Inspection task execution method and device for electric power pipe gallery and computer equipment

Abstract

The invention relates to an inspection task execution method and device for an electric power pipe gallery and computer equipment. The method comprises the following steps: in a process of controlling an inspection robot to execute an inspection task, obtaining actual inspection information of an electric power pipe gallery sent by the inspection robot; according to the actual inspection information, updating the twin virtual space at the previous time point to obtain a current twin virtual space; based on the twinning inspection information in the current twinning virtual space, predicting the operation state of the electric power pipe gallery; when the operation state is an abnormal operation state, the inspection robot is controlled to stop executing the inspection task, and abnormal information and abnormal optimization strategy information of the abnormal information are determined; and according to the abnormal optimization strategy information and the abnormal information, the actual inspection information is updated until the predicted operation state is in a normal state, and the inspection robot is controlled to continue to execute the inspection task based on the latest obtained actual inspection information. By adopting the method, the inspection task of the electric power pipe gallery can be efficiently executed.

Description

Technical Field

[0001] The present application relates to the technical field of industrial inspection, and particularly to a method, device, and computer equipment for executing inspection tasks of a power pipe gallery. Background Art

[0002] A power pipe gallery is an integrated pipe gallery that combines facilities such as power cables, communication, lighting, monitoring, and fire prevention. It is mainly used for laying and accommodating power cables to provide stable and reliable power supply for the city. With the development of power pipe galleries, it is necessary to inspect the power pipe galleries to detect whether the information of equipment, structures, and the working state of robots in the power pipe galleries is normal through the obtained inspection information.

[0003] In traditional technologies, methods for processing inspection information of power pipe galleries include: designing an inspection robot equipped with an anomaly recognition model to continuously detect the physical condition of equipment through infrared, acoustic, and visual sensors. While obtaining the inspection information of the power pipe gallery, it can automatically identify and report equipment anomalies.

[0004] However, the current method for executing inspection tasks of power pipe galleries has the technical problem of being inefficient. Summary of the Invention

[0005] Based on this, it is necessary to provide an efficient method, device, computer equipment, computer-readable storage medium, and computer program product for executing inspection tasks of power pipe galleries in view of the above technical problems.

[0006] In a first aspect, the present application provides a method for executing an inspection task of a power pipe gallery, including:

[0007] During the process of controlling an inspection robot to execute an inspection task, obtain the actual inspection information of the power pipe gallery sent by the inspection robot, and determine the twin virtual space of the power pipe gallery at the previous time point, where the twin virtual space is used to simulate the actual working state of the power pipe gallery;

[0008] Update step: Update the twin virtual space at the previous time point according to the actual inspection information to obtain the current twin virtual space;

[0009] Predict the operating state of the power pipe gallery based on the twin inspection information that matches the actual inspection information in the current twin virtual space;

[0010] In the case where the operating state is an abnormal operating state, control the inspection robot to abort the execution of the inspection task, search for abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information;

[0011] Update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return the update steps until the predicted operating state is the normal operating state;

[0012] Based on the latest obtained actual inspection information, control the inspection robot to continue to execute the inspection task.

[0013] In a second aspect, the present application also provides an inspection task execution device for a power pipe gallery, including:

[0014] An actual inspection information acquisition module, configured to acquire the actual inspection information of the power pipe gallery sent by the inspection robot during the process of controlling the inspection robot to execute the inspection task, and determine the twin virtual space at the previous time point corresponding to the power pipe gallery, wherein the twin virtual space is used to simulate the actual working state of the power pipe gallery;

[0015] A twin virtual space construction module, for the update step: update the twin virtual space at the previous time point according to the actual inspection information to obtain the current twin virtual space;

[0016] A pipe gallery operating state prediction module, configured to predict the operating state of the power pipe gallery based on the twin inspection information matching the actual inspection information in the current twin virtual space;

[0017] An abnormal optimization strategy information generation module, configured to, when the operating state is an abnormal operating state, control the inspection robot to abort the execution of the inspection task, search for abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information;

[0018] An actual inspection information update module, configured to update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return the update steps until the predicted operating state is the normal operating state;

[0019] An inspection task execution module, configured to control the inspection robot to continue to execute the inspection task based on the latest obtained actual inspection information.

[0020] In a third aspect, the present application also provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0021] During the process of controlling the inspection robot to execute the inspection task, acquire the actual inspection information of the power pipe gallery sent by the inspection robot, and determine the twin virtual space at the previous time point corresponding to the power pipe gallery, wherein the twin virtual space is used to simulate the actual working state of the power pipe gallery;

[0022] Update step: Update the twin virtual space at the previous time point according to the actual inspection information to obtain the current twin virtual space;

[0023] Predict the operating state of the power pipe gallery based on the twin inspection information that matches the actual inspection information in the current twin virtual space;

[0024] In the case where the operating state is an abnormal operating state, control the inspection robot to abort the inspection task, search for abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information;

[0025] Update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return to the update step until the predicted operating state is a normal operating state;

[0026] Based on the latest obtained actual inspection information, control the inspection robot to continue to execute the inspection task.

[0027] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0028] During the process of controlling the inspection robot to execute the inspection task, obtain the actual inspection information of the power pipe gallery sent by the inspection robot, and determine the twin virtual space at the previous time point corresponding to the power pipe gallery, where the twin virtual space is used to simulate the actual working state of the power pipe gallery;

[0029] Update step: Update the twin virtual space at the previous time point according to the actual inspection information to obtain the current twin virtual space;

[0030] Predict the operating state of the power pipe gallery based on the twin inspection information that matches the actual inspection information in the current twin virtual space;

[0031] In the case where the operating state is an abnormal operating state, control the inspection robot to abort the inspection task, search for abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information;

[0032] Update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return to the update step until the predicted operating state is a normal operating state;

[0033] Based on the latest obtained actual inspection information, control the inspection robot to continue to execute the inspection task.

[0034] In a fifth aspect, the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0035] In the process of controlling an inspection robot to perform an inspection task, obtain the actual inspection information of the power cable tunnel sent by the inspection robot, and determine the twin virtual space of the power cable tunnel at the previous time point, where the twin virtual space is used to simulate the actual working state of the power cable tunnel;

[0036] Update step: Update the twin virtual space at the previous time point according to the actual inspection information to obtain the current twin virtual space;

[0037] Predict the operating state of the power cable tunnel based on the twin inspection information in the current twin virtual space that matches the actual inspection information;

[0038] In the case where the operating state is an abnormal operating state, control the inspection robot to abort the inspection task, search for abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information;

[0039] Update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return to the update step until the predicted operating state is a normal operating state;

[0040] Based on the latest obtained actual inspection information, control the inspection robot to continue to perform the inspection task.

[0041] In the above method, device, computer device, computer-readable storage medium, and computer program product for performing the inspection task of the power cable tunnel, in the process of controlling the inspection robot to perform the inspection task, update the twin virtual space at the previous time point through the actual inspection information of the power cable tunnel sent by the inspection robot, so as to predict in advance the operating state of the power cable tunnel based on the twin inspection information in the current twin virtual space obtained after the update, and in the case where the operating state is an abnormal operating state, control the inspection robot to abort the inspection task, search for abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information; finally, update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return to the update step until the predicted operating state is a normal operating state, and based on the latest obtained actual inspection information, control the inspection robot to continue to perform the inspection task in the power cable tunnel in a normal operating state. In the whole process, compared with the method of detecting the abnormality of the power cable tunnel only when an abnormality has occurred, the present application can predict whether the power cable tunnel is in an abnormal operating state before an abnormality occurs by constructing the current twin virtual space, and then perform abnormal optimization on the power cable tunnel in time, so that the efficiency of the inspection robot in performing the inspection task is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the accompanying drawings required for the description of the embodiments of the present application or related technologies. Obviously, the accompanying drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0043] Figure 1 It is an application environment diagram of the inspection task execution method for the power pipe gallery in an embodiment;

[0044] Figure 2 It is a schematic flowchart of the inspection task execution method for the power pipe gallery in an embodiment;

[0045] Figure 3 It is a schematic flowchart of the inspection task execution method for the power pipe gallery in another embodiment;

[0046] Figure 4 It is a schematic diagram of the operation logic among five layers in the digital twin system of the power pipe gallery in an embodiment;

[0047] Figure 5 It is a structural block diagram of the inspection task execution device for the power pipe gallery in an embodiment;

[0048] Figure 6 It is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0049] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the following further details the present application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are used to explain the present application and are not used to limit the present application.

