data of the final degradation region can be achieved by querying the data source, interface path, and field coordinates corresponding to the region from the metadata directory, providing precise location for resource release and link adjustment.

[0087] Based on the configured location data, progress status, and impact weights, a final resource allocation scheme for the synchronization task is generated. The synchronization system allocates resources according to the final resource allocation plan and adjusts the degraded synchronization route.

[0088] The final resource allocation scheme can be a fine-grained resource scheduling instruction set for degraded tasks, generated based on configuration location data, progress status, and impact weights. This can be used to achieve dynamic resource rebalancing and ensure the execution of critical paths. In one specific embodiment, the final resource allocation scheme can be obtained by releasing the resource quota corresponding to the final degraded region and reallocating the saved resources to high-weight path nodes. Generating the final resource allocation scheme for synchronization tasks based on configuration location data, progress status, and impact weights can involve releasing the resource reservations corresponding to the configuration location and reallocating the released resources to non-degraded paths according to their impact weights. For example, this operation can be achieved by updating Kubernetes resource request / limit parameters or adjusting the concurrency of message queue consumers and the size of the memory buffer, thereby enabling dynamic resource optimization and focusing on critical synchronization paths. The synchronization system allocates resources according to the final resource allocation scheme and adjusts the degraded synchronization route. This can be done by executing resource scheduling instructions and removing the transmission steps corresponding to the final degraded region from the degraded synchronization route, thus completing a closed loop from decision-making to execution and ensuring the implementation of the degraded strategy.

[0089] For example, in an emergency scenario where resources are exhausted during the synchronization of cross-border supply chain finance settlement data, the data interface synchronization method for the supply chain system in this embodiment could be as follows: A multinational corporation needs to synchronize cross-border payment instructions (high impact weight) for the day, but during execution, a sudden network congestion causes memory resources to be quickly exhausted. The system first predicts that only 500 records (remaining system resource data) can be processed within the remaining 10 seconds, while the target downgrade synchronization area contains 2000 records. Combining the impact weight, the maximum adjustment range is calculated to be 1500 records. The candidate downgrade area range is set as a pruning subset of 1500 records. It is divided into 4 candidate areas (500 records each) according to the area size (2000 records), corresponding to different currency settlement batches. Business value assessment shows that the USD batch has the highest value because it involves core suppliers and is close to the deadline; the JPY batch has the lowest value because it is settled the next day. The system selects the JPY area as the final downgrade area and extracts its configuration location (Bank of Japan API interface, field path). The final resource allocation plan freed up the memory and connection pool resources of the JPY channel, reallocated them entirely to the USD and EUR channels, and removed the JPY node from the degraded synchronization route. The synchronization task successfully completed the core settlement data synchronization under resource constraints.

[0090] Furthermore, to achieve the above objectives, the present invention also provides a data interface synchronization device for a supply chain system, the device comprising: a memory, a processor, and a data interface synchronization program for a supply chain system stored in the memory and executable on the processor, the data interface synchronization program for a supply chain system being configured to implement the steps of the data interface synchronization method for a supply chain system as described in any one of the above descriptions.

[0091] In addition, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a data interface synchronization program for a supply chain system, wherein the data interface synchronization program for a supply chain system, when executed by a processor, implements the steps of the data interface synchronization method for a supply chain system as described above.

[0092] Other embodiments or specific implementations of the data interface synchronization device for the supply chain system described in this invention can be found in the above-described method embodiments, and will not be repeated here.

[0093] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A data interface synchronization method for a supply chain system, characterized in that, The method includes: Obtain heterogeneous interface data from each node in the supply chain and the progress status of the current synchronization task. Extract data from the heterogeneous interface data based on the preset synchronization range of the synchronization task to obtain the target synchronization source data. Obtain the initial synchronization link of the synchronization task; collect interface load data within a preset range of the subsequent synchronization task based on the initial synchronization link and the progress status; re-plan the initial synchronization link based on the interface load data and the target synchronization source data to obtain an update synchronization scheme. Obtain the current resource status of the synchronization system and the business data of each node in the supply chain; obtain the limit synchronization range of the synchronization task based on the current resource status and the target synchronization source data; and determine the target synchronization partition based on the limit synchronization range and the business data of each node. Based on the current resource status, determine whether the synchronization task is about to fail. If it is determined that the synchronization task is about to fail, then determine the target downgrade synchronization area based on the progress status, the business data of each node, and the current resource status.

2. The data interface synchronization method for a supply chain system as described in claim 1, characterized in that, The steps of acquiring heterogeneous interface data from each node of the supply chain and the progress status of the current synchronization task, and extracting data from the heterogeneous interface data based on the preset synchronization range of the synchronization task to obtain the target synchronization source data are as follows: Obtain heterogeneous interface data of the supply chain and the progress status of the current synchronization task; group the heterogeneous interface data according to the preset synchronization range to obtain grouped interface data. Based on the synchronization task, extract the influencing factors that affect the execution of the synchronization task; Based on the aforementioned influencing factors, the group interface data is filtered to obtain the target synchronization source data.

3. The data interface synchronization method for a supply chain system as described in claim 2, characterized in that, The step of collecting interface load data within a preset range for the subsequent synchronization task based on the initial synchronization link and the progress status specifically includes: Based on the initial synchronization link and the progress status, determine the progress position of the synchronization task on the initial synchronization link; Based on the progress position, the subsequent synchronization direction of the synchronization task is determined; Based on the subsequent synchronization direction, collect interface response delay data, interface concurrency data, data format difference data, and business update frequency data within a preset range for the subsequent synchronization task; Based on the interface response latency data and the interface concurrency data, the network load within the preset range is obtained, and initial synchronization load data is generated based on the network load. Based on the data format difference data and the business update frequency data, an auxiliary synchronization constraint distribution map is generated, and the auxiliary synchronization constraint distribution map is added to the initial synchronization load data to generate interface load data.

