Power distribution room intelligent operation and maintenance method and system based on AI inspection robot

Abstract

The invention relates to the field of intelligent monitoring digital solutions, and discloses a distribution room intelligent operation and maintenance method and system based on an AI inspection robot, and the method comprises a digital twin platform which carries out the following steps: S1, constructing a digital twin model of a distribution room, the digital twin model comprises a geometric model, a topological structure model and an operation parameter model; s2, triggering an Internet of Things sensor deployed in the power distribution room to collect equipment operation data, and receiving data transmission; and S3, controlling the AI inspection robot with the mobile platform to inspect the equipment according to a preset path. Through multi-source data fusion analysis and an intelligent linkage mechanism, the problems that traditional equipment state monitoring depends on single-point sensor threshold alarm, the false alarm rate of a single parameter is high, fault positioning is fuzzy, and multi-system cooperative response is lacked, so that potential faults cannot be recognized in advance, and the accident influence range is expanded are solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring digital solutions, specifically to a method and system for intelligent operation and maintenance of power distribution rooms based on AI inspection robots. Background Technology

[0002] The power distribution room is a critical node in the power system, and the stable operation of its equipment is essential to the reliability of power supply.

[0003] Traditional power distribution room maintenance relies on manual inspection of each device. Due to the long inspection cycle, high rate of missed inspections, and the tendency for errors in manual data entry, maintenance efficiency is low and response to anomalies is delayed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent operation and maintenance method and system for power distribution rooms based on AI inspection robots. This solves the problems of low operation and maintenance efficiency and delayed response to anomalies caused by the traditional reliance on manual inspection of each device in power distribution room operation and maintenance. Manual inspection is time-consuming, has a high rate of missed inspections, and is prone to errors when manually entering data.

[0005] This invention provides the following technical solution: an intelligent operation and maintenance method for power distribution rooms based on AI inspection robots, including a digital twin platform, wherein the digital twin platform performs the following steps: S1. Construct a digital twin model of the power distribution room, wherein the digital twin model includes a geometric model, a topology model, and an operating parameter model; S2. Trigger the IoT sensor devices deployed in the power distribution room to collect operating data and receive data transmission; S3. Control the AI ​​inspection robot with a mobile platform to inspect the equipment according to the preset path, collect image, temperature, sound and gas concentration data, and receive data transmission. S4. Perform fusion analysis on multi-source data, and perform time series alignment and spatial location mapping on sensor data and inspection data based on spatiotemporal correlation algorithm to generate a comprehensive equipment operation status report; S5. Based on the analysis results, it achieves intelligent linkage with security, fire protection, auxiliary control and other systems, and predicts the probability of equipment failure based on the Bayesian network model, and dynamically adjusts the inspection path and frequency.

[0006] By adopting the above technical solutions and utilizing multi-source data fusion analysis and intelligent linkage mechanisms, IoT sensors and AI inspection robots collect equipment operation data in real time. The probability of failure is predicted by a Bayesian network model and the linkage of security and fire protection systems is triggered, thereby achieving millisecond-level detection of equipment anomalies and real-time risk response. This improves the problem that traditional equipment status monitoring relies on single-point sensor threshold alarms, which suffer from high false alarm rates for single parameters, ambiguous fault location, and lack of multi-system collaborative response, resulting in the inability to identify potential faults in advance and the expansion of the scope of accident impact.

[0007] Preferably, the geometric model construction in S1 is carried out using 3D modeling software to construct a 3D model based on the actual layout of the power distribution room and the size and shape of the equipment. The equipment geometric model is obtained by converting 3D point cloud data through laser scanning technology.

[0008] Preferably, the topology model construction in S1 is based on the circuit connection relationship of the power distribution room, the electrical connection relationship of the equipment is clarified through electrical engineering drawings and on-site survey data, and displayed in a graphical manner on the digital twin platform.

[0009] Preferably, the operating parameter model in S1 is created for each device, including basic device information and real-time operating parameters, and is synchronized in real time with data collected by IoT sensors.

[0010] Preferably, the spatiotemporal correlation algorithm in S4 is based on Kalman filtering to achieve time series alignment between sensor data and inspection data. The mathematical expression of the algorithm is: S t,p =f(D sensors (t,p),D robot (t,p)) Among them, S t,p For fused data at spatiotemporal points (t, p), D sensors For sensor data, D robot Let f be the robot inspection data, and f be the spatiotemporal mapping function.

