A new energy station mobile inspection operation and maintenance management system and data linkage method

Abstract

This invention discloses a mobile inspection and maintenance management system and data linkage method for new energy power plants, specifically relating to the field of power plant operation and maintenance technology. The system includes a mobile inspection terminal and a power plant edge server. The mobile inspection terminal supports offline inspection and has a built-in local rule parser and instruction cache queue. The power plant edge server includes a multi-source data aggregation module, a linkage rule engine module, and an event distribution module. The linkage rule engine module dynamically optimizes rule parameters based on a machine learning model. The method collects and standardizes multi-source events, uses the rule engine for matching and decision-making, generates and distributes linkage instructions, and achieves closed-loop verification. This invention realizes intelligent fusion of multi-source data and automatic business linkage, constructing a resilient operation and maintenance system integrating online and offline processes, significantly improving operation and maintenance response speed, reliability, and intelligence level.

Description

Technical Field

[0001] This invention relates to the field of power plant operation and maintenance management technology, and more specifically, to a mobile inspection and maintenance management system and data linkage method for new energy power plants. Background Technology

[0002] With the rapid development of new energy industries such as wind power and photovoltaics, the operation and maintenance of their power stations face unique challenges such as wide equipment distribution, complex environment, and high safety requirements. Traditional operation and maintenance models mainly rely on manual periodic inspections and paper records, which have problems such as low efficiency, data lag, and difficulty in traceability.

[0003] To improve efficiency, existing technologies include electronic inspection systems based on mobile terminals, which enable information entry and query by scanning equipment QR codes. Meanwhile, centralized monitoring platforms, such as data acquisition and monitoring control systems, can remotely monitor equipment operating parameters. In addition, technologies such as video surveillance and drone inspections are gradually being applied to site safety and equipment inspection.

[0004] However, the above-mentioned technical solutions are mostly isolated and have significant shortcomings: the mobile inspection system has a single function, focusing on recording and reporting, and cannot be intelligently linked with real-time monitoring data and safety alarms; the centralized monitoring platform operates independently, and its alarm information is difficult to automatically trigger and close the loop of specific on-site inspection or maintenance processes; the data standards of each subsystem are inconsistent and the business is fragmented, which makes the process from problem discovery, analysis and judgment to on-site handling heavily dependent on manual coordination and experience, resulting in slow response. Moreover, in remote site areas with poor network signals, mobile inspection operations are prone to interruption and data cannot be synchronized.

[0005] Therefore, how to break down the data and business barriers between various operation and maintenance subsystems of new energy power stations, realize the intelligent integration and automatic linkage of multi-source information, and ensure business continuity under unstable network conditions has become an urgent technical problem to be solved in order to improve the operation and maintenance efficiency and safety level of new energy power stations. In view of this, the present invention provides a mobile inspection and maintenance management system and data linkage method for new energy power stations. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a mobile inspection and maintenance management system and data linkage method for new energy power stations to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a mobile inspection and maintenance management system for new energy power stations, comprising a mobile inspection terminal and a power station edge server; The mobile inspection terminal is used to perform inspection tasks on site, collect data, and support offline operation; The site edge server is deployed locally at the new energy power station to realize real-time aggregation of multi-source data, local analysis and business linkage triggering; The site edge server includes: The multi-source data aggregation module is used to access and integrate in real time equipment operation status data from the equipment monitoring system, security identification event data from the video surveillance system, image analysis data from the drone inspection system, and on-site operation data from the mobile inspection terminal. The linkage rule engine module has several pre-set linkage rules, which are used to perform real-time matching on the event stream after the multi-source data aggregation module is integrated. When a specific rule is matched, the corresponding linkage instruction is generated. The linkage rule engine module also includes a rule optimization unit, which is used to construct an optimization sample set based on the historical execution success rate of linkage instructions, closed-loop verification time and feedback data, and to dynamically optimize the rule parameters, triggering conditions or priorities in the linkage rule base using a gradient boosting decision tree model. The gradient boosting decision tree model takes event type, device type, time period and initial parameter threshold as input features, and closed-loop verification time as prediction target. The model is trained periodically, and the parameter thresholds or task priority weights in the rules are automatically fine-tuned according to the feature importance of the model output. The event distribution module is used to distribute linkage instructions to the corresponding target endpoints, including mobile inspection terminals, production management systems, or on-site audible and visual alarms.

