A method, device and equipment for optimizing inspection routes

Abstract

The present invention provides a method, device and equipment for optimizing inspection routes, the method comprising: obtaining inspection data of unmanned aerial vehicles; determining the connection relationship between each device to be inspected in the inspection area based on the inspection data and the data of all devices to be inspected in the inspection area; constructing an integer linear programming model based on the inspection data and the connection relationship, the integer linear programming model is used to achieve adaptive allocation of inspection tasks for all unmanned aerial vehicles, so that the total flight time consumed by all unmanned aerial vehicles to complete their respective inspection tasks is minimized; based on solving the integer linear programming model, an inspection task allocation scheme that meets the total flight time minimization condition is obtained; at least based on the inspection task allocation scheme, the required target unmanned aerial vehicles, the inspection tasks to be performed by each target unmanned aerial vehicle and the optimal inspection route are determined. The inspection route optimization method provided by the present invention is used to accurately optimize the inspection route of unmanned aerial vehicles.

Description

Technical Field

[0001] The present invention belongs to the technical field of power facility inspection, and more specifically, relates to an inspection route optimization method, device and equipment. Background Art

[0002] In the power system, power facilities are the core components of energy transmission and distribution, and their safe and stable operation is crucial to ensuring social and economic activities. Most power facilities such as power grids, cables, and towers are exposed to complex and changeable natural environments for a long time, and have been subjected to multiple tests such as wind and rain erosion, temperature changes, and human damage for a long time. The risk of failure cannot be ignored. In order to ensure the safe, stable and efficient operation of power facilities, regular inspections are particularly important. However, the traditional manual inspection mode is limited by human and material resources. It is not only inefficient and costly, but also faces great challenges to the safety of inspectors when facing complex terrain or bad weather. With the development of drone technology, drone inspections have gradually become an important means of power operation and maintenance with its advantages of high efficiency, high safety, and wide field of view.

[0003] Although drone inspection technology has made significant progress, there are still some challenges and shortcomings. In particular, in terms of route planning, many current solutions fail to fully consider the endurance of drones, resulting in frequent battery replacement during large-scale power facility inspection missions, which seriously affects inspection efficiency. At the same time, unreasonable route planning may also cause drones to fly unnecessary flights, further increasing energy consumption and limiting the widespread application of drone inspection technology. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a patrol route optimization method, device and equipment for optimizing the patrol route of a drone.

[0005] The present invention provides a method for optimizing inspection routes, comprising:

[0006] Obtaining inspection data of the drone, the inspection data including route data, nest data, drone data, and data of equipment to be inspected that has completed the inspection;

[0007] Determine the connection relationship between the various devices to be inspected in the inspection area based on the inspection data and the data of all the devices to be inspected in the inspection area, wherein the connection relationship is related to the flight time when the drone flies between different devices to be inspected, the location information of the devices to be inspected, and the flight range of the drone;

[0008] An integer linear programming model is constructed based on the inspection data and the connection relationship, and the integer linear programming model is used to achieve adaptive allocation of inspection tasks for all drones so as to minimize the total flight time consumed by all drones when completing their respective inspection tasks;

[0009] Based on solving the integer linear programming model, an inspection task allocation scheme that satisfies the total flight time minimization condition is obtained;

[0010] The required target UAVs, the inspection tasks to be performed by each target UAV, and the optimal inspection route are determined based at least on the inspection task allocation scheme.

[0011] In some embodiments, obtaining the inspection data of the drone includes:

[0012] The drone is controlled to perform a test inspection task, and the route data, the machine nest data, the drone data and the data of the equipment to be inspected that has completed the inspection are obtained.

[0013] In some embodiments, the route data includes a starting location, an end location, and a flight time of the corresponding route;

[0014] The nest data includes the location information of the nest and the data information of the equipped drone;

[0015] The drone data includes the drone’s take-off time, landing time, flight time, and charging time;

[0016] The data of the equipment to be inspected includes the location information of the equipment to be inspected and the required inspection time.

