Intelligent inspection monitoring and early warning system and method for high-voltage cable gallery robot

Abstract

The invention discloses an intelligent inspection monitoring and early warning system and method for a high-voltage cable gallery robot, and the key points of the technical scheme are that a robot management platform is used for adding, editing and deleting a plurality of track inspection robots, configuring robot parameters, and controlling the robots to be remotely turned on and turned off, to be automatically charged and to return with low electric quantity; the operation and maintenance management platform comprises a monitoring module, an inspection management module, an alarm management module, an inspection report module and a statistical analysis module; the intelligent image recognition platform adopts a deep learning model to recognize specific defects in images based on crack, water leakage and cable sample image training; the data support layer comprises a relational database, a remote dictionary service memory database and a professional network video recorder; the communication layer supports 4G / 5G, local area network and optical fiber communication, and adopts a webpage real-time communication technology to realize video stream forwarding. An efficient and accurate inspection mode with early warning capability is formed, and the operation safety and management efficiency of the high-voltage cable gallery are remarkably enhanced.

Description

Technical Field

[0001] This invention relates to the field of intelligent inspection and monitoring technology, and more specifically to a robotic intelligent inspection, monitoring and early warning system and method for high-voltage cable corridors. Background Technology

[0002] High-voltage cable corridors are the core infrastructure for power transmission in power plants. The high-voltage cables laid inside are constantly in a closed, humid environment, making them prone to defects such as overheating (e.g., loose joints), insulation cracks (e.g., aging), and water leakage. If not addressed promptly, these defects can lead to major accidents such as cable burnout and line tripping. Manual inspection is labor-intensive, has limited daily coverage, and has a high rate of missing early-stage, localized high temperatures and micro-cracks. Existing single-robot inspection systems lack a unified management mechanism, cannot adapt to multi-corridor, long-distance layouts, have rigid inspection task configurations, and fixed inspection routes and cycles. Furthermore, the uneven lighting and complex backgrounds within the corridors result in low accuracy in identifying high-voltage cable cracks and leaks. Additionally, the poor coordination between the robot and the terminal system prevents the formation of a unified monitoring, management, and analysis platform, resulting in a rigid system framework that struggles to adapt to different track structures or add new analytical functions. Therefore, existing technologies have shortcomings. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a robot-based intelligent inspection, monitoring, and early warning system and method for high-voltage cable corridors. This system constructs three major platforms: robot management, operation and maintenance management, and intelligent image recognition. By combining a collaborative architecture of a data support layer and a communication layer, it achieves an efficient, accurate, and early warning-enabled inspection mode and intelligent closed-loop management throughout the entire process.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] This invention proposes an intelligent inspection, monitoring and early warning system for high-voltage cable corridors using robots, comprising a robot management platform, an operation and maintenance management platform, an intelligent image recognition platform, a data support layer and a communication layer;

[0006] The robot management platform is used to add, edit, and delete multiple track inspection robots, configure robot parameters, and control the robots to remotely turn on and off, charge autonomously, and return to base when the battery is low.

[0007] The operation and maintenance management platform includes a monitoring module, an inspection management module, an alarm management module, an inspection report module, and a statistical analysis module.

[0008] The intelligent image recognition platform uses a deep learning model, trained on sample images of cracks, leaks, and cables, to identify specific defects in the images.

[0009] The data support layer includes a relational database, a remote dictionary service in-memory database, and a professional network video recorder;

[0010] The communication layer supports 4G / 5G, local area network, and fiber optic communication, and uses real-time web communication technology to forward video streams.

[0011] As a further improvement of the present invention, the robot management platform displays the robot's online status and patrol status in real time, and the robot parameters include charging threshold, minimum working power, obstacle avoidance distance, working frequency, autonomous shutdown power, and sensor enable status.

[0012] As a further improvement of the present invention, the monitoring module supports real-time inspection, multi-screen real-time video monitoring, and video recording query and retrieval; the inspection management module adopts multi-level configuration, supports pipeline definition, batch generation / manual input of stations, drag-and-drop sorting of routes, strategy formulation, and timed task cycle setting; the alarm management module displays cable high temperature, crack, and water leakage alarm information, and links the defect location and thermal imaging image; the inspection report module generates exportable inspection reports; and the statistical analysis module realizes multi-dimensional statistics of inspection frequency, alarm frequency, and temperature trend.