[0050] The inspection task execution method for the power pipe gallery provided by the embodiments of the present application can be applied to the application environment as Figure 1 shown. Among them, the terminal 102 communicates with the inspection robot 104 through the network.

[0051] The user triggers the inspection task execution control of the power cable tunnel on the terminal 102. The terminal 102 responds to the trigger operation of the terminal 102 and controls the inspection robot 104 to execute the inspection task of the power cable tunnel. During the process of the inspection robot 104 executing the inspection task, the inspection robot 104 obtains the actual inspection information of the power cable tunnel and sends the actual inspection information of the power cable tunnel to the terminal 102. The terminal 102 obtains the actual inspection information of the power cable tunnel sent by the inspection robot and determines the twin virtual space at the previous time point corresponding to the power cable tunnel. Among them, the twin virtual space is used to simulate the actual working state of the power cable tunnel. Update step: Update the twin virtual space at the previous time point according to the actual inspection information to obtain the current twin virtual space. Predict the operating state of the power cable tunnel based on the twin inspection information matching the actual inspection information in the current twin virtual space. In the case where the operating state is an abnormal operating state, control the inspection robot 104 to abort the execution of the inspection task, search for abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information. Update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return to the update step until the predicted operating state is a normal operating state. Control the inspection robot 104 to continue to execute the inspection task based on the latest obtained actual inspection information. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, and smart glasses, etc.

[0052] In an exemplary embodiment, as Figure 2 shown, a method for executing an inspection task of a power cable tunnel is provided. Taking the method applied to Figure 1 the terminal 102 in

[0053] S100, during the process of controlling the inspection robot to execute the inspection task, obtain the actual inspection information of the power cable tunnel sent by the inspection robot, and determine the twin virtual space at the previous time point corresponding to the power cable tunnel.

[0054] Among them, the power cable tunnel is a comprehensive tunnel integrating power cables, communication, lighting, monitoring, and fire prevention facilities, etc. It is mainly used for laying and accommodating power cables to provide stable and reliable power supply for the city. The twin virtual space is used to simulate the actual working state of the power cable tunnel.

[0055] The inspection robot uses a mobile robot as a carrier, a visible light camera, an infrared thermal imager or other detection instruments as a payload system, and the multi-field information fusion of machine vision, electromagnetic field, GPS (Global Positioning System), and GIS (Geographic Information System) as the navigation system for the robot's autonomous movement and autonomous inspection, and an embedded computer as the software and hardware development platform for the control system. The inspection robot usually consists of parts such as the robot body, sensors, control system, positioning system, and communication system. Some inspection robots are also equipped with multiple sensors such as lidar, millimeter-wave radar, and ultrasonic radar to ensure the accuracy and effectiveness of data.

[0056] The actual inspection information is the inspection information detected by the inspection robot, including physical power pipe gallery entities such as power pipe gallery inspection robots, sensors, power supply equipment, and staff, as well as the set of corresponding working states of the power pipe gallery.

[0057] Specifically, the user triggers the inspection task execution control of the power pipe gallery at the terminal. The terminal responds to the trigger operation of the terminal, controls the inspection robot to execute the inspection task of the power pipe gallery. During the process of the inspection robot executing the inspection task, the inspection robot obtains the actual inspection information of the power pipe gallery and sends the actual inspection information of the power pipe gallery to the terminal; the terminal obtains the actual inspection information of the power pipe gallery sent by the inspection robot and determines the twin virtual space at the corresponding previous time point of the power pipe gallery. The actual inspection information can be obtained according to multiple sensor devices. The twin virtual space at the previous time point can be the initially constructed twin virtual space or the twin virtual space updated at the previous time point.

[0058] S200, Update step: Update the twin virtual space at the previous time point according to the actual inspection information to obtain the current twin virtual space.

[0059] Among them, the twin virtual space is the product of the integration of digital technology and spatial concepts. It uses digital twin technology to create a virtual model in the virtual space that is highly similar to the physical entity in the real world. This virtual model can reflect and synchronize the state, behavior, and performance of the physical entity in real time, thereby realizing the two-way mapping and interaction between the physical world and the virtual world.

[0060] Specifically, determine the twin virtual space at the corresponding previous time point of the power pipe gallery, and based on the actual inspection information, for example, based on the physical power pipe gallery entity information such as power pipe gallery inspection robots, sensors, power supply equipment, and staff, update the twin virtual space at the previous time point to be consistent with the entity information in the real world to obtain the current twin virtual space.

[0061] The main advantages of this application combined with the twin system are as follows: (1) From passive response to active prevention: Predict risks through simulation to reduce the probability of sudden anomalies; (2) From single-point monitoring to global collaboration: Integrate physical entities, data, models, and business logics to improve decision-making efficiency; (3) From experience-driven to data-driven: Optimize the operation and maintenance strategy based on the full-life cycle data to reduce human misjudgment.

[0062] S300, based on the twin inspection information that matches the actual inspection information in the current twin virtual space, predicts the operating status of the power pipe gallery.

[0063] Specifically, to obtain the twin inspection information that matches the actual inspection information in the current twin virtual space, the operating status of the power pipe gallery can be predicted based on the twin inspection information. The operating status of the power pipe gallery can indicate that the power pipe gallery is in an abnormal operating state or a normal operating state. Predicting the operating status of the power pipe gallery based on the twin inspection information means predicting whether the operating condition of the power pipe gallery is abnormal when the inspection information is the twin inspection information. In practical applications, the abnormal operating condition of the power pipe gallery can include many types, such as abnormal operation of equipment in the power pipe gallery, abnormal operation of inspection robots, or abnormal environmental information in a certain area, etc.

[0064] S400, when the operating status is an abnormal operating state, controls the inspection robot to abort the execution of the inspection task, searches for abnormal information from the twin inspection information, and detects the abnormal optimization strategy information corresponding to the abnormal information.

[0065] Specifically, when the operating status is an abnormal operating state, the inspection robot is controlled to abort the execution of the inspection task. At this time, it is necessary to repair the abnormality of the power pipe gallery. First, it is necessary to search for abnormal information from the twin inspection information and generate the abnormal optimization strategy information corresponding to the abnormal information based on the abnormal information. Among them, generating the abnormal optimization strategy information corresponding to the abnormal information based on the abnormal information can include: generating the abnormal type of the abnormal information based on the abnormal information and generating the abnormal optimization strategy information corresponding to the abnormal type.

[0066] In an exemplary embodiment, the abnormal optimization strategy information can be sent to the inspection robot in the form of a JSON format instruction.

[0067] S500, updates the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and returns to the update step until the predicted operating status is a normal operating state.

[0068] Specifically, the abnormal optimization strategy information and the abnormal information are sent to the inspection robot, and the inspection robot updates the actual inspection information according to the abnormal optimization strategy information and the abnormal information; alternatively, the abnormal optimization strategy information and the abnormal information are sent to the staff, and the staff updates the actual inspection information according to the abnormal optimization strategy information and the abnormal information. Updating the actual inspection information means repairing the abnormal information that causes the power pipe gallery to be in an abnormal operating state, and updating the current twin virtual space again according to the repaired actual inspection information to predict the operating state of the power pipe gallery again until the predicted operating state is a normal operating state.

