Management and control warehouse system for power monitoring and management and control method thereof

Abstract

The invention discloses a management and control warehouse system for power monitoring and a management and control method thereof, and relates to the technical field of power monitoring, and the system comprises a local warehouse module, an updating mechanism optimization module, a system integration and compatibility module, and an operation and maintenance management and monitoring module. The local warehouse module, the updating mechanism optimization module, the system integration and compatibility module and the operation and maintenance management and monitoring module transmit data to each other through a database, and the local warehouse module supports distributed deployment of a main warehouse and branch warehouses and adapts to a layered architecture of a power system. Temperature, voltage and vibration sensor data are integrated through the decision support unit and the sensor data fusion unit, a comprehensive monitoring instrument panel is generated, the decision support unit provides operation and maintenance suggestions based on a risk scoring model, manual intervention is reduced, the operation and maintenance cost is reduced, the service life of equipment is prolonged, smart cities and renewable energy application are supported, and the system is suitable for popularization and application. And the efficient and reliable solution of the management and control warehouse system is improved.

Description

Technical Field

[0001] This invention relates to the field of power monitoring technology, specifically to a power monitoring and control warehouse system and its control method. Background Technology

[0002] Power monitoring systems are the core support for the safe operation of modern power grids. Their management warehouses, as infrastructure for data storage, processing, and operation and maintenance scheduling, directly impact monitoring efficiency and reliability. Traditional warehouses often use centralized databases, which are prone to single-point-of-failure risks and struggle to support the massive amounts of data from distributed energy sources such as solar and wind power. The fundamental contradictions of existing power monitoring warehouse systems lie in: the conflict between the surge in data volume and lagging processing capacity; the contradiction between the need for cross-system collaboration and isolated protocols; and the disconnect between the demands for intelligent operation and maintenance and traditional manual methods. In particular, the passive response mode in operation and maintenance management has become the biggest bottleneck restricting power grid reliability. Furthermore, existing power monitoring management warehouse systems cannot reduce operation and maintenance costs or shorten equipment lifespan, do not support smart city and renewable energy applications, and have inefficient utilization rates.

[0003] The existing power monitoring and control warehouse system has the following shortcomings: 1. Patent document CN119398381A discloses a power material supply management and control system and method. "The power material supply management and control system includes a central control center, sub-control centers, material warehouses, mobile control terminals, and a data processing platform; the power material supply management and control method includes the following steps: S1, material demand forecasting; S2, intelligent scheduling; S3, real-time monitoring; S4, risk assessment and early warning; S5, emergency response. This power material supply management and control system and method can improve material supply efficiency, enhance decision support capabilities, make decisions more accurate and efficient, help optimize resource allocation, improve overall operational efficiency, increase supply chain transparency and visibility, strengthen risk management, ensure the safe and stable operation of the power grid, improve emergency response speed, optimize resource allocation, help reduce inventory costs, improve capital utilization efficiency, achieve optimal resource allocation, and better meet the power material needs of various regions." However, existing power monitoring and control warehouse systems cannot reduce maintenance costs or equipment lifespan, do not support smart city and renewable energy applications, and have inefficient utilization rates. Summary of the Invention

[0004] The purpose of this invention is to provide a power monitoring and control warehouse system and its control method, in order to solve the technical problems mentioned in the background art, namely, that existing power monitoring and control warehouse systems cannot reduce operation and maintenance costs, shorten equipment lifespan, do not support smart city and renewable energy applications, and have inefficient utilization rates.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a control warehouse system for power monitoring, comprising: a local warehouse module, an update mechanism optimization module, a system integration and compatibility module, and an operation and maintenance management and monitoring module. The local warehouse module, update mechanism optimization module, system integration and compatibility module, and operation and maintenance management and monitoring module transmit data to each other through a database. The local warehouse module supports distributed deployment of main warehouse and sub-warehouses, adapts to the layered architecture of the power system, and supports real-time acquisition and compressed storage of power monitoring data. The operation and maintenance management and monitoring module includes a sensor data fusion unit, an AI anomaly detection unit, and an automated response unit.