[0017] In some embodiments, determining the connection relationship between the devices to be inspected in the inspection area based on the inspection data and the data of all devices to be inspected in the inspection area includes:

[0018] Determine the flight time between any two devices to be detected among all devices to be detected in the inspection area based on the inspection data;

[0019] A route time matrix is ​​constructed based on the plurality of flight times.

[0020] In some embodiments, the method further comprises:

[0021] Based on the route time matrix, the location information of the drone and the location information of the equipment to be inspected, the Dijkstra algorithm is used to calculate and determine the first time matrix and the second time matrix, wherein the first time matrix is ​​the shortest flight time matrix of each drone to each equipment to be inspected, and the second time matrix is ​​the shortest flight time matrix between all the equipment to be inspected, and each element in the first time matrix represents the shortest flight time from a specific drone to the target equipment to be inspected, and each element in the second time matrix represents the shortest flight time from a specific equipment to be inspected to the target equipment to be inspected;

[0022] The shortest flight route of the UAV to different devices to be inspected and the shortest flight route between any two devices to be inspected are determined based on the inspection data, the first time matrix and the second time matrix.

[0023] In some embodiments, the method further comprises:

[0024] Based on the take-off time, landing time, inspection time of the equipment to be inspected, the shortest flight time of the drone to each equipment to be inspected and the flight time of the drone in the inspection data, it is determined that each drone can reach and inspect the first equipment to be inspected in the inspection area, and the second equipment to be inspected that the drone cannot reach and inspect.

[0025] In some embodiments, the integer linear programming model comprises:

[0026]

[0027] Said is the number of devices to be inspected, is the number of drones, For drones To the equipment to be inspected The shortest flight time, is a binary decision variable representing the drone Whether to inspect equipment points ,in Indicates inspection. Indicates no inspection.

[0028] In some embodiments, the inspection task allocation scheme includes the number of drones required to perform the inspection task, the specific selected drones, and the equipment to be inspected that each drone is responsible for inspecting under the condition of minimizing the total flight time;

[0029] The step of determining the required target drones, the inspection tasks to be performed by each target drone, and the optimal inspection route based at least on the inspection task allocation scheme includes:

[0030] Determine the required target drones and the equipment that each target drone needs to inspect based on the inspection task allocation plan;

[0031] Determine the shortest flight route and the flight route with the shortest flight time for the target UAV to each device to be inspected based on the connection relationship;

[0032] The optimal inspection route is determined based on the required target drones, the equipment that each target drone needs to inspect, the shortest flight route of the target drone to each equipment to be inspected, and the flight route corresponding to the shortest flight time.

[0033] Another embodiment of the present invention also provides an inspection route optimization device, including:

[0034] An acquisition module is used to obtain the inspection data of the UAV, wherein the inspection data includes route data, nest data, UAV data, and data of equipment to be inspected that has completed the inspection;

[0035] A first determination module is used to determine the connection relationship between the various devices to be inspected in the inspection area according to the inspection data and the data of all the devices to be inspected in the inspection area, wherein the connection relationship is related to the flight time when the drone flies between different devices to be inspected, the location information of the devices to be inspected, and the flight range of the drone;

[0036] A construction module, used to construct an integer linear programming model based on the inspection data and the connection relationship, wherein the integer linear programming model is used to achieve adaptive allocation of inspection tasks for all drones so as to minimize the total flight time consumed by all drones when completing their respective inspection tasks;

[0037] A calculation module, used for obtaining an inspection task allocation scheme that satisfies a total flight time minimization condition by solving the integer linear programming model;

[0038] The second determination module is used to determine the required target drones, the inspection tasks to be performed by each target drone, and the optimal inspection route based at least on the inspection task allocation plan.

