Intelligent power distribution operation and maintenance robot inspection method and system

Abstract

The invention discloses an intelligent power distribution operation and maintenance robot inspection method and system, and the method comprises the steps: obtaining historical environment data, and historical operation data and corresponding historical load data of each power device in a target power distribution network, and carrying out the correlation aging analysis of the historical operation data, the historical environment data and the historical load data, obtaining a fault risk level of each power device, performing device association analysis on the historical load data of all the power devices and the topological structure of the target power distribution network, determining an importance level of each power device, and calculating an inspection key level of each power device in combination with the fault risk level; and performing dynamic path inspection according to all the inspection key levels and the topological structure. According to the intelligent power distribution operation and maintenance robot inspection method and system provided by the embodiment of the invention, the inspection efficiency of power equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an intelligent distribution operation and maintenance robot inspection method and system. Background Art

[0002] Grid power inspection refers to the regular or irregular inspection, testing and maintenance of various equipment, lines and facilities in the power system to ensure their normal operation and timely discovery of potential problems. Inspection helps to detect potential safety hazards, such as overheating of equipment, insulation damage, etc., so as to take preventive measures to ensure the normal operation of power equipment, reduce power outages caused by equipment failures, and improve the reliability and stability of power supply.

[0003] However, existing inspection technologies often adopt fixed inspection strategies and cannot be adjusted according to the actual operating conditions of the power grid. Key equipment may not be inspected in time, while low-risk equipment is frequently inspected, resulting in low inspection efficiency.

[0004] Therefore, how to optimize the inspection strategy to improve the inspection efficiency has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides an intelligent distribution operation and maintenance robot inspection method and system to solve the technical problem that existing inspection technologies often adopt fixed inspection strategies and cannot be adjusted according to the actual operating conditions of the power grid. Key equipment may not be inspected in time, while low-risk equipment is frequently inspected, resulting in low inspection efficiency.

[0006] To solve the above technical problem, an embodiment of the present invention provides an intelligent distribution operation and maintenance robot inspection method.

[0007] Obtain historical environmental data, historical operation data of each power equipment in the target distribution network, and historical load data of each of the power equipment; Perform associated aging analysis on the historical operation data, the historical environmental data, and the historical load data to obtain the failure risk level of each of the power equipment; wherein, the associated aging analysis reflects the influence of environmental factors and load on the normal operation of the equipment; Perform equipment association analysis on the historical load data of all the power equipment and the topological structure of the target distribution network to obtain the importance level of each of the power equipment; wherein, the equipment association analysis reflects the key equipment for identifying fault propagation according to the topological structure and the load distribution characteristics of all the power equipment; The inspection critical level of each power device is calculated based on the linear relationship between the failure risk level and the corresponding importance level of each power device; the target distribution network is dynamically path-inspected according to all the inspection critical levels and the topological structure, where the dynamic path inspection is designed to determine the current inspection device according to the sorting result of the inspection critical levels of all the power devices to be inspected, and determine the next inspection device based on the inspection critical levels of the remaining power devices to be inspected, the connection relationship between the remaining power devices to be inspected and the current inspection device.

[0008] As one of the preferred solutions, the correlation aging analysis of the historical operation data, the historical environment data and the historical load data to obtain the failure risk level of each power device includes: Determine the environmental stress index of each power device based on the correlation characteristics between the historical operation data and the historical environment data, and determine the load stress index of each power device based on the correlation characteristics between the historical operation data and the historical load data; Perform weighted summation on the environmental stress index and the corresponding load stress index of each power device to determine the comprehensive aging index of each power device; Perform clustering analysis on the comprehensive aging indexes of all the power devices to determine the thresholds of each failure risk level; Compare and analyze the comprehensive aging index of each power device with all the thresholds to obtain the failure risk level of each power device.

[0009] As one of the preferred solutions, the determination of the environmental stress index of each power device based on the correlation characteristics between the historical operation data and the historical environment data includes: Perform first feature extraction on each historical operation data to obtain each operation feature vector of each power device, where the operation feature vector reflects the failure characteristics of the corresponding power device, and the operation feature vector includes the start-stop times and the overload times; Perform second feature extraction on each historical environment data to obtain each environmental feature vector; Take the Pearson correlation coefficient between each environmental feature vector and the operation feature vector of each power device as the environmental stress index of each power device, where the environmental stress index reflects the influence of environmental factors on the normal operation of the device.

