Power pipe gallery inspection management method, system, device and equipment and storage medium

Abstract

The invention relates to an electric power pipe gallery inspection management method, system, device and equipment and a storage medium. The method comprises the following steps: acquiring space structure information of an electric power pipe gallery and machine information of a digital twin robot; based on the space structure information of the electric power pipe gallery and the machine information of the digital twin robots, determining electric power pipe gallery areas in charge of different types of digital twin robots; sending an inspection scheduling instruction to a plurality of different types of digital twin robots, wherein the inspection scheduling instruction is used for indicating the digital twin robots to execute inspection tasks in different power pipe gallery areas; inspection data fed back by the digital twin robot are received, fault prediction is carried out on the electric power pipe gallery based on the inspection data, a fault prediction result is obtained, and the inspection data are obtained by collecting data of a corresponding electric power pipe gallery area through a sensor carried by the digital twin robot; and performing early warning based on the fault prediction result. The method is beneficial to improving the inspection flexibility of the electric power pipe gallery.

Description

Technical Field

[0001] The present application relates to the technical field of electrical engineering, and particularly to a method, system, device, computer device, computer-readable storage medium, and computer program product for power pipe gallery inspection and management. Background Art

[0002] With the rapid development of technology and the continuous advancement of the urbanization process, the construction scale and standards of modern cities are increasing day by day. A power pipe gallery is an underground facility that centrally buries power transmission and distribution, communication, control, monitoring, and other lines, as well as other equipment, within a certain physical space range by setting up supporting facilities such as maintenance channels and drainage systems. It is mainly used for cable laying in the central area of the city and is applicable to power engineering construction such as cable laying in the central area of the city. Since it is an important part of urban infrastructure, the safety management of power pipe galleries is also crucial.

[0003] The traditional inspection method for power pipe galleries mainly relies on manual inspection, which has low efficiency and is easily interfered by various factors, with obvious limitations. It can be seen that the current inspection of power pipe galleries lacks flexibility. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, system, device, computer device, computer-readable storage medium, and computer program product for power pipe gallery inspection and management that can improve the flexibility of inspection.

[0005] In a first aspect, the present application provides a method for power pipe gallery inspection and management, including:

[0006] Obtain the spatial structure information of the power pipe gallery and the machine information of different types of digital twin robots;

[0007] Based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robots, determine the power pipe gallery areas responsible for different types of digital twin robots;

[0008] Send inspection scheduling instructions to multiple different types of digital twin robots, where the inspection scheduling instructions are used to instruct the digital twin robots to perform inspection tasks in different power pipe gallery areas;

[0009] Receive the inspection data feedback by the digital twin robots, and based on the inspection data, perform fault prediction on the power pipe gallery to obtain a fault prediction result, where the inspection data is obtained by the sensors carried by the digital twin robots to collect data of the corresponding power pipe gallery areas;

[0010] Give an early warning based on the fault prediction result.

[0011] In one embodiment, the machine information includes the capability parameters of the digital twin robot. Based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robot, determining the areas of the power pipe gallery responsible for different types of digital twin robots includes:

[0012] Obtaining the spatial structure information of each area of the power pipe gallery;

[0013] Matching the spatial structure information of each area of the power pipe gallery with the capability parameters of the digital twin robot to determine the areas of the power pipe gallery responsible for different types of digital twin robots.

[0014] In one embodiment, the machine information further includes remaining battery power, load capacity, and fault status information;

[0015] Based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robot, determining the areas of the power pipe gallery responsible for different types of digital twin robots further includes:

[0016] Based on a preset status evaluation criterion and the remaining battery power, load capacity, and fault status information of the digital twin robot, determining the status score of the digital twin robot;

[0017] Based on the status score of the digital twin robot and the spatial structure information of each area of the power pipe gallery, determining the areas of the power pipe gallery responsible for different types of digital twin robots.

[0018] In one embodiment, based on the inspection data, performing fault prediction on the power pipe gallery to obtain a fault prediction result, including:

[0019] Based on a trained fault prediction model and the inspection data, predicting potential faults of the power pipe gallery to obtain a fault prediction result, where the fault prediction model is trained based on historical inspection data collected by different types of digital twin robots.

[0020] In one embodiment, the method further includes:

[0021] When the fault prediction result indicates the existence of potential faults, generating a preventive maintenance recommendation report based on the fault prediction result and pushing the preventive maintenance recommendation report.

[0022] In a second aspect, the present application further provides a power pipe gallery inspection management system, including a management platform and multiple different types of digital twin robots that are communicatively connected, and multiple different sensors are carried by the multiple different types of digital twin robots;

[0023] A management platform is used to execute the steps in the embodiments of the power pipe gallery inspection management method as described in any one of the above, dispatch digital twin robots to perform inspection tasks in different power pipe gallery areas, receive the inspection data fed back by the digital twin robots, predict faults in the power pipe gallery to obtain fault prediction results, and issue warnings based on the fault prediction results.

[0024] A digital twin robot is used to respond to the received power pipe gallery inspection instruction, collect data of the corresponding power pipe gallery area through the equipped sensors to obtain inspection data, and feed back the inspection data to the management platform.

[0025] In one of the embodiments, multiple different types of digital twin robots include at least two of digital twin humanoid robots, digital twin wheeled quadruped robots, digital twin gas self-growing soft robots, and digital twin wall-climbing robots.

[0026] A digital twin humanoid robot is used to collect environmental monitoring data of the power pipe gallery area and electrical equipment status data of the power pipe gallery area through the equipped sensors, and feed back the environmental monitoring data and electrical equipment status data to the management platform.

[0027] A digital twin wheeled quadruped robot is used to monitor the ground of the power pipe gallery area through the equipped sensors to obtain ground monitoring data, and feed back the ground monitoring data to the management platform.

[0028] A digital twin gas self-growing soft robot is used to monitor the narrow space of the power pipe gallery area through the equipped sensors to obtain narrow space monitoring data, and feed back the narrow space monitoring data to the management platform.

[0029] A digital twin wall-climbing robot is used to monitor the wall surface of the power pipe gallery area through the equipped sensors to obtain wall surface monitoring data, and feed back the wall surface monitoring data to the management platform.

[0030] In one of the embodiments, the digital twin humanoid robot is equipped with a temperature and humidity sensor, a gas sensor, a vision sensor, and an infrared sensor:

[0031] The temperature and humidity sensor is used to collect temperature and humidity data in the power pipe gallery area and send the temperature and humidity data to the management platform.