4. The data interface synchronization method for a supply chain system as described in claim 3, characterized in that, The step of replanning the initial synchronization link based on the interface load data and the target synchronization source data to obtain an updated synchronization scheme is as follows: Based on the interface load data, a multidimensional load distribution within the preset range is obtained, and a synchronization resource space model is constructed according to the multidimensional load distribution. Obtain the task priority parameters and data volume requirements of the synchronization task, and obtain the maximum concurrency, maximum latency tolerance, maximum error tolerance, and maximum resource consumption limit of the synchronization task based on the task priority parameters and the data volume requirements. The limit constraints for the synchronization task are generated based on the maximum concurrency, the maximum latency tolerance, the maximum error tolerance, and the maximum resource consumption limit. Based on the aforementioned limit constraints, multi-dimensional data matching is performed in the synchronization resource space model to generate a synchronization resource availability corridor. Extract the topology of the available corridors for the synchronization resources, generate a secure synchronization link based on the topology, and replan the initial synchronization link based on the secure synchronization link to obtain an update synchronization scheme.

5. The data interface synchronization method for a supply chain system as described in claim 1, characterized in that, The steps of obtaining the extreme synchronization range of the synchronization task based on the current resource status and the target synchronization source data, and determining the target synchronization partition based on the extreme synchronization range and the service data of each node, are as follows: Based on the current resource status, the synchronization health value of the synchronization task is obtained, and it is determined whether the synchronization health value is lower than a preset health threshold. If it is determined that the synchronization health value is lower than the health threshold, then it is determined that the synchronization task needs to adjust the synchronization strategy. Based on the current resource status, the extreme synchronization range of the synchronization task is extracted, and the business data of each node is extracted based on the extreme synchronization range to obtain the target synchronization candidate range; A traversal query is performed within the target synchronization candidate range to determine whether a preset standard synchronization partition exists. If the preset standard synchronization partition exists, the target synchronization partition of the preset standard synchronization partition is extracted. Extract the configuration data of the target synchronization partition, generate a synchronization control scheme for the synchronization task based on the configuration data and the progress status, and control the synchronization task to execute on the target synchronization partition according to the synchronization control scheme.

6. The data interface synchronization method for a supply chain system as described in claim 5, characterized in that, After determining that the preset standard synchronization partition exists, the step of extracting the target synchronization partition of the preset standard synchronization partition further includes: If it is determined that the preset standard synchronization partition does not exist, the minimum availability requirement, minimum bandwidth requirement, and minimum data consistency requirement of the synchronization task are obtained, and the minimum availability requirement, minimum bandwidth requirement, and minimum data consistency requirement are integrated to generate the minimum synchronization conditions. Based on the minimum synchronization condition, the business data of each node, and the target synchronization candidate range, a location query is performed to obtain multiple non-standard candidate synchronization partitions. Extract the synchronization condition data and resource distance data from the current progress position for each of the non-standard candidate synchronization partitions; Based on the current resource status, the urgency value of the synchronization task that needs to be adjusted is obtained; Based on the synchronization condition data, the resource distance data, and the urgency value, multiple non-standard candidate synchronization partitions are filtered to obtain the target non-standard synchronization partition; Based on the target non-standard synchronization partition and the progress status of the synchronization task, a backup synchronization control scheme is generated for the synchronization task, and the synchronization task is controlled to execute in the target non-standard synchronization partition according to the backup synchronization control scheme.

7. The data interface synchronization method for a supply chain system as described in claim 4, characterized in that, The step of determining the target degradation synchronization region based on the progress status, the business data of each node, and the current resource status is as follows: Based on the current resource status, the failure trend of the synchronization task is obtained, and the initial degradation direction is obtained based on the failure trend; The data size of the synchronization task is obtained based on the task parameters of the synchronization task, and the impact weight when the synchronization task fails is obtained based on the data size and the task priority. By combining the progress status, the target synchronization source data, and the influence weight, the initial degradation direction is corrected to obtain the final degradation direction; Based on the final degradation direction and the impact weight, a degradation synchronization route for the synchronization task is generated, and the target degradation synchronization area is determined by combining the degradation synchronization route and the business data of each node.

8. The data interface synchronization method for a supply chain system as described in claim 7, characterized in that, After determining the target degradation synchronization area by combining the degradation synchronization route and the service data of each node, the method further includes: Based on the current resource status, extract the remaining system resource data that the synchronization task could call before the synchronization failed; Based on the influence weights and the remaining system resource data, the maximum range of adjustments that the synchronization task can generate during the degradation process is obtained; Based on the maximum range adjustment amount and the target degradation synchronization region, a candidate degradation region range is generated, and the region size of the target degradation synchronization region is extracted. The candidate degradation region is divided according to the region size to generate multiple alternative degradation regions, and the business value data of each alternative degradation region is extracted. Select the candidate degradation region with the lowest business value data as the final degradation region, and extract the configuration location data of the final degradation region; Based on the configured location data, the progress status, and the influence weight, a final resource allocation scheme for the synchronization task is generated. The synchronization system allocates resources according to the final resource allocation scheme and adjusts the downgraded synchronization route.

9. A data interface synchronization device for a supply chain system, characterized in that, The device includes: a memory, a processor, and a data interface synchronization program for a supply chain system stored in the memory and executable on the processor, the data interface synchronization program for a supply chain system being configured to implement the steps of the data interface synchronization method for a supply chain system as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a data interface synchronization program for a supply chain system, which, when executed by a processor, implements the steps of the data interface synchronization method for a supply chain system as described in any one of claims 1 to 8.

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