Enterprise data real-time analysis and synchronization system based on intelligent governance

Abstract

The invention relates to the technical field of data processing, and discloses an enterprise data real-time analysis and synchronization system based on intelligent governance, and the method comprises the steps: obtaining real-time state data reflecting the current operation condition of the system; based on the real-time state data, through a pre-configured time sequence prediction model, generating a prediction result of a system load in a future preset time period; maintaining a strategy library comprising at least two data synchronization strategies; dynamically selecting a data synchronization strategy adaptive to the prediction result of the system load from a strategy library as a target synchronization strategy according to the prediction result of the system load and a preset business service level protocol; and executing the data synchronization task according to the target synchronization strategy. According to the method and the device, a load prediction mechanism is introduced, so that a high-throughput strategy can be switched in advance or resource capacity expansion can be completed before a load peak arrives, and sudden system performance drop or service interruption caused by untimely strategy switching is effectively avoided.

Description

Technical Field

[0001] This application relates to the field of computer data processing technology, and more specifically, to a real-time analysis and synchronization system for enterprise data based on intelligent governance. Background Technology

[0002] Data synchronization is a key technology for ensuring data consistency and real-time performance in modern enterprise distributed systems. To improve synchronization efficiency and resolve data conflicts, existing technologies have explored various approaches.

[0003] In practice, the applicant has found that existing technologies still have significant technical bottlenecks when dealing with current complex business scenarios. On the one hand, the synchronization strategies adopted by these technical solutions are usually statically preset or passively responsive; when the system faces drastic load fluctuations, this fixed strategy is difficult to dynamically balance the latency and consistency of data synchronization, resulting in performance degradation under high load or resource waste under low load.

[0004] On the other hand, when dealing with concurrent data updates, existing conflict resolution methods mostly focus on physical-level conflict detection, such as timestamp-based write-overwrite rules. This mechanism cannot deeply understand the business semantics carried by the data, which may lead to incorrect conflict resolution, resulting in data inconsistency or loss of business information. Summary of the Invention

[0005] The objective of this application is to provide a method for real-time analysis and synchronization of enterprise data based on intelligent governance, comprising: real-time monitoring of one or more status indicators in a data processing system to obtain real-time status data reflecting the current operating status of the system; based on the real-time status data, generating a prediction result of the system load within a preset future time period through a pre-configured time series prediction model; maintaining a strategy library including at least two data synchronization strategies, wherein the at least two data synchronization strategies have different trade-offs in data processing latency and data consistency guarantee level; dynamically selecting a data synchronization strategy adapted to the prediction result of the system load from the strategy library as a target synchronization strategy according to the prediction result of the system load and a preset business service level agreement; and executing a data synchronization task according to the target synchronization strategy, wherein, when the target synchronization strategy is a preset semantic merging strategy, the method further includes identifying multiple concurrent data update operations targeting the same business entity and accessing predefined business semantic metadata to resolve conflicts of the multiple concurrent data update operations according to the business semantic metadata.

[0006] Optionally, the status indicators include, but are not limited to: the data generation rate of the data source, the message backlog depth of the message queue, the CPU utilization of the data processing node, or the network transmission bandwidth.

[0007] Optionally, the data synchronization strategies in the strategy library include at least: an optimistic synchronization strategy for low-load scenarios that aims to minimize processing latency; and a transactional synchronization strategy for high-conflict-risk scenarios that ensures strong data consistency.

[0008] Optionally, the conflict resolution of the multiple concurrent data update operations based on the business semantic metadata includes: accessing the business semantic metadata to obtain business attributes of different fields, wherein the business attributes define the merging of fields, the idempotency of updates, or the priority of update operations; merging field updates that are determined to be merging and non-conflicting based on the business attributes, and selectively adjudicating field updates that are determined to be conflicting based on the priority of the update operations.

[0009] Optionally, it also includes: based on the predicted system load, proactively sending resource scheduling instructions to the underlying computing resource platform before the predicted load peak arrives, so as to flexibly adjust the number or specifications of computing nodes used to perform the data synchronization task.