[0032] The gas sensor is used to collect gas concentration data in the power pipe gallery area and send the gas concentration data to the management platform.

[0033] The vision sensor is used to collect image data in the power pipe gallery area and send the image data to the management platform.

[0034] The infrared sensor is used to collect the status data of electrical equipment in the power pipe gallery area and send the status data of the electrical equipment to the management platform.

[0035] In one embodiment, the digital twin humanoid robot, the digital twin wheeled quadruped robot, the digital twin gas self-growing soft robot, and the digital twin wall-climbing robot are also used to perform anomaly detection based on the inspection data to obtain an anomaly detection result. When the anomaly detection result indicates that there is an anomaly in the power pipe gallery, an anomaly message is generated and the anomaly message is fed back to the management platform.

[0036] In a third aspect, the present application also provides a power pipe gallery inspection management device, including:

[0037] A data acquisition module, configured to acquire the spatial structure information of the power pipe gallery and the machine information of different types of digital twin robots;

[0038] A collaborative scheduling module, configured to determine the power pipe gallery areas responsible for different types of digital twin robots based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robots; send inspection scheduling instructions to multiple different types of digital twin robots, and the inspection scheduling instructions are used to instruct the digital twin robots to perform inspection tasks in different power pipe gallery areas;

[0039] A fault prediction module, configured to receive the inspection data fed back by the digital twin robot, and perform fault prediction on the power pipe gallery based on the inspection data to obtain a fault prediction result. The inspection data is obtained by collecting data of the corresponding power pipe gallery area through the sensors carried by the digital twin robot;

[0040] A fault warning module, configured to perform warning based on the fault prediction result.

[0041] In a fourth 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 steps in any one of the embodiments of the above-mentioned power pipe gallery inspection management method are implemented.

[0042] In a fifth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in any one of the embodiments of the above-mentioned power pipe gallery inspection management method are implemented.

[0043] In a sixth aspect, the present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps in any one of the embodiments of the above-mentioned power pipe gallery inspection management method are implemented.

[0044] The above power pipe gallery inspection management method, device, computer equipment, computer-readable storage medium and computer program product, on the one hand, is different from the single robot management method in the traditional inspection management method. By introducing multiple different types of digital twin robots, according to the spatial structure information of the power pipe gallery and the machine information of the digital twin robots, different types of digital twin robots are uniformly coordinated and scheduled to perform different inspection tasks, thereby reducing the limitations of manual inspection, improving the efficiency, flexibility and safety of inspection. At the same time, based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robots, the power pipe gallery areas responsible for different types of digital twin robots are allocated, which is conducive to improving the accuracy of inspection through the characteristics of different types of digital twin robots, improving the adaptability to the inspection of complex power pipe gallery environments, and further improving the flexibility of inspection; on the other hand, the inspection data fed back by the digital twin robots is conducive to quickly identifying and coping with potential risks, avoiding the occurrence of major accidents, reducing the risks of manual operations, and enhancing the operational safety and stability of the power pipe gallery. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is an application environment diagram of the power pipe gallery inspection management method in an embodiment;

[0047] Figure 2 It is a flowchart of the power pipe gallery inspection management method in an embodiment;

[0048] Figure 3 It is a flowchart of the power pipe gallery inspection management method in a detailed embodiment;

[0049] Figure 4 It is a structural block diagram of the power pipe gallery inspection management device in another embodiment;

[0050] Figure 5 It is a structural block diagram of the power pipe gallery inspection management system in another embodiment;

[0051] Figure 6 It is an internal structure diagram of the computer equipment in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] In order to make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] The power pipe gallery inspection and management method provided by the embodiment of the present application can be applied to an application environment as Figure 1 shown. Among them, the management platform 120 communicates with multiple different types of digital twin robots 140 through the network respectively. The management platform 120 obtains the power pipe gallery space structure information and the machine information of the digital twin robot 140, and based on the power pipe gallery space structure information and the machine information of the digital twin robot 140, determines the power pipe gallery areas responsible for different types of digital twin robots 140. Subsequently, the management platform 120 sends inspection scheduling instructions to multiple different types of digital twin robots 140. The inspection scheduling instructions are used to instruct the digital twin robots 140 to perform inspection tasks in different power pipe gallery areas. The digital twin robots 140 respond to the received inspection scheduling instructions, collect data of the corresponding power pipe gallery areas through the sensors carried by themselves, obtain inspection data, and feedback the inspection data to the management platform 120. After that, the management platform 120 performs fault prediction on the power pipe gallery based on the inspection data, obtains a fault prediction result, and issues an early warning based on the fault prediction result.

[0054] The management platform includes, but is not limited to, a personal computer, a laptop computer, and an Internet of Things device, etc.

[0055] In an exemplary embodiment, as Figure 2 shown, a power pipe gallery inspection and management method is provided. Taking the method applied to the management platform 120 in Figure 1 as an example, the following steps S100 to S500 are included. Among them:

[0056] S100, obtain the power pipe gallery space structure information and the machine information of different types of digital twin robots.

[0057] Among them, the power pipe gallery space structure information may include the size information, layout information, and equipment location of the power pipe gallery, etc. The digital twin robot is a digital model constructed in the virtual space through digital twin technology to simulate the physical entity. The machine information of the digital twin robot may include the attributes and functional characteristic parameters of the digital twin robot, etc.

[0058] In practical applications, a power pipe gallery model can be constructed through methods such as image measurement and laser scanning. The size information, layout information, and equipment locations of the power pipe gallery can be obtained through the power pipe gallery model. The attributes and functional characteristic parameters of the digital twin robot can be obtained from the technical documentation of the digital twin robot. Among them, the attributes can include type, model, size, etc. The type can be a wheeled robot, a tracked robot, a humanoid robot, an aerial robot, etc. The functional characteristic parameters can include the sensors equipped on the digital twin robot and the performance parameters of the sensors.

[0059] S200. Based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robot, determine the areas of the power pipe gallery responsible for by different types of digital twin robots.

[0060] In practical applications, it can be that, based on the spatial structure information of the power pipe gallery in advance, the power pipe gallery is divided into multiple power pipe gallery areas. Specifically, the area division and classification are carried out according to the size information, layout information, and equipment types in each power pipe gallery area. For example, after the area division, there are power pipe gallery areas such as straight pipe gallery areas, narrow areas, and substation areas. Among them, the straight pipe gallery area mainly contains cables and pipelines; the narrow area can be a space with a narrow space and limited channels, such as the narrow space through which power supply lines and water pipelines pass; the substation area can be a power pipe gallery area containing substation equipment.