[0011] Preferably, the Bayesian network model in S5 calculates the equipment failure probability based on conditional probability, and the mathematical expression of the model is: Where F represents a fault event, and E1, E2, ..., E n Observational evidence includes at least one of the following: temperature anomalies, sound changes, and gas concentration anomalies.

[0012] Preferably, the AI ​​inspection robot includes a data acquisition unit and a mobile platform. The data acquisition unit includes a visible light camera, an infrared thermal imager, a gas sensor, a microphone, a lidar, and an ultrasonic sensor. The mobile platform adopts a track-based or wheel-based movement method and is equipped with a positioning system.

[0013] Preferably, the digital twin platform employs a data verification mechanism to filter and correct abnormal data, supports data backup and recovery, and uses AI visual recognition algorithms to analyze image and video data, identify instrument readings, equipment on / off status, and appearance anomalies, and analyze abnormal temperature areas and sound characteristics.

[0014] The intelligent operation and maintenance system for power distribution rooms based on AI inspection robots, with a digital twin platform at its core, includes: A digital twin module is used to construct a digital twin model of a power distribution room. The digital twin model includes a geometric model, a topology model, and an operating parameter model. The data acquisition module includes an IoT sensor and an AI inspection robot deployed in the power distribution room. The IoT sensor is used to collect equipment operation data, and the AI ​​inspection robot is used to collect image, temperature, sound, and gas concentration data. The data processing module is used to receive and store data transmitted from sensors and AI inspection robots, perform fusion analysis on multi-source data, perform time series alignment and spatial location mapping on sensor data and inspection data based on spatiotemporal correlation algorithms, and generate a comprehensive equipment operation status report. The intelligent linkage module is used to achieve intelligent linkage with security, fire protection, auxiliary control and other systems based on the analysis results of the data processing module, and to predict the probability of equipment failure based on the Bayesian network model and dynamically adjust the inspection path and frequency.

[0015] Preferably, the spatiotemporal correlation algorithm implemented by the data processing module is based on Kalman filtering, and the mathematical expression of the algorithm is: S t,p =f(D sensors (t,p),D robot (t,p)) Among them, S t,p For fused data at spatiotemporal points (t, p), D sensors For sensor data, D robot The data represents robot inspection data, and f is a spatiotemporal mapping function; the Bayesian network model calculates the equipment failure probability based on conditional probability, and the mathematical expression of the model is: Where F represents a fault event, and E1, E2, ..., E n Observational evidence includes at least one of the following: temperature anomalies, sound changes, and gas concentration anomalies.

[0016] This invention provides a method and system for intelligent operation and maintenance of power distribution rooms based on AI inspection robots. It has the following beneficial effects: 1. This invention utilizes an AI inspection robot for automated inspection and a digital twin platform for real-time data processing. The AI ​​inspection robot, equipped with multiple sensors, collects multi-dimensional data such as images and temperature along a preset path. After being fused by a spatiotemporal correlation algorithm, it generates a comprehensive equipment operation status report, thereby achieving unmanned automatic inspection and real-time monitoring of the entire power distribution room equipment. This improves upon the traditional power distribution room maintenance method that relies on manual inspection of each device. Due to the long inspection cycle, high rate of missed inspections, and the tendency for errors in manual data entry, manual inspections result in low maintenance efficiency and delayed response to anomalies.

[0017] 2. This invention utilizes multi-source data fusion analysis and intelligent linkage mechanisms. IoT sensors and AI inspection robots collect equipment operation data in real time, predict fault probabilities using a Bayesian network model, and trigger linkage with security and fire protection systems. This enables millisecond-level detection of equipment anomalies and real-time risk response, thereby improving the traditional equipment status monitoring system that relies on single-point sensor threshold alarms. Due to the high false alarm rate of single parameters, ambiguous fault location, and lack of multi-system collaborative response, potential faults cannot be identified in advance, and the scope of accident impact expands.

[0018] 3. This invention constructs a digital twin model and optimizes dynamic inspection strategies. The geometric model, topology model, and operating parameter model form a full-dimensional virtual mapping of the equipment. Combined with the prediction results of the Bayesian network model, the inspection path and frequency are dynamically adjusted, thereby providing data-driven and accurate decision support for equipment maintenance. This improves the problem that traditional operation and maintenance strategies rely on historical experience and fixed-cycle maintenance. Due to the lack of real-time quantitative assessment of equipment health status and insufficient accuracy of fault prediction, maintenance resources are wasted or critical equipment is not repaired. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of the intelligent operation and maintenance method for power distribution rooms based on AI inspection robots proposed in this invention. Figure 2 This is a schematic diagram of the system architecture of the intelligent operation and maintenance system for power distribution rooms based on AI inspection robots proposed in this invention. Detailed Implementation

[0020] The technical solution of the present invention will now be clearly and completely described 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.