[0008] Preferably, the mobile inspection terminal further includes a local rule parser and an instruction cache queue; The local rule parser is used to provide prompts or generate temporary tasks for the current inspection operation when the network is offline, based on the pre-loaded simplified rule base and the locally cached key device status. The simplified rule base is stored in the local memory of the mobile inspection terminal and contains a set of frequently triggered IF-THEN rules. The rule conditions only involve the device ID, historical alarm type and current inspection item. The actions are limited to pop-up prompts or the creation of low-priority temporary tasks. The simplified rule base is automatically generated by the site edge server periodically analyzing the linkage rule engine logs and using the FP-Growth frequent itemset mining algorithm to extract high-frequency condition item combinations. After compression, it is pushed to the mobile inspection terminal for updates. The instruction cache queue is used to temporarily store data to be reported generated by inspection and linkage instructions pushed by the event distribution module when the network is offline. Synchronization is performed after the network is restored. If there is a conflict between the locally stored data and the server record during synchronization, the record with the latest timestamp on the server shall prevail, and a conflict log shall be generated for auditing.

[0009] Preferably, the linkage rules in the linkage rule engine module include at least monitoring-inspection linkage rules; The monitoring-inspection linkage rule is configured such that when the equipment operation status data indicates that a certain operating parameter of a specific device is abnormal, a special re-inspection task for that specific device is automatically generated and pushed to the mobile inspection terminal responsible for that device through the event distribution module.

[0010] Preferably, the linkage rules in the linkage rule engine module also include security-process linkage rules; The safety-process linkage rule is configured to automatically trigger audible and visual alarms, generate safety violation records, and send lockout commands to relevant devices or warning information to the management terminal through the event distribution module, depending on the severity of the violation, when safety identification event data identifies unsafe behavior or violation of regulations by personnel.

[0011] Preferably, the system also includes a group cloud platform, which includes a global policy management module for defining and maintaining global linkage policies applicable to all or some of the subordinate sites, and distributing the global linkage policies to the linkage rule engine module in the corresponding site edge server of each site; The global linkage strategy is distributed via HTTPS protocol in JSON format. The strategy data includes strategy ID, version number, list of applicable sites, and rule content. After receiving the policy, the site edge server verifies the version number. If it is higher than the local version, it replaces the old policy and returns a confirmation message to the group cloud platform.

[0012] Preferably, the global linkage strategy includes a technical supervision project association strategy, which is used to associate the technical supervision projects stipulated by the group with specific inspection items, maintenance task templates or equipment monitoring parameter thresholds in the site edge server, and automatically complete the filling and uploading of technical supervision-related data when the site edge server performs linkage.

[0013] Preferably, the system also includes a data association module, which is integrated into the site edge server or the group cloud platform. The data association module is used to attach equipment identifier, timestamp, geographic information and event type label to each event, and establish a relationship map between inspection records, equipment defects, maintenance work orders, monitoring alarms and safety events with the equipment identifier as the core. The data association module is implemented using a graph database, with the device's unique identifier as the node's primary key. When a new event arrives, its device ID, timestamp, and geographical coordinates are extracted. If the time difference between the event and the existing node event is less than a preset time window and the geographical distance is less than a preset range, a new event child node is created under that device node, and a connection is established with the relevant defect, work order, or alarm node through a relation edge based on spatiotemporal proximity. Based on the relationship graph, it provides traceability and visualization analysis of equipment operation and maintenance data throughout the entire life cycle.

[0014] This invention also provides a data linkage method, applied to the aforementioned mobile inspection and maintenance management system for new energy power plants, comprising the following steps: S1. Event Collection and Aggregation: Continuously collect inspection events from mobile inspection terminals, status events from equipment monitoring systems, risk events from security monitoring systems, and inspection events from drone systems, and aggregate them to the site edge server; S2. Event Standardization and Correlation: Standardize and encapsulate the aggregated heterogeneous events, extract key elements, and associate them with specific physical devices and responsible entities through device identification and spatiotemporal information; S3. Rule matching and linkage decision: Input the standardized event stream into the linkage rule engine and match it with the preset linkage rule library. If the match is successful, the corresponding linkage instruction will be generated. S3a, Rule Optimization: Based on the historical execution success rate of linkage instructions, closed-loop verification time and feedback data, an optimization sample set is constructed. A gradient boosting decision tree model is adopted, with closed-loop verification time as the prediction target and event type, device type, time period and initial parameter threshold as input features. The model is trained periodically, and the parameter threshold or task priority weight in the rules is automatically fine-tuned according to the feature importance and prediction results of the model output, so as to achieve dynamic optimization of the rule base. S4. Command Distribution and Execution: Through the event distribution module, the linkage command is distributed to one or more target endpoints for execution in real time and in a targeted manner; S5. Closed-loop feedback and verification: Receive instruction execution feedback from the target endpoint and verify whether the business process corresponding to the initial triggering event has formed a closed loop.