[0010] Optionally, it also includes: after executing the data synchronization task, periodically or event-triggeredly initiating a data consistency verification process to verify the consistency status between the target data and the source data through checksum or sampling comparison; when data inconsistency is detected, triggering a data compensation process or data rollback operation according to a preset repair strategy.

[0011] Optionally, the step of dynamically selecting a data synchronization strategy adapted to the predicted system load from the strategy library as the target synchronization strategy based on the predicted system load and the preset service level agreement is intended to dynamically optimize one or more predefined system performance objectives, including end-to-end synchronization latency, data throughput per unit time, or overall system resource utilization efficiency.

[0012] Optionally, it also includes: continuously monitoring the task health status and key performance indicators of the data synchronization task during the execution of the data synchronization task; and, when the execution of the data synchronization task is detected to have failed or the performance indicators deviate from expectations, retrying the task, switching to a backup synchronization link, or migrating the synchronization task to a healthy computing node according to a preset fault tolerance mechanism.

[0013] Optionally, it further includes: using the expected performance threshold corresponding to the target synchronization strategy as a baseline; comparing the measured actual performance index with the expected performance threshold after executing the data synchronization task; and generating and sending alarm information when the actual performance index continuously deviates from the expected performance threshold and exceeds a predetermined range.

[0014] The second objective of this application is to provide a real-time enterprise data analysis and synchronization system based on intelligent governance, comprising: a status monitoring module for real-time monitoring of one or more status indicators in the data processing system to obtain real-time status data reflecting the current operating status of the system; a load prediction engine, which integrates a time series prediction model to generate prediction results of the system load within a preset time period based on the real-time status data and a pre-configured time series prediction model; a strategy library for storing and managing at least two data synchronization strategies with different trade-offs in data processing latency and data consistency guarantee levels; a strategy selector for dynamically selecting a data synchronization strategy adapted to the prediction result of the system load from the strategy library as the target synchronization strategy based on the prediction result generated by the load prediction engine and a preset business service level agreement; and a synchronization executor configured to execute data synchronization tasks according to the target synchronization strategy; the synchronization executor also integrates a conflict resolution unit, which is activated when the target synchronization strategy is a preset semantic merging strategy, for identifying multiple concurrent data update operations targeting the same business entity and accessing predefined business semantic metadata to resolve conflicts between the multiple concurrent data update operations according to the business semantic metadata.

[0015] This application introduces a load prediction mechanism, enabling the data synchronization system to shift from passive response to proactive adaptation. The system can preemptively switch to a high-throughput strategy or expand resources before peak loads arrive, effectively preventing sudden performance drops or service interruptions due to untimely strategy switching. Similarly, when a load trough is predicted, the system can switch to a resource-saving strategy, reducing unnecessary operating costs and achieving a dynamic optimal balance between system performance and resource consumption. By introducing business semantic metadata, it surpasses traditional conflict resolution methods based on physical attributes, allowing the system to understand the business logic of the data and distinguish between business-compatible, mergeable concurrent updates and truly mutually exclusive updates requiring arbitration. This significantly reduces data overwriting and information loss caused by misjudged conflicts, thereby significantly improving the accuracy of synchronized data and the integrity of business logic. By integrating proactive resource scheduling, data consistency verification and repair, and comprehensive fault tolerance mechanisms, the data synchronization process exhibits greater robustness in terms of self-healing capabilities and business continuity when facing anomalies such as underlying resource bottlenecks, data drift, and node failures. By continuously monitoring the performance of synchronization tasks and comparing the actual performance with the expected performance, abnormal deviations in system performance can be detected in a timely manner. Combined with an automatic alarm mechanism, maintenance personnel can quickly locate problems, shorten troubleshooting time, and significantly improve the maintenance efficiency of complex distributed systems. Attached Figure Description

[0016] Figure 1This is a block diagram of the enterprise data real-time analysis and synchronization system based on intelligent governance, as described in this application.

