A substation inspection robot autonomous inspection method and system

Abstract

The present application relates to the technical field of substation inspection robots, and discloses a substation inspection robot autonomous inspection method and system. The method collects current environmental parameters of the substation and real-time state information of the inspection robot, and constructs an inspection environment model; analyzes historical inspection records, classifies historical inspection strategies into multiple experience levels, and establishes a multi-layer experience pool; calculates intelligent inspection strategies required to achieve a target inspection state in the inspection environment model; uses a double-channel coordination mechanism to real-time correct the intelligent inspection strategies and experience inspection strategies to respond to dynamic changes in the substation environment, and generates a final inspection strategy; executes control instructions based on the final inspection strategy, completes the inspection task, and records the actual inspection state; compares the deviation reasons of the actual inspection state and the target inspection state, adjusts the experience content in the multi-layer experience pool according to the deviation reasons, and optimizes the experience data.

Description

Technical Field

[0001] This invention relates to the field of substation inspection robot technology, specifically to an autonomous inspection method and system for substation inspection robots. Background Technology

[0002] In power systems, substations are a crucial link in ensuring stable power transmission and distribution, and the stable operation of their equipment is essential for the safety of the entire power grid. Traditionally, substation inspections have relied mainly on manual labor, but this method faces many insurmountable challenges.

[0003] Manual inspections require maintenance personnel to regularly travel to the substation site, which is not only time-consuming but also involves transportation costs. If the substation is located in a remote area with inconvenient transportation, the difficulty and cost of travel for maintenance personnel will increase further. During the journey to the site, they may also encounter traffic congestion, inclement weather, and other situations that affect the timeliness of the inspection. Moreover, manual inspections pose personal safety risks. Substations contain dangerous factors such as high voltage and high current; in the event of equipment failure or abnormality, maintenance personnel may be directly exposed to danger, threatening their lives.

[0004] Traditional manual inspection methods are relatively simple, relying mainly on the senses of maintenance personnel and basic tools. This approach is greatly affected by weather and environmental factors. In severe weather conditions, such as heavy rain, heavy snow, and dense fog, the observation and testing work of maintenance personnel is severely hampered, making it difficult to accurately determine the equipment status. The accuracy and efficiency of manual inspections largely depend on the skills and experience of the personnel. Differences in the professional level and working conditions of different maintenance personnel can easily lead to deviations in inspection results, causing equipment defects or anomalies to go undetected in a timely manner, which can then develop into serious faults and affect the stable operation of the power grid.

[0005] With rapid economic development, the power grid is expanding, the number of substations is increasing, and the complexity of equipment is also rising. This has led to a significant increase in inspection workload, while the number of professional maintenance personnel is growing relatively slowly, resulting in a growing contradiction between insufficient personnel and the increasing workload. Manual inspection is inefficient and cannot meet the needs of the rapidly developing power grid. To solve the problems of traditional manual inspection, substation inspection robots have emerged, but existing robot inspection technology still has many limitations.

[0006] Some robots employing magnetic navigation for inspection require laying magnetic wires on the ground and placing necessary electronic tags along the inspection path to send commands to the robot to coordinate its actions. This method is not only costly and complex to install initially, but also cumbersome to maintain later. Failure of the magnetic wires or electronic tags can disrupt the robot's normal operation. Furthermore, these robots have a limited range of inspection capabilities, often only able to perform simple tasks, making it difficult to meet the demands of comprehensive, multi-parameter inspection of substation equipment. In terms of obstacle avoidance, these robots perform poorly; they cannot respond flexibly to sudden obstacles or environmental changes, potentially leading to inspection interruptions or even robot damage.

[0007] The working mode of some single inspection robots also has obvious shortcomings. In large-scale medium and high voltage substations, there are many areas that need to be inspected. The inspection efficiency of a single robot is low, and it is difficult to complete the comprehensive inspection task within the specified time. Moreover, once the robot malfunctions during the inspection, it cannot continue to complete the remaining inspection tasks, resulting in the interruption of the inspection work and requiring manual intervention.

[0008] Most existing inspection robots are unable to effectively inspect the complex magnetic field environment of substations. Substations contain various electrical devices that generate complex magnetic fields, and abnormal magnetic fields may indicate potential equipment failures. However, most current robots lack the ability to effectively detect and analyze magnetic fields, making it difficult to identify anomalies and provide timely warnings of magnetic field-related equipment malfunctions to maintenance personnel. Summary of the Invention

[0009] The purpose of this invention is to provide an autonomous inspection method and system for substation inspection robots to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides an autonomous inspection method for a substation inspection robot, the method comprising:

[0011] Collect current environmental parameters of the substation and real-time status information of the inspection robot to build an inspection environment model;

[0012] Analyze historical inspection records, and classify historical inspection strategies into multiple experience levels based on the confidence level of historical inspection strategies and the gap between historical inspection results and expected goals, and build a multi-layered experience pool.

[0013] Based on the preset target inspection state in the inspection environment model, the intelligent inspection strategy required to achieve the target inspection state is calculated, and at the same time, the experience inspection strategy is extracted from the multi-layer experience pool based on the strategy confidence and state matching degree.

[0014] A dual-path coordination mechanism is adopted to make real-time corrections to the intelligent inspection strategy and the experience-based inspection strategy in response to the dynamic changes in the substation environment and to generate the final inspection strategy.

[0015] Execute control commands based on the final inspection strategy, complete the inspection task, and record the actual inspection status;

[0016] Compare the deviations between the actual inspection status and the target inspection status, and adjust the experience content in the multi-layered experience pools according to the reasons for the deviations to optimize the experience data.

[0017] Preferably, the step of collecting the current environmental parameters of the substation and the real-time status information of the inspection robot to construct the inspection environment model includes:

[0018] Environmental parameter data are collected using a multi-source sensor array deployed within the substation. The multi-source sensor array includes temperature sensors, humidity sensors, vibration sensors, and electromagnetic sensors.

[0019] The robot's visual perception system captures image sequences of the inspection area, and image recognition technology is used to analyze the equipment status and robot pose.

[0020] Multi-source sensor data and image sequence information are spatiotemporally fused to construct a dynamically updated digital twin model, which serves as an inspection environment model.

[0021] Preferably, the step of analyzing historical inspection records and classifying historical inspection strategies into multiple experience levels based on the confidence level of historical inspection strategies and the gap between historical inspection results and expected targets, and constructing a multi-layered experience pool, includes:

[0022] Extract the execution records of the inspection strategy from the historical database, including strategy parameters, inspection results, and timestamps;

[0023] Statistical analysis methods were used to calculate the success rate and deviation rate of each historical inspection strategy.

[0024] Based on the threshold ranges of success rate and deviation indicators, historical inspection strategies are automatically divided into core experience layer, standard experience layer, and basic experience layer, forming a hierarchical experience pool.

[0025] Preferably, the step of calculating the intelligent inspection strategy required to achieve the target inspection state based on the preset target inspection state in the inspection environment model, and extracting the experience inspection strategy from the multi-layer experience pool based on the strategy confidence and state matching degree, includes:

[0026] The current environmental parameters and robot status are input into the intelligent decision-making system, and an optimized inspection strategy is generated with the target inspection status as a constraint.

[0027] Perform pattern matching operations in a multi-layered experience pool to filter out historical strategy cases that are highly similar to the current target inspection status;

[0028] By combining optimized inspection strategies with historical strategy cases using strategy synthesis technology, a preliminary strategy set is generated.

[0029] Preferably, the step of using a dual-path coordination mechanism to modify the intelligent inspection strategy and the experience-based inspection strategy in real time to respond to dynamic changes in the substation environment and generate the final inspection strategy includes:

[0030] Continuously monitor substation environmental parameters and robot operating indicators, and set anomaly detection thresholds;

[0031] When the monitored data exceeds the anomaly detection threshold, it automatically switches to the experience inspection strategy channel and selects an appropriate strategy from the core experience layer.

[0032] Within the normal monitoring range, maintain the intelligent inspection strategy channel as the dominant force, and regularly evaluate the applicability of the experience-based strategy to achieve dynamic strategy adjustment.

