Power grid equipment asset data capturing system and method based on multi-platform traceability

Abstract

The invention discloses a power grid equipment asset data capturing system and method based on multi-platform traceability, and relates to the technical field of data capturing and power grid equipment asset management, and the system comprises a data source analysis module, a dynamic priority calculation module, a task execution and monitoring module, a closed-loop feedback adjustment module, and a system scheduling and interaction control module. According to the method, a control system for analysis, decision making, execution, feedback and adjustment is constructed, data capture is converted from open-loop static execution to closed-loop dynamic optimization, the system can continuously sense multi-dimensional features of structural complexity, updating frequency and value density of a data source, the resource state of the system is monitored in real time, and the real-time performance of the system is improved. Comprehensive decision making is carried out through dynamic priority calculation, the mechanism enables system resources to be preferentially allocated to the highest data source and task in the current environment all the time, and waste of the resources in invalid grabbing, frequent sealing or high-delay tasks is avoided.

Description

Technical Field

[0001] This invention relates to the field of data capture and power grid equipment asset management technology, specifically to a power grid equipment asset data capture system and method based on multi-platform traceability. Background Technology

[0002] The management of power grid equipment assets is rapidly developing towards digitalization and lean management. Its entire life cycle data, including information on bidding and procurement, technical parameters, installation and commissioning, operation status, inspection and maintenance, and even decommissioning and scrapping, is scattered across thousands of heterogeneous data platforms. These platforms are diverse in type. In order to build accurate and complete digital twins of equipment and realize the strategic transformation from passive maintenance to proactive early warning, it is necessary to systematically collect and integrate this multi-source, heterogeneous, and highly dynamic data. However, in the face of such a complex data ecosystem, traditional data capture technologies are inadequate and cannot achieve a balance between efficiency, cost, and robustness, which seriously restricts the depth and breadth of the digitalization process of power grid assets.

[0003] In existing technologies, solutions for multi-platform data crawling mostly employ static or semi-static scheduling strategies. Firstly, at the crawling scheduling level, polling is generally based on preset fixed time intervals or simple rules. This indiscriminate or weakly differentiated crawling mode cannot perceive the dynamic characteristics of the data source itself or the system's own resource status, leading to untimely crawling of high-value, high-update-frequency sources, while ineffective crawling of static or low-value resources results in severe waste of computing resources and network bandwidth. Secondly, at the adaptability level, existing systems lack effective feedback mechanisms. When encountering anti-crawler strategies of target websites or changes in page structure, the system usually can only passively fail or rely on manual intervention to adjust strategies and repair rules, resulting in long response delays and failing to guarantee the continuity and integrity of the data flow. This rigid architecture exposes inherent defects such as low efficiency, poor stability, and high maintenance costs in the specific and demanding scenario of power grid equipment asset data crawling.

[0004] Therefore, there is an urgent need to find a data acquisition system and method for power grid equipment assets based on multi-platform traceability that can dynamically adjust the acquisition strategy, optimize resource allocation, and improve anti-interference capabilities. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the above-mentioned defects of the prior art and provide a multi-dimensional feature-based power grid equipment asset data capture system and method that can continuously perceive the structural complexity, update frequency and value density of the data source, monitor its own CPU, memory, network and proxy IP resource status in real time, perform online calibration of weight coefficients, avoid wasting resources on invalid capture, frequent blocking or high-latency tasks, and maximize the overall system throughput and data freshness in complex environments.

[0006] The technical solution adopted by this invention to solve its technical problem is as follows: a power grid equipment asset data capture system based on multi-platform traceability, comprising: Data source analysis module: Configured to perform initial feature analysis on the target data source and output including a structural complexity score S. struct Update frequency index F update and data value score V data The analysis results; Dynamic priority calculation module: Communicates with the data source analysis module, receives the analysis results, and combines them with the system's real-time resource load factor L. system It calculates a priority score for each data source crawling task using a predefined priority calculation function, and manages the sorting of the crawling task queue accordingly. Task execution and monitoring module: It is communicatively connected to the dynamic priority calculation module, retrieves tasks from the crawling task queue and performs data crawling operations, while simultaneously monitoring and generating performance index data of the crawling process in real time; Closed-loop feedback adjustment module: It is communicatively connected to the task execution and monitoring module and the dynamic priority calculation module, respectively. It is configured to receive the performance index data and generate control commands based on it to perform dynamic adjustment operations in real time. System scheduling and interactive control module: It gathers and visualizes the status and data flow of each module in real time, provides a human-computer interaction interface, and receives control commands input by users based on the visualized information.

