A grid-based intelligent inspection method and system for electric power drones

Abstract

The present invention discloses a grid-based intelligent inspection method and system for electric power drones, including: selecting pole towers that need to be spliced ​​for routes within a certain set coverage range of a machine nest; determining the inspection order of each pole tower with the goal of shortest drone flight path; determining the head and tail waypoints of the drone from the machine nest to the inspection pole tower, or from the previous inspection pole tower to the next inspection pole tower, or from the inspection pole tower to the machine nest; determining the altitude between the head and tail waypoints, and determining the flight strategy between the machine nest and the pole tower and between two pole towers based on the altitude; based on the inspection order of each pole tower and the flight strategy, a total route for drone inspection is obtained. The present invention determines the inspection order and flight strategy of the pole towers with the goal of shortest inspection distance, splices drone inspections into a total route for single-flight inspections of more than two pole towers, avoids waste of resources with the optimal inspection order, and improves inspection efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of power line inspection, and in particular to a grid-based intelligent inspection method and system for power unmanned aerial vehicles. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Traditional power line (including power grid transmission / distribution lines) inspections often adopt the "human patrol" method, which requires people to reach the pole tower to complete the inspection task. This inspection method often requires a lot of manual participation and is greatly affected by geographical factors. The inspection efficiency is very low. The inspection system urgently needs to develop in a smarter, more intensive and more efficient direction. In this case, the "smart patrol" method represented by the machine nest came into being. As a warehouse for storing drones, the machine nest not only undertakes the storage function of drones, but also is responsible for drone charging, information exchange and other functions. After the machine nest is deployed, the drone can realize intelligent and autonomous inspection of power lines within the surrounding coverage area, which greatly liberates human resources and improves inspection efficiency.

[0004] However, the following technical problems still exist in the process of UAV intelligent inspection:

[0005] (1) Currently, drones deployed in the machine nests can only take off from designated machine nests to perform inspection tasks due to the actual environment, and return to designated machine nests after the mission is completed. At the same time, due to the complex environment around the power line towers, the inspection shooting points have high requirements. In order to ensure the inspection effect and personal and property safety, each tower has a relatively fixed inspection route. In the past, when pilots arrived at the line inspection site and manually operated drones to inspect the line, they could only rely on a single professional power line and perform inspection tasks in the order of tower numbers. After each task was completed, they returned to the designated machine nest; each drone could only execute a single route based on one tower for each mission, and could not fly more than two tower inspection routes at a time, resulting in low inspection efficiency and waste of resources.

[0006] (2) Due to the limitations of the drone’s own backhaul data processing capabilities and comprehensive business processing capabilities, in order to avoid the problem of the server being unable to support the service pressure, the number of towers inspected by the drone in a single inspection cannot be too large.

[0007] (3) The deployment of drone nests is mostly considered according to a single inspection route, with one drone nest deployed for each inspection route. This method cannot maximize the coverage of the tower by drone nests, and often the number of drone nests in the entire deployment area is greater than the actual demand, increasing the inspection cost. Summary of the invention

[0008] In order to solve the above problems, the present invention proposes a grid-based intelligent inspection method and system for electric power UAVs, which can realize the reasonable deployment of UAV nests, and within the coverage of a certain nest, by reasonably determining the inspection order of the towers, the routes of each tower are spliced ​​into a total route to form a grid-based inspection strategy; at the same time, the server pressure is effectively monitored, which can improve the inspection efficiency, ensure the smooth execution of the inspection tasks, and realize the grid-based intelligent inspection of UAVs.

[0009] In some embodiments, the following technical solutions are adopted:

[0010] A grid-based intelligent inspection method for electric power drones, comprising:

[0011] Determine the towers within the coverage area of ​​each machine nest;

[0012] With the goal of shortest drone flight path, determine the inspection order of each tower;

[0013] Determine the head and tail waypoints of the drone from the machine nest to the inspection tower, or from the previous inspection tower to the next inspection tower, or from the inspection tower to the machine nest; determine the altitude between the head and tail waypoints, and determine the flight strategy between the machine nest and the tower and between two towers based on the altitude;

[0014] Based on the inspection sequence of each tower and the flight strategy, a total inspection route for the UAV is obtained, and the UAV is controlled to perform power inspection according to the total inspection route.

[0015] As an optional solution, the inspection order of each tower is determined with the shortest flight path of the drone as the goal. The specific process is as follows:

[0016] Based on the distance from the first waypoint of each tower inspection route to the machine nest, determine the first target inspection tower of the total splicing route;

[0017] Get the last waypoint M of the first target inspection tower a1 , calculate the first waypoint of the remaining towers except the first target inspection tower to M a1 distance, select distance M a1 The pole tower with the smallest waypoint is used as the second target inspection tower;

[0018] Get the last waypoint M of the second target inspection tower a2 , calculate the first waypoint of the remaining towers except the first and second target inspection towers to M a2 distance, select distance M a2 The tower with the smallest waypoint is the third target inspection tower.

[0019] And so on, until the inspection order of all towers is determined.

[0020] As an optional solution, the altitude between the head and tail waypoints is determined, and the flight strategy between the aircraft nest and the tower and between the two towers is determined based on the altitude, specifically including:

[0021] Determine the altitudes of a set of head and tail waypoints and multiple locations set in between. When the altitudes of all locations between the head and tail waypoints are lower than the altitudes of the head and tail waypoints, the drone flies directly from the head waypoint to the tail waypoint.

[0022] or,

[0023] Determine the altitudes of a set of head and tail waypoints and multiple positions set in between. If the altitude of the head waypoint is higher than the altitudes of all positions in between the head and tail waypoints, and the altitude of at least one position in the middle is higher than the altitude of the tail waypoint, introduce an intermediate waypoint, which is located directly above the tail waypoint and has the same altitude as the head waypoint; the drone first flies from the head waypoint to the intermediate waypoint, and then flies from the intermediate waypoint to the tail waypoint;

[0024] or,

[0025] Determine the altitudes of a set of head and tail waypoints and multiple positions set in between. If the altitude of the tail waypoint is higher than the altitudes of all positions in between the head and tail waypoints, and the altitude of at least one position in the middle is higher than the altitude of the head waypoint, introduce an intermediate waypoint, which is located directly above the head waypoint and has the same altitude as the tail waypoint; the drone first flies from the head waypoint to the intermediate waypoint, and then flies from the intermediate waypoint to the tail waypoint;

[0026] or,

[0027] Determine the altitudes of a group of head and tail waypoints and multiple positions set in between. If the altitude of at least one position in the middle is higher than the altitude of the head waypoint and the tail waypoint, introduce the first intermediate waypoint and the second intermediate waypoint. The first intermediate waypoint is directly above the head waypoint, and the second intermediate waypoint is directly above the tail waypoint. The heights of the first intermediate waypoint and the second intermediate waypoint are both higher than the highest value of the altitudes of all the positions in the middle, and higher than the set distance; the drone flies from the head waypoint to the first intermediate waypoint, then from the first intermediate waypoint to the second intermediate waypoint, and finally from the second intermediate waypoint to the tail waypoint.

