A multi-dimensional inspection method for power grid bird deterrents

Abstract

The embodiment of the present application discloses a multi-dimensional inspection method for power grid bird deterrents, which is used to perform multi-faceted inspections of bird deterrents installed on power grid equipment through drones, and determine the working efficiency of the bird deterrents to determine whether the bird deterrents need repair or maintenance. The multi-dimensional inspection method for power grid bird deterrents can detect the image of the inspected bird deterrents by detecting drones and determine the working status of the inspected bird deterrents based on the image information analysis, and can conveniently determine whether there are abnormalities in the working status of the inspected bird deterrents without the help of manpower. The present application can detect bird deterrents and determine whether there are abnormalities in the working status of the inspected bird deterrents without the help of manpower; various components of the bird deterrents can be detected separately and determined whether there are abnormalities; the working status and performance of each component of the inspected bird deterrent can be comprehensively and quantitatively determined.

Description

Technical Field

[0001] The present application relates to the field of power grids, and in particular to a multi-dimensional inspection method for power grid bird deterrents. Background Art

[0002] In modern society, power supply is one of the indispensable infrastructures, and the normal operation of power grid facilities is crucial to maintaining social stability and economic development. However, power grid facilities often face problems caused by birds, especially birds nesting on power grid facilities, which may cause equipment damage, power outages and safety risks.

[0003] Usually, power grid departments install bird deterrents on power grid facilities to drive away birds and prevent them from nesting on power grid facilities. However, power grid facilities are generally distributed in remote and inaccessible areas. Therefore, the inspection and maintenance of bird deterrents installed on power grid facilities becomes a challenging task.

[0004] Traditional inspection methods mainly rely on manual inspections, which not only consumes a lot of time and manpower, but also poses certain safety risks. In addition, personnel may not be able to cover all power grid facilities during the inspection process, resulting in potential problems not being discovered and resolved in a timely manner.

[0005] Although image-based inspection technology has been developed in recent years, there are still some challenges for the inspection of power grid bird deterrents. Power grid facilities are usually located at high altitudes or in complex environments, so image acquisition is difficult, and image processing analysis alone cannot accurately determine whether the bird deterrent is working properly and whether it needs repair or maintenance. Summary of the invention

[0006] In order to conveniently determine whether there is any abnormality in the working state of a bird deterrent installed on a power grid device, the present application provides a multi-dimensional inspection method for a power grid bird deterrent.

[0007] The multi-dimensional inspection method of the power grid bird deterrent of the present application adopts the following technical solutions:

[0008] A multi-dimensional inspection method for a power grid bird deterrent comprises the following steps:

[0009] A1, control the preset detection drone to enter the monitoring area of ​​the preset bird deterrent to be inspected;

[0010] A2, obtaining appearance image information of the inspected bird deterrent through the camera module of the inspection drone in the monitoring area;

[0011] A3, determining the device type information of the inspected bird deterrent according to the appearance image information by using a preset bird deterrent identification algorithm;

[0012] A4, obtaining the device parameter information of the inspected bird deterrent in the preset device database according to the device type information;

[0013] A5, retrieve the preset inspection strategy from the preset inspection strategy database according to the equipment parameter information;

[0014] A6, controlling the detection drone to move within the monitoring area using a patrol strategy;

[0015] A7, obtaining the response action image information of the detected bird deterrent through the camera module of the detection drone;

[0016] A8, determining the abnormality of the inspected bird deterrent by using a preset bird deterrent analysis algorithm according to the response action image information.

[0017] By adopting the above technical solution, the multi-dimensional inspection method of the power grid bird deterrent can perform image recognition on the inspected bird deterrent through the detection drone and judge the working status of the inspected bird deterrent based on the image information analysis, and can conveniently determine whether there is any abnormality in the working status of the inspected bird deterrent without manpower.

[0018] Optionally, the multi-dimensional inspection method of the power grid bird deterrent further comprises the following steps:

[0019] B1, determining the installation position information of the inspected bird deterrent in the response action image information according to the appearance image information;

[0020] B2, controlling the detection drone to approach the bird deterrent to be detected and maintain a preset detection distance according to the installation location information;

[0021] B3, identifying the reflective element of the inspected bird deterrent through the camera module of the detection drone and obtaining reflective action image information of the reflective element;

[0022] B4, obtaining brightness fluctuation data through the light detection module of the detection drone;

[0023] B5, calculating the reflective effectiveness according to the reflective action image information and the brightness fluctuation data using a preset reflective effectiveness algorithm.

[0024] By adopting the above technical solution, the multi-dimensional inspection method of the power grid bird deterrent can more accurately detect the reflective performance of the reflective element of the inspected bird deterrent.

[0025] Optionally, the multi-dimensional inspection method of the power grid bird deterrent further comprises the following steps:

[0026] C1, determining the bird deterrent communication protocol of the communication module of the bird deterrent under inspection according to the device parameter information;

[0027] C2, at the detection distance, the communication module of the detection drone attempts to establish a communication connection with the communication module of the detected bird deterrent using the bird deterrent communication protocol;

[0028] C3, determining whether the detection drone has successfully established a communication connection with the detected bird deterrent;

[0029] C4, if the judgment result is yes, obtaining the connection signal strength information within the preset strength detection time through the communication module of the detected bird deterrent, and calculating the signal stability of the detected bird deterrent by using the preset signal stability algorithm according to the connection signal strength information;

[0030] C5, if the judgment result is no, the preset signal stability default value is defined as the signal stability.

[0031] By adopting the above technical solution, the multi-dimensional inspection method of the power grid bird deterrent can determine whether the communication module of the inspected bird deterrent can work normally, and can detect the communication performance of the communication module of the inspected bird deterrent.