[0013] As a further improvement of the present invention, the timed task configuration of the inspection management module includes setting the task cycle, start time and end time, and the timed task generates automatic inspection tasks according to the cycle.

[0014] As a further improvement of the present invention, the inspection management module also includes configuring a planar map and marking the stations on the map;

[0015] This invention proposes an intelligent inspection, monitoring, and early warning method for high-voltage cable corridors using robots, employing the aforementioned intelligent inspection, monitoring, and early warning system for high-voltage cable corridors, comprising:

[0016] S1: Add and configure parameters of multi-track inspection robots through the robot management platform;

[0017] S2: Through the inspection management module of the operation and maintenance management platform, pipeline definition, site configuration, line configuration, policy configuration and scheduled task configuration are completed in sequence;

[0018] S3: Trigger a timed task, the robot inspects along a preset route, and uploads real-time inspection location information, visible light video, thermal imaging video and sensor data to the platform;

[0019] S4: The intelligent image recognition platform uses the YOLO-v5 model to analyze inspection data and identify cable defects such as high temperature, cracks, and water leakage.

[0020] S5: If a defect is detected, the alarm management module will trigger an alarm and display the defect location area, temperature, video frame and alarm information.

[0021] S6: After the inspection task is completed, the inspection report module generates an inspection report, and the statistical analysis module performs multi-dimensional statistics on the inspection data.

[0022] S7: Professional network video recorder stores inspection videos, and the database stores inspection data, alarm information and report data.

[0023] As a further improvement of the present invention, when the site configuration in step S2 adopts the batch inspection generation method, the number of generated sites is adapted to the total length of the pipeline, and the route configuration is achieved by dragging and dropping the sites for sorting.

[0024] As a further improvement of the present invention, the identification of cable high temperature, cracks, and water leakage defects in step S4 includes:

[0025] Identify cable, crack, and leakage defects and their locations based on visible light video frames;

[0026] The area where the cable is located is mapped onto thermal imaging video frames to extract the temperature characteristics of the area where the cable is located;

[0027] The temperature characteristics are compared with a preset threshold. If the temperature exceeds the threshold, it is determined to be a high-temperature defect in the cable.

[0028] As a further improvement of the present invention, the alarm information in step S5 includes alarm level, alarm content and abnormal location, wherein the abnormal location is accurate to a specific station and the distance relative to that station.

[0029] As a further improvement of the present invention, the multi-dimensional statistics in step S6 include the number of inspections, the number of alarms, the inspection duration, and the temperature change trend by time and site.

[0030] The beneficial effects of this invention are as follows: By constructing three major platforms—robot management, operation and maintenance management, and intelligent image recognition—and combining them with a collaborative architecture of data support and communication layers, a fully intelligent closed-loop management system for inspection and maintenance is achieved. Through a complete link from robot configuration and multi-level inspection task configuration to automated execution, and leveraging the synchronous acquisition of visible light and thermal imaging video streams, defect identification, and WebRTC low-latency transmission technology, key defects can be accurately identified in real time, triggering alarms and visual linkage displays. Furthermore, through inspection report generation and multi-dimensional statistical analysis, in-depth mining and trend assessment of inspection data are achieved, ultimately forming a highly efficient, accurate, and proactive operation and maintenance model with early warning capabilities and full traceability, significantly improving the safety assurance capabilities and operation and maintenance management efficiency of high-voltage cable corridors. Attached Figure Description

[0031] Figure 1 This is a diagram illustrating the overall architecture of a robotic intelligent inspection, monitoring, and early warning system for high-voltage cable corridors according to the present invention.

[0032] Figure 2 This is a flowchart illustrating the steps of an intelligent inspection, monitoring, and early warning method for high-voltage cable corridors using a robot, as described in this invention.

[0033] Figure 3 A flowchart illustrating the inspection task configuration for this invention;

[0034] Figure 4 This is a flowchart illustrating the steps of identifying defects based on an intelligent image recognition platform according to the present invention. Detailed Implementation

[0035] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof.

[0036] Identical parts are indicated by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, while the terms "bottom surface," "top surface," "inner," and "outer" refer to directions toward or away from the geometric center of a specific part, respectively.

