A control system for unmanned low-altitude inspection of power transmission lines

Abstract

The application discloses a control system for low-altitude inspection of a power transmission line by a UAV, relates to the technical field of UAV inspection, and comprises a posture control system, a power system is used to change the flight posture of the UAV, an image acquisition system is used to acquire the surrounding environment image during the flight of the UAV and generate an auxiliary image, a plurality of sensors are used to measure the height of a tower, the length of a cable and the distance between the UAV and a feature point, a cable sag model and a posture correction model are used to determine the flight posture change of the UAV at different positions according to the position of the feature point calculated by the image processing system, and the flight posture adjustment data of the UAV is updated according to the relationship between the plurality of feature points and the distance of the UAV. The application further corrects the position of the UAV by using environmental features, so that the UAV can complete accurate inspection in an electromagnetic interference area.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) inspection technology, specifically a control system for low-altitude UAV inspection of power transmission lines. Background Technology

[0002] Drones have played a crucial role in low-altitude inspections of power transmission lines, significantly improving inspection efficiency and safety. Drones can perform automated inspections along pre-set routes and at predetermined points, reducing errors and risks associated with manual operation. Equipped with infrared cameras and lidar, drones can collect detailed data, promptly identifying equipment anomalies and potential faults. Drone inspections are fast and can cover large areas of power transmission lines. Furthermore, drone inspections eliminate the risks of manual tower climbing and high-altitude operations, reducing the risk of falls and electric shocks for workers.

[0003] CN105790155B discloses an autonomous UAV inspection system for power transmission lines based on differential GPS, comprising a UAV and a ground station control system. The UAV's flight control system receives waypoint information from the autonomous tower inspection waypoint generation system of the ground station control system and controls the flight system to fly according to the waypoint information. The waypoint information includes the UAV's latitude and longitude, altitude, nose direction, and gimbal angle. This application utilizes geographic information technology to accurately identify power transmission line towers, uses differential GPS positioning technology to accurately locate the UAV platform in flight, uses power transmission line tower inspection technology to fix inspection points, and uses control technology to accurately control the angle displacement of the gimbal, thus realizing an autonomous UAV inspection system and method for power transmission lines.

[0004] However, the basis for applying differential GPS is that, within the same region, factors affecting the real-time single-point positioning accuracy of GPS, such as atmospheric ionospheric delay error, satellite ephemeris error, and satellite clock error, have the same or similar impact on the base station and its neighboring users. Therefore, the distance between the receiver and the base station affects the accuracy after correction; the greater the distance, the worse the accuracy of the correction, especially when the receiver and the base station lack a common satellite reference. Furthermore, during inspections, UAVs also face electromagnetic interference, which can lead to GPS signal loss or compass failure, affecting the stability and navigation accuracy of the UAV.

[0005] When a drone loses its GPS signal or its compass fails, its flight attitude changes, causing the images captured by the drone to deviate and affecting the integrity of the power transmission line. Therefore, in the face of electromagnetic interference, it is particularly important to ensure the accuracy of the drone's inspection path when the GPS signal is lost or the compass fails. To this end, this application proposes a control system that ensures the accuracy of the drone's inspection path when inspecting power transmission lines at low altitudes in the presence of electromagnetic interference. Summary of the Invention

[0006] One of the objectives of this invention is to provide a control system for low-altitude inspection of power transmission lines by unmanned aerial vehicles (UAVs), which ensures the positional accuracy of the UAV during the inspection process when electromagnetic interference causes GPS signal loss or compass failure.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: a control system for low-altitude inspection of power transmission lines by unmanned aerial vehicles (UAVs), the control system being used to control the UAV to perform power transmission line inspections, including an attitude control system for controlling the flight attitude of the UAV, the attitude control system being connected to the power system of the UAV, and using the power system to change the flight attitude of the UAV.

[0008] The image acquisition system includes an image acquisition unit that acquires images of power transmission lines corresponding to the inspection path during UAV inspections.