[0028] As an optional solution, after obtaining the total route of the drone inspection, it also includes:

[0029] The time taken by the drone to inspect the total route is compared with the flight time that the drone's current battery power can support to determine whether the drone can complete the inspection mission.

[0030] As an optional scheme, the time taken by the drone to inspect the total route includes the time t′ required to inspect the towers, and the flight time t″ between the towers; wherein the flight time t″ between the towers includes the time required from the machine nest to the first target inspection tower, the time required between the two inspection towers, and the time required from the last tower to the machine nest. The flight time t″ between the towers is determined according to the flight strategy.

[0031] As an optional solution, it also includes: deploying a microservice architecture in the drone, and transmitting the inspection data collected during the drone inspection process to the drone's microservice architecture in real time, which receives the data and identifies defects; in the above process, the operating data of each service in the microservice architecture is monitored in real time, and the service pressure of the server where each microservice is located is determined. For servers with excessive service pressure, tasks are automatically diverted to other servers. If there is no server that meets the diversion conditions, a new server with corresponding functions is actively created to alleviate data processing pressure.

[0032] As an optional solution, before determining the towers within the coverage range of each drone nest, it also includes: determining the location of the drone nest; specifically:

[0033] For two parallel transmission or distribution lines, the towers of each line are numbered in sequence according to their distance from the substation;

[0034] When the total inspection complexity of the first pole tower of the first line and the first pole tower of the second line is less than or equal to n times the cruising capacity of the drone, the first pole tower of the first line and the first pole tower of the second line are included in the inspection range of the first drone nest; n is a set value;

[0035] Add a second pole tower on the first line, and determine whether the total inspection complexity is less than or equal to n times the cruising capability of the drone. If so, include the second pole tower on the first line in the inspection range of the first drone nest;

[0036] Add a second pole tower for the second line, and determine whether the total inspection complexity is less than or equal to n times the cruising capability of the drone. If so, include the second pole tower for the second line in the inspection range of the first drone nest.

[0037] By analogy, the third pole tower of the first line and the third pole tower of the second line are added, until the cruising capability of the drone is n times the total inspection complexity;

[0038] For all towers included in the inspection range of the first drone nest, connect the largest numbered tower of the first line with the smallest numbered tower of the second line, and connect the largest numbered tower of the second line with the smallest numbered tower of the first line. The intersection of the two lines is the nest layout position within the inspection range.

[0039] The first pole towers of the two lines that were not included in the inspection range of the previous drone nest are used as the first pole towers of the new first line and the first pole towers of the second line, and the site selection process is continued for subsequent pole towers until the locations of all drone nests are obtained;

[0040] Among them, the total inspection complexity is the sum of the inspection time of the drone for each tower and the round-trip flight time required for the drone to inspect the tower.

[0041] For two non-parallel transmission lines or distribution lines, the towers of each line are numbered in sequence according to their distance from the substation;

[0042] First, determine whether the total inspection complexity of the first tower of the first line and the first tower of the second line is less than or equal to n times the cruising capability of the drone. If so, determine the tower position according to the above method; n is a set value;

[0043] Otherwise, deploy a machine nest for each line. The specific process is as follows:

[0044] When the total inspection complexity of the first tower and the Nth tower is greater than n times the cruising capability of the drone, and the total inspection complexity of the first tower and the N-1th tower is less than or equal to n times the cruising capability of the drone, a drone nest is deployed between the first tower and the N-1th tower, and the first tower to the N-1th tower are included in the inspection range of the nest; N is a positive integer greater than 2; the coordinates of the tower in the middle position with a smaller number are taken as the location of the nest;

[0045] For the pole towers that are not included in the inspection range of the machine nest, determine whether the total inspection complexity of the first pole tower of the first line and the first pole tower of the second line is less than or equal to n times the cruising capability of the drone. If so, determine the position of the pole tower according to the above method; otherwise, continue to deploy the machine nest for each line separately.

[0046] In other embodiments, the following technical solutions are adopted:

[0047] A grid-based intelligent inspection system for electric power drones, comprising:

[0048] The inspection tower determination module is used to select the towers that need to be spliced ​​for the route within a certain set machine nest coverage area;

[0049] The inspection sequence determination module is used to determine the inspection sequence of each tower with the goal of minimizing the UAV flight path;

[0050] The flight strategy determination module is used to determine the head and tail waypoints of the drone from the machine nest to the inspection tower, or from the previous inspection tower to the next inspection tower, or from the inspection tower to the machine nest; determine the altitude between the head and tail waypoints, and determine the flight strategy between the machine nest and the tower and between two towers based on the altitude;

[0051] The route splicing module is used to obtain a total route for UAV inspection based on the inspection sequence of each tower and the flight strategy, and control the UAV to perform power inspection according to the total route.

[0052] In other embodiments, the following technical solutions are adopted:

[0053] A drone for inspecting power line towers, characterized in that the above-mentioned grid-based intelligent inspection method for power drones is adopted to realize inspection route splicing;

[0054] Alternatively, including the above-mentioned electric drone grid intelligent inspection system.

[0055] In other embodiments, the following technical solutions are adopted:

[0056] A terminal device includes a processor and a memory, wherein the processor is used to implement various instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the above-mentioned grid-based intelligent inspection method of electric power drones.

[0057] In other embodiments, the following technical solutions are adopted:

[0058] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the above-mentioned grid-based intelligent inspection method of electric power drones.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] (1) The present invention creatively proposes a grid-based intelligent inspection method for electric power UAVs, which takes the shortest inspection distance as the goal, autonomously plans the inspection sequence and flight strategy of pole towers, and splices the original inspection of a single pole tower by the UAV into a single-flight total inspection route of more than two pole towers, thereby avoiding waste of resources with the optimal inspection sequence, while improving the efficiency of UAV inspections and achieving full grid coverage of pole tower inspections; by reasonably formulating the flight strategy between the head and tail waypoints, the safety of UAV flight is effectively guaranteed, the safety and reliability of the UAV's autonomous obstacle avoidance are improved, and the intelligent inspection of electric power UAVs is realized.

[0061] (2) The present invention creatively proposes a microservice pressure self-balancing method for alleviating the pressure of microservice processing data, customizes intelligent monitoring modules for each microservice of the drone inspection background system, and monitors the operation status of each microservice in real time; at the same time, customizes the monitoring value judgment mechanism and the custom weight ratio and threshold for the microservice; actively creates new microservices to alleviate data processing pressure and achieve self-balancing of microservice pressure; effectively ensures the stability of real-time transmission of inspection data during the drone long cruise line inspection, and improves the drone's return data processing capability and comprehensive business processing capability.

