Multi-UAV collaborative power re-inspection method and system based on communication technology

Abstract

The present invention discloses a multi-UAV collaborative power re-inspection method and system based on communication technology, which belongs to the field of power system inspection technology, including: S1, constructing a 3D inspection map to determine an instruction set and obtain corresponding node inspection information; S2, analyzing the node inspection information to determine its own abnormal points and related abnormal points and then determine the abnormal tracing network; segmenting the inspection 3D map according to the abnormal tracing network to obtain target abnormal areas and non-abnormal areas; S3, respectively constructing a first target execution path and a second target execution path through a minimum tree generation algorithm; S4, determining a regional information interaction period based on the first target execution path and / or the second target execution path; S5, the UAV performs a power re-inspection operation based on a multi-frequency communication strategy combined with the regional information interaction period; the scheme can ensure the real-time and reliability of information interaction, and at the same time, the UAVs that are mutually primary and standby can ensure the smooth execution of the inspection task, significantly improving the efficiency of the UAV power inspection.

Description

Technical Field

[0001] The present invention relates to the field of power system inspection technology, and in particular to a multi-UAV collaborative power re-inspection method and system based on communication technology. Background Art

[0002] With the continuous development of the power system, power facilities are increasingly distributed, especially in remote mountainous areas where power lines are complex and intertwined. Traditional manual inspection methods face many difficulties in these areas, such as complex terrain and inconvenient transportation, and it is difficult to meet the efficiency and accuracy requirements of power inspection.

[0003] The emergence of drone inspection technology provides a new way to solve this problem. However, existing drone inspection methods still have some shortcomings. When facing power failures, if drones only check the fault points one by one, the efficiency is low. For example, in a complex mountain power network, faults may occur at multiple points or be related, but existing drone inspections lack effective fault analysis and regional division mechanisms, and cannot quickly locate the fault area and reasonably plan the inspection route.

[0004] In addition, drones often work independently during the inspection process, and information exchange is limited. Once a drone encounters a fault or signal interference, the information it collects cannot be shared with other drones in a timely manner, resulting in interruptions or duplications in the inspection work, which seriously affects the overall inspection efficiency. Moreover, in terms of communication, a single communication frequency band is difficult to adapt to the complex mountain environment, and the signal is unstable, further reducing the reliability and efficiency of the inspection. Therefore, how to achieve collaborative inspections of multiple drones in complex environments, ensure the effectiveness and real-time nature of data collection, and the autonomous response capabilities of drones when signals are lost, has become an urgent problem to be solved.

[0005] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0006] In view of the problem of low inspection efficiency of existing drone inspections when facing power failures, the present invention provides a multi-drone collaborative power re-inspection method and system based on communication technology. By performing correlation analysis on abnormal situations, the target abnormal area and non-abnormal area are determined and the corresponding target execution path is generated. Two drones can independently perform corresponding inspection tasks, and can share inspection information in real time during the inspection process. The information exchange period is determined according to the real-time interaction situation, which can ensure the real-time and reliability of information interaction. At the same time, the drones that serve as the main and backup for each other can ensure the smooth execution of the inspection tasks, thereby significantly improving the efficiency of drone power inspections.

[0007] In a first aspect, a technical solution provided in an embodiment of the present invention is a multi-UAV collaborative power re-inspection method based on communication technology, comprising the following steps:

[0008] S1. Build a 3D inspection map, determine the instruction set corresponding to the inspection point according to the inspection scenario, and execute the instruction set to obtain the corresponding node inspection information;

[0009] S2. Analyze the node inspection information to determine its own abnormal points and determine related abnormal points according to the node correlation coefficient; determine the abnormal tracing network according to the flow direction of its own abnormal points and related abnormal points; segment the inspection 3D map according to the abnormal tracing network to obtain the target abnormal area and non-abnormal area;

[0010] S3, constructing a first target execution path corresponding to the target abnormal area and a second target execution path corresponding to the non-abnormal area respectively by using a minimum tree generation algorithm;

[0011] S4, determining the regional information interaction period based on the communication strength and communication flow of each inspection point in the first target execution path and / or the second target execution path;

[0012] S5. The UAV performs power re-inspection operations based on the multi-frequency communication strategy combined with the regional information interaction period.

[0013] Preferably, S1 comprises the following steps:

[0014] S11. Determine the inspection 3D map of each area to be inspected according to the topological structure and geographical characteristics of the distribution network;

[0015] S12. Obtain the equipment type corresponding to the inspection scene, and determine the inspection instruction information according to the spatial position of the inspection point corresponding to the equipment type; S13. Obtain the inspection instruction information of the inspection points in turn to construct an instruction set, and the UAV obtains the corresponding node inspection information according to the inspection instruction information in the instruction set.

[0016] Preferably, in S2, analyzing the node inspection information to determine the node's own abnormal points and determining related abnormal points according to the node correlation coefficient comprises the following steps:

[0017] S201, extracting abnormal characteristic factors of node inspection information, and determining the inspection point corresponding to the abnormal characteristic factor whose status value exceeds the set safety threshold range as its own abnormal point;

[0018] S202. Determine the node correlation coefficient corresponding to each abnormal point according to the fault network corresponding to the historical fault situation of the inspection point, take the inspection point whose node correlation coefficient is greater than the set threshold as the relevant abnormal point, determine the target fault network according to the relevant abnormal point and the abnormal point itself, and determine the inspection point in the target fault network excluding the abnormal point itself as the relevant abnormal point.

[0019] As a preferred embodiment, in S2, an abnormal tracing network is determined according to the flow direction of the abnormal point itself and the related abnormal points; and the inspection 3D map is segmented according to the abnormal tracing network to obtain the target abnormal area and the non-abnormal area; the steps include:

[0020] S211, taking the own abnormal point as the primary inspection point of the target fault network, and taking the related abnormal points as the secondary inspection points of the target fault network;

[0021] S212, determining a first-level directed tracing path according to the tidal current direction between the first-level inspection points; determining a second-level directed tracing path according to the tidal current direction between the second-level inspection points; constructing an abnormal tracing network according to the first-level directed tracing path and the second-level directed tracing path, and determining the spatial clustering center of the abnormal network according to the intersection of the paths;

[0022] S213, determining the initial abnormal area according to the radiation distance between the cluster center and each abnormal point itself, and determining the fuzzy abnormal area according to the radiation distance between the cluster center and each related abnormal point; determining the target abnormal area by fusing the boundaries of the initial abnormal area and the fuzzy abnormal area;

[0023] S214: Determine the area in the inspection 3D map excluding the target abnormal area as a non-abnormal area.