[0037] The term "and / or" in the following text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0038] like Figure 1 As shown, this embodiment provides a robot-based intelligent inspection, monitoring, and early warning system for high-voltage cable corridors, including a robot management platform, an operation and maintenance management platform, an intelligent image recognition platform, a data support layer, and a communication layer;

[0039] The robot management platform is used to add, edit, and delete multiple track inspection robots, configure robot parameters, and control the robots to remotely turn on and off, charge autonomously, and return to base when the battery is low.

[0040] The operation and maintenance management platform includes a monitoring module, an inspection management module, an alarm management module, an inspection report module, and a statistical analysis module.

[0041] The intelligent image recognition platform uses a deep learning model, trained on sample images of cracks, leaks, and cables, to identify specific defects in the images;

[0042] The data support layer includes relational databases, remote dictionary service in-memory databases, and professional network video recorders;

[0043] The communication layer supports 4G / 5G, LAN, and fiber optic communication, and uses real-time web communication technology to forward video streams.

[0044] Specifically, the system is based on a microservice architecture and a browser and server architecture. The robot management platform configures the inspection robot to perform inspections and data collection, the intelligent image recognition platform is responsible for identifying and judging the on-site data, and the operation and maintenance management platform is responsible for presenting, analyzing, and making decisions on the detection and early warning results. Each performs its own function and works closely together to realize a complete operation and maintenance chain from task assignment, automatic execution, intelligent recognition, automatic alarm, manual confirmation, and decision support, thereby realizing full-process management of intelligent inspection, monitoring, and early warning of high-voltage cable corridor robots.

[0045] Furthermore, in this embodiment, the robot management platform is used to add, edit, and delete multiple track inspection robots, configure robot parameters, and control the robots to remotely power on / off, autonomously charge, and return to base when low on battery. Specifically, the robot management platform realizes full lifecycle management of multiple track inspection robots. As a basic function of the system, the inspection robot data is stored in a relational database in the data support layer. It supports editing and deleting existing inspection robots or adding new ones. When adding a robot, relevant information such as the robot's IP address, serial number, model, type, and activation status ("enabled" / "disabled") needs to be entered. The inspection robots are monitored through ultra-wideband (UWB), and the online status ("online" / "offline") and patrol status ("idle" / "in patrol") of each robot are displayed in real time. By editing the robot's activation status to "enabled", the system issues a command to the corresponding inspection robot through the communication layer. After receiving the command, the inspection robot triggers the power-on process, realizing remote power-on of the inspection robot, which facilitates subsequent management and use.

[0046] Furthermore, in this embodiment, the robot management platform displays the robot's online status and patrol status in real time. The robot parameters include charging threshold, minimum operating power, obstacle avoidance distance, operating frequency, autonomous shutdown power, and sensor enable status.

[0047] Specifically, the robot management platform can display real-time information such as the robot IP, online status, and patrol status of all inspection robots, allowing users to understand the current usage of the inspection robots. Simultaneously, by editing the inspection robot information, core parameters can be flexibly configured to control the robot's patrolling with specified characteristics, including the robot's charging threshold (e.g., 10%), minimum operating battery level (e.g., 30%), obstacle avoidance distance (using a fusion of lidar and ultrasonic sensors for obstacle avoidance), operating frequency (once every 2 hours), autonomous shutdown battery level (e.g., 5%), and sensor enable / disable status. The above data are example data and are not required in this embodiment. Based on the configured parameters, the robot can automatically perform autonomous charging (e.g., automatically returning to the charging dock when the battery level is below the charging threshold) and low-battery return (e.g., automatically returning to the dock when the battery level is below the minimum operating battery level). The low-battery return setting is to prevent the inspection robot from running out of power and shutting down during the return trip if it is too far from the charging dock.

[0048] Furthermore, in this embodiment, the operation and maintenance management platform includes a monitoring module, an inspection management module, an alarm management module, an inspection report module, and a statistical analysis module. The monitoring module supports real-time inspection, multi-screen real-time video monitoring, and video recording query and retrieval. The inspection management module adopts multi-level configuration, supporting pipeline definition, batch generation / manual input of sites, drag-and-drop sorting of routes, strategy formulation, and timed task cycle setting. The alarm management module displays alarm information for cable high temperature, cracks, and water leakage, and links the defect location and thermal imaging image. The inspection report module generates exportable inspection reports. The statistical analysis module realizes multi-dimensional statistics of inspection frequency, alarm frequency, and temperature trend.