[0062] (2) The present invention creatively proposes a server task diversion and expansion method to alleviate the service processing pressure of the server where the microservice is located (hereinafter referred to as the server). The method customizes an intelligent monitoring module for each microservice of the drone inspection background system to monitor the operation status of each server in real time; at the same time, it customizes the monitoring value judgment mechanism and the custom weight ratio and threshold for each microservice; the system automatically diverts the tasks of the server with excessive pressure to other servers (i.e., servers deploying the same microservice); if there are no other servers or other servers cannot support the diversion of this part of the task, a new server corresponding to the microservice is actively created to alleviate the data processing pressure and achieve server pressure relief. It effectively ensures the stability of the real-time transmission of inspection data during the drone's long cruise line inspection, and improves the drone's return data processing capability and comprehensive business processing capability.

[0063] (3) The present invention creatively proposes a UAV route planning method based on distributed machine nests. By constructing a machine nest site selection model between multiple lines, it realizes the sharing of machine nests during the inspection of multiple transmission / distribution lines, provides basic support and guarantee for the main planning of UAV inspection routes, improves the utilization rate of machine nests, and reduces inspection costs.

[0064] Other features and advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flow chart of a grid-based intelligent inspection method for electric power drones in an embodiment of the present invention;

[0066] Figure 2 (a)-(b) are schematic diagrams of altitudes corresponding to different flight strategies;

[0067] Figure 3 A schematic diagram of a service pressure monitoring process in an embodiment of the present invention;

[0068] Figure 4 This is a schematic diagram of determining the location of a drone nest on a single route in an embodiment of the present invention;

[0069] Figure 5 This is a schematic diagram of determining the locations of drone nests on two parallel routes in an embodiment of the present invention;

[0070] Figure 6 Schematic diagram of determining the locations of drone nests on two intersecting routes in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0072] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0073] Embodiment 1

[0074] In one or more embodiments, a grid-based intelligent inspection method for electric power drones is disclosed, combining Figure 1 , specifically including the following process:

[0075] (1) Select the tower that needs to be spliced ​​within a certain set aircraft nest coverage area;

[0076] In this embodiment, it is first determined whether the basic route conditions are met, that is, whether the selected tower is located within the coverage range of the machine nest. If there are inspection towers that exceed the coverage range of the machine nest, the route splicing is terminated; if all the towers are within the coverage range of the machine nest, the route splicing process is performed.

[0077] (2) Determine the inspection order of each tower with the goal of minimizing the UAV flight path;

[0078] Specifically, read the first waypoint of each tower route. If there are n transmission and distribution towers that need to be spliced, it means that there are n inspection routes. Select the first waypoint of these n routes, which are N1, N2, N3, ..., N. n , the location of the machine nest is set as N0, and the three-dimensional coordinate system is constructed with the location of the machine nest N0, then N1, N2, N3, ..., N n The coordinates in the coordinate system are (x n1 ,y n1 , z n1 )、(xn2 ,y n2 , z n2 )、(x n3 ,y n3 , z n3 ),...,(x nn ,y nn , z nn ), calculate the straight-line distance from each point to the machine nest That is, the distance between N1 and the machine nest The distance between N2 and the machine nest ...、N n Distance from the nest Compare all the distances obtained and take the minimum value, that is, D0 = min{S 01 , S 02 , ..., S 0n}.

[0079] If the waypoint corresponding to D0 is N a , the waypoint belongs to the ath tower, then the ath tower is the No. 1 inspection tower, which is the first target inspection tower in the route splicing process.

[0080] After determining the first target inspection tower, take the last point M of the tower route a (x ma ,y ma , z ma ), calculate the remaining n-1 waypoints (N1, N2, ..., N a-1 、N a+1 ,...,N n ) and point M a The straight-line distance between N1 and M a Distance ...、N n With M a Distance Compare all the distances obtained and take the minimum value D1=min{S 11 , S 12 , ..., S 1n}. If the waypoint corresponding to D1 is N b , the waypoint belongs to the bth tower, then the bth tower is the No. 2 inspection tower, which serves as the second target inspection tower in the route splicing process.

[0081] Similarly, obtain the last waypoint M of the second target inspection tower a2 , calculate the first waypoint of the remaining towers except the first and second target inspection towers to M a2 distance, select distance M a2The tower with the smallest waypoint is the third target inspection tower.

[0082] And so on, the inspection order of all towers is obtained.

[0083] (3) Determine the head and tail waypoints of the drone from the nest to the inspection tower, or from the previous inspection tower to the next inspection tower, or from the inspection tower to the nest; determine the altitude between the head and tail waypoints, and determine the flight strategy between the nest and the tower and between two towers based on the altitude;

[0084] The route of the drone for inspecting pole towers is a preset route, which has avoidance strategies for obstacles such as pole towers, power cables, and trees to ensure flight safety. However, during the flight between pole towers, that is, the route splicing between pole towers, appropriate strategies still need to be adopted to ensure the flight safety of the drone.

[0085] In this embodiment, the head and tail waypoints are defined, specifically including: the position of the machine nest is the head waypoint, and the first waypoint of the first target inspection tower is the corresponding tail waypoint; the last waypoint of the mth target inspection tower is the head waypoint, and the first waypoint of the m+1th target inspection tower is the corresponding tail waypoint; the last waypoint of the last target inspection tower is the head waypoint, and the position of the machine nest is the corresponding tail waypoint.

[0086] Get the altitudes of a set of head and tail waypoints and a series of multiple locations in between, and then compare the altitudes of the head and tail waypoints with the altitudes of multiple locations in between through 3D point cloud and GPS map. According to the comparison, the following four different flight strategies are obtained:

[0087] ①Strategy 1: Fly directly from the first waypoint to the last waypoint;

[0088] Combined with Figure 2(a), when the altitudes of a series of locations between the head and tail waypoints are lower than the altitudes of the head and tail waypoints, it means that there are no obstacles blocking the two waypoints. In this case, the drone can fly directly from the head waypoint to the tail waypoint.

[0089] ② Strategy 2: Fly at the altitude of the head waypoint, fly to the middle waypoint, and land at the tail waypoint;

[0090] Combined with Figure 2(b), when the altitude of the head waypoint is higher than the altitude of the middle series of positions, and the altitude of a point in the middle series of positions is higher than the altitude of the tail waypoint, it means that there are some obstacles blocking the head and tail waypoints. At this time, if you fly directly from the head waypoint to the tail waypoint, flight safety cannot be guaranteed. Therefore, an intermediate waypoint is introduced, whose coordinates are the latitude and longitude of the tail waypoint and the altitude of the head waypoint (that is, the intermediate waypoint is directly above the tail waypoint and has the same altitude as the head waypoint).

[0091] The aircraft flies from the head waypoint to the middle waypoint first, and then lands at the tail waypoint after reaching the middle waypoint, which can effectively avoid obstacles between the waypoints.

[0092] ③Strategy 3: Ascend from the first waypoint, reach the middle waypoint, and fly to the last waypoint;

[0093] Combined with Figure 2(c), when the altitude of the tail waypoint is higher than the altitude of the middle series of positions, and the altitude of a point in the middle series of positions is higher than the altitude of the head waypoint, an intermediate waypoint is introduced accordingly, whose coordinates are the latitude and longitude of the head waypoint and the altitude of the tail waypoint (that is, the intermediate waypoint is directly above the head waypoint and has the same altitude as the tail waypoint).