[0039] Another embodiment of the present invention further provides an electronic device, including:

[0040] one or more processors;

[0041] a memory configured to store one or more programs;

[0042] When the one or more programs are executed by the one or more processors, the one or more processors implement the inspection route optimization method as described in any one of the embodiments above.

[0043] The beneficial effect of the present invention lies in that by acquiring inspection data, including route data, machine nest data, drone data and data of equipment to be inspected; secondly, extracting features from the inspection data, constructing an air traffic network for drones, and calculating the shortest flight time matrix from each drone to each equipment point to be inspected and the shortest flight time matrix between equipment points to be inspected; then, evaluating and determining whether the drone can reach all equipment to be inspected, and recording the information of equipment that cannot be reached; finally, based on the shortest flight time matrix and the drone's endurance, the total inspection flight time minimization problem is converted into an integer linear programming algorithm model. By solving this model, the optimal inspection route for each drone can be determined. Based on the method provided by this application, it is possible to optimize drone inspection routes, reduce inspection costs, and improve the level of intelligence of power facility inspections while ensuring inspection efficiency.

[0044] Other features and advantages of the present application will be described in the subsequent description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims and drawings.

[0045] The technical solution of the present application is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a flowchart of the inspection route optimization method in an embodiment of the present invention.

[0048] Figure 2 It is a flowchart of a method for optimizing an inspection route in another embodiment of the present invention.

[0049] Figure 3 The figure is a flow chart of a method for optimizing inspection routes in another embodiment of the present invention.

[0050] Figure 4 It is a structural block diagram of the inspection route optimization device in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, but are not intended to limit the present invention.

[0052] It should be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the following description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope of the invention will occur to those skilled in the art.

[0053] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the general description of the invention given above and the detailed description of the embodiments given below, serve to explain the principles of the invention.

[0054] These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the accompanying drawings.

[0055] The above and other aspects, features and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0056] Specific embodiments of the present invention are described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present invention, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that obscure the present invention. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present invention in a variety of ways with substantially any suitable detailed structure.

[0057] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," which may all refer to one or more of the same or different embodiments according to the present invention.

[0058] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0059] like Figure 1 As shown, the present invention includes a method for optimizing inspection routes, including:

[0060] S1: Obtaining inspection data of the drone, the inspection data including route data, nest data, drone data and data of equipment to be inspected that has completed the inspection;

[0061] S2: Determine the connection relationship between the devices to be inspected in the inspection area based on the inspection data and the data of all the devices to be inspected in the inspection area, wherein the connection relationship is related to the flight time when the drone flies between different devices to be inspected, the location information of the devices to be inspected, and the flight range of the drone;

[0062] S3: constructing an integer linear programming model based on the inspection data and the connection relationship, wherein the integer linear programming model is used to achieve adaptive allocation of inspection tasks for all drones, so as to minimize the total flight time consumed by all drones when completing their respective inspection tasks;

[0063] S4: Based on solving the integer linear programming model, an inspection task allocation scheme that satisfies the total flight time minimization condition is obtained;

[0064] S5: Determine the required target drones, the inspection tasks to be performed by each target drone, and the optimal inspection route based at least on the inspection task allocation plan.

[0065] The method in this embodiment is a method for optimizing the inspection route of unmanned aerial vehicles (UAVs) in response to the unreasonable inspection route planning of UAVs in existing solutions. It can be specifically but not limited to being applied in the inspection scenario of power facilities. Based on the above content, the method described in this embodiment can be summarized as obtaining inspection data, including route data, machine nest data, UAV data and data of equipment to be inspected; secondly, extracting features from the inspection data, building an air traffic network for UAVs, and calculating the shortest flight time matrix from each UAV to each equipment point to be inspected and the shortest flight time matrix between equipment points to be inspected; then, evaluating and determining whether the UAV can reach all equipment to be inspected, and recording the information of equipment that cannot be reached; finally, based on the shortest flight time matrix and the UAV endurance time, the problem of minimizing the total inspection flight time is converted into an integer linear programming algorithm model. By solving this model, the optimal inspection route for each UAV can be determined. That is, the method of this embodiment aims to optimize the flight routes of each UAV through an integer linear programming algorithm, while taking into account the scenario in which multiple UAVs jointly participate in the inspection mission, so as to determine the flight plan with the shortest total flight time, thereby improving the efficiency and flexibility of UAV inspections.