[0049] Specifically, the operation and maintenance management platform realizes full-process control and data visualization of inspections. The monitoring module includes three main functions: real-time inspection, real-time video monitoring, and video recording query and retrieval. Real-time inspection uses web-based real-time communication technology at the communication layer to receive visible light video, thermal imaging video, and inspection robot status (such as power, voltage, current, speed, etc.) collected by the inspection robot in real time. This data is cached in a remote dictionary service (Redis) in-memory database (to improve access speed) and then synchronously updated to the visualization interface. Users can view the status, visible light video, thermal imaging video, and alarm information of each inspection robot based on the inspection robot IP index, enhancing data correlation. Real-time video monitoring allows users to select multiple inspection robot IPs and retrieve corresponding visible light video and thermal imaging video from the database, enabling multi-window split-screen display. Video recording query and retrieval is based on a high-definition professional network video recorder (NVR), which stores the received video collected by the inspection robot in real time. Users can retrieve corresponding video segments by robot name and time range for easy review and confirmation.

[0050] The inspection management module adopts a five-level hierarchical configuration of "pipeline-site-route-strategy-scheduled task," adapting to complex corridor layouts. It supports pipeline definition, batch generation / manual input of sites, drag-and-drop sorting of routes, strategy formulation, and scheduled task cycle settings. Specifically, the inspection management module includes pipeline management, site management, route management, strategy management, and scheduled task management. Pipeline management defines the names of pipelines, shafts, corridors, etc., associating them with their corresponding actual total length, depth, and number of sites, matching user business terminology, and displaying all pipeline information through a visual interface. All inspections are based on sites on the pipeline, therefore site management is necessary. Site management supports manual input or batch generation of inspection sites. Batch generation is based on a preset number of sites, evenly dividing the total pipeline length to automatically generate equidistant inspection sites. However, for key equipment such as high-voltage joints and insulation supports, manual input is used for the high-voltage corridor in this embodiment. In route management, a route is essentially an ordered set of stations representing the patrol route of an inspection robot. A route is constructed by selecting stations in an orderly fashion, and the robot follows this route, passing through all stations to complete the inspection. In strategy management, a strategy is the execution rule corresponding to the route. Requirements for the inspection robot's patrol and data collection are added to the route. For example, strategy A for a route might specify that the robot stays at each station for 15 seconds and collects thermal images at a frequency of 1 frame per second. An inspection robot using strategy A on this route will execute according to its specified requirements. Furthermore, a route can have multiple strategies to match different inspection needs. Strategy management allows for the addition, deletion, and modification of strategies on the route. Scheduled task management configures scheduled tasks for the strategies; each scheduled task forms an automatic inspection task periodically.

[0051] Furthermore, in this embodiment, the timed task configuration of the inspection management module includes setting the task cycle, start time, and end time, and the timed task generates automatic inspection tasks according to the cycle.

[0052] Specifically, the scheduled task configuration of the inspection management module allows you to configure the task period (e.g., every 10 minutes), start time, and end time (accurate to the second), and select an inspection robot. The system will generate an inspection task at each periodic time based on the strategy and timing rules, and send it to the inspection robot through the transport layer. After receiving the task, the inspection robot will automatically start the inspection process.

[0053] Furthermore, in this embodiment, the inspection management module also includes configuring a planar map and marking the stations on the map.

[0054] Specifically, map management supports importing planar maps and visual annotation of sites. Import a planar map of the actual project scenario, and set the map name and scale. After importing the map, add annotations in the visualization interface to associate the created sites with the corresponding physical locations on the map. The annotated map is integrated into the real-time inspection interface, overlaid with the robot's real-time location icon (updated according to UWB positioning data) and site identifiers to form a linked display.