[0017] Figure 2 This is a schematic diagram of the enterprise data real-time analysis and synchronization method based on intelligent governance in this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following will provide a detailed explanation of the technical solutions through an enterprise-level application scenario of order-inventory-logistics data synchronization on a large e-commerce platform. However, those skilled in the art should understand that this embodiment is merely exemplary and not intended to limit the scope of protection of this application. The core ideas of this application are also applicable to various other distributed application scenarios requiring efficient and reliable data synchronization, such as financial transaction data distribution, IoT device status synchronization, and content delivery networks for multinational corporations.

[0021] Reference Figure 1 This diagram illustrates a block diagram of a real-time enterprise data analysis and synchronization system based on intelligent governance, according to an embodiment of this application. The system can be deployed in a cloud server cluster, local data center, or hybrid cloud environment, and includes a status monitoring module, a load prediction engine, a policy library, a policy selector, and a synchronization executor. The synchronization executor further integrates a conflict resolution unit.

[0022] In a typical e-commerce platform environment, a series of business events such as user order placement, payment, merchant shipment, and logistics status updates will generate massive data changes in an instant. These changes must be reliably and in real time synchronized from their source system to multiple downstream systems. The source system includes order management system, payment gateway, etc., while the downstream systems include inventory center, warehouse management system, logistics tracking system, and data analysis platform, etc.

[0023] Reference Figure 2 The diagram illustrates a flowchart of a real-time analysis and synchronization method for enterprise data based on intelligent governance according to an embodiment of this application, which specifically includes the following steps.

[0024] S101: Monitor one or more status indicators in the real-time data processing system to obtain real-time status data reflecting the current operating status of the system.

[0025] Specifically, the status monitoring module is designed as a network of distributed, lightweight data acquisition agents, strategically deployed at key nodes along the data synchronization link. The real-time monitored status indicators are multi-dimensional and multi-layered, aiming to provide a comprehensive picture of the overall system operation status. In this e-commerce embodiment, these status indicators include, but are not limited to: the data generation rate of the data source, the message backlog depth of the message queue, and the CPU utilization or network transmission bandwidth of the data processing nodes.

[0026] Specifically, the data generation rate of a data source directly measures how quickly the source system generates new data or changes existing data. For example, in an order management system, this can be specified as the number of new orders generated per second or the number of successfully paid orders per minute. During major e-commerce promotional events, this rate can instantly surge to tens or even hundreds of times the normal rate. Real-time monitoring of this metric provides the most direct basis for predicting the system's inbound traffic pressure.

[0027] In modern distributed architectures, data synchronization often leverages message queues to decouple systems and smooth traffic flow. The message backlog depth of a message queue precisely measures the number of messages waiting to be processed. For example, if the backlog depth of the order payment success message queue subscribed to by the inventory center shows a continuous upward trend, it clearly indicates that the processing capacity of the inventory deduction service can no longer keep up with the speed of order generation, and a system bottleneck has formed. This is a key indicator of the health status of the task.

[0028] The CPU utilization of a data processing node directly reflects the load on the computing node performing data synchronization tasks. For example, if the CPU utilization of a microservice instance responsible for executing inventory deduction logic remains above 90% for an extended period, it indicates that the node is nearing saturation, and any additional tasks could lead to a sharp increase in processing latency.

[0029] In synchronization scenarios involving cross-data center or cross-regional data transmission, network bandwidth can easily become a performance bottleneck. Network transmission bandwidth is used to monitor the actual network throughput and saturation on the synchronization link. For example, in businesses that need to synchronize domestic order data to overseas warehousing systems, close monitoring of the network transmission bandwidth usage of cross-border dedicated lines is crucial.

[0030] The status monitoring module collects the aforementioned status indicator data from multiple levels, including the operating system layer, middleware layer, and the application itself. The collected status indicator data is aggregated into a structured time-series data stream with precise timestamps and then sent to the load prediction engine.

[0031] S102: Based on the real-time status data, a prediction result of the system load within a preset time period is generated through a pre-configured time series prediction model.

[0032] After obtaining real-time status indicator data, instead of simply making reactive judgments based on the current state, we use complex mathematical models to delve into the hidden patterns in historical data and scientifically extrapolate them to the future.