[0066] Therefore, the method provided based on the embodiment can optimize the drone inspection route, reduce the inspection cost, and improve the intelligent inspection level of power facilities or other field facilities while ensuring the inspection efficiency.

[0067] When a drone performs an inspection mission, the route usually involves the following links:

[0068] 1. The drone takes off from the nest, rises to a safe height through the take-off and landing route, and then arrives at the take-off and landing point;

[0069] 2. Start from the take-off and landing point and go to the entrance of the equipment route along the cross-equipment route;

[0070] 3. Enter the equipment flight route and fly according to the predetermined route (to carry out inspection work on a certain equipment to be inspected);

[0071] 4. After completing the flight of the equipment route, return to the exit of the equipment route;

[0072] 5. Start from the exit of the current equipment route, go to the entrance of the next equipment route through the cross-equipment route, and repeat steps 3 and 4 until the route flight of all assigned equipment to be inspected is completed;

[0073] 6. Start from the exit of the last equipment route and follow the cross-equipment route to the take-off and landing point of the planned landing nest;

[0074] 7. Safely land at the designated aircraft nest via the take-off and landing route.

[0075] Based on the above-mentioned flight process of the UAV, the inspection data of the UAV is obtained, including:

[0076] S6: Control the UAV to perform a test inspection task, and obtain the route data, the machine nest data, the UAV data, and the data of the equipment to be inspected that has completed the inspection.

[0077] For example, after the staff uses a handheld remote control to operate the drone to complete the inspection task for the test, the flight route and other data are recorded to obtain the inspection data, which includes the route data, the nest data, the drone data and the data of the equipment to be inspected after the inspection. Among them, the route data includes the starting position, the end position and the flight time of the corresponding route; the nest data includes the location information of the nest and the data information of the equipped drone; the drone data includes the take-off time, landing time, endurance time and charging time of the drone; the equipment data to be inspected includes the location information of the equipment to be inspected and the required inspection time.

[0078] In order to improve the accuracy of subsequent data processing, the system in this embodiment performs a preprocessing operation on the obtained inspection data to remove erroneous data and incomplete data in the data set.

[0079] In another embodiment, based on the foregoing content, it can be known that the connection relationship is related to the flight time when the drone flies between different devices to be inspected, the location information of the devices to be inspected, and the flight range of the drone. Therefore, the connection relationship between the devices to be inspected in the inspection area is determined based on the inspection data and the data of all devices to be inspected in the inspection area, including:

[0080] S7: Determine the flight time between any two devices to be detected among all devices to be detected in the inspection area based on the inspection data;

[0081] S8: Constructing a route time matrix based on the multiple flight times.

[0082] S9: Based on the route time matrix, the location information of the drone and the location information of the equipment to be inspected, the Dijkstra algorithm is used to calculate and determine the first time matrix and the second time matrix, wherein the first time matrix is ​​the shortest flight time matrix of each drone to each equipment to be inspected, and the second time matrix is ​​the shortest flight time matrix between all the equipment to be inspected, and each element in the first time matrix represents the shortest flight time from a specific drone to the target equipment to be inspected, and each element in the second time matrix represents the shortest flight time from a specific equipment to be inspected to the target equipment to be inspected;

[0083] S10: Determine the shortest flight route for the drone to fly to different devices to be inspected and the shortest flight route between any two devices to be inspected based on the inspection data, the first time matrix and the second time matrix.

[0084] S11: Based on the take-off time, landing time, inspection time of the equipment to be inspected, the shortest flight time of the drone to each equipment to be inspected and the flight time of the drone in the inspection data, determine the first equipment to be inspected that each drone can reach and inspect in the inspection area, and the second equipment to be inspected that the drone cannot reach and inspect.