[0094] The aircraft flies from the head waypoint to the middle waypoint first, and then flies to the tail waypoint at the same altitude as the tail waypoint after reaching the middle waypoint. This can also effectively avoid obstacles between waypoints.

[0095] ④Strategy 4: Ascend from the first waypoint, reach the middle waypoint 1, fly to the middle waypoint 2, and land at the last waypoint

[0096] Combined with Figure 2(d), when the altitude of a point in the middle series of positions is higher than the altitude of the first and last waypoints, it means that there is an obvious obstacle blocking the first and last waypoints. At this time, two middle waypoints are selected.

[0097] The longitude and latitude of the first intermediate waypoint are the longitude and latitude of the head waypoint, and the longitude and latitude of the second intermediate waypoint are the longitude and latitude of the tail waypoint, that is, the first intermediate waypoint is directly above the head waypoint, and the second intermediate waypoint is directly above the tail waypoint; the altitudes of the first intermediate waypoint and the second intermediate waypoint are both higher than the highest value of the altitudes of all intermediate positions, and higher than a set distance (for example, 5m); this embodiment chooses to further increase the set altitude in order to avoid possible mobility obstacles such as people and vehicles.

[0098] The aircraft first rises from the first waypoint to the first intermediate waypoint, then moves horizontally to the second intermediate waypoint, and finally lands at the last waypoint. This process can effectively avoid obstacles between the two waypoints and ensure flight safety.

[0099] (4) Based on the inspection sequence of each tower and the flight strategy, a total inspection route for the UAV is obtained, and the UAV is controlled to perform power inspection according to the total inspection route.

[0100] As an implementation method, after obtaining the total inspection route of the drone, it also includes: comparing the time taken by the drone to inspect the total inspection route with the flight time that the current power of the drone can support, to determine whether the drone can complete the inspection task.

[0101] Specifically, after establishing the flight sequence of the towers and the flight strategy between towers, a complete drone inspection route can be obtained, which starts and ends at the machine nest and covers all the inspection target towers. At this time, the machine nest needs to combine the route with the current status of the drone to determine whether the drone's endurance in the current machine nest is greater than the flight time required to complete the inspection.

[0102] The time required for flying can be divided into the time required for inspecting the tower t′ and the flight time between towers t″.

[0103] Considering that the inspection route of the tower is fixed, for the n towers that need to be inspected in the route, the time required to inspect the first target inspection tower can be regarded as a fixed value t1, the time required to inspect the second target inspection tower can be regarded as a fixed value t2, and so on. The time required to inspect the nth target inspection tower can be regarded as a fixed value t n The time required to inspect all towers is t′=t1+t2+…+t n .

[0104] After the flight strategy between towers is determined, the flight distance between towers can also be determined. The flight distance between the drone flying from the nest to the first waypoint of the first target inspection tower can be regarded as D 01 The flight distance between the last waypoint of the first target inspection tower and the first waypoint of the second target inspection tower can be regarded as D 12 By analogy, the flight distance of the last tower (the nth target inspection tower) returning to the machine nest can be regarded as D n0 The flying speed of the drone is v, so the total time required for the drone to travel the distance is:

[0105]

[0106] Assume that the flight time that the drone battery can support is T, determine whether it meets:

[0107]

[0108] If the conditions are met, the drone can execute the spliced ​​route; if not, it means that the spliced ​​route exceeds the flight capability of the drone and the drone cannot execute the route.

[0109] As an optional implementation method, after the route is spliced, the drone needs to transmit a large amount of data / pictures / videos, etc., which may face the problem of excessive pressure on the drone server. In order to improve the return data processing capability and comprehensive business processing capability of the drone during the autonomous inspection process, this embodiment deploys a microservice architecture in the drone. The inspection data collected during the drone inspection process is transmitted to the drone's microservice architecture in real time, and the microservice architecture receives the data and performs defect identification; in the above process, the operation data of each service in the microservice architecture is monitored in real time, and the service pressure of the server where each microservice is located (the server where the microservice is located, hereinafter referred to as the server) is determined. For servers with excessive service pressure, the system automatically diverts tasks to other servers (i.e., servers deploying the same microservice); the specific diversion method is: determine the number of tasks that each other server can handle through the pressure monitoring mechanism, and perform task diversion accordingly. If there is no other server or other servers cannot support this part of the task diversion, a new server with this function is actively created to relieve the pressure of data processing.

[0110] Microservice architecture is an architectural pattern that advocates dividing a single application into a group of small services that coordinate and cooperate with each other to provide ultimate value to users. Each service runs in its own independent process, and services communicate with each other using lightweight communication mechanisms (usually Restful API based on HTTP). Each service is built around a specific business and can be independently deployed to a production environment, a quasi-production environment, etc. In addition, a unified and centralized service management mechanism should be avoided as much as possible. For a specific service, the appropriate language and tool should be selected to build it according to the business context. This embodiment is based on the springcloud architecture, which is the most commonly used microservice framework. It relies on Spring Boot and has the advantages of rapid development, continuous delivery, and easy deployment.

[0111] Specifically, combined Figure 3 In this embodiment, a microservice pressure monitoring module is created. For servers that are judged to have excessive service pressure, the system automatically diverts tasks to other servers (i.e., servers that deploy the same microservice). If there are no other servers or other servers cannot support the diversion of this part of the task, a new microservice of this function is actively created to alleviate data processing pressure; for example:

[0112] In the pressure monitoring microservice, the RedisExporter indicator interface of the Redis cache is implemented to realize the function of monitoring the memory value and network traffic occupied by the Redis cache, so as to control the service pressure of related servers such as memory and network traffic, and ensure the stability of the memory and network of the drone autonomous inspection and control system.

[0113] In the pressure monitoring microservice, the interface of MySQL database MySQLExporter is implemented to realize the function of monitoring the number of database connections and disk space, so as to control the server pressure of database connection and disk space services and ensure the stability of database connection and disk of the drone autonomous inspection and control system.

[0114] For microservices related to image / video stream reception, a custom indicator interface is implemented according to the business logic of the service to continuously monitor the server pressure related to image and video stream reception during the image and video stream return process, thereby ensuring the stability of the drone autonomous inspection system when images and videos are returned.

[0115] For other modules of the drone autonomous inspection system, a weighted election model is adopted to select microservices that are more likely to generate service pressure, implement a unified interface for monitoring service pressure, and ensure the stability of microservices with a high weight of service pressure in the drone autonomous inspection system.

[0116] In order to implement the above monitoring process, this embodiment uses Prometheus as an indicator collector to collect monitoring indicators of Redis, MySQL and other microservices such as video streaming microservices, image return microservices, and data return microservices.

[0117] Establish an indicator database and an indicator processor. The indicator database is set to store the monitoring thresholds of various monitoring indicator interfaces and the processing mechanism of pressure monitoring, which is used to judge the server pressure. The indicator processor processes the data collected by the indicator collector according to the corresponding monitoring thresholds and processing mechanisms taken out from the indicator database, and returns the server pressure monitoring results.