[0010] As one of the preferred solutions, the determination of the load stress index of each power device based on the correlation characteristics between the historical operation data and the historical load data includes: Perform third feature extraction on each of the historical load data to obtain respective load feature vectors of each of the power devices; Use the Pearson correlation coefficient between each load feature vector of each power device and each of the operation feature vectors as the load stress index of each power device, where the load stress index reflects the impact of the load on the normal operation of the device.

[0011] As one preferred solution, the device association analysis of the historical load data of all the power devices and the topological structure of the target distribution network to obtain the importance level of each power device includes: Construct an adjacency matrix of the target distribution network based on the topological structure using a graph structure model, analyze the adjacency matrix, and calculate the degree of the vertex corresponding to each power device; Determine each critical path in the topological structure based on the degrees of the vertices corresponding to adjacent power devices in the topological structure; Use the number of critical paths containing the current power device as the critical index of the current power device, and use the product of the proportion of the historical load data of each power device and the critical index of each power device as the power critical factor of each power device; Perform normalization processing on the power critical factors of all the power devices to obtain the importance levels of each power device.

[0012] As one preferred solution, the calculation of the inspection critical level of each power device based on the linear relationship between the failure risk level of each power device and the corresponding importance level includes: Based on the inspection mode, perform weighted summation of the failure risk level of each power device and the corresponding importance level to obtain the inspection critical factor of each power device, where the weight assignment for the weighted summation is determined by the inspection mode; Perform normalization processing on the inspection critical factors of all the power devices to determine the inspection critical levels of each power device.

[0013] As one preferred solution, the historical environmental data at least includes temperature, humidity, wind speed, and rainfall.

[0014] As one preferred solution, the method further includes: Before performing correlation aging analysis on the historical operation data, the historical environmental data, and the historical load data to obtain the failure risk level of each power device, data preprocessing is performed on the historical environmental data, the historical operation data, and the historical load data respectively.

[0015] As one of the preferred solutions, the method further includes: During the dynamic path inspection, an inspection log of the target distribution network is generated, where the inspection log at least includes the inspection device number, the detection time, and the abnormal type.

[0016] Another embodiment of the present invention provides an intelligent distribution operation and maintenance robot inspection system, including: A data acquisition module for obtaining historical environmental data, historical operation data of each power device in the target distribution network, and historical load data of each power device; A failure risk assessment module for performing correlation aging analysis on the historical operation data, the historical environmental data, and the historical load data to obtain the failure risk level of each power device; wherein, the correlation aging analysis reflects the influence of environmental factors and load on the normal operation of the device; A key device assessment module for performing device association analysis on the historical load data of all power devices and the topological structure of the target distribution network to obtain the importance level of each power device; wherein, the device association analysis reflects the key devices for identifying fault propagation according to the topological structure and the load distribution characteristics of all power devices; A real-time inspection module for calculating the inspection key level of each power device based on the linear relationship between the failure risk level of each power device and the corresponding importance level; performing dynamic path inspection on the target distribution network according to all the inspection key levels and the topological structure, where the dynamic path inspection is designed to determine the current inspection device according to the sorting result of the inspection key levels of all power devices to be inspected, and determine the next inspection device based on the inspection key levels of the remaining power devices to be inspected, the connection relationship between the remaining power devices to be inspected and the current inspection device.