[0024] Preferably, constructing a first target execution path corresponding to the target abnormal area by a minimum tree generation algorithm includes the following steps:

[0025] S301, acquiring the spatial coordinate information of all inspection points in the target abnormal area and the connection relationship between the inspection points to construct a first inspection point set;

[0026] S302, acquiring the spatial distances between the inspection points based on the spatial coordinate information to construct a first spatial distance matrix belonging to the first inspection point set;

[0027] S303, determining the minimum distance in the first spatial distance matrix, arbitrarily selecting an inspection node corresponding to the minimum distance as the root node of the tree, using the minimum edge information as the edge information of the tree, and using another inspection node corresponding to the minimum distance as the root node of the next inspection;

[0028] S304, execute S303 in rounds to obtain a minimum tree structure belonging to the target abnormal area, and obtain a first target execution path using a root node in the minimum tree structure and its corresponding edge.

[0029] Preferably, constructing the second target execution path corresponding to the non-abnormal area by a minimum tree generation algorithm includes the following steps:

[0030] S311, acquiring the spatial coordinate information of all inspection points in the non-abnormal area and the connection relationship between the inspection points to construct a second inspection point set;

[0031] S312, acquiring the spatial distances between the inspection points based on the spatial coordinate information to construct a second spatial distance matrix belonging to the second inspection point set;

[0032] S313, determining the minimum distance in the second spatial distance matrix, arbitrarily selecting an inspection node corresponding to the minimum distance as the root node of the tree, using the minimum edge information as the edge information of the tree, and using another inspection node corresponding to the minimum distance as the root node of the next inspection;

[0033] S314, execute S303 in rounds to obtain the minimum tree structure belonging to the non-abnormal area, and obtain the second target execution path with the root node in the minimum tree structure and its corresponding edge.

[0034] Preferably, determining the regional information interaction period based on the communication strength and communication flow of each inspection point in the first target execution path includes the following steps:

[0035] S401, determining first duration information of acquiring information at each abnormal node and first flight duration information corresponding to the distance between adjacent nodes in a first target execution path;

[0036] Synchronously, determining second duration information of the second target execution path acquiring information at each non-abnormal node and second flight duration information corresponding to the distance between adjacent nodes;

[0037] S402, determining a first flow value of node inspection information obtained by each abnormal inspection node; determining a corresponding first information interaction period according to the real-time perceived signal strength and flow value; the first information interaction period is within the time period range of the second duration information or the second flight duration information;

[0038] Synchronously, determining a second flow value of the node inspection information obtained by each non-abnormal inspection node; determining a corresponding second information interaction period according to the real-time perceived signal strength and the flow value, wherein the second information interaction period is within the time period range of the first duration information or the first flight duration information;

[0039] S403: Negotiate and determine the regional information interaction period of the corresponding drone according to the first information interaction period and the second information interaction period.

[0040] Preferably, S5, the UAV performs a power re-inspection operation based on a multi-frequency communication strategy combined with a regional information interaction period, including the following steps:

[0041] S51, extracting time scale information, coding information and information vector of each period in the regional information interaction period;

[0042] S52, determining the communication frequency band according to the spatial distance between the UAVs and the time scale information to generate a frequency band switching instruction adapted to the current time period;

[0043] S53, determining the UAV information receiving and sending instructions for each time period according to the information vector and the coding information;

[0044] S54. The UAV performs a power recheck operation within the regional information interaction period in response to the frequency band switching instruction and the UAV information receiving and sending instruction.

[0045] In the second aspect, the present application also proposes a technical solution: a multi-UAV collaborative power re-inspection system, including: an information collection module: constructing a 3D inspection map, determining an instruction set corresponding to an inspection point according to the inspection scenario, and executing the instruction set to obtain the corresponding node inspection information;

[0046] Information analysis module: Analyze node inspection information to determine its own abnormal points and determine related abnormal points based on node correlation coefficients; determine the abnormal tracing network based on the flow direction of its own abnormal points and related abnormal points; segment the inspection 3D map based on the abnormal tracing network to obtain the target abnormal area and non-abnormal area;

[0047] Path generation module: constructs the first target execution path corresponding to the target abnormal area and the second target execution path corresponding to the non-abnormal area respectively through the minimum tree generation algorithm;

[0048] An interaction period generation module is used to determine the regional information interaction period based on the communication strength and communication flow of each inspection point in the first target execution path and / or the second target execution path;

[0049] Execution module: The drone performs power re-inspection operations based on the multi-frequency communication strategy and the regional information interaction period.

[0050] In the third aspect, the present application also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, it implements the steps of the above-mentioned multi-UAV collaborative power re-inspection method based on communication technology.

[0051] In a fourth aspect, the present application also proposes a storage medium, which stores computer-executable instructions. When the computer-executable instructions are loaded and executed by a processor, the steps of the multi-UAV collaborative power re-inspection method based on communication technology as mentioned above are implemented.

[0052] The present invention has at least the following substantial beneficial effects:

[0053] (1) In a complex power system, the devices are interconnected and the failure of one device may trigger a chain reaction. The present invention can accurately determine the abnormal point itself by extracting abnormal characteristic factors (such as temperature, electrical signal, status signal, etc.) from the node inspection information and according to the set safety threshold range; further determine the node correlation coefficient according to the historical fault conditions of the inspection point, find the related abnormal points and build an abnormal tracing network; based on the abnormal tracing network, determine the spatial clustering center according to the flow direction of the abnormal point itself and the related abnormal points, and then divide the target abnormal area and non-abnormal area, so that the division of the inspection area is more scientific and reasonable. Through the above technical scheme, the fault propagation path can be comprehensively sorted out, and the correlation of the fault can be deeply understood, so as to locate the root cause of the fault more accurately and provide a clear direction for subsequent maintenance work.

[0054] (2) In order to solve the problem of poor information exchange during drone inspections, the present invention determines the regional information exchange period based on the communication strength and communication flow of each inspection point in the first target execution path and the second target execution path; by accurately calculating the information acquisition time, flight time and flow value of abnormal nodes and non-abnormal nodes, and combining the real-time perceived signal strength, the information exchange period is reasonably determined; ensuring that the drone can perform efficient information exchange during the period of stable signals and sufficient resources during the inspection process, avoiding information transmission delays or interruptions, and improving the real-time and reliability of information exchange.