[0118] This embodiment applies the above-mentioned microservice monitoring strategy throughout the entire route of the drone inspection. The specific process includes:

[0119] The autonomous inspection system of the drone sends autonomous inspection tasks to the drone, and the drone begins to perform inspection tasks according to the plan of the spliced ​​total route. During the flight, the drone returns real-time data / pictures / videos to the autonomous inspection system. By implementing a custom indicator interface, the server where the data / picture / video transmission microservice is located is monitored to find out the service pressure related parameters caused to the data / picture / video transmission server, such as: real-time data return pressure parameters, real-time picture return pressure parameters, real-time video return pressure parameters, etc., and transmit them to the microservice indicator processor for judgment.

[0120] During the flight of the drone, data such as wind speed, flight altitude, and alarm information are sent back to the inspection system in real time. At the same time, the microservice that sends back data uses the indicator interface it implements to send service pressure-related parameters, such as flight status feedback pressure parameters, to the microservice indicator processor for judgment.

[0121] After the drone inspection is completed, the image is transmitted to the machine nest, which then transmits it back to the system. The inspection image receiving microservice of the drone autonomous inspection system obtains service pressure-related parameters in real time through the indicator interface it implements, such as the image return pressure parameters, etc. The pressure coefficient collector collects the information in real time and hands it over to the microservice indicator processor for judgment.

[0122] After receiving the inspection pictures sent back, the drone inspection system will perform intelligent defect identification on the inspection pictures. The indicator interface implemented by the intelligent identification algorithm microservice will start to monitor the service pressure-related parameters of the server, such as intelligent identification service pressure parameters, and send them back to the pressure coefficient collector.

[0123] Other microservices selected according to service weights are monitored in real time according to their respective implemented indicator interfaces. In this embodiment, the weight ratio of each microservice pressure parameter in the calculation can be set before the system is run, and the weighted average calculation of each parameter according to the weight ratio is performed to obtain the result when calculating the total microservice server pressure.

[0124] After collecting the above service pressure related parameters, the microservice indicator processor takes out the service pressure monitoring threshold corresponding to the corresponding parameter from the corresponding indicator database, and compares the obtained parameter with the service pressure monitoring threshold; if it is higher than the threshold, it outputs the judgment result that the corresponding server pressure is too high, and the system automatically diverts the task to other servers (i.e., servers deploying the same microservice). If there is no other server or other servers cannot support the diversion of this part of the task, a corresponding new server is created. Register the new microservice server, start running, and start monitoring the pressure of the server where the new microservice is located.

[0125] Embodiment 2

[0126] In one or more embodiments, a grid-based intelligent inspection method for electric power drones is disclosed. The method of this embodiment is based on the grid-based intelligent inspection method for electric power drones described in Example 1. Before determining the pole towers within the coverage range of the machine nest, the layout location of the electric power inspection drone machine nest is first determined.

[0127] Since the interval between two adjacent transmission / distribution lines is generally far, and the interval between three adjacent transmission / distribution lines is even farther, it is rare for one machine nest to cover three transmission / distribution lines. Therefore, this embodiment only describes the situation where two adjacent transmission / distribution lines are uniformly planned and deployed with drone machine nests. The specific process is as follows:

[0128] (1) Combination Figure 5 , for two parallel transmission lines or distribution lines, the towers of each line are numbered in sequence according to the distance from the substation;

[0129] (1-1) When the total inspection complexity of the first pole tower of the first line and the first pole tower of the second line is less than or equal to n times the cruising capability of the drone, the first pole tower of the first line and the first pole tower of the second line are included in the inspection range of the first drone nest; n is a set value, and in this embodiment, n=2.

[0130] Add a second pole tower on the first line, and determine whether the total inspection complexity is less than or equal to n times the cruising capability of the drone. If so, include the second pole tower on the first line in the inspection range of the first drone nest;

[0131] Add a second pole tower for the second line, and determine whether the total inspection complexity is less than or equal to n times the cruising capability of the drone. If so, include the second pole tower for the second line in the inspection range of the first drone nest.

[0132] By analogy, the third pole tower of the first line and the third pole tower of the second line are added, until the cruising capability of the drone is n times the total inspection complexity;

[0133] For all towers included in the inspection range of the first drone nest, connect the largest numbered tower of the first line with the smallest numbered tower of the second line, and connect the largest numbered tower of the second line with the smallest numbered tower of the first line. The intersection of the two lines is the nest layout position within the inspection range.

[0134] The first pole towers of the two lines that were not included in the inspection range of the previous drone nest are used as the first pole towers of the new first line and the first pole towers of the second line, and the site selection process is continued for subsequent pole towers until the locations of all drone nests are obtained;

[0135] Among them, the total inspection complexity is the sum of the inspection time of the drone for each tower and the round-trip flight time required for the drone to inspect the tower.

[0136] In this embodiment, the towers of the two lines starting from the substation are numbered 1, 2, 3, ..., n in order from near to far, and the total inspection complexity of the two towers G1 and g1 is recorded as T G1g1 , the distance between G1 and g1 is SG1g1 ,but:

[0137]

[0138] Total inspection complexity:

[0139]

[0140] (1-2) If the total inspection complexity of the first tower of the first line and the first tower of the second line is greater than n times the cruising capability of the drone, it means that the first towers G1 and g1 of the two tower lines cannot meet the cruising capability of a single drone nest. Then the location of the drone nest is determined according to the site selection method of a single tower line. The specific process is as follows:

[0141] When the total inspection complexity of the first tower and the Nth tower is greater than n times the cruising capability of the drone, and the total inspection complexity of the first tower and the N-1th tower is less than or equal to n times the cruising capability of the drone, a drone nest is deployed between the first tower and the N-1th tower, and the first tower to the N-1th tower are included in the inspection range of the nest; wherein N is a positive integer greater than 2; the coordinates of the tower in the middle position with a smaller number are taken as the position of the nest, that is, when N-1 is an even number, the coordinates of the N / 2th tower are taken as the nest position, and when N-1 is an odd number, the position of the (N-1) / 2th tower is taken as the nest position.

[0142] Specifically, starting from the substation, the deployment is carried out in sequence along the pole tower line direction. The location of the drone nest needs to be considered according to the complexity Tn and three-dimensional coordinates (GXn, GYn, GZn) of each tower. Figure 4 As shown, select tower G1 (GX1, GY1, GZ1), whose complexity is T1, as a point on the circle with inspection radius, and then find tower G2 (GX2, GY2, GZ2), whose complexity is T2, and find the total inspection complexity of the two towers G1 and G2, recorded as T G1G2 , the distance between G1 and G2 is S G1G2 ,but:

[0143]

[0144] Total complexity:

[0145]

[0146] If T G1G2 >2*T, that is, the total inspection complexity of the two towers G1 and G2 is recorded as T G1G2The drone inspection capacity T is greater than 2 times, indicating that deploying a drone nest between the G1 and G2 towers cannot meet the capacity of simultaneously inspecting the G1 and G2 towers. In this case, the G1 tower will be given priority. Based on the principle of the shortest inspection path, the drone nest A1 is deployed at point G1 to achieve the purpose of the shortest inspection path.