[0085] For example, the route data is converted into the connection relationship between all nodes (devices to be inspected) in the inspection area and saved as a route time matrix, where each value of the matrix represents the flight time between two nodes. Based on the route time matrix, the location information of the drone and the location information of the equipment to be inspected, the Dijkstra algorithm is used to calculate two key matrices, the first time matrix and the second time matrix. The first time matrix is ​​the shortest flight time matrix from each drone to each device to be inspected, and the second time matrix is ​​the shortest flight time matrix between any two devices to be inspected. Each element in the two matrices represents the shortest flight time from a specific starting position to the corresponding target position. At the same time, the shortest flight route of each drone to each device to be inspected (that is, each location point passed along the way) and the shortest flight route between each device to be inspected are recorded. This information can be stored in a structure matrix, that is, a matrix composed of the equipment to be inspected and each location point passed along the flight route. In addition, the system will also determine whether all the equipment to be inspected in the detection area can be inspected by the drone based on the drone's take-off time, landing time, inspection time of the equipment to be inspected, the shortest flight time from the drone to the corresponding equipment to be inspected, and the drone's endurance time, and correspondingly determine the information of the equipment to be inspected that cannot be reached by each drone, as well as the equipment to be inspected that can be reached by each drone.

[0086] Furthermore, in this embodiment, the system is based on the shortest flight time matrix from each drone to each device to be inspected and the drone's endurance information, and constructs the problem of minimizing the total flight time of all devices to be inspected as a problem of solving an integer linear programming algorithm model, that is, by constructing an algorithm model and solving it to achieve the above goal. The integer linear programming model includes:

[0087]

[0088] Objective Function It is to minimize the total flight time corresponding to all tasks assigned to the UAV. is the number of devices to be inspected, is the number of drones, For drones To the equipment to be inspected The shortest flight time, For drones The battery life is a binary decision variable representing the drone Whether to inspect equipment points ,in Indicates inspection. Indicates no inspection.

[0089] The solution of the model must satisfy the following constraints:

[0090]

[0091] The first constraint ensures that each device to be inspected is inspected by at least one drone. The second constraint ensures that the flight time of each drone does not exceed its endurance. The last two constraints ensure that is a binary variable, i.e. Can only take values ​​of 0 or 1.

[0092] By solving the integer linear programming model, it is possible to determine the number of drones required, the specific drones selected, and the allocation plan for the equipment to be inspected that each drone is responsible for, under the condition of minimizing the total flight time. That is, the inspection task allocation plan includes the number of drones required to perform the inspection task, the specific drones selected, and the equipment to be inspected that each drone is responsible for, under the condition of minimizing the total flight time;

[0093] The step of determining the required target drones, the inspection tasks to be performed by each target drone, and the optimal inspection route based at least on the inspection task allocation scheme includes:

[0094] S12: Determine the required target drones and the equipment that each target drone needs to inspect based on the inspection task allocation plan;

[0095] S13: Determine the shortest flight route and the flight route with the shortest flight time for the target UAV to each device to be inspected based on the connection relationship;

[0096] S14: Determine the optimal inspection route based on the required target drones, the equipment that each target drone needs to inspect, the shortest flight route of the target drone to each equipment to be inspected, and the flight route corresponding to the shortest flight time.

[0097] In order to further illustrate that the inspection route optimization method can optimize the inspection route of drones and improve inspection efficiency, it is now described in detail as follows in combination with specific application examples:

[0098] For example, based on the pre-processed drone route data information, an air traffic network of drones is constructed, and each network node accurately marks the location of each device to be inspected in the inspection area. Then, based on the provided data of the equipment to be inspected, the location data of the nest, and the data of the drones equipped with the nest, a linear programming mathematical model is constructed to minimize the total flight time. By solving and calculating the model, the optimal inspection route for each drone is determined. When displaying the optimal inspection route, the location of each drone selected to perform the task can be highlighted in combination with a geographic diagram. For example, each drone corresponds to an identification color, and the lines connecting these location points can intuitively display the optimal inspection route followed by each drone.