[0032] Optionally, the multi-dimensional inspection method of the power grid bird deterrent further comprises the following steps:

[0033] D1, controlling the detection drone to hover at a preset hovering height directly above the inspected bird deterrent according to the installation position information;

[0034] D2, obtaining the impeller image information of the bird-repelling impeller of the inspected bird-repelling device through the shooting module of the inspection drone;

[0035] D3, determining the impeller size coefficient according to the hovering height and the impeller image information using a preset impeller size estimation algorithm;

[0036] D4, driving the bird-repelling impeller of the inspected bird-repelling device to rotate by the inspection drone;

[0037] D5, obtaining the impeller speed data of the bird-repelling impeller through the communication module of the inspected bird-repelling device;

[0038] D6, calculates the impeller efficiency according to the impeller size factor and the impeller speed data using a preset impeller efficiency algorithm.

[0039] By adopting the above technical solution, the multi-dimensional inspection method of the power grid bird deterrent can quickly detect the working status of the bird-repelling impeller of the inspected bird deterrent by hovering the inspection drone.

[0040] Optionally, the multi-dimensional inspection method of the power grid bird deterrent further comprises the following steps:

[0041] E1, controlling the detection drone to move around the inspected bird deterrent according to the installation location information and a preset surrounding strategy;

[0042] E2, obtaining relative position information through the positioning module of the detection drone according to the installation position information, and obtaining brightness data information corresponding to the relative position information through the light detection module of the detection drone, wherein the relative position information includes a plurality of relative position data, the brightness data information includes a plurality of brightness data, and the relative position data and the brightness data correspond to each other one by one;

[0043] E3, screening the brightness data information according to a preset brightness threshold, defining the brightness data greater than the brightness threshold as valid brightness data, and defining the relative position data corresponding to the valid brightness data as valid position data;

[0044] E4, determining the effective reflection angle according to the installation position information and all effective position data using a preset effective reflection angle algorithm.

[0045] By adopting the above technical solution, the multi-dimensional inspection method of the power grid bird deterrent can measure the effective reflection angle of the reflective element of the inspected bird deterrent to determine the effective range of the reflective element of the inspected bird deterrent.

[0046] Optionally, the multi-dimensional inspection method of the power grid bird deterrent further comprises the following steps:

[0047] F1, obtaining the current time, and sending the preset virtual position information of the flying bird to the detected bird deterrent through the communication module of the detecting drone and defining the current time as the command time;

[0048] F2, obtaining the anti-response image information of the reflective element of the inspected bird deterrent through the camera module of the inspection drone;

[0049] F3, determining the response time of the reflective element according to the response image information using a preset motion recognition algorithm;

[0050] F4, calculates and determines the response delay based on the difference between the command time and the response time;

[0051] F5, controlling the detection drone to move to the position corresponding to the virtual position information of the flying bird, and obtaining the response brightness value through the light detection module of the detection drone;

[0052] F6, obtain the brightness data with the largest value in the brightness data information and define it as the optimal brightness value;

[0053] F7, calculates the response accuracy according to the response delay, response brightness value and optimal brightness value using the preset response accuracy algorithm.

[0054] By adopting the above technical solution, the multi-dimensional inspection method of the power grid bird deterrent can detect the response efficiency of the reflective element by generating a virtual flying bird position toward the inspected bird deterrent.

[0055] Optionally, the power grid bird deterrent inspection method further includes the following steps:

[0056] G1, using a preset surveying and mapping surround algorithm to control the detection drone to surround the power grid tower on which the bird deterrent to be inspected is installed, and obtaining tower image information through the camera module of the detection drone;

[0057] G2, generating tower model data according to the tower image information using a preset modeling algorithm;

[0058] G3, matching the tower model data in a preset tower model database with a preset tower matching algorithm to determine the tower type information;

[0059] G4, obtaining the location data of bird damage on similar towers from a preset tower bird damage history database according to the tower type information;

[0060] G5, determine the high-frequency area information of bird damage based on the bird damage location data of each similar tower pole using a preset distribution density algorithm;

[0061] G6, calculating the installation effectiveness of the inspected bird deterrent according to the bird damage high frequency area information and the installation location information using a preset installation effectiveness algorithm.

[0062] By adopting the above technical solution, the multi-dimensional inspection method of the power grid bird deterrent can determine the effectiveness of the position where the inspected bird deterrent is installed on the tower.

[0063] Optionally, the bird deterrent recognition algorithm of the multi-dimensional inspection method for power grid bird deterrents includes the following steps:

[0064] H1, determining component type information according to the appearance image information using a preset component recognition algorithm;

[0065] H2, determine matching device information based on component type information in the device database;

[0066] H3, obtaining corresponding matching device image information from the device database according to the matching device information;

[0067] H4, calculating the device image difference using a preset difference algorithm according to the appearance image information and the matching device graphic information;

[0068] H5, judging whether the difference of the device images is less than the preset difference threshold;

[0069] H6, if the returned result is yes, the matching device information is defined as the device type information.

[0070] By adopting the above technical solution, the power grid bird deterrent inspector can determine the device type information of the inspected bird deterrent through the appearance image information of the inspected bird deterrent.

[0071] Optionally, the multi-dimensional inspection method of the power grid bird deterrent further comprises the following steps:

[0072] J1, calculating the equipment health value of the inspected bird deterrent according to the reflection effectiveness, signal stability, impeller efficiency, effective reflection angle, response accuracy and installation effectiveness using a preset equipment health value algorithm;

[0073] The device health value algorithm is:

[0074] H=a*L+b*S+c*F+d*A+e*R+f*P,

[0075] Among them, H is the equipment health value, a is the preset reflection effectiveness adjustment coefficient, L is the reflection effectiveness, b is the preset signal stability adjustment coefficient, S is the signal stability, c is the preset impeller efficiency adjustment coefficient, F is the impeller efficiency, d is the preset effective reflection angle adjustment coefficient, A is the effective reflection angle, e is the preset response accuracy adjustment coefficient, R is the response accuracy, f is the preset installation effectiveness adjustment coefficient, and P is the installation effectiveness;

[0076] J2, determine whether the device health value is less than the preset health value threshold;

[0077] J3, if the returned result is yes, then an equipment maintenance report is generated according to the reflection effectiveness, signal stability, impeller efficiency, effective reflection angle, response accuracy and installation effectiveness using a preset analysis algorithm.