[0069] In an exemplary embodiment, after the actual inspection information is updated, the power pipe gallery provides real-time optimization feedback to the terminal to inform the terminal that the actual inspection information has been successfully optimized. The optimization feedback in this application can be the execution result or status response information generated by a physical layer entity (such as an inspection robot or a staff member, etc.) after performing a specific operation, and these information are transmitted back to the data layer and the model layer in real time through sensors or control systems to verify the effectiveness of the optimization operation and optimize subsequent decisions. For example, its specific content includes:

[0070] (1) Operation feedback of the inspection robot: Task execution feedback: Completion status of the inspection path (such as "Path A has been completed" or "Path B is interrupted due to an obstacle"); Machine operation result (such as "Device image acquisition successful / failed"); Action execution feedback: Activation status of the fire extinguishing device (such as "Fire extinguishing device is abnormal"); Infrared temperature measurement result of the equipment (such as "The temperature of the cable joint has dropped from 80°C to 50°C"); Abnormal response feedback: Obstacle avoidance operation record (such as "The robot has bypassed the obstacle, and the new path is B"); Emergency stop warning (such as "Battery overheating, task has been forced to pause").

[0071] (2) Feedback of personnel operations: Manual operation record of the staff (such as "The damaged cable has been replaced"); Result of emergency event handling (such as "Personnel have been evacuated, start extinguishing the fire").

[0072] (3) System-level collaborative feedback: Instruction response status: Execution result of the instruction issued by the digital twin system (such as "The optimization instruction of the model layer has been executed, and the energy consumption has been reduced by 15%"); Result of multi-robot collaborative task assignment (such as "Robot B takes over the task, and the remaining battery power is sufficient"); Environmental intervention feedback: Regulation effect of the ventilation / drainage system (such as "The CO concentration has dropped from 500 ppm (Parts Per Million) to the safety threshold of 100 ppm").

[0073] S600, based on the latest obtained actual inspection information, control the inspection robot to continue to perform the inspection task.

[0074] Specifically, obtain the latest actual inspection information obtained when the predicted operating state is the normal operating state. Based on this actual inspection information, control the inspection robot to continue to perform the inspection task, that is, in the environment where the power pipe gallery is in the normal operating state, control the inspection robot to continue to perform the inspection task. For example, when the twin inspection information corresponding to the actual inspection information detected by the inspection robot indicates that there is an obstacle in a certain area, the inspection robot stops performing the inspection task, and when this obstacle is removed from the area, the inspection robot continues to perform the inspection task.

[0075] In the above method for executing the inspection task of the power pipe gallery, during the process of controlling the inspection robot to perform the inspection task, the actual inspection information of the power pipe gallery sent by the inspection robot is used to update the twin virtual space at the previous time point, so as to predict the operating state of the power pipe gallery in advance based on the twin inspection information that matches the actual inspection information in the updated current twin virtual space. And when the operating state is the abnormal operating state, control the inspection robot to stop performing the inspection task, find the abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information; finally, update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return to the update step until the predicted operating state is the normal operating state, and based on the latest obtained actual inspection information, control the inspection robot to continue to perform the inspection task in the power pipe gallery in the normal operating state. In the whole process, compared with the method that can only detect the abnormality of the power pipe gallery when an abnormality has occurred, this application can predict whether the power pipe gallery is in the abnormal operating state before the abnormality occurs by constructing the current twin virtual space, and then optimize the abnormality of the power pipe gallery in time, making the process of the inspection robot performing the inspection task more efficient.

[0076] In an exemplary embodiment, a photographing device and multiple sensors are installed on the inspection robot, and the sensors include a position sensor, an environment sensor, and a power sensor; as Figure 3 shown, S100 includes:

[0077] S120, during the process of controlling the inspection robot to perform the inspection task, obtain the task execution information of the inspection robot, the actual inspection image captured by the photographing device, the robot positioning information monitored by the position sensor, the environment information monitored by the environment sensor, and the device operation information monitored by the power sensor.

[0078] S140, based on the actual inspection image, detect the object positioning information of the target object.

[0079] S160, based on the task execution information, the object positioning information, the robot positioning information, the environment information, and the device operation information, generate the actual inspection information.

[0080] S180. Determine the twin virtual space corresponding to the power pipe gallery at the previous time point.

[0081] Among them, the photographing device includes, but is not limited to, devices with photographing functions such as cameras and video recorders. There is more than one actual inspection image, and the task execution information refers to the task completion situation when the inspection robot executes tasks according to the preset operation trajectory, including, but not limited to, the task completion rate, the battery power of the inspection robot, and the operation feedback information of the inspection robot, etc.

[0082] Specifically, during the process of controlling the inspection robot to execute inspection tasks, the inspection robot can detect various information from itself and the surrounding environment. The various detected information includes a multi-dimensional real-time parameter set of physical layer entities (robots, devices, environments, and personnel, etc.), specifically including the following:

[0083] (1) Task execution information: including the task execution status of the inspection robot, such as the task completion rate of the inspection robot, the movement trajectory, the operation feedback of the robot, and the battery power, etc.

[0084] (2) Robot positioning information: including the real-time position of the inspection robot, which can be obtained through the positioning data of the position sensor. In practical applications, the position sensor can be a device such as a lidar.

[0085] (3) Environmental information: that is, the internal environment data of the pipe gallery, including temperature, humidity, smoke concentration, and oxygen content, etc. In addition, it also includes the concentration of dangerous gases, such as the concentrations of gases such as methane, carbon monoxide, and hydrogen sulfide. The environmental information can be monitored by environmental sensors.

[0086] (4) Object positioning information: The object in this application generally refers to a staff member. The object positioning information refers to the staff member positioning information. The object positioning information can generally be obtained by performing target detection on the target object from the actual inspection images taken by the photographing device. Further, for an object wearing a wearable device, the object positioning information can also be directly obtained through the wearable device. In addition, the actual inspection information also includes object operation records, such as operation logs for equipment maintenance and emergency intervention, etc.

[0087] (5) Equipment operation information: including power supply equipment parameters and equipment abnormal indicators. Among them, the power supply equipment parameters include current, voltage, power, insulation status, and partial discharge, etc., and the equipment abnormal indicators include overheating (detected by thermal imaging), mechanical wear, etc.

[0088] By obtaining the task execution information, object positioning information, robot positioning information, environmental information, and equipment operation information sent by the above inspection robot, actual inspection information is generated.

[0089] In some exemplary embodiments, the actual inspection information of the power cable tunnel further includes the event trigger information of the power cable tunnel. The event trigger information of the power cable tunnel is not obtained by the inspection robot, but can be directly sent to the terminal through the communication bus of the power cable tunnel. The event trigger information of the power cable tunnel includes, but is not limited to, the emergency event trigger flag and the emergency response status. Among them, the emergency event trigger flag includes warning signals such as fire, gas leakage or equipment short circuit, and the emergency response status includes operation feedback such as activation of the fire extinguishing device or startup of the ventilation system.

[0090] In an exemplary embodiment, the generated actual inspection information can also be processed such as data cleaning, structured processing and multimodal fusion. For example, multimodal fusion can refer to the spatio-temporal alignment of sensor data and camera images, and the cleaned structured data can be the historical operation records of equipment in the time series database.

[0091] In the above embodiments, by comprehensively and multi-dimensionally obtaining the actual inspection information of the power cable tunnel, it can prepare for accurately updating the twin virtual space at the previous time point to obtain the current twin virtual space in the following text.

[0092] In an exemplary embodiment, updating the twin virtual space at the previous time point according to the actual inspection information includes: updating the three-dimensional environmental space model in the twin virtual space at the previous time point according to the environmental information; updating the dynamic model of the power equipment in the twin virtual space at the previous time point according to the equipment operation information; updating the running trajectory of the virtual inspection robot in the twin virtual space at the previous time point according to the environmental information, task execution information and robot positioning information, where the virtual inspection robot corresponds to the inspection robot; updating the position information of the target object in the twin virtual space at the previous time point according to the object positioning information.