[0033] Preferably, the step of executing control instructions based on the final inspection strategy, completing the inspection task, and recording the actual inspection status includes:

[0034] The final inspection strategy is analyzed into a sequence of specific action instructions, including movement path, detection points, and data acquisition commands;

[0035] The robot is controlled to perform inspection tasks according to the instruction sequence, and completes equipment status acquisition and image recording at each inspection point;

[0036] By integrating robot sensor data and visual feedback, a real-time inspection status report is generated, including abnormal equipment readings and environmental changes.

[0037] Preferably, the step of comparing the deviation between the actual inspection status and the target inspection status, and adjusting the experience content in the multi-layer experience pool according to the deviation reasons to optimize the experience data includes:

[0038] Calculate the difference between the actual inspection time and the target inspection time, as well as the difference in task completion rate;

[0039] Analyze the root causes of the discrepancies and identify key influencing factors;

[0040] Revise the strategy content in the multi-layered experience pool based on influencing factors, and adjust the weights and case data of the experience layer.

[0041] Preferably, in the step of calculating the intelligent inspection strategy required to achieve the target inspection state, the generation of the intelligent inspection strategy focuses on resource optimization, including minimizing robot energy consumption, optimizing inspection path length, and reducing sensor usage frequency.

[0042] In the step of extracting experience inspection strategies from the multi-layer experience pool, the selection of experience inspection strategies is based on case library reasoning, retrieving similar scenarios from historical successful cases, and combining real-time matching degree evaluation to determine the final strategy.

[0043] Preferably, in the step of using a dual-path coordination mechanism to make real-time corrections to the intelligent inspection strategy and the experience-based inspection strategy, the dual-path coordination mechanism has a fault-tolerant function. When the main strategy channel fails, the backup strategy channel is immediately activated to ensure the continuity of the inspection process.

[0044] Preferably, the present invention also includes an autonomous inspection system for a substation inspection robot, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described autonomous inspection method for a substation inspection robot.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] This invention constructs a comprehensive and accurate inspection environment model by collecting current environmental parameters of the substation and real-time status information of the inspection robot. The substation environment is complex and variable; environmental factors such as temperature, humidity, and electromagnetic interference constantly affect the operating status of equipment, while the inspection robot's own power level and hardware status also affect the successful completion of inspection tasks. By comprehensively collecting this information, the substation environment can be perceived. Previous inspection technologies may only focus on some equipment parameters, failing to grasp the overall operating status of the substation. The model constructed by this invention integrates various types of information. Whether in harsh weather conditions with high temperature and humidity, or under special operating conditions with complex electromagnetic environments, this model can accurately reflect the actual situation of the substation, laying a solid foundation for the scientific conduct of inspection work.

[0047] By deeply analyzing historical inspection records and classifying historical inspection strategies into multiple experience levels based on their confidence levels and the gap between historical inspection results and expected targets, a multi-layered experience pool was established. This process fully leverages valuable experience from past inspection work. Each inspection is a practical exercise, containing methods and strategies for dealing with various situations. Through the systematic sorting and classification of these experiences, a rich experience repository has been formed. When calculating the intelligent inspection strategy required to achieve the target inspection state, experience inspection strategies are simultaneously extracted from the multi-layered experience pool based on strategy confidence and state matching degree, achieving an organic combination of intelligent analysis and historical experience. This is more scientific, reasonable, and efficient than simply relying on intelligent algorithms or past experience to formulate inspection strategies. When facing abnormal overheating issues caused by equipment aging, the experience pool may store effective inspection strategies for handling similar problems in the past. Combined with the analysis of the current equipment state and environmental factors by intelligent algorithms, highly targeted and reliable inspection strategies can be quickly generated, greatly improving the quality and efficiency of inspection work.

[0048] The key to this invention's adaptability to dynamic changes in the substation environment lies in its dual-path coordination mechanism, which enables real-time correction of both intelligent and experience-based inspection strategies. Substation operation is not static; new equipment failures and sudden environmental changes can occur at any time. Traditional inspection strategies, once established, often lack flexibility and are difficult to adjust quickly in the face of these changes. This invention's dual-path coordination mechanism, however, can monitor the dynamic changes in the substation environment in real time. When sudden changes in environmental parameters or new equipment anomalies are detected, it rapidly corrects both the intelligent and experience-based inspection strategies. In the event of sudden, strong electromagnetic interference, the dual-path coordination mechanism promptly adjusts the inspection robot's detection frequency, focus, and path to avoid the interference affecting the detection results, ensuring the timeliness and adaptability of the inspection strategy and guaranteeing successful completion of inspection tasks under various complex and changing conditions. Attached Figure Description

[0049] Figure 1 This is a schematic diagram illustrating the working principle of the autonomous inspection method for substation inspection robots described in this invention.

[0050] Figure 2 A flowchart outlining the steps involved in building an inspection environment model;

[0051] Figure 3 A flowchart outlining the steps involved in building a multi-layered experience pool. Detailed Implementation

[0052] 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.

[0053] Please see Figure 1 This invention provides an autonomous inspection method and system for substation inspection robots. The method integrates environmental perception, historical experience learning, and dynamic strategy adjustment to achieve autonomous inspection. The method begins by collecting current environmental parameters of the substation and real-time status information of the inspection robot, including temperature, humidity, vibration, and electromagnetic data, as well as the robot's visual feedback. The collected data is used to construct an inspection environment model, which is dynamically updated in the form of a digital twin to reflect the real-time status of the substation. Historical inspection records are analyzed, and based on the confidence level of historical inspection strategies and the gap between historical inspection results and expected goals, historical inspection strategies are classified into multiple experience levels, such as a core experience layer, a standard experience layer, and a basic experience layer. A multi-layered experience pool stores strategy cases with different levels of reliability. Based on the preset target inspection state in the inspection environment model, an intelligent inspection strategy required to achieve the target inspection state is calculated. The intelligent inspection strategy focuses on resource optimization, such as minimizing energy consumption. Experienced inspection strategies are extracted from the multi-layered experience pool based on strategy confidence and state matching degree; the selection of experienced inspection strategies is based on reasoning from the case library. A dual-path coordination mechanism is employed to continuously revise both the intelligent and experience-based inspection strategies. This mechanism is fault-tolerant and responds to dynamic changes in the substation environment, generating a final inspection strategy. Control commands based on this final strategy are executed, parsed into specific action sequences, completing the inspection task and recording the actual inspection status. Deviations between the actual and target inspection statuses are compared to identify the causes, including time differences and task completion variations. Based on these deviations, the experience content in the multi-layered experience pool is adjusted to optimize the experience data and enable continuous learning.

[0054] Example 1: See Figure 2Environmental parameter data is collected using a multi-source sensor array deployed within the substation. This array includes temperature sensors, humidity sensors, vibration sensors, and electromagnetic sensors. These sensors are distributed and installed around key equipment in the substation. The temperature sensor uses a platinum resistance PT100 element to measure the surface temperature of the equipment; the humidity sensor monitors air humidity based on the capacitive principle; the vibration sensor uses a piezoelectric accelerometer to detect mechanical vibration; and the electromagnetic sensor captures changes in magnetic field strength using a Hall effect element. The data acquisition frequency of the multi-source sensor array is dynamically adjusted according to the inspection task requirements, with a higher sampling rate used for monitoring points in high-temperature areas and a standard sampling rate used in regular areas. Sensor data is transmitted to a data concentrator via industrial Ethernet or wireless Zigbee protocol, and CRC check and retransmission mechanisms are used during transmission to ensure data integrity.