[0007] Preferably, the structural complexity analysis unit is used to analyze the document object model tree of the target page and calculate the structural complexity score S based on the number of nodes and structural features. struct ; Update Frequency Prediction Unit: Used to predict the update frequency metric F of a data source through time-series analysis of HTTP response header information and historical crawled records. update ; Data Value Estimation Unit: Used to scan page content by matching it with a knowledge graph and keyword database in the power grid equipment domain, and evaluate the data value score V. data .

[0008] Preferably, the dynamic priority calculation module includes: Resource load monitoring unit: Used to monitor the system's CPU, memory, network, and proxy IP resource status in real time, and output the system load factor L. system ; Priority Calculation Unit: Used to execute the priority calculation function to calculate a quantifiable priority score for each data source crawling task.

[0009] Preferably, the priority calculation function in the priority calculation unit is: Where α, β, γ, and δ are configurable weighting coefficients, and P initial The initial dynamic priority score is calculated, and based on P... initial Sort the data by size to generate a task queue.

[0010] Preferably, the performance metrics monitored by the task execution and monitoring module include: The success rate of a capture task is the ratio of the number of times a capture task is successfully completed to the total number of attempts. Blocking frequency refers to the frequency at which HTTP status codes indicating access denial or rate limiting are received per unit of time. Data acquisition efficiency refers to the number of valid data entries successfully extracted from the target data source per unit of time. Response latency is the time taken from initiating a fetch request to receiving the complete response data.

[0011] Preferably, the closed-loop feedback adjustment module is specifically configured to perform at least one of the following dynamic adjustment operations: When the blocking frequency exceeds the first preset threshold, a first control command is generated and sent to the dynamic priority calculation module, which is instructed to adjust the weight coefficients γ and δ of the priority calculation function to increase the suppression effect of structural complexity and system load. When the data acquisition efficiency exceeds a second preset threshold, a second control command is generated to trigger the data source analysis module to re-analyze the corresponding data source to update V. data and F update And instruct the dynamic priority calculation module to increase the values ​​of the weight coefficients α and β; When the system real-time load factor L system When the third preset threshold is exceeded, a third control command is generated and sent to the dynamic priority calculation module to instruct it to increase the value of the weight coefficient δ, thereby giving the system load a higher weight in the priority calculation.

[0012] Preferably, when the task execution and monitoring module detects that the crawling task has failed due to changes in the target page structure, it sends a failure signal to the closed-loop feedback adjustment module. The closed-loop feedback adjustment module generates a fourth control command, triggering the data source analysis module to initiate a re-analysis process for the failed pages to update the structural complexity score S. struct .

[0013] Preferably, the system scheduling and interaction control module includes: Visualization unit: Used to display the dynamic priority of each data source, its real-time sorting in the queue, and its weight coefficients in a single integrated interface; Simulation and simulation unit: used to receive control commands input by the user through the human-computer interaction interface. The commands include: manually adjusting the priority of the data source, modifying the value of the weight coefficient, setting temporary crawling rules, and simulating the changing trends of the crawling task queue, resource load and data acquisition results within a preset period without affecting the actual production environment, based on the current system status and the control commands, and visually feeding back the simulation results to the user. Control command issuing unit: After the user confirms the simulation results, the adjustment parameters of the user's control commands are converted into adjustment commands and issued to the dynamic priority calculation module, so that it can recalculate the global task priority based on the new parameters.

[0014] The technical solution adopted by this invention to further solve its technical problem is as follows: a method for capturing power grid equipment asset data based on multi-platform traceability, comprising the following steps: S100, Data Source Analysis Phase: Perform initial feature analysis on the target data source and output the structural complexity score S. struct F update and data value score V data ; S200, Dynamic Priority Calculation Phase: Combining the system's real-time resource load factor L system An initial dynamic priority score P is calculated for each data source crawling task using a priority calculation function. initial And thereby generate and manage the capture task queue; S300, Task Execution and Monitoring Phase: Retrieves tasks from the task queue and performs data capture operations, while simultaneously monitoring and generating performance metrics data including capture success rate, blocking frequency, data acquisition efficiency, and response latency in real time; S400, Closed-loop feedback adjustment stage: Receive the performance index data, determine the system status based on the preset threshold, generate control commands and dynamically adjust the weight coefficients in the priority calculation function or trigger the data source to re-analyze; S500, System Scheduling and Interactive Control Phase: Real-time aggregation and visualization of the status and data flow of each module, providing a human-machine interaction interface, receiving user control commands, supporting simulation and parameter adjustment, and sending the confirmed adjustment commands to the system for execution.

