An Adaptive Real-Time Database Redundancy Synchronization Method and System Based on OPC UA and Artificial Intelligence

Abstract

This disclosure discloses an adaptive real-time database redundancy synchronization method and system based on OPC UA and artificial intelligence. The method includes: point-level redundancy modeling based on the OPC UA address space; configuring hierarchical distributed data streams; establishing a point-level fault detection and switching mechanism; point-level incremental synchronization and version control; and AI-enhanced monitoring and adaptive optimization. This invention achieves fine-grained allocation of computing and storage resources through point-level redundancy; regional policy inheritance and batch configuration effectively improve configuration efficiency; fault detection and switching can reach millisecond levels, meeting high availability requirements; incremental synchronization and referencing patterns effectively suppress redundant traffic, significantly saving network bandwidth; the synchronization method is built entirely based on the OPC UA standard, facilitating integration with third-party systems and ensuring strong compatibility; early fault warning and preventative maintenance are achieved through machine learning technology, reducing unexpected downtime; and an adaptive optimization mechanism based on reinforcement learning continuously improves system performance.

Description

Technical Field

[0001] This invention relates to the field of control system technology, and in particular to an adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence. Background Technology

[0002] In modern industrial automation control systems, SCADA / DCS systems (SCADA: Data Acquisition and Monitoring Control System; DCS: Distributed Control System) serve as the core of production scheduling and field control, placing stringent requirements on the high availability, data consistency, and real-time switching of real-time data services. Redundancy synchronization mechanisms are key technologies to ensure the continuous and stable operation of the system.

[0003] Traditional real-time database redundancy solutions generally employ coarse-grained architectures such as whole-machine redundancy and whole-database redundancy, resulting in significant waste of computing, storage, and network resources and low overall system resource utilization. During primary / backup device switching, reliance on heartbeat timeouts or service failure detection takes several seconds, failing to meet millisecond-level seamless switching requirements, leading to long switching times and data loss. Regarding data synchronization, the lack of a clear data flow definition mechanism results in data conflicts or malfunctioning control commands. In terms of redundancy strategies, the inability to configure differentiated synchronization strategies for different measurement points makes it difficult to balance real-time performance, bandwidth consumption, and operational permissions. Furthermore, existing redundancy solutions often use proprietary protocols or external middleware, failing to fully utilize the capabilities of the OPC UA (a universal cross-platform, secure, and standardized communication and information modeling protocol for industrial automation) standard, thus deviating from the OPC UA standard.

[0004] Therefore, it is necessary to improve one or more of the problems existing in the above-mentioned related technical solutions.

[0005] It should be noted that this section is intended to provide background or context for the technical solutions of this disclosure as set forth in the claims. The description herein does not constitute an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0006] The purpose of this invention is to provide an adaptive real-time database redundancy synchronization method and system based on OPC UA and artificial intelligence, thereby overcoming, to at least some extent, one or more problems caused by the limitations and defects of related technologies.

[0007] This invention first provides an adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence, comprising: S1. Measure point-level redundancy modeling based on OPC UA address space: Redundancy attributes are extended for OPC UA variable nodes, and independent and identifiable redundancy configuration information is set for the measure points corresponding to each variable node; using OPC UA standard reference types or custom references, the topological connection between primary and backup measure points is explicitly established in the OPC UA address space to form a visual redundancy map; a default redundancy strategy is defined in the region or device group node, and its child nodes automatically inherit it; S2, Configure layered distributed data flow: For the three-layer architecture of center-plant-field, configure mirror, contain, and reference data flow modes according to the measurement point or region, and establish corresponding OPC UA subscription and command replication routing rules; S3 establishes a measurement point-level fault detection and switching mechanism: multi-dimensional health monitoring is performed through heartbeat messages, data quality codes, OPC UA service status, and application layer function probes, and the faults are classified into minor faults, serious faults, and suspected faults; incremental data synchronization between primary and backup devices, alarm status migration, and switching process that notifies all clients of switching information through OPC UA events are executed. S4, Measurement Point-Level Incremental Synchronization and Version Control: Add change markers to measurement points, drive incremental synchronization based on change markers, perform field-level difference comparison for complex data types, and perform object-level, unit-level, and project-level version tracking, comparison, rollback, and online incremental activation for redundant configurations; S5, AI-enhanced monitoring and adaptive optimization: Collects real-time and historical time-series data, preprocesses it, and inputs it into a machine learning model to perform health monitoring, fault prediction, and preventive maintenance triggering. Through reinforcement learning agents, it dynamically adjusts synchronization strategies and redundant configuration parameters to complete adaptive optimization.