[0015] Preferably, in step S3, the rule configuration in the linkage rule base supports IF-THEN logic, and the behavior of the THEN part includes one or more combinations of generating a new task, creating a defect work order, sending an early warning notification, pushing knowledge base entries, triggering equipment locking, or reporting supervision data.

[0016] Preferably, the method further includes an offline collaboration step: When the mobile inspection terminal is offline, it provides real-time prompts to the inspection personnel based on the locally cached key device status and the pre-loaded simplified rule base. The simplified rule base contains a set of frequently triggered IF-THEN rules, which are automatically generated and pushed for updates by the site edge server based on recent frequent linkage rules. Data generated and instructions received during the inspection process are temporarily stored in a local instruction cache queue. Once the network is restored, the data is automatically synchronized and instructions are exchanged with the site edge server, triggering subsequent cloud-based linkage processes to ensure the continuity of business processes.

[0017] The technical effects and advantages of this invention are as follows: 1. This invention realizes intelligent fusion and proactive linkage of multi-source data, fundamentally solving the problem of delayed operation and maintenance response of new energy power stations. By deploying an edge server with integrated rule engine locally at the power station, the system can collect and analyze heterogeneous data such as SCADA, video surveillance, drones and mobile inspections in real time. Once a preset abnormal or risk pattern is identified, linkage instructions can be automatically generated and dispatched, transforming the traditional serial process that relies on manual coordination into a system-driven parallel process, realizing a leap from passive response to proactive early warning and handling. 2. This invention constructs an online-offline integrated resilient operation and maintenance system, ensuring the continuity and reliability of business execution in complex network environments. By designing a local rule parsing and instruction caching mechanism on the mobile terminal, the inspection operation can still be intelligently guided and fully executed when the network is interrupted. Once the network is restored, the data and instructions can be automatically synchronized and seamlessly integrated into the background linkage closed loop, effectively overcoming the inherent challenge of uneven network coverage in new energy power stations and ensuring that the entire process of business is uninterrupted and without omission. 3. This invention introduces a data-driven rule self-optimization mechanism, enabling the system to continuously evolve and learn. By analyzing feedback data such as the execution success rate and closed-loop duration of historical linkage instructions, the system dynamically optimizes rule parameters, triggering conditions, and task priorities using machine learning or statistical methods. This not only improves the accuracy of existing rules but also helps to discover potential optimization strategies, promoting the continuous evolution of operation and maintenance management from static automation to dynamic intelligence, and providing core support for improving the safety and economy of long-term site operation. Attached Figure Description

[0018] Figure 1 This is a system module diagram of the present invention.

[0019] Figure 2 This is a flowchart of the method of the present invention.

[0020] The attached diagram is labeled as follows: 100, Mobile Inspection Terminal; 101, Local Rule Parser; 102, Instruction Cache Queue; 200, Site Edge Server; 201, Multi-Source Data Aggregation Module; 202, Linkage Rule Engine Module; 203, Event Distribution Module; 3, Group Cloud Platform; 301, Global Policy Management Module; 400, Data Association Module. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0022] Example 1 This application provides a mobile inspection and maintenance management system for new energy power plants, which aims to solve the problems of low maintenance efficiency, delayed response and difficulty in safety management caused by the dispersed equipment, isolated subsystems and poor network conditions of wind power, photovoltaic and other new energy power plants.