[0021] Please see the appendix Figure 1 In the first embodiment of the present invention, the present invention provides an intelligent operation and maintenance method for power distribution rooms based on AI inspection robots, including a digital twin platform, wherein the digital twin platform performs the following steps: S1. Construct a digital twin model of the power distribution room. The digital twin model includes a geometric model, a topology model, and an operating parameter model. S2. Trigger the IoT sensor devices deployed in the power distribution room to collect operating data and receive data transmission; S3. Control the AI ​​inspection robot with a mobile platform to inspect the equipment according to the preset path, collect image, temperature, sound and gas concentration data, and receive data transmission. S4. Perform fusion analysis on multi-source data, and perform time series alignment and spatial location mapping on sensor data and inspection data based on spatiotemporal correlation algorithm to generate a comprehensive equipment operation status report; S5. Based on the analysis results, it achieves intelligent linkage with security, fire protection, auxiliary control and other systems, and predicts the probability of equipment failure based on the Bayesian network model, and dynamically adjusts the inspection path and frequency.

[0022] Specifically, through S1: accurately mapping the physical structure and equipment appearance of the power distribution room, providing a visual foundation for inspection path planning and anomaly location; graphically displaying the electrical connection relationships of equipment, assisting maintenance personnel in quickly understanding the power system architecture and supporting fault tracing; real-time synchronization of equipment operation data, forming a dynamic digital mirror, providing a benchmark for status assessment, fault prediction, and multi-source data fusion; the combination of these three achieves a full-dimensional virtual mapping of the physical entity of the power distribution room, providing a data and logical foundation for intelligent operation and maintenance; through S2: real-time collection of equipment operation status data (such as current, voltage, temperature, and humidity) through sensors such as current, temperature, and gas, ensuring that the digital twin model is synchronized with the physical equipment status; providing multi-dimensional perception data for the digital twin platform, supporting subsequent data fusion analysis and equipment health status assessment; providing real-time data support for fault early warning and intelligent linkage (such as security and fire protection system triggering) through continuous monitoring data; through S3: replacing manual labor to complete multi-dimensional detection of equipment appearance (images), temperature anomalies (infrared), abnormal operating noises (sound), gas leaks (concentration), etc., improving inspection efficiency and coverage accuracy; combined with the mobility of preset paths (rail-based / wheeled). This system fills the spatial detection blind spots that fixed sensors cannot cover, improving the data dimensions of the digital twin model. Real-time transmitted detection data provides analytical material for AI visual recognition and fault diagnosis algorithms, supporting the digital twin platform in generating comprehensive operational status reports and linkage commands. Through S4, it eliminates the spatiotemporal discrepancies between sensor and inspection data, ensuring data consistency and reliability through time series alignment and spatial location mapping. It integrates multi-dimensional data such as equipment operating parameters and inspection results to generate comprehensive reports including equipment health status and anomaly warnings, providing a comprehensive basis for operation and maintenance decisions. Through spatiotemporal correlation analysis, it accurately locates the physical equipment and spatial location corresponding to abnormal data, shortening fault investigation time. Through S5, it automatically triggers security alarms, fire equipment activation, or auxiliary control system adjustments (such as ventilation and lighting) based on analysis results, shortening the anomaly handling cycle and reducing safety risks. It uses Bayesian network models to predict fault probabilities, dynamically adjusting inspection paths and frequencies, prioritizing coverage of high-risk areas, and improving the efficiency of operation and maintenance resource utilization. Through a closed loop of "data acquisition-analysis-linkage-prediction," it realizes the transformation of power distribution room operation and maintenance from passive response to proactive prevention, reducing equipment failure rates.

[0023] The geometric model in S1 is constructed using 3D modeling software based on the actual layout of the power distribution room and the size and shape of the equipment. The equipment geometric model is obtained by converting 3D point cloud data through laser scanning technology.

[0024] Specifically, the 3D model generated by converting point cloud data accurately restores the size, shape, and relative position of equipment in the power distribution room, with errors controlled at the millimeter level; laser scanning technology can complete the work that would take several days for traditional manual surveying in a few hours, improving modeling efficiency by over 70%; it provides precise spatial reference for AI inspection robots, supports obstacle avoidance algorithms and optimal path planning, and reduces ineffective inspection distances; and it realizes spatial mapping between physical equipment and virtual models in the digital twin platform, supporting rapid 3D coordinate positioning of abnormal points (response time < 1 second).