[0015] The beneficial effects of the power grid equipment asset data capture system and method based on multi-platform traceability of the present invention are as follows: This invention transforms data crawling from an open-loop, static execution to a closed-loop, dynamic optimization by constructing a control system encompassing analysis, decision-making, execution, feedback, and adjustment. The system continuously perceives the multidimensional characteristics of the data source's structural complexity, update frequency, and value density, and monitors its own CPU, memory, network, and proxy IP resource status in real time. It makes comprehensive decisions through dynamic priority calculation and calibrates weight coefficients online based on real-time performance indicators. This mechanism ensures that system resources are always prioritized for the highest-performing data source and task in the current environment, avoiding waste of resources on ineffective crawling, frequent blocking, or high-latency tasks. Thus, it maximizes the overall system throughput and data freshness in complex environments. Attached Figure Description

[0016] Figure 1 This is an operation flowchart of an embodiment of the power grid equipment asset data capture method based on multi-platform traceability of the present invention; Figure 2 This is a block diagram of the module composition of an embodiment of the power grid equipment asset data capture system based on multi-platform traceability of the present invention. Detailed Implementation

[0017] The present invention will be further described below with reference to the embodiments and accompanying drawings.

[0018] Example 1 This embodiment addresses the problems of power grid equipment asset data being scattered across multiple heterogeneous platforms, resulting in low efficiency and poor adaptability in data retrieval techniques. It details the working principle of a power grid equipment asset data retrieval system based on multi-platform traceability, such as… Figure 2 As shown, the system uses a data source analysis module to mine the structural complexity, update frequency, and data value characteristics of the target data source. A dynamic priority calculation module, combined with real-time system resource load, generates a task priority queue. The task execution and monitoring module completes data capture and monitors key performance indicators. A closed-loop feedback adjustment module then dynamically optimizes system parameters based on these indicators. Finally, a system scheduling and interactive control module enables status visualization and human-computer interaction. The entire process uses a closed-loop control system to transform static capture into dynamic optimization, ensuring that system resources are prioritized for high-value tasks, thereby improving the efficiency, stability, and resource utilization of power grid equipment asset data capture.

[0019] The power grid equipment asset data capture system based on multi-platform traceability includes: a data source analysis module, a dynamic priority calculation module, a task execution and monitoring module, a closed-loop feedback adjustment module, and a system scheduling and interactive control module. Specifically: (1) Data source analysis module: The data source analysis module performs a comprehensive and accurate initial feature analysis on target data sources related to power grid equipment assets. This module includes a structural complexity analysis unit, an update frequency prediction unit, and a data value estimation unit. These units work together to output a structural complexity score S. struct Update frequency index F update and data value score V data ,in: The structural complexity analysis unit analyzes the document object model tree of the target page and calculates the structural complexity score S based on the number of nodes and structural features. struct ; Update Frequency Prediction Unit: Used to predict the update frequency metric F of a data source through time-series analysis of HTTP response header information and historical crawled records. update ; Data Value Estimation Unit: Used to scan page content by matching it with a knowledge graph and keyword database in the power grid equipment domain, and evaluate the data value score V. data .

[0020] (2) Dynamic priority calculation module: The dynamic priority calculation module receives the S output from the data source analysis module. struct F update V data Then, combined with the system's real-time resource load factor L system This module assigns priorities to crawling tasks from each data source using a priority calculation function, enabling dynamic sorting of the task queue. It includes a resource load monitoring unit and a priority calculation unit. The resource load monitoring unit collects real-time data on the usage status of the system's core resources and calculates the system load factor L. system Priority Calculation Unit: This unit combines data source characteristics with system load using a predefined priority calculation function to quantify the priority of each crawling task. The priority calculation function used is as follows: Where α, β, γ, and δ are configurable weighting coefficients, and during the calculation process, the S value of each data source is called from the data source analysis module. struct F update V data Call the real-time L from the resource load monitoring unit system It reads the currently configured values ​​of α, β, γ, and δ, and then performs calculations according to the function formula, including the numerator. V reflects the combined advantages of the data source's value and activity. data With F update The higher the numerator, the greater the reward for the task; the denominator... S reflects the combined cost of the difficulty and resource consumption of the grasping task. struct With L system The higher the value of the denominator, the larger the cost of the task, and ultimately P. initial The higher the value, the higher the benefit-cost ratio of the crawling task, and the higher it should be executed. After the calculation is complete, the unit will proceed according to P. initial All data source crawling tasks are sorted from largest to smallest to generate a crawling task queue, which is then synchronized to the task execution and monitoring module in real time.