[0099] like Figure 4 As shown, another embodiment of the present invention also provides an inspection route optimization device 100, including:

[0100] An acquisition module is used to obtain the inspection data of the UAV, wherein the inspection data includes route data, nest data, UAV data, and data of equipment to be inspected that has completed the inspection;

[0101] A first determination module is used to determine the connection relationship between the various devices to be inspected in the inspection area according to the inspection data and the data of all the devices to be inspected in the inspection area, wherein the connection relationship is related to the flight time when the drone flies between different devices to be inspected, the location information of the devices to be inspected, and the flight range of the drone;

[0102] A construction module, used to construct an integer linear programming model based on the inspection data and the connection relationship, wherein the integer linear programming model is used to achieve adaptive allocation of inspection tasks for all drones so as to minimize the total flight time consumed by all drones when completing their respective inspection tasks;

[0103] A calculation module, used for obtaining an inspection task allocation scheme that satisfies a total flight time minimization condition by solving the integer linear programming model;

[0104] The second determination module is used to determine the required target drones, the inspection tasks to be performed by each target drone, and the optimal inspection route based at least on the inspection task allocation plan.

[0105] In some embodiments, obtaining the inspection data of the drone includes:

[0106] The UAV is controlled to execute a test inspection task, and the route data, the machine nest data, the UAV data and the data of the equipment to be inspected that has completed the inspection are obtained.

[0107] In some embodiments, the route data includes a starting location, an end location, and a flight time of the corresponding route;

[0108] The nest data includes the location information of the nest and the data information of the equipped drone;

[0109] The drone data includes the drone’s take-off time, landing time, flight time, and charging time;

[0110] The data of the equipment to be inspected includes the location information of the equipment to be inspected and the required inspection time.

[0111] In some embodiments, determining the connection relationship between the devices to be inspected in the inspection area based on the inspection data and the data of all devices to be inspected in the inspection area includes:

[0112] Determine the flight time between any two devices to be detected among all devices to be detected in the inspection area based on the inspection data;

[0113] A route time matrix is ​​constructed based on the plurality of flight times.

[0114] In some embodiments, the first determining module is further configured to:

[0115] Based on the route time matrix, the location information of the drone and the location information of the equipment to be inspected, the Dijkstra algorithm is used to calculate and determine the first time matrix and the second time matrix, wherein the first time matrix is ​​the shortest flight time matrix of each drone to each equipment to be inspected, and the second time matrix is ​​the shortest flight time matrix between all the equipment to be inspected, and each element in the first time matrix represents the shortest flight time from a specific drone to the target equipment to be inspected, and each element in the second time matrix represents the shortest flight time from a specific equipment to be inspected to the target equipment to be inspected;

[0116] The shortest flight route of the UAV to different devices to be inspected and the shortest flight route between any two devices to be inspected are determined based on the inspection data, the first time matrix and the second time matrix.

[0117] In some embodiments, the first determining module is further configured to:

[0118] Based on the take-off time, landing time, inspection time of the equipment to be inspected, the shortest flight time of the drone to each equipment to be inspected and the flight time of the drone in the inspection data, it is determined that each drone can reach and inspect the first equipment to be inspected in the inspection area, and the second equipment to be inspected that the drone cannot reach and inspect.

[0119] In some embodiments, the integer linear programming model comprises:

[0120]

[0121] Said is the number of devices to be inspected, is the number of drones, For drones To the equipment to be inspected The shortest flight time, is a binary decision variable representing the drone Whether to inspect equipment points ,in Indicates inspection. Indicates no inspection.