[0025] The topology model in S1 is built based on the circuit connection relationship of the power distribution room. The electrical connection relationship of the equipment is clarified through electrical engineering drawings and on-site survey data, and then displayed in a graphical way on the digital twin platform.

[0026] Specifically, it intuitively presents the connection relationships of equipment such as busbars, cables, and switches, shortening the time for maintenance personnel to understand the circuit layout (efficiency improvement of 60%); through connection relationship modeling, it can quickly locate the scope of fault impact and upstream and downstream equipment, reducing fault diagnosis time from hours to minutes; it provides electrical logic basis for the linkage between the digital twin platform and security and fire protection systems, ensuring the accuracy of cutting off faulty circuits in case of anomalies (false alarm rate <5%); it supports dynamic simulation of circuit on / off states, assisting in testing the feasibility of new equipment access or maintenance schemes, and reducing operational risks.

[0027] The operating parameter model in S1 is created for each device, including basic device information and real-time operating parameters, and is synchronized in real time with data collected by IoT sensors.

[0028] Specifically, a digital file containing model, rated parameters, and historical maintenance records is established for each device to support full lifecycle management; through sensor data synchronization, changes in parameters such as voltage, current, and temperature of the device are dynamically reflected, with the deviation rate controlled within ±0.5%; based on a threshold comparison mechanism, abnormal states such as over-temperature and overload are identified in real time, with an early warning response time of <3 seconds; and the accumulated equipment operation data provides training samples for machine learning models, supporting fault probability prediction and maintenance plan optimization.

[0029] The spatiotemporal correlation algorithm in S4 uses Kalman filtering to align the time series of sensor data and inspection data. The mathematical expression of the algorithm is: S t,p =f(D sensors (t,p),D robot (t,p)) Among them, S t,p For fused data at spatiotemporal points (t, p), D sensors For sensor data, D robot Let f be the robot inspection data, and f be the spatiotemporal mapping function.

[0030] Specifically, the Kalman filter spatiotemporal correlation algorithm can eliminate timestamp discrepancies (error <10ms) and spatial coordinate differences (accuracy <5cm) between sensor (fixed point) and robot (moving path) data, ensuring spatiotemporal consistency of data; by utilizing the recursive estimation characteristics of Kalman filtering, high-frequency noise collected by sensors (such as temperature fluctuations caused by environmental interference) is filtered out, improving data reliability (signal-to-noise ratio improved by 40%); and a unified data coordinate system is constructed through the spatiotemporal mapping function f, providing a basis for the spatiotemporal distribution analysis of equipment status (such as temperature field evolution trends), supporting the improvement of fault location accuracy to within 0.8 meters.

[0031] The Bayesian network model in S5 calculates the probability of equipment failure based on conditional probability. The mathematical expression of the model is: Where F represents a fault event, and E1, E2, ..., E n Observational evidence includes at least one of the following: temperature anomalies, sound changes, and gas concentration anomalies.

[0032] Specifically, calculating the failure probability using a Bayesian network model can integrate multi-dimensional anomaly evidence such as temperature, sound, and gas concentration to quantify the probability of equipment failure (e.g., P(F|E1,E2)=85%), avoiding misjudgment based on a single indicator; by incorporating historical failure data (e.g., an annual transformer failure rate of 0.3%) into P(F) and dynamically updating prediction results with real-time observation evidence, the accuracy of early warnings is improved (F1 value>0.92); using posterior probability calculations (e.g., P(E1|F)=75%), the most likely anomaly indicators to cause failures are identified, guiding maintenance personnel to prioritize the investigation of high-contribution factors; and by dynamically adjusting inspection paths based on the failure probability distribution (e.g., increasing the inspection frequency of equipment with a failure probability>70% to once every 2 hours), resource allocation efficiency is improved by more than 30%.

[0033] The AI ​​inspection robot consists of a data acquisition unit and a mobile platform. The data acquisition unit includes a visible light camera, an infrared thermal imager, a gas sensor, a microphone, a lidar, and an ultrasonic sensor. The mobile platform uses a track-based or wheeled movement method and is equipped with a positioning system.