[0017] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: Obtain historical environmental data, historical operation data of each power device in the target distribution network, and corresponding historical load data. Conduct correlation aging analysis on the historical operation data, historical environmental data, and historical load data to obtain the failure risk level of each power device. The failure risk level quantifies the degree of equipment hidden danger. By correlating the influence of environmental factors and load on equipment aging, high-risk and low-risk devices are distinguished, avoiding "one-size-fits-all" inspections, making inspection resources tilt towards high-risk devices, and reducing the probability of sudden failures; conduct equipment correlation analysis on the historical load data of all power devices and the topological structure of the target distribution network to determine the importance level of each power device. Based on the topological structure and load distribution, locate the key devices for fault propagation. Prioritized inspections can block cascading failures. The importance level reflects the impact of power devices on the overall situation, avoiding large-scale power outages caused by local failures and improving overall reliability; calculate the inspection key level of each power device according to the importance level and failure risk level; conduct dynamic path inspections based on all inspection key levels and the topological structure. Among them, dynamic path inspections are designed to determine the current inspection device according to the sorting result of the inspection key levels of all power devices to be inspected, and determine the next inspection device based on the inspection key levels of the remaining power devices to be inspected, the connection relationship between the remaining power devices to be inspected and the current inspection device, which can support inserting sudden high-priority tasks, improve the response speed. By prioritizing inspections of high-risk and high-importance devices, faults can be detected in a timely manner, and corresponding measures can be taken to block cascading failures, avoid fault spread, and improve inspection efficiency. Brief Description of the Drawings

[0018] Figure 1 It is a flowchart of an intelligent distribution operation and maintenance robot inspection method in one embodiment of the present invention; Figure 2 It is a structural block diagram of an intelligent distribution operation and maintenance robot inspection system in one embodiment of the present invention. Detailed Embodiments

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] In the description of this application, the terms "first", "second", "third", etc. are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0021] In the description of this application, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down", and similar expressions used herein are only for illustrative purposes and do not indicate or imply that the indicated device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0022] In the description of this application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0023] An embodiment of the present invention provides a patrol inspection method for an intelligent power distribution operation and maintenance robot. Specifically, please refer to Figures 1 to 2 , Figure 1 which shows a flowchart of a patrol inspection method for an intelligent power distribution operation and maintenance robot in one of the embodiments of the present invention, Figure 2 and which shows a structural block diagram of a patrol inspection system for an intelligent power distribution operation and maintenance robot in one of the embodiments of the present invention.

[0024] The flowchart of a patrol inspection method for an intelligent power distribution operation and maintenance robot in one of the embodiments of the present invention includes the following steps S1 to S4, specifically as follows: Step S1: Obtain historical environmental data, historical operation data of each power device in the target power distribution network, and historical load data of each power device.

[0025] Step S2: Conduct correlation aging analysis on historical operation data, historical environment data, and historical load data to obtain the failure risk level of each power device; among them, the correlation aging analysis reflects the influence of environmental factors and load on the normal operation of the device.

[0026] Step S3: Conduct device association analysis on the historical load data of all power devices and the topological structure of the target distribution network to obtain the importance level of each power device; among them, the device association analysis reflects the identification of key devices for fault propagation based on the topological structure and the load distribution characteristics of all power devices.

[0027] Step S4: Calculate the inspection key level of each power device based on the linear relationship between the failure risk level and the corresponding importance level of each power device; conduct dynamic path inspection on the target distribution network according to all inspection key levels and the topological structure, where the dynamic path inspection is designed to determine the current inspection device according to the sorting result of the inspection key levels of all power devices to be inspected, and determine the next inspection device based on the inspection key levels of the remaining power devices to be inspected, the connection relationship between the remaining power devices to be inspected and the current inspection device.

[0028] In one embodiment, the historical environment data in step S1 includes at least temperature, humidity, wind speed, and rainfall, and the historical operation data includes at least voltage, current, and power.

[0029] In one embodiment, in step S2, conducting correlation aging analysis on historical operation data, historical environment data, and historical load data to obtain the failure risk level of each power device includes: Step S201: Determine the environmental stress index of each power device based on the correlation characteristics between historical operation data and historical environment data; Step S202: Determine the load stress index of each power device based on the correlation characteristics between historical operation data and historical load data; Step S203: Perform weighted summation on the environmental stress index and the corresponding load stress index of each power device to determine the comprehensive aging index of each power device; Step S204: Conduct cluster analysis on the comprehensive aging indexes of all power devices to determine the thresholds of each failure risk level; Step S205: Compare and analyze the comprehensive aging index of each power device with all thresholds to obtain the failure risk level of each power device.