[0008] In this invention, in S1, the redundancy attributes include: redundancy mode, primary server identifier, backup server identifier, synchronization strategy, and fault detection parameters.

[0009] In this invention, the processing rules for the three standardized data stream modes in S2 include: Mirror mode: Local nodes are read-only caches, and persistent OPC UA subscriptions are established to synchronize upstream center data in real time; Includes the following mode: Local nodes serve as readable and writable data sources and caches, supporting the issuance of local control commands and synchronizing state with upstream nodes through subscription; Reference mode: A temporary subscription update is triggered when a client makes a request and the cache expires.

[0010] In this invention, the fault classification determination rule in S3 includes: Minor faults trigger retries and alarms; Immediately initiate a switchover for critical faults; Initiate a diagnostic process for suspected faults.

[0011] In this invention, during the switching process in S3, the process of pausing the main test point data update and control, migrating the historical context and control mode, and updating the main / standby identifier in the address space is also executed.

[0012] In this invention, in S5, health monitoring uses an LSTM or Prophet time series analysis model to establish a baseline of normal behavior, monitors data deviation in real time, and outputs fault warnings. Fault prediction uses a Random Forest or XGBoost classification model to output the fault type and impact range, triggering preventive maintenance; Adaptive optimization employs a reinforcement learning agent, using system performance metrics as feedback to dynamically adjust the synchronization cycle, synchronization mode, and redundant node parameters, automatically matching the optimal configuration.

[0013] This invention also provides an adaptive real-time database redundancy synchronization system based on OPC UA and artificial intelligence, used to implement the above-described method, the system comprising: Redundancy Configuration Manager: Used to perform OPC UA address space measurement point-level redundancy modeling, redundancy attribute configuration, zone policy inheritance and batch configuration; Synchronization Engine: Used for performing incremental synchronization, field-level difference comparison, configuration version management, and online incremental download; Status monitor: used to perform multi-dimensional health monitoring, data quality code detection, and fault classification. Switching controller: Used to perform measurement point-level switching, state transition, primary / standby identifier update, and OPC UA event notification; Machine learning engine: used for time series data preprocessing, health baseline modeling, fault prediction inference, and reinforcement learning adaptive optimization; Log auditing module: Used to record synchronization, switching, failure, configuration change and artificial intelligence optimization logs.