[0055] (3) In order to solve the problem of inspection interruption caused by drone failure, the present invention adopts a collaborative working mode of two drones, which are responsible for the inspection tasks of the target abnormal area and the non-abnormal area respectively, and serve as backup for each other; when one of the drones fails, the other drone can quickly take over its task based on the shared information and continue to complete the inspection work, thus ensuring the continuity of the inspection task. In power inspection, time is of the essence. Timely discovery and handling of faults can avoid the occurrence of power accidents. This mutual backup mechanism effectively improves the ability of the inspection system to respond to emergencies and ensures the safe and stable operation of the power system.

[0056] (4) In order to overcome the problem of unstable signals in a single communication frequency band in a complex mountainous environment, the UAV performs power re-inspection operations based on a multi-frequency communication strategy combined with the regional information exchange period; during the inspection process, the communication frequency band is determined according to the spatial distance between the UAVs and the time scale information, and a frequency band switching instruction is generated; in a mountainous environment, factors such as terrain and vegetation have a greater impact on the communication signal, and the degree of signal attenuation in different areas is different; through the multi-frequency communication strategy, the UAV can flexibly select a more suitable communication frequency band according to actual conditions, thereby ensuring the stability of communication and ensuring unimpeded information transmission between the UAV and the ground control center and between the UAVs.

[0057] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0059] Figure 1 This is a flow chart of a multi-UAV collaborative power re-inspection method based on communication technology according to an embodiment of the present invention.

[0060] Figure 2 A flow chart is generated for the first target execution path of an embodiment of the present invention.

[0061] Figure 3 A flow chart is generated for the second target execution path of an embodiment of the present invention.

[0062] Figure 4 The present invention is a flowchart of determining a regional information interaction period according to an embodiment of the present invention.

[0063] Figure 5 The present invention is a flowchart of a power recheck operation according to an embodiment of the present invention.

[0064] Figure 6 This is a structural block diagram of a multi-UAV collaborative power re-inspection system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0066] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0067] Embodiment 1:

[0068] This embodiment proposes a multi-UAV collaborative power re-inspection method based on communication technology, aiming to solve the technical problem of efficient collaborative power re-inspection by multiple UAVs; Figure 1 As shown, the following steps are included:

[0069] S1. Build a 3D inspection map, determine the instruction set corresponding to the inspection point according to the inspection scenario, and execute the instruction set to obtain the corresponding node inspection information.

[0070] As an optional embodiment, S1 includes the following steps:

[0071] S11. Determine a 3D inspection map of each area to be inspected based on the topological structure and geographical features of the distribution network.

[0072] It is understandable that when constructing a 3D inspection map, it is first necessary to comprehensively collect the topological structure data of the distribution network, among which the topological structure data of the distribution network can be obtained from the operation and maintenance management system of the power company, and the geographical feature data is collected through satellite remote sensing, aerial surveying or ground measurement. The topological structure of the distribution network includes information such as the connection method of the power line, the location of the substation, the distribution of the power tower, etc., which can accurately reflect the logical relationship between the power facilities. At the same time, combined with geographical feature information, such as terrain height, slope, landform type (mountain, forest, river, etc.) and obstacle distribution (buildings, bridges, etc.). For example, in mountainous areas, the terrain is undulating, and the power lines may be erected along valleys or ridges. It is necessary to accurately depict the changes in terrain height to ensure the safety of drone flight and accurate coverage of the inspection area. Using professional geographic information system (GIS) software or surveying tools, these data are integrated and visualized to generate a three-dimensional inspection 3D map. In the map, power facilities are represented by specific icons or models, and the terrain is presented in the form of contour lines, three-dimensional surfaces, etc., providing an intuitive and accurate basis for subsequent inspection planning.

[0073] S12: Obtain the device type corresponding to the inspection scene, and determine the inspection instruction information according to the spatial position of the inspection point corresponding to the device type.

[0074] It is understandable that for different inspection scenarios, such as power towers, transformer boxes, lightning arresters, and cables, the corresponding equipment types are first obtained through image recognition technology, equipment identification, or pre-entered equipment databases. For each type of equipment, the spatial location of the corresponding inspection point is determined based on its structural characteristics, operating requirements, and potential fault risk points. For example, for power towers, inspection points may include tower bases, key connection parts of the tower body, insulator strings, lightning arresters, etc.; for transformer boxes, inspection points include box doors, heat sinks, wiring terminals, etc. According to the relative positions of these inspection points on the equipment, combined with the coordinate system in the inspection 3D map, their precise spatial coordinates are determined. Then, according to the flight performance, sensor type, and shooting requirements of the drone, the corresponding inspection instruction information is generated. These command information includes attitude control information such as the flight altitude of the drone (for example, for inspections on the top of power towers, the flight altitude needs to be a certain distance higher than the tower to obtain a comprehensive view), the bird's-eye view (for example, when photographing insulator strings, the bird's-eye view needs to be adjusted to clearly observe the surface condition), the roll angle (to keep the fuselage horizontal and stable when flying along power lines), and the yaw angle (to adjust the heading of the drone according to the inspection path), as well as the working parameters of the sensor (such as the focal length, resolution, shooting mode, etc. of the camera).

[0075] S13, sequentially obtain the inspection command information of the inspection points to construct an instruction set, and the drone obtains the corresponding node inspection information according to the inspection command information in the instruction set.

[0076] It can be understood that the inspection instruction information of each inspection point is obtained in turn to construct an instruction set. For example, the instruction set can be recorded as: L = [L1, L2, ···, L n ], i∈n, n is the total number of inspection points, where L i The command information representing the i-th inspection point is a command data containing information of multiple dimensions, including but not limited to (flight altitude, roll angle, yaw angle, etc.). The setting of the command data can be automatically adapted and set according to the historical inspection data. During the inspection process, the drone flies to each inspection point in sequence according to the order in the command set. At each inspection point, the drone adjusts its own posture and sensor parameters according to the command information, performs operations such as shooting and detection on the equipment, and obtains node inspection information. The node inspection information includes the status parameter information of the equipment, such as the inclination of the power tower (measured by the onboard inclination sensor), the temperature in the transformer box (using the temperature sensor), the current and voltage values ​​of the cable (through the current and voltage transformer), etc.; it also includes status alarm information, such as whether the equipment has abnormal heating (detected by infrared thermal imaging), whether there is a discharge phenomenon (using the electric field sensor), whether there is foreign matter attached (image recognition analysis), etc. The drone transmits these node inspection information back to the ground control center in real time or stores it locally, providing a data basis for subsequent abnormal analysis.

[0077] S2. Analyze the node inspection information to determine its own abnormal points and determine related abnormal points according to the node association coefficient; determine the abnormal tracing network according to the flow direction of its own abnormal points and related abnormal points; and segment the inspection 3D map according to the abnormal tracing network to obtain the target abnormal area and non-abnormal area.