[0122] In some embodiments, the inspection task allocation scheme includes the number of drones required to perform the inspection task, the specific selected drones, and the equipment to be inspected that each drone is responsible for inspecting under the condition of minimizing the total flight time;

[0123] The step of determining the required target drones, the inspection tasks to be performed by each target drone, and the optimal inspection route based at least on the inspection task allocation scheme includes:

[0124] Determine the required target drones and the equipment that each target drone needs to inspect based on the inspection task allocation plan;

[0125] Determine the shortest flight route and the flight route with the shortest flight time for the target UAV to each device to be inspected based on the connection relationship;

[0126] The optimal inspection route is determined based on the required target drones, the equipment that each target drone needs to inspect, the shortest flight route of the target drone to each equipment to be inspected, and the flight route corresponding to the shortest flight time.

[0127] Another embodiment of the present invention further provides an electronic device, including:

[0128] one or more processors;

[0129] a memory configured to store one or more programs;

[0130] When the one or more programs are executed by the one or more processors, the one or more processors implement the inspection route optimization method as described in any one of the embodiments above.

[0131] Furthermore, an embodiment of the present invention also provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the inspection route optimization method described above is implemented. It should be understood that each solution in this embodiment has the corresponding technical effect in the above method embodiment, which will not be repeated here.

[0132] Furthermore, an embodiment of the present invention also provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions, which, when executed, enable at least one processor to perform an inspection route optimization method such as that in the embodiment described above.

[0133] It should be noted that the computer storage medium of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate or transmit a program configured to be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, antenna, optical cable, RF, etc., or any suitable combination of the above.

[0134] In addition, it should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0135] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0136] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

Claims

1. A method for optimizing inspection routes, characterized in that: include: Obtaining inspection data of the drone, the inspection data including route data, nest data, drone data, and data of equipment to be inspected that has completed the inspection; Determine the connection relationship between the various devices to be inspected in the inspection area based on the inspection data and the data of all the devices to be inspected in the inspection area, wherein the connection relationship is related to the flight time when the drone flies between different devices to be inspected, the location information of the devices to be inspected, and the flight range of the drone; An integer linear programming model is constructed based on the inspection data and the connection relationship, and the integer linear programming model is used to achieve adaptive allocation of inspection tasks for all drones so as to minimize the total flight time consumed by all drones when completing their respective inspection tasks; Based on solving the integer linear programming model, an inspection task allocation scheme that satisfies the total flight time minimization condition is obtained; Determine the required target drones, the inspection tasks to be performed by each target drone, and the optimal inspection route based at least on the inspection task allocation scheme; Wherein, the determining of the connection relationship between the devices to be inspected in the inspection area based on the inspection data and the data of all devices to be inspected in the inspection area includes: Determine the flight time between any two devices to be detected among all devices to be detected in the inspection area based on the inspection data; constructing a route time matrix based on a plurality of said flight times; The method further comprises: Based on the route time matrix, the location information of the drone and the location information of the equipment to be inspected, the Dijkstra algorithm is used to calculate and determine the first time matrix and the second time matrix, wherein the first time matrix is ​​the shortest flight time matrix of each drone to each equipment to be inspected, and the second time matrix is ​​the shortest flight time matrix between all the equipment to be inspected, and each element in the first time matrix represents the shortest flight time from a specific drone to the target equipment to be inspected, and each element in the second time matrix represents the shortest flight time from a specific equipment to be inspected to the target equipment to be inspected; The shortest flight route of the UAV to different devices to be inspected and the shortest flight route between any two devices to be inspected are determined based on the inspection data, the first time matrix and the second time matrix.

2. The inspection route optimization method according to claim 1, characterized in that: The obtaining of the inspection data of the drone includes: The drone is controlled to perform a test inspection task, and the route data, the machine nest data, the drone data and the data of the equipment to be inspected that has completed the inspection are obtained.