[0006] Preferably, the operation and maintenance management and monitoring module further includes a real-time monitoring unit. The real-time monitoring unit processes sensor data streams through edge computing nodes to generate equipment health indices. The AI ​​anomaly detection unit uses a convolutional neural network model to analyze historical data, predict the probability of failure and the time of failure. The automated response unit triggers alarms or performs self-healing operations based on the prediction results. The self-healing operation involves isolating faulty equipment or adjusting the load. The AI ​​anomaly detection unit dynamically optimizes model parameters through reinforcement learning algorithms to improve prediction accuracy to over 95%.

[0007] Preferably, the operation and maintenance management and monitoring module further includes a decision support unit, a sensor data fusion unit that integrates temperature, voltage and vibration sensor data to generate a comprehensive monitoring dashboard, and a decision support unit that provides operation and maintenance suggestions based on a risk scoring model to reduce manual intervention.

[0008] Preferably, the AI ​​anomaly detection unit further includes a federated learning training module, a dynamic feature selector, and a model drift correction mechanism. The federated learning training module is a distributed model training framework deployed among multiple edge computing nodes. Each node uses local power monitoring data to train a CNN sub-model and updates the global model parameters through encrypted gradient aggregation. The dynamic feature selector automatically filters input features based on the real-time operating conditions of the equipment to eliminate the interference of noise data on the model. The real-time operating conditions of the equipment are temperature fluctuations or load change rates. The model drift correction mechanism triggers an incremental learning process when the prediction error continuously exceeds a threshold, injecting the latest fault samples to reconstruct the decision boundary. The threshold can be adjusted manually.

[0009] Preferably, the automated response unit comprises a risk quantification assessment layer, a strategy matching library, and a feedback optimization loop. The risk quantification assessment layer generates a risk level matrix based on the fault probability and impact range output by the AI ​​anomaly detection unit. The level matrix has 0-5 levels. The strategy matching library stores predefined risk level matrix response action chains. The risk level matrix response action chains are as follows: Level 0-2 is set to normal; Level 3 automatically adjusts the load of adjacent devices and issues an early warning; Level 4 starts the backup power supply and isolates the faulty section; Level 5 triggers the entire system security protocol and synchronously notifies the operation and maintenance personnel. The feedback optimization loop records the execution effect of the response actions and updates the weights of the strategy matching library through a reinforcement learning algorithm.

[0010] Preferably, the operation and maintenance management and monitoring module integrates a blockchain verification subsystem. The blockchain verification subsystem includes cross-domain operation and maintenance collaboration, an audit trail chain, and model copyright protection. Cross-domain operation and maintenance collaboration involves writing the equipment prediction results, response actions, and execution timestamps into a private chain for verification by other power monitoring system nodes. The audit trail chain automatically verifies the compliance of operation and maintenance operations based on smart contracts, and triggers a freezing mechanism for abnormal operations. Model copyright protection records the AI ​​model version and training data through hash fingerprints to prevent unauthorized tampering.

[0011] Preferably, the update mechanism optimization module includes an automatic update mechanism and a configuration file template. The automatic update mechanism ensures that the latest software packages can be synchronized to the offline environment in a timely manner. The configuration file template facilitates the device to point to the local repository, realizing one-click update configuration. The update mechanism optimization module stores RPM, DEB and binary files, and the development format parser automatically extracts metadata, which includes version number, dependencies and applicable systems. The update mechanism optimization module builds an offline mirror site, which uses apt-mirror and createrepo to generate standard repository index files.

[0012] Preferably, the system integration and compatibility module is compatible with different versions of Linux operating systems and NingSi operating systems installed in the local repository and power monitoring system. The system integration and compatibility module supports seamless use of yum and apt commands. The system integration and compatibility module supports both Cron scheduled tasks and event-triggered modes. The Cron scheduled task and event-triggered modes are incremental synchronization from an external trusted source to the local repository. The external trusted source is the vendor's offline package server. It supports different systems on the station side to upgrade and update from a unified trusted repository through yum and apt commands. The upgrade and update is automatically mapped to the local repository API by yuminstall. The system integration and compatibility module provides a standardized API interface that is compatible with a variety of external power monitoring devices and protocols.