[0078] By adopting the above technical solution, the multi-dimensional inspection method of the power grid bird deterrent can comprehensively and quantitatively judge the working status and performance of each component of the inspected bird deterrent for reference by the staff to make disposal.

[0079] In summary, the present application includes at least one of the following beneficial technical effects:

[0080] 1. The bird deterrent can be inspected without human intervention and whether there is any abnormality in the working state of the inspected bird deterrent;

[0081] 2. The various components of the bird deterrent can be tested separately to determine whether there are any abnormalities;

[0082] 3. The working status and performance of each component of the inspected bird deterrent can be comprehensively and quantitatively determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a flow chart of a multi-dimensional inspection method of a power grid bird deterrent in the present application. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0085] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0086] The embodiment of the present application discloses a multi-dimensional inspection method for a power grid bird deterrent, which is used to perform a multi-faceted inspection of a bird deterrent installed on a power grid device through a drone, and determine the working efficiency of the bird deterrent to determine whether the bird deterrent needs repair or maintenance.

[0087] like Figure 1 As shown, the multi-dimensional inspection method of the power grid bird deterrent includes the following steps:

[0088] A1, control the preset detection drone to enter the monitoring area of ​​the preset bird deterrent to be inspected;

[0089] The detection drone is a controlled drone selected by staff to detect bird deterrents;

[0090] The inspected bird deterrent is the one selected by the worker or the control background for inspection;

[0091] The monitoring area is a certain area around the inspected bird deterrent. An area can be artificially set with the inspected bird deterrent as the center, or a certain monitoring area can be estimated based on the parameter performance of the inspected bird deterrent.

[0092] A2, obtaining appearance image information of the inspected bird deterrent through the camera module of the inspection drone in the monitoring area;

[0093] Appearance image information refers to the image information of the appearance of the inspected bird deterrent, which can be obtained by shooting from multiple angles through the camera module of the inspection drone;

[0094] There are various types of bird deterrents, some are passive bird deterrents that work continuously with wind or solar energy; some are intelligent bird deterrents that can identify approaching birds based on their own sensing modules and respond by controlling their own bird repelling working modules to drive away the birds;

[0095] For different bird deterrents, it is necessary to make a type judgment in advance so that the working module can be determined according to the type of bird deterrent and targeted detection can be carried out.

[0096] A3, determining the device type information of the inspected bird deterrent according to the appearance image information by using a preset bird deterrent identification algorithm;

[0097] The bird deterrent device recognition algorithm is an image recognition algorithm set by the staff. It can perform feature recognition through the appearance image information of the inspected bird deterrent device, and match it in the database to determine the type of the inspected bird deterrent device. It can train the image samples of the bird deterrent device through a preset recognition model, and extract the corresponding recognition features to complete the recognition of the bird deterrent device through the recognition features.

[0098] The device type information is information about the type of the bird deterrent being inspected.

[0099] A4, obtaining the device parameter information of the inspected bird deterrent in the preset device database according to the device type information;

[0100] The equipment database is a database of various bird deterrents that have been used and preset by the staff;

[0101] Equipment parameter information is the parameter data of various bird deterrents, including shape, size, color, working module, etc. A database of bird deterrents can be established through big data, and bird deterrents can be identified through shape, size, color, etc. information to improve safety and reliability.

[0102] A5, retrieve the preset inspection strategy from the preset inspection strategy database according to the equipment parameter information;

[0103] The inspection strategy data is a database pre-stored with various inspection strategies set by the staff for calling to control the inspection drone;

[0104] The inspection strategy is the detection method of the detection drone set by the staff. Different detection methods can be pre-set according to different types of bird deterrents or different working modules of bird deterrents, so that the detection drone can perform targeted detection after obtaining the equipment parameter information of the inspected bird deterrent.

[0105] A6, controlling the detection drone to move within the monitoring area using a patrol strategy;

[0106] The detection drone is controlled to move through a patrol strategy, so that the detection drone can detect the bird deterrent from multiple angles.

[0107] A7, obtaining the response action image information of the detected bird deterrent through the camera module of the detection drone;

[0108] The response action graphic information is image information of the action performed by the inspected bird deterrent, which can be used to determine whether the inspected bird deterrent is working properly. The response action image information can be continuous video image information, continuous photo image information with a certain time interval, or any other image information with content that can be used for analysis and identification.

[0109] A8, determining the abnormality of the inspected bird deterrent by using a preset bird deterrent analysis algorithm according to the response action image information;

[0110] The bird deterrent analysis algorithm is an analysis algorithm set by the staff, which is used to identify and judge whether the bird deterrent can work normally and the working status according to the response action image information, such as by identifying various types of the inspected bird deterrent in the response action image.

[0111] Through the above steps, the multi-dimensional inspection method for power grid bird deterrents can perform image recognition on the inspected bird deterrents through the detection drone and determine the working status of the inspected bird deterrents based on the image information analysis, and can conveniently determine whether there is any abnormality in the working status of the inspected bird deterrents without the help of manpower.

[0112] Furthermore, the multi-dimensional inspection method for power grid bird deterrents further comprises the following steps for detecting the reflective performance of the reflective element of the inspected bird deterrent:

[0113] B1, determining the installation position information of the inspected bird deterrent in the response action image information according to the appearance image information;

[0114] The installation location information is the location information where the inspected bird deterrent is installed and positioned;

[0115] According to the appearance image information and by responding to the action image information, the position of the inspected bird deterrent can be located by using the existing visual SLAM algorithm to determine the installation position information of the inspected bird deterrent.