[0061] Obtain the inspection requirements corresponding to different power pipe gallery areas, such as environmental monitoring, equipment inspection, and fault diagnosis. According to the inspection requirements of different power pipe gallery areas, compare and match with the type of digital twin robot, the equipped sensors, and the performance parameters of the sensors, and determine the digital twin robot that meets the inspection requirements of the power pipe gallery area for each power pipe gallery area. Exemplarily, the inspection requirements for the straight pipe gallery area are environmental monitoring and cable inspection, and it can be that a tracked robot undertakes the inspection task of this area; the inspection requirements for the substation area are equipment operation, status inspection, and fault diagnosis, and it can be that a humanoid robot is responsible for the inspection task of this area; the inspection requirement for the top area of the pipe gallery is environmental monitoring, and it can be that a crawling robot is responsible for the inspection task of this area.

[0062] S300. Send an inspection scheduling instruction to multiple different types of digital twin robots. The inspection scheduling instruction is used to instruct the digital twin robot to perform an inspection task in different power pipe gallery areas.

[0063] Among them, the inspection scheduling instruction can carry the power pipe gallery area, the inspection path, and the inspection requirements.

[0064] In practical applications, after determining the power cable tunnel areas responsible for different types of digital twin robots, inspection scheduling instructions are generated for each digital twin robot according to the inspection paths and inspection requirements of the power cable tunnel areas, and the inspection scheduling instructions are sent to the corresponding digital twin robots, so that after receiving the inspection scheduling instructions, the digital twin robots can parse and obtain the power cable tunnel areas responsible for inspection, as well as the corresponding inspection paths and inspection requirements of the power cable tunnel areas.

[0065] S400, receive the inspection data fed back by the digital twin robot, and based on the inspection data, conduct fault prediction on the power cable tunnel to obtain a fault prediction result. The inspection data is obtained by collecting data of the corresponding power cable tunnel area through the sensors carried by the digital twin robot.

[0066] Among them, the inspection data may include environmental monitoring data and equipment status data. The fault prediction result may include the fault prediction time and the fault prediction type.

[0067] In practical applications, in response to the inspection scheduling instructions, the digital twin robot collects data of its corresponding power cable tunnel area through the sensors carried by itself to obtain inspection data. The types of sensors carried by different types of digital twin robots may be different. Exemplarily, digital twin robot A is equipped with a temperature and humidity sensor and a gas sensor, and monitors the environment of the power cable tunnel area through the temperature and humidity sensor and the gas sensor to collect temperature and humidity data and gas concentration data, and integrates them to obtain environmental detection data; digital twin robot B is equipped with a temperature sensor and a vibration sensor to monitor the status of the power equipment in the power cable tunnel area, collect the temperature data and vibration data of the equipment, and integrate them to obtain equipment status data. During the inspection process of the digital twin robot, it monitors in real time through the sensors and feeds back the inspection data in real time. The management platform stores the environmental monitoring historical data and equipment status historical data within a historical time period, as well as the corresponding fault types and fault time points. The fault types may include electrical equipment faults, temperature and humidity exceeding the standard, gas leakage, etc. After receiving the inspection data, statistical methods such as time series analysis methods may be used to capture the data change trends of different fault types according to the fault types and fault time points corresponding to the environmental monitoring historical data and equipment status historical data stored locally, as well as the received environmental monitoring data and equipment status data, and conduct fault prediction to obtain a fault prediction result.

[0068] S500, issue a warning based on the fault prediction result.

[0069] In practical applications, it can be based on a pre-set time interval threshold (such as 15 days). When the fault prediction time is not greater than the preset time interval, the fault prediction results and the detection time are integrated to generate a warning message and push the warning message. The method of pushing the warning message can include displaying the warning message on a display, giving a warning prompt by emitting a specific sound or vibration pattern, giving a visual warning prompt by flashing an indicator light, etc. It can be understood that the method of pushing the warning message can be any one of the foregoing methods or a combination of any multiple methods.

[0070] In this embodiment, on the one hand, different from the single robot management method in the traditional patrol management method, by introducing multiple different types of digital twin robots, according to the power pipe gallery space structure information and the machine information of the digital twin robots, different types of digital twin robots are coordinated and dispatched to perform different patrol tasks, thus reducing the limitations of manual patrol, improving the efficiency, flexibility and safety of patrol. At the same time, based on the power pipe gallery space structure information and the machine information of the digital twin robots, the power pipe gallery areas responsible for different types of digital twin robots are allocated, which is conducive to improving the accuracy of patrol through the characteristics of different types of digital twin robots, improving the adaptability to the complex power pipe gallery environment patrol, and further improving the flexibility of patrol; on the other hand, according to the patrol data fed back by the digital twin robots, it is conducive to quickly identifying and coping with potential risks, avoiding the occurrence of major accidents, reducing the risks of manual operations, and enhancing the operation safety and stability of the power pipe gallery.

[0071] In an exemplary embodiment, the machine information includes the capability parameters of the digital twin robots. Based on the power pipe gallery space structure information and the machine information of the digital twin robots, determining the power pipe gallery areas responsible for different types of digital twin robots includes:

[0072] Obtain the space structure information of each power pipe gallery area.

[0073] Match the space structure information of each power pipe gallery area with the capability parameters of the digital twin robots to determine the power pipe gallery areas responsible for different types of digital twin robots.

[0074] Among them, the space structure information of the power pipe gallery area can include the dimension information and structural features of the area, such as length, width, height change, whether there is a bend, and whether there is a branch, etc. The capability parameters can include the moving speed, obstacle-crossing ability, sensor configuration and operation ability of the digital twin robots. The sensor configuration can include the types of sensors equipped and the sensor performance parameters; the operation ability can be the abilities of grasping, carrying, switch operation, etc.