[0033] The engine integrates one or more pre-configured time series forecasting models. "Pre-configured" means that the model type, hyperparameters, etc., have been thoroughly trained and finely tuned using offline data. These models can be classic statistical models, such as autoregressive integral moving average models that excel at capturing linear trends and seasonal variations in data, or more advanced machine learning or deep learning models, such as Long Short-Term Memory (LSTM) networks. LTM networks, with their unique gating structure, are able to learn more complex nonlinear relationships and long-term dependencies in data. For example, a well-trained LTM network can autonomously learn and summarize valuable and complex patterns from a year's worth of historical data, such as drastically different load patterns between weekdays and weekends, the inevitable deterministic peak loads triggered by major promotional events, and the occurrence of moderate-intensity loads lasting for several hours when a certain type of product is launched.

[0034] In this e-commerce embodiment, the load prediction engine first continuously receives multi-dimensional, high-frequency status indicator data streams from the status monitoring module. It can also perform a series of preprocessing on the raw data, such as smoothing noise, filling missing values, and even constructing more representative composite features, such as using the product of CPU utilization and message backlog depth as a more sensitive indicator of overall system pressure. Then, the engine inputs the processed recent historical data into a pre-trained long short-term memory network and outputs a prediction sequence of key load indicators for a preset time period in the future. For example, the processed data of the past 24 hours can be input into the pre-trained long short-term memory network, and then a prediction sequence of the order generation rate or the message queue backlog trend for the next 30 minutes can be output to quantify the uncertainty of the prediction.

[0035] This step provides an early warning time. For example, before the peak of the Double Eleven promotion on e-commerce platforms at midnight, such as at 11:45 pm, the system can accurately predict, based on historical data models and the recent sharp increase in user activity, that an order surge of 100 times the normal level will occur within an hour after midnight. This completely changes the predicament of existing technologies being able to only passively and chaotically cope with sudden loads.

[0036] S103: Maintain a policy library that includes at least two data synchronization strategies, wherein the at least two data synchronization strategies have different trade-off attributes in terms of data processing latency and data consistency guarantee level.

[0037] The strategy library can be viewed as a predefined strategy toolbox, which contains a variety of synchronization strategies designed for different scenarios and with distinct trade-offs. The strategy library can include several representative strategies: optimistic synchronization strategy, transactional synchronization strategy, and semantic merging strategy.

[0038] Specifically, the optimistic synchronization strategy prioritizes minimizing processing latency and maximizing system throughput, typically performing data synchronization asynchronously and non-transactionally. For example, during off-peak hours, when a user adds an item to their shopping cart, this change can be synchronized to the backend product recommendation and analysis system using this strategy. The synchronization operation is quickly submitted to the message queue, and the source system immediately considers the operation complete, without waiting for final confirmation from the downstream system. This strategy is based on the assumption of an extremely low probability of data conflicts; even if conflicts occur, their business impact is relatively minor and can be resolved through subsequent asynchronous reconciliation processes. Therefore, it is highly suitable for business scenarios with low load and low conflict risk.

[0039] Transactional synchronization strategies, in stark contrast to optimistic strategies, prioritize strong data consistency. They typically employ strict transaction control mechanisms such as two-phase commit, distributed locks, or atomic commit protocols based on consensus algorithms. In this e-commerce implementation, when a user completes payment, the system needs to simultaneously mark the order as paid in the order system and accurately deduct the corresponding inventory from the inventory system. This strategy is essential in this situation. These two operations are tightly wrapped in a distributed transaction to ensure that they either both succeed or both fail, thus completely eliminating serious data inconsistencies such as payment without inventory deduction or inventory deduction without updated order status. While extremely reliable, this strategy has relatively long processing latency and consumes more system resources. It is primarily suitable for core business scenarios with high conflict risk and stringent data consistency requirements.

[0040] Semantic merging is an intelligent strategy that falls between the two mentioned above. It is specifically designed to handle scenarios with high-concurrency updates to the same business entity. Instead of simply using locking and mutual exclusion to avoid conflicts, it attempts to deeply understand the business intent of each update operation and intelligently merge them. Its specific implementation mechanism will be detailed in the subsequent conflict resolution section. This strategy is particularly suitable for scenarios with medium to high loads, frequent concurrent updates, and business logic that is often non-mutually exclusive.