[0030] In one embodiment, in step S201, determining the environmental stress index of each power device based on the correlation characteristics between historical operation data and historical environment data includes: Perform first feature extraction on each piece of historical operation data to obtain various operation feature vectors of each power device. Among them, the operation feature vector reflects the fault characteristics of the corresponding power device, and the operation feature vector includes the start-stop times and overload times; Perform second feature extraction on each piece of historical environment data to obtain various environment feature vectors; Take the Pearson correlation coefficient between each environment feature vector and the operation feature vector of each power device as the environment stress index of each power device. Among them, the environment stress index reflects the influence of environmental factors on the normal operation of the device.

[0031] In one embodiment, step S202 of determining the load stress index of each power device based on the correlation characteristics between historical operation data and historical load data includes: Perform third feature extraction on each piece of historical load data to obtain various load feature vectors of each power device; Take the Pearson correlation coefficient between each load feature vector of each power device and various operation feature vectors as the load stress index of each power device. Among them, the load stress index reflects the influence of the load on the normal operation of the device.

[0032] In one embodiment, step S3 of performing device association analysis on the historical load data of all power devices and the topological structure of the target distribution network to obtain the importance level of each power device includes: Use a graph structure model to construct the adjacency matrix of the target distribution network based on the topological structure, analyze the adjacency matrix, and calculate the degree of the vertex corresponding to each power device; determine each key path in the topological structure based on the degrees of the vertices corresponding to adjacent power devices in the topological structure; Take the number of key paths containing the current power device as the key index of the current power device, and take the product of the proportion of the historical load data of each power device and the key index of each power device as the power key factor of each power device; Perform normalization processing on the power key factors of all power devices to obtain the importance levels of each power device.

[0033] In one embodiment, step S4 of calculating the inspection key level of each power device based on the linear relationship between the fault risk level and the corresponding importance level of each power device includes: Based on the inspection mode, perform weighted summation of the fault risk level and the corresponding importance level of each power device to obtain the inspection key factor of each power device. Among them, the weight assignment of the weighted summation is determined by the inspection mode; perform normalization processing on the inspection key factors of all power devices to determine the inspection key levels of each power device.

[0034] An intelligent power distribution operation and maintenance robot inspection method provided by this embodiment further includes: Before performing associated aging analysis on historical operation data, historical environment data, and historical load data to obtain the fault risk level of each power device, data preprocessing is respectively performed on the historical environment data, historical operation data, and historical load data.

[0035] An intelligent power distribution operation and maintenance robot inspection method provided by this embodiment further includes: During the dynamic path inspection, an inspection log of the target power distribution network is generated, where the inspection log at least includes the inspected device number, detection time, and abnormal type.

[0036] Compared with the prior art, the beneficial effects of an intelligent power distribution operation and maintenance robot inspection method provided by an embodiment of the present invention are as follows: Obtain historical environment data, historical operation data of each power device in the target power distribution network, and corresponding historical load data, perform associated aging analysis on the historical operation data, historical environment data, and historical load data to obtain the fault risk level of each power device. The fault risk level quantifies the degree of equipment hidden danger. By associating the influence of environmental factors and load on equipment aging, high-risk and low-risk devices are distinguished, avoiding "one-size-fits-all" inspection, making inspection resources tilt towards high-risk devices, and reducing the probability of sudden failures; perform equipment association analysis on the historical load data of all power devices and the topological structure of the target power distribution network to determine the importance level of each power device. Based on the topological structure and load distribution, locate the key devices for fault propagation. Prioritizing inspections can block cascading failures. The importance level reflects the impact of power devices on the overall situation, avoiding large-scale power outages caused by local failures and improving overall reliability; calculate the inspection key level of each power device according to the importance level and fault risk level; perform dynamic path inspection according to all inspection key levels and the topological structure. Among them, the dynamic path inspection is designed to determine the current inspected device according to the sorting result of the inspection key levels of all power devices to be inspected, and determine the next inspected device based on the inspection key levels of the remaining power devices to be inspected, the connection relationship between the remaining power devices to be inspected and the current inspected device. It can support inserting sudden high-priority tasks, improve the response speed. By prioritizing inspections of high-risk and high-importance devices, faults can be detected in a timely manner, and corresponding processing can be carried out to block cascading failures, avoid fault spread, and improve inspection efficiency.