[0078] As an optional embodiment, analyzing the node inspection information to determine the node's own abnormal points and determining related abnormal points according to the node correlation coefficient includes the following steps:

[0079] S201, extracting abnormal characteristic factors of node inspection information, and determining the inspection point corresponding to the abnormal characteristic factor whose status value exceeds the set safety threshold range as its own abnormal point.

[0080] It is understandable that a variety of abnormal characteristic factors are accurately extracted from the node inspection information collected by drones, and the abnormal characteristic factors characterize the key parameters of the operating status of power equipment. For example, the temperature signal is an important basis for judging whether the equipment is overheated. When the parts such as transformers and cable joints are operating normally, the temperature should be within a certain range. If it exceeds the set safety threshold (such as the transformer oil temperature exceeds 85°C, the cable joint temperature exceeds 70°C), there may be hidden dangers of failure; abnormal fluctuations in electrical signals (such as voltage and current) can reflect problems such as short circuits, open circuits or load imbalance in the circuit. If the voltage deviation exceeds ±5% of the rated value or the current suddenly changes, it is abnormal; the status signal includes the opening and closing status of the equipment, the operating mode, etc. If the switch status does not match the preset status or the equipment operation mode is abnormal, it is also considered abnormal. Through real-time monitoring of these abnormal characteristic factors, the inspection points corresponding to the abnormal characteristic factors whose status values ​​exceed the set safety threshold range are accurately determined as their own abnormal points.

[0081] S202. Determine the node correlation coefficient corresponding to each abnormal point according to the fault network corresponding to the historical fault situation of the inspection point, take the inspection point whose node correlation coefficient is greater than the set threshold as the relevant abnormal point, determine the target fault network according to the relevant abnormal point and the abnormal point itself, and determine the inspection point in the target fault network excluding the abnormal point itself as the relevant abnormal point.

[0082] It is understandable that the fault network data corresponding to the historical fault conditions of the inspection points are deeply mined. These data record the correlation between the inspection points when the equipment failed in the past. A correlation analysis algorithm (such as the Pearson correlation coefficient calculation method) is used to calculate the node correlation coefficient between each abnormal point and other inspection points (the calculation principle of the correlation algorithm is mastered by those skilled in the art and will not be described here). The calculated node correlation coefficient is compared with the set threshold (such as 0.7), and the inspection points greater than the threshold are regarded as related abnormal points. A target fault network is constructed based on the related abnormal points and the abnormal points. In this network, each node represents the inspection point, and the edge represents the correlation between the nodes. The inspection points in the target fault network except for the abnormality are determined as related abnormal points. For example, if a power tower is determined to be a self-abnormal point, and through correlation analysis, it is found that the inspection points of several power cables adjacent to it have a high node correlation coefficient with the tower, then these cable inspection points can be determined as related abnormal points.

[0083] As an optional embodiment, an abnormal tracing network is determined according to the flow direction of the abnormal point itself and the related abnormal points; and the inspection 3D map is segmented according to the abnormal tracing network to obtain the target abnormal area and the non-abnormal area; the steps include:

[0084] S211. Use the own abnormal point as the primary inspection point of the target fault network, and use the related abnormal points as the secondary inspection points of the target fault network.

[0085] It is understandable that the previously identified abnormal points are clearly defined as the first-level inspection points of the target fault network. These points are the direct source or key parts of the fault. The related abnormal points are used as the second-level inspection points of the target fault network. They are related to the first-level inspection points and may be affected by the fault of the first-level inspection point or play a conductive role in the fault propagation process; this hierarchical division helps to sort out the logical order of fault propagation and provide a clear structural framework for the subsequent construction of the abnormal tracing network.

[0086] S212, determining a first-level directed tracing path according to the flow direction between the first-level inspection points; determining a second-level directed tracing path according to the flow direction between the second-level inspection points; constructing an abnormal tracing network according to the first-level directed tracing path and the second-level directed tracing path, and determining the spatial clustering center of the abnormal network according to the intersection of the paths.

[0087] It can be understood that the first-level directed tracing path is determined according to the flow direction between the first-level inspection points. In the power system, the flow direction reflects the transmission direction of electric energy, and the flow direction information is obtained by monitoring equipment such as current transformers. For example, in the transmission line, electric energy flows from the substation to the user end. If the current direction of a certain line section is opposite to the normal flow direction or abnormal diversion occurs, it can be determined that the line section is part of the first-level directed tracing path. Similarly, the second-level directed tracing path is determined according to the flow direction between the second-level inspection points. Then, the first-level directed tracing path and the second-level directed tracing path are integrated to construct an abnormal tracing network. The nodes in the network are inspection points, and the directed edges represent the flow direction and the possible paths of fault propagation. The spatial clustering center of the abnormal network is determined by calculating the intersection of the paths in the network. This clustering center is usually located in the core area of ​​the fault impact range and is an important reference point for the subsequent division of abnormal areas.

[0088] S213, determining the initial abnormal area according to the radiation distance between the cluster center and each abnormal point itself, and determining the fuzzy abnormal area according to the radiation distance between the cluster center and each related abnormal point; and determining the target abnormal area by fusing the boundaries of the initial abnormal area and the fuzzy abnormal area.

[0089] It can be understood that the initial abnormal area is determined according to the radiation distance between the cluster center and each abnormal point. The radiation distance can be set according to the fault impact range characteristics of the power equipment and the actual operation experience. For example, for a transformer fault, its initial abnormal area may be a circular area with a radius of 50 meters centered on the transformer. The fuzzy abnormal area is determined by the radiation distance between each related abnormal point. Since the fault correlation of the related abnormal points is relatively weak and the certainty of its impact range is low, it is represented by a fuzzy area. For example, for related abnormal points caused by cable faults, its fuzzy abnormal area may be a strip area with a certain length and gradient width along the cable direction. Finally, by merging the boundaries of the initial abnormal area and the fuzzy abnormal area, the overlapping parts and transition conditions of the two areas are comprehensively considered to determine the target abnormal area, so that the division of the abnormal area is more scientific and reasonable, and can accurately cover the range where faults may exist.

[0090] S214: Determine the area in the inspection 3D map excluding the target abnormal area as a non-abnormal area.

[0091] It is understandable that the remaining area after removing the target abnormal area from the inspection 3D map is determined as the non-abnormal area. After determining the target abnormal area, it is marked and divided on the inspection 3D map, and the remaining area is the non-abnormal area. In the subsequent inspection planning, different inspection strategies and resource allocations can be adopted for the target abnormal area and the non-abnormal area. The inspection focus of the non-abnormal area is to monitor the normal operating status of the equipment to ensure that there are no potential faults, while the target abnormal area requires more in-depth and detailed re-inspection and troubleshooting, which improves the pertinence of troubleshooting.