[0013] Preferably, the operation and maintenance management and monitoring module also includes a warehouse operation status monitoring tool to track warehouse availability in real time, configure a log management system, and record four-tuple logs. The four-tuple logs contain the operation time, account, object, and result. The operation and maintenance management and monitoring module supports SQL-style retrieval and visual analysis to ensure that the update process is transparent and traceable.

[0014] Preferably, the control method of this system includes the following steps: Step S1, Warehouse Setup: Initialize the distributed storage architecture and configure data collection rules; Step S2, Update and Optimize: Perform incremental data updates and monitor the completeness of the updates; Step S3, System Integration: Connect to external devices via API interface to achieve protocol adaptation; Step S4, Operation and Maintenance Monitoring: Analyze equipment data in real time, use AI models to predict faults, and automatically execute response actions. Operation and maintenance monitoring includes data preprocessing, model inference, and decision output to achieve end-to-end automated operation and maintenance management.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention utilizes a real-time monitoring unit to process sensor data streams through edge computing nodes and generate equipment health indices. An AI anomaly detection unit analyzes historical data using a convolutional neural network model to predict failure probabilities and failure times. An automated response unit triggers alarms or performs self-healing operations based on the prediction results. Self-healing operations involve isolating faulty equipment or adjusting the load. The AI ​​anomaly detection unit dynamically optimizes model parameters using reinforcement learning algorithms, improving prediction accuracy to over 95%. The operation and maintenance management and monitoring module also includes a decision support unit. A sensor data fusion unit integrates temperature, voltage, and vibration sensor data to generate a comprehensive monitoring dashboard. The decision support unit provides operation and maintenance suggestions based on a risk scoring model, reducing manual intervention and achieving lower operation and maintenance costs, extended equipment lifespan, and support for smart city and renewable energy applications, thus providing an efficient and reliable solution for warehouse management systems. 2. This invention utilizes a federated learning training model to deploy a distributed model training framework across multiple edge computing nodes. Each node trains a CNN sub-model using local power monitoring data, updates global model parameters through encrypted gradient aggregation, and employs a dynamic feature selector to automatically filter input features based on real-time equipment operating conditions, eliminating interference from noisy data. Real-time equipment operating conditions include temperature fluctuations or load change rates. The model drift correction mechanism triggers an incremental learning process when the prediction error continuously exceeds a threshold, injecting the latest fault samples to reconstruct the decision boundary. The threshold can be manually adjusted, thus overcoming the limitations of traditional centralized training. This improves the model's generalization ability while protecting data privacy, and dynamic feature optimization keeps the prediction accuracy stable above 96%, while also adapting to dynamic changes in power grid load. 3. This invention generates a risk level matrix (0-5 levels) based on the fault probability and impact range output by the AI ​​anomaly detection unit through a risk quantification assessment layer. The strategy matching library stores predefined risk level matrix response action chains. The risk level matrix response action chains are as follows: Level 0-2 is set to normal; Level 3 automatically adjusts the load of adjacent equipment and issues an early warning; Level 4 starts the backup power supply and isolates the faulty section; Level 5 triggers the entire system safety protocol and simultaneously notifies the operation and maintenance personnel. The feedback optimization loop records the execution effect of the response action and updates the weights of the strategy matching library through a reinforcement learning algorithm. This achieves a leap from single action triggering to hierarchical strategy chain execution, shortens the response decision time to within 200ms, and reduces the error rate, significantly improving the accuracy and safety of power grid fault handling. 4. This invention utilizes a blockchain verification subsystem comprising cross-domain operation and maintenance collaboration, an audit trail chain, and model copyright protection. Cross-domain operation and maintenance collaboration involves writing equipment prediction results, response actions, and execution timestamps into a private chain for verification by other power monitoring system nodes. The audit trail chain automatically verifies the compliance of operation and maintenance operations based on smart contracts, triggering a freezing mechanism for abnormal operations. Model copyright protection records AI model versions and training data using hash fingerprints to prevent unauthorized tampering. This invention integrates blockchain and AI prediction in power monitoring and operation and maintenance, solving the trust problem in multi-entity collaboration, and providing technical protection for model intellectual property rights. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the warehouse management system of the present invention; Figure 2 This is a schematic diagram of the operation and maintenance management and monitoring module of the present invention; Figure 3 This is a schematic diagram of the workflow of the AI ​​anomaly detection unit of the present invention; Figure 4 This is a schematic diagram of the workflow of the automated response unit of the present invention; Figure 5 This is a schematic diagram of the workflow of the blockchain verification subsystem of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1: Please refer to Figure 1 and Figure 2A power monitoring and control warehouse system includes: a local warehouse module, an update mechanism optimization module, a system integration and compatibility module, and an operation and maintenance management and monitoring module. The local warehouse module, update mechanism optimization module, system integration and compatibility module, and operation and maintenance management and monitoring module transmit data to each other through a database. The local warehouse module supports distributed deployment of main warehouse and sub-warehouses, adapts to the layered architecture of the power system, and supports real-time acquisition and compressed storage of power monitoring data. The operation and maintenance management and monitoring module includes a sensor data fusion unit, an AI anomaly detection unit, and an automated response unit.