[0055] The robot's visual perception system captures image sequences of the inspection area. This system consists of a high-definition visible light camera and an infrared thermal imager. The visible light camera has a resolution of 1920x1080 pixels, and the infrared thermal imager has a thermal sensitivity of 0.05℃. Image sequences are continuously acquired at a rate of 25 frames per second. The visual perception system is mounted on a robot gimbal, which has pitch ±90° and rotation 360° capabilities. Image recognition technology is used to analyze the device status and robot pose. This technology is based on the YOLOv5 deep learning architecture for device target detection, and robot pose analysis uses the ARUCO marker recognition algorithm to calculate three-dimensional spatial coordinates. The image processing workflow includes Gaussian filtering for noise reduction, histogram equalization enhancement, and Canny edge detection. The processed image features are then input into the recognition model for inference. Multi-source sensor data and image sequence information are spatiotemporally synchronized and fused. The spatiotemporal synchronization and fusion adopts the IEEE 1588 precision clock protocol to align sensor timestamps, and spatial coordinate mapping is achieved through a grid map established by the robot SLAM system. A dynamically updated digital twin model is constructed as the inspection environment model. The digital twin model is developed using the Unity3D engine, and the model elements include transformer architecture, circuit breaker model, and conductor topology. The dynamic update mechanism is based on differential data stream technology, transmitting only changed data to reduce network load. The digital twin model supports multi-dimensional data display, with temperature distribution overlaid with thermal layers and equipment status marked with color codes.

[0056] The deployment scheme of the multi-source sensor array takes into account the electromagnetic compatibility requirements of the substation. The sensor housing adopts a metal shielding design, and magnetic rings are added to the signal cables to suppress interference. The sensor calibration cycle is every 30 days, and the calibration process uses a standard temperature source and vibration table for calibration. The optical parameters of the visual perception system are automatically adjusted according to the ambient light. Infrared supplementary lighting is activated in dark environments, and a neutral density filter is used in strong light conditions. The training dataset of the image recognition model contains 100,000 images of substation equipment, and the dataset annotation follows the PASCALVOC standard. The update logic of the digital twin model adopts an event-driven architecture. Circuit breaker tripping events trigger local model updates, and temperature over-limit events activate alarm layers. Model rendering optimization uses LOD technology, displaying a simplified model from a far-view perspective and showcasing fine structures from a close-view perspective. The fault diagnosis function of the multi-source sensor array is implemented through a self-test protocol. The sensors periodically send heartbeat packets, and failure to respond within a timeout triggers an alarm event. The image storage of the visual perception system uses H.265 encoding compression to save storage space while maintaining image quality. The structured storage of image recognition results uses a MySQL database, with each record containing a timestamp, device ID, and anomaly type.

[0057] The accuracy verification method for spatiotemporal synchronization fusion uses a laser tracker to measure spatial deviation, with time synchronization errors controlled within milliseconds. Version management of the digital twin model uses a Git system, generating commit records for each update. The power supply system for the multi-source sensor array uses a combination of solar panels and supercapacitors to ensure continuous operation in rainy weather. The lens cleaning mechanism of the visual perception system is designed with an automatic scraping device to regularly remove dust and water stains. The data interface of the digital twin model follows the OPCUA standard, supporting bidirectional communication with the SCADA system; the model visualization terminal supports touch interaction, allowing maintenance personnel to manually mark key areas of concern. The mounting bracket for the multi-source sensor array is made of corrosion-resistant stainless steel, and the base is designed with vibration-damping rubber pads. Image transmission latency optimization for the visual perception system uses JPEG2000 progressive transmission technology, prioritizing thumbnail transmission in low-bandwidth environments. The completed inspection environment model outputs an interactive 3D scene, supporting virtual roaming and equipment information query functions; the model data backup strategy adopts an off-site disaster recovery solution, with real-time synchronization between master and slave databases. Electromagnetic compatibility testing of the multi-source sensor array was conducted according to the GB / T17626 standard, and the radiated emission index met the Class A limit. Performance benchmarking of the visual perception system used a standard test chart with a center resolution of 2000 lines. Mesh optimization of the digital twin model employed surface subdivision technology to reduce the number of vertices while preserving detail; the model's physics engine integrated rigid body dynamics simulation to predict device collision behavior. Communication encryption for the multi-source sensor array used the AES-256 algorithm to prevent malicious data tampering. Focus control of the visual perception system employed an autofocus algorithm, and the focus evaluation function was based on the Tenengrad gradient operator.

[0058] A quality control system was established during the construction of the inspection environment model, with verification points at each integration stage. The model accuracy acceptance standard requires a spatial error of less than 5 cm and a temporal jitter of less than 100 milliseconds. The lifespan assessment of the multi-source sensor array was based on accelerated aging tests, achieving an MTBF of 100,000 hours. Optical distortion correction for the visual perception system adopted the Zhang Zhengyou calibration method, with radial distortion coefficients k1 and k2 controlled within 0.01. Lightweight processing of the digital twin model utilized instantiation rendering technology, with models of identical equipment sharing memory storage. The model animation system supports demonstrations of equipment operation processes, such as the opening and closing of disconnect switches. The lightning protection device for the multi-source sensor array adopted a combination design of gas discharge tubes and TVS diodes. The white balance algorithm of the visual perception system was based on the gray world assumption, with a color reproduction error ΔE of less than 3. The final inspection environment model was published on both web and mobile platforms, supporting cross-terminal adaptive display. The model update log recorded the changes and operators involved in each modification. The operation and maintenance management platform for the multi-source sensor array integrated equipment asset management functions, and the firmware upgrade of the visual perception system supported remote OTA (Over-The-Air) updates. Collision detection in digital twin models uses the separating axis theorem to accurately pinpoint the minimum distance between devices.

[0059] Example 2: See Figure 3This process analyzes historical inspection records and categorizes historical inspection strategies into multiple experience levels based on their confidence level and the gap between historical inspection results and expected targets, thus constructing a multi-layered experience pool. Simultaneously, it calculates the intelligent inspection strategy required to achieve the target inspection state based on the preset target inspection state in the inspection environment model, and extracts the experienced inspection strategies from the multi-layered experience pool based on strategy confidence and state matching degree. Execution records of the inspection strategies are extracted from the historical database. The historical database uses a time-series database architecture to store structured data. Execution records include strategy parameters such as path planning algorithm type and sensor configuration scheme, inspection results such as equipment anomaly detection reports and task completion status, and timestamps accurate to the millisecond level. The query interface of the historical database uses SQL to perform data retrieval operations, with search conditions including time range, robot model, and environment type. Statistical analysis methods were used to calculate the success rate and deviation rate for each historical inspection strategy. The statistical analysis applied hypothesis testing to calculate the probability of successful strategy execution. The success rate was defined as the ratio of the number of completed tasks to the total number of executions, and the deviation rate was calculated by the root mean square error between the actual inspection results and the expected target. A sliding time window technique was used to process time-series data, with the window size set to the most recent 100 inspection tasks. Based on the threshold ranges of the success rate and deviation rates, historical inspection strategies were automatically divided into a core experience layer, a standard experience layer, and a basic experience layer to form a hierarchical experience pool. The threshold ranges were dynamically determined using a clustering algorithm. The threshold for the core experience layer was set to a success rate greater than 95% and a deviation rate less than 5%, the threshold for the standard experience layer was set to a success rate between 80% and 95% and a deviation rate between 5% and 15%, and the basic experience layer included all other strategies. The storage structure of the hierarchical experience pool was implemented using a Redis database, with each layer of experience data stored in a hash table and an inverted index built to accelerate queries.

[0060] The current environmental parameters and robot status are input into the intelligent decision-making system. The current environmental parameters include temperature readings, humidity levels, and electromagnetic field strength. The robot status includes battery level, movement speed, and sensor health status. The intelligent decision-making system is built on a reinforcement learning framework, generating optimized inspection strategies with the target inspection state as a constraint. The target inspection state is defined as a multi-objective optimization problem that minimizes full equipment coverage detection and energy consumption. Pattern matching is performed in a multi-layered experience pool. The pattern matching operation uses a cosine similarity algorithm to calculate the feature vector distance between the current target inspection state and historical strategy cases. The feature vector includes the mean environmental parameters, robot state vector, and time features. Historical strategy cases with high similarity to the current target inspection state are selected, with a similarity threshold set above 0.8. The selection results are sorted in descending order of matching score. A strategy synthesis technique combines the optimized inspection strategy with historical strategy cases to generate a preliminary strategy set. The strategy synthesis technique uses a genetic algorithm for strategy crossover and mutation operations, and the combination methods include parameter-weighted fusion and rule-based logical merging. The output format of the preliminary strategy set is a JSON object, containing the strategy ID, execution parameters, and expected performance indicators. The maintenance process for the historical database includes regular data cleaning and backup operations. Data cleaning rules remove duplicate records and outliers, and the backup strategy uses incremental backups performed daily. Statistical analysis methods utilize a distributed computing framework, with the Spark engine processing large-scale historical data in parallel. The hierarchical experience pool update mechanism is driven by event triggers; the experience pool reclassification process is automatically triggered upon completion of a new inspection task. The training data for the intelligent decision-making system comes from successful cases in the historical database, and the training process uses an offline batch processing mode to update model parameters weekly. Performance optimization for pattern matching operations is achieved through pre-computed feature matrices, which are then loaded into GPU memory to accelerate similarity calculations. The validation phase of the strategy synthesis technology employs simulated environment testing, where a digital twin model is used to render a real substation scenario.