[0041] S104: Based on the predicted system load and the preset service level agreement, dynamically select a data synchronization strategy from the strategy library that is compatible with the predicted system load as the target synchronization strategy.

[0042] The strategy selector bases its decisions on two main inputs: first, the load forecasting results provided by the load forecasting engine, which clearly indicate the future stress level of the system; and second, a predefined SLA (Service Level Agreement). An SLA is a set of explicit business rules that defines the priority order of various performance metrics, such as latency, consistency, and availability, under different business scenarios. For example, an SLA might stipulate that during a major sales event like Singles' Day (November 11th), order creation success rate has the highest priority, and minute-level latency in inventory display is tolerable; however, during routine financial reconciliation, 100% data accuracy has the highest priority, and longer processing times are acceptable.

[0043] The decision-making logic within the strategy selector can be a complex rule engine or an optimization model based on a cost function. The decision-making process can be illustrated through the following scenarios: Scenario 1: The prediction engine reports that the system load is stable and low within the next hour, and the SLA indicates a normal operating period. In this case, the strategy selector will choose an optimistic synchronization strategy to minimize resource consumption and provide the fastest user response. Scenario 2: The prediction engine issues a warning that a surge in orders will occur in five minutes, and the SLA defines a critical promotional period, making preventing system crashes the top priority. In this case, the strategy selector may choose an optimized version of the optimistic synchronization strategy, quickly writing all inventory deduction requests to a high-throughput persistent log, which is then handled asynchronously by dynamically scaling consumers. Simultaneously, it may reduce the priority of non-core data synchronization tasks. Scenario 3: High-frequency concurrent updates to the inventory of a popular product are detected, indicating a high risk of conflict. However, using a transactional synchronization strategy might block users. In this case, a semantic merging strategy will be chosen to attempt to merge the concurrent inventory update requests.

[0044] This series of decisions fully demonstrates the dynamic optimization capability of this application. The purpose of the strategy selector is to dynamically optimize one or more predefined system performance objectives, such as end-to-end synchronization delay, data throughput per unit time, or overall system resource utilization efficiency, under predicted boundary conditions.

[0045] S105: Perform a data synchronization task according to the target synchronization strategy, wherein when the target synchronization strategy is a preset semantic merging strategy, the method further includes identifying multiple concurrent data update operations for the same business entity and accessing predefined business semantic metadata to resolve conflicts between the multiple concurrent data update operations according to the business semantic metadata.

[0046] The synchronizer strictly follows the instructions from the strategy selector, using the selected data synchronization strategy to efficiently move and process data.

[0047] When the selected data synchronization strategy is an optimistic synchronization strategy or a transactional synchronization strategy, the synchronization executor will call the corresponding standard library or framework to perform the operation. For example, for a transactional strategy, database operations may be performed through a connection pool that supports distributed transactions.

[0048] Understandably, in this scenario, the synchronous executor will identify multiple concurrent update operations targeting the same business entity and hand them over to its internally integrated conflict resolution unit for specialized processing.

[0049] Specifically, the conflict resolution unit can obtain processing guidance from predefined business semantic metadata, thus completely surpassing the traditional simple last writer wins logic.

[0050] The business semantic metadata meticulously defines the business attributes and conflict handling rules for each data field. For example, the Order ID field is defined as a primary key and is immutable; any attempt to modify it is considered an illegal operation and should be immediately rejected and an alert issued. The Order Status field follows a predefined state machine flow; in the event of a conflict, the state with the highest priority and conforming to the flow logic is selected as the final result. The Delivery Address field is defined as an overwriteable field, allowing new values ​​to directly replace old values, and arbitration can be performed based on the update timestamp or the priority of the update source. The Customer Remarks field is an appendable field; concurrent updates will intelligently concatenate its contents with a specific delimiter, rather than overwriting it. The Loyalty Points field is defined as a commutative, accumulative field; the system will summarize the incremental values ​​from multiple concurrent update requests and apply them to the current base value to ensure the accuracy of the points. For composite object fields such as invoice information, merging orthogonal updates is supported. For example, if one update modifies the invoice header and another updates modify the tax number, these two operations do not affect each other in terms of business and can be safely merged into a complete invoice information object without losing any information.