[0009] The image acquisition system also includes an image assistance unit, which uses a movable camera device to acquire images of the surrounding environment during the flight of the UAV and generate auxiliary images;

[0010] Also includes:

[0011] The measurement system uses multi-sensor fusion to measure tower height, cable length, and distance between the UAV and feature points. The measurement system also includes at least one measuring device for measuring factors affecting the UAV's flight attitude, which acquires environmental data that affects the UAV's flight attitude.

[0012] The image processing system acquires auxiliary images, identifies environmental features in the auxiliary images used for UAV localization, constructs a graph showing the relationship between the UAV and feature points in the environmental features, calculates the distance between the UAV and the feature points, and determines the UAV's position.

[0013] The cable sag model is constructed using tower data and cable length to determine the image acquisition range during UAV inspection. The UAV inspection path is determined based on the cable sag model, and the flight attitude of the UAV in the transmission line corresponding to the cable sag model is set according to the inspection path.

[0014] The attitude correction model acquires the UAV's flight attitude and makes synchronous adjustments based on the UAV's flight attitude. It determines the impact of environmental data on the UAV's environment and determines the changes in the UAV's flight attitude at different positions based on the position of the UAV's distance feature points calculated by the image processing system. It then iterates the UAV's flight attitude adjustment data by combining the relationship between multiple feature points and the UAV's distance.

[0015] In one or more embodiments of the present invention, multi-sensor fusion includes a height sensor for determining the tower altitude and tower height, a laser ranging unit for measuring cable length, and an acoustic ranging sensor for acquiring the distance between the UAV and the feature point.

[0016] Determine the height relationship between the two towers and the support point data of the tower support cables, and determine the height difference between the support points;

[0017] The acoustic ranging sensor, combined with auxiliary images, determines the distance between the UAV and feature points. Based on the acoustic ranging sensor and auxiliary images, three-dimensional data of the feature point locations can be constructed.

[0018] In one or more embodiments of the present invention, the measuring device is an airflow detection sensor, which acquires the airflow speed, airflow direction and turbulence level around the drone during the inspection process, and determines the airflow state and the relationship between the airflow and the drone through the airflow data.

[0019] In one or more embodiments of the present invention, the method for constructing a relationship graph between the UAV and feature points, calculating the distance between the UAV and feature points, and determining the UAV's position is as follows:

[0020] Acquire an auxiliary image, extract multiple environmental feature ranges from the auxiliary image using image processing algorithms, and determine at least one feature point in each environmental feature;

[0021] The acoustic ranging sensor is emitted by the UAV toward environmental features to determine three-dimensional data within the range of the environmental features;

[0022] Continuously acquire auxiliary images, match feature points in multiple auxiliary images, and determine at least one high-precision feature point;

[0023] Determine the distance between the high-precision feature points detected by the acoustic ranging sensor and the UAV;

[0024] The drone's position is determined based on the distance between the drone and high-precision feature points in multiple auxiliary images.

[0025] In one or more embodiments of the present invention, the method for establishing the cable sag model is as follows:

[0026] Obtain the height h of two adjacent towers of the line to be inspected. A and h B Cable support point data h Ax and h Bx And the cable length L;

[0027] Calculate the weight w per unit length of the cable based on the cable type;

[0028] Calculate the horizontal distance D between the two towers based on the tower height and cable support point data;

[0029] Determine the catenary parameters in the catenary equation and calculate the cable sag S.

[0030] In one or more embodiments of the present invention, the formula for calculating the horizontal distance D is as follows:

[0031]

[0032] Among them, h A The height of tower A, h B h is the height of tower B. Ax For the cable support point data of tower A, h Bx Data for the cable support points of tower B;

[0033] The equation of the catenary is:

[0034]

[0035] Where a is the catenary parameter, x is the horizontal distance, and y is the vertical distance;

[0036] The catenary parameter 'a' is calculated using an iterative method, such that 'a' satisfies the following condition:

[0037]

[0038] The formula for calculating cable sag S is as follows:

[0039]

[0040] Draw the cable arc shape using the cable sag S.