[0037] Another embodiment of the present invention provides an intelligent power distribution operation and maintenance robot inspection system, including: A data acquisition module 11, configured to obtain historical environment data, historical operation data of each power device in the target power distribution network, and historical load data of each power device; A fault risk assessment module 12, configured to perform associated aging analysis on historical operation data, historical environment data, and historical load data to obtain the fault risk level of each power device; wherein the associated aging analysis reflects the impact of environmental factors and load on the normal operation of the device; A key device assessment module 13, configured to perform device association analysis on the historical load data of all power devices and the topological structure of the target distribution network to obtain the importance level of each power device; wherein the device association analysis reflects the identification of key devices for fault propagation based on the topological structure and the load distribution characteristics of all power devices; A real-time inspection module 14, configured to calculate the inspection key level of each power device based on the linear relationship between the fault risk level and the corresponding importance level of each power device; perform dynamic path inspection on the target distribution network according to all inspection key levels and the topological structure, wherein the dynamic path inspection is designed to determine the current inspection device according to the sorting result of the inspection key levels of all power devices to be inspected, and determine the next inspection device based on the inspection key levels of the remaining power devices to be inspected, the connection relationship between the remaining power devices to be inspected and the current inspection device.

[0038] The above embodiments merely represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. An intelligent power distribution operation and maintenance robot inspection method, characterized in that: The method comprises: Acquire historical environmental data, historical operating data of each power device in the target distribution network, and historical load data of each of the power devices; Performing a correlation aging analysis on the historical operation data, the historical environmental data and the historical load data to obtain a failure risk level of each of the power equipment; wherein the correlation aging analysis reflects the impact of environmental factors and loads on the normal operation of the equipment; Performing device association analysis on the historical load data of all the power devices and the topological structure of the target power distribution network to obtain the importance level of each of the power devices; wherein the device association analysis reflects the identification of key devices for fault propagation based on the topological structure and the load distribution characteristics of all the power devices; The inspection critical level of each of the power equipment is calculated based on the linear relationship between the fault risk level of each of the power equipment and the corresponding importance level; the target distribution network is subjected to dynamic path inspection according to all the inspection critical levels and the topological structure, wherein the dynamic path inspection is designed to determine the current inspection device according to the sorting results of the inspection critical levels of all the power equipment to be inspected, and to determine the next inspection device based on the inspection critical levels of the remaining power equipment to be inspected, the connection relationship between the remaining power equipment to be inspected and the current inspection device.

2. The intelligent power distribution operation and maintenance robot inspection method according to claim 1 is characterized in that: The performing of correlation aging analysis on the historical operation data, the historical environment data and the historical load data to obtain the failure risk level of each of the power equipment includes: Determine the environmental stress index of each of the electric power equipment based on the correlation characteristics between the historical operation data and the historical environmental data, and determine the load stress index of each of the electric power equipment based on the correlation characteristics between the historical operation data and the historical load data; Taking a weighted sum of the environmental stress index and the corresponding load stress index of each of the electric devices to determine a comprehensive aging index of each of the electric devices; Performing cluster analysis on the comprehensive aging index of all the power equipment to determine the threshold of each fault risk level; The comprehensive aging index of each of the electric equipment is compared and analyzed with all the thresholds to obtain the failure risk level of each of the electric equipment.

3. The intelligent power distribution operation and maintenance robot inspection method according to claim 2 is characterized in that: The determining of the environmental stress index of each of the power equipment based on the correlation characteristics between the historical operation data and the historical environmental data includes: Performing a first feature extraction on each of the historical operation data to obtain each operation feature vector of each of the electric power equipment, wherein the operation feature vector reflects the fault feature of the corresponding electric power equipment, and the operation feature vector includes the number of starts and stops and the number of overloads; Performing a second feature extraction on each of the historical environmental data to obtain each environmental feature vector; The Pearson correlation coefficient between each of the environmental characteristic vectors and the operating characteristic vector of each of the electric equipment is used as the environmental stress index of each of the electric equipment, wherein the environmental stress index reflects the influence of environmental factors on the normal operation of the equipment.