[0023] As attached Figure 1 The diagram shown is an architectural schematic of one embodiment of the system in this application. The system mainly includes a mobile inspection terminal 100, a site edge server 200, and an optional group cloud platform 3. The site edge server 200 is deployed locally at the new energy site and is connected to the mobile inspection terminal 100 via a wireless network and to the group cloud platform 3 via a wide area network. Specifically: The mobile inspection terminal 100 is a smart handheld device with a dedicated application installed. It is the core operating tool for on-site maintenance personnel. Its configuration is used to: receive and execute inspection, re-inspection or maintenance tasks issued by the site edge server 200; obtain equipment information by scanning the QR code attached to the device; and perform item-by-item inspection and data collection according to the standardized inspection items displayed by the application. Data collection methods include text recording, on-site photography and audio recording. Specifically, to adapt to the poor network signal conditions in some areas of the station, the mobile inspection terminal 100 also includes an offline support unit. This unit enables the terminal to still perform core operation processes when the network is offline. The offline support unit specifically includes: The local rule parser 101 is used to provide context-aware prompts for the current inspection operation in the offline state, based on the pre-loaded simplified rule base and locally cached key device status information, or to automatically generate temporary tasks according to preset conditions. The simplified rule base is stored in the local memory of the mobile inspection terminal 100 and contains a set of frequently triggered IF-THEN rules (e.g., no more than 50 rules). The rule conditions only involve device ID, historical alarm type and current inspection item. The actions are limited to pop-up prompts or the creation of low-priority temporary tasks. The simplified rule base is automatically generated and pushed to be updated by the site edge server 200 each time the network connection with the mobile inspection terminal 100 is restored, based on the high-frequency linkage rules of the past 30 days. As a preferred embodiment, the site edge server 200 analyzes the logs of the linkage rule engine regularly (e.g., weekly), extracts the frequently occurring conditional item combinations (such as {device ID = "FAN05", alarm type = "vibration overlimit"}) using the FP-Growth frequent item set mining algorithm, automatically generates no more than 50 IF-THEN rules from them, and after compression encoding, pushes them to the mobile patrol terminal 100 to reduce the storage and parsing overhead of the terminal.

[0024] When the mobile patrol terminal 100 establishes a network connection with the site edge server 200 each time, it first sends the version number of the local simplified rule library to the server. The server compares the version numbers. If the local version is lower than the latest version of the server, it automatically pushes the updated rule library. After the terminal receives it, it replaces the old rule library and records the update log. If the network connection resumes after a short interruption and the version number remains unchanged, the push is not repeated to reduce communication overhead.

[0025] The instruction cache queue 102 is used to temporarily store two types of data in the offline state: one is the data to be reported generated by the patrol; the other is the linkage instructions pushed by the site edge server 200 that may be received through intermittent networks. When the network resumes, the instruction cache queue 102 automatically performs a synchronization operation to achieve seamless connection and continuous execution of the operation process during network interruption and recovery.

[0026] In addition, when conflicts are found between the locally cached data and the server-side records during the synchronization process (such as duplicate patrol records of the same device at the same time point), the system takes the record with the latest server timestamp as the standard and generates a conflict log for subsequent auditing to ensure data consistency and traceability.

[0027] The site edge server 200, as the intelligent processing center of the system, is deployed locally at the site and is responsible for real-time aggregation, analysis, and linkage triggering of multi-source data. It mainly includes the following modules: The multi-source data aggregation module 201 is used to access and fuse heterogeneous data streams from different subsystems in real time through standard interface protocols. The specific data sources accessed include: Device operation status data from the device monitoring system (such as the SCADA system), for example, fan power, speed, temperatures of various parts, and vibration values; Security recognition event data from the intelligent video monitoring system, which are structured alarm events, such as "person enters the designated area without wearing a safety helmet"; Image analysis data from the UAV automatic patrol system, for example, the defect report of "cracks on the surface of the fan blade" output by the artificial intelligence image recognition algorithm; On-site operation data from the mobile inspection terminal 100, such as QR code records, inspection results, and multimedia attachments; This module converts the above data into a unified internal standard event format, forming a continuous event stream.

[0028] The linkage rule engine module 202 has a built-in configurable linkage rule library. The rules are described using "IF-THEN" logic and define the response actions that should be triggered by a specific event or combination of events. Exemplary rules include: Monitoring-inspection linkage rule: Configured as follows: IF SCADA reports that a certain operating parameter of a specific device is abnormal (e.g., the oil temperature of the fan gearbox is >85℃ for 5 minutes) AND the recent normal inspection status of the device is normal THEN generate instruction: "Create a high-priority special re-inspection task for this specific device and push it to the responsible mobile inspection terminal 100". Safety-process linkage rule: Configured as follows: IF When the event "Video analysis system identifies unsafe behavior or violation of regulations by personnel (such as operation without a ticket)" is received, THEN generate the instruction: "Immediately trigger on-site audible and visual alarms and send warning information and safety violation records to relevant management personnel".

[0029] The linkage rule engine module 202 also includes a rule optimization unit for dynamic rule optimization. This unit constructs an optimization sample set based on the historical execution success rate of linkage instructions, closed-loop verification time, and feedback data. It adopts a regression model, such as gradient boosting decision tree, with "closed-loop verification time" as the prediction target and event type, equipment type, time period, and initial parameter threshold as input features. The model is trained periodically, and the parameter thresholds in the rules are automatically fine-tuned (e.g., adjusting the oil temperature threshold from 85℃ to 83℃) or task priority weights according to the feature importance and prediction results of the model output, thereby achieving dynamic optimization of the rule base.