[0093] Specifically, in this application, it is necessary to update the twin virtual space at the previous time point with the actual inspection information at the current time point in the real world to obtain the current twin virtual space. At this time, the update process can be: updating the three-dimensional environmental space model in the twin virtual space at the previous time point according to the environmental information, updating the dynamic model of the power equipment in the twin virtual space at the previous time point according to the equipment operation information; updating the running trajectory of the virtual inspection robot corresponding to the inspection robot in the twin virtual space at the previous time point according to the environmental information, task execution information and robot positioning information; updating the position information of the target object in the twin virtual space at the previous time point according to the object positioning information. Through the above steps, the simulation of parameters such as personnel, equipment, inspection process and environment in the real world is realized to obtain the current twin virtual space at the current time point, and then the operation status of the power cable tunnel is predicted.

[0094] In an exemplary embodiment, the three-dimensional environmental space model can be updated through tools such as ANSYS Mechanical or COMSOL Multiphysics. For example, the three-dimensional temperature field model can be updated by simulating the overheating of cables in a power cable tunnel through the ANSYS Mechanical tool and solving the Fourier heat conduction equation. Additionally, the gas diffusion environment at the current time point can be simulated through COMSOL Multiphysics based on the coupling of the Navier-Stokes equation and the diffusion equation.

[0095] In an exemplary embodiment, the dynamic model of power equipment can be updated through the MATLAB Simulink tool.

[0096] In an exemplary embodiment, controlling the virtual inspection robot to perform inspection tasks according to the row trajectory in the twin virtual space can be simulated through the AnyLogic tool, that is, by defining the robot movement rules and obstacle avoidance logic, and simulating the collaborative inspection of multiple robots based on Agent-Based modeling.

[0097] In the above embodiments, through the actual inspection information, the simulation of parameters such as personnel, equipment, the inspection process of the inspection robot, and the environment in the real world can be accurately carried out to accurately update the twin virtual space at the previous time point.

[0098] In an exemplary embodiment, the twin inspection information includes twin task execution information, twin object positioning information, twin robot positioning information, twin environment information, and twin equipment operation information; based on the twin inspection information that matches the actual inspection information in the current twin virtual space, the operating state of the power cable tunnel is predicted, including: predicting the regional operating state of each area in the power cable tunnel based on the twin environment information and the twin equipment operation information; predicting the robot task execution state of the power cable tunnel based on the twin environment information, the twin task execution information, and the twin robot positioning information; predicting the equipment operation state of the power cable tunnel based on the twin equipment operation information and the twin object positioning information; and determining the operating state of the power cable tunnel based on the regional operating state, the robot task execution state, and the equipment operation state.

[0099] Specifically, the twin inspection information includes twin task execution information that matches the task execution information, twin object positioning information that matches the task execution information, twin robot positioning information that matches the task execution information, twin environment information that matches the task execution information, and twin equipment operation information that matches the task execution information.

[0100] Based on at least one piece of twin inspection information, the operating state of the power cable tunnel can be predicted, as exemplified below:

[0101] The first method: Divide the power pipe gallery into multiple regions. Based on the twin environment information and twin device operation information of each region, the regional operation status of each region in the power pipe gallery can be predicted. For example, whether the environment information of a certain region is normal or abnormal, or whether there are devices with abnormal operation in a certain region, etc. In one embodiment, based on the twin environment information and twin device operation information such as the real-time temperature, gas concentration, and device current of each region, a cable temperature distribution cloud map and a methane diffusion path prediction map can be generated. Through the cable temperature distribution cloud map, local overheating regions can be predicted, and based on the methane diffusion path, gas hazard regions can be predicted. By combining the local overheating regions and gas hazard regions, the operation status of the power pipe gallery can be obtained.

[0102] The second method: Predict the robot task execution status of the power pipe gallery based on the twin environment information, twin task execution information, and twin robot positioning information. Based on the twin environment information, the position information of obstacles in the environment is generated, and based on the obstacle position information, twin task execution information, and twin robot positioning information, the robot task execution status of the power pipe gallery is predicted. For example, predict whether there are conflicts in the tasks executed by the robot, such as conflicts like path intersections, or predict the success rate when the robot executes the fire extinguishing task.

[0103] Thirdly, based on the operating information of twin devices and the positioning information of twin objects, predict the operating status of the power pipe gallery. For example, methods such as Isolation Forest, LSTM (Long Short-Term Memory), or wavelet transform can be used to predict the operating status of the power pipe gallery's devices. Among them, the Isolation Forest method is implemented through the Scikit-learn library in Python to detect abnormal fluctuations in device current; the LSTM method is constructed through the TensorFlow framework to model the time-series data of vibration signals and trigger an alarm when the reconstruction error exceeds the threshold; the wavelet transform method uses the PyWavelets library to decompose partial discharge signals and extract the pulse rise time and amplitude characteristics. Through the various methods exemplified above, overheating of cable joints can be diagnosed by comparing temperature data with historical baselines, and deformation of transformer windings can be detected through vibration signal spectrum analysis. For another example, the Transformer model implemented by PyTorch can also be used to predict the remaining life of devices using the multivariate time series corresponding to the operating information of twin devices. Additionally, the Fault Tree Analysis method can be used to construct a cable short-circuit fault logic tree using the ReliaSoft tool, calculate the probability importance of cable short-circuit faults, and obtain the remaining life curve of the cable or the device health score to predict the operating status of the power pipe gallery's devices through the operating information of twin devices. Furthermore, by combining the operating information of twin devices and the positioning information of twin objects, the operating status of the power pipe gallery's devices can be predicted. For example, by determining whether the staff is repairing at the area where the abnormal device is located, the operating status of the power pipe gallery can be predicted as a high-risk or low-risk status.

[0104] Finally, by combining the regional operating status, the robot task execution status, and the device operating status, the operating status of the power pipe gallery is obtained. That is to say, when at least one of the regional operating status, the robot task execution status, and the device operating status represents an abnormal state, the operating status of the power pipe gallery also represents an abnormal state; when the regional operating status, the robot task execution status, and the device operating status all represent normal states, the operating status of the power pipe gallery also represents a normal state.

[0105] In the above embodiments, through twin information such as twin task execution information, twin object positioning information, twin robot positioning information, twin environment information, and twin device operating information, the regional operating status, robot task execution status, and device operating status of the power pipe gallery can be comprehensively and accurately predicted to accurately obtain the operating status of the power pipe gallery. Moreover, the determination of the operating status of the power pipe gallery is not limited to the above regional operating status, robot task execution status, and device operating status, and can also be determined by combining the operating status of more objects in the power pipe gallery.

[0106] In an exemplary embodiment, the actual inspection information is updated according to the abnormal optimization strategy information and the abnormal information, including: locating the abnormal actual inspection information corresponding to the abnormal information from the actual inspection information; and updating the abnormal actual inspection information according to the abnormal optimization strategy information.

[0107] Specifically, since the abnormal information is the abnormal information in the twin virtual space, the abnormal information is part of the twin inspection information, and the twin inspection information matches the actual inspection information, therefore, the abnormal actual inspection information corresponding to the abnormal information can be located from the actual inspection information, and then the abnormal actual inspection information can be updated according to the abnormal optimization measurement information to repair the abnormality of the power pipe gallery in the physical world, so that the power pipe gallery is in a normal operation state.

[0108] In this embodiment, by locating the abnormal actual inspection information corresponding to the abnormal information from the actual inspection information, the abnormal actual inspection information can be accurately updated according to the abnormal optimization strategy information, so that the power pipe gallery is in a normal operation state.

[0109] In an exemplary embodiment, the abnormal information is the twin environment information; detecting the abnormal optimization strategy information corresponding to the abnormal information includes: generating the inspection task priority of the inspection robot according to the twin environment information; and generating the target running trajectory of the inspection robot based on the inspection task priority, and using the target running trajectory as the abnormal optimization strategy information corresponding to the twin environment information.