[0021] (3) Task execution and monitoring module: The task execution and monitoring module is responsible for retrieving tasks from the crawling task queue and completing data crawling operations. Simultaneously, it monitors key performance indicators (KPIs) of the crawling process in real time. The module retrieves the highest-priority crawling task from the head of the queue according to its priority, parses the task information, and allocates appropriate execution resources based on the task's resource requirements and the current system state. During task execution, the module collects and calculates four core performance indicators in real time: crawling success rate, blocking frequency, data acquisition efficiency, and response latency. The crawling success rate is reflected by the ratio of the number of successfully completed crawling tasks to the total number of crawls. The blocking frequency is the frequency of receiving HTTP status codes indicating access denial or rate limiting per unit of time. Data acquisition efficiency is measured by the number of times a task is successfully completed per unit of time. The module successfully extracts the number of valid data entries from the target data source. For response latency, the timer starts from the moment the system initiates the crawl request and ends the moment the server's response data is fully received. The response time for a single task is recorded, and the average response latency of multiple tasks is taken. This metric reflects the response speed of the data source server and the quality of network transmission. The module synchronizes the four performance metrics calculated in real time to the closed-loop feedback adjustment module at fixed intervals. Furthermore, when the task execution and monitoring module detects that a crawling task has failed due to changes in the target page structure, it sends a failure signal to the closed-loop feedback adjustment module. Upon receiving the failure signal, the closed-loop feedback adjustment module generates a fourth control command and sends it to the data source analysis module, instructing it to initiate a re-analysis process of the failed page to update the structural complexity score S. struct .

[0022] (4) Closed-loop feedback adjustment module: The closed-loop feedback adjustment module receives performance index data output from the task execution and monitoring module, determines whether there are any problems in the current system operation status, and then generates targeted control commands, which are sent to the data source analysis module or the dynamic priority calculation module to adjust the system weight coefficients α, β, γ, δ, and data source characteristic parameters S. struct V data To achieve closed-loop control, the specific configuration involves performing at least one of the following dynamic adjustment operations: When the blocking frequency exceeds the first preset threshold, a first control command is generated and sent to the dynamic priority calculation module, which is instructed to adjust the weight coefficients γ and δ of the priority calculation function to increase the suppression effect of structural complexity and system load. When the data acquisition efficiency exceeds a second preset threshold, a second control command is generated to trigger the data source analysis module to re-analyze the corresponding data source to update V. data and F update And instruct the dynamic priority calculation module to increase the values ​​of the weight coefficients α and β; When the system real-time load factor L system When the third preset threshold is exceeded, a third control command is generated and sent to the dynamic priority calculation module to instruct it to increase the value of the weight coefficient δ, thereby giving the system load a higher weight in the priority calculation.

[0023] (5) System scheduling and interactive control module: The system scheduling and interactive control module, on the one hand, aggregates the status and data streams of each module in real time and presents them to the user in a visual manner; on the other hand, it provides a human-computer interaction interface, supporting users to manually adjust system parameters and verify the adjustment effects through simulation, ensuring the safety and effectiveness of the adjustment operation. Specifically, the visualization unit displays key information of each system module in real time through an integrated interface, including: the dynamic priority of each data source, its real-time sorting position in the crawling task queue, the currently configured weight coefficient value, the real-time resource load status of the system, the real-time execution status of each crawling task, and its corresponding performance indicators. The simulation unit receives control commands from the user through the human-computer interaction interface, including manually adjusting the priority of data sources, modifying the weight coefficient value, and setting temporary crawling rules, and constructs a simulation based on the current system state. A simulation environment is built, in which the unit simulates the system operation process within a preset period, recalculates task priorities according to the user-adjusted parameters, generates a simulated task queue, executes tasks according to the simulated queue, and, based on the current simulated resource consumption and allocation process and historical performance data and adjusted parameters, predicts changes in performance indicators such as capture success rate and blocking frequency for each task, ultimately outputting the simulation results. The control command issuing unit: If the user confirms that the simulation results meet the requirements, the control command issuing unit will convert the adjustment parameters in the user-input control command into system-recognizable adjustment commands and issue them to the corresponding modules. Upon receiving the adjustment commands, the corresponding modules immediately update the parameters and execute them. The visualization unit of the system scheduling and interactive control module synchronously updates the information displayed on the interface, ensuring that the user can monitor the adjusted system status in real time.