[0075] In practical applications, the spatial structure information of the power pipe gallery area can be obtained through the power pipe gallery model. Matching the spatial structure information of each power pipe gallery area with the capability parameters of the digital twin robot can include matching the spatial structure and capability parameters of the power pipe gallery, as well as matching the inspection task categories and capability parameters of the power pipe gallery. By setting priorities for different matching items and using optimization algorithms (such as genetic algorithms and particle swarm optimization algorithms, etc.), the allocation scheme for the power pipe gallery area is determined, thereby determining the power pipe gallery areas responsible for different types of digital twin robots. Specifically, the matching of the spatial structure and capability parameters of the power pipe gallery can be based on the spatial structure information of the power pipe gallery area and the moving speed and obstacle-crossing ability of the digital twin robot. Exemplarily, according to the length, width, height changes, and whether there are also bends in the power pipe gallery area, the moving ability and obstacle-crossing ability of the digital twin robot are matched; the matching of the inspection task categories and capability parameters of the power pipe gallery can be based on the inspection task categories corresponding to the power pipe gallery area (such as equipment inspection, maintenance, fault troubleshooting, etc.) and the sensor configuration and operation ability of the digital twin robot, and the matching is carried out through the preset corresponding relationship between the inspection task categories and (sensor configuration and operation ability). Set priorities for each matching item, and the setting of priorities can be determined according to the importance of the inspection tasks. After determining the power pipe gallery areas responsible for different types of digital twin robots, the process of different types of digital twin robots performing inspection tasks in their corresponding power pipe gallery areas can also be simulated through the digital twin platform to verify the feasibility and efficiency of the allocation scheme, and the allocation scheme is adjusted according to the verification results.

[0076] In this embodiment, by matching the spatial structure information of each power pipe gallery area with the capability parameters of the digital twin robot for inspection area allocation, it is beneficial to improve the efficiency and accuracy of inspection and maintenance work.

[0077] In an exemplary embodiment, the machine information further includes remaining battery power, load capacity, and fault status information. Based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robot, determining the power pipe gallery areas responsible for different types of digital twin robots further includes:

[0078] Based on the preset status evaluation criteria and the remaining battery power, load capacity, and fault status information of the digital twin robot, determine the status score of the digital twin robot.

[0079] Based on the status score of the digital twin robot and the spatial structure information of each power pipe gallery area, determine the power pipe gallery areas responsible for different types of digital twin robots.

[0080] Among them, the remaining battery level can be the remaining percentage of the robot's battery. The load capacity can be the current task volume or weight being carried, as well as whether it is within the safe range. The fault status can include whether there are hardware or software faults and their severity.

[0081] In practical applications, the set state evaluation indicators include the remaining battery level, load capacity, and fault status information. Weights are assigned to each indicator in advance. For example, the weights of the remaining battery level, load capacity, and fault status information are 0.4, 0.3, and 0.3 respectively. The scoring criteria for each indicator are determined. Exemplarily, for the remaining battery level, the scoring criteria can be: if the remaining battery level is greater than 50%, 40 points are obtained; if it is between 20% - 50%, the score is linearly obtained; if it is less than 20%, 0 points are obtained. For the load capacity, the scoring criteria can be: if the load is less than 80%, 30 points are obtained; if it is between 80% - 100%, the score is linearly obtained; if it exceeds 100%, 0 points are obtained. For the fault status, the scoring criteria can be: 30 points are obtained for no fault; 10 points are deducted for minor faults (not affecting the main function); for major faults (affecting the main function), more than 20 points are deducted until 0 points. For each digital twin robot, according to the scoring criteria corresponding to the state evaluation indicators, the scores of each state evaluation indicator of the digital twin robot are determined. According to the weights corresponding to the state evaluation indicators, the state score of the digital twin robot is obtained by weighted summation.

[0082] After determining the state scores of each digital twin robot, based on the state scores of the digital twin robots and the spatial structure information of each power cable tunnel area, it can be determined that the power cable tunnel areas responsible for different types of digital twin robots are as follows. According to the spatial structure information of the power cable tunnel area, the inspection complexity of the power cable tunnel area is determined. For example, the inspection complexity of a linear or open power cable tunnel area is relatively low; the inspection complexity of a power cable tunnel area with a complex layout is relatively high; the inspection complexity of a power cable tunnel area with a longer inspection distance is relatively high, etc. Allocations are made according to the state score and the inspection complexity of the power cable tunnel area to determine the power cable tunnel areas responsible for different types of digital twin robots. Exemplarily, for robots with a high state score (such as a state score greater than 80 points), they are preferentially assigned to areas with a higher inspection complexity; for robots with a medium state score (such as a state score between 60 and 80 points), they are assigned to areas with a medium inspection complexity; for robots with a low state score (such as a state score less than 60 points), they are assigned to areas with a lower inspection complexity.

[0083] In this embodiment, regional allocation is performed according to the state scores of the digital twin robots and the spatial structure information of each power cable tunnel area, which helps to reduce the impact of the state of inspection robots on inspections and improves resource utilization.

[0084] In an exemplary embodiment, based on the inspection data, fault prediction is performed on the power pipeline corridor to obtain a fault prediction result, including:

[0085] Based on the trained fault prediction model and inspection data, the potential faults of the power pipeline corridor are predicted to obtain the fault prediction results. The fault prediction model is trained based on the historical inspection data collected by different types of digital twin robots.

[0086] In practical applications, it is possible to obtain inspection data within a historical time period and annotate the inspection data. The annotation information is used to indicate whether a fault has occurred, the time point of the fault, and the type of fault. An initial fault prediction model is pre-built based on a regression model and a convolutional neural network. The neural network model may include, but is not limited to, linear regression, logistic regression, and ARIMA models. By iteratively training the initial fault prediction model, the fault development trend is learned until the preset training end condition is reached, and a trained fault prediction model is obtained. Among them, the preset training end condition may be that the loss function value is less than a preset loss threshold for a consecutive preset number of times. Taking the inspection data fed back by the digital twin robot as input, the trained fault prediction model is called to output the fault prediction time and fault prediction type of the power corridor.

[0087] In this embodiment, the inspection data is analyzed by a model to predict potential faults in the power corridor, thereby improving the efficiency and accuracy of fault prediction. At the same time, it is beneficial to take corresponding protective measures according to the fault prediction results, thereby improving the stability and safety of the power corridor operation.

[0088] In an exemplary embodiment, the method further comprises:

[0089] When the fault prediction result indicates the existence of a potential fault, a preventive maintenance recommendation report is generated based on the fault prediction result and the preventive maintenance recommendation report is pushed.