[0110] Specifically, when there is only one inspection task, based on the twin environment information, generate the target running trajectory of the inspection robot, and use the target running trajectory as the abnormal optimization strategy information corresponding to the twin environment information. For example, based on the A* algorithm (ROS Navigation Stack, a collection of 2D navigation function packages): Based on the input corridor map and abnormal twin environment information, plan the global path of the robot. In addition, through the abnormal twin environment information, use the dynamic window method and lidar data technology to generate the target running trajectory of the inspection robot for real-time local obstacle avoidance. Another example is that according to the abnormal twin environment information, use the Unity3D engine to build a virtual corridor, preview the effect of the ventilation strategy, and generate the target running trajectory of the inspection robot. Further, when there is more than one inspection task, the inspection task priorities of multiple inspection tasks can also be determined according to the twin environment information; based on the inspection task priorities of multiple inspection tasks and the twin environment information, generate the target running trajectory of the inspection robot, and use the target running trajectory as the abnormal optimization strategy information corresponding to the twin environment information. At this time, after determining the inspection task priorities, robot task execution scheduling information can be generated. The robot task execution scheduling information includes task identification, task execution time, task execution robot, etc. A feasibility report on the equipment maintenance plan can also be issued after passing the verification in the virtual environment. For example, according to the genetic algorithm in the DEAP (Distributed Evolutionary Algorithms in Python, a distributed evolutionary algorithm framework) library, calculate the minimum energy consumption and maximum coverage rate during the operation of the inspection robot according to the twin environment information to generate inspection task priorities, realize multi-objective optimization of robot task allocation, and based on the inspection task priorities, update the running trajectory of the inspection robot, that is, first reach the task location with a higher inspection task priority, and then reach the task location with a lower inspection task priority. Further, the running trajectory from each task location to the next task location can also be determined according to the twin environment information.

[0111] In the above embodiments, according to the twin environment information, generate the inspection task priorities of the inspection robot, and based on the inspection task priorities of the inspection robot, update the running trajectory of the inspection robot, so that the abnormal optimization strategy information of the abnormal information can be accurately generated.

[0112] In an exemplary embodiment, the abnormal information is the operation information of the twin device; detecting the abnormal optimization strategy information corresponding to the abnormal information includes: according to the operation information of the twin device, generating the device abnormal positioning point and the device abnormal type corresponding to the device abnormal positioning point; based on the device abnormal positioning point and the device abnormal type, generating the abnormal optimization strategy information corresponding to the operation information of the twin device.

[0113] Specifically, based on the operating information of the twin devices, device anomaly location points are generated, and the operating information of the twin devices is processed through deep learning methods to obtain the device anomaly types corresponding to the device anomaly location points, or the device anomaly types corresponding to the device anomaly location points are obtained through the mapping relationship between the operating information of the twin devices and the device anomaly types. Based on the device anomaly location points and the device anomaly types, maintenance warning information for the device anomaly location points is generated. For example, when the device anomaly location point is point A and the device anomaly type is type a, the generated maintenance warning information for the device anomaly location point is: The device at point A has an anomaly of type a and needs to be repaired in a timely manner. Further, anomaly optimization strategy information matching the maintenance warning information can also be generated based on the maintenance warning information. The anomaly optimization strategy information can be that for the device at point A having an anomaly of type a, measure 1 matching type a needs to be taken at point A to optimize the device. Even further, the present application can also generate the remaining life of the device based on the operating information of the twin devices, generate a device health score based on the remaining life of the device, and perform preventive maintenance on the devices in the power pipe gallery based on the device health score.

[0114] In this embodiment, based on the operating information of the twin devices, device anomaly location points and the device anomaly types corresponding to the device anomaly location points are generated. Based on the device anomaly location points and the device anomaly types, anomaly optimization strategy information corresponding to the operating information of the twin devices can be accurately generated.

[0115] In an exemplary embodiment, the present application provides a method for executing inspection tasks of a power pipe gallery, which realizes automated and intelligent inspection and emergency response of the power pipe gallery through the collaborative work of the physical layer, model layer, data layer, function layer, and application layer. Specifically, 1. The physical layer mainly refers to physical power pipe gallery entities such as power pipe gallery inspection robots, sensors, power supply equipment, and staff, as well as the set of corresponding working states of the power pipe gallery, responsible for the physical space realization of the overall power pipe gallery, and having functions such as self-awareness, self-decision-making, underlying data collection and transmission; 2. The model layer mainly refers to the digital twin system's virtual power pipe gallery and the simulation, analysis, optimization, and decision-making of its corresponding working states in the virtual space; 3. The data layer refers to the power pipe gallery twin data service platform, responsible for providing data support services for the physical power pipe gallery, virtual power pipe gallery, and power pipe gallery service system operation of the digital twin system, and having data life cycle management and processing functions such as the production, processing, integration, and fusion of power pipe gallery twin data; 4. The function layer is responsible for providing multiple pipe gallery operation functions such as intelligent scheduling, collaborative maintenance planning, device operation status monitoring, inspection quality management, operation process monitoring, device health management, and energy consumption analysis for the operation and management of the power pipe gallery. 5. The application layer refers to the intelligent task requirements for the specific operation and maintenance of the power pipe gallery, including key tasks such as intelligent inspection, precise control, and reliable operation and maintenance.

[0116] As Figure 4 shown, the operation logic between the five layers in the digital twin system of the power pipe gallery constructed by this method is as follows:

[0117] The physical layer, through sensor devices, sends the real-time data of physical entities in the physical environment (such as sensor data, device operation status, location information and operation feedback of inspection robots, object positioning information of staff, etc.) to the data layer and provides the required real-time feedback for the model layer.

[0118] Relying on the data of the physical layer and the computing power of the data layer, and combining with the simulation model, the model layer conducts virtual simulation, data analysis and management optimization on the incoming data, and then transfers the processing results (simulation results, optimization suggestions and virtual device operation status feedback) to the data layer and the function layer to support the actual operation and intelligent decision-making management of the power pipe gallery.

[0119] The data layer is the data bridge between physical entities and virtual entities. It mainly transfers the data incoming from the physical layer to the function layer and the model layer (raw data and historical record data), and at the same time receives the data processed by the model layer to ensure that the function layer and the application layer can make decisions and control through effective data. It can provide full-element data support and provide data life cycle management and processing functions such as production, processing, integration and fusion.

[0120] The function layer can receive the data provided by the data layer for analysis, make intelligent decisions, realize the control and scheduling of the physical layer, and transfer the results (scheduling instructions and execution feedback of optimization suggestions) to the application layer to provide decision support and information feedback for the application layer. Specifically, the function layer includes basic functions and extended functions. The basic functions include but are not limited to: data acquisition, data storage, data management, device operation status monitoring, computing resource support and algorithm support, etc. The extended functions include but are not limited to: inspection route planning, energy efficiency optimization analysis, device health management, device anomaly detection, intelligent scheduling, task allocation, device anomaly warning, emergency response and virtual simulation, etc. In addition, the function layer also provides function interfaces, and the function interfaces include simulation training, virtual simulation, real-time data transmission and intelligent algorithms, etc.

[0121] The application layer receives data (equipment monitoring and inspection data) from the function layer, helps users understand the operation status of the power pipe gallery system through visualization and perform intervention operations, and the application layer transmits operation instructions to the function layer to execute specific inspection, maintenance, and rescue tasks. The above five layers jointly drive the operation of the entire system through mutual cooperation and data transfer, ensuring that the power pipe gallery twin system can effectively monitor, analyze, and optimize the status of the power pipe gallery, realize the two-way linkage between the physical space and the virtual space, and finally provide users with precise control and reliable maintenance. In addition, the application layer also provides functions such as intelligent inspection, precise control, reliable operation and maintenance, result visualization, and three-dimensional virtual mapping.

[0122] In one embodiment, for the model layer:

[0123] I. Incoming data types: The core data processed by the model layer includes: (1) Physical layer data: sensor data (temperature, humidity, gas concentration, etc.), equipment operation parameters (current, voltage, etc.), inspection robot status (position, battery power, and operation logs); (2) Preprocessed data of the data layer: cleaned structured data (historical operation records of equipment in the time series database), fused multimodal data (spatiotemporal alignment of sensor data and camera images).