[0019] The operation and maintenance management and monitoring module also includes a real-time monitoring unit. This unit processes sensor data streams through edge computing nodes to generate equipment health indices. An AI anomaly detection unit uses a convolutional neural network model to analyze historical data, predicting failure probabilities and failure times. An automated response unit triggers alarms or performs self-healing operations based on the prediction results. Self-healing operations involve isolating faulty equipment or adjusting the load. The AI ​​anomaly detection unit dynamically optimizes model parameters using reinforcement learning algorithms, improving prediction accuracy to over 95%. The operation and maintenance management and monitoring module also includes a decision support unit. A sensor data fusion unit integrates temperature, voltage, and vibration sensor data to generate a comprehensive monitoring dashboard. The decision support unit provides operation and maintenance suggestions based on a risk scoring model, reducing manual intervention.

[0020] The AI ​​anomaly detection unit also includes a federated learning training module, a dynamic feature selector, and a model drift correction mechanism. The federated learning training module is a distributed model training framework deployed among multiple edge computing nodes. Each node uses local power monitoring data to train a CNN sub-model and updates the global model parameters through encrypted gradient aggregation. The dynamic feature selector automatically filters input features based on the real-time operating conditions of the equipment to eliminate the interference of noisy data on the model. The real-time operating conditions of the equipment are temperature fluctuations or load change rates. The model drift correction mechanism triggers an incremental learning process when the prediction error continuously exceeds a threshold, injecting the latest fault samples to reconstruct the decision boundary. The threshold can be adjusted manually.

[0021] The automated response unit consists of a risk quantification assessment layer, a strategy matching library, and a feedback optimization loop. The risk quantification assessment layer generates a risk level matrix based on the fault probability and impact range output by the AI ​​anomaly detection unit. The level matrix ranges from 0 to 5. The strategy matching library stores predefined risk level matrix response action chains. The risk level matrix response action chains are as follows: Level 0-2 is set to normal; Level 3 automatically adjusts the load of adjacent devices and issues an early warning; Level 4 starts the backup power supply and isolates the faulty section; Level 5 triggers the entire system's safety protocol and simultaneously notifies the operation and maintenance personnel. The feedback optimization loop records the execution effect of the response actions and updates the weights of the strategy matching library through a reinforcement learning algorithm.

[0022] The operation and maintenance management and monitoring module integrates a blockchain verification subsystem, which includes cross-domain operation and maintenance collaboration, an audit trail chain, and model copyright protection. Cross-domain operation and maintenance collaboration involves writing equipment prediction results, response actions, and execution timestamps into a private chain for verification by other power monitoring system nodes. The audit trail chain automatically verifies the compliance of operation and maintenance operations based on smart contracts, and triggers a freezing mechanism for abnormal operations. Model copyright protection records the AI ​​model version and training data through hash fingerprints to prevent unauthorized tampering.