[0041] In one or more embodiments of the present invention, the inspection path of the UAV is determined based on the cable sag and the optimal inspection distance. The inspection path corresponds to the entire cable length of the sag and is adjusted in combination with the cable curvature change.

[0042] The cable is divided into several different areas, and the optimal inspection path is determined by combining the obstacles in each area.

[0043] Based on the altitude and position of the optimal inspection path, set the drone flight attitude for the corresponding area.

[0044] In one or more embodiments of the present invention, the attitude correction model acquires the control data of the attitude control system for the UAV, and adjusts the UAV state in the attitude correction model according to the control data of the UAV. The attitude correction model acquires the environmental data acquired by the measuring device, and further adjusts the UAV state according to the attitude of the UAV controlled by the attitude control system in combination with the environmental data.

[0045] In one or more embodiments of the present invention, the method for determining the attitude offset position of a UAV using airflow data is as follows:

[0046] The airflow detection sensor acquires airflow data and filters the airflow data to remove noise;

[0047] An airflow model was established based on airflow data to analyze the impact of airflow on the flight attitude of the UAV.

[0048] Determine the flight attitude data of the UAV in the attitude control system and calculate the attitude deviation;

[0049] After determining the attitude deviation, the position of the UAV's movement deviation per unit time is determined, and the UAV is adjusted to the standard position per unit time set by the attitude control system based on the deviation position.

[0050] In one or more embodiments of the present invention, the flight attitude changes of the UAV at different positions are determined based on the position of the UAV from the feature point calculated by the image processing system. The method for determining the UAV flight attitude adjustment data is as follows:

[0051] Determine the positional relationship between feature points and the UAV, and calculate the distance d between the UAV and each feature point. PA and angle θ PA ;

[0052]

[0053] The feature point coordinates are (x1, y1), and the initial position of the UAV is (x0, y0, z0).

[0054] Determine the drone's set flight attitude at its current location. Calculate the standard position P'(x',y',z') within a unit time t;

[0055]

[0056] After a unit of time, the deviation position P (x”, y”, z”) of the UAV is determined by the feature points, and the flight attitude deviation ΔP between the deviation position and the standard position is determined;

[0057]

[0058] The flight attitude adjustment data of the UAV is determined based on the flight attitude deviation.

[0059] Through the above technical solution, the present invention has the following beneficial effects:

[0060] 1. This application is used to control a drone. When the drone is subjected to electromagnetic interference during inspection, causing GPS loss and compass failure, and making it impossible to stably control the drone's attitude, the application uses environmental characteristics to further correct the drone's position, ensuring that the drone can complete accurate inspections even in areas with electromagnetic interference.

[0061] 2. During the inspection process of the UAV, establish a cable sag model between the two towers, determine the cable arc based on the cable sag, set the optimal inspection distance for the UAV, determine the inspection path for the UAV, ensure the effectiveness and efficiency of the UAV inspection along the inspection path, and set the flight attitude of the UAV at different positions accordingly.

[0062] 3. Acquire airflow data around the drone during operation, determine the impact of airflow data on the drone's flight attitude, and further determine the changes in the drone's flight attitude by combining the drone's offset position. After the drone experiences positional deviation, the drone's position can be corrected based on the changes in the drone's flight attitude.

[0063] 4. Determine low-altitude feature points and their 3D data to identify high-precision feature points. This avoids the problem of insufficient positional accuracy of feature points caused by visual differences. Furthermore, it enables the determination of stable feature points within the environmental feature range after deviations occur in the location of feature points, thus ensuring the accuracy of the UAV's position.

[0064] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the system of the present invention;

[0066] Figure 2 This is a schematic diagram showing the positional relationship between the UAV and the feature points according to the present invention. Detailed Implementation

[0067] The following describes several embodiments of the present invention with reference to the accompanying drawings. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. And features of different embodiments may be interchanged if feasible.

[0068] Unless otherwise defined, all terms used herein (including technical and scientific terms) have their ordinary meanings, which are understandable to those skilled in the art. Furthermore, the definitions of the foregoing terms in commonly used dictionaries should be interpreted in the context of this specification as having the meaning consistent with the relevant field of this invention. Unless specifically defined, these terms will not be construed as having idealized or overly formal meanings.