[0061] The historical database employs AES-256 encryption to protect sensitive information, and access control is based on a role-based access control model to limit the scope of operations. The visualization tools for statistical analysis methods integrate Tableau dashboards to display indicator trends and support interactive drill-down analysis. Query optimization in the hierarchical experience pool uses Bloom filters to reduce false positives, and a caching mechanism stores frequently accessed data. The real-time inference engine of the intelligent decision-making system is deployed on edge computing nodes, with inference latency controlled within 100 milliseconds. Feature engineering for pattern matching operations includes standardization and dimensionality reduction, and principal component analysis retains 95% of variance information. The output evaluation of the strategy synthesis technique uses a multi-objective evaluation function, with evaluation function weights set by domain experts. The historical database's storage partitioning strategy is based on time ranges, with each partition storing one quarter's worth of data for ease of management. The anomaly detection module of the statistical analysis methods integrates the Isolation Forest algorithm to identify outlier strategies. Data consistency in the hierarchical experience pool is ensured through ACID transactions, and write operations employ a pessimistic locking mechanism. Model version management in the intelligent decision-making system uses a Git system to track change history, and the rollback function supports rapid recovery of older versions. The distributed deployment of pattern matching operations employs a consistent hashing algorithm to balance the load, automatically reassigning tasks in case of node failure. The iterative optimization process of strategy synthesis technology includes an A / B testing framework, conducting online experiments to compare the performance of different synthesis strategies. The historical database compression algorithm uses Zstandard to reduce storage space usage, achieving a compression ratio of 3:1. Statistical analysis report generation is automated and executed on a scheduled basis, with reports including indicator summaries and recommended measures. The hierarchical experience pool monitoring and alarm settings include threshold alarm rules, sending notifications when core experience layer data is abnormal. The intelligent decision-making system's input validation module checks data format and range, logging invalid inputs and rejecting them. Semantic enhancement technology for pattern matching operations incorporates knowledge graph reasoning, with the knowledge graph constructed based on the substation equipment topology. The rule engine for strategy synthesis technology supports DSL-based custom rules, allowing business personnel to flexibly adjust the synthesis logic.

[0062] The historical database migration tool supports cross-platform data transfer, ensuring data integrity and consistency during the migration process. Benchmarking of statistical analysis methods uses standard datasets to verify accuracy, with the test environment isolated from the production environment to avoid interference. Backup and recovery drills for the hierarchical experience pool are performed monthly, with drill scripts simulating various failure scenarios. The performance profiling tool for the intelligent decision-making system identifies bottlenecks, focusing optimization on computationally intensive operations. The federated learning framework for pattern matching operations allows cross-site data collaboration, employing differential privacy technology for privacy protection. The standardized interface definition for policy synthesis technology is a REST API, allowing third-party systems to integrate policy generation services. The historical database archiving strategy moves expired data to cold storage, with a five-year retention period for archived data. Parameter tuning for statistical analysis methods uses grid search to explore optimal configurations. Capacity planning for the hierarchical experience pool predicts future demand based on historical growth trends. The intelligent decision-making system's fault recovery mechanism includes a backup model instance, automatically switching in case of primary instance failure. The incremental learning capability of pattern matching operations adapts to changes in data distribution, ensuring uninterrupted online model updates. The multi-objective optimization solver for policy synthesis technology supports constraint priority settings, with high-priority constraints strictly satisfied. Historical database audit logs record all data access operations, and log analysis tools detect suspicious behavior. The confidence interval calculation for statistical analysis methods applies the Bootstrap method to improve estimation robustness. Data quality monitoring in a hierarchical experience pool calculates integrity and accuracy metrics. The interpretive module of the intelligent decision-making system generates the basis for policy decisions, and the interpretation results are described in natural language. An approximation algorithm for pattern matching operations handles ultra-large-scale datasets, with approximation errors controlled within acceptable ranges. The parallel computing architecture of policy synthesis technology utilizes multi-core CPUs to concurrently execute synthesis tasks.

[0063] The query optimizer for historical databases analyzes execution plans and selects the optimal index. The time-series forecasting module, incorporating statistical analysis methods, integrates an ARIMA model to predict future values ​​of metrics. A hierarchical experience pool with redundant storage design deploys replicas across different availability zones. The intelligent decision-making system sets resource limits, including memory usage caps and CPU time limits. Hardware acceleration for pattern matching operations utilizes FPGA chips to improve computational speed. The code generator for policy synthesis technology compiles high-level policy descriptions into executable instructions.

[0064] Example 3: This section describes the process of using a dual-path coordination mechanism to real-time modify intelligent and experience-based inspection strategies in response to dynamic changes in the substation environment and generate the final inspection strategy. The dual-path coordination mechanism is fault-tolerant. Continuous monitoring of substation environmental parameters and robot operating indicators is performed. Monitoring data sources include temperature sensor arrays, humidity monitoring networks, vibration accelerometer groups, electromagnetic field strength meters, and the robot's built-in gyroscope, fuel gauge, and motor encoder. Monitoring data is sampled at 100-millisecond intervals using an Industrial Internet of Things (IIoT) protocol. The data stream is smoothed using Kalman filtering before being input to the anomaly detection module. A dynamic adaptive algorithm is used to set the anomaly detection threshold. The algorithm calculates the threshold boundary based on the probability distribution characteristics of historical monitoring data. The temperature parameter threshold is automatically adjusted according to seasonal changes, and the vibration parameter threshold is dynamically updated in relation to the equipment's operating status.

[0065] When monitored data exceeds the anomaly detection threshold, the system automatically switches to the experience-based inspection strategy channel. This switching action is completed within 10 milliseconds by the state machine controller. The experience-based inspection strategy channel selects an appropriate strategy from the core experience layer. The selection process is based on a multi-dimensional matching algorithm to calculate the similarity between the current anomaly scenario and historical cases. The matching dimensions include anomaly type, severity, and time characteristics. Within the normal monitoring range, the system maintains the intelligent inspection strategy channel as the dominant channel. The intelligent inspection strategy channel uses a model predictive control algorithm to optimize the inspection path in real time. The applicability of the experience-based strategy is periodically evaluated, with an evaluation cycle set at 5 minutes. The evaluation method involves strategy simulation testing using a digital twin system. The dual-path coordination mechanism is implemented using a microservice design. The intelligent strategy channel and the experience-based strategy channel run as independent services, exchanging data between the channels through a message middleware.

[0066] The fault tolerance of the dual-path coordination mechanism is achieved through multiple safeguards. When the primary strategy channel fails, the heartbeat detection mechanism triggers channel switching within 500 milliseconds; the backup strategy channel remains resident in memory in a hot standby state, ensuring the continuity of strategy execution during the switching process. The anomaly detection model for monitoring data uses the isolated forest algorithm to identify anomalies. Model training uses normal operating data from the past 30 days, and the anomaly judgment threshold is dynamically calibrated based on false alarm rate requirements. The strategy selection algorithm for the experience-based inspection strategy channel incorporates priority rules: in emergency anomaly scenarios, the strategy with the shortest handling time is prioritized, while in ordinary anomaly scenarios, the strategy with the optimal resource consumption is selected. The optimization algorithm for the intelligent inspection strategy channel integrates constraint satisfaction technology, processing robot motion constraints and device access constraints in real time; strategy outputs are issued for execution after feasibility verification. The simulation environment for evaluating the applicability of the experience strategy is built based on a discrete event simulation engine, with a simulation step size set to 1 second. Evaluation metrics include task completion rate and resource utilization. Communication security for the dual-path coordination mechanism uses TLS encrypted transmission, and the message format follows the Protobuf protocol standard to ensure data parsing efficiency.