[0124] II. Simulation technical means: (1) Multiphysics simulation: Tools and algorithms used: ANSYS Mechanical: used for cable overheating simulation, constructing a three-dimensional temperature field model, and solving the Fourier heat conduction equation; COMSOL Multiphysics: simulating gas diffusion (coupling based on the Navier-Stokes equation and the diffusion equation); MATLAB Simulink: constructing a dynamic model of power equipment; Input data: real-time temperature, gas concentration, and equipment current of sensors; Output results: cloud map of cable temperature distribution (predicting local overheating points) and methane diffusion path prediction (marking dangerous areas); (2) Dynamic behavior simulation: Tools and algorithms used: AnyLogic: simulating multi-robot collaborative inspection based on Agent-Based modeling (defining robot movement rules and obstacle avoidance logic); Gazebo: physical engine simulating robot movement (such as simulating image acquisition behavior); Input data: real-time position of robots, task list, and coordinates of environmental obstacles; Output results: robot task conflict warning (such as path crossing) and prediction of the success rate of fire extinguishing operations (based on virtual environment testing).

[0125] III. Analytical Technical Means: (1) Anomaly Detection and Diagnosis: Algorithms and Implementations Used: Isolation Forest: Implemented using the Scikit-learn library in Python to detect abnormal fluctuations in device current; LSTM: Constructed through the TensorFlow framework to model the time-series data of vibration signals and trigger an alarm when the reconstruction error exceeds the threshold; Wavelet Transform: Decompose partial discharge signals using the PyWavelets library to extract the pulse rise time and amplitude features; Application Scenarios: Overheating Diagnosis of Cable Joints (comparison of temperature data with historical baselines), Detection of Transformer Winding Deformation (spectrum analysis of vibration signals); (2) Trend Prediction and Health Assessment: Algorithms and Implementations Used: Transformer Model (implemented in PyTorch): Predict the remaining life of equipment for multivariate time series (such as input current, vibration, temperature); Fault Tree Analysis: Use the ReliaSoft tool to construct a cable short-circuit fault logic tree and calculate the probability importance; Output Results: Cable remaining life curve (accurate to days), Equipment health score (0 - 100 points, based on multi-index weighting).

[0126] IV. Optimization Technical Means: (1) Dynamic Path Planning: Algorithms and Implementations Used: A* Algorithm (ROS Navigation Stack): Global path planning for robots (inputting the corridor map and target points); Dynamic Window Method: Real-time local obstacle avoidance (based on lidar data, implemented in Python); Input Data: Robot's current position, obstacle coordinates, and task priorities; Output Results: Real-time updated inspection path (JSON format instructions sent to the robot), Safe evacuation path in case of fire (avoiding high-temperature areas); (2) Resource Scheduling and Policy Optimization: Algorithms and Implementations Used: Genetic Algorithm (DEAP library): Multi-objective optimization of robot task allocation (minimizing energy consumption and maximizing coverage); Digital Twin Sandbox: Construct a virtual corridor based on the Unity3D engine to preview the effect of ventilation strategies; Output Results: Robot scheduling table (task ID, execution time, and executing robot), Feasibility report on equipment maintenance plan (issued after verification in the virtual environment).

[0127] In an exemplary embodiment, the operation process of the functional layer includes: (1) Data integration and status perception: Input: real-time data from the physical layer (sensors, robots, and devices), cleaned data from the data layer, and simulation results from the model layer (such as equipment life prediction); Operation interface: Display the global status of the pipe gallery (temperature distribution, robot position, and equipment health score, etc.) through a visualization interface; (2) Intelligent analysis and decision generation: Input: simulation optimization suggestions and historical data patterns provided by the model layer (such as equipment abnormal patterns); Decision interface: Maintenance personnel can select predefined strategies (such as "automatic fire extinguishing") or manually adjust parameters (such as modifying the inspection frequency) on the operation interface; (3) Instruction issuance and execution: Output: Send instructions to the physical layer (robot path coordinates and equipment start / stop commands), and at the same time push work orders to the staff terminal; Operation interface: Real-time tracking of instruction execution status (such as robot movement trajectory and fire extinguishing progress bar); (4) Closed-loop feedback and optimization: Input: operation results returned by the physical layer (such as temperature data after fire extinguishing), and new parameters calibrated by the model layer; Operation interface: Generate operation and maintenance reports (such as energy consumption analysis reports and equipment maintenance records), and support manual review and strategy iteration.

[0128] Furthermore, the collaborative means between the functional layer and the model layer are shown in Table 1 below:

[0129] Table 1 Collaborative means between the functional layer and the model layer

[0130]

[0131] In one embodiment, generally speaking, the twin virtual space and the multi-purpose intelligent inspection robot play a key role in the efficient inspection of the power pipe gallery. Through automated intelligent inspection, the robot can move autonomously along a predetermined path and use a dual-spectrum pan-tilt and a variety of sensors to monitor the operation status of equipment and environmental parameters in real time. In response to problems such as abnormal cable temperature, equipment abnormalities, or excessive harmful gas concentration, the system can quickly detect and issue warning signals. In terms of real-time monitoring, all data collected by the robot is transmitted to the digital twin system in real time, and the system simulates and analyzes the data to ensure synchronous updates between the physical device and the virtual model. This real-time nature enables the system to perform intelligent scheduling and management of various operations in the power pipe gallery, respond to abnormal situations in a timely manner, thereby improving the safety and operation efficiency of the power pipe gallery. In addition, through integrated data analysis and decision support, the intelligent management enables the inspection robot not only to perform daily inspection tasks but also to provide fast and accurate response measures in case of emergencies. Through the synergistic effect of these functions, this application significantly improves the inspection efficiency, abnormal response speed, and intelligent level of operation and management of the power pipe gallery, ensuring the safe and stable operation of the entire system.

[0132] Specifically, the digital twin robot is a multi-purpose intelligent inspection robot that can cooperate with the digital twin system. As an important part of the digital twin system for power pipe corridors, the digital twin robot plays a key role in daily inspection and emergency rescue.

[0133] During daily inspections, the robot can efficiently and comprehensively monitor the operation status of cables and equipment in the power pipe corridor. Its main functions are as follows: (1) Defect identification and status monitoring: The robot regularly inspects each area of the pipe corridor through the dual-spectrum pan-tilt head it carries. The dual-spectrum pan-tilt head integrates visible light and thermal imaging devices, which can capture images omnidirectionally at 360° and perform infrared temperature measurement. This system allows the robot to conduct real-time monitoring of the power pipe corridor and equipment from all directions and multiple angles during the inspection, identify problems such as pipe corridor defects and equipment abnormalities. Combining the thermal imaging data, the digital twin system can accurately analyze the operation status of the equipment, thereby warning of equipment abnormalities and preventing the occurrence of major equipment abnormalities; (2) Gas monitoring and safety warning: During the inspection task, the robot is also equipped with a variety of gas sensors for monitoring dangerous gases in the pipe corridor. The robot can collect the concentration data of combustible gases and toxic gases such as methane, carbon monoxide, and hydrogen sulfide in real time. When the density of these gases reaches the dangerous threshold, the robot will immediately send a warning signal to the digital twin system to notify the staff to take necessary safety protection measures. This real-time monitoring and warning function improves the operation safety of the power pipe corridor and reduces the probability of dangerous accidents; (3) Automatic inspection and scheduling function: The robot is connected to various equipment in the pipe corridor through the digital twin system and can be remotely controlled and preset with an automatic inspection path. Through the automatic inspection function, the robot can conduct full-coverage inspections of the pipe corridor according to the preset route and collect data in real time. Under the scheduling of the digital twin system, the robot can not only complete the inspection task independently but also timely feedback real-time data to the staff and adjust the inspection plan by itself according to the situation. This function makes up for the defects of traditional manual inspections, which are time-consuming and laborious, and improves the inspection efficiency.