[0116] B2, controlling the detection drone to approach the bird deterrent to be detected and maintain a preset detection distance according to the installation location information;

[0117] The detection distance is a distance set by the staff or selected by the control background, which is used to keep the detection drone at an appropriate distance from the bird deterrent being detected, thereby improving the stability and accuracy of the detection.

[0118] B3, identifying the reflective element of the inspected bird deterrent through the camera module of the detection drone and obtaining reflective action image information of the reflective element;

[0119] Reflective elements are set in bird deterrents to drive away birds by reflecting sunlight. There are passive reflective elements for continuous reflection; there are also active reflective elements that can accurately reflect and drive away birds according to their positions to improve the bird deterrent effect. Reflective elements generally drive away birds by reflecting sunlight.

[0120] The reflective action image information is image information of the action of the reflective element when it is working, which is obtained by the camera module and can be used to determine whether the reflective element is in a working state.

[0121] B4, obtaining brightness fluctuation data through the light detection module of the detection drone;

[0122] The light detection module is used to detect the brightness of the light of the reflective element. The brightness data of the reflected light of the reflective element can be obtained in real time through the light detection module.

[0123] The brightness fluctuation data is the reflective brightness of the reflective element continuously obtained by the light detection module within a certain period of time.

[0124] B5, calculating the reflective effectiveness using a preset reflective effectiveness algorithm according to the reflective action image information and the brightness fluctuation data;

[0125] The reflective effectiveness algorithm is an algorithm set by the staff for calculating the reflective effectiveness of the reflective element;

[0126] By matching the reflective action image information with the brightness fluctuation data, the brightness data detected by the light detection module when the reflective element performs the reflective action can be obtained to obtain effective brightness data, and then statistical calculations can be performed based on these effective brightness data to determine the reflective effectiveness of the reflective element of the inspected bird deterrent.

[0127] Through the above steps, the multi-dimensional inspection method of the power grid bird deterrent can accurately detect the reflective performance of the reflective element of the inspected bird deterrent.

[0128] Furthermore, the multi-dimensional inspection method for power grid bird deterrents also includes the following steps for detecting the communication module of the inspected bird deterrent:

[0129] C1, determining the bird deterrent communication protocol of the communication module of the bird deterrent under inspection according to the device parameter information;

[0130] The communication protocol of the bird deterrent is the rule used by the communication module of the detected bird deterrent to communicate with other devices. Communication modules of different types or signals usually have different communication protocols. If the detection drone cannot obtain the corresponding communication protocol, it will not be able to accurately establish a communication connection with the communication module of the detected bird deterrent.

[0131] The communication protocol of the bird deterrent under inspection can be obtained directly in the data information through the device parameter information.

[0132] C2, at the detection distance, the communication module of the detection drone attempts to establish a communication connection with the communication module of the detected bird deterrent using the bird deterrent communication protocol;

[0133] After obtaining the communication protocol of the bird deterrent, the detection drone can establish a communication connection with the communication module of the detected bird deterrent through the communication module.

[0134] C3, determining whether the detection drone has successfully established a communication connection with the detected bird deterrent;

[0135] Whether the communication module of the bird deterrent under inspection can work normally is confirmed by whether the communication connection is successful.

[0136] C4, if the judgment result is yes, obtaining the connection signal strength information within the preset strength detection time through the communication module of the detected bird deterrent, and calculating the signal stability of the detected bird deterrent by using the preset signal stability algorithm according to the connection signal strength information;

[0137] The strength detection time is a time length set by the staff, and is used to detect the connection signal strength information of the communication module of the detected bird deterrent within the time length;

[0138] The connection signal strength information is the signal strength of the communication connection between the communication module of the detection drone and the communication module of the detected bird deterrent, and the data can usually be obtained directly from the communication module;

[0139] The signal stability algorithm is an algorithm set by the staff for detecting the signal stability of the communication connection between the communication module of the detection drone and the communication module of the detected bird deterrent according to the connection signal strength information, which can be obtained by processing the connection signal strength information using a certain statistical method;

[0140] The signal stability is a value reflecting the stability of the communication connection between the communication module of the detection drone and the communication module of the detected bird deterrent, which can quantify the stability of the communication continuity.

[0141] C5, if the judgment result is no, the preset signal stability default value is defined as the signal stability.

[0142] The default value of signal stability is the default value set by the staff. It is used when it is impossible to establish a communication connection or obtain signal strength. The default value is usually 0.

[0143] Through the above steps, the multi-dimensional inspection method of the power grid bird deterrent can determine whether the communication module of the inspected bird deterrent can work normally, and can detect the communication performance of the communication module of the inspected bird deterrent.

[0144] Furthermore, the multi-dimensional inspection method for power grid bird deterrents also includes the following steps for detecting the bird-repelling impeller of the inspected bird deterrent:

[0145] D1, controlling the detection drone to hover at a preset hovering height directly above the inspected bird deterrent according to the installation position information;

[0146] The hovering height is the relative height between the detection drone and the inspected bird deterrent set by a worker or a control background;

[0147] By hovering the detection drone just above the inspected bird deterrent, the downward wind force generated by the detection drone when hovering can drive the bird-repelling impeller to inspect the bird-repelling impeller;

[0148] For example, for the same inspection UAV, its own weight is unchanged, and the lift required when the inspection UAV is hovering is also unchanged, and the downward wind force when the UAV is hovering is almost the same. The driving force of the bird-repellent impeller can be estimated based on the preset fixed hovering height and the UAV's own weight, and then the impeller can be judged whether it can work smoothly according to the rotation speed of the impeller and the force it receives, so as to determine whether the impeller needs to be repaired or maintained.