[0014] The technical solution provided by this invention may include the following beneficial effects: This invention presents an adaptive real-time database redundancy synchronization method and system based on OPC UA and artificial intelligence. Through point-level redundancy, it achieves fine-grained allocation of computing and storage resources; region-level policy inheritance and batch configuration significantly reduce engineering complexity and effectively improve configuration efficiency; fault detection and switching can be achieved at the millisecond level, meeting high availability requirements; incremental synchronization and referencing mode effectively suppress redundant traffic, significantly saving network bandwidth; the synchronization method is built entirely on the OPC UA standard, facilitating integration with third-party systems and exhibiting strong standard compatibility; machine learning technology enables early fault warning and preventative maintenance, reducing unexpected downtime; and an adaptive optimization mechanism based on reinforcement learning continuously improves system performance. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0016] Figure 1 A flowchart illustrating an exemplary embodiment of the present disclosure of an adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence; Figure 2 This diagram illustrates the overall architecture of the synchronization system in an exemplary embodiment of this disclosure. Figure 3 This diagram illustrates point-level redundancy modeling in the OPC UA address space according to an exemplary embodiment of this disclosure. Figure 4 A comparative diagram illustrating the working principles of three data flow modes (mirroring / inclusion / reference) in exemplary embodiments of this disclosure is shown. Figure 5 This diagram illustrates a measurement-level disturbance-free handover process in an exemplary embodiment of this disclosure. Figure 6 This diagram illustrates the incremental synchronization and version control mechanism in an exemplary embodiment of this disclosure. Figure 7 This diagram illustrates an AI-driven health monitoring and fault prediction process in an exemplary embodiment of this disclosure. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0018] Furthermore, the accompanying drawings are merely illustrative diagrams of embodiments of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0019] This example implementation first provides an adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence. Please refer to [link / reference]. Figure 1 This method may include: S1-S5, as follows: S1. Measure point-level redundancy modeling based on OPC UA address space: Redundancy attributes are extended for OPC UA variable nodes, and independent and identifiable redundancy configuration information is set for the measure points corresponding to each variable node; using OPC UA standard reference types or custom references, the topological connection between primary and backup measure points is explicitly established in the OPC UA address space to form a visual redundancy map; a default redundancy strategy is defined in the region or device group node, and its child nodes automatically inherit it; S2, Configure layered distributed data flow: For the three-layer architecture of center-plant-field, configure mirror, contain, and reference data flow modes according to the measurement point or region, and establish corresponding OPC UA subscription and command replication routing rules; S3 establishes a measurement point-level fault detection and switching mechanism: multi-dimensional health monitoring is performed through heartbeat messages, data quality codes, OPC UA service status, and application layer function probes, and the faults are classified into minor faults, serious faults, and suspected faults; incremental data synchronization between primary and backup devices, alarm status migration, and switching process that notifies all clients of switching information through OPC UA events are executed. S4, Measurement Point-Level Incremental Synchronization and Version Control: Add change markers to measurement points, drive incremental synchronization based on change markers, perform field-level difference comparison for complex data types, and perform object-level, unit-level, and project-level version tracking, comparison, rollback, and online incremental activation for redundant configurations; S5, AI-enhanced monitoring and adaptive optimization: Collects real-time and historical time-series data, preprocesses it, and inputs it into a machine learning model to perform health monitoring, fault prediction, and preventive maintenance triggering. Through reinforcement learning agents, it dynamically adjusts synchronization strategies and redundant configuration parameters to complete adaptive optimization.

[0020] In this embodiment, point-level redundancy enables fine-grained allocation of computing and storage resources; region-level policy inheritance and batch configuration significantly reduce engineering complexity and effectively improve configuration efficiency; fault detection and switching can be achieved at the millisecond level, meeting high availability requirements; incremental synchronization and reference mode effectively suppress redundant traffic and significantly save network bandwidth; the synchronization method is built entirely based on the OPC UA standard, facilitating integration with third-party systems and exhibiting strong standard compatibility; early fault warning and preventative maintenance are achieved through machine learning technology, reducing unexpected downtime; and an adaptive optimization mechanism based on reinforcement learning continuously improves system performance.

[0021] This application presents a redundancy synchronization method that integrates "measurement point-level modeling, hierarchical data flow configuration, incremental synchronization, seamless switching, and AI enhancement." Using the OPC UA address space as a unified carrier, redundancy relationships are endogenized in node attributes and reference structures. Simultaneously, machine learning technology is used to achieve intelligent monitoring, fault prediction, and adaptive optimization, representing a paradigm shift from "server redundancy" to "measurement point redundancy."

[0022] Please refer to this application. Figure 2 , Figure 2 The demonstration showcases a three-tiered deployment structure comprising a demonstration center, plant stations, and field equipment, along with the redundancy and synchronization relationships and data flow directions between OPC UA servers at each level. Equipment data (speed, temperature, pressure, etc.) is collected directly at the field sites. This data is then sent to the plant stations, which in turn transmit it to the center. The center then issues scheduling instructions to the plant stations, which in turn send these instructions to the field sites. One center can correspond to multiple plant stations, and each plant station can correspond to multiple field sites. Each field site is configured with primary and backup measurement points (or primary and backup equipment) for data collection.

[0023] The specific process of each step in the above embodiments will be described below.

[0024] S1, a point-level redundancy modeling mechanism based on the OPC UA address space. Please refer to... Figure 3 , Figure 3 This demonstrates how a device node and its subordinate measurement points (such as temperature and pressure) are organized through HasComponent references and connected to its replicas on the backup server through custom HasRedundantCopy references, while also annotating the redundancy attributes of each node.