[0023] The update mechanism optimization module includes an automatic update mechanism and configuration file templates. The automatic update mechanism ensures that the latest software packages can be synchronized to the offline environment in a timely manner. The configuration file templates facilitate devices to point to the local repository, enabling one-click update configuration. The update mechanism optimization module stores RPM, DEB, and binary files, and develops a format parser to automatically extract metadata, which includes version number, dependencies, and applicable systems. The update mechanism optimization module also builds an offline mirror site, which uses apt-mirror and createrepo to generate standard repository index files.

[0024] The system integration and compatibility module ensures compatibility between the local repository and the power monitoring system with different versions of Linux operating systems and NingSi operating systems. It supports seamless use of the yum and apt commands and supports both Cron scheduled tasks and event-triggered modes. Both modes enable incremental synchronization from an external trusted source to the local repository, with the external trusted source being the vendor's offline package server. It supports upgrades and updates from a unified trusted repository via the yum and apt commands for different systems on the site. Upgrades and updates are automatically mapped to the local repository API using yuminstall. The system integration and compatibility module provides standardized API interfaces, compatible with various external power monitoring devices and protocols.

[0025] The operation and maintenance management and monitoring module also includes a warehouse operation status monitoring tool, which tracks warehouse availability in real time, configures a log management system, and records four-tuple logs. The four-tuple logs contain the operation time, account, object, and result. The operation and maintenance management and monitoring module supports SQL-style retrieval and visual analysis to ensure that the update process is transparent and traceable.

[0026] The control methods of this system include the following steps: Step S1, Warehouse Setup: Initialize the distributed storage architecture and configure data collection rules; Step S2, Update and Optimize: Perform incremental data updates and monitor the completeness of the updates; Step S3, System Integration: Connect to external devices via API interface to achieve protocol adaptation; Step S4, Operation and Maintenance Monitoring: Analyze equipment data in real time, use AI models to predict faults, and automatically execute response actions. Operation and maintenance monitoring includes data preprocessing, model inference, and decision output to achieve end-to-end automated operation and maintenance management.

[0027] The operation and maintenance management and monitoring module also includes a real-time monitoring unit. This unit processes sensor data streams through edge computing nodes to generate equipment health indices. An AI anomaly detection unit uses a convolutional neural network model to analyze historical data, predicting failure probabilities and failure times. An automated response unit triggers alarms or performs self-healing operations based on the prediction results. Self-healing operations involve isolating faulty equipment or adjusting the load. The AI ​​anomaly detection unit dynamically optimizes model parameters using reinforcement learning algorithms, improving prediction accuracy to over 95%. The operation and maintenance management and monitoring module also includes a decision support unit. A sensor data fusion unit integrates temperature, voltage, and vibration sensor data to generate a comprehensive monitoring dashboard. The decision support unit provides operation and maintenance suggestions based on a risk scoring model, reducing manual intervention, thereby lowering operation and maintenance costs, extending equipment lifespan, supporting smart city and renewable energy applications, and improving the efficiency and reliability of warehouse management systems.

[0028] Example 2: Please refer to Figure 1 and Figure 3 A power monitoring and control warehouse system includes an AI anomaly detection unit that further comprises a federated learning training module, a dynamic feature selector, and a model drift correction mechanism. The federated learning training module deploys a distributed model training framework across multiple edge computing nodes. Each node trains a CNN sub-model using local power monitoring data and updates global model parameters through encrypted gradient aggregation. The dynamic feature selector automatically filters input features based on real-time equipment operating conditions, eliminating interference from noisy data. Real-time equipment operating conditions are temperature fluctuations or load change rates. The model drift correction mechanism triggers an incremental learning process when the prediction error continuously exceeds a threshold, injecting the latest fault samples to reconstruct the decision boundary. The threshold can be manually adjusted, thus overcoming the limitations of traditional centralized training, improving model generalization ability while protecting data privacy, and ensuring that the prediction accuracy remains stable above 96% through dynamic feature optimization, adapting to dynamic changes in power grid load.