[0069] like Figure 1 As shown, the present invention provides a control system for low-altitude inspection of power transmission lines by unmanned aerial vehicles (UAVs), which is used to improve the accuracy of the inspection path during the inspection of power transmission lines by UAVs, and to avoid the problem that the UAVs deviate from the preset inspection path after being subjected to electromagnetic interference, thus affecting the comprehensiveness of the inspection data.

[0070] In one embodiment, the control system is used to control a drone to perform power line inspection, including an attitude control system for controlling the drone's flight attitude, which is connected to the drone's power system and uses the power system to change the drone's flight attitude.

[0071] The image acquisition system includes an image acquisition unit that acquires images of power transmission lines corresponding to the inspection path during UAV inspections.

[0072] The image acquisition system also includes an image assistance unit, which uses a movable camera device to acquire images of the surrounding environment during the flight of the UAV and generate auxiliary images;

[0073] Also includes:

[0074] The measurement system uses multi-sensor fusion to measure tower height, cable length, and distance between the UAV and feature points. The measurement system also includes at least one measuring device for measuring factors affecting the UAV's flight attitude, which acquires environmental data that affects the UAV's flight attitude.

[0075] The image processing system acquires auxiliary images, identifies environmental features in the auxiliary images used for UAV localization, constructs a graph showing the relationship between the UAV and feature points in the environmental features, calculates the distance between the UAV and the feature points, and determines the UAV's position.

[0076] The cable sag model is constructed using tower data and cable length to determine the image acquisition range during UAV inspection. The UAV inspection path is determined based on the cable sag model, and the flight attitude of the UAV in the transmission line corresponding to the cable sag model is set according to the inspection path.

[0077] The attitude correction model acquires the UAV's flight attitude and makes synchronous adjustments based on the UAV's flight attitude. It determines the impact of environmental data on the UAV's environment and determines the changes in the UAV's flight attitude at different positions based on the position of the UAV's distance feature points calculated by the image processing system. It then iterates the UAV's flight attitude adjustment data by combining the relationship between multiple feature points and the UAV's distance.

[0078] In one feasible approach, the image acquisition system is an acquisition system for UAVs to acquire image data. The image acquisition unit and the image auxiliary unit are both part of the image acquisition system. Since the image acquisition unit needs to inspect the power transmission line, an additional image auxiliary unit is used to acquire images of the surrounding environment. The auxiliary images are used to assist in the UAV's positioning.

[0079] Due to electromagnetic interference, when a drone loses its GPS signal or its compass malfunctions, its attitude will change under the influence of the external environment. The acquisition range corresponding to the change in attitude will also change. The positioning of auxiliary images can further ensure the stability of the drone's flight attitude when subjected to electromagnetic interference.

[0080] During the flight of a drone, the flight attitude corresponds to the position that the drone can reach after a period of time. When determining the cable sag at different positions, the drone's flight attitude is set, and the changes in the drone's flight attitude are continuously determined by using feature points during the flight. By combining the flight attitude with the changes in the distance between the feature points and the drone, the drone's flight attitude can be accurately corrected.

[0081] In one embodiment, the multi-sensor fusion includes a height sensor for determining the tower altitude and tower height, a laser ranging unit for measuring cable length, and an acoustic ranging sensor for acquiring the distance between the UAV and the feature point.

[0082] Determine the height relationship between the two towers and the support point data of the tower support cables, and determine the height difference between the support points;

[0083] The acoustic ranging sensor, combined with auxiliary images, determines the distance between the UAV and feature points. Based on the acoustic ranging sensor and auxiliary images, three-dimensional data of the feature point locations can be constructed.

[0084] In one feasible approach, by determining the distance data between the tower, cable, and UAV and the feature points, a cable sag model is constructed and the UAV is positioned, enabling the UAV to determine its flight attitude by relying on multiple feature points at low altitudes during low-altitude inspections.