[0025] When extending redundant attributes of an OPC UA variable node, custom attribute fields are extended within the OPC UA variable node, including: RedundancyMode (Redundancy mode: Mirror / Include / Reference); PrimaryServerID / BackupServerID (Primary Server Identifier, Backup Server Identifier); SyncPolicy (Synchronization strategy: Realtime / Periodic / OnDemand); DataSource (identifies the actual source of the data); HeartbeatInterval / TimeoutThreshold (heartbeat interval, timeout threshold).

[0026] Each measurement point is assigned independent and identifiable redundancy configuration information, enabling the system to perform differentiated redundancy management based on measurement points, replacing the traditional coarse-grained redundancy at the whole machine / whole warehouse level.

[0027] When modeling redundancy, the following approach is used to make reference relationships explicit: By utilizing standard OPC UA reference types (such as HasComponent, Organizes) or custom references (such as HasRedundantCopy), the topological connections between primary and backup test points are explicitly established in the address space, forming a visual redundancy map. The structured and visualized primary-backup correspondence is embedded into the standard address space, enabling the redundant topology to be automatically resolved by the system, avoiding cross-domain data chaos and instruction errors.

[0028] The design incorporates a regional policy inheritance scheme: It supports defining default redundancy policies on area or device group nodes, with child nodes automatically inheriting these policies. This supports partial overriding, enabling "one-time configuration, batch application." This significantly reduces engineering configuration workload while retaining flexible single-point adjustment capabilities, improving system maintainability.

[0029] S2 is a data flow configuration mechanism under a layered distributed architecture.

[0030] Please refer to Figure 4 For a typical three-tier architecture of "center-plant-field", three standardized data flow patterns are defined: Mirror mode: Local nodes are only for caching and are read-only; the system automatically establishes a persistent OPC UA subscription to synchronize central data in real time.

[0031] Include mode: The local node is both a data source and a cache, and can be read and written; it supports the issuance of local control commands and synchronizes the state with the upstream through subscription.

[0032] Reference mode: Temporary subscription updates are triggered only when the client requests the service and the cache expires. It is suitable for low-frequency access scenarios and significantly reduces network load.

[0033] The hierarchical distributed data flow configuration matches the field-plant-center hierarchy, allowing data to flow reasonably according to roles; it distinguishes between read-only cache, local controllability, and on-demand access to avoid privilege escalation and conflicts; it adopts three standardized data flow modes and subscribes on demand, significantly saving bandwidth.

[0034] It supports batch setting of data flow modes by region, avoiding point-by-point configuration and improving engineering efficiency; at the same time, it supports the "command repeat" mechanism to ensure that cross-domain control commands are correctly routed.

[0035] S3, a measurement point-level fault detection and seamless switching mechanism.

[0036] Please refer to Figure 5 This application performs health monitoring of the system from multiple dimensions, such as using heartbeat messages (between primary and backup), data quality code anomaly detection, OPC UA service status monitoring, and application layer function probes (such as periodically reading key values) to perform multi-dimensional health monitoring.

[0037] After a fault is detected, it is classified and determined: Minor fault: Retry + Alarm; Critical fault: Immediately switch between primary and backup equipment; Suspected fault: Initiate diagnostic process.

[0038] When a switch is required, execute the following hassle-free switchover process: (1) Suspend the data update and control of the main measurement points; (2) Perform the final incremental synchronization to ensure that the data of the backup test points are consistent; (3) Save and migrate alarm status, historical context, and control mode; (4) Update the primary / backup identifier in the address space; (5) Notify all clients via OPC UA event; (6) Restore the complete operating status at the test point.

[0039] This application employs multi-dimensional detection at the measurement point level to refine the detection granularity; it adopts hierarchical fault determination to quickly determine whether to switch between primary and backup measurement points, avoiding problems caused by fault downtime and heartbeat timeout switching; it migrates alarms, historical contexts, and control modes to ensure uninterrupted service; it achieves millisecond-level worry-free switching; and it proactively notifies all clients through OPC UA events.

[0040] S4, please refer to Figure 6 This application adopts an incremental synchronization and version control mechanism, including the following specific contents: Change tag-driven synchronization: Add a VersionStamp or ChangeSequenceNumber to each measurement point to synchronize only the changed items.

[0041] Difference synchronization algorithm: Supports field-level difference comparison for complex types such as structures and arrays, minimizing the amount of data transmitted.

[0042] Configuration version management: Redundant configurations are incorporated into the project version control system, supporting object-level, unit-level, and project-level configuration change tracking, comparison, and rollback.