[0134] In addition to daily inspections, robots also play an important role in emergency rescue. The main functions are: (1) Fire extinguishing and disaster control: The robot is equipped with a fire extinguishing device in addition to the inspection function, which is designed to intervene immediately to extinguish fires when emergency situations such as fires occur. The application of this device is particularly suitable for fires caused by abnormal cables in power corridors. The robot can quickly extinguish the fire through its fire extinguishing system, prevent the fire from spreading, and minimize property losses. In addition, the robot can also operate in harsh environments such as high temperature and smoke, which plays a key role in emergency rescue; (2) Ability to operate in hazardous environments: When emergency situations such as fires occur, power corridors are usually accompanied by hazardous environmental factors such as high temperature and toxic gas leakage. With its high durability and the gas sensors it carries, the robot can work in environments that are difficult for people to enter, monitor changes in gas concentration and temperature in the corridor, and transmit data in the corridor in real time. This capability not only ensures the safety of the rescue mission, but also provides data support for subsequent response decisions.

[0135] The work of the intelligent inspection robot depends not only on its own hardware and software configuration, but also on the support of the digital twin system of the power pipeline corridor. In actual operation, the robot achieves the following collaborative work through the scheduling and management of the digital twin system:

[0136] (1) Data collection and feedback: The various data collected by the robot during the inspection process will be transmitted to the digital twin system in real time. At the same time, the system integrates and analyzes the data, and predicts and judges the operating status of the equipment in the power corridor through virtual simulation. Through this process, staff can adjust and optimize the inspection tasks; (2) Rapid transmission of operation instructions: When an abnormal situation occurs in the corridor, the digital twin system analyzes the real-time data provided by the robot and quickly issues instructions to the robot, commanding it to perform operations such as fire extinguishing, investigation or evacuation. This rapid transmission of operation instructions ensures the efficiency and accuracy of emergency rescue tasks and avoids delays and misjudgments that may be caused by human command.

[0137] Furthermore, the innovation of the inspection task execution method of the power pipeline corridor of the present application lies in:

[0138] (1) Innovative design of multi-layer system architecture: This application proposes a digital twin system architecture for power pipeline corridors based on five layers: physical layer, model layer, data layer, function layer, and application layer. Through effective data transmission and collaborative work, each layer can achieve all-round and intelligent management of the power pipeline corridor, solving the technical bottleneck of the traditional single-layer structure that is difficult to take into account the real-time linkage of physical entities and virtual models.

[0139] (2) Multi-functional integration of intelligent inspection robots: The intelligent inspection robots integrate a variety of sensors, dual-spectrum pan-tilt units, and fire extinguishing devices. The robots can not only efficiently complete daily inspection tasks but also possess the capabilities of defect identification, gas monitoring, and emergency rescue. This multi-functional integrated design greatly improves the operation safety and inspection efficiency of power cable galleries.

[0140] (3) Cyber-physical fusion technology combining virtual and real: Through cyber-physical fusion technology, this application realizes the synchronous update and dynamic consistency between physical devices in the power cable gallery and the digital twin model. In particular, the implementation of physical fusion, model fusion, data fusion, and function fusion enables the virtual power cable gallery to reflect the operating state of the physical cable gallery in real time, thus achieving precise control and intelligent decision-making.

[0141] (4) Intelligent decision-making and scheduling system for collaborative work: In the function layer of this application, through intelligent algorithms and data analysis, it realizes the intelligent scheduling of inspection robots and the monitoring of equipment operating states. When an abnormality occurs in the power cable gallery, the system can automatically analyze and quickly issue instructions, ensuring the efficiency and accuracy of emergency rescue tasks and significantly reducing the delays and misjudgments that may be caused by manual operations.

[0142] (5) Real-time fusion and processing of multi-source heterogeneous data: This application introduces real-time fusion and processing technology for multi-source heterogeneous data in the data layer. By uniformly managing and analyzing the data collected by inspection robots and sensors, it ensures the integrity and consistency of the data, providing a reliable data basis for the analysis and decision-making of the digital twin system.

[0143] In addition, compared with the prior art, this application has the following advantages:

[0144] (1) Efficient inspection and monitoring: Compared with the traditional manual inspection method, the digital twin robots in this application can achieve automated and efficient inspection operations. The various sensors and dual-spectrum pan-tilt units equipped on the robots can comprehensively monitor the operating states of the equipment in the power cable gallery, promptly detect potential abnormal hidden dangers, greatly improving the inspection efficiency and accuracy, and avoiding omissions and delays in manual inspections.

[0145] (2) Comprehensive function integration: The digital twin robots in this application not only have the conventional inspection function but also integrate functions of defect identification, gas monitoring, and fire extinguishing and rescue, and can quickly respond in case of emergencies. This multi-functional integrated design significantly enhances the response ability of power cable galleries in emergencies, while most of the inspection systems in the prior art have single functions and are difficult to comprehensively cover the complex requirements of power cable galleries.

[0146] (3)Intelligent management with virtual-real interaction: This application realizes the two-way interaction between the physical power pipe gallery and the virtual twin model through cyber-physical fusion technology, enabling real-time monitoring and optimization of the operation status of the power pipe gallery. Compared with the existing static simulation system, the digital twin system of this application can dynamically update the model to keep the virtual pipe gallery synchronized with the actual pipe gallery, thereby improving the accuracy and reliability of management decisions.

[0147] (4)Intelligent scheduling and decision support: The function layer of this application can automatically schedule inspection robots and provide intelligent decision support through intelligent algorithms and big data analysis. When an abnormal situation occurs, the system can quickly analyze and respond, reducing the time cost and error rate of manual intervention. In contrast, the inspection systems in the prior art usually lack such highly intelligent decision-making and scheduling capabilities.

[0148] (5)Multi-source data fusion and efficient processing: The data layer in this application introduces real-time fusion and processing technology for multi-source heterogeneous data, which can uniformly manage and analyze data from different sensors and devices to ensure the consistency and integrity of the data. This efficient processing ability of multi-source data greatly improves the analysis and decision-making level of the system. In contrast, data processing in the prior art is usually scattered and lacks systematicness, affecting the overall efficiency.

[0149] (6)Improving operation safety and reducing costs: Through the collaborative work of intelligent inspection robots and the digital twin system, this application greatly reduces the frequency and cost of manual inspections, and also reduces the risk of safety accidents caused by equipment abnormalities. In contrast, manual inspections in the prior art are not only costly but also pose safety hazards in dangerous environments. This application effectively solves these problems and improves the overall operation safety of the power pipe gallery.

[0150] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order restriction, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0151] Based on the same application concept, an embodiment of the present application further provides an inspection task execution device for a power pipe gallery for implementing the inspection task execution method for the power pipe gallery involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the inspection task execution device for the power pipe gallery provided below can refer to the limitations on the inspection task execution method for the power pipe gallery in the above text, and will not be elaborated here.

[0152] In an exemplary embodiment, as Figure 5 shown, an inspection task execution device for a power pipe gallery is provided, including: an actual inspection information acquisition module 100, a twin virtual space construction module 200, a pipe gallery operation state prediction module 300, an abnormal optimization strategy information generation module 400, an actual inspection information update module 500, and an inspection task execution module 600, where:

[0153] The actual inspection information acquisition module 100 is configured to obtain the actual inspection information of the power pipe gallery sent by the inspection robot during the process of controlling the inspection robot to execute the inspection task, and determine the twin virtual space of the power pipe gallery at the previous time point, where the twin virtual space is used to simulate the actual working state of the power pipe gallery;

[0154] The twin virtual space construction module 200 is used to update the step: update the twin virtual space at the previous time point according to the actual inspection information to obtain the current twin virtual space;

[0155] The pipe gallery operation state prediction module 300 is configured to predict the operation state of the power pipe gallery based on the twin inspection information matching the actual inspection information in the current twin virtual space;

[0156] The abnormal optimization strategy information generation module 400 is configured to, when the operation state is an abnormal operation state, control the inspection robot to abort the execution of the inspection task, find abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information;

[0157] The actual inspection information update module 500 is configured to update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return to the update step until the predicted operation state is a normal operation state;

[0158] The inspection task execution module 600 is configured to control the inspection robot to continue to execute the inspection task based on the latest obtained actual inspection information.