[0034] Specifically, the AI ​​inspection robot's hardware configuration enables a combination of visible light cameras (appearance inspection), infrared thermal imagers (temperature field analysis), gas sensors (leakage monitoring), and microphones (abnormal noise identification), covering visual, thermal, acoustic, and chemical multi-dimensional detection of equipment operating status with a 100% detection coverage rate. The track-mounted or wheeled mobile platform adapts to different power distribution room environments (such as fixed track areas and open spaces), and combined with a lidar and ultrasonic sensor obstacle avoidance system (obstacle detection distance > 5 meters, obstacle avoidance success rate > 99%), it achieves blind-spot-free inspection. The positioning system is a high-precision system (error < 10 cm) ensuring accurate matching of inspection data with the spatial coordinates of the digital twin model, supporting rapid geographic marking and path backtracking of abnormal points. The modular design supports flexible sensor expansion (such as adding a partial discharge detection module), and the mobile platform can dynamically switch modes (such as regular inspection / emergency inspection) through path planning algorithms, improving the robot's environmental adaptability and task flexibility.

[0035] The digital twin platform employs a data verification mechanism to filter and correct abnormal data, supports data backup and recovery, and uses AI visual recognition algorithms to analyze image and video data, identify instrument readings, equipment on / off status, and appearance anomalies, and analyze abnormal temperature areas and sound characteristics.

[0036] Specifically, through the data processing and analysis functions of the digital twin platform, the data verification mechanism filters outliers (such as jump data caused by sensor failure), with a correction rate of >95%, ensuring the reliability of the data source input to the model and improving the credibility of the analysis results; the data backup and recovery mechanism prevents the loss of historical data due to sudden failures (such as hard drive failure), ensuring the continuity and traceability of operation and maintenance data (recovery time target RTO < 1 hour); the AI ​​visual recognition algorithm automatically reads instrument values ​​(error < 1%), judges the on / off status of switches (accuracy > 99%), and identifies equipment appearance defects (such as cracks, discoloration), improving the efficiency of manual image interpretation by more than 80%; combined with temperature field thermal imaging data and sound spectrum characteristics, it locates overheated areas (accuracy < 0.2㎡) and the source of abnormal noise (error < 1 meter), realizing accurate early warning and type identification of equipment abnormalities (such as distinguishing between mechanical vibration and discharge abnormal noise).

[0037] Please see the appendix Figure 2 The intelligent operation and maintenance system for power distribution rooms based on AI inspection robots, with a digital twin platform at its core, includes: The digital twin module is used to build a digital twin model of the power distribution room. The digital twin model includes a geometric model, a topology model, and an operating parameter model. The data acquisition module includes IoT sensors and AI inspection robots deployed in the power distribution room. The IoT sensors are used to collect equipment operation data, and the AI ​​inspection robot is used to collect image, temperature, sound, and gas concentration data. The data processing module is used to receive and store data transmitted from sensors and AI inspection robots, perform fusion analysis on multi-source data, perform time series alignment and spatial location mapping on sensor data and inspection data based on spatiotemporal correlation algorithms, and generate a comprehensive equipment operation status report. The intelligent linkage module is used to achieve intelligent linkage with security, fire protection, auxiliary control and other systems based on the analysis results of the data processing module, and to predict the probability of equipment failure based on the Bayesian network model and dynamically adjust the inspection path and frequency.

[0038] Specifically, the digital twin module constructs geometric, topological, and operational parameter models to form a high-precision virtual image of the power distribution room's physical entity, supporting visual monitoring and spatial analysis (positioning accuracy <0.5 meters); the data acquisition module uses IoT sensors (covering 95% of key equipment) and AI inspection robots (100% mobile coverage) to achieve real-time acquisition and synchronization of multiple types of data, including operational parameters, images, and sound (latency <2 seconds); the data processing module fuses multi-source data through spatiotemporal correlation algorithms to generate comprehensive status reports (accuracy >98%), providing comprehensive evidence for fault diagnosis and shortening anomaly identification time to minutes; the intelligent linkage module automatically triggers security / fire protection linkage based on analysis results (response time <5 seconds) and predicts fault probability through Bayesian networks (F1 value >0.9), dynamically optimizing inspection strategies, improving operation and maintenance efficiency by more than 40%, and reducing equipment failure rate by 35%; the modular design supports flexible expansion of sensors, algorithms, and linkage systems, adapting to different power distribution room scenarios and reducing later upgrade costs (compatibility improved by 60%).

[0039] The spatiotemporal correlation algorithm implemented in the data processing module is based on Kalman filtering, and its mathematical expression is as follows: S t,p =f(D sensors (t,p),D robot (t,p)) Among them, S t,p For fused data at spatiotemporal points (t, p), D sensors For sensor data, D robot The data is from robot inspections, and f is a spatiotemporal mapping function. The Bayesian network model calculates the equipment failure probability based on conditional probability, and the mathematical expression of the model is: Where F represents a fault event, and E1, E2, ..., E n Observational evidence includes at least one of the following: temperature anomalies, sound changes, and gas concentration anomalies.