[0147] If T G1G2 ≤2*T, it means that a drone nest can be deployed between the two towers G1 and G2, which can meet the inspection capabilities of the two towers G1 and G2 respectively. It is also necessary to determine whether the drone nest meets the autonomous inspection capability of the G3 tower. The judgment basis is to calculate the total complexity of the two towers G1 and G3, recorded as T G1G3 , the distance between G1 and G3 is S G1G3 ,but:

[0148]

[0149] Total complexity:

[0150]

[0151] If T G1G3 ≤2*T, that is, the total inspection complexity of the two towers G1 and G3 is recorded as T G1G3 ≤2 times the drone inspection capacity T, indicating that a drone nest can be deployed between the two towers G1 and G3, which can meet the inspection capacity of the three towers G1, G2, and G3, and then continue to judge the next tower G4 until it is not less than or equal to 2*T. If it is not satisfied, it is considered that the drone nest A1 can only meet the inspection capacity of the two towers G1 and G2, and the site selection interval of the drone nest A1 (X1, Y1, Z1) is:

[0152]

[0153] Among them, formula (3) ensures that the drone nest site A1 is on the line between tower G1 and tower G2. According to the principle that the straight line between two points is the shortest, the drone nest is located on a straight line from the inspection route between the two towers G1 and G2, ensuring the shortest inspection flight distance.

[0154] (2) Combination Figure 6 , for two non-parallel (e.g. crossing) transmission lines or distribution lines, the towers of each line are numbered in sequence according to their distance from the substation;

[0155] First, determine whether the total inspection complexity of the first tower of the first line and the first tower of the second line is less than or equal to n times the cruising capability of the drone. If so, determine the tower position according to the method described in (1-1) above; n is a set value, and in this embodiment, n=2.

[0156] Otherwise, according to the above method (1-2), a machine nest is deployed for each line.

[0157] For the pole towers that are not included in the inspection range of the machine nest, determine whether the total inspection complexity of the first pole tower of the first line and the first pole tower of the second line is less than or equal to n times the cruising capability of the drone. If so, determine the position of the pole tower according to the method described in (1-1) above; otherwise, continue to deploy the machine nest for each line according to the method described in (1-2) above.

[0158] Embodiment 3

[0159] In one or more embodiments, a grid-based intelligent inspection system for electric power drones is disclosed, specifically comprising:

[0160] The inspection tower determination module is used to select the towers that need to be spliced ​​for the route within a certain set machine nest coverage area;

[0161] The inspection sequence determination module is used to determine the inspection sequence of each tower with the goal of minimizing the UAV flight path;

[0162] The flight strategy determination module is used to determine the head and tail waypoints of the drone from the machine nest to the inspection tower, or from the previous inspection tower to the next inspection tower, or from the inspection tower to the machine nest; determine the altitude between the head and tail waypoints, and determine the flight strategy between the machine nest and the tower and between two towers based on the altitude;

[0163] The route splicing module is used to obtain a total route for UAV inspection based on the inspection sequence of each tower and the flight strategy, and control the UAV to perform power inspection according to the total route.

[0164] As an optional implementation, the power drone grid intelligent inspection system also includes:

[0165] The cruise time calculation module is used to compare the time taken by the drone to inspect the total route with the flight time that the drone's current battery power can support, to determine whether the drone can complete the inspection task.

[0166] As an optional implementation, the power drone grid intelligent inspection system also includes:

[0167] The service pressure monitoring module is used to deploy the microservice architecture in the drone. The inspection data collected during the drone inspection process is transmitted to the drone's microservice architecture in real time, and the microservice architecture receives the data and identifies defects. During the above process, the operation data of each service in the microservice architecture is monitored in real time to determine the service pressure of the server where each microservice is located. For servers with excessive service pressure, tasks are automatically diverted to other servers. If there is no server that meets the diversion conditions, a new server with corresponding functions is actively created to alleviate data processing pressure.

[0168] As an optional method, the power drone grid intelligent inspection system also includes:

[0169] The machine nest position determination module is used to determine the layout position of the drone machine nest. The specific layout position determination method adopts the method described in the second embodiment.

[0170] It should be noted that the specific implementation methods of the above modules have been described in Example 1 and will not be described in detail here.

[0171] Embodiment 4

[0172] In one or more embodiments, a drone for inspecting power line towers is disclosed, which adopts the grid-based intelligent inspection method for power drones described in Example 1 to control the drone to inspect according to the generated general route.

[0173] Alternatively, it includes the electric UAV grid intelligent inspection system described in Example 2.

[0174] Embodiment 5

[0175] In one or more embodiments, a terminal device is disclosed, including a server, the server including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the grid-based intelligent inspection method of the power drone in Embodiment 1 when executing the program. For the sake of brevity, it will not be described here.

[0176] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0177] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0178] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.

[0179] Embodiment 6

[0180] In one or more embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the grid-based intelligent inspection method for electric power drones described in Example 1.

[0181] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A grid-based intelligent inspection method for electric power drones, characterized in that: include: Determine the towers within the coverage area of ​​each machine nest; Within a certain coverage area of ​​a certain drone nest, the inspection order of each tower is determined with the shortest flight path of the drone as the goal; Determine the head and tail waypoints of the drone from the machine nest to the inspection tower, or from the previous inspection tower to the next inspection tower, or from the inspection tower to the machine nest; determine the altitude between the head and tail waypoints, and determine the flight strategy between the machine nest and the tower and between two towers based on the altitude; Based on the inspection sequence of each tower and the flight strategy, a total inspection route of the drone is obtained, and the drone is controlled to perform power inspection according to the total inspection route; Deploy a microservice architecture in the drone. The inspection data collected during the drone inspection process is transmitted to the drone's microservice architecture in real time. The microservice architecture receives the data and performs defect identification. In the above process, the operation data of each service in the microservice architecture is monitored in real time to determine the service pressure of the server where each microservice is located. For servers with excessive service pressure, tasks are automatically diverted to other servers. If there is no server that meets the diversion conditions, a new server with corresponding functions is actively created to alleviate data processing pressure. The microservice monitoring strategy used in the entire route of drone inspection includes the following processes: The UAV autonomous inspection system sends autonomous inspection tasks to the UAV, and the UAV starts to perform inspection tasks according to the plan of the spliced ​​total route; during the flight, the UAV returns real-time data / pictures / videos to the autonomous inspection system, and by implementing a custom indicator interface, monitors the server where the data / picture / video transmission microservice is located, finds out the service pressure related parameters caused to the data / picture / video transmission server, including real-time data return pressure parameters, real-time picture return pressure parameters, and real-time video return pressure parameters, and transmits them to the microservice indicator processor for judgment; During the flight of the drone, the wind speed, flight altitude, and alarm information data are sent back to the inspection system in real time. At the same time, the microservice that sends back the data uses the indicator interface it implements to pass the service pressure-related parameters to the microservice indicator processor for judgment; After the drone inspection is completed, the image is transmitted to the machine nest, and then transmitted back to the system; the inspection image receiving microservice of the drone autonomous inspection system obtains service pressure related parameters in real time through the indicator interface it implements, and the pressure coefficient collector collects the data in real time and hands it over to the microservice indicator processor for judgment; After receiving the inspection pictures sent back, the drone inspection system will perform intelligent defect identification on the inspection pictures. The indicator interface implemented by the intelligent identification algorithm microservice will start to monitor the service pressure-related parameters of the server and send them back to the pressure coefficient collector.