[0085] Since environmental features in auxiliary images can cause visual discrepancies, an acoustic ranging sensor is used to detect the distance to these features, determine the three-dimensional data of their locations, and construct a three-dimensional model of the environmental feature locations based on this data. This data can also be used to analyze the location and occlusion of feature points, thereby further improving the accuracy of feature point measurements.

[0086] In one embodiment, the measuring device is an airflow detection sensor, which acquires the airflow speed, airflow direction and turbulence level around the drone during the inspection process, and determines the airflow state and the relationship between the airflow and the drone through the airflow data.

[0087] In one feasible approach, after determining the inspection path of the UAV, the flight attitude change of the UAV is set. Without the influence of the external environment, the flight attitude of the UAV will not change significantly within a certain controllable range. However, changes in airflow will directly affect the flight attitude of the UAV. Therefore, the measuring device is set as an airflow detection sensor. The corresponding attitude change of the UAV can be determined through airflow data. During the continued inspection of the UAV, the attitude can be adjusted in a timely manner.

[0088] In one embodiment, the method for constructing a relationship graph between the drone and feature points, calculating the distance between the drone and the feature points, and determining the drone's position is as follows:

[0089] Acquire an auxiliary image, extract multiple environmental feature ranges from the auxiliary image using image processing algorithms, and determine at least one feature point in each environmental feature;

[0090] The acoustic ranging sensor is emitted by the UAV toward environmental features to determine three-dimensional data within the range of the environmental features;

[0091] Continuously acquire auxiliary images, match feature points in multiple auxiliary images, and determine at least one high-precision feature point;

[0092] Determine the distance between the high-precision feature points detected by the acoustic ranging sensor and the UAV;

[0093] The drone's position is determined based on the distance between the drone and high-precision feature points in multiple auxiliary images.

[0094] Drones determine their position through image feature points using visual positioning. However, due to the large amount of data in the auxiliary images, which contain multiple environmental feature ranges and high-precision feature points, the computational load required is also significant.

[0095] In one feasible approach, three-dimensional data of the environmental feature range in the auxiliary image is determined by an acoustic ranging sensor. Only the auxiliary image is needed to determine the environmental features, and the feature points in the environmental features are all determined by the three-dimensional data. This reduces the computational load of calculating feature points in multiple auxiliary images and improves the distance accuracy between the UAV and the feature points.

[0096] In order to avoid changes in feature points due to the influence of the external environment, multiple feature points are determined within the environmental feature range. 3D data can reflect the shape changes of environmental features, and feature points with no change or small change are identified as high-precision feature points to ensure the accuracy of the UAV's position.

[0097] In one embodiment, the cable sag model is established as follows:

[0098] Obtain the height h of two adjacent towers of the line to be inspected. A and h B Cable support point data h Ax and h Bx And the cable length L;

[0099] Calculate the weight w per unit length of the cable based on the cable type;

[0100] Calculate the horizontal distance D between the two towers based on the tower height and cable support point data;

[0101] Determine the catenary parameters in the catenary equation and calculate the cable sag S.

[0102] In one feasible approach, cable sag is calculated using tower data and cable data, which reflects the cable's change state between two towers. Based on the cable sag and the optimal inspection distance range of the drone, the inspection path of the drone is determined. Since the optimal inspection distance is a certain range, the final determined drone inspection path is a range.

[0103] In one embodiment, the horizontal distance D is calculated using the following formula:

[0104]

[0105] Among them, h A The height of tower A, h B h is the height of tower B. Ax For the cable support point data of tower A, h Bx Data for the cable support points of tower B;

[0106] The equation of the catenary is:

[0107]

[0108] Where a is the catenary parameter, x is the horizontal distance, and y is the vertical distance;

[0109] The catenary parameter 'a' is calculated using an iterative method, such that 'a' satisfies the following condition:

[0110]

[0111] The formula for calculating cable sag S is as follows:

[0112]

[0113] Draw the cable arc shape using the cable sag S.

[0114] In one feasible approach, the catenary equation is used to calculate and determine the sag state, and data between two towers is obtained. Since drone inspections are routine inspections, it is only necessary to obtain tower and cable data at different locations during the first drone inspection. In subsequent inspections, the sag can be quickly calculated using the previous data. When multiple cables extend to the same or different locations on a tower, the cable sag between two towers can be quickly determined based on the corresponding tower data and the length of the cables during the erection process.