[0055] Furthermore, the alarm management module within the inspection management module generates structured alarm information based on the defect identification results of cables (high temperature, cracks, and leaks) and the corresponding inspection robot information. All alarm information is then visualized, including the inspection robot name, alarm type, alarm content, alarm location, and alarm level. Simultaneously, it displays images of the defect locations (visualized or thermal images). For example, a thermal image is displayed for high-temperature cables, with the high-temperature area and maximum temperature clearly marked. The inspection report module generates exportable PDF inspection reports based on sensor data (temperature, humidity), visible light / thermal images, and alarm records from each autonomous inspection. The statistical analysis module supports multi-dimensional data statistics, including inspection analysis (temperature / humidity trends over time, displayed as line charts), site analysis (number of inspections and alarms per site), and report management (number of inspections, inspection duration, and number of sites within a period), all displayed synchronously on a visual page. The entire operation and maintenance management platform achieves full-process management before, during, and after inspections, adapting to various environments and possessing high applicability.

[0056] In a further embodiment of this invention, the intelligent image recognition platform employs a deep learning model, trained on sample images of cracks, leaks, and cables, to identify specific defects in the images.

[0057] Specifically, the intelligent image recognition platform employs the YOLO-v5 deep learning model. Compared to traditional image processing techniques, it can handle complex corridor environments and improve the accuracy of identifying cracks, leaks, and high-temperature defects. A training dataset is constructed based on crack, leak, and cable sample images collected from the internet. The deep learning model receives visible light / thermal imaging video frames (images) uploaded by the inspection robot, extracts multi-scale features from the images, classifies and identifies cracks, leaks, and high-temperature cable defects, and outputs confidence scores, locations, and temperatures, which are then linked to the alarm management module.

[0058] Furthermore, in this embodiment, the data support layer includes a relational database, a remote dictionary service in-memory database, and a professional network video recorder.

[0059] Specifically, the data support layer provides data access and storage for the aforementioned platforms. A relational database stores inspection robot parameters, inspection task configurations, alarm records, and inspection report data; a remote dictionary service (Redis) in-memory database caches the inspection robot's real-time status and video, improving data access speed for real-time inspections; and a professional network video recorder stores the video collected by the inspection robot, ensuring video stability. The data support layer guarantees data storage and efficient access.

[0060] Furthermore, in this embodiment, the communication layer supports 4G / 5G, local area network, and fiber optic communication, and uses real-time web communication technology to forward video streams.

[0061] Specifically, the communication layer needs to meet the requirements of high-efficiency transmission, support 4G / 5G, LAN, and fiber optic communication methods, and adapt to different corridor network environments; it should use WebRTC technology to forward video streams, effectively reducing real-time video latency; and it should integrate the Message Queuing Telemetry Transmission (MQTT) protocol to realize the transmission of sensor data and control commands between the robot and the platform.

[0062] With its low-latency video communication and stable data support, the intelligent inspection, monitoring and early warning robot for high-voltage cable corridors transforms the traditional passive inspection that relies on manual labor into an efficient, accurate and reliable proactive early warning and predictive maintenance mode, significantly improving the safety assurance capability and maintenance management efficiency of high-voltage cable corridors.

[0063] Furthermore, this embodiment provides a method for intelligent inspection, monitoring, and early warning of high-voltage cable corridors using robots, employing the aforementioned intelligent inspection, monitoring, and early warning system for high-voltage cable corridors, including:

[0064] S1: Add and configure parameters of multi-track inspection robots through the robot management platform;

[0065] S2: Through the inspection management module of the operation and maintenance management platform, pipeline definition, site configuration, line configuration, policy configuration and scheduled task configuration are completed in sequence;

[0066] S3: Trigger a timed task, the robot inspects along a preset route, and uploads real-time inspection location information, visible light video, thermal imaging video and sensor data to the platform;

[0067] S4: The intelligent image recognition platform uses the YOLO-v5 model to analyze inspection data and identify cable defects such as high temperature, cracks, and water leakage.

[0068] S5: If a defect is detected, the alarm management module will trigger an alarm and display the defect location area, temperature, video frame and alarm information.

[0069] S6: After the inspection task is completed, the inspection report module generates an inspection report, and the statistical analysis module performs multi-dimensional statistics on the inspection data.

[0070] S7: Professional network video recorder stores inspection videos, and the database stores inspection data, alarm information and report data.