2. The grid-based intelligent inspection method for electric power drones according to claim 1, characterized in that: With the shortest flight path of the drone as the goal, determine the inspection order of each tower. The specific process is as follows: Based on the distance from the first waypoint of each tower inspection route to the machine nest, determine the first target inspection tower of the total splicing route; Get the last waypoint of the first target inspection tower , calculate the first waypoint of the remaining towers except the first target inspection tower to Distance, select distance The pole tower with the smallest waypoint is used as the second target inspection tower; Get the last waypoint of the second target inspection tower , calculate the first waypoint to the remaining towers except the first and second target inspection towers Distance, select distance The tower with the smallest waypoint is the third target inspection tower. And so on, until the inspection order of all towers is determined.

3. The grid-based intelligent inspection method for electric power drones according to claim 1, characterized in that: The head and tail waypoints specifically include: The position of the machine nest is taken as the head waypoint, and the first waypoint of the first target inspection tower is taken as the corresponding tail waypoint; The last waypoint of the previous target inspection tower is used as the head waypoint, and the first waypoint of the next target inspection tower is used as the corresponding tail waypoint; The last waypoint of the last target inspection tower is taken as the head waypoint, and the position of the machine nest is taken as the corresponding tail waypoint.

4. A grid-based intelligent inspection method for electric power drones as described in claim 1 or 3, characterized in that: Determine the altitude between the head and tail waypoints, and determine the flight strategy between the aircraft nest and the tower and between two towers based on the altitude, specifically including: Determine the altitudes of a set of head and tail waypoints and multiple locations set in between. When the altitudes of all locations in between the head and tail waypoints are lower than the altitudes of the head and tail waypoints, the drone flies directly from the head waypoint to the tail waypoint.

5. A grid-based intelligent inspection method for electric power drones as described in claim 1 or 3, characterized in that: Determine the altitude between the head and tail waypoints, and determine the flight strategy between the aircraft nest and the tower and between two towers based on the altitude, specifically including: Determine the altitudes of a set of head and tail waypoints and multiple positions set in between. If the altitude of the head waypoint is higher than the altitudes of all positions in between the head and tail waypoints, and the altitude of at least one position in the middle is higher than the altitude of the tail waypoint, introduce an intermediate waypoint, which is located directly above the tail waypoint and has the same altitude as the head waypoint; the drone first flies from the head waypoint to the intermediate waypoint, and then flies from the intermediate waypoint to the tail waypoint.

6. A grid-based intelligent inspection method for electric power drones as described in claim 1 or 3, characterized in that: Determine the altitude between the head and tail waypoints, and determine the flight strategy between the aircraft nest and the tower and between two towers based on the altitude, specifically including: Determine the altitudes of a set of head and tail waypoints and multiple positions set in between. If the altitude of the tail waypoint is higher than the altitudes of all positions in between the head and tail waypoints, and the altitude of at least one position in the middle is higher than the altitude of the head waypoint, introduce an intermediate waypoint, which is located directly above the head waypoint and has the same altitude as the tail waypoint; the drone first flies from the head waypoint to the intermediate waypoint, and then flies from the intermediate waypoint to the tail waypoint.

7. A grid-based intelligent inspection method for electric power drones as described in claim 1 or 3, characterized in that: Determine the altitude between the head and tail waypoints, and determine the flight strategy between the aircraft nest and the tower and between two towers based on the altitude, specifically including: Determine the altitudes of a group of head and tail waypoints and multiple positions set in between. If the altitude of at least one position in the middle is higher than the altitude of the head waypoint and the tail waypoint, introduce the first intermediate waypoint and the second intermediate waypoint. The first intermediate waypoint is directly above the head waypoint, and the second intermediate waypoint is directly above the tail waypoint. The heights of the first intermediate waypoint and the second intermediate waypoint are both higher than the highest value of the altitudes of all the positions in the middle, and higher than the set distance; the drone flies from the head waypoint to the first intermediate waypoint, then from the first intermediate waypoint to the second intermediate waypoint, and finally from the second intermediate waypoint to the tail waypoint.

8. The grid-based intelligent inspection method for electric power drones according to claim 1, characterized in that: After obtaining the total route of the drone inspection, it also includes: The time taken by the drone to inspect the total route is compared with the flight time that the drone's current battery power can support to determine whether the drone can complete the inspection mission.

9. The grid-based intelligent inspection method for electric power drones according to claim 8, characterized in that: The time taken by the drone to inspect the total route includes the time required to inspect the towers , and the flight time between towers ; Among them, the flight time between towers Including the time required from the machine nest to the first target inspection tower, the time required between the two inspection towers, and the time required from the last tower to the machine nest, and the flight time between towers Determined by flight strategy.

10. The grid-based intelligent inspection method for electric power drones according to claim 1, characterized in that: The monitored microservices include: Redis cache, MySQL database, microservices related to data / image / video transmission and reception, and microservices for defect identification.

11. The grid-based intelligent inspection method for electric power drones according to claim 10, characterized in that: For the monitored microservices, collect parameters related to service pressure and compare them with the corresponding service pressure monitoring threshold. If the parameters are higher than the threshold, it is judged that the pressure on the server where the video stream microservice is located is too high. Determine the number of tasks that other servers can handle and determine whether the diversion conditions are met. If so, divert the traffic to other servers. Otherwise, actively create a new server for this function to relieve the pressure of data processing.

12. The grid-based intelligent inspection method for electric power drones according to claim 1, characterized in that: According to the weighted election model, select microservices that are more likely to generate service pressure for monitoring; Set the weight ratio of each microservice pressure parameter. When calculating the total microservice pressure, the weighted average of each parameter is calculated according to the weight ratio to obtain the result.

13. The grid-based intelligent inspection method for electric power drones according to claim 1, characterized in that: Before determining the towers within the coverage of each drone nest, the process also includes: determining the location of the drone nest; specifically: For two parallel transmission or distribution lines, the towers of each line are numbered in sequence according to their distance from the substation; When the total inspection complexity of the first pole tower of the first line and the first pole tower of the second line is less than or equal to n times the cruising capacity of the drone, the first pole tower of the first line and the first pole tower of the second line are included in the inspection range of the first drone nest; n is a set value; Add a second pole tower on the first line, and determine whether the total inspection complexity is less than or equal to n times the cruising capability of the drone. If so, include the second pole tower on the first line in the inspection range of the first drone nest; Add a second pole tower for the second line, and determine whether the total inspection complexity is less than or equal to n times the cruising capability of the drone. If so, include the second pole tower for the second line in the inspection range of the first drone nest. By analogy, the third pole tower of the first line and the third pole tower of the second line are added, until the cruising capability of the drone is n times the total inspection complexity; For all towers included in the inspection range of the first drone nest, connect the largest numbered tower of the first line with the smallest numbered tower of the second line, and connect the largest numbered tower of the second line with the smallest numbered tower of the first line. The intersection of the two lines is the nest layout position within the inspection range. The first pole towers of the two lines that were not included in the inspection range of the previous drone nest are used as the first pole towers of the new first line and the first pole towers of the second line, and the site selection process is continued for subsequent pole towers until the locations of all drone nests are obtained; Among them, the total inspection complexity is the sum of the inspection time of the drone for each tower and the round-trip flight time required for the drone to inspect the tower.