[0092] S3. Construct a first target execution path corresponding to the target abnormal area and a second target execution path corresponding to the non-abnormal area respectively through a minimum tree generation algorithm.

[0093] As an optional embodiment, the first target execution path corresponding to the target abnormal area is constructed by a minimum tree generation algorithm, such as Figure 2 As shown, the following steps are included:

[0094] S301, acquiring the spatial coordinate information of all inspection points in the target abnormal area and the connection relationship between the inspection points to construct a first inspection point set.

[0095] It is understandable that for the target abnormal area, the spatial coordinate information of all inspection points is first accurately obtained from the inspection 3D map and the abnormal area related data determined by the previous analysis. These coordinate information are based on a specific geographic coordinate system (such as the WGS-84 coordinate system) and can accurately represent the position of the inspection point in three-dimensional space. At the same time, the connection relationship between each inspection point is sorted out, including the physical connection between power equipment (such as two power towers connected by cables) and logical associations (such as the association between a transformer box and multiple related equipment). Integrate these inspection points and their connection relationships to construct the first inspection point set PA = [P1, P2, ···, P m ],i∈m,P i xi represents the i-th inspection point, m is the number of inspection points in the first inspection point set, and each inspection point includes its spatial coordinates (xi, yi, zi) and identification information of other inspection points connected to it.

[0096] S302: Acquire the spatial distances between the inspection points based on the spatial coordinate information to construct a first spatial distance matrix belonging to the first inspection point set.

[0097] It can be understood that the spatial distance between two inspection points is calculated based on the spatial coordinate information of each inspection point in the first inspection point set. The spatial distance is calculated using the three-dimensional spatial distance formula Where dij represents the spatial distance between inspection point i and the inspection point. By traversing all inspection point pairs in the first inspection point set, an m×m first spatial distance matrix D1=(dij) is calculated, where i, j∈m. The matrix fully describes the relative distance relationship between each inspection point in the target abnormal area, providing a data basis for the subsequent determination of the minimum distance and construction of the minimum tree.

[0098] S303, determine the minimum distance in the first spatial distance matrix, arbitrarily select an inspection node corresponding to the minimum distance as the root node of the tree, use the minimum edge information as the edge information of the tree, and use another inspection node corresponding to the minimum distance as the root node of the next inspection.

[0099] It can be understood that in the first spatial distance matrix D1, the minimum distance value is found. Since there may be multiple identical minimum distance values, an inspection node corresponding to the minimum distance is arbitrarily selected as the root node r1 of the tree. At the same time, the edge information corresponding to the minimum distance (i.e., the connection relationship between the root node r1 and another inspection node) is used as the initial edge of the tree. Then, another inspection node corresponding to the minimum distance is used as the root node candidate for the next inspection, ready to enter the next round of iteration.

[0100] S304, execute S303 in rounds to obtain a minimum tree structure belonging to the target abnormal area, and obtain a first target execution path using a root node in the minimum tree structure and its corresponding edge.

[0101] It can be understood that the operation of S303 is performed in rounds. In each round of iteration, the remaining inspection point set is updated (excluding the inspection points that have been selected into the tree structure), the distance between the current remaining inspection points and the selected root node (or the node in the constructed tree structure) is recalculated, the new minimum distance and its corresponding inspection node are found, added to the tree structure, and the edge information of the tree is updated. This process continues until all inspection points in the target abnormal area are included in the minimum tree structure. The final minimum tree structure T1 = (r1, E1), where r1 is the root node, and E1) is a set containing all edge information. The root node in the minimum tree structure and its corresponding edges are connected in a certain order (such as depth-first search or breadth-first search order), and the first target execution path LA corresponding to the target abnormal area is obtained = [P i1 ,P i2 ,···,P im ], where P ij Indicates the jth inspection point on the path.

[0102] As an optional embodiment, a second target execution path corresponding to the non-abnormal area is constructed by a minimum tree generation algorithm, such as Figure 3 As shown, the following steps are included:

[0103] S311, acquiring the spatial coordinate information of all inspection points in the non-abnormal area and the connection relationship between the inspection points to construct a second inspection point set;

[0104] S312, acquiring the spatial distances between the inspection points based on the spatial coordinate information to construct a second spatial distance matrix belonging to the second inspection point set;

[0105] S313, determining the minimum distance in the second spatial distance matrix, arbitrarily selecting an inspection node corresponding to the minimum distance as the root node of the tree, using the minimum edge information as the edge information of the tree, and using another inspection node corresponding to the minimum distance as the root node of the next inspection;

[0106] S314, execute S303 in rounds to obtain the minimum tree structure belonging to the non-abnormal area, and obtain the second target execution path with the root node in the minimum tree structure and its corresponding edge.

[0107] It can be understood that the second target execution path is generated in the same manner as the first target execution path. Specifically, the spatial coordinate information and connection relationship of all inspection points in the non-abnormal area are obtained to construct the second inspection point set PB = [P m+1 ,P m+2 ,···,P n ], based on the spatial coordinates of these inspection points, calculate the spatial distances between each other, and construct a second spatial distance matrix D2 = (dij), where i, j∈n; determine the minimum distance in the second spatial distance matrix, select the corresponding inspection node as the root node r2, determine the initial edge e2, and use another node as the root node candidate for the next inspection. In S314, the above operations are performed in rounds to construct a minimum tree structure belonging to the non-abnormal area, and then the second target execution path corresponding to the non-abnormal area is obtained. The above operations are performed in rounds to construct a minimum tree structure T2 = (r2, E2) belonging to the non-abnormal area, and then the second target execution path corresponding to the non-abnormal area is obtained. LB = [P j1 ,P j2 ,···,P jm ].

[0108] S4. Determine the regional information interaction period based on the communication strength and communication flow of each inspection point in the first target execution path and / or the second target execution path.

[0109] As an optional embodiment, Figure 4 As shown, S4 includes the following steps:

[0110] S401, determining first duration information of acquiring information at each abnormal node and first flight duration information corresponding to the distance between adjacent nodes in a first target execution path;

[0111] Synchronously, second duration information of the second target execution path acquiring information at each non-abnormal node and second flight duration information corresponding to the distance between adjacent nodes are determined.