[0030] Specifically, as a preferred embodiment, the rule optimization unit adopts an XGBoost regression model, using event type code, device type code, time period (hour), and original parameter threshold as input features, and training with the historical closed-loop processing time of the linkage rule as a label. The importance of features is analyzed through SHAP values. If it is found that the "parameter threshold" has a significant and negative correlation with the closed-loop time, the system automatically fine-tunes the rule threshold in the direction that minimizes the closed-loop time predicted by the model. For example, the oil temperature alarm threshold of the fan gearbox is gradually reduced from 85℃ to 83℃ until the closed-loop time predicted by the model no longer decreases significantly. At the same time, the model can also output the feature importance ranking to help maintenance personnel discover potential key influencing factors and further optimize the linkage strategy.

[0031] The rule optimization unit updates the fine-tuned parameter thresholds or priority weights to the rule base of the linkage rule engine module (202) in real time, replacing the original rules and taking effect in subsequent event flow matching. At the same time, the system continuously collects execution data under the new rules and incorporates it into the next round of training sample set, forming a closed-loop self-optimization mechanism of collection-training-optimization-deployment-re-collection.

[0032] The event distribution module 203 is used to receive the linkage instructions generated by the linkage rule engine module 202, and distribute them to one or more specified target endpoints in real time and accurately according to the content of the instructions. The target endpoints include the mobile inspection terminal 100, the production management system, the on-site sound and light alarm in the station, or the group cloud platform 3.

[0033] Optionally, for group-level management application scenarios, the system can be expanded to include a group cloud platform 3, which mainly includes: The global policy management module 301 is used to define and maintain unified management policies and linkage rule templates applicable to all or some of the subordinate new energy power stations. Administrators can formulate standardized policies in this module, such as "A Level 1 alarm will be triggered if personnel are not wearing insulated shoes in all power station booster stations." Global linkage policies are distributed to the edge servers 200 of each power station via HTTPS protocol in JSON format. The policy data includes policy ID, version number, list of applicable power stations, and rule content. After receiving the policy, the edge server 200 verifies the version number. If it is higher than the local version, the old policy is replaced, and a confirmation message is returned to the group cloud platform 3. If a policy with the same name exists locally and has been manually modified, the local policy is retained and marked as 'overwritten', and an alarm is sent to the administrator.

[0034] In some preferred embodiments, an important strategy defined by the global strategy management module 301 is the technical supervision project association strategy. This strategy is used to digitally bind the technical supervision projects (such as insulation supervision and relay protection supervision) uniformly stipulated by the group company with the specific operation and maintenance business objects in the site. For example, the "insulation resistance test" project is associated with the inspection items of related equipment in the system, the corresponding maintenance test electronic templates, and the insulation monitoring parameter thresholds in SCADA. When the site edge server 200 performs any linkage involving these business objects, the system can automatically extract and generate technical supervision data that meets the format requirements and complete the automatic filling to the group's technical supervision platform.

[0035] Optionally, the system may also include a data association module 400, which can be integrated into the site edge server 200 or the group cloud platform 3. This module is implemented using a graph database (such as Neo4j) and is used to attach a unified metadata tag to each event (including inspection records, defect work orders, monitoring alarms, and security events) flowing within the system. The tag includes at least a unique device identifier, a precise timestamp, geographic information, and event type.

[0036] The system uses device identifiers as core nodes to automatically construct and maintain a relational graph between all associated data entities. When a new event arrives, its device ID, timestamp, and geographic coordinates are extracted. If the time difference between the event and an existing node event is less than a preset window (e.g., 2 hours) and the geographic distance is less than a preset range (e.g., 100 meters), a new event child node is created under that device node, and a connection is established with related defect, work order, or alarm nodes through the 'RELATED_TO' relation edge. For multi-source events of the same device, a nearest neighbor matching strategy based on timestamps is used for association. Based on this graph, the system can provide comprehensive traceability and visualization analysis functions for the entire lifecycle operation and maintenance data of the device. For example, when a user queries a specific wind turbine, the system can immediately associate and display all its historical inspection records, past defect handling processes, related monitoring parameter curves, and surrounding safety event logs, providing data support for in-depth root cause analysis of faults and preventive maintenance decisions.