[0067] Monitoring data is stored using a time-series database cluster, with a data retention strategy that differentiates between normal and abnormal data; normal data is retained for 7 days, and abnormal data for 30 days for analysis and optimization. The adaptive adjustment algorithm for anomaly detection thresholds incorporates a sliding window mechanism, with the window size dynamically changing based on data stability; threshold updates avoid frequent fluctuations using a lag adjustment method. Case matching optimization for the experience-based inspection strategy channel employs index pre-construction technology, and case feature vectors use locality-sensitive hashing to accelerate similarity calculation. Real-time optimization computational load is balanced across multiple computing nodes for the intelligent inspection strategy channel, with tasks distributed among nodes using consistent hashing. The results of the experience strategy applicability evaluation are fed back to the experience pool management system, triggering weight adjustments for the experience strategies. Fault injection testing of the dual-path coordination mechanism is conducted periodically, with test cases covering scenarios such as network outages, node crashes, and data anomalies. The monitoring data quality control module detects sensor failures; data from failed sensors is marked and excluded from anomaly detection. The anomaly detection thresholds are tiered, distinguishing between warning thresholds and alarm thresholds; warning thresholds trigger strategy fine-tuning, while alarm thresholds trigger channel switching. The rollback mechanism of the experience inspection strategy channel has a default strategy library, which activates the basic protection strategy when matching fails.

[0068] The intelligent inspection strategy channel's performance monitoring tracks strategy generation latency and resource consumption, with monitoring data used for system optimization. The confidence level calculation for the applicability assessment of empirical strategies uses a Bayesian probability model; manual review is triggered when the confidence level falls below a threshold. The dual-path coordination mechanism's deployment architecture employs multi-availability zone deployment, automatically switching to a backup availability zone in case of a single availability zone failure. Real-time stream processing of monitoring data uses window aggregation operations, with the window size configured according to business needs. The historical adjustment records of anomaly detection thresholds are stored in audit logs, and log analysis tools identify threshold adjustment patterns. The empirical inspection strategy channel's strategy execution effect tracking records the actual performance of the strategies, with performance data used to enhance the case library. Algorithm parameter tuning for the intelligent inspection strategy channel uses reinforcement learning, aiming to balance strategy quality and computational overhead. The simulation acceleration technology for the empirical strategy applicability assessment uses time scaling, with real-time scaling used for critical periods. Fault-tolerant drills for the dual-path coordination mechanism are performed monthly, with drill reports recording system response time and successful recovery rate. Anomaly correlation analysis of monitoring data identifies multi-sensor collaborative anomalies, with correlation rules configured based on device topology. Cluster synchronization of anomaly detection thresholds ensures consistency across multiple detection nodes. The experience inspection strategy channel supports the parallel execution of multiple strategies, and the combined strategies select the optimal result through a voting mechanism.

[0069] The intelligent inspection strategy channel employs a progressive update strategy, gradually replacing older models with new ones after validation. Decision trees are used for scenario classification in the experience-based strategy applicability assessment, with differentiated evaluation criteria for different scenarios. The dual-path coordination mechanism's resource isolation ensures that a single channel failure does not affect other channels. Compressed transmission of monitoring data reduces network bandwidth consumption, with lossy or lossless compression selected based on data type. Robust statistical methods are used for handling outliers in anomaly detection thresholds to avoid extreme values ​​interfering with threshold calculations. The experience-based inspection strategy channel's case update mechanism regularly incorporates new cases, while older cases are phased out based on usage frequency. Load monitoring in the intelligent inspection strategy channel predicts resource demands, and the elastic scaling mechanism automatically adjusts computing resources based on load. The multi-dimensional evaluation matrix for experience-based strategy applicability assessment includes dimensions such as time efficiency, resource consumption, and risk control. The system logs of the dual-path coordination mechanism use a structured format, and the log analysis platform provides a real-time monitoring dashboard. Data traceability records the data flow path, and traceability information supports problem localization. The sensitivity adjustment of the anomaly detection threshold is tiered according to business needs, with high sensitivity used for monitoring critical equipment. The strategy verification of the experience inspection strategy channel is completed in a sandbox environment, and it can only be put into actual use after the verification is passed.

[0070] The intelligent inspection strategy channel maintains multiple optimization algorithms, selecting the most suitable algorithm based on the problem characteristics. Specifically, when the problem focuses on path planning optimization, the A* algorithm or an improved apprentice algorithm is chosen to suit the equipment distribution topology and obstacle avoidance requirements. When the problem emphasizes multi-objective resource optimization (such as simultaneously considering energy consumption, path length, and sensor usage frequency), a non-dominated sorting genetic algorithm is selected to meet the needs of finding optimal solutions under multiple constraints. When the problem involves robot motion control parameter optimization, a PID control algorithm and its adaptive adjustment variant are selected to meet the needs of improving path tracking and positioning accuracy. When the problem involves sensor data processing and anomaly detection, the Isolation Forest algorithm is selected to meet the needs of identifying anomalies in environmental parameters and equipment status, ensuring accurate matching between the algorithm and the specific problem scenario.

[0071] The automated report generation for the applicability assessment of experience-based strategies generates an assessment summary, which is then pushed to the operations and maintenance management system. Version management of the dual-path coordination mechanism supports canary releases, with new versions gradually rolled out to the production environment. The entire dual-path coordination mechanism operates in a self-decision-making closed loop, with monitoring data driving strategy selection and strategy execution feedback optimizing mechanism parameters. Fault tolerance ensures system resilience, maintaining basic inspection capabilities even in abnormal scenarios. Multi-dimensional analysis of monitoring data reveals patterns of environmental change, and intelligent adjustment of anomaly detection thresholds improves system adaptability. The case library for the experience-based inspection strategy channel continuously evolves, with new experiences constantly enriching the strategy selection space. Algorithm iteration in the intelligent inspection strategy channel improves strategy quality, optimizing both efficiency and reliability. The coordination logic of the dual-path coordination mechanism dynamically balances innovation and conservatism, maintaining strategy advancement while inheriting historical experience. The efficiency improvement formula of the dual-path coordination mechanism describes the optimization relationship of channel switching decisions:

[0072]

[0073] in: This indicates the overall score for channel switching. Represents the number of monitoring indicators. This represents the weight coefficient of the i-th indicator. It is the normalization function for the i-th index. The real-time reading corresponding to the i-th monitoring indicator. This is the baseline value for the anomaly detection threshold. This reflects the rate of environmental change. The optimal formula for channel switching decisions quantitatively assesses the timing of channel switching. The numerator is a weighted average of various monitoring indicators, while the denominator incorporates dynamic environmental factors to adjust the switching sensitivity.

[0074] Example 4: Executing control commands based on the final inspection strategy completes the inspection task and records the actual inspection status. It also compares the deviation between the actual and target inspection status to identify the causes and adjusts the experience content in the multi-layer experience pool to optimize the experience data. The process of parsing the final inspection strategy into a sequence of specific action commands uses a syntax parser to process the strategy description language. The command sequence includes a list of coordinate points for the movement path, an array of device identifiers for detection points, and a set of parameters for the data acquisition command. The movement path planning uses the A* algorithm to calculate the optimal path, the detection point sorting is based on device priority logic, and the data acquisition command configures the sensor sampling rate and duration. The robot executes the inspection task according to the command sequence. The robot's motion control system receives the path point sequence and adjusts the hub motor speed through a PID controller. Data acquisition is triggered after the positioning accuracy at each detection point reaches the centimeter level. Device status acquisition calls a multi-source sensor array to read temperature, humidity, and vibration data. Image recording uses a visual perception system to capture high-definition images and infrared thermal imaging. The system integrates robot sensor data and visual feedback to generate actual inspection status reports. The data integration platform uses the Apache Kafka stream processing engine to aggregate data streams in real time. The report content is stored in a structured JSON format, including equipment reading anomaly markers and environmental change event logs.