[0112] It can be understood that for the abnormal nodes in the first target execution path, the first time information ta1, ta2, ..., tam required to obtain information at each abnormal node is first determined, including the time for the drone to hover at the abnormal node and adjust the posture to obtain accurate data, and the time for the sensor to collect data and perform preliminary processing. For example, for infrared thermal imaging detection of power equipment, the drone may need to hover for 10-30 seconds to obtain stable temperature image data, plus data transmission and processing time, the total duration may vary from 30 to 60 seconds. At the same time, according to the distance da1, da2, ..., dam-1 between adjacent abnormal nodes and the average flight speed v0 of the drone (such as the average flight speed of the drone in a mountainous environment is 10-15 meters / second), the first flight time information fa1, fa2, ..., fam-1 corresponding to the distance between adjacent nodes is calculated, and the calculation formula is fai=dai / v0. Synchronously, for the non-abnormal nodes in the second target execution path, the second time information tb1, tb2, ..., tbk for obtaining information at each non-abnormal node is determined. Since the detection of non-abnormal nodes is relatively simple, the time to obtain information may be short, such as 5-15 seconds. Similarly, according to the distances db1, db2, ···, dbm-1 between adjacent non-abnormal nodes and the flight speed v1 of the drone, the second flight duration information fa1, fb2, ···, fbm-1 is calculated.

[0113] S402, determining a first flow value of node inspection information obtained by each abnormal inspection node; determining a corresponding first information interaction period according to the real-time perceived signal strength and flow value; the first information interaction period is within the time period range of the second duration information or the second flight duration information;

[0114] Synchronously, determine the second flow value of the node inspection information obtained by each non-abnormal inspection node; determine the corresponding second information interaction time period based on the real-time perceived signal strength and flow value, and the second information interaction time period is within the time period range of the first duration information or the first flight duration information.

[0115] It can be understood that for each abnormal inspection node, the first flow value qa1, qa2, ···, qam of the node inspection information obtained is determined. This depends on factors such as the data type, accuracy and transmission frequency collected by the sensor. For example, the image data flow collected by the high-definition camera is large, while the sensor data flow such as temperature and voltage is relatively small. According to the real-time perceived signal strength Sa1, Sa2, ···, Sam (the signal strength can be measured by the power of the signal received by the drone communication module, the signal-to-noise ratio and other indicators) and the flow value qai, the corresponding first information interaction period Ia1, Ia2, ···, Iam is determined. When the signal strength is weak, in order to ensure the accuracy of data transmission, it is necessary to reduce the transmission rate, thereby extending the information interaction period; and when the signal strength is good and the flow is small, the information interaction period can be appropriately shortened. At the same time, it is stipulated that the first information interaction period Iai is within the time period range of the second duration information tbj or the second flight duration information fnj, so as to avoid conflicts with the inspection operation of non-abnormal nodes and make rational use of time resources. Synchronously, for each non-abnormal inspection node, the second flow value qb1, qb2, ···, qbk of the node inspection information obtained is determined, and the corresponding second information interaction time period Ib1, Ib2, ···, Ibk is determined according to the real-time perceived signal strength Sb1, Sb2, ···, Sbk, and the second information interaction time period Ibi is within the time period range of the first duration information tai or the first flight duration information fai.

[0116] S403: Negotiate and determine the regional information interaction period of the corresponding drone according to the first information interaction period and the second information interaction period.

[0117] It is understandable that after the information exchange periods of abnormal nodes and non-abnormal nodes are determined respectively, negotiation is required to determine the regional information exchange period of the corresponding drone. This negotiation process takes into account multiple factors, such as the drone's task priority (drones inspecting abnormal areas may have higher priorities), overall inspection efficiency, and reasonable allocation of communication resources. For example, if the drone in the abnormal area has poor signal strength during a certain period of time and requires a long time for information exchange, while the drone in the non-abnormal area has good signal and the information exchange period is short, then the information exchange period of the drone in the non-abnormal area can be appropriately adjusted to give priority to ensuring the data transmission of the drone in the abnormal area. In this way, under the premise of meeting the inspection task requirements of the two areas, an optimal regional information exchange period is determined, so that drones can exchange information efficiently and stably without affecting their respective inspection progress.

[0118] S5. The UAV performs power re-inspection operations based on the multi-frequency communication strategy combined with the regional information interaction period.

[0119] As a preferred embodiment, S5, the UAV performs power re-inspection operation based on the multi-frequency communication strategy combined with the regional information interaction period, such as Figure 5 As shown, the following steps are included:

[0120] S51, extracting the time scale information, coding information and information vector of each time period in the regional information interaction period.

[0121] It can be understood that before the start of each regional information exchange period, the UAV extracts the time scale information of the period from the local storage or real-time received data T = [t start , t end ], where t start Indicates the start time of the period, t end Indicates the end time of the time period. At the same time, the coding information C is obtained, including the data coding format (such as binary code, Gray code, etc.) and coding parameters (such as coding rate, check bit, etc.). The coding information determines the efficiency and reliability of data transmission. In addition, the information vector Where vi represents the transmission priority, data volume and transmission direction of different types of information (such as image data, status parameter data, etc.). The transmission priority is determined by the importance level of the equipment, the scope of fault impact and the historical repair time of the fault. A specific method is: transmission priority = importance level * fault impact scope * historical repair time of the fault. It can be understood that for the status parameter data of key equipment, its transmission priority may be higher, while for some auxiliary monitoring data, the priority is lower.

[0122] S52: Determine the communication frequency band according to the spatial distance between the UAVs and the time scale information to generate a frequency band switching instruction suitable for the current time period.

[0123] It is understandable that the drone obtains the spatial distance d between it and other drones in real time through its own positioning system (such as GPS / Beidou navigation system) and communication module. At the same time, combined with the time scale information T of the current regional information interaction period, the communication frequency band is determined by comprehensively considering both factors; a specific form of the communication frequency band is:

[0124] f=f min +(f max -f min )×g(t)×h(d); Among them, f min and f maxis the lowest and highest frequency bands supported by the drone communication module; g(t) is the time scale function, h(t) is the space scale function; α is the time scale change trend; β is the space scale change trend, t0 is the time scale threshold parameter, d0 is the space distance threshold parameter. In complex terrain environments such as mountainous areas, the signal propagation characteristics of different frequency bands are quite different. For example, in short-distance communication (such as d < 500 meters) and short time scale (such as t end -t start <10 seconds), a high frequency band (such as 5.8 GHz band) may be selected because the high frequency band signal has a large bandwidth and can achieve high-speed data transmission to meet the needs of transmitting a large amount of data in a short time; while in long-distance communication (such as d>1000 meters) or long time scale (such as t end -t start >20 seconds), low frequency bands (such as 900MHz band) have long signal propagation distance and strong penetration ability, which is more suitable for stable communication. According to the preset frequency band selection strategy (which can be determined based on a large amount of experimental data and actual scenario tests), a frequency band switching instruction suitable for the current period is generated, and the instruction content includes parameters such as the target frequency band to switch to and the switching time.