[0159] In one embodiment, a shooting device and multiple sensors are installed on the patrol robot. The sensors include a position sensor, an environment sensor, and a power sensor. The actual patrol information acquisition module 100 is further configured to acquire the task execution information of the patrol robot, the actual patrol images captured by the shooting device, the robot positioning information monitored by the position sensor, the environment information monitored by the environment sensor, and the device operation information monitored by the power sensor. Based on the actual patrol images, detect the object positioning information of the target object. Based on the task execution information, the object positioning information, the robot positioning information, the environment information, and the device operation information, generate the actual patrol information.

[0160] In one embodiment, the twin virtual space construction module 200 is further configured to update the three-dimensional environment space model in the twin virtual space at the previous time point according to the environment information; update the power equipment dynamic model in the twin virtual space at the previous time point according to the device operation information; update the operation trajectory of the virtual patrol robot in the twin virtual space at the previous time point according to the environment information, the task execution information, and the robot positioning information, where the virtual patrol robot corresponds to the patrol robot; update the position information of the target object in the twin virtual space at the previous time point according to the object positioning information.

[0161] In one embodiment, the twin patrol information includes twin task execution information, twin object positioning information, twin robot positioning information, twin environment information, and twin device operation information. The utility tunnel operation state prediction module 300 is further configured to predict the regional operation state of each region in the power utility tunnel based on the twin environment information and the twin device operation information; predict the robot task execution state of the power utility tunnel based on the twin environment information, the twin task execution information, and the twin robot positioning information; predict the device operation state of the power utility tunnel based on the twin device operation information and the twin object positioning information; determine the operation state of the power utility tunnel based on the regional operation state, the robot task execution state, and the device operation state.

[0162] In one embodiment, the actual patrol information update module 500 is further configured to locate the abnormal actual patrol information corresponding to the abnormal information from the actual patrol information; update the abnormal actual patrol information according to the abnormal optimization strategy information.

[0163] In one embodiment, the abnormal information is the twin environment information. The abnormal optimization strategy information generation module 400 is further configured to generate the patrol task priority of the patrol robot; update the patrol task priority according to the twin environment information; generate the target operation trajectory of the patrol robot based on the updated patrol task priority, and use the target operation trajectory as the abnormal optimization strategy information corresponding to the twin environment information.

[0164] In one embodiment, the abnormal information is the operation information of the twin device; the abnormal optimization strategy information generation module 400 is further configured to generate a device abnormal positioning point and a corresponding device abnormal type according to the operation information of the twin device; and generate abnormal optimization strategy information corresponding to the operation information of the twin device based on the device abnormal positioning point and the device abnormal type.

[0165] Each module in the above-mentioned inspection task execution device of the power pipe gallery can be stored in the memory of the computer device in whole or in part in the form of software, hardware and their combination, so that the processor can call and execute the operations corresponding to each module above.

[0166] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 6 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the actual inspection information of the power pipe gallery. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for executing an inspection task of a power pipe gallery.

[0167] Those skilled in the art can understand that Figure 6 the structure shown in is a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0168] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented.

[0169] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above-mentioned method embodiments are implemented.

[0170] In one embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps in the above method embodiments.

[0171] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, a database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., and are not limited thereto.

[0172] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope recorded in this application.

[0173] The above embodiments only represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several variations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for executing inspection tasks of a power pipe gallery, characterized in that, The method includes: During the process of controlling the inspection robot to perform the inspection task, obtaining the actual inspection information of the power cable gallery sent by the inspection robot, and determining the twin virtual space of the power cable gallery at the previous time point, where the twin virtual space is used to simulate the actual working state of the power cable gallery; Update step: According to the actual inspection information, update the twin virtual space at the previous time point to obtain the current twin virtual space; Predict the operating state of the power cable gallery based on the twin inspection information that matches the actual inspection information in the current twin virtual space; When the operating state is an abnormal operating state, control the inspection robot to abort the inspection task, search for abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information; Update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return to the update step until the predicted operating state is a normal operating state; Based on the latest obtained actual inspection information, control the inspection robot to continue to perform the inspection task.

2. The method according to claim 1, characterized in that, A photographing device and multiple sensors are installed on the inspection robot, and the sensors include a position sensor, an environment sensor, and an electric power sensor; The obtaining the actual inspection information of the power cable gallery sent by the inspection robot includes: Obtaining the task execution information of the inspection robot, the actual inspection image captured by the photographing device, the robot positioning information monitored by the position sensor, the environment information monitored by the environment sensor, and the device operation information monitored by the electric power sensor; Based on the actual inspection image, detecting the object positioning information of the target object; Generating the actual inspection information based on the task execution information, the object positioning information, the robot positioning information, the environment information, and the device operation information.

3. The method according to claim 2, characterized in that, The updating the twin virtual space at the previous time point according to the actual inspection information includes: Updating the three-dimensional environment space model in the twin virtual space at the previous time point according to the environment information; Updating the power equipment dynamic model in the twin virtual space at the previous time point according to the device operation information; Updating the running track of the virtual inspection robot in the twin virtual space at the previous time point according to the environment information, the task execution information, and the robot positioning information, where the virtual inspection robot corresponds to the inspection robot; Updating the position information of the target object in the twin virtual space at the previous time point according to the object positioning information.

4. The method according to claim 2, wherein The twin inspection information includes twin task execution information, twin object positioning information, twin robot positioning information, twin environment information, and twin device operation information; The predicting the operating state of the power cable gallery based on the twin inspection information that matches the actual inspection information in the current twin virtual space includes: Predicting the regional operating state of each region in the power cable gallery based on the twin environment information and the twin device operation information; Predict the robot task execution status of the power cable tunnel based on the twin environment information, the twin task execution information, and the twin robot positioning information; Predict the equipment operation status of the power cable tunnel based on the twin equipment operation information and the twin object positioning information; Determine the operation status of the power cable tunnel based on the regional operation status, the robot task execution status, and the equipment operation status.

5. The method according to claim 1, wherein The updating of the actual inspection information according to the abnormal optimization strategy information and the abnormal information includes: Locate the abnormal actual inspection information corresponding to the abnormal information from the actual inspection information; Update the abnormal actual inspection information according to the abnormal optimization strategy information.

6. The method according to claim 1, characterized in that, The abnormal information is twin environment information; the detection of the abnormal optimization strategy information corresponding to the abnormal information includes: Generate the inspection task priority of the inspection robot according to the twin environment information; Generate the target operation trajectory of the inspection robot based on the inspection task priority, and use the target operation trajectory as the abnormal optimization strategy information corresponding to the twin environment information.

7. The method according to claim 1, characterized in that The abnormal information is twin equipment operation information; the detection of the abnormal optimization strategy information corresponding to the abnormal information includes: Generate the equipment abnormal positioning point and the equipment abnormal type corresponding to the equipment abnormal positioning point according to the twin equipment operation information; Generate the abnormal optimization strategy information corresponding to the twin equipment operation information based on the equipment abnormal positioning point and the equipment abnormal type.

8. An inspection task execution device for a power pipe gallery, characterized in that, The device includes: An actual inspection information acquisition module, configured to acquire the actual inspection information of the power cable tunnel sent by the inspection robot during the process of controlling the inspection robot to execute the inspection task, and determine the twin virtual space of the power cable tunnel at the previous time point, where the twin virtual space is used to simulate the actual working state of the power cable tunnel; A twin virtual space construction module, configured to update the step: update the twin virtual space at the previous time point according to the actual inspection information to obtain the current twin virtual space; A tunnel operation status prediction module, configured to predict the operation status of the power cable tunnel based on the twin inspection information matching the actual inspection information in the current twin virtual space; An abnormal optimization strategy information generation module, configured to, when the operation status is an abnormal operation status, control the inspection robot to abort the execution of the inspection task, search for abnormal information from the twin inspection information, and detect the abnormal optimization strategy information corresponding to the abnormal information; An actual inspection information update module, configured to update the actual inspection information according to the abnormal optimization strategy information and the abnormal information, and return to the update step until the predicted operation status is a normal operation status; An inspection task execution module, configured to control the inspection robot to continue to execute the inspection task based on the latest acquired actual inspection information.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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