[0115] For example, the total cable length L = 350, the cable weight per unit length w = 3, and the tower height A h. A =158, Tower B height h B =350, the height h of the cable at the support point of tower A Ax =100, the height h of the cable at the support point of tower B Bx =320, the horizontal distance between the two towers D = 292;

[0116] Choose an initial value a0 = 100, and gradually adjust the value of a through iterative method. After determining a suitable value of a, calculate the sag.

[0117] After iterating and obtaining a = 150, calculate the cosh value in the catenary equation:

[0118]

[0119] The calculated sag is S≈78.6;

[0120] Draw the cable arc, where the X value ranges from 0 to 292, and calculate the corresponding Y value:

[0121]

[0122] like Figure 2As shown, in one embodiment, the UAV inspection path is determined based on the cable sag and the optimal inspection distance. The inspection path corresponds to the entire cable length of the sag, and the inspection path is adjusted in combination with the cable curvature variation.

[0123] The cable is divided into several different areas, and the optimal inspection path is determined by combining the obstacles in each area.

[0124] Based on the altitude and position of the optimal inspection path, set the drone flight attitude for the corresponding area.

[0125] The flight attitude of a drone includes pitch angle, roll angle, and yaw angle.

[0126] In one feasible approach, obstacles around the cable are determined based on the cable's condition during installation, and the optimal inspection path for the drone is determined based on these obstacles. Since the cable has sag, the cable is divided into multiple areas, and different flight attitudes are set in different areas, enabling the drone to better inspect the power transmission line under these flight attitudes.

[0127] In one embodiment, the attitude correction model acquires control data of the attitude control system for the UAV, and adjusts the UAV state in the attitude correction model according to the UAV control data. The attitude correction model acquires environmental data acquired by the measurement device, and further adjusts the UAV state according to the attitude of the UAV controlled by the attitude control system in combination with the environmental data.

[0128] In one feasible approach, the drone attitude corresponds to the position that the drone can reach per unit time. During the drone's movement in a specific attitude, it is affected by external environmental data, causing the drone's attitude to change. However, the external changes caused by the external environment cannot be determined by the attitude control system. The positional deviation of the drone in flight with a fixed attitude is determined by determining the environmental data.

[0129] In one embodiment, the method for determining the attitude offset position of the UAV using airflow data is as follows:

[0130] The airflow detection sensor acquires airflow data and filters the airflow data to remove noise;

[0131] An airflow model was established based on airflow data to analyze the impact of airflow on the flight attitude of the UAV.

[0132] Determine the flight attitude data of the UAV in the attitude control system and calculate the attitude deviation;

[0133] After determining the attitude deviation, the position of the UAV's movement deviation per unit time is determined, and the UAV is adjusted to the standard position per unit time set by the attitude control system based on the deviation position.

[0134] In one feasible approach, when the drone is adjusted to the standard position, the calculated drone movement time is the sum of the time it takes for the drone to move to the deviation position and the time it takes for the drone to move from the deviation position to the standard position. By utilizing the drone's chasing state after moving to the deviation position, the inspection efficiency of the drone can be adjusted to avoid changes in inspection time due to drone deviation, thereby further ensuring inspection efficiency.

[0135] The flight attitude data of the drone includes pitch angle, roll angle, and yaw angle.

[0136] In one embodiment, the flight attitude changes of the UAV at different positions are determined based on the position of the UAV from the feature points calculated by the image processing system. The method for determining the UAV flight attitude adjustment data is as follows:

[0137] Determine the positional relationship between feature points and the UAV, and calculate the distance d between the UAV and each feature point. PA and angle θ PA ;

[0138]

[0139] The feature point coordinates are (x1, y1), and the initial position of the UAV is (x0, y0, z0).