[0037] As a preferred embodiment, to avoid erroneous associations caused by accidental co-occurrence, the system introduces a confidence threshold when establishing relationship edges. Only when the frequency of two events occurring simultaneously within the same time window and geographical range on the same device exceeds a preset threshold (for example, the proportion of alarms and inspection records associated with the same device within 1 hour is >60% of the total number of associations), a strong association edge is established. Other events are marked as weakly associated or temporarily not associated for manual review.

[0038] Example 2 This embodiment provides a data linkage method, applied to the mobile inspection and maintenance management system for new energy power stations in Embodiment 1, such as... Figure 2 The diagram shown is a flowchart of one embodiment of the method of this application. The method includes the following steps: S1. Event Collection and Aggregation: The system runs continuously, collecting events from multiple heterogeneous sources in real time, including: inspection events from the mobile inspection terminal 100 (such as task completion reports), equipment status events from the equipment monitoring system (such as parameter limit alarms), risk events from the security monitoring system (such as personnel behavior alarms), and analysis events from the drone inspection system (such as defect identification reports). All events are aggregated in real time to the site edge server 200 deployed locally at the site. S2, Event Standardization and Correlation The 200 edge servers at the site perform standardized encapsulation processing on the aggregated raw events, extracting key elements from each event, such as device ID, timestamp, geographical location, and event type. Then, through device identification and spatiotemporal information, these events are automatically associated with specific equipment, responsible teams, and personnel in the physical world, forming standardized event objects with rich context. S3, Rule Matching and Linked Decision Making The standardized event flow is input into the linkage rule engine in the site edge server 200 in real time. The engine loads the pre-set linkage rule library for fast matching. The rules in the rule library support "IF-THEN" logic configuration. The actions defined in the "THEN" part can include one or more combinations of: generating a new task, creating a defect work order, sending an early warning notification, pushing knowledge base entries, triggering equipment locking, or automatically reporting supervision data. When the event flow meets the triggering condition of a certain rule, the engine immediately generates the corresponding executable linkage instruction. S3a, Rule Optimization This step is executed in parallel with S3 or triggered periodically, independent of real-time event stream processing. After generating the linkage instruction, the system also performs a rule optimization step. The linkage rule engine module 202 continuously collects historical execution data for each linkage instruction. The data includes at least: the success rate of instruction execution, the closed-loop verification time from triggering to problem verification closure, and the feedback results after execution (such as re-inspection conclusions and parameters after defect elimination). These data constitute the optimization sample set. The system's built-in optimizer uses machine learning models, such as regression models based on gradient boosting decision trees (such as XGBoost), with the closed-loop verification time as the prediction target and event type, device type, time period, and initial parameter threshold as input features. The model is trained periodically, and based on the feature importance and prediction results of the model output, the parameter thresholds in the rules are automatically fine-tuned to improve accuracy, the task priority is dynamically adjusted according to the closed-loop efficiency, or new potential rules are recommended to the administrator based on frequent event pattern mining. This allows the system to learn from historical operation and maintenance experience and continuously improve the accuracy and efficiency of linkage decisions.

[0039] As a preferred embodiment, the model employs the XGBoost algorithm, using the features of each linkage record in historical data (such as device type code, abnormal parameter value, and time period code) as input, and the actual time from triggering to final confirmation of the linkage as a label for training. After training, by calculating the contribution of each feature to the model output (SHAP value), the system identifies the feature with the greatest impact on the closed-loop time. If the SHAP value of the "parameter threshold" is found to be high and negatively correlated with the closed-loop time, the system automatically adjusts the threshold in the direction of shortening the closed-loop time, for example, adjusting "oil temperature > 85℃" to "oil temperature > 83℃", and continuously monitors the effect of the adjustment to form closed-loop optimization.