[0125] S53, determining the UAV information receiving and sending instructions for each time period according to the information vector and the coding information.

[0126] It can be understood that according to the extracted information vector and coding information C, determine the information sending and receiving instructions I of the drone in each period. First, determine the order of data transmission according to the transmission priority in the information vector. For high-priority data (such as fault alarm information), transmission is arranged first, and more communication resources (such as larger transmission bandwidth, higher transmission power) are allocated. According to the coding information, determine the encoding and decoding methods of the data to ensure the accuracy and integrity of the data during transmission. For example, if the coding information indicates the use of error correction coding, the drone performs error correction coding on the data before sending the data, and the receiver performs the corresponding decoding operation. At the same time, according to the amount of data and the transmission direction in the information vector, determine the rate and direction of data transmission (whether to send data to other drones or receive data). If the amount of data is large and the transmission direction is sending, the drone will adjust the parameters such as the sending buffer size and the transmission rate to ensure that the data can be sent smoothly.

[0127] S54. The UAV performs a power recheck operation within the regional information interaction period in response to the frequency band switching instruction and the UAV information receiving and sending instruction.

[0128] It is understandable that after receiving the frequency band switching instruction F, the drone immediately performs the frequency band switching operation, adjusts the working frequency band of the communication module, and ensures that the communication with other drones is carried out on the optimal frequency band. Then, according to the information sending and receiving instruction I, the data sending and receiving operations are performed during the regional information interaction period. In the process of performing the power re-inspection operation, the drone further detects and evaluates the power equipment in the target abnormal area or non-abnormal area according to the preset inspection task process and the received information (such as abnormal conditions found by other drones, instructions from the ground control center, etc.). For example, in an abnormal area, if the information of a suspected fault point found by other drones is received, the drone can adjust the flight path, give priority to more detailed detection of the fault point (such as close-up shooting, multi-sensor joint detection, etc.), and promptly feedback the detection results to other drones and the ground control center to achieve collaborative re-inspection and improve the accuracy and efficiency of fault diagnosis.

[0129] Embodiment 2:

[0130] This application also proposes a technical solution of a multi-UAV collaborative power re-inspection system, such as Figure 6 As shown, including:

[0131] Information collection module 101: constructs a 3D inspection map, determines the instruction set corresponding to the inspection point according to the inspection scenario, and executes the instruction set to obtain the corresponding node inspection information;

[0132] Information analysis module 102: Analyze node inspection information to determine its own abnormal points and determine related abnormal points according to node association coefficients; determine an abnormal tracing network according to the flow directions of its own abnormal points and related abnormal points; segment the inspection 3D map according to the abnormal tracing network to obtain target abnormal areas and non-abnormal areas;

[0133] Path generation module 103: constructs a first target execution path corresponding to the target abnormal area and a second target execution path corresponding to the non-abnormal area respectively through a minimum tree generation algorithm;

[0134] Interaction period generation module 104: determining the regional information interaction period based on the communication strength and communication flow of each inspection point in the first target execution path and / or the second target execution path;

[0135] Execution module 105: The UAV performs power re-inspection operations based on the multi-frequency communication strategy and the regional information interaction period.

[0136] This embodiment has at least the following technical effects: by constructing a 3D inspection map and instruction set, node inspection information is fully obtained, providing a rich data basis for subsequent analysis. When analyzing node inspection information, multiple factors are comprehensively considered to determine abnormal points and traceability networks, and target abnormal areas and non-abnormal areas are accurately divided, which greatly improves the accuracy of fault location and changes the traditional blind troubleshooting mode. The target execution path constructed by the minimum tree generation algorithm is used to scientifically plan the inspection route of the drone, reduce flight costs, and improve the inspection efficiency of a single drone. When multiple drones work together, they can also avoid mutual interference, further improving the overall inspection speed. The regional information exchange period is determined based on communication intensity and traffic, and combined with a multi-frequency communication strategy, the drone can flexibly switch frequency bands for information exchange according to actual conditions. In the complex and changeable power inspection environment, the efficiency, stability and timeliness of information interaction are guaranteed, and close coordination between drones is achieved. When a drone encounters a fault or signal interference, another drone can quickly take over the task with shared information to ensure uninterrupted inspection work, significantly enhance the reliability and adaptability of the system, and effectively ensure the safe and stable operation of the power system.

[0137] Embodiment 3:

[0138] The present application also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the steps of the multi-UAV collaborative power re-inspection method based on communication technology are implemented.

[0139] Embodiment 4:

[0140] An optional embodiment also provided in the embodiments of the present invention is: a storage medium, which stores computer-executable instructions. When the computer-executable instructions are loaded and executed by a processor, the steps of a multi-UAV collaborative power re-inspection method based on communication technology are implemented.

[0141] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0142] In the embodiments provided in the present application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structure described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, structures or units, which can be electrical, mechanical or other forms.

[0143] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0144] In addition, each functional unit in the embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0145] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0146] The specific implementation described above is a preferred implementation of the multi-UAV collaborative power re-inspection method and system based on communication technology of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A multi-UAV collaborative power re-inspection method based on communication technology, characterized by: The steps include: S1. Build a 3D inspection map, determine the instruction set corresponding to the inspection point according to the inspection scenario, and execute the instruction set to obtain the corresponding node inspection information; S2. Analyze the node inspection information to determine its own abnormal points and determine related abnormal points according to the node correlation coefficient; determine the abnormal tracing network according to the flow direction of its own abnormal points and related abnormal points; segment the inspection 3D map according to the abnormal tracing network to obtain the target abnormal area and non-abnormal area; S3, constructing a first target execution path corresponding to the target abnormal area and a second target execution path corresponding to the non-abnormal area respectively by using a minimum tree generation algorithm; S4, determining the regional information interaction period based on the communication strength and communication flow of each inspection point in the first target execution path and / or the second target execution path; S5. The UAV performs power re-inspection operations based on the multi-frequency communication strategy combined with the regional information interaction period; The S4 comprises the following steps: S401, determining first duration information of acquiring information at each abnormal node and first flight duration information corresponding to the distance between adjacent nodes in a first target execution path; Synchronously, determining second duration information of the second target execution path acquiring information at each non-abnormal node and second flight duration information corresponding to the distance between adjacent nodes; S402, determining a first flow value of node inspection information obtained by each abnormal inspection node; Determine a corresponding first information interaction period according to the real-time perceived signal strength and traffic value; The first information interaction period is within the period of the second duration information or the second flight duration information; Synchronously, determining a second flow value of the node inspection information obtained by each non-abnormal inspection node; Determine a corresponding second information interaction period according to the real-time perceived signal strength and traffic value, where the second information interaction period is within the time period of the first duration information or the first flight duration information; S403: Negotiate and determine the regional information interaction period of the corresponding drone according to the first information interaction period and the second information interaction period.