[0090] In practical applications, when the fault prediction results indicate the existence of a potential fault, a preventive maintenance recommendation report is generated based on the fault prediction time and the fault prediction type, and the preventive maintenance recommendation report is pushed. Wherein, the preventive maintenance recommendation report is generated based on the fault prediction time and the fault prediction type, and illustratively, the preventive maintenance recommendation report information includes maintenance measures corresponding to the fault prediction type. Exemplarily, the fault and prediction type is an electrical equipment failure, and the corresponding maintenance measures may be to locate the fault point using high-voltage testing equipment, partial discharge detectors, etc., and to repair or replace damaged parts or equipment. The fault prediction type is excessive humidity, and the corresponding maintenance measures may be to strengthen sealing measures and install moisture-proof devices.

[0091] In this embodiment, automatically generating a corresponding preventive maintenance recommendation report according to the fault prediction result is beneficial to improving the preventive maintenance efficiency for potential faults, thereby improving the reliability and safety of the operation of the power pipe gallery.

[0092] To make a clearer description of the power pipe gallery inspection management method provided in this application, a specific embodiment and the attached Figure 3 are used for illustration. The specific embodiment includes the following steps:

[0093] S1. Obtain the spatial structure information of the power pipe gallery and the machine information of different types of digital twin robots. The machine information includes the capability parameters of the digital twin robots.

[0094] S2. Obtain the spatial structure information of each power pipe gallery area, match the spatial structure information of each power pipe gallery area with the capability parameters of the digital twin robots, and determine the power pipe gallery areas responsible for different types of digital twin robots.

[0095] S3. Send inspection scheduling instructions to multiple different types of digital twin robots. The inspection scheduling instructions are used to instruct the digital twin robots to perform inspection tasks in different power pipe gallery areas.

[0096] S4. Receive the inspection data fed back by the digital twin robots, predict the potential faults of the power pipe gallery based on the trained fault prediction model and the inspection data, and obtain the fault prediction result. The fault prediction model is trained based on the historical inspection data collected by different types of digital twin robots.

[0097] S5. Give an alarm based on the fault prediction result. When the fault prediction result indicates the existence of potential faults, generate a preventive maintenance recommendation report based on the fault prediction result and push the preventive maintenance recommendation report.

[0098] It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0099] In an exemplary embodiment, as Figure 4As shown, a power pipe gallery inspection management device 600 is provided, including: a data acquisition module 610, a collaborative scheduling module 620, a fault prediction module 630, and a fault warning module 640, where:

[0100] The data acquisition module 610 is used to acquire the spatial structure information of the power pipe gallery and the machine information of different types of digital twin robots;

[0101] The collaborative scheduling module 620 is used to determine the power pipe gallery areas responsible for different types of digital twin robots based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robots; send inspection scheduling instructions to multiple different types of digital twin robots, and the inspection scheduling instructions are used to instruct the digital twin robots to perform inspection tasks in different power pipe gallery areas;

[0102] The fault prediction module 630 is used to receive the inspection data fed back by the digital twin robots, and based on the inspection data, perform fault prediction on the power pipe gallery to obtain a fault prediction result. The inspection data is obtained by collecting data of the corresponding power pipe gallery area through sensors carried by the digital twin robots;

[0103] The fault warning module 640 is used to give a warning based on the fault prediction result.

[0104] In an exemplary embodiment, the data acquisition module 610 is further used to acquire the spatial structure information of each power pipe gallery area;

[0105] The collaborative scheduling module 620 is further used to match the spatial structure information of each power pipe gallery area with the capability parameters of the digital twin robots to determine the power pipe gallery areas responsible for different types of digital twin robots.

[0106] In an exemplary embodiment, the collaborative scheduling module 620 is further used to determine the status score of the digital twin robots based on a preset status evaluation criterion and the remaining power, load capacity, and fault status information of the digital twin robots, and determine the power pipe gallery areas responsible for different types of digital twin robots based on the status score of the digital twin robots and the spatial structure information of each power pipe gallery area.

[0107] In an exemplary embodiment, the fault prediction module 630 is further used to predict potential faults of the power pipe gallery based on a trained fault prediction model and the inspection data to obtain a fault prediction result. The fault prediction model is trained based on historical inspection data collected by different types of digital twin robots.

[0108] In an exemplary embodiment, the fault warning module 630 is further configured to generate a preventive maintenance recommendation report based on the fault prediction result and push the preventive maintenance recommendation report when the fault prediction result indicates the existence of a potential fault.

[0109] Each module in the above-mentioned power pipe gallery inspection management device 600 can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0110] In an exemplary embodiment, as Figure 1 shown, a power pipe gallery inspection management system is provided, including a management platform 120 and a plurality of different types of digital twin robots 140 that are communicatively connected. The plurality of different types of digital twin robots are equipped with a plurality of different sensors. Among them:

[0111] The management platform 120 is configured to adopt the steps in the embodiments of any of the above-mentioned power pipe gallery inspection management methods to schedule the digital twin robots to perform inspection tasks in different power pipe gallery areas, receive the inspection data fed back by the digital twin robots, perform fault prediction on the power pipe gallery, obtain a fault prediction result, and perform warning based on the fault prediction result.

[0112] The digital twin robot 140 is configured to collect data of the corresponding power pipe gallery area through the equipped sensors in response to the received power pipe gallery inspection instruction, obtain inspection data, and feed the inspection data back to the management platform.

[0113] In practical applications, the management platform obtains the power pipe gallery spatial structure information and the machine information of the digital twin robots. Based on the power pipe gallery spatial structure information and the machine information of the digital twin robots, it determines the power pipe gallery areas responsible for different types of digital twin robots, sends inspection scheduling instructions to the plurality of different types of digital twin robots. The inspection scheduling instructions are used to instruct the digital twin robots to perform inspection tasks in different power pipe gallery areas, receive the inspection data fed back by the digital twin robots, perform fault prediction on the power pipe gallery based on the inspection data, obtain a fault prediction result, and perform warning based on the fault prediction result. Refer to the steps in the embodiments of the above-mentioned power pipe gallery inspection management method, which will not be elaborated here.

[0114] In response to the inspection scheduling instruction, the digital twin robot collects data of its corresponding power pipe gallery area through the sensors carried by itself to obtain inspection data. The types of sensors carried by different types of digital twin robots can be different. Exemplarily, digital twin robot A is equipped with temperature and humidity sensors and gas sensors, and monitors the environment of the power pipe gallery area through the temperature and humidity sensors and gas sensors to collect temperature and humidity data and gas concentration data, and integrates them to obtain environmental detection data; digital twin robot B is equipped with temperature sensors and vibration sensors to monitor the state of power equipment in the power pipe gallery area, collect temperature data and vibration data of the equipment, and integrate them to obtain equipment status data. During the inspection process of the digital twin robot, the inspection data is monitored and fed back in real time through the sensors.