[0051] Based on the aforementioned metadata, the specific processing flow of the conflict resolution unit is as follows: First, the unit identifies and aggregates multiple update requests for the same order ID that arrive within a very short time window. Then, it obtains the relevant business semantic metadata for the entity type to which the order ID belongs. Finally, the unit meticulously analyzes the fields involved in all concurrent update requests and applies the rules defined in the metadata. For orthogonal updates, such as modifying the shipping address and adding customer remarks, these operations are merged into a single equivalent database update, avoiding mutual overwriting. For cumulative updates, such as concurrent additions of loyalty point increments from different business activities, these are intelligently merged into a single +50 operation, ensuring no points are lost. For state conflicts, such as when an order status simultaneously receives both user cancellation and payment success notifications from the payment gateway, the unit will selectively adjudicate based on preset state machine priorities, selecting "cancelled" as the final status, while simultaneously recording the payment event for potential subsequent refund processes.

[0052] Through this highly refined processing based on business semantics, this application fundamentally solves the problem of superficial conflict resolution in the background technology. Instead of blindly discarding information, it preserves the business intent of each concurrent update to the greatest extent, thereby achieving higher quality and higher fidelity data synchronization.

[0053] This application can also transform predictive capabilities into forward-looking control over underlying computing resources. When the strategy selector makes a decision based on the prediction results, it can proactively send resource scheduling instructions to the underlying computing resource platform.

[0054] Understandably, the computing resource platform here can be a Kubernetes cluster based on container technology, or an elastic computing service provided by major public cloud vendors. These platforms all utilize their powerful resource scheduling capabilities through APIs (Application Programming Interfaces).

[0055] In e-commerce scenarios, such as at 11:45 PM before midnight on Singles' Day, the load prediction engine accurately predicts that the microservice cluster responsible for inventory deduction will face 200 times the request pressure within 10 minutes after midnight. Upon receiving this high-confidence prediction, the strategy selector quickly determines that the current number of computing nodes is far from sufficient to handle the load (e.g., only 10 service instances). Therefore, it immediately calls the Kubernetes API to issue a command to elastically scale up the number of replicas of the microservice deployment from 10 to 200. The Kubernetes platform then begins creating and starting 190 new service instances. In this way, when the traffic surge arrives at midnight, a cluster of 200 service instances is ready to process the massive inventory deduction requests in parallel, allowing the system to smoothly pass through the peak period and avoiding critical issues such as request timeouts. After the predicted peak period, the system can automatically scale down, releasing excess resources to save operating costs.

[0056] Although the synchronization method in this application strives for completeness, in complex distributed environments, data may still drift and enter an inconsistent state due to unforeseen factors such as network partitions and software defects. To address this, this application introduces a closed-loop verification and repair mechanism to further ensure the eventual consistency of data.

[0057] Specifically, this verification and repair mechanism typically runs as a background task independent of the main synchronization process. Its implementation is flexibly triggered; it can be periodic, such as performing a daily consistency check on all order data from the previous day during low-business periods; or it can be event-triggered, such as immediately performing a special verification on order data generated during a major promotional event after the event ends. Verification methods include efficient verification and comparison of massive amounts of data, as well as precise location of discrepancies in specific records through sampling comparison. Once an inconsistency is detected, the system will automatically execute a preset repair strategy. This repair strategy might be a data compensation process, retrieving and applying the missing data from the source; or it might be a data rollback operation, restoring the erroneous data to a known correct version. For complex situations that cannot be automatically repaired, the system will generate a detailed discrepancy report and trigger an alarm, requiring manual intervention by professionals.

[0058] This closed-loop process of verification and repair ensures that even minor errors that may occur during the synchronization process can be detected and corrected in a timely manner, providing a strong guarantee for the long-term stable operation of the system and the final correctness of the data.

[0059] This application also fully considers various transient or permanent failures that may be encountered during the execution of data synchronization tasks, and incorporates a comprehensive fault tolerance mechanism to significantly improve the robustness of the system.