[0075] The difference between the actual inspection time and the target inspection time is calculated using high-precision clock synchronization technology, with the time difference calculated to the millisecond level and jitter eliminated by averaging through a sliding window. The difference in task completion is quantified based on the completion ratio of a predefined task list, which covers the number of equipment inspections and the completeness of data types. The root cause analysis of the differences is performed using a root cause analysis framework, integrating fault tree analysis and correlation calculations to identify key influencing factors such as sudden electromagnetic pulses in environmental interference or path planning deviations in strategy execution errors. Based on these influencing factors, the strategy content in the multi-layered experience pool is revised. Revision operations use database transactions to ensure consistency, and the weights of the experience layers are adjusted based on the severity scores of the influencing factors. Case data updates employ incremental learning to incorporate new experiences. During the generation of the actual inspection status report, the data verification module checks the reasonableness boundaries of sensor readings; invalid data is marked and excluded from the report. The report distribution system pushes the data to the monitoring center via REST API and simultaneously stores it locally as a compressed archive file. The analysis of the time difference considers fluctuations in robot motion performance. The motion performance model establishes a baseline value based on historical data; deviations exceeding a threshold trigger a performance calibration process. The calculation of task completion difference values ​​incorporates weighting factors, with higher weighting for critical equipment detection tasks. The overall difference value score reflects the overall task execution performance. Environmental interference identification utilizes a pattern matching algorithm. The algorithm's training dataset includes various interference scenarios, and the identification results are categorized and stored for trend analysis. Strategy execution errors are tracked and recorded in operation logs. Log analysis tools pinpoint the time and context of error occurrences, and error types are encoded into standard codes for easy statistical analysis.

[0076] The multi-layered experience pool adjustment mechanism incorporates version control, generating a new version record with each revision, and supporting rollback operations. The weight adjustment algorithm utilizes gradient descent to optimize the influence distribution of the experience layer, with weight value normalization ensuring a constant sum. The case data update strategy distinguishes between major deviations and minor fluctuations; major deviations trigger case reconstruction, while minor fluctuations only adjust case metadata. The visualization interface of the actual inspection status report displays deviation trend charts, allowing maintenance personnel to interactively drill down into detailed data. The cause analysis of time difference integrates time series anomaly detection, with the detection algorithm identifying periodic patterns and sudden anomalies. The root cause analysis of task completion difference values ​​utilizes an expert system rule base, built based on domain knowledge, with the inference engine outputting a list of possible causes. Environmental interference modeling uses physical simulation tools to simulate the electromagnetic field distribution and temperature gradient of the substation, quantifying the interference impact. Corrective measures for strategy execution errors are automatically generated, with correction logic mapping error codes to predefined actions. The optimization effect of the multi-layered experience pool is evaluated using an A / B testing framework, comparing strategy performance before and after adjustments, with evaluation metrics including inspection efficiency and resource consumption. The long-term storage of actual inspection status reports adopts a data lake architecture, which supports multimodal querying and analysis. A time difference compensation mechanism incorporates a predictive model, which uses machine learning to forecast future time deviations. An early warning system for task completion discrepancies sets multi-level thresholds, and early warning messages are pushed to the operations and maintenance terminal in real time.

[0077] Environmental interference mitigation strategies are dynamically adjusted, with a mitigation strategy library containing multiple solutions, selected based on interference type and intensity. Preventative measures for strategy execution errors strengthen the verification process, simulating key steps before strategy execution. Distributed synchronization across multi-layered experience pools ensures data consistency across multiple replicas, using the Paxos algorithm to handle network partitions. The generation frequency of actual inspection status reports is adjusted based on task complexity, increasing the reporting frequency for more complex tasks for closer monitoring. Statistical analysis of time differences utilizes hypothesis testing methods to verify the significance of deviations. A root cause analysis tool for task completion differences integrates visual charts, displaying the contribution ratio of causes. Historical pattern mining of environmental interference uses clustering algorithms to identify common interference combinations. Error eradication processes are incorporated into a continuous improvement cycle, periodically reviewing error data. Access control for multi-layered experience pools strengthens security policies, restricting modifications based on role-based permissions. Integrity verification of actual inspection status reports uses hash algorithms, with hash values ​​stored on a tamper-proof blockchain. A real-time monitoring dashboard for time differences displays dynamic changes and supports custom alarm rules. A repair plan for the task completion discrepancy is automatically generated, which includes specific action items and a timeline.

[0078] The environmental interference adaptation mechanism learns from historical response effects and adjusts the interference identification sensitivity. Feedback on policy execution errors is collected from operations personnel, and the feedback system optimizes error classification accuracy. Performance monitoring of the multi-layered experience pool tracks query latency, and monitoring data is used for database optimization. The compression algorithm for actual inspection status reports is selected based on data type: LZ77 compression is used for text data, and JPEG2000 compression is used for image data. The time difference calibration process references a standard time source, which is synchronized via the NTP protocol. The report template for task completion difference values ​​is configurable, and the template editor allows for custom fields. Simulation tests of environmental interference are conducted in a sandbox environment that replicates real substation conditions. The root cause analysis report of policy execution errors generates a natural language summary, which is pushed to the knowledge management system. The backup strategy for the multi-layered experience pool adopts a geographically distributed, multi-active architecture, and backup data is encrypted and stored in cloud storage. The generation pipeline for actual inspection status reports processes data streams in parallel, and pipeline load balancing prevents bottlenecks. The trend prediction of time differences uses an ARIMA model, with model parameters updated periodically to adapt to changes. The optimization objective of the task completion difference value is to balance quality and cost, and the optimization model solves for the Pareto optimal solution.

[0079] Environmental interference isolation techniques utilize shielding and filtering, with shielding materials selected based on interference frequency characteristics. Measures to reduce strategy execution errors include training robot operators on common error cases. Query optimization in the multi-layered experience pool uses index merging technology to improve the performance of complex queries. Audit logs for actual inspection status reports record all accesses, and these logs are periodically analyzed for safety incidents. The time difference compensation algorithm considers robot aging factors, with the aging model based on equipment lifespan data. Root cause analysis of task completion differences integrates a machine learning classifier, with classifier feature engineering including temporal and contextual features. The real-time environmental interference response system features a low-latency architecture, triggering immediate adjustments. The strategy execution error detection algorithm incorporates a confusion matrix evaluation, with the evaluation results optimizing the algorithm's threshold. Data cleaning operations in the multi-layered experience pool are performed periodically, removing expired and invalid data. The actual inspection status report generation system is designed for high availability, with redundant deployment to avoid single points of failure. The time difference analysis tool provides an API interface supporting third-party system integration. A visualization tool for task completion differences uses the D3.js library, allowing interactive exploration of data relationships. A historical event database for environmental interference is constructed, with event tags categorizing interference types. Time tracking is used to correct policy execution errors, and this time data is used for efficiency improvement. A multi-layered experience pool caching mechanism stores hot data, and the cache invalidation strategy is based on the LRU algorithm. Signature verification of actual inspection status reports ensures authenticity, and digital certificate technology is used for signatures. A root cause analysis tool for time differences supports collaborative annotation, enriching the analysis dimensions with annotated data. An automated diagnostic system for task completion discrepancies integrates a rule engine, which supports dynamically loaded rules. See Table 1 for the classification of deviation causes and corresponding experience adjustment actions.

[0080] Table 1: Mapping Table of Deviation Causes and Experience-Based Adjustments

[0081]

[0082] The use of a deviation cause-experience adjustment mapping table is integrated into the deviation analysis process. The analysis engine automatically triggers adjustment actions by querying the table's mapping relationships. The table content is reviewed and updated regularly, with the update cycle synchronized with the equipment maintenance plan. Deviation data from actual inspection status reports is input into the table for lookup. The lookup algorithm matches the deviation category and root cause, outputting suggested adjustment actions. The execution of adjustment actions is automated through database scripts, which verify the consistency of the adjusted data. The table maintenance interface allows maintenance personnel to manually add new mapping relationships, and the maintenance log records all changes.