[0115] In this embodiment, different from the single robot management method in the traditional inspection management method, multiple different types of digital twin robots are introduced into the system. The management platform coordinates and schedules different types of digital twin robots to perform different inspection tasks according to the spatial structure information of the power pipe gallery and the machine information of the digital twin robots, thereby reducing the limitations of manual inspection, improving the efficiency, flexibility and safety of inspection. At the same time, it improves the adaptability to the inspection of the complex power pipe gallery environment and further improves the flexibility of inspection; on the other hand, the inspection data fed back by the digital twin robot is conducive to quickly identifying and coping with potential risks, avoiding the occurrence of major accidents, and reducing the risks of manual operation, enhancing the operation safety and stability of the power pipe gallery.

[0116] In an exemplary embodiment, as Figure 5 shown, multiple different types of digital twin robots 140 include at least two of digital twin humanoid robots 142, digital twin wheeled quadruped robots 144, digital twin gas self-growing soft robots 146, and digital twin wall-climbing robots 148. Among them:

[0117] The digital twin humanoid robot 142 is used to collect environmental monitoring data of the power pipe gallery area and electrical equipment status data of the power pipe gallery area through the carried sensors, and feed back the environmental monitoring data and electrical equipment status data to the management platform 120.

[0118] Among them, the environmental monitoring data may include temperature and humidity and gas concentration. The electrical equipment status data may include the operating status of the electrical equipment.

[0119] In practical applications, digital twin robots are designed in the shape of humans, simulating human actions and behaviors, and having high operation accuracy and adaptability. The sensors for collecting environmental monitoring data can be temperature and humidity sensors and gas sensors. The sensors for collecting electrical equipment status data can be temperature sensors and vibration sensors. The digital twin humanoid robot can analyze the inspection and scheduling instructions to obtain the power pipe gallery area and the inspection path, and collect data on the power pipe gallery area through the mounted sensors according to the inspection path, monitor the temperature and humidity in the power pipe gallery to obtain temperature and humidity data for preventing cable insulation damage caused by dampness and high temperature; monitor the concentration of harmful gases (such as hydrogen sulfide, carbon dioxide, and total volatile organic compounds, etc.) in the power pipe gallery to obtain gas concentration data, and timely detect harmful gas leakage caused by insulation aging or equipment failure. The digital twin humanoid robot can open and close the cabinet door switch of the substation, press buttons and perform screen operations, monitor the status of electrical equipment through temperature sensors and vibration sensors to obtain electrical equipment status data, and feedback it to the management platform through the wireless communication network.

[0120] The digital twin wheeled quadruped robot 144 is used to monitor the ground of the power pipe gallery area through the mounted sensors to obtain ground monitoring data, and feedback the ground monitoring data to the management platform 120.

[0121] Among them, the ground monitoring data can include inspection images and videos of the pipe gallery ground.

[0122] In practical applications, the digital twin wheeled quadruped robot combines the characteristics of wheeled and legged movement, is suitable for ground inspection, takes into account flat ground and complex terrains, and has good obstacle-crossing ability and long battery life. It can be equipped with an image sensor to collect inspection images and videos of the pipe gallery ground through the image sensor, and feedback them to the management platform through the wireless communication network.

[0123] The digital twin gas self-growing soft robot 146 is used to monitor the narrow space of the power pipe gallery area through the mounted sensors to obtain narrow space monitoring data, and feedback the narrow space monitoring data to the management platform 120.

[0124] In practical applications, the digital twin gas self-growing soft robot has a flexible structure, is made of flexible materials driven by gas, and can change its shape under specific conditions. It can adapt to narrow space environments and extend to hard-to-reach areas through the self-growing mechanism. It can be equipped with a high-precision image sensor to collect images and videos of the narrow space to obtain narrow space monitoring data, and feedback it to the management platform through the wireless communication network.

[0125] The digital twin wall-climbing robot 148 is used to monitor the wall surface of the power cable gallery area through the carried sensors, obtain the wall surface monitoring data, and feed back the wall surface monitoring data to the management platform 120.

[0126] In practical applications, the digital twin robot can move on the cable gallery wall or ceiling through the adsorption mechanism and perform inspection tasks at high places or on vertical surfaces. It can collect pictures or videos of the cable gallery wall and ceiling through the carried image sensors, obtain the wall surface monitoring data, and feed it back to the management platform through the wireless communication network.

[0127] In this embodiment, by introducing multiple different types of digital twin robots, it is beneficial to reasonably allocate different types of digital twin robots to perform specific tasks according to the characteristics of different inspection tasks, improve the coverage of the power cable gallery inspection, and thus improve the inspection efficiency and reliability.

[0128] In other embodiments, the digital twin robot is also equipped with a lidar, a structure scanner, a time-of-flight sensor, and a stereo vision sensor. The digital twin robot uses the lidar, structure scanner, time-of-flight sensor, and stereo vision sensor to detect abnormalities in electrical equipment based on laser scanning, laser point cloud, and visible light scanning modeling methods, such as detecting cable dropping from the bracket and damage to the cable surface. Specifically, it can reconstruct the external structure of the electrical equipment through the laser point cloud, detect structural defects such as depressions, cracks, bends, and displacements on the surface, obtain the structure detection results, and feed back the structure detection results to the management platform, which helps to monitor the integrity of the electrical equipment appearance.

[0129] In other embodiments, the digital twin humanoid robot is also equipped with a visible light sensor. It collects high-definition images of electrical equipment through the visible light sensor and feeds them back to the management platform. The management platform detects obvious appearance defects such as surface contamination, rust, cracks, and local wear based on the images of the electrical equipment.

[0130] In other embodiments, the digital twin humanoid robot is also equipped with an acoustic sensor (such as an acoustic imager). It collects acoustic imaging data of electrical equipment through the acoustic imager and feeds it back to the management platform. The management platform detects whether abnormal sound waves are emitted during the operation of the electrical equipment based on the acoustic imaging data, such as partial discharge, mechanical friction, or abnormal vibration, which helps to locate potential fault points according to the detection results.

[0131] In other embodiments, the digital twin humanoid robot is also used to detect whether there is partial discharge in the cable insulation layer through the local discharge detector installed, obtain the local discharge detection results, and feed back the local discharge detection results to the management platform. The management platform predicts the risks of insulation performance degradation, aging, or damage based on the received local discharge detection results and historical local discharge detection results.