[0043] Online incremental download: Supports modifying redundancy policies at runtime and applying them incrementally without restarting the system, meeting debugging and maintenance needs.

[0044] By using incremental synchronization and version control mechanisms, only changed data is synchronized, abandoning full synchronization, which significantly reduces network transmission load and improves synchronization efficiency; relying on version stamps and change sequence numbers to mark the data modification time sequence, it ensures that the data time sequence of the primary and backup test points is consistent and the content is unified; it accurately locates data differences, reduces invalid data interaction, and saves system storage and computing resources; it provides complete data version basis for fault switching, avoiding data loss and data disorder during switching; it supports historical change tracing, which facilitates anomaly investigation and improves system maintainability and operational stability.

[0045] S5, please refer to Figure 7 This application employs an AI-enhanced intelligent monitoring and fault prediction method, specifically including: (1) Health monitoring based on machine learning: Use time series analysis (such as LSTM, Prophet) to train on historical data to establish a baseline for normal behavior; Real-time monitoring of the deviation between current data and baseline provides early warning of potential faults.

[0046] (2) Fault prediction and preventive maintenance: Use classification algorithms (such as Random Forest and XGBoost) to predict potential future failures and their impact. Based on the forecast results, preventative maintenance measures are implemented in advance to reduce unplanned downtime.

[0047] (3) Intelligent optimization and self-adjustment: A reinforcement learning agent is introduced to dynamically adjust parameters such as synchronization strategy and redundancy configuration based on the actual running effect; Automatically identify the optimal configuration combination to improve overall system performance.

[0048] This application employs AI-enhanced intelligent monitoring and fault prediction technology to monitor node status, data quality, and synchronization links in real time, enabling proactive identification of potential problems and moving away from traditional passive fault response. Through algorithmic analysis of operational patterns, it predicts risks such as link anomalies, synchronization failures, and node offline events in advance, providing early warning of faults. Based on load, latency, and the operational status of measurement points, it adaptively optimizes redundancy strategies and synchronization parameters to improve system adaptability. This reduces the frequency of sudden failures, lowers the cost of manual inspections and maintenance interventions, and enhances the level of automated operation and maintenance. It also ensures the continuous and stable operation of SCADA / DCS, strengthening the overall system reliability and redundancy tolerance.

[0049] This embodiment integrates AI technology into the original redundancy synchronization framework, which not only solves the inherent problems of the traditional static redundancy mechanism, but also creates a new generation of "intelligent, adaptive, and predictable" industrial automation redundancy architecture, with significant technological advancement and industrial application value.

[0050] In the above embodiments, no specific restrictions are placed on the machine learning model. For example, deep neural networks can be used instead of LSTM for health monitoring. The fault prediction software can be deployed on the edge (local server) or in the cloud, and the choice can be made flexibly according to network conditions. Configuration optimization can be based on rule template matching rather than deep learning, which is suitable for small-scale systems.

[0051] This application also provides an adaptive real-time database redundancy synchronization system based on OPC UA and artificial intelligence, used to implement the synchronization method described in any of the above embodiments. The system includes: Redundancy Configuration Manager: Used to perform OPC UA address space measurement point-level redundancy modeling, redundancy attribute configuration, zone policy inheritance and batch configuration; Synchronization Engine: Used for performing incremental synchronization, field-level difference comparison, configuration version management, and online incremental download; Status monitor: used to perform multi-dimensional health monitoring, data quality code detection, and fault classification. Switching controller: Used to perform measurement point-level switching, state transition, primary / standby identifier update, and OPC UA event notification; Machine learning engine: used for time series data preprocessing, health baseline modeling, fault prediction inference, and reinforcement learning adaptive optimization; Log auditing module: Used to record synchronization, switching, failure, configuration change and artificial intelligence optimization logs.

[0052] Regarding the system in the above embodiments, the specific manner in which each component performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0053] Experiments were conducted using the synchronization method proposed in this application. The results show that, compared with traditional real-time database redundancy schemes, this application improves resource utilization by more than 30%, reduces switching time by 90%, improves configuration efficiency by 70%, and saves network bandwidth by more than 50%.