[0149] D2, obtaining the impeller image information of the bird-repelling impeller of the inspected bird-repelling device through the shooting module of the inspection drone;

[0150] The bird-repelling impeller is installed in the bird-repelling device and is used for passive or active rotation to scare away birds and prevent them from nesting.

[0151] The impeller image information is an image of the bird-repelling impeller, and can be used to analyze information such as the shape, size, and color of the bird-repelling impeller.

[0152] D3, determining the impeller size coefficient according to the hovering height and the impeller image information using a preset impeller size estimation algorithm;

[0153] The impeller size estimation algorithm is an algorithm set by the staff, which is used to estimate the size of the bird-repelling impeller according to the hovering height and the impeller image information, and determine the impeller size coefficient according to the impeller size;

[0154] The impeller size factor is calculated by the impeller size estimation algorithm and can quantitatively reflect the size of the impeller.

[0155] D4, driving the bird-repelling impeller of the inspected bird-repelling device to rotate by the inspection drone;

[0156] The downward wind force generated by the detection drone when hovering can drive the bird-repelling impeller to detect the bird-repelling impeller.

[0157] D5, obtaining the impeller speed data of the bird-repelling impeller through the communication module of the inspected bird-repelling device;

[0158] The impeller speed data is the speed data of the bird-repelling impeller, which can be measured by the impeller speed sensor of the inspected bird-repelling device and sent to the detection drone through the communication module.

[0159] D6, calculating the impeller efficiency according to the impeller size factor and the impeller speed data using a preset impeller efficiency algorithm;

[0160] The impeller efficiency algorithm is an algorithm set by the staff to calculate the impeller efficiency based on the impeller size factor and impeller speed data;

[0161] Impeller efficiency is a quantitative value that reflects the working efficiency of the bird-repelling impeller.

[0162] Through the above steps, the multi-dimensional inspection method for power grid bird deterrents can quickly detect the working status of the bird-repelling impeller of the inspected bird deterrent by hovering the inspection drone.

[0163] Furthermore, the multi-dimensional inspection method for power grid bird deterrents also includes the following steps for detecting the effective reflection angle of the reflective element of the inspected bird deterrent:

[0164] E1, controlling the detection drone to move around the inspected bird deterrent according to the installation location information and a preset surrounding strategy;

[0165] The surround strategy is the movement mode of the detection drone around the inspected bird deterrent set by the staff. For example, a fixed surround distance can be set to control the detection drone to move around the inspected bird deterrent according to the installation location information, so as to collect the reflective data of the reflective element from all directions of the inspected bird deterrent at equal distances, so that the reflective effectiveness of each orientation can be judged according to the reflective data and the effective reflective angle of the reflective element can be obtained.

[0166] E2, obtaining relative position information through the positioning module of the detection drone according to the installation position information, and obtaining brightness data information corresponding to the relative position information through the light detection module of the detection drone, wherein the relative position information includes a plurality of relative position data, the brightness data information includes a plurality of brightness data, and the relative position data and the brightness data correspond to each other one by one;

[0167] The relative position information is the position relationship information between the detection drone and the detected bird deterrent, reflecting the relative position between the two;

[0168] The brightness data information is the brightness data measured by the light detection module of the detection drone at different relative positions;

[0169] The relative position data is data of the reaction position relationship between the detection drone and the detected bird deterrent at a certain relative position;

[0170] The brightness data is the brightness data of the reflective element detected by the light detection module of the detection drone at a certain relative position.

[0171] E3, screening the brightness data information according to a preset brightness threshold, defining the brightness data greater than the brightness threshold as valid brightness data, and defining the relative position data corresponding to the valid brightness data as valid position data;

[0172] The brightness threshold is a brightness value set by the staff, which is used to exclude brightness data that is invalid or inefficient for bird repelling.

[0173] Effective brightness data refers to brightness data that is greater than the brightness threshold and has a bird-repelling effect;

[0174] The effective position data is the relative position data corresponding to the effective brightness data, and can reflect the various positions in which the reflective element of the inspected bird deterrent can achieve the bird-repelling effect.

[0175] E4, determining the effective reflection angle according to the installation position information and all effective position data using a preset effective reflection angle algorithm.

[0176] The effective reflection angle algorithm is an algorithm set by the staff, which can calculate the corresponding spatial orientation and angle based on multiple effective position data;

[0177] The effective reflection angle is the angle information centered on the inspected bird deterrent, which can reflect the direction and angle in which the reflective element of the inspected bird deterrent can achieve the bird-repelling effect;

[0178] For example, by taking the installation position of the inspected bird deterrent as the center of the circle, the angles from the effective positions of each reflection to the installation position can be calculated, and an angle range can be generated by combining all the angles, which can be used as the effective reflection angle.

[0179] Through the above method, the multi-dimensional inspection method of the power grid bird deterrent can measure the effective reflection angle of the reflective element of the inspected bird deterrent to determine the effective range of the reflective element of the inspected bird deterrent.

[0180] Furthermore, the multi-dimensional inspection method for power grid bird deterrents also includes the following steps for detecting the response accuracy of the reflective element of the inspected bird deterrent:

[0181] F1, obtaining the current time, and sending the preset virtual position information of the flying bird to the detected bird deterrent through the communication module of the detecting drone and defining the current time as the command time;

[0182] The current moment is the current time, which can be obtained through the processing system or time-related module of the inspection drone;

[0183] The virtual position information of the flying bird is the virtual position information of the flying bird generated by the detection drone, and is used to enable the detected bird deterrent to respond according to the virtual information;

[0184] The instruction time is the time when the detection drone sends the virtual position information of the flying bird to the detected bird deterrent.