4. The intelligent power distribution operation and maintenance robot inspection method according to claim 3 is characterized in that: The determining of the load stress index of each of the power equipment based on the correlation characteristics between the historical operation data and the historical load data comprises: Performing a third feature extraction on each of the historical load data to obtain each load feature vector of each of the electric power equipment; The Pearson correlation coefficient between each of the load characteristic vectors and each of the operation characteristic vectors of each of the power equipment is used as the load stress index of each of the power equipment, wherein the load stress index reflects the impact of the load on the normal operation of the equipment.

5. The intelligent power distribution operation and maintenance robot inspection method according to claim 1 is characterized in that: The performing device association analysis on the historical load data of all the power devices and the topological structure of the target distribution network to obtain the importance level of each of the power devices includes: Using a graph structure model to construct an adjacency matrix of the target distribution network based on the topological structure, analyzing the adjacency matrix, and calculating the degree of the vertex corresponding to each of the power equipment; Determining each critical path in the topological structure based on the degrees of the vertices corresponding to the adjacent power devices in the topological structure; The number of the critical paths including the current power equipment is used as the critical index of the current power equipment, and the product of the proportion of the historical load data of each power equipment and the critical index of each power equipment is used as the power critical factor of each power equipment; The power key factors of all the power equipment are normalized to obtain the importance level of each power equipment.

6. The intelligent power distribution operation and maintenance robot inspection method according to claim 1 is characterized in that: The calculating the inspection criticality level of each of the power equipment based on the linear relationship between the fault risk level of each of the power equipment and the corresponding importance level includes: Based on the inspection mode, the failure risk level and the corresponding importance level of each of the power equipment are weighted and summed to obtain the inspection key factor of each of the power equipment, wherein the weight distribution of the weighted sum is determined by the inspection mode; The inspection key factors of all the electric power equipment are normalized to determine the inspection key level of each electric power equipment.

7. The intelligent power distribution operation and maintenance robot inspection method according to claim 1 is characterized in that: The historical environmental data includes at least temperature, humidity, wind speed and rainfall.

8. The intelligent power distribution operation and maintenance robot inspection method according to claim 1 is characterized in that: The method further comprises: Before performing the associated aging analysis on the historical operation data, the historical environment data and the historical load data to obtain the failure risk level of each of the power equipment, the historical environment data, the historical operation data and the historical load data are respectively preprocessed.

9. The intelligent power distribution operation and maintenance robot inspection method according to claim 1 is characterized in that: The method further comprises: During the dynamic path inspection, an inspection log of the target distribution network is generated, wherein the inspection log at least includes an inspection device number, a detection time, and an abnormality type.

10. An intelligent power distribution operation and maintenance robot inspection system, characterized in that: The system comprises: A data acquisition module, used to obtain historical environmental data, historical operating data of each power device in the target distribution network, and historical load data of each of the power devices; A fault risk assessment module, used to perform an associated aging analysis on the historical operation data, the historical environmental data and the historical load data to obtain a fault risk level of each of the power equipment; wherein the associated aging analysis reflects the impact of environmental factors and loads on the normal operation of the equipment; A key equipment evaluation module, used to perform equipment association analysis on the historical load data of all the power equipment and the topological structure of the target distribution network to obtain the importance level of each power equipment; wherein the equipment association analysis reflects the key equipment for fault propagation identification based on the topological structure and the load distribution characteristics of all the power equipment; A real-time inspection module is used to calculate the inspection critical level of each of the power equipment based on the linear relationship between the fault risk level of each of the power equipment and the corresponding importance level; and to perform dynamic path inspection on the target distribution network according to all the inspection critical levels and the topological structure, wherein the dynamic path inspection is designed to determine the current inspection device according to the sorting results of the inspection critical levels of all the power equipment to be inspected, and to determine the next inspection device based on the inspection critical levels of the remaining power equipment to be inspected, the connection relationship between the remaining power equipment to be inspected and the current inspection device.

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