[0071] Specifically, such as Figure 2 As shown, this method relies on the aforementioned system to achieve intelligent operation of the entire inspection process. Steps S1 and S2 are pre-inspection configurations, configuring the standby inspection robots and inspection tasks through the inspection management modules of the robot management platform and the operation and maintenance management platform, respectively. Steps S3-S5 are in-inspection detections, acquiring real-time multimodal inspection data and sending it to the intelligent image recognition platform to identify defects and activate the alarm management module to display specific alarm information, providing operation and maintenance personnel with intuitive decision-making basis. Steps S6 and S7 are post-inspection summaries, automatically generating structured inspection reports, and using the statistical analysis module to perform multi-dimensional in-depth mining of historical data to assess status trends. Videos generated throughout the process are archived and stored by a professional network video recorder, and all inspection data, alarm records, and reports are persistently stored in the database, ensuring the traceability of the operation and maintenance process and the integrity of data assets. This method, from task configuration, intelligent execution, accurate early warning to analysis and archiving, forms an efficient, closed-loop intelligent operation and maintenance management chain.

[0072] Furthermore, step S1 involves adding and configuring the parameters of the multi-track inspection robot through the robot management platform. Specifically, the SN code, IP address, and model of the inspection robot to be deployed are entered into the robot management platform, such as SN code HD202301001, IP address 192.168.4.105, and model name "track robot". The core parameters of the inspection robot are configured, such as charging threshold of 10%, minimum working power of 30%, and obstacle avoidance distance of 50cm. The activation status is set to "enabled". The inspection robot is remotely powered on and its online status is monitored through UWB positioning. The above data is example data and is not required in this embodiment.

[0073] Furthermore, in step S2, the inspection management module of the operation and maintenance management platform sequentially completes pipeline definition, site configuration, route configuration, policy configuration, and scheduled task configuration. When the site configuration adopts a batch inspection generation method, the number of generated sites is adapted to the total pipeline length. The route configuration is achieved by dragging and dropping sites for sorting. Specifically, as follows... Figure 3As shown, the hierarchical configuration is completed through the inspection management module of the operation and maintenance management platform. First, the pipeline is defined, and the total length, depth, cable type, and number of stations are entered. The pipeline stations are generated in batches. For example, if the number of pipeline stations is 4, the total pipeline length is evenly divided to automatically generate equidistant inspection stations, thus generating stations 0, 1, 2, and 3. Stations 0, 1, and 2 are arranged in an orderly manner by dragging and dropping to generate line R1. A strategy is formulated for the line, namely the requirements for inspection robot patrol and data collection. The start time, end time, and repetition period are set for this strategy. The inspection robot configured in step S1 is selected, a scheduled task is generated, and the inspection task is generated and sent to the inspection robot according to the cycle.

[0074] Furthermore, step S3 triggers a timed task, during which the robot inspects along a preset route and uploads real-time inspection location information, visible light video, thermal imaging video, and sensor data to the platform. Specifically, when the timed task is initiated, the inspection robot receives the inspection task, inspects and monitors along the preset route, collects video data through visible light and thermal imaging cameras, collects environmental data through temperature and humidity sensors, and uploads the data to the system via the communication layer (fiber optic / 4G). The video is then displayed in real time via web-based real-time communication technology.

[0075] Furthermore, in step S4, the intelligent image recognition platform uses a YOLO-v5 model to analyze inspection data and identify cable defects such as high temperatures, cracks, and leaks. Specifically, the intelligent image recognition platform receives video frames (images) for defect identification. For visible light video frames, it uses a pre-trained YOLO-v5 deep learning model to identify cracks, leaks, and cable defects, and outputs the confidence level and the location of the defect. For thermal imaging video frames, it extracts the temperature value of the area where the cable is located from the video frame and compares it with a preset threshold (such as 30℃) to determine whether it is a high temperature. The defect identification results (type, location, temperature) are then pushed to the operation and maintenance management platform.

[0076] Furthermore, step S4 identifies cable defects such as high temperature, cracks, and leakage, including:

[0077] Identify cable, crack, and leakage defects and their locations based on visible light video frames;

[0078] The area where the cable is located is mapped onto thermal imaging video frames to extract the temperature characteristics of the area where the cable is located;

[0079] The temperature characteristics are compared with a preset threshold. If the temperature exceeds the threshold, it is determined to be a high-temperature defect in the cable.