14. The grid-based intelligent inspection method for electric power drones according to claim 1, characterized in that: Before determining the towers within the coverage of each drone nest, the process also includes: determining the location of the drone nest; specifically: For two non-parallel transmission lines or distribution lines, the towers of each line are numbered in sequence according to their distance from the substation; First, determine whether the total inspection complexity of the first pole tower of the first line and the first pole tower of the second line is less than or equal to n times the cruising capability of the drone, and if so, determine the pole tower position according to the method of claim 13; n is a set value; Otherwise, deploy a machine nest for each line. The specific process is as follows: When the total inspection complexity of the first tower and the Nth tower is greater than n times the cruising capability of the drone, and the total inspection complexity of the first tower and the N-1th tower is less than or equal to n times the cruising capability of the drone, a drone nest is deployed between the first tower and the N-1th tower, and the first tower to the N-1th tower are included in the inspection range of the nest; N is a positive integer greater than 2; the coordinates of the tower in the middle position with a smaller number are taken as the location of the nest; For the pole towers that are not included in the inspection range of the machine nest, determine whether the total inspection complexity of the first pole tower of the first line and the first pole tower of the second line is less than or equal to n times the cruising capability of the drone. If so, determine the position of the pole tower according to the method of claim 13; otherwise, continue to deploy the machine nest for each line separately.

15. A grid-based intelligent inspection system for electric power drones, characterized in that: include: The inspection tower determination module is used to select the towers that need to be spliced ​​for the route within a certain set machine nest coverage area; The inspection sequence determination module is used to determine the inspection sequence of each tower with the goal of minimizing the UAV flight path; The flight strategy determination module is used to determine the head and tail waypoints of the drone from the machine nest to the inspection tower, or from the previous inspection tower to the next inspection tower, or from the inspection tower to the machine nest; determine the altitude between the head and tail waypoints, and determine the flight strategy between the machine nest and the tower and between two towers based on the altitude; A route splicing module is used to obtain a total route for UAV inspection based on the inspection sequence of each tower and the flight strategy, and control the UAV to perform power inspection according to the total route; The service pressure monitoring module is used to deploy the microservice architecture in the drone. The inspection data collected during the drone inspection process is transmitted to the drone's microservice architecture in real time, and the microservice architecture receives the data and performs defect identification. In the above process, the operation data of each service in the microservice architecture is monitored in real time to determine the service pressure of the server where each microservice is located. For servers with excessive service pressure, tasks are automatically diverted to other servers. If there is no server that meets the diversion conditions, a new server with corresponding functions is actively created to alleviate data processing pressure. The microservice monitoring strategy used in the entire route of drone inspection includes the following processes: The UAV autonomous inspection system sends autonomous inspection tasks to the UAV, and the UAV starts to perform inspection tasks according to the plan of the spliced ​​total route; during the flight, the UAV returns real-time data / pictures / videos to the autonomous inspection system, and by implementing a custom indicator interface, monitors the server where the data / picture / video transmission microservice is located, finds out the service pressure related parameters caused to the data / picture / video transmission server, including real-time data return pressure parameters, real-time picture return pressure parameters, and real-time video return pressure parameters, and transmits them to the microservice indicator processor for judgment; During the flight of the drone, the wind speed, flight altitude, and alarm information data are sent back to the inspection system in real time. At the same time, the microservice that sends back the data uses the indicator interface it implements to send the service pressure-related parameters and the flight status feedback pressure parameters to the microservice indicator processor for judgment; After the drone inspection is completed, the image is transmitted to the machine nest, and then transmitted back to the system; the inspection image receiving microservice of the drone autonomous inspection system obtains service pressure related parameters in real time through the indicator interface it implements, and the pressure coefficient collector collects the data in real time and hands it over to the microservice indicator processor for judgment; After receiving the inspection pictures sent back, the drone inspection system will perform intelligent defect identification on the inspection pictures. The indicator interface implemented by the intelligent identification algorithm microservice will start to monitor the service pressure-related parameters of the server and send them back to the pressure coefficient collector.

16. The grid-based intelligent inspection system for electric power drones according to claim 15, characterized in that: Also includes: The cruise time calculation module is used to compare the time taken by the drone to inspect the total route with the flight time that the drone's current battery power can support, to determine whether the drone can complete the inspection task.

17. The grid-based intelligent inspection system for electric power drones according to claim 15, characterized in that: Also includes: The machine nest position determination module is used to determine the layout location of the drone machine nest.

18. The grid-based intelligent inspection system for electric power drones according to claim 15, characterized in that: Also includes: The tower inspection sequence module is used to determine the inspection sequence of each tower with the shortest UAV flight path as the goal. The specific process is as follows: Based on the distance from the first waypoint of each tower inspection route to the machine nest, determine the first target inspection tower of the total splicing route; Get the last waypoint of the first target inspection tower , calculate the first waypoint of the remaining towers except the first target inspection tower to Distance, select distance The pole tower with the smallest waypoint is used as the second target inspection tower; Get the last waypoint of the second target inspection tower , calculate the first waypoint to the remaining towers except the first and second target inspection towers Distance, select distance The tower with the smallest waypoint is the third target inspection tower. And so on, until the inspection order of all towers is determined.

19. The grid-based intelligent inspection system for electric power drones according to claim 15, characterized in that: Also includes: The head and tail waypoints determination module, the head and tail waypoints specifically include: The position of the machine nest is taken as the head waypoint, and the first waypoint of the first target inspection tower is taken as the corresponding tail waypoint; The last waypoint of the previous target inspection tower is used as the head waypoint, and the first waypoint of the next target inspection tower is used as the corresponding tail waypoint; The last waypoint of the last target inspection tower is taken as the head waypoint, and the position of the machine nest is taken as the corresponding tail waypoint.

20. A drone for inspecting power line towers, characterized in that: The grid-based intelligent inspection method of electric UAVs according to any one of claims 1 to 14 is adopted; Alternatively, it comprises the electric UAV grid intelligent inspection system as described in any one of claims 15-19.

21. A terminal device, comprising a processor and a memory, wherein the processor is used to implement each instruction; and the memory is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the grid-based intelligent inspection method for electric drones as described in any one of claims 1-14.

22. A computer-readable storage medium storing a plurality of instructions, characterized in that: The instructions are suitable for being loaded by a processor of a terminal device and executing the grid-based intelligent inspection method for electric drones as described in any one of claims 1-14.

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