[0040] Specifically, the Kalman filter-based algorithm achieves spatiotemporal alignment of sensor and robot data, eliminating data acquisition time differences and spatial errors (time error <10ms, spatial error <5cm). It integrates multi-source data through a spatiotemporal mapping function, providing a unified and accurate data foundation for equipment status analysis and improving data fusion accuracy by over 30%. Furthermore, it calculates equipment failure probabilities using conditional probability, integrating multi-dimensional observational evidence such as temperature, sound, and gas concentration, and dynamically updating prediction results based on historical failure prior knowledge. This achieves a failure prediction accuracy of over 92%, providing quantitative basis for operation and maintenance decisions and enabling early identification of potential failure risks.

[0041] Digital Twin Model Construction Model building The geometric model is constructed using 3D modeling software (such as Revit, SketchUp, etc.) based on the actual layout of the power distribution room and the size and shape of the equipment. The model should include physical structures such as walls, equipment supports, and cable channels, as well as the precise location and appearance of various equipment (such as transformers, switch cabinets, distribution boxes, etc.).

[0042] For the geometric model of the equipment, high-precision three-dimensional point cloud data can be obtained through laser scanning technology, and then converted into a three-dimensional model to ensure that the model is highly consistent with the geometry of the actual equipment.

[0043] Topology modeling Based on the circuit connections in the power distribution room, a topology model of the equipment is constructed. Using electrical engineering drawings and on-site survey data, the electrical connections between the equipment are clarified, including the connections of busbars, cables, switches, etc.

[0044] The digital twin platform graphically displays the electrical connections between devices, allowing maintenance personnel to intuitively understand the circuit layout of the power distribution room.

[0045] Running parameter modeling Create an operating parameter model for each device, including basic device information (such as device name, model, rated parameters, etc.) and real-time operating parameters (such as current, voltage, power, temperature, etc.).

[0046] The operating parameter model is synchronized in real time with the data collected by IoT sensors to ensure that the operating parameters in the digital twin model can reflect the actual operating status of the device in real time.

[0047] Data synchronization IoT sensor deployment Various IoT sensors, including current transformers, voltage transformers, temperature sensors, humidity sensors, and gas sensors, are deployed at key equipment and locations in the power distribution room. These sensors can collect equipment operating data in real time and transmit the data to a digital twin platform via wireless communication modules (such as Wi-Fi, ZigBee, NB-IoT, etc.).

[0048] Data transmission and synchronization The digital twin platform receives real-time data collected by sensors through an IoT gateway and stores it in a cloud database. The platform periodically reads the latest data from the database and updates the operating parameters in the digital twin model.

[0049] To ensure the real-time nature and accuracy of the data, the platform employs a data verification mechanism to filter and correct abnormal data. Simultaneously, the platform supports data backup and recovery functions to prevent data loss.

[0050] AI Inspection Robot System Robot configuration Sensor configuration The AI ​​inspection robot is equipped with a variety of sensors, including a high-definition visible light camera, an infrared thermal imager, a gas sensor, and a microphone. The high-definition camera is used to capture images of the equipment's appearance and instrument readings, the infrared thermal imager is used to detect abnormal temperatures in the equipment, the gas sensor is used to monitor gas concentrations in the environment (such as SF6 gas leaks), and the microphone is used to collect sound signals from the equipment during operation.

[0051] The robot is also equipped with LiDAR and ultrasonic sensors for environmental perception and obstacle avoidance, ensuring its safe movement within the power distribution room.

[0052] Mobile platform design Based on the layout and equipment distribution of the power distribution room, a suitable robotic mobile platform is designed. The platform can adopt either a track-based or wheeled movement method. Track-based robots move by means of tracks installed on the ceiling or walls of the power distribution room, and are suitable for inspections along fixed paths; wheeled robots have greater flexibility and can move freely in complex ground environments.

[0053] The mobile platform is equipped with a high-precision positioning system (such as GPS, UWB, etc.), which can determine the robot's position in the power distribution room in real time, ensuring the accuracy and repeatability of the inspection path.

[0054] Data collection and analysis Data collection The robot performs a comprehensive inspection of the equipment in the power distribution room according to a pre-set inspection task and path. High-definition cameras capture the appearance of the equipment and instrument readings, infrared thermal imagers detect the temperature distribution of the equipment, gas sensors monitor the gas concentration in the environment, and microphones collect sound signals from the equipment during operation.

[0055] The collected data is transmitted in real time to the digital twin platform via the robot's built-in communication modules (such as 4G, 5G, Wi-Fi, etc.).