[0029] Example 3: Please refer to Figure 1 and Figure 4A power monitoring and control warehouse system includes an automated response unit comprising a risk quantification assessment layer, a strategy matching library, and a feedback optimization loop. The risk quantification assessment layer generates a risk level matrix (0-5 levels) based on the fault probability and impact range output by an AI anomaly detection unit. The strategy matching library stores predefined risk level matrix response action chains. These chains are configured as follows: Levels 0-2 are set to normal; Level 3 automatically adjusts the load of adjacent equipment and issues a warning; Level 4 activates backup power and isolates the faulty section; and Level 5 triggers the entire system's safety protocol and simultaneously notifies maintenance personnel. The feedback optimization loop records the execution effect of the response actions and updates the weights of the strategy matching library using a reinforcement learning algorithm. This achieves a leap from single-action triggering to hierarchical strategy chain execution, shortening the response decision time to within 200ms and reducing the error rate, significantly improving the accuracy and safety of power grid fault handling.

[0030] Example 4: Please refer to Figure 1 and Figure 5 A control warehouse system for power monitoring is disclosed. The operation and maintenance management and monitoring module integrates a blockchain verification subsystem. The blockchain verification subsystem includes cross-domain operation and maintenance collaboration, an audit trail chain, and model copyright protection. Cross-domain operation and maintenance collaboration involves writing equipment prediction results, response actions, and execution timestamps into a private chain for verification by other power monitoring system nodes. The audit trail chain automatically verifies the compliance of operation and maintenance operations based on smart contracts, and triggers a freezing mechanism for abnormal operations. Model copyright protection records the AI ​​model version and training data through hash fingerprints to prevent unauthorized tampering. This system integrates blockchain and AI prediction in power monitoring and operation and maintenance, solves the trust problem in multi-entity collaboration, and provides technical protection for model intellectual property rights.

[0031] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A control warehouse system for power monitoring, characterized in that, include: The system comprises a local warehouse module, an update mechanism optimization module, a system integration and compatibility module, and an operation and maintenance management and monitoring module. These modules transmit data to each other via a database. The local warehouse module supports distributed deployment of main warehouses and sub-warehouses, adapts to the hierarchical architecture of the power system, and supports real-time acquisition and compressed storage of power monitoring data. The operation and maintenance management and monitoring module includes a sensor data fusion unit, an AI anomaly detection unit, and an automated response unit.

2. The power monitoring and control warehouse system according to claim 1, characterized in that: The operation and maintenance management and monitoring module also includes a real-time monitoring unit. The real-time monitoring unit processes sensor data streams through edge computing nodes to generate equipment health indices. The AI ​​anomaly detection unit uses a convolutional neural network model to analyze historical data, predict the probability of failure and the time of failure. The automated response unit triggers alarms or performs self-healing operations based on the prediction results. The self-healing operation involves isolating faulty equipment or adjusting the load. The AI ​​anomaly detection unit dynamically optimizes model parameters through reinforcement learning algorithms, improving prediction accuracy to over 95%.

3. A power monitoring and control warehouse system according to claim 2, characterized in that: The operation and maintenance management and monitoring module also includes a decision support unit, a sensor data fusion unit that integrates temperature, voltage and vibration sensor data to generate a comprehensive monitoring dashboard, and a decision support unit that provides operation and maintenance suggestions based on a risk scoring model to reduce manual intervention.

4. A power monitoring and control warehouse system according to claim 1, characterized in that: The AI ​​anomaly detection unit also includes a federated learning training module, a dynamic feature selector, and a model drift correction mechanism. The federated learning training module is a distributed model training framework deployed among multiple edge computing nodes. Each node uses local power monitoring data to train a CNN sub-model and updates the global model parameters through encrypted gradient aggregation. The dynamic feature selector automatically filters input features based on the real-time operating conditions of the equipment to eliminate the interference of noisy data on the model. The real-time operating conditions of the equipment are temperature fluctuations or load change rates. The model drift correction mechanism triggers an incremental learning process when the prediction error continuously exceeds a threshold, injecting the latest fault samples to reconstruct the decision boundary. The threshold can be adjusted manually.