[0054] It should be noted that although several modules of the system for executing actions are mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided into multiple modules for embodiment. Components shown as modules 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 modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0055] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. An adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence, characterized in that, include: S1. Measure point-level redundancy modeling based on OPC UA address space: Redundancy attributes are extended for OPC UA variable nodes, and independent and identifiable redundancy configuration information is set for the measure points corresponding to each variable node; using OPC UA standard reference types or custom references, the topological connection between primary and backup measure points is explicitly established in the OPC UA address space to form a visual redundancy map; a default redundancy strategy is defined in the region or device group node, and its child nodes automatically inherit it; S2, Configure layered distributed data flow: For the three-layer architecture of center-plant-field, configure mirror, contain, and reference data flow modes according to the measurement point or region, and establish corresponding OPC UA subscription and command replication routing rules; S3 establishes a measurement point-level fault detection and switching mechanism: multi-dimensional health monitoring is performed through heartbeat messages, data quality codes, OPC UA service status, and application layer function probes, and the faults are classified into minor faults, serious faults, and suspected faults; incremental data synchronization between primary and backup devices, alarm status migration, and switching process that notifies all clients of switching information through OPC UA events are executed. S4, Measurement Point-Level Incremental Synchronization and Version Control: Add change markers to measurement points, drive incremental synchronization based on change markers, perform field-level difference comparison for complex data types, and perform object-level, unit-level, and project-level version tracking, comparison, rollback, and online incremental activation for redundant configurations; S5, AI-enhanced monitoring and adaptive optimization: Collects real-time and historical time-series data, preprocesses it, and inputs it into a machine learning model to perform health monitoring, fault prediction, and preventive maintenance triggering. Through reinforcement learning agents, it dynamically adjusts synchronization strategies and redundant configuration parameters to complete adaptive optimization.

2. The adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence according to claim 1, characterized in that, In S1, the redundancy attributes include: redundancy mode, primary server identifier, backup server identifier, synchronization strategy, and fault detection parameters.

3. The adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence according to claim 1, characterized in that, In S2, the processing rules for the three standardized data flow patterns include: Mirror mode: Local nodes are read-only caches, and persistent OPC UA subscriptions are established to synchronize upstream center data in real time; Includes the following mode: Local nodes serve as readable and writable data sources and caches, supporting the issuance of local control commands and synchronizing state with upstream nodes through subscription; Reference mode: A temporary subscription update is triggered when a client makes a request and the cache expires.

4. The adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence according to claim 1, characterized in that, In S3, the fault classification rules include: Minor faults trigger retries and alarms; Immediately initiate a switchover for critical faults; Initiate a diagnostic process for suspected faults.

5. The adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence according to claim 1, characterized in that, In S3, during the switching process, the process of pausing the main test point data update and control, migrating the historical context and control mode, and updating the main / standby identifier in the address space is also executed.

6. The adaptive real-time database redundancy synchronization method based on OPC UA and artificial intelligence according to claim 1, characterized in that, In S5, health monitoring uses LSTM or Prophet time series analysis models to establish a baseline of normal behavior, monitors data deviation in real time, and outputs fault warnings. Fault prediction uses a Random Forest or XGBoost classification model to output the fault type and impact range, triggering preventive maintenance; Adaptive optimization employs a reinforcement learning agent, using system performance metrics as feedback to dynamically adjust the synchronization cycle, synchronization mode, and redundant node parameters, automatically matching the optimal configuration.

7. An adaptive real-time database redundancy synchronization system based on OPC UA and artificial intelligence, used to implement the method described in any one of claims 1 to 6, characterized in that, The system includes: Redundancy Configuration Manager: Used to perform OPC UA address space measurement point-level redundancy modeling, redundancy attribute configuration, zone policy inheritance and batch configuration; Synchronization Engine: Used for performing incremental synchronization, field-level difference comparison, configuration version management, and online incremental download; Status monitor: used to perform multi-dimensional health monitoring, data quality code detection, and fault classification. Switching controller: Used to perform measurement point-level switching, state transition, primary / standby identifier update, and OPC UA event notification; Machine learning engine: used for time series data preprocessing, health baseline modeling, fault prediction inference, and reinforcement learning adaptive optimization; Log auditing module: Used to record synchronization, switching, failure, configuration change and artificial intelligence optimization logs.

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