[0040] S4. Command Distribution and Execution The linkage instructions generated in step S3 or optimized in step S3a are distributed in real time and directionally to one or more target endpoints for execution through the event distribution module 203. For example, a special equipment inspection instruction can be sent to the terminal of the responsible inspection personnel and the nearby drone nest at the same time, and a safety alarm instruction can trigger the on-site sound and light alarm and notify the duty room at the same time. S5, Closed-Loop Feedback and Verification After the target endpoint executes the instruction, it sends the execution result back to the system. After receiving the feedback, the system starts the closed-loop verification process: the feedback result is compared and analyzed with the original event that initially triggered the linkage to confirm whether the corresponding business process has been completed effectively and correctly. For example, after the re-inspection task triggered by the "high oil temperature" alarm is completed, the system will automatically verify whether the real-time oil temperature data of the device in SCADA has returned to the normal range, which serves as one of the core verification bases for the business closed loop. This implementation also includes offline collaboration steps. This step ensures the system's resilience in unstable network environments. When the mobile inspection terminal 100 is offline, the following steps are performed: The terminal calls the local rule parser 101 to provide real-time operation prompts to the inspection personnel based on the pre-loaded simplified rule base and cached device status. The simplified rule base is stored in the local memory of the mobile device and contains no more than 50 frequently triggered IF-THEN rules. It is automatically generated and pushed for updates by the site edge server 200 based on the high-frequency linkage rules of the past 30 days. All inspection data generated and instructions received during offline periods are stored in the local instruction cache queue 102. When a terminal enters the network coverage area, it automatically synchronizes all data and instructions in the cache queue with the site edge server 200. After receiving the event data generated during the offline period, the server immediately incorporates it into the standard event handling process (i.e., steps S1-S5), thereby ensuring that even if the business occurs during the network interruption, it can be seamlessly incorporated into the overall intelligent management closed loop, thus guaranteeing the continuity and integrity of the business process.

[0041] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A new energy station mobile inspection operation and maintenance management system, characterized in that: Includes a mobile inspection terminal (100) and a site edge server (200). The mobile inspection terminal (100) is used to perform on-site inspection tasks, collect data, and support offline operation. The site edge server (200) is deployed locally at the new energy site to realize real-time aggregation of multi-source data, local analysis and business linkage triggering; The site edge server (200) includes: The multi-source data aggregation module (201) is used to access and integrate in real time equipment operation status data from the equipment monitoring system, security identification event data from the video surveillance system, image analysis data from the drone inspection system, and on-site operation data from the mobile inspection terminal (100). The linkage rule engine module (202) has several preset linkage rules, which are used to perform real-time matching on the event stream after fusion by the multi-source data aggregation module (201). When a specific rule is matched, a corresponding linkage instruction is generated. The linkage rule engine module (202) also includes a rule optimization unit, which is used to construct an optimization sample set based on the historical execution success rate of linkage instructions, closed-loop verification time and feedback data, and to use a gradient boosting decision tree model to dynamically optimize the rule parameters, triggering conditions or priorities in the linkage rule base. The gradient boosting decision tree model takes event type, device type, time period and initial parameter threshold as input features, closed-loop verification time as prediction target, and periodically trains the model. It automatically fine-tunes the parameter thresholds or task priority weights in the rules according to the feature importance of the model output. The event distribution module (203) is used to distribute linkage instructions to the corresponding target endpoints, including mobile inspection terminal (100), production management system or on-site sound and light alarm.

2. The new energy station mobile inspection operation and maintenance management system according to claim 1, characterized in that: The mobile inspection terminal (100) also includes a local rule parser (101) and an instruction cache queue (102). The local rule parser (101) is used to provide prompts or generate temporary tasks for the current inspection operation in the offline state of the network, based on the pre-loaded simplified rule base and the locally cached key status of the device. The simplified rule base is stored in the local memory of the mobile inspection terminal (100) and contains a set of frequently triggered IF-THEN rules. The rule conditions only involve the device ID, historical alarm type and current inspection item. The actions are limited to pop-up prompts or the creation of low-priority temporary tasks. The simplified rule base is automatically generated by the site edge server (200) after periodically analyzing the linkage rule engine log and extracting the combination of high-frequency condition items using the FP-Growth frequent itemset mining algorithm. After compression, it is pushed to the mobile inspection terminal (100) for updates. The instruction cache queue (102) is used to temporarily store the data to be reported generated by the inspection and the linkage instructions pushed by the event distribution module (203) when the network is offline, and to perform synchronization after the network is restored. If there is a conflict between the locally stored data and the server record during synchronization, the record with the latest timestamp on the server shall prevail, and a conflict log shall be generated for auditing. 3.The new energy station mobile inspection operation and maintenance management system according to claim 1, characterized in that: The linkage rules in the linkage rule engine module (202) include at least monitoring-inspection linkage rules; The monitoring-inspection linkage rule is configured as follows: when the equipment operation status data indicates that a certain operating parameter of a specific equipment is abnormal, a special re-inspection task for that specific equipment is automatically generated and pushed to the mobile inspection terminal (100) responsible for that equipment through the event distribution module (203).

4. The new energy station mobile inspection operation and maintenance management system according to claim 3, characterized in that: The linkage rules in the linkage rule engine module (202) also include security-process linkage rules; The safety-process linkage rule is configured as follows: when the safety identification event data identifies unsafe behavior or violation of regulations by personnel, an audible and visual alarm is automatically triggered, a safety violation record is generated, and depending on the severity, a lockout command is sent to the relevant equipment or a warning message is sent to the management terminal through the event distribution module (203).