[0083] The analysis of time differences references the time deviation categories in the table, which are further subdivided into sudden delays and gradual offsets, with different subcategories corresponding to differentiated adjustments. The mapping of task completion difference values ​​considers completion deviation categories, which are segmented based on the completion ratio of the task list. The classification of environmental interference causes is expanded in the table to include physical interference and logical interference; physical interference includes weather changes, and logical interference includes software failures. The mapping of strategy execution errors distinguishes between syntax errors and semantic errors; syntax errors correct the strategy parser, while semantic errors adjust the strategy logic. Adjustments to multi-layered experience pools are encoded as standard opcodes in the table, which are parsed and executed by the adjustment engine. The efficiency of generating actual inspection status reports is improved through table optimization, and table indexes accelerate deviation category lookups. Root cause analysis of time differences utilizes historical mapping data from the table; historical data trains predictive models to improve analysis accuracy. The verification of adjustments to task completion difference values ​​is conducted through simulation testing, with test results used to optimize table content. Table entries for environmental interference are linked to weather forecast data, which is integrated into the cause prediction system. Table updates for strategy execution errors are based on error statistics, with the statistical frequency determining the urgency of updates.

[0084] Example 5: When calculating the intelligent inspection strategy required to achieve the target inspection state, the focus is on resource optimization, including minimizing robot energy consumption, optimizing inspection path length, and reducing sensor usage frequency. The specific implementation of selecting strategies based on case library reasoning when extracting experience inspection strategies from a multi-layered experience pool is also included. Taking a spring preventative inspection task at a 220kV substation as an example, the target inspection state requires completing infrared temperature measurement and visible light imaging of all 128 high-voltage devices within the substation. Constraints include a robot battery life limit of 4 hours, a total inspection path length not exceeding 5 kilometers, and a continuous sensor operating temperature range of -10℃ to 50℃. The intelligent inspection strategy is generated using a multi-objective optimization algorithm to handle the resource optimization problem. The algorithm inputs the current battery level (78%), ambient temperature (25℃), and equipment distribution topology. The optimization model establishes three objective functions: an energy consumption objective function to minimize the sum of motor drive and sensor power consumption; a path length objective function to optimize the shortest path traversing all devices; and a sensor usage frequency objective function to control the number of infrared camera activations to extend their lifespan.

[0085] The resource optimization calculation process uses the non-dominated sorting genetic algorithm NSGA-II to solve for the Pareto optimal solution set, with a population size of 200 and 1000 iterations. The energy consumption model is based on the robot's kinematic parameters; the power consumption calculation formula for straight-line travel incorporates a slope factor, and the power consumption calculation for turning considers the track slippage coefficient. Path optimization integrates equipment detection priorities, scheduling critical equipment (such as the main transformer) for detection in the first half of the path to avoid omissions when power is insufficient. Path planning constraints include obstacle avoidance rules, and the obstacle map is updated in real time using laser SLAM. Sensor usage frequency optimization employs a dynamic scheduling algorithm, allocating detection time according to equipment importance; the infrared temperature measurement time for the main transformer is set to 30 seconds, and the isolation switch time is reduced to 10 seconds. The sensor sleep strategy shuts down unnecessary sensors during movement, keeping only the navigation sensors running.

[0086] The case database retrieval process selects historical successful cases from the standardized experience layer of a multi-layered experience pool. Retrieval criteria are set as spring inspection, temperature range 20℃-30℃, and number of devices 100-150. Similarity calculation uses multi-dimensional feature matching; the feature vector includes an average ambient temperature of 25.3℃, humidity of 45%, and task type: preventative inspection. Real-time matching evaluation considers the current robot hardware status; the difference between the current robot model TR-2024 and the historical case robot model TR-2022 is corrected using performance coefficients. The matching score is calculated using a weighted Euclidean distance formula, with environmental features weighted at 0.6, equipment features at 0.3, and robot features at 0.1. The search results return 5 similar cases, sorted in descending order of matching score. The highest-scoring case with a matching score of 0.92 corresponds to the inspection record from April 15, 2023. The strategy generation system integrates the three Pareto optimal solutions output by the NSGA-II algorithm with five historical strategies retrieved from the case database. The integration process uses a decision matrix to evaluate each candidate strategy. The decision matrix metrics include estimated energy consumption (kWh), path length (meters), sensor usage count, historical success rate, and real-time matching degree. Weight allocation is determined using the analytic hierarchy process (AHP), with a total weight of 0.7 for resource optimization metrics and 0.3 for case matching metrics. Matrix scoring uses a weighted sum model, and the strategy with the highest score is selected as the final strategy. The specific parameters of the final strategy are as follows: path planning uses an improved apprentice algorithm to generate a circular path with a total length of 4.2 kilometers; the sensor scheduling scheme allocates an infrared camera duty cycle of 60%; and energy management sets the movement speed to a constant 0.8 meters per second.

[0087] During the strategy execution phase, the robot control system parses the final strategy into an executable instruction sequence, which includes coordinates of 36 path points and operation commands for 128 detection points. Motion control employs an adaptive PID controller to track the path, and positioning errors are corrected to ±2 cm using GPS-RTK and LiDAR fusion. At each detection point, the sensor trigger protocol first activates a visible light camera to capture three multi-angle images, then activates an infrared thermal imager for 30 seconds of temperature acquisition. Data is packaged and transmitted using a 4G network for compression, with the compression algorithm dynamically selecting JPEG or H.265 format based on network bandwidth. The case library update mechanism is activated upon task completion, generating new cases for successful strategies in a standard format. Case features include actual energy consumption of 3.2 kWh, path length of 4280 meters, and sensor utilization efficiency rating of A. Cases undergo quality verification before being added to the library, with verification standards requiring 100% task completion and no significant deviations. The weights of the multi-layered experience pool are dynamically updated based on strategy performance. The resource optimization metrics performed well in this task, and the weight coefficient of resource optimization-related cases increased by 0.05. The case retrieval index reconstruction adopted inverted index optimization, and the keyword "spring preventive inspection" for the newly added cases was added to the index dictionary.

[0088] The resource optimization model's parameter learning utilizes the data from this task; the error between actual and predicted battery consumption data is used to correct the rolling friction coefficient in the energy consumption model. The path optimization algorithm incorporates detour experience from actual driving paths and updates the obstacle database to include newly discovered construction isolation zone locations. Sensor usage frequency optimization is adjusted based on the value of equipment temperature data. Actual analysis shows that the variance of circuit breaker temperature data is large; subsequent strategies will increase the circuit breaker detection frequency to 15 seconds per device. The similarity algorithm for case library inference is continuously optimized. Cases with a matching degree of 0.92 in this task have been proven to be fully applicable. The environmental temperature feature weight in the feature weight vector of cases with a matching degree of 0.92 has been increased from 0.6 to 0.65. A time decay factor is introduced into the matching degree calculation, increasing the timeliness weight of recent cases by 10%. The weight configuration of the strategy fusion decision matrix initiates an adaptive adjustment process; during periods of resource scarcity (such as when battery power is below 50%), the resource optimization weight automatically increases to 0.8.