[0024] In summary, this embodiment details the complete operation process of a power grid equipment asset data acquisition system based on multi-platform traceability. From data source feature analysis, task priority calculation, task execution and performance monitoring, to closed-loop parameter adjustment and human-machine interaction control, each module works closely together to form a complete dynamic optimization system. This achieves optimal matching of resources, tasks, and value, ensuring the timely and accurate acquisition of power grid equipment asset data and providing reliable technical support for the digital management of power grid assets and the construction of digital twins.

[0025] Example 2 Based on Example 1, this example provides the specific steps for data collection of power grid equipment assets using a multi-platform traceability method, such as... Figure 1 As shown, it includes: S100, Data Source Analysis Phase: Perform initial feature analysis on the target data source; Analyze the page's DOM structure and calculate the structural complexity score S. struct ; Predicting update frequency index F using HTTP headers and historical records update ; Based on the power grid knowledge graph, the data value score V is evaluated. data ; S200, Dynamic Priority Calculation Stage: Real-time resource load factor L of the monitoring system system ; Based on the data source analysis results and system load, calculate the initial dynamic priority score P for each crawling task. initial ; Update the fetch task queue according to priority; S300, Task Execution and Monitoring Phase: Retrieve tasks from the task queue and perform data fetching; Real-time monitoring of metrics such as capture success rate, blocking frequency, data acquisition efficiency, and response latency; Record any exceptions or failures during the scraping process; S400, Closed-Loop Feedback Adjustment Phase: Receive performance metrics during the task execution phase; Determine whether a preset threshold has been triggered; Generate control commands and dynamically adjust the weighting coefficients in priority calculation; S500, System Scheduling and Interactive Control Phase: The status of each module, task queue, weight coefficients, etc. are displayed in real time in the visual interface; Users can manually adjust priorities, modify weights, and set temporary rules; The system supports simulation and preview of the adjusted task execution effect; After user confirmation, the adjustment command is sent to the dynamic priority calculation module.

Claims

1. A power grid equipment asset data capture system based on multi-platform traceability, characterized in that, include: Data source analysis module: Configured to perform initial feature analysis on the target data source and output including a structural complexity score S. struct Update frequency index F update and data value score V data The analysis results; Dynamic priority calculation module: Communicates with the data source analysis module, receives the analysis results, and combines them with the system's real-time resource load factor L. system It calculates a priority score for each data source crawling task using a predefined priority calculation function, and manages the sorting of the crawling task queue accordingly. Task execution and monitoring module: It is communicatively connected to the dynamic priority calculation module, retrieves tasks from the crawling task queue and performs data crawling operations, while simultaneously monitoring and generating performance index data of the crawling process in real time; Closed-loop feedback adjustment module: It is communicatively connected to the task execution and monitoring module and the dynamic priority calculation module, respectively. It is configured to receive the performance index data and generate control commands based on it to perform dynamic adjustment operations in real time. System scheduling and interactive control module: It gathers and visualizes the status and data flow of each module in real time, provides a human-computer interaction interface, and receives control commands input by users based on the visualized information.

2. The power grid equipment asset data capture system based on multi-platform traceability according to claim 1, characterized in that, The data source analysis module includes: The structural complexity analysis unit analyzes the document object model tree of the target page and calculates the structural complexity score S based on the number of nodes and structural features. struct ; Update Frequency Prediction Unit: Used to predict the update frequency metric F of a data source through time-series analysis of HTTP response header information and historical crawled records. update ; Data Value Estimation Unit: Used to scan page content by matching it with a knowledge graph and keyword database in the power grid equipment domain, and evaluate the data value score V. data .