[0185] F2, obtaining the response image information of the reflective element of the inspected bird deterrent through the camera module of the inspection drone;

[0186] The response image information is image information in which the inspected bird deterrent controls the reflective element to respond after receiving the virtual position information of the flying bird.

[0187] F3, determining the response time of the reflective element according to the response image information using a preset motion recognition algorithm;

[0188] The motion recognition algorithm is an image recognition algorithm set by the staff. It can identify the action in the image according to the image content and derive the time when the action occurs. The corresponding response moment can be calculated by training the image recognition algorithm through a large number of samples, based on the moment when the reflective element moves, recording the reaction moment of each training sample, and marking the relationship between the environmental information and the reaction moment. The recognition is then completed through the training model, and the corresponding motion recognition algorithm is generated. The theoretical response moment can be calculated according to the corresponding position range.

[0189] The response time is the time when the reflective element performs a response action in response to the image information.

[0190] F4, calculates and determines the response delay based on the difference between the command time and the response time;

[0191] The response delay is the time difference between the detected bird deterrent receiving the virtual position information of the flying bird and controlling the reflective element to make a response action, which is used to quantitatively reflect the response speed of the detected bird deterrent.

[0192] F5, controlling the detection drone to move to the position corresponding to the virtual position information of the flying bird, and obtaining the response brightness value through the light detection module of the detection drone;

[0193] The response brightness value is the brightness value of the reflective element of the detected bird deterrent measured by the detection drone at the position corresponding to the virtual position information of the flying bird.

[0194] F6, obtain the brightness data with the largest value in the brightness data information and define it as the optimal brightness value;

[0195] The optimal brightness value is the maximum value in the brightness data information, and can be used to estimate the maximum brightness value that the inspected bird repellent can achieve.

[0196] F7, calculates the response accuracy according to the response delay, response brightness value and optimal brightness value using a preset response accuracy algorithm;

[0197] The response accuracy algorithm is an algorithm set by the staff and is used to comprehensively calculate the working efficiency of the reflective element of the inspected bird deterrent;

[0198] The response accuracy is a value calculated by comprehensively considering the response delay, the response brightness value and the optimal brightness value, and is used to quantitatively reflect the response efficiency of the reflective element of the inspected bird deterrent.

[0199] Through the above steps, the multi-dimensional inspection method of the power grid bird deterrent can detect the response efficiency of the reflective element by generating a virtual flying bird position toward the inspected bird deterrent.

[0200] Furthermore, the multi-dimensional inspection method for power grid bird deterrents also includes the following steps for detecting the installation effectiveness of the inspected bird deterrents:

[0201] G1, using a preset surveying and mapping surround algorithm to control the detection drone to surround the power grid tower on which the bird deterrent to be inspected is installed, and obtaining tower image information through the camera module of the detection drone;

[0202] The mapping and circling algorithm is an algorithm set by the staff to control the detection drone to circle the above-mentioned bird deterrent device under inspection;

[0203] The tower pole image information is an image of the tower pole on which the inspected bird deterrent is installed.

[0204] G2, generating tower model data according to the tower image information using a preset modeling algorithm;

[0205] The modeling algorithm is an algorithm set by the staff and is used to build a three-dimensional model of the tower according to the tower image information;

[0206] The tower model data is three-dimensional model data of the tower on which the inspected bird deterrent is installed.

[0207] G3, matching the tower model data in a preset tower model database with a preset tower matching algorithm to determine the tower type information;

[0208] The tower model database is a database pre-set by the staff that stores three-dimensional models of various towers. The model data of various towers can be obtained through prior surveying or modeling.

[0209] The tower matching algorithm is a matching algorithm set by the staff, which is used to compare the tower model data with the data in the tower model database to find tower data that is the same or similar to the tower image information;

[0210] The tower type information is the type data of the tower on which the inspected bird deterrent is installed, and is obtained by matching in a tower model database.

[0211] G4, obtaining the location data of bird damage on similar towers from a preset tower bird damage history database according to the tower type information;

[0212] The tower bird damage history database is a database pre-set by the staff that stores various tower bird damage information, such as bird damage image information, bird damage location information on the tower, etc.

[0213] The bird damage location data of the same type of towers are all the bird damage information that has occurred on towers with the same tower type information in the past;

[0214] G5, determine the high-frequency area information of bird damage based on the bird damage location data of each similar tower pole using a preset distribution density algorithm;

[0215] The distribution density algorithm is an algorithm set by the staff, which is used to calculate the probability of bird damage occurring in different areas of the tower based on the location data of bird damage on all similar towers;

[0216] The high-frequency area information of bird damage is the area location information where bird damage is most likely to occur on the tower.

[0217] G6, calculating the installation effectiveness of the inspected bird deterrent according to the bird damage high frequency area information and the installation location information using a preset installation effectiveness algorithm.

[0218] The installation effectiveness algorithm is an algorithm set by the staff and is used to calculate the installation effectiveness of the inspected bird deterrent according to the bird damage high frequency area information and the installation location information;

[0219] The installation effectiveness is a quantitative value of the effectiveness of the installation position of the inspected bird deterrent on the tower.

[0220] Through the above steps, the multi-dimensional inspection method of the power grid bird deterrent can determine the effectiveness of the position where the inspected bird deterrent is installed on the tower.

[0221] Furthermore, the bird deterrent recognition algorithm of the power grid bird deterrent inspection party includes the following steps:

[0222] H1, determining component type information according to the appearance image information using a preset component recognition algorithm;

[0223] The component recognition algorithm is an algorithm set by the staff, and is used to identify each component of the inspected bird deterrent in the appearance image information through an image recognition algorithm;

[0224] The component type information is the corresponding information of each component of the inspected bird deterrent identified and determined by a component identification algorithm;

[0225] The image information of various existing bird deterrents and their various components can be trained through AI algorithms such as machine learning or neural networks to generate an efficient and highly accurate component recognition algorithm for targeted identification of various components of bird deterrents.