[0089] An anomaly handling mechanism plays a crucial role in resource optimization. When a sudden downpour occurs during an actual inspection, the system automatically triggers an emergency search of the case database, adding the weather condition "rain" to the search criteria. Matched rainy cases suggest activating a waterproof mode strategy, with adjustments including reducing movement speed to 0.5 m / s and adding sensor anti-false alarm algorithms. The robustness of the resource optimization model under abnormal conditions is continuously enhanced through these cases, and a rainy-day energy consumption correction coefficient is incorporated into the energy consumption model. The strategy effectiveness evaluation system generates a detailed comparison report, showing that this task saved 12% of energy compared to a pure algorithm strategy and shortened the path by 8% compared to a pure experience strategy. The retrieval accuracy of the case database reasoning was verified through this task, with cases with a matching degree of 0.85 or higher achieving 95% applicability. The trade-offs between resource optimization objectives are quantified, and the conversion formula for an increase of 0.1 kWh in energy consumption for every 100 meters increase in path length is recorded in the knowledge base. In long-term operation, the resource optimization algorithm and the case library inference complement each other. The resource optimization algorithm pursues theoretical optimality under normal conditions, while the case library inference provides empirical solutions under special operating conditions. The outputs of the two methods are weighted and fused to avoid extreme decisions, and the weight coefficients are dynamically adjusted according to the environmental stability index. The policy adaptability during substation equipment expansion is achieved through case library expansion, with new equipment cases generated from similar equipment cases through transfer learning. The system learning mechanism records the full-link data of each policy decision, and the decision log includes algorithm output, case matching details, fusion weights, and execution results; the log analysis tool identifies blind spots in the resource optimization model. The case library maintenance process establishes quality gates; new cases must pass three simulation verifications before being officially added to the library, and expired cases are archived to the historical database every quarter.

[0090] The resource optimization algorithm's online learning function integrates an incremental learning framework. This framework fine-tunes model parameters with new data every week to prevent model drift. The retrieval efficiency of the case library inference is improved through vectorization technology, with the feature vector library loaded into GPU memory to accelerate similarity calculation. The modular design of the strategy generation system supports hot-swappable algorithm components; new resource optimization algorithms can be integrated for testing as long as they meet the interface standards. Real-world application scenarios demonstrate the system's ability to handle complex conditions. When faced with multiple constraints such as battery power below 50%, the addition of emergency detection tasks, and unstable communication networks, the resource optimization algorithm recalculates the Pareto front, and the case library inference retrieves simplified strategies for emergency situations. The strategy fusion module initiates a multi-round voting mechanism to ultimately generate emergency strategies that shorten the path to 3 kilometers, prioritize the detection of critical equipment, and adopt offline storage. During system iteration and upgrades, the resource optimization model introduces deep reinforcement learning to replace traditional optimization algorithms. The deep reinforcement learning network structure includes a 128-dimensional input layer, 3 hidden layers, and a multi-objective output layer. Case library inference combines graph neural network technology, which incorporates device topology relationships into similarity calculations. Real-time monitoring data of resource consumption is fed back to the optimization loop. When the monitoring data detects that battery aging is causing capacity decay, the resource optimization algorithm automatically adjusts the decay coefficient of the energy consumption prediction formula.

[0091] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for autonomous inspection of a substation by a robot, characterized in that, The method includes: Collect current environmental parameters of the substation and real-time status information of the inspection robot to build an inspection environment model; Analyze historical inspection records, and classify historical inspection strategies into multiple experience levels based on the confidence level of historical inspection strategies and the gap between historical inspection results and expected goals, and build a multi-layered experience pool. Based on the preset target inspection state in the inspection environment model, the intelligent inspection strategy required to achieve the target inspection state is calculated, and at the same time, the experience inspection strategy is extracted from the multi-layer experience pool based on the strategy confidence and state matching degree. A dual-path coordination mechanism is adopted to make real-time corrections to the intelligent inspection strategy and the experience-based inspection strategy in response to the dynamic changes in the substation environment and to generate the final inspection strategy. Execute control commands based on the final inspection strategy, complete the inspection task, and record the actual inspection status; Compare the deviations between the actual inspection status and the target inspection status, and adjust the experience content in the multi-layered experience pools according to the reasons for the deviations to optimize the experience data.

2. The autonomous inspection method for substation inspection robots as described in claim 1, characterized in that, The steps for collecting current environmental parameters of the substation and real-time status information of the inspection robot to construct an inspection environment model include: Environmental parameter data are collected using a multi-source sensor array deployed within the substation. The multi-source sensor array includes temperature sensors, humidity sensors, vibration sensors, and electromagnetic sensors. The inspection robot's visual perception system captures image sequences of the inspection area, and image recognition technology is used to analyze the equipment status and robot pose. Multi-source sensor data and image sequence information are spatiotemporally fused to construct a dynamically updated digital twin model, which serves as an inspection environment model.

3. The autonomous inspection method for substation inspection robots as described in claim 2, characterized in that, The steps involved in analyzing historical inspection records, classifying historical inspection strategies into multiple experience levels based on the confidence level of the strategies and the gap between historical inspection results and expected targets, and constructing a multi-layered experience pool include: Extract the execution records of the inspection strategy from the historical database, including strategy parameters, inspection results, and timestamps; Statistical analysis methods were used to calculate the success rate and deviation rate of each historical inspection strategy. Based on the threshold ranges of success rate and deviation indicators, historical inspection strategies are automatically divided into core experience layer, standard experience layer, and basic experience layer, forming a hierarchical experience pool.

4. The autonomous inspection method for substation inspection robots as described in claim 3, characterized in that, The step of calculating the intelligent inspection strategy required to achieve the target inspection state based on the preset target inspection state in the inspection environment model, and extracting the experience inspection strategy from the multi-layer experience pool based on the strategy confidence and state matching degree, includes: The current environmental parameters and robot status are input into the intelligent decision-making system, and an optimized inspection strategy is generated with the target inspection status as a constraint. Perform pattern matching operations in a multi-layered experience pool to filter out historical strategy cases that are highly similar to the current target inspection status; By combining optimized inspection strategies with historical strategy cases using strategy synthesis technology, a preliminary strategy set is generated.

5. The autonomous inspection method for substation inspection robots as described in claim 4, characterized in that, The steps of using a dual-path coordination mechanism to modify the intelligent inspection strategy and the experience-based inspection strategy in real time to respond to dynamic changes in the substation environment and generate the final inspection strategy include: Continuously monitor substation environmental parameters and robot operating indicators, and set anomaly detection thresholds; When the monitored data exceeds the anomaly detection threshold, it automatically switches to the experience inspection strategy channel and selects an appropriate strategy from the core experience layer. Within the normal monitoring range, maintain the intelligent inspection strategy channel as the dominant force, and regularly evaluate the applicability of the experience-based strategy to achieve dynamic strategy adjustment.

6. The autonomous inspection method for substation inspection robots as described in claim 5, characterized in that, The steps of executing control commands based on the final inspection strategy, completing the inspection task, and recording the actual inspection status include: The final inspection strategy is analyzed into a sequence of specific action instructions, including movement path, detection points, and data acquisition commands; The robot is controlled to perform inspection tasks according to the instruction sequence, and completes equipment status acquisition and image recording at each inspection point; By integrating robot sensor data and visual feedback, a real-time inspection status report is generated, including abnormal equipment readings and environmental changes.

7. The autonomous inspection method for substation inspection robots as described in claim 6, characterized in that, The steps of comparing the deviation between the actual inspection status and the target inspection status, adjusting the experience content in the multi-layer experience pool according to the deviation reasons, and optimizing the experience data include: Calculate the difference between the actual inspection time and the target inspection time, as well as the difference in task completion rate; Analyze the root causes of the discrepancies and identify key influencing factors; Revise the strategy content in the multi-layered experience pool based on key influencing factors, and adjust the weights and case data of the experience layer.

8. The autonomous inspection method for substation inspection robots as described in claim 4, characterized in that, In the step of calculating the intelligent inspection strategy required to achieve the target inspection state, the generation of the intelligent inspection strategy focuses on resource optimization, including minimizing robot energy consumption, optimizing inspection path length, and reducing sensor usage frequency. In the step of extracting experience inspection strategies from the multi-layer experience pool, the selection of experience inspection strategies is based on case library reasoning, retrieving similar scenarios from historical successful cases, and combining real-time matching degree evaluation to determine the final strategy.

9. The autonomous inspection method for substation inspection robots as described in claim 5, characterized in that, In the step of using a dual-path coordination mechanism to make real-time corrections to the intelligent inspection strategy and the experience-based inspection strategy, the dual-path coordination mechanism has a fault-tolerant function. When the main strategy channel fails, the backup strategy channel is immediately activated to ensure the continuity of the inspection process.

10. A substation inspection robot autonomous inspection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the autonomous inspection method for substation inspection robots according to any one of claims 1 to 9.

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