[0132] In other embodiments, the management platform is also used to perform real-time fusion based on data feedback from digital twin humanoid robots, digital twin wheeled and footed robot dogs, digital twin gas self-growing soft robots, and digital twin wall-climbing robots, and to construct a multi-dimensional physical field through algorithms such as deep neural networks and Kalman filtering to achieve a comprehensive and accurate assessment of the cable body status, thereby facilitating early detection and early warning of potential hidden dangers and improving the safety of cable operation.

[0133] In an exemplary embodiment, the digital twin humanoid robot 142 is equipped with a temperature and humidity sensor, a gas sensor, a visual sensor, and an infrared sensor:

[0134] The temperature and humidity sensor is used to collect temperature and humidity data in the power pipeline corridor area and send the temperature and humidity data to the management platform 120.

[0135] The gas sensor is used to collect gas concentration data in the power pipeline corridor area and send the gas concentration data to the management platform 120.

[0136] The visual sensor is used to collect image data in the power pipeline corridor area and send the image data to the management platform 120.

[0137] The infrared sensor is used to collect electrical equipment status data in the power pipeline corridor area and send the electrical equipment status data to the management platform 120.

[0138] In actual applications, the digital twin humanoid robot is equipped with temperature and humidity sensors to monitor the temperature and humidity changes in the power corridor area and collect temperature and humidity data; the gas sensor (such as sulfur hexafluoride sensor) is used to monitor the concentration of specific gases in the power corridor area and collect gas concentration data; the visual sensor (such as a camera) is used to collect pictures and videos in the power corridor area and obtain image data; the infrared sensor (such as visible light and thermal imaging camera) is used to obtain the temperature distribution of electrical equipment through thermal imaging, and the abnormal hot spots are identified through machine vision. The abnormal temperature area is determined based on the abnormal hot spots. Usually, high temperature spots may indicate poor heat dissipation, insulation aging overload or local short circuit. Therefore, the operating status of the electrical equipment is determined based on whether there is an abnormal temperature area, and the electrical equipment status data is obtained. Real-time collection feeds the collected data back to the management platform.

[0139] In other embodiments, the digital twin wheeled quadruped robot is equipped with a lidar sensor, a temperature and humidity sensor, and an image sensor. Among them, the lidar sensor is used to detect whether there are obstacles on the ground, obtain the obstacle detection result, and avoid obstacles according to the obstacle detection result; the temperature and humidity sensor is used to collect the temperature and humidity data in the power cable tunnel area, and the image sensor is used to collect the image data of the tunnel ground. The digital twin wheeled quadruped robot feeds back the temperature and humidity data and the image data of the ground to the management platform.

[0140] The digital twin gas self-growing soft robot is equipped with a micro camera for collecting high-precision image data of narrow spaces and feeding back the image data to the management platform. The digital twin wall-climbing robot is equipped with an image sensor for collecting the image data of the tunnel wall and the top, and feeding back the image data to the management platform.

[0141] In this embodiment, for different types of digital twin robots, by integrating different sensors and technical means, it is possible to effectively cover all corners of the power cable tunnel, expanding the inspection coverage. This is conducive to the all-round monitoring and maintenance of the power cable tunnel system. Further, it enhances the ability to detect potential problems, helps to take preventive measures in a timely manner, and ensures the stable operation of the power system.

[0142] In an exemplary embodiment, the digital twin humanoid robot, the digital twin wheeled quadruped robot, the digital twin gas self-growing soft robot, and the digital twin wall-climbing robot are also used to perform anomaly detection based on the inspection data, obtain the anomaly detection result. When the anomaly detection result indicates that there is an anomaly in the power cable tunnel, an anomaly message is generated and the anomaly message is fed back to the management platform.

[0143] Among them, the anomaly message may include the anomaly type and the anomaly occurrence time.

[0144] In practical applications, digital twin humanoid robots, digital twin wheeled quadruped robots, digital twin gas self-growing soft robots, and digital twin wall-climbing robots have anomaly detection functions. Corresponding anomaly detection methods can be set in advance according to the data collected by the sensors equipped with each type of digital twin robot. Whether there is an anomaly is judged through the anomaly detection method and the collected sensor data. For example, corresponding anomaly detection thresholds are set for temperature data, humidity data, and gas concentration data respectively. The anomaly detection thresholds can be set according to power industry standards or based on historical data and experience. When there is inspection data exceeding the corresponding anomaly detection threshold, it is determined that there is an anomaly, and the anomaly type and the time of anomaly occurrence are recorded. The anomaly type can include too high temperature, too high humidity, and too high gas concentration, etc. Exemplarily, if the temperature data exceeds the corresponding anomaly detection threshold, the anomaly type can include too high temperature; if the humidity data exceeds the corresponding anomaly detection threshold, the anomaly type can include too high humidity; if the gas concentration data exceeds the corresponding anomaly detection threshold, the anomaly type can include too high gas concentration. Anomaly information is generated based on the anomaly type and the time of anomaly occurrence. The anomaly information can also include the abnormal data, and the anomaly information is fed back to the monitoring and management platform. In other embodiments, the management platform analyzes the anomaly information and automatically generates countermeasures and pushes the countermeasures. The digital twin robot judges whether to return to the safe area according to the anomaly information.

[0145] In this embodiment, the digital twin robot independently performs anomaly detection, which is beneficial to timely discovering potential failure risks, thereby facilitating improving the timeliness of power cable tunnel maintenance, and further facilitating improving the reliability of power cable tunnel operation.

[0146] In other embodiments, the digital twin robot is also used to share real-time inspection data with the remaining digital twin robots other than itself, such as including inspection progress, environmental information, detected abnormal conditions, etc. Through information synchronization, multiple robots can form a coordinated operation network.

[0147] In other embodiments, during the inspection process, the digital twin robot is also used to feed back its own real-time status, remaining power, task progress, etc. to the management platform. The management platform is also used to dynamically adjust inspection task allocation and path planning according to factors such as the real-time status, remaining power, and task progress of each robot. When a robot cannot complete the inspection task due to insufficient power, the system can automatically transfer its task to other robots to ensure the continuity and overall efficiency of the task.

[0148] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 6As shown in the figure. 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. 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 inspecting and managing a power pipe gallery.

[0149] Those skilled in the art can understand that Figure 6 the structure shown in the figure is only a block diagram of some structures 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 certain components, or have different component arrangements.