[0080] Specifically, such as Figure 4As shown, the YOLO-v5 deep learning model, pre-trained with a large amount of image data, can accurately identify cables, cracks, leaks, confidence levels, and their locations in visible light video frames. Cracks and leaks can be directly identified without further processing. However, high-temperature defects require the extraction of temperature features from thermal imaging video frames for judgment. Directly extracting the temperature features of the entire thermal imaging video frame might focus on non-cable areas, leading to false alarms. Therefore, it is necessary to accurately locate the cable area in the thermal imaging image. Using the cable location identified from the visible light video frame, coordinate mapping is used to map it to the thermal imaging video frame, obtaining the cable area in the thermal imaging video frame. This area serves as the temperature measurement area. Subsequently, only the temperature features of this area are extracted, such as calculating the highest temperature within the area. The temperature features (e.g., the highest temperature) are compared with a preset threshold (e.g., 30 degrees Celsius). If the temperature exceeds the preset threshold, it is judged as a high-temperature cable defect. This process identifies high-temperature, crack, and leak defects in cables, and the defect identification results are pushed to the operation and maintenance management platform.

[0081] Furthermore, if a defect is detected in step S5, the alarm management module triggers an alarm, displaying the defect's location, temperature, video frame, and alarm information. Specifically, if a defect is detected, the alarm management module triggers an alarm, generates alarm information, updates the alarm information list, and displays it visually on the page. It simultaneously displays the defect's location, temperature, and video frame. When a cable has a high-temperature defect, it displays the alarm's thermal imaging video frame, marking the defect's location and overlaying it with the highest temperature. When a crack or leak is detected, it displays the alarm's visible light video frame, marking the defect's location. Simultaneously, alarm information is pushed to maintenance and management personnel's terminals (such as mobile phones and computers) via pop-up windows, reminding them to handle the issue promptly.

[0082] Furthermore, the alarm information in step S5 includes alarm level, alarm content, and abnormal location. The abnormal location is combined with UWB positioning data and is accurate to the specific station and the distance relative to that station.

[0083] Furthermore, after the inspection task in step S6 is completed, the inspection report module generates an inspection report, and the statistical analysis module performs multi-dimensional statistics on the inspection data. These multi-dimensional statistics include the number of inspections, the number of alarms, the inspection duration, and the temperature change trend by time and site.

[0084] Specifically, after the inspection task is completed, the inspection report module generates a PDF inspection report for the robot's inspection task. This report includes sensor data (temperature, humidity), visible light / thermal imaging images, and alarm records. The visible light / thermal imaging images display all defect images collected during the entire inspection process. Multiple images can be displayed, such as a visible light image indicating the defect location and a thermal imaging image indicating the defect location and temperature. If no defects are detected during the inspection, "None" is displayed. The alarm records document all alarm information collected during the entire inspection process. The statistical analysis module calculates the number of inspections, alarms, and average inspection time by time and site, and generates a trend curve with time on the horizontal axis and temperature on the vertical axis, providing data support for optimizing inspection strategies.

[0085] Furthermore, step S7 uses a professional network video recorder to store inspection recordings, and a database to store inspection data, alarm information, and report data. Specifically, the NVR stores the entire inspection video, the relational database stores the inspection robot parameters, task configurations, alarm information, and inspection reports, and Redis stores the inspection robot's real-time status, video, and defect identification results to ensure rapid subsequent retrieval.

[0086] Based on the implementation of the above-mentioned intelligent inspection, monitoring and early warning method for high-voltage cable corridors using robots, this method significantly improves the automation level and operation and maintenance efficiency of cable corridor inspections by integrating robot control, multimodal data acquisition, deep learning analysis and integrated platform operation and maintenance.

[0087] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A robotic intelligent inspection, monitoring, and early warning system for high-voltage cable corridors, characterized in that, It includes a robot management platform, an operation and maintenance management platform, an intelligent image recognition platform, a data support layer, and a communication layer; The robot management platform is used to add, edit, and delete multiple track inspection robots, configure robot parameters, and control the robots to remotely turn on and off, charge autonomously, and return to base when the battery is low. The operation and maintenance management platform includes a monitoring module, an inspection management module, an alarm management module, an inspection report module, and a statistical analysis module. The intelligent image recognition platform uses a deep learning model, trained on sample images of cracks, leaks, and cables, to identify specific defects in the images. The data support layer includes a relational database, a remote dictionary service in-memory database, and a professional network video recorder; The communication layer supports 4G / 5G, local area network, and fiber optic communication, and uses real-time web communication technology to forward video streams.