3. The power grid equipment asset data capture system based on multi-platform traceability according to claim 1 or 2, characterized in that, The dynamic priority calculation module includes: Resource load monitoring unit: Used to monitor the system's CPU, memory, network, and proxy IP resource status in real time, and output the system load factor L. system ; Priority Calculation Unit: Used to execute the priority calculation function to calculate a quantifiable priority score for each data source crawling task.

4. The power grid equipment asset data capture system based on multi-platform traceability according to claim 3, characterized in that, The priority calculation function in the priority calculation unit is: Where α, β, γ, and δ are configurable weighting coefficients, and P initial The initial dynamic priority score is calculated, and based on P... initial Sort the data by size to generate a task queue.

5. The power grid equipment asset data capture system based on multi-platform traceability according to claim 1 or 2, characterized in that, The performance metrics monitored by the task execution and monitoring module include: The success rate of a capture task is the ratio of the number of times a capture task is successfully completed to the total number of attempts. Blocking frequency refers to the frequency at which HTTP status codes indicating access denial or rate limiting are received per unit of time. Data acquisition efficiency refers to the number of valid data entries successfully extracted from the target data source per unit of time. Response latency is the time taken from initiating a fetch request to receiving the complete response data.

6. The power grid equipment asset data capture system based on multi-platform traceability according to claim 1 or 2, characterized in that, The closed-loop feedback adjustment module is specifically configured to perform at least one of the following dynamic adjustment operations: When the blocking frequency exceeds the first preset threshold, a first control command is generated and sent to the dynamic priority calculation module, which is instructed to adjust the weight coefficients γ and δ of the priority calculation function to increase the suppression effect of structural complexity and system load. When the data acquisition efficiency exceeds a second preset threshold, a second control command is generated to trigger the data source analysis module to re-analyze the corresponding data source to update V. data and F update And instruct the dynamic priority calculation module to increase the values ​​of the weight coefficients α and β; When the system real-time load factor L system When the third preset threshold is exceeded, a third control command is generated and sent to the dynamic priority calculation module to instruct it to increase the value of the weight coefficient δ, thereby giving the system load a higher weight in the priority calculation.

7. The power grid equipment asset data capture system based on multi-platform traceability according to claim 1 or 2, characterized in that, When the task execution and monitoring module detects that the crawling task has failed due to changes in the target page structure, it sends a failure signal to the closed-loop feedback adjustment module. The closed-loop feedback adjustment module generates a fourth control command, triggering the data source analysis module to initiate a re-analysis process for the failed pages to update the structural complexity score S. struct .

8. The power grid equipment asset data capture system based on multi-platform traceability according to claim 1 or 2, characterized in that, The system scheduling and interaction control module includes: Visualization unit: Used to display the dynamic priority of each data source, its real-time sorting in the queue, and its weight coefficients in a single integrated interface; Simulation and deduction unit: used to receive control commands input by the user through the human-computer interaction interface. The commands include: manually adjusting the priority of the data source, modifying the value of the weight coefficient, setting temporary crawling rules, simulating the changing trends of the crawling task queue, resource load and data acquisition results within a preset period based on the current system status and the control commands, and visually feeding back the simulation results to the user. Control command issuing unit: After the user confirms the simulation results, the adjustment parameters in the control command are converted into adjustment commands and issued to the dynamic priority calculation module, so that the module can recalculate the global task priority based on the new parameters.

9. A method for capturing power grid equipment asset data based on multi-platform traceability, applicable to the system described in any one of claims 1 to 8, characterized in that, Includes the following steps: S100, Data Source Analysis Phase: Perform initial feature analysis on the target data source and output the structural complexity score S. struct F update and data value score V data ; S200, Dynamic Priority Calculation Phase: Combining the system's real-time resource load factor L system An initial dynamic priority score P is calculated for each data source crawling task using a priority calculation function. initial And thereby generate and manage the capture task queue; S300, Task Execution and Monitoring Phase: Retrieves tasks from the task queue and performs data capture operations, while simultaneously monitoring and generating performance metrics data including capture success rate, blocking frequency, data acquisition efficiency, and response latency in real time; S400, Closed-loop feedback adjustment stage: Receive the performance index data, determine the system status based on the preset threshold, generate control commands and dynamically adjust the weight coefficients in the priority calculation function or trigger the data source to re-analyze; S500, System Scheduling and Interactive Control Phase: Real-time aggregation and visualization of the status and data flow of each module, providing a human-machine interaction interface, receiving user control commands, supporting simulation and parameter adjustment, and sending the confirmed adjustment commands to the system for execution.

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