[0060] This fault-tolerance mechanism continuously monitors the health status and key performance indicators of synchronization tasks, and automatically triggers corresponding processing logic when anomalies are detected. This includes task retry strategies for transient failures such as network jitter, typically employing exponential backoff algorithms. For network link issues, the system can automatically switch to a backup synchronization link to ensure data transmission continuity. If the computing node itself fails, the system's health check mechanism, combined with the underlying resource scheduling platform, such as a Kubernetes cluster, can automatically migrate the task to a healthy node for re-execution; this process is transparent to the business layer. Furthermore, to prevent malformed or logically unprocessable messages from blocking the entire queue, a dead-letter queue mechanism can be used to isolate these error messages for subsequent analysis and manual processing.

[0061] To further improve the observability and manageability of the system, this application introduces a performance monitoring and alarm mechanism based on a dynamic baseline. The working principle is as follows: When the strategy selector selects a target synchronization strategy, it not only issues an execution instruction, but also sets an expected performance threshold for the strategy under the current predicted load conditions as a baseline. The expected performance threshold is dynamic. For example, the expected end-to-end latency of the optimistic synchronization strategy under low load may be set to less than 50 milliseconds, but under high load, this expectation may be reasonably relaxed to less than 200 milliseconds. This dynamic expected performance threshold forms the baseline for performance monitoring. During task execution, the system continuously measures actual performance metrics and compares them in real time with the currently effective baseline. If the actual performance consistently and significantly deviates from this baseline—for example, if the actual latency exceeds 200% of the baseline for five consecutive minutes—even if the actual latency has not yet reached a global, static, hard limit threshold, the system can readily identify this as an anomaly. Once such a significant deviation is detected, the system immediately generates and sends a rich intelligent alarm message. For example, the warning message might be: "Current strategy is optimistic synchronization, expected latency <50ms, but actual average latency of 160ms has persisted for 5 minutes; possible cause: slow response from downstream databases." This precise alarm provides operations and maintenance personnel with a clear direction for troubleshooting, greatly shortening fault location time and helping them discover potential, hidden performance degradation issues in the system early, thus preventing problems before they occur.

[0062] In summary, the enterprise data real-time analysis and synchronization method and system based on intelligent governance disclosed in this application organically combine cutting-edge time series forecasting technology, dynamic strategy selection mechanism based on business service level agreements, and deep conflict resolution mechanism based on business semantic metadata. Furthermore, through a series of enhanced functions such as proactive resource scheduling, closed-loop consistency verification, comprehensive fault tolerance, and intelligent alarms, a complete and powerful technical system is constructed, successfully overcoming the core drawbacks of rigid synchronization strategies and superficial conflict resolution that have long existed in existing technologies.

[0063] Therefore, this application demonstrates a new generation of data synchronization solution that can proactively adapt to fluctuations in business load, intelligently understand and maximize the preservation of data business value, and exhibit extremely high resilience and manageability in complex production environments. It not only significantly improves the efficiency and quality of data synchronization in distributed systems but also provides a solid guarantee for the continuity, accuracy, and reliability of core enterprise businesses during the wave of digital transformation, thus possessing extremely high technical value and broad application prospects.

[0064] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0065] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0066] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0067] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0068] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0069] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for real-time analysis and synchronization of enterprise data based on intelligent governance, characterized in that, include: Real-time monitoring of one or more status indicators in the data processing system to obtain real-time status data reflecting the current operating status of the system; Based on the real-time status data, a pre-configured time series prediction model is used to generate a prediction result of the system load within a preset time period in the future. Maintain a strategy library that includes at least two data synchronization strategies, wherein the at least two data synchronization strategies have different trade-off attributes in terms of data processing latency and data consistency guarantee level; Based on the predicted system load and the preset service level agreement, a data synchronization strategy that is compatible with the predicted system load is dynamically selected from the strategy library as the target synchronization strategy. The method performs a data synchronization task according to the target synchronization strategy. When the target synchronization strategy is a preset semantic merging strategy, the method further includes identifying multiple concurrent data update operations for the same business entity and accessing predefined business semantic metadata to resolve conflicts between the multiple concurrent data update operations based on the business semantic metadata.