[0150] In an exemplary embodiment, a computer device is 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 any one of the above embodiments of the method for inspecting and managing a power pipe gallery are implemented.

[0151] In an 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 any one of the above embodiments of the method for inspecting and managing a power pipe gallery are implemented.

[0152] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in any one of the above embodiments of the method for inspecting and managing a power pipe gallery are implemented.

[0153] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0154] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium 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 logics, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., and are not limited thereto.

[0155] 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.

[0156] The above-described embodiments merely 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 on 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 modifications and improvements can still be made, and these all fall within 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 inspecting and managing a power pipe gallery, characterized in that, The method includes: Obtaining the spatial structure information of the power pipe gallery and the machine information of different types of digital twin robots; Based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robots, determining the power pipe gallery areas responsible for different types of digital twin robots; Sending inspection scheduling instructions to multiple different types of digital twin robots, where the inspection scheduling instructions are used to instruct the digital twin robots to perform inspection tasks in different power pipe gallery areas; Receiving the inspection data fed back by the digital twin robots, and based on the inspection data, performing fault prediction on the power pipe gallery to obtain a fault prediction result, where the inspection data is obtained by the sensors carried by the digital twin robots collecting data of the corresponding power pipe gallery areas; Issuing an early warning based on the fault prediction result.

2. The method according to claim 1, wherein The machine information includes the capability parameters of the digital twin robots. The determining, based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robots, of the power pipe gallery areas responsible for different types of digital twin robots includes: Obtaining the spatial structure information of each power pipe gallery area; Matching the spatial structure information of each power pipe gallery area with the capability parameters of the digital twin robots to determine the power pipe gallery areas responsible for different types of digital twin robots.

3. The method according to claim 2, wherein The machine information further includes remaining battery power, load capacity, and fault status information; The determining, based on the spatial structure information of the power pipe gallery and the machine information of the digital twin robots, of the power pipe gallery areas responsible for different types of digital twin robots further includes: Based on a preset status evaluation criterion and the remaining battery power, load capacity, and fault status information of the digital twin robots, determining the status score of the digital twin robots; Based on the status score of the digital twin robots and the spatial structure information of each power pipe gallery area, determining the power pipe gallery areas responsible for different types of digital twin robots.

4. The method according to claim 1, characterized in that, The performing, based on the inspection data, of fault prediction on the power pipe gallery to obtain a fault prediction result includes: Based on a trained fault prediction model and the inspection data, predicting potential faults of the power pipe gallery to obtain a fault prediction result, where the fault prediction model is trained based on historical inspection data collected by different types of digital twin robots.

5. The method according to claim 4, characterized in that, The method further includes: In the case where the fault prediction result indicates the existence of potential faults, generating a preventive maintenance recommendation report based on the fault prediction result and pushing the preventive maintenance recommendation report.

6. A power pipe gallery inspection and management system, characterized in that, The system includes a management platform and multiple different types of digital twin robots that are communicatively connected, and multiple different sensors are carried by the multiple different types of digital twin robots; The management platform is used to adopt the power pipe gallery inspection management method as described in any one of claims 1 to 5, schedule the digital twin robots to perform inspection tasks in different power pipe gallery areas, receive the inspection data fed back by the digital twin robots, perform fault prediction on the power pipe gallery to obtain a fault prediction result, and issue an early warning based on the fault prediction result; The digital twin robot is used to collect data of the corresponding power pipe gallery area through the equipped sensors in response to the received power pipe gallery inspection instruction, obtain inspection data, and feedback the inspection data to the management platform.

7. The system according to claim 6, characterized in that, The multiple different types of digital twin robots include at least two of: digital twin humanoid robots, digital twin wheeled quadruped robots, digital twin gas self-growing soft robots, and digital twin wall-climbing robots; The digital twin humanoid robot is used to collect environmental monitoring data of the power pipe gallery area and electrical equipment status data of the power pipe gallery area through the equipped sensors, and feedback the environmental monitoring data and the electrical equipment status data to the management platform; The digital twin wheeled quadruped robot is used to monitor the ground of the power pipe gallery area through the equipped sensors, obtain ground monitoring data, and feedback the ground monitoring data to the management platform; The digital twin gas self-growing soft robot is used to monitor the narrow space of the power pipe gallery area through the equipped sensors, obtain narrow space monitoring data, and feedback the narrow space monitoring data to the management platform; The digital twin wall-climbing robot is used to monitor the wall surface of the power pipe gallery area through the equipped sensors, obtain wall surface monitoring data, and feedback the wall surface monitoring data to the management platform.

8. The system according to claim 7, characterized in that, The digital twin humanoid robot is equipped with a temperature and humidity sensor, a gas sensor, a vision sensor, and an infrared sensor: The temperature and humidity sensor is used to collect temperature and humidity data in the power pipe gallery area and send the temperature and humidity data to the management platform; The gas sensor is used to collect gas concentration data in the power pipe gallery area and send the gas concentration data to the management platform; The vision sensor is used to collect image data in the power pipe gallery area and send the image data to the management platform; The infrared sensor is used to collect electrical equipment status data of the power pipe gallery area and send the electrical equipment status data to the management platform.

9. The system according to claim 8, wherein The digital twin humanoid robot, the digital twin wheeled quadruped robot, the digital twin gas self-growing soft robot, and the digital twin wall-climbing robot are also used to perform anomaly detection based on the inspection data, obtain an anomaly detection result, generate anomaly information when the anomaly detection result indicates that there is an anomaly in the power pipe gallery, and feedback the anomaly information to the management platform.

10. An inspection and management device for a power pipe gallery, characterized in that, The device includes: A data acquisition module, used to acquire power pipe gallery spatial structure information and machine information of different types of digital twin robots; A collaborative scheduling module, used to determine the power pipe gallery areas responsible for different types of digital twin robots based on the power pipe gallery spatial structure information and the machine information of the digital twin robots; send an inspection scheduling instruction to the multiple different types of digital twin robots, and the inspection scheduling instruction is used to instruct the digital twin robot to perform an inspection task in different power pipe gallery areas; A fault prediction module, configured to receive the inspection data fed back by the digital twin robot, and based on the inspection data, perform fault prediction on the power pipe gallery to obtain a fault prediction result, where the inspection data is obtained by the sensors carried by the digital twin robot to collect data of the corresponding power pipe gallery area; A fault warning module, configured to issue a warning based on the fault prediction result.

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