2. The intelligent inspection, monitoring, and early warning system for high-voltage cable corridors using robots according to claim 1, characterized in that, The robot management platform displays the robot's online status and patrol status in real time. The robot parameters include charging threshold, minimum operating power, obstacle avoidance distance, operating frequency, autonomous shutdown power, and sensor enable status.

3. The intelligent inspection, monitoring, and early warning system for high-voltage cable corridors using robots according to claim 1, characterized in that, The monitoring module supports real-time inspection, multi-screen real-time video monitoring, and video recording query and retrieval; the inspection management module adopts multi-level configuration and supports pipeline definition, batch generation / manual input of stations, drag-and-drop sorting of routes, strategy formulation, and timed task cycle setting; the alarm management module displays alarm information for cable high temperature, cracks, and water leakage, and links the defect location and thermal imaging screen; the inspection report module generates exportable inspection reports. The statistical analysis module enables multi-dimensional statistics on the number of inspections, alarms, and temperature trends.

4. The intelligent inspection, monitoring, and early warning system for high-voltage cable corridors using robots according to claim 3, characterized in that, The scheduled task configuration of the inspection management module includes setting the task cycle, start time, and end time. The scheduled task generates automatic inspection tasks according to the cycle.

5. The intelligent inspection, monitoring, and early warning system for high-voltage cable corridors using robots according to claim 1, characterized in that, The inspection management module also includes configuring a planar map and marking the stations on the map.

6. A method for intelligent inspection, monitoring, and early warning of high-voltage cable corridors using a robot, comprising the intelligent inspection, monitoring, and early warning system for high-voltage cable corridors as described in any one of claims 1-5, characterized in that... include: S1: Add and configure parameters of multi-track inspection robots through the robot management platform; S2: Through the inspection management module of the operation and maintenance management platform, pipeline definition, site configuration, line configuration, policy configuration and scheduled task configuration are completed in sequence; S3: Trigger a timed task, the robot inspects along a preset route, and uploads real-time inspection location information, visible light video, thermal imaging video and sensor data to the platform; S4: The intelligent image recognition platform uses the YOLO-v5 model to analyze inspection data and identify cable defects such as high temperature, cracks, and water leakage. S5: If a defect is detected, the alarm management module will trigger an alarm and display the defect location area, temperature, video frame and alarm information. S6: After the inspection task is completed, the inspection report module generates an inspection report, and the statistical analysis module performs multi-dimensional statistics on the inspection data. S7: Professional network video recorder stores inspection videos, and the database stores inspection data, alarm information and report data.

7. The intelligent inspection, monitoring, and early warning method for high-voltage cable corridors using robots according to claim 6, characterized in that, When the site configuration in step S2 is generated in batch inspection mode, the number of generated sites is adapted to the total length of the pipeline, and the route configuration is achieved by dragging and dropping the sites to sort them.

8. A method for intelligent inspection, monitoring, and early warning of high-voltage cable corridors using robots, as described in claim 6, is characterized in that... The identification of cable defects such as high temperature, cracks, and leakage in step S4 includes: Identify cable, crack, and leakage defects and their locations based on visible light video frames; The area where the cable is located is mapped onto thermal imaging video frames to extract the temperature characteristics of the area where the cable is located; The temperature characteristics are compared with a preset threshold. If the temperature exceeds the threshold, it is determined to be a high-temperature defect in the cable.

9. A method for intelligent inspection, monitoring, and early warning of high-voltage cable corridors using robots according to claim 6, characterized in that, The alarm information mentioned in step S5 includes the alarm level, alarm content, and abnormal location, with the abnormal location accurate to the specific station and the distance relative to that station.

10. A method for intelligent inspection, monitoring, and early warning of high-voltage cable corridors using robots according to claim 6, characterized in that, The multi-dimensional statistics mentioned in step S6 include the number of inspections, the number of alarms, the inspection duration, and the temperature change trend by time and site.

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