2. The method for real-time analysis and synchronization of enterprise data based on intelligent governance according to claim 1, characterized in that, The status indicators include, but are not limited to: the data generation rate of the data source, the message backlog depth of the message queue, the CPU utilization of the data processing node, or the network transmission bandwidth.

3. The method for real-time analysis and synchronization of enterprise data based on intelligent governance according to claim 1, characterized in that, The data synchronization strategies in the strategy library include at least: an optimistic synchronization strategy for low-load scenarios that aims to minimize processing latency; and a transactional synchronization strategy for high-conflict-risk scenarios that ensures strong data consistency.

4. The method for real-time analysis and synchronization of enterprise data based on intelligent governance according to claim 1, characterized in that, The conflict resolution of the multiple concurrent data update operations based on the business semantic metadata includes: Access the business semantic metadata to obtain the business attributes of different fields, wherein the business attributes define the field's mergibility, idempotency of update, or priority of update operation; Based on the business attributes, field updates that are determined to be mergeable and non-conflicting are merged, and field updates that are determined to be conflicting are selectively adjudicated according to the priority of the update operations.

5. The method for real-time analysis and synchronization of enterprise data based on intelligent governance according to claim 1, characterized in that, Also includes: Based on the predicted system load, before the predicted load peak is reached, a resource scheduling instruction is proactively sent to the underlying computing resource platform to flexibly adjust the number or specifications of computing nodes used to perform the data synchronization task.

6. The method for real-time analysis and synchronization of enterprise data based on intelligent governance according to claim 1, characterized in that, Also includes: After executing the data synchronization task, a data consistency verification process is initiated periodically or triggered by events to verify the consistency status between the target data and the source data through checksum or sampling comparison. When data inconsistency is detected, a data compensation process or data rollback operation is triggered according to the preset repair strategy.

7. The method for real-time analysis and synchronization of enterprise data based on intelligent governance according to claim 1, characterized in that, The step of dynamically selecting a data synchronization strategy adapted to the predicted system load from the strategy library as the target synchronization strategy based on the predicted system load and the preset service level agreement is intended to dynamically optimize one or more predefined system performance objectives, including end-to-end synchronization latency, data throughput per unit time, or overall system resource utilization efficiency.

8. The method for real-time analysis and synchronization of enterprise data based on intelligent governance according to claim 1, characterized in that, Also includes: During the execution of the data synchronization task, the task health status and key performance indicators of the data synchronization task are continuously monitored. When the data synchronization task fails or its performance deviates from expectations, the system will, according to a preset fault tolerance mechanism, retry the task, switch to a backup synchronization link, or migrate the synchronization task to a healthy computing node.

9. The method for real-time analysis and synchronization of enterprise data based on intelligent governance according to claim 1, characterized in that, Also includes: The expected performance threshold corresponding to the target synchronization strategy is used as the baseline; After performing the data synchronization task, the measured actual performance metrics are compared with the expected performance threshold. When the actual performance metric continuously deviates from the expected performance threshold and exceeds the predetermined range, an alarm message is generated and sent.

10. A real-time enterprise data analysis and synchronization system based on intelligent governance, characterized in that, include: The status monitoring module is used to monitor one or more status indicators in the data processing system in real time to obtain real-time status data that reflects the current operating status of the system. The load prediction engine integrates a time series prediction model, which is used to generate prediction results of the system load within a preset time period based on the real-time status data and through a pre-configured time series prediction model. A strategy library for storing and managing at least two data synchronization strategies that have different trade-offs in data processing latency and data consistency guarantee levels; The strategy selector is used to dynamically select a data synchronization strategy that is compatible with the prediction results of the system load from the strategy library as the target synchronization strategy, based on the prediction results generated by the load prediction engine and the preset service level agreement. A synchronization executor is configured to perform data synchronization tasks according to the target synchronization strategy; The synchronous executor also integrates a conflict resolution unit, which is activated when the target synchronization strategy is a preset semantic merging strategy. This conflict resolution unit is used to identify multiple concurrent data update operations targeting the same business entity and access predefined business semantic metadata to resolve conflicts between the multiple concurrent data update operations based on the business semantic metadata.

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