[0226] H2, determine matching device information based on component type information in the device database;

[0227] The matching device information is the device information corresponding to the bird deterrent that can match the component type information in the device database, that is, the types or models of the bird deterrent that include all the identified components.

[0228] H3, obtaining corresponding matching device image information from the device database according to the matching device information;

[0229] The matching device image information is the image information of the bird deterrent corresponding to the matching device information in the device database.

[0230] H4, calculating the device image difference using a preset difference algorithm according to the appearance image information and the matching device graphic information;

[0231] The difference algorithm is an image algorithm set by the staff to compare the appearance image information and the matching device graphic information to determine the difference between the device images;

[0232] The device image difference is a quantitative value of the difference between the appearance image information and the matching device graphic information. The smaller the device image difference is, the more likely the type of the inspected bird deterrent is the corresponding model in the matching device graphic information.

[0233] H5, judging whether the difference of the device images is less than the preset difference threshold;

[0234] The difference threshold is a standard value set by the staff and is used to determine whether the device image difference between the appearance image information and the matching device graphic information meets expectations.

[0235] H6, if the returned result is yes, the matching device information is defined as the device type information.

[0236] If the device image difference between the appearance image information and the matching device graphic information is less than the difference threshold, it can be determined that the type of the inspected bird deterrent is the same as the type of the bird deterrent in the matching device graphic information, and then the device type information of the inspected bird deterrent is determined.

[0237] Through the above steps, the power grid bird deterrent inspector can determine the device type information of the inspected bird deterrent through the appearance image information of the inspected bird deterrent.

[0238] Furthermore, the multi-dimensional inspection method for power grid bird deterrents also includes the following steps for comprehensively and quantitatively determining the equipment health value of the inspected bird deterrent:

[0239] J1, calculating the equipment health value of the inspected bird deterrent according to the reflection effectiveness, signal stability, impeller efficiency, effective reflection angle, response accuracy and installation effectiveness using a preset equipment health value algorithm;

[0240] The device health value algorithm is:

[0241] H=a*L+b*S+c*F+d*A+e*R+f*P,

[0242] Among them, H is the equipment health value, a is the preset reflection effectiveness adjustment coefficient, L is the reflection effectiveness, b is the preset signal stability adjustment coefficient, S is the signal stability, c is the preset impeller efficiency adjustment coefficient, F is the impeller efficiency, d is the preset effective reflection angle adjustment coefficient, A is the effective reflection angle, e is the preset response accuracy adjustment coefficient, R is the response accuracy, f is the preset installation effectiveness adjustment coefficient, and P is the installation effectiveness;

[0243] The device health value algorithm is an algorithm that comprehensively considers the reflective effectiveness, signal stability, impeller efficiency, effective reflective angle, response accuracy and installation effectiveness of the inspected bird deterrent, and is used to quantitatively calculate the device health of the inspected bird deterrent;

[0244] In addition, the staff can make different changes and adjustments to the reflection effectiveness adjustment coefficient, signal stability coefficient, impeller efficiency coefficient, effective reflection angle coefficient, response accuracy coefficient and installation effectiveness coefficient according to needs or different situations to meet the actual situation.

[0245] J2, determine whether the device health value is less than the preset health value threshold;

[0246] The health value threshold is a standard value set by the staff to determine whether the inspected bird deterrent needs repair or maintenance.

[0247] J3, if the result returned is yes, then generate the equipment maintenance report according to the reflection effectiveness, signal stability, impeller efficiency, effective reflection angle, response accuracy and installation effectiveness with the preset analysis algorithm;

[0248] If the device health value is less than the health value threshold, it means that the inspected bird deterrent needs to be repaired or maintained;

[0249] The analysis algorithm is an algorithm set by the staff and is used to generate equipment maintenance reports based on reflection effectiveness, signal stability, impeller efficiency, effective reflection angle, response accuracy, and installation effectiveness;

[0250] The equipment maintenance report is a report reflecting the various aspects of the inspected bird deterrent, which can be read by the staff and they can make disposal decisions on the inspected bird deterrent with reference to the contents of the report.

[0251] Through the above steps, the multi-dimensional inspection method of the power grid bird deterrent can comprehensively and quantitatively judge the working status and performance of each component of the inspected bird deterrent for reference by the staff to make disposal.

[0252] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0253] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A multi-dimensional inspection method for power grid bird deterrents, characterized in that: The following steps are involved: A1, control the preset detection drone to enter the monitoring area of ​​the preset bird deterrent to be inspected; A2, obtaining appearance image information of the inspected bird deterrent through the camera module of the inspection drone in the monitoring area; A3, determining the device type information of the inspected bird deterrent according to the appearance image information by using a preset bird deterrent identification algorithm; A4, obtaining the device parameter information of the inspected bird deterrent in the preset device database according to the device type information; A5, retrieve the preset inspection strategy from the preset inspection strategy database according to the equipment parameter information; A6, controlling the detection drone to move within the monitoring area using a patrol strategy; A7, obtaining the response action image information of the detected bird deterrent through the camera module of the detection drone; A8, determining the abnormality of the inspected bird deterrent by using a preset bird deterrent analysis algorithm according to the response action image information; B1, determining the installation position information of the inspected bird deterrent in the response action image information according to the appearance image information; B2, controlling the detection drone to approach the bird deterrent to be detected and maintain a preset detection distance according to the installation location information; B3, identifying the reflective element of the inspected bird deterrent through the camera module of the detection drone and obtaining reflective action image information of the reflective element; B4, obtaining brightness fluctuation data through the light detection module of the detection drone; B5, calculating the reflective effectiveness using a preset reflective effectiveness algorithm according to the reflective action image information and the brightness fluctuation data; C1, determining the bird deterrent communication protocol of the communication module of the bird deterrent under inspection according to the device parameter information; C2, at the detection distance, the communication module of the detection drone attempts to establish a communication connection with the communication module of the detected bird deterrent using the bird deterrent communication protocol; C3, determining whether the detection drone has successfully established a communication connection with the detected bird deterrent; C4, if the judgment result is yes, obtaining the connection signal strength information within the preset strength detection time through the communication module of the detected bird deterrent, and calculating the signal stability of the detected bird deterrent by using the preset signal stability algorithm according to the connection signal strength information; C5, if the judgment result is no, the preset signal stability default value is defined as the signal stability.