5. A power monitoring and control warehouse system according to claim 1, characterized in that: The automated response unit includes a risk quantification assessment layer, a strategy matching library, and a feedback optimization loop. The risk quantification assessment layer generates a risk level matrix based on the fault probability and impact range output by the AI ​​anomaly detection unit. The level matrix has 0-5 levels. The strategy matching library stores predefined risk level matrix response action chains. The risk level matrix response action chain is as follows: Level 0-2 is set to normal; Level 3 automatically adjusts the load of adjacent devices and issues an early warning; Level 4 starts the backup power supply and isolates the faulty section; Level 5 triggers the entire system's safety protocol and synchronously notifies the operation and maintenance personnel. The feedback optimization loop records the execution effect of the response action and updates the weights of the strategy matching library through a reinforcement learning algorithm.

6. A power monitoring and control warehouse system according to claim 1, characterized in that: The operation and maintenance management and monitoring module integrates a blockchain verification subsystem, which includes cross-domain operation and maintenance collaboration, an audit trail chain, and model copyright protection. Cross-domain operation and maintenance collaboration involves writing equipment prediction results, response actions, and execution timestamps into a private chain for verification by other power monitoring system nodes. The audit trail chain automatically verifies the compliance of operation and maintenance operations based on smart contracts, and triggers a freezing mechanism for abnormal operations. Model copyright protection records the AI ​​model version and training data through hash fingerprints to prevent unauthorized tampering.

7. A power monitoring and control warehouse system according to claim 1, characterized in that: The update mechanism optimization module includes an automatic update mechanism and configuration file templates. The automatic update mechanism ensures that the latest software packages can be synchronized to the offline environment in a timely manner. The configuration file templates facilitate devices to point to the local repository, enabling one-click update configuration. The update mechanism optimization module stores RPM, DEB, and binary files, and a development format parser automatically extracts metadata, which includes version number, dependencies, and applicable systems. The update mechanism optimization module also builds an offline mirror site, which uses apt-mirror and createrepo to generate standard repository index files.

8. A power monitoring and control warehouse system according to claim 7, characterized in that: The system integration and compatibility module is compatible with different versions of Linux operating systems and NingSi operating systems installed in the local repository and power monitoring system. The system integration and compatibility module supports seamless use of yum and apt commands. The system integration and compatibility module supports both Cron scheduled tasks and event-triggered modes. The Cron scheduled task and event-triggered modes are incremental synchronization from an external trusted source to the local repository. The external trusted source is the vendor's offline package server. It supports different systems on the station side to upgrade and update from a unified trusted repository through yum and apt commands. Upgrades and updates are automatically mapped to the local repository API by yuminstall. The system integration and compatibility module provides a standardized API interface that is compatible with a variety of external power monitoring devices and protocols.

9. A power monitoring and control warehouse system according to claim 1, characterized in that: The operation and maintenance management and monitoring module also includes a warehouse operation status monitoring tool, which tracks warehouse availability in real time, configures a log management system, and records four-tuple logs. The four-tuple logs contain the operation time, account, object, and result. The operation and maintenance management and monitoring module supports SQL-style retrieval and visual analysis to ensure that the update process is transparent and traceable.

10. A control method for a power monitoring and control warehouse system, applicable to the power monitoring and control warehouse system described in any one of claims 1-9, characterized in that, The control methods of this system include the following steps: Step S1, Warehouse Setup: Initialize the distributed storage architecture and configure data collection rules; Step S2, Update and Optimize: Perform incremental data updates and monitor the completeness of the updates; Step S3, System Integration: Connect to external devices via API interface to achieve protocol adaptation; Step S4, Operation and Maintenance Monitoring: Analyze equipment data in real time, use AI models to predict faults, and automatically execute response actions. Operation and maintenance monitoring includes data preprocessing, model inference, and decision output to achieve end-to-end automated operation and maintenance management.

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