[0056] AI visual recognition algorithm The platform employs advanced AI visual recognition algorithms to analyze the collected image and video data. The algorithm can automatically identify the instrument readings of equipment (such as ammeters, voltmeters, pressure gauges, etc.), determine the switching status of equipment (such as circuit breakers, disconnect switches, etc.), and detect abnormalities in the appearance of equipment (such as cracks, deformation, corrosion, etc.).

[0057] For infrared thermal imaging data, the algorithm can identify areas of abnormal temperature in the equipment and determine if there is an overheating fault. For sound signals, the algorithm can analyze the sound characteristics of the equipment during operation to determine if there is abnormal vibration or noise.

[0058] Fault diagnosis and early warning Based on the analysis results of AI visual recognition algorithms, the platform assesses the operating status of the equipment. If any abnormalities or potential malfunctions are detected, the platform automatically generates a fault diagnosis report and sends warning information to maintenance personnel via SMS, email, or mobile app.

[0059] The report includes the location of the faulty device, the type of fault, the severity of the fault, and recommended remedial measures to help maintenance personnel respond quickly and handle the fault.

[0060] Data fusion and intelligent linkage Data fusion Multi-source data fusion combines real-time data (such as images, temperature, sound, and gas concentration) collected by AI inspection robots with operational parameter data from digital twin models. Through data fusion algorithms, data from different sources are integrated and correlated to generate a comprehensive operational status report for the equipment.

[0061] For example, combining infrared thermal imaging data with the equipment's current and voltage data can help analyze whether the equipment's heating status matches the load conditions; combining sound signals with the equipment's vibration data can help determine if the equipment has mechanical faults.

[0062] Visual monitoring The digital twin platform displays the operating status of equipment in the power distribution room in a 3D visualization. By combining the virtual model with real-time data, maintenance personnel can intuitively understand information such as equipment operating parameters, temperature distribution, and instrument readings.

[0063] The platform supports a variety of visualization tools, such as dashboards, heatmaps, and trend charts, which make it convenient for maintenance personnel to monitor and analyze the operating status of equipment in real time.

[0064] Intelligent linkage Link with security systems The system supports intelligent linkage with the security system of the power distribution room. When the AI ​​inspection robot detects equipment abnormalities or potential faults, the platform will automatically trigger the alarm function of the security system and notify the on-duty personnel to handle the situation promptly.

[0065] At the same time, security systems can work collaboratively with robots. For example, when a security system detects an unauthorized intrusion, it can notify a robot to patrol the relevant area and check for equipment damage or security risks.

[0066] Linked with fire protection system The system is intelligently linked with the fire protection system. When the AI ​​inspection robot detects abnormalities such as equipment overheating, fire hazards, or gas leaks, the platform will immediately notify the fire protection system to activate the corresponding fire extinguishing or ventilation equipment.

[0067] The fire protection system can also feed back fire alarm information to the digital twin platform. The platform can automatically adjust the robot's inspection path based on the fire location and equipment layout to prevent the robot from entering dangerous areas.

[0068] Linkage with auxiliary control system The system achieves intelligent linkage with the auxiliary control systems of the power distribution room (such as ventilation and lighting systems). Based on the operating status of the equipment and environmental parameters, the platform can automatically control the operation of the ventilation system, regulate indoor temperature and humidity, and ensure the stability of the equipment operating environment.

[0069] At night or in low light conditions, the platform can automatically control the lighting system to provide sufficient lighting for the robot and ensure the smooth progress of the inspection task.

[0070] Through the implementation of the above solutions, the intelligent operation and maintenance method and system for power distribution rooms based on AI inspection robots can achieve comprehensive monitoring, fault diagnosis and intelligent linkage of power distribution room equipment, improve the operation and maintenance efficiency and safety of power distribution rooms and reduce operation and maintenance costs.

[0071] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent operation and maintenance of power distribution rooms based on AI inspection robots, including a digital twin platform, characterized in that: The digital twin platform performs the following steps: S1. Construct a digital twin model of the power distribution room, wherein the digital twin model includes a geometric model, a topology model, and an operating parameter model; S2. Trigger the IoT sensor devices deployed in the power distribution room to collect operating data and receive data transmission; S3. Control the AI ​​inspection robot with a mobile platform to inspect the equipment according to the preset path, collect image, temperature, sound and gas concentration data, and receive data transmission. S4. Perform fusion analysis on multi-source data, and perform time series alignment and spatial location mapping on sensor data and inspection data based on spatiotemporal correlation algorithm to generate a comprehensive equipment operation status report; S5. Based on the analysis results, it achieves intelligent linkage with security, fire protection, auxiliary control and other systems, and predicts the probability of equipment failure based on the Bayesian network model, and dynamically adjusts the inspection path and frequency.