2. The multi-dimensional inspection method of power grid bird deterrent according to claim 1 is characterized in that: Further comprising the steps of: D1, controlling the detection drone to hover at a preset hovering height directly above the inspected bird deterrent according to the installation position information; D2, obtaining the impeller image information of the bird-repelling impeller of the inspected bird-repelling device through the shooting module of the inspection drone; D3, determining the impeller size coefficient according to the hovering height and the impeller image information using a preset impeller size estimation algorithm; D4, driving the bird-repelling impeller of the inspected bird-repelling device to rotate by the inspection drone; D5, obtaining the impeller speed data of the bird-repelling impeller through the communication module of the inspected bird-repelling device; D6, calculates the impeller efficiency according to the impeller size factor and the impeller speed data using a preset impeller efficiency algorithm.

3. The multi-dimensional inspection method of power grid bird deterrent according to claim 2 is characterized in that: Further comprising the steps of: E1, controlling the detection drone to move around the inspected bird deterrent according to the installation location information and a preset surrounding strategy; E2, obtaining relative position information through the positioning module of the detection drone according to the installation position information, and obtaining brightness data information corresponding to the relative position information through the light detection module of the detection drone, wherein the relative position information includes a plurality of relative position data, the brightness data information includes a plurality of brightness data, and the relative position data and the brightness data correspond to each other one by one; E3, screening the brightness data information according to a preset brightness threshold, defining the brightness data greater than the brightness threshold as valid brightness data, and defining the relative position data corresponding to the valid brightness data as valid position data; E4, determining the effective reflection angle according to the installation position information and all the effective position data using a preset effective reflection angle algorithm.

4. The multi-dimensional inspection method of power grid bird deterrent according to claim 3 is characterized in that: Further comprising the steps of: F1, obtaining the current time, and sending the preset virtual position information of the flying bird to the detected bird deterrent through the communication module of the detecting drone and defining the current time as the command time; F2, obtaining the anti-response image information of the reflective element of the inspected bird deterrent through the camera module of the inspection drone; F3, determining the response time of the reflective element according to the response image information using a preset motion recognition algorithm; F4, calculates and determines the response delay based on the difference between the command time and the response time; F5, controlling the detection drone to move to the position corresponding to the virtual position information of the flying bird, and obtaining the response brightness value through the light detection module of the detection drone; F6, obtain the brightness data with the largest value in the brightness data information and define it as the optimal brightness value; F7, calculates the response accuracy according to the response delay, response brightness value and optimal brightness value using a preset response accuracy algorithm.

5. The multi-dimensional inspection method of power grid bird deterrent according to claim 4 is characterized in that: Further comprising the steps of: G1, using a preset surveying and mapping surround algorithm to control the detection drone to surround the power grid tower on which the bird deterrent to be inspected is installed, and obtaining tower image information through the camera module of the detection drone; G2, generating tower model data according to the tower image information using a preset modeling algorithm; G3, matching the tower model data in a preset tower model database with a preset tower matching algorithm to determine the tower type information; G4, obtaining the location data of bird damage on similar towers from a preset tower bird damage history database according to the tower type information; G5, determine the high-frequency area information of bird damage based on the bird damage location data of each similar tower pole using a preset distribution density algorithm; G6, calculating the installation effectiveness of the inspected bird deterrent according to the bird damage high frequency area information and the installation location information using a preset installation effectiveness algorithm.

6. The multi-dimensional inspection method of power grid bird deterrent according to claim 5 is characterized in that: The bird deterrent recognition algorithm includes the following steps: H1, determining component type information according to the appearance image information using a preset component recognition algorithm; H2, determine matching device information based on component type information in the device database; H3, obtaining corresponding matching device image information from the device database according to the matching device information; H4, calculating the device image difference using a preset difference algorithm according to the appearance image information and the matching device graphic information; H5, judging whether the difference of the device images is less than the preset difference threshold; H6, if the returned result is yes, the matching device information is defined as the device type information.

7. The multi-dimensional inspection method of power grid bird deterrent according to claim 6 is characterized in that: Further comprising the steps of: J1, calculating the equipment health value of the inspected bird deterrent according to the reflection effectiveness, signal stability, impeller efficiency, effective reflection angle, response accuracy and installation effectiveness using a preset equipment health value algorithm; J2, determine whether the device health value is less than the preset health value threshold; J3, if the result returned is yes, then generate the equipment maintenance report according to the reflection effectiveness, signal stability, impeller efficiency, effective reflection angle, response accuracy and installation effectiveness with the preset analysis algorithm; The device health value algorithm is: H=a*L+b*S+c*F+d*A+e*R+f*P, Among them, H is the equipment health value, a is the preset reflection effectiveness adjustment coefficient, L is the reflection effectiveness, b is the preset signal stability adjustment coefficient, S is the signal stability, c is the preset impeller efficiency adjustment coefficient, F is the impeller efficiency, d is the preset effective reflection angle adjustment coefficient, A is the effective reflection angle, e is the preset response accuracy adjustment coefficient, R is the response accuracy, f is the preset installation effectiveness adjustment coefficient, and P is the installation effectiveness.

Images