[0140] Determine the drone's set flight attitude at its current location. Calculate the standard position P'(x',y',z') within a unit time t;

[0141]

[0142] After a unit of time, the deviation position P (x”, y”, z”) of the UAV is determined by the feature points, and the flight attitude deviation ΔP between the deviation position and the standard position is determined;

[0143]

[0144] The flight attitude adjustment data of the UAV is determined based on the flight attitude deviation.

[0145] In one feasible approach, after GPS signal loss or compass failure, the influence of airflow on the drone causes a deviation in the drone's flight attitude that is not controlled by the attitude control system. In order to calculate the drone's attitude deviation more accurately, multiple feature points are combined to determine the drone's standard flight position and deviation position. The difference between the two positions is used to calculate the flight attitude deviation. Based on the flight attitude deviation, the drone's attitude is adjusted to restore its attitude and position.

[0146] Although the present invention has been disclosed in conjunction with the above embodiments, it is not intended to limit the present invention. Any person skilled in the art can make various modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A control system for unmanned aerial vehicle low-altitude inspection of power transmission lines, the control system being configured to control an unmanned aerial vehicle to inspect a power transmission line, the control system comprising an attitude control system configured to control an attitude of the unmanned aerial vehicle, the attitude control system being connected to a power system of the unmanned aerial vehicle, the power system being configured to change the attitude of the unmanned aerial vehicle; an image acquisition system comprising an image auxiliary unit, the image auxiliary unit being configured to acquire images of an environment surrounding the unmanned aerial vehicle during flight of the unmanned aerial vehicle and generate auxiliary images; characterized in that the control system further comprising: a measurement system configured to measure a height of a tower, a length of a cable, and a distance between the unmanned aerial vehicle and a feature point by multi-sensor fusion, the measurement system further comprising at least one measurement device configured to measure an environment affecting the attitude of the unmanned aerial vehicle, the measurement device being configured to acquire environment data affecting the attitude of the unmanned aerial vehicle; an image processing system configured to acquire the auxiliary images, determine environment features in the auxiliary images for positioning of the unmanned aerial vehicle, construct a relationship graph of the unmanned aerial vehicle and the feature points in the environment features, calculate distances between the unmanned aerial vehicle and the feature points, and determine a position of the unmanned aerial vehicle; a cable sag model configured to be constructed based on the height of the tower and the length of the cable, determine an image acquisition range of the unmanned aerial vehicle during inspection, determine an inspection path of the unmanned aerial vehicle based on the cable sag model, and set the attitude of the unmanned aerial vehicle in the power transmission line corresponding to the cable sag model based on the inspection path; an attitude correction model configured to acquire the attitude of the unmanned aerial vehicle, adjust the attitude of the unmanned aerial vehicle synchronously based on the attitude of the unmanned aerial vehicle, determine an influence of the environment data on the environment of the unmanned aerial vehicle, determine changes in the attitude of the unmanned aerial vehicle at different positions based on the position of the unmanned aerial vehicle calculated by the image processing system, and update the attitude adjustment data of the unmanned aerial vehicle based on a relationship between the feature points and the unmanned aerial vehicle; a method of constructing a relationship graph of the unmanned aerial vehicle and the feature points, calculating distances between the unmanned aerial vehicle and the feature points, and determining a position of the unmanned aerial vehicle, the method comprising: acquiring the auxiliary images, extracting a plurality of environment feature ranges from the auxiliary images by an image processing algorithm, and determining at least one feature point in each environment feature; emitting a sound wave ranging sensor from the unmanned aerial vehicle to the environment feature to determine three-dimensional data in the environment feature range; continuously acquiring the auxiliary images, matching the feature points in the plurality of auxiliary images, and determining at least one high-precision feature point; determining a distance between the high-precision feature point and the unmanned aerial vehicle detected by the sound wave ranging sensor; determining a position of the unmanned aerial vehicle based on the distance between the unmanned aerial vehicle and the high-precision feature points in the plurality of auxiliary images; determining an inspection path of the unmanned aerial vehicle based on the cable sag and an optimal inspection distance, the inspection path corresponding to the entire length of the cable with the sag, and adjusting the inspection path based on an arc-shaped change of the cable; dividing the cable into a plurality of different regions, and determining an optimal inspection path based on obstacles in different regions; setting the attitude of the unmanned aerial vehicle corresponding to different regions based on the height and position of the optimal inspection path.