3. The inspection route optimization method according to claim 2, characterized in that: The route data includes the starting point location, the end point location and the flight time of the corresponding route; The nest data includes the location information of the nest and the data information of the equipped drone; The drone data includes the drone’s take-off time, landing time, flight time, and charging time; The data of the equipment to be inspected includes the location information of the equipment to be inspected and the required inspection time.

4. The inspection route optimization method according to claim 1, characterized in that: The method further comprises: Based on the take-off time, landing time, inspection time of the equipment to be inspected, the shortest flight time of the drone to each equipment to be inspected and the flight time of the drone in the inspection data, it is determined that each drone can reach and inspect the first equipment to be inspected in the inspection area, and the second equipment to be inspected that the drone cannot reach and inspect.

5. The inspection route optimization method according to claim 1, characterized in that: The integer linear programming model comprises: Said is the number of devices to be inspected, is the number of drones, For drones To the equipment to be inspected The shortest flight time, is a binary decision variable representing the drone Whether to inspect equipment points ,in Indicates inspection. Indicates no inspection.

6. The inspection route optimization method according to claim 1, characterized in that: The inspection task allocation scheme includes the number of drones required to perform the inspection task, the specific drones selected, and the equipment to be inspected that each drone is responsible for inspecting, under the condition of minimizing the total flight time; The step of determining the required target drones, the inspection tasks to be performed by each target drone, and the optimal inspection route based at least on the inspection task allocation scheme includes: Determine the required target drones and the equipment that each target drone needs to inspect based on the inspection task allocation plan; Determine the shortest flight route and the flight route with the shortest flight time for the target UAV to each device to be inspected based on the connection relationship; The optimal inspection route is determined based on the required target drones, the equipment that each target drone needs to inspect, the shortest flight route of the target drone to each equipment to be inspected, and the flight route corresponding to the shortest flight time.

7. A patrol route optimization device, characterized in that: include: An acquisition module is used to obtain the inspection data of the UAV, wherein the inspection data includes route data, nest data, UAV data, and data of equipment to be inspected that has completed the inspection; A first determination module is used to determine the connection relationship between the various devices to be inspected in the inspection area according to the inspection data and the data of all the devices to be inspected in the inspection area, wherein the connection relationship is related to the flight time when the drone flies between different devices to be inspected, the location information of the devices to be inspected, and the flight range of the drone; A construction module, used to construct an integer linear programming model based on the inspection data and the connection relationship, wherein the integer linear programming model is used to achieve adaptive allocation of inspection tasks for all drones so as to minimize the total flight time consumed by all drones when completing their respective inspection tasks; A calculation module, used for obtaining an inspection task allocation scheme that satisfies a total flight time minimization condition by solving the integer linear programming model; A second determination module is used to determine the required target drones, the inspection tasks to be performed by each target drone, and the optimal inspection route based at least on the inspection task allocation plan; Wherein, the determining of the connection relationship between the devices to be inspected in the inspection area based on the inspection data and the data of all devices to be inspected in the inspection area includes: Determine the flight time between any two devices to be detected among all devices to be detected in the inspection area based on the inspection data; constructing a route time matrix based on a plurality of said flight times; The first determining module is further used for: Based on the route time matrix, the location information of the drone and the location information of the equipment to be inspected, the Dijkstra algorithm is used to calculate and determine the first time matrix and the second time matrix, wherein the first time matrix is ​​the shortest flight time matrix of each drone to each equipment to be inspected, and the second time matrix is ​​the shortest flight time matrix between all the equipment to be inspected, and each element in the first time matrix represents the shortest flight time from a specific drone to the target equipment to be inspected, and each element in the second time matrix represents the shortest flight time from a specific equipment to be inspected to the target equipment to be inspected; The shortest flight route of the UAV to different devices to be inspected and the shortest flight route between any two devices to be inspected are determined based on the inspection data, the first time matrix and the second time matrix.

8. An electronic device, characterized in that: include: one or more processors; a memory configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the inspection route optimization method according to any one of claims 1 to 6.

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