5. The new energy station mobile inspection operation and maintenance management system according to claim 1, characterized in that: The system also includes a group cloud platform (3), which includes a global policy management module (301) for defining and maintaining global linkage policies applicable to all or some of the subordinate sites, and distributing the global linkage policies to the linkage rule engine module (202) in the site edge server (200) corresponding to each site. The global linkage strategy is distributed via HTTPS protocol in JSON format. The strategy data includes strategy ID, version number, list of applicable sites, and rule content. After receiving the policy, the site edge server (200) verifies the version number. If it is higher than the local version, it replaces the old policy and returns a confirmation message to the group cloud platform (3).

6. The new energy station mobile inspection operation and maintenance management system according to claim 5, characterized in that: The global linkage strategy includes a technical supervision project association strategy, which is used to associate the technical supervision projects stipulated by the group with specific inspection items, maintenance task templates or equipment monitoring parameter thresholds in the site edge server (200), and automatically complete the filling and uploading of technical supervision-related data when the site edge server (200) performs linkage.

7. The new energy station mobile inspection operation and maintenance management system according to claim 1, characterized in that: The system also includes a data association module (400), which is integrated into the site edge server (200) or the group cloud platform (3). The data association module (400) is used to attach equipment identifier, timestamp, geographic information and event type label to each event, and establish a relationship map between inspection records, equipment defects, maintenance work orders, monitoring alarms and safety events with equipment identifier as the core. The data association module (400) is implemented using a graph database. The device unique identifier is used as the node primary key. When a new event arrives, its device ID, timestamp and geographical coordinates are extracted. If the time difference between the event time and the existing node event is less than a preset time window and the geographical distance is less than a preset range, a new event child node is created under the device node, and a connection is established with the relevant defect, work order or alarm node through the relationship edge based on the spatiotemporal proximity. Based on the relationship graph, it provides traceability and visualization analysis of equipment operation and maintenance data throughout the entire life cycle.

8. A data linkage method applied to the new energy station mobile inspection operation and maintenance management system of any one of claims 1-7, characterized in that: Includes the following steps: S1. Event collection and aggregation: Continuously collect inspection events from mobile inspection terminals (100), status events from equipment monitoring systems, risk events from security monitoring systems, and inspection events from drone systems, and aggregate them to the site edge server (200). S2. Event Standardization and Correlation: Standardize and encapsulate the aggregated heterogeneous events, extract key elements, and associate them with specific physical devices and responsible entities through device identification and spatiotemporal information; S3. Rule matching and linkage decision: Input the standardized event stream into the linkage rule engine and match it with the preset linkage rule library. If the match is successful, the corresponding linkage instruction will be generated. S3a, Rule Optimization: Based on the historical execution success rate of linkage instructions, closed-loop verification time and feedback data, an optimization sample set is constructed. A gradient boosting decision tree model is adopted, with closed-loop verification time as the prediction target and event type, device type, time period and initial parameter threshold as input features. The model is trained periodically, and the parameter threshold or task priority weight in the rules is automatically fine-tuned according to the feature importance and prediction results of the model output, so as to achieve dynamic optimization of the rule base. S4. Command Distribution and Execution: Through the event distribution module (203), the linkage command is distributed to one or more target endpoints for execution in real time and in a targeted manner; S5. Closed-loop feedback and verification: Receive instruction execution feedback from the target endpoint and verify whether the business process corresponding to the initial triggering event has formed a closed loop. 9.The data linkage method of claim 8, wherein: In step S3, the rule configuration in the linkage rule base supports IF-THEN logic, and the behavior of the THEN part includes one or more combinations of generating a new task, creating a defect work order, sending an early warning notification, pushing knowledge base entries, triggering equipment locking, or reporting supervision data. 10.The data linkage method of claim 8, wherein: The method also includes an offline collaboration step: When the mobile inspection terminal (100) is offline, it provides real-time prompts to the inspection personnel based on the locally cached key device status and the pre-loaded simplified rule base. The simplified rule base contains a set of frequently triggered IF-THEN rules, which are automatically generated and pushed for updates by the site edge server (200) based on recent frequent linkage rules. Data generated and instructions received during the inspection process are temporarily stored in the local instruction cache queue. After the network is restored, the data is automatically synchronized and instructions are exchanged with the site edge server (200), and subsequent cloud linkage processes are triggered to ensure the continuity of business processes.

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