2. The multi-UAV collaborative power re-inspection method based on communication technology according to claim 1 is characterized by: S1 includes the following steps: S11. Determine the inspection 3D map of each area to be inspected according to the topological structure and geographical characteristics of the distribution network; S12, obtaining the device type corresponding to the inspection scene, and determining the inspection instruction information according to the spatial position of the inspection point corresponding to the device type; S13, sequentially obtain the inspection command information of the inspection points to construct an instruction set, and the drone obtains the corresponding node inspection information according to the inspection command information in the instruction set.

3. The multi-UAV collaborative power re-inspection method based on communication technology according to claim 1 is characterized by: In S2, the node inspection information is analyzed to determine the abnormal point of the node itself and the related abnormal points are determined according to the node correlation coefficient; the steps include: S201, extracting abnormal characteristic factors of node inspection information, and determining the inspection point corresponding to the abnormal characteristic factor whose status value exceeds the set safety threshold range as its own abnormal point; S202. Determine the node correlation coefficient corresponding to each abnormal point according to the fault network corresponding to the historical fault situation of the inspection point, take the inspection point whose node correlation coefficient is greater than the set threshold as the relevant abnormal point, determine the target fault network according to the relevant abnormal point and the abnormal point itself, and determine the inspection point in the target fault network excluding the abnormal point itself as the relevant abnormal point.

4. The multi-UAV collaborative power re-inspection method based on communication technology according to claim 1 or 3 is characterized in that: In S2, the abnormal tracing network is determined according to the flow direction of the abnormal point itself and the related abnormal points; the inspection 3D map is segmented according to the abnormal tracing network to obtain the target abnormal area and the non-abnormal area; the steps include: S211, taking the own abnormal point as the primary inspection point of the target fault network, and taking the related abnormal points as the secondary inspection points of the target fault network; S212, determining a first-level directed tracing path according to the tidal current direction between the first-level inspection points; determining a second-level directed tracing path according to the tidal current direction between the second-level inspection points; constructing an abnormal tracing network according to the first-level directed tracing path and the second-level directed tracing path, and determining the spatial clustering center of the abnormal network according to the intersection of the paths; S213, determining the initial abnormal area according to the radiation distance between the cluster center and each abnormal point itself, and determining the fuzzy abnormal area according to the radiation distance between the cluster center and each related abnormal point; determining the target abnormal area by fusing the boundaries of the initial abnormal area and the fuzzy abnormal area; S214: Determine the area in the inspection 3D map excluding the target abnormal area as a non-abnormal area.

5. The multi-UAV collaborative power re-inspection method based on communication technology according to claim 1 is characterized by: Constructing a first target execution path corresponding to the target abnormal area through a minimum tree generation algorithm includes the following steps: S301, acquiring the spatial coordinate information of all inspection points in the target abnormal area and the connection relationship between the inspection points to construct a first inspection point set; S302, acquiring the spatial distances between the inspection points based on the spatial coordinate information to construct a first spatial distance matrix belonging to the first inspection point set; S303, determining the minimum distance in the first spatial distance matrix, arbitrarily selecting an inspection node corresponding to the minimum distance as the root node of the tree, using the minimum edge information as the edge information of the tree, and using another inspection node corresponding to the minimum distance as the root node of the next inspection; S304, execute S303 in rounds to obtain a minimum tree structure belonging to the target abnormal area, and obtain a first target execution path using a root node in the minimum tree structure and its corresponding edge.

6. The multi-UAV collaborative power re-inspection method based on communication technology according to claim 1 or 5, characterized in that: The second target execution path corresponding to the non-abnormal area is constructed by the minimum tree generation algorithm, including the following steps: S311, acquiring the spatial coordinate information of all inspection points in the non-abnormal area and the connection relationship between the inspection points to construct a second inspection point set; S312, acquiring the spatial distances between the inspection points based on the spatial coordinate information to construct a second spatial distance matrix belonging to the second inspection point set; S313, determining the minimum distance in the second spatial distance matrix, arbitrarily selecting an inspection node corresponding to the minimum distance as the root node of the tree, using the minimum edge information as the edge information of the tree, and using another inspection node corresponding to the minimum distance as the root node of the next inspection; S314, execute S303 in rounds to obtain the minimum tree structure belonging to the non-abnormal area, and obtain the second target execution path with the root node in the minimum tree structure and its corresponding edge.

7. The multi-UAV collaborative power re-inspection method based on communication technology according to claim 1 is characterized by: S5. The UAV performs power re-inspection operation based on the multi-frequency communication strategy combined with the regional information interaction period, including the following steps: S51, extracting time scale information, coding information and information vector of each period in the regional information interaction period; S52, determining the communication frequency band according to the spatial distance between the UAVs and the time scale information to generate a frequency band switching instruction adapted to the current time period; S53, determining the UAV information receiving and sending instructions for each time period according to the information vector and the coding information; S54. The UAV performs a power recheck operation within the regional information interaction period in response to the frequency band switching instruction and the UAV information receiving and sending instruction.

8. A multi-UAV collaborative power re-inspection system, applicable to the multi-UAV collaborative power re-inspection method based on communication technology as claimed in any one of claims 1 to 7, characterized in that: include: Information collection module: builds a 3D inspection map, determines the instruction set of the corresponding inspection point according to the inspection scenario, and executes the instruction set to obtain the corresponding node inspection information; Information analysis module: Analyze node inspection information to determine its own abnormal points and determine related abnormal points based on node correlation coefficients; determine the abnormal tracing network based on the flow direction of its own abnormal points and related abnormal points; According to the abnormal tracing network, the inspection 3D map is segmented to obtain the target abnormal area and non-abnormal area; Path generation module: constructs the first target execution path corresponding to the target abnormal area and the second target execution path corresponding to the non-abnormal area respectively through the minimum tree generation algorithm; An interaction period generation module is used to determine the regional information interaction period based on the communication strength and communication flow of each inspection point in the first target execution path and / or the second target execution path; Execution module: The drone performs power re-inspection operations based on the multi-frequency communication strategy and the regional information interaction period.

9. An electronic device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the steps of the multi-UAV collaborative power re-inspection method based on communication technology as described in any one of claims 1 to 7 are implemented.

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