2. The control system for low altitude inspection of transmission lines by UAVs according to claim 1, characterized in that, the multi-sensor fusion comprises a height sensor configured to determine an altitude of a tower and a height of the tower, a laser ranging unit configured to measure a length of a cable, and a sound wave ranging sensor configured to acquire a distance between the unmanned aerial vehicle and a feature point; determining a height relationship between two towers and support point data of the towers supporting a cable, and determining a height difference between the support points. The acoustic ranging sensor combines with the auxiliary image to determine the distance between the UAV and the feature point, and the three-dimensional data of the feature point position can be constructed according to the acoustic ranging sensor combined with the auxiliary image.

3. The control system for low altitude inspection of transmission lines by UAVs according to claim 1, characterized in that, The measuring device is an air flow detection sensor, which obtains the air flow speed, air flow direction and turbulence degree around the UAV body during the inspection process, determines the air flow state and the relationship between the air flow and the UAV through the air flow data.

4. The control system for low altitude inspection of transmission lines by UAVs according to claim 1, characterized in that, The cable sag model is established as follows: Obtaining heights of two adjacent tower poles of a line to be inspected h A and h B , cable support point data h Ax and h Bx and cable length L ; Calculating the weight within the unit length of the cable according to the cable model w ; Calculating the horizontal distance between the two towers based on the tower height and cable support point data D ; Determining parameters of a catenary equation, calculating cable sag S .

5. The control system for low-altitude inspection of power transmission lines by drones according to claim 4, characterized in that, Horizontal distance D The calculation formula is as follows: ； wherein, h A is the height of tower pole A, h B is the height of tower pole B, h Ax is the cable support point data of tower pole A, h Bx is the cable support point data of tower pole B; The catenary equation is: ； wherein, a is the catenary parameter, x is the horizontal distance, y is the vertical distance; Calculating the catenary parameters by an iterative method a such that a satisfies the following conditions: ； Cable sag S The formula for calculating the sag is as follows: ； The cable arc is drawn through the cable sag S.

6. The control system for low altitude inspection of transmission lines by UAVs according to claim 1, characterized in that, The attitude correction model obtains the control data of the UAV from the attitude control system, and adjusts the UAV state in the attitude correction model according to the control data of the UAV. The attitude correction model obtains the environmental data obtained by the measuring device, and further adjusts the UAV state according to the attitude of the UAV controlled by the attitude control system combined with the environmental data.

7. The control system for low-altitude inspection of power transmission lines by UAVs according to claim 3, characterized in that, The air flow detection sensor obtains the air flow data, and filters the air flow data to remove noise; An air flow model is established based on the air flow data to analyze the influence of the air flow on the flight attitude of the UAV; The flight attitude data of the UAV in the attitude control system is determined, and the attitude deviation is calculated; The attitude deviation is determined, and the motion deviation position of the UAV per unit time is calculated, and the UAV is adjusted to the standard position per unit time set by the attitude control system according to the motion deviation position of the UAV per unit time.

8. The control system for low altitude inspection of transmission lines by UAVs according to claim 1, characterized in that, The flight attitude adjustment data of the UAV is determined by combining the relationship between the multiple feature points and the distance of the UAV: Determine the position relationship between the feature points and the UAV, calculate the distance d between the UAV and each feature point PA and the angle θ PA ; ； ； Wherein, the feature point coordinates are (x1, y1, z1), and the initial position of the UAV is (x0, y0, z0). determining a set flight attitude of the UAV at the current position θ 0 ,ψ 0 ,φ 0 calculating a standard position P'(x', y', z') in a unit time t; ； The deviation position P’’(x’’, y’’, z’’) of the UAV is determined through the feature point after a unit time, and the flight attitude deviation ΔP between the deviation position and the standard position is determined. The flight attitude adjustment data of the UAV is determined according to the flight attitude deviation.

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