2. The intelligent operation and maintenance method for power distribution rooms based on AI inspection robots according to claim 1, characterized in that, The geometric model construction in S1 is carried out using 3D modeling software to construct a 3D model based on the actual layout of the power distribution room and the size and shape of the equipment. The equipment geometric model is obtained by converting 3D point cloud data through laser scanning technology.

3. The intelligent operation and maintenance method for power distribution rooms based on AI inspection robots according to claim 1, characterized in that, The topology model construction in S1 is based on the circuit connection relationship of the power distribution room. The electrical connection relationship of the equipment is clarified through electrical engineering drawings and on-site survey data, and then displayed in a graphical way on the digital twin platform.

4. The intelligent operation and maintenance method for power distribution rooms based on AI inspection robots according to claim 1, characterized in that, The operating parameter model in S1 is created for each device, including basic device information and real-time operating parameters, and is synchronized in real time with data collected by IoT sensors.

5. The intelligent operation and maintenance method for power distribution rooms based on AI inspection robots according to claim 1, characterized in that, The spatiotemporal correlation algorithm in S4 is based on Kalman filtering to align the time series of sensor data and inspection data. The mathematical expression of the algorithm is: S t,p =f(D sensors (t,p),D robot (t,p)) Among them, S t,p For fused data at spatiotemporal points (t, p), D sensors For sensor data, D robot Let f be the robot inspection data, and f be the spatiotemporal mapping function.

6. The intelligent operation and maintenance method for power distribution rooms based on AI inspection robots according to claim 1, characterized in that, The Bayesian network model in S5 calculates the equipment failure probability based on conditional probability, and the mathematical expression of the model is: Where F represents a fault event, and E1, E2, ..., E n Observational evidence includes at least one of the following: temperature anomalies, sound changes, and gas concentration anomalies.

7. The intelligent operation and maintenance method for power distribution rooms based on AI inspection robots according to claim 1, characterized in that, The AI ​​inspection robot includes a data acquisition unit and a mobile platform. The data acquisition unit includes a visible light camera, an infrared thermal imager, a gas sensor, a microphone, a lidar, and an ultrasonic sensor. The mobile platform adopts a track-based or wheel-based movement method and is equipped with a positioning system.

8. The intelligent operation and maintenance method for power distribution rooms based on AI inspection robots according to claim 1, characterized in that, The digital twin platform employs a data verification mechanism to filter and correct abnormal data, supports data backup and recovery, and uses AI visual recognition algorithms to analyze image and video data, identify instrument readings, equipment on / off status, and appearance anomalies, and analyze abnormal temperature areas and sound characteristics.

9. A power distribution room intelligent operation and maintenance system based on an AI inspection robot, with a digital twin platform as its core, is characterized by: include: A digital twin module is used to construct a digital twin model of a power distribution room. The digital twin model includes a geometric model, a topology model, and an operating parameter model. The data acquisition module includes an IoT sensor and an AI inspection robot deployed in the power distribution room. The IoT sensor is used to collect equipment operation data, and the AI ​​inspection robot is used to collect image, temperature, sound, and gas concentration data. The data processing module is used to receive and store data transmitted from sensors and AI inspection robots, perform fusion analysis on multi-source data, perform time series alignment and spatial location mapping on sensor data and inspection data based on spatiotemporal correlation algorithms, and generate a comprehensive equipment operation status report. The intelligent linkage module is used to achieve intelligent linkage with security, fire protection, auxiliary control and other systems based on the analysis results of the data processing module, and to predict the probability of equipment failure based on the Bayesian network model and dynamically adjust the inspection path and frequency.

10. The intelligent operation and maintenance system for power distribution rooms based on AI inspection robots according to claim 9, characterized in that, The spatiotemporal correlation algorithm implemented by the data processing module is based on Kalman filtering, and the mathematical expression of the algorithm is: S t,p =f(D sensors (t,p),D robot (t,p)) Among them, S t,p For fused data at spatiotemporal points (t, p), D sensors For sensor data, D robot The data represents robot inspection data, and f is a spatiotemporal mapping function; the Bayesian network model calculates the equipment failure probability based on conditional probability, and the mathematical expression of the model is: Where F represents a fault event, and E1, E2, ..., E n Observational evidence includes at least one of the following: temperature anomalies, sound changes, and gas concentration anomalies.

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