A distribution line drone autonomous inspection method and system based on GNSS positioning

Abstract

The invention discloses a distribution line unmanned aerial vehicle autonomous inspection method and system based on GNSS positioning, which relates to the field of unmanned aerial vehicle positioning and navigation technology, including: receiving multi-band GNSS signals through an unmanned aerial vehicle to obtain an initial position, and simultaneously starting inertial navigation technology INS to record the acceleration and angular velocity of the unmanned aerial vehicle; fusing the GNSS signal with inertial navigation system INS data to obtain high-precision position coordinates; real-time monitoring of the signal-to-noise ratio (SNR) of the GNSS signal around the pole tower, and when interference is detected, performing anti-interference processing and outputting fused positioning data; combining the fused positioning data with differential data of a ground base station, performing high-precision correction, and calculating the final positioning coordinates; updating the coordinates of the pole tower according to the final positioning coordinates and the theoretical coordinates of the pole tower; planning an inspection path based on the corrected pole tower coordinates, detecting obstacles on the flight path, calculating a safe distance, and obtaining an obstacle avoidance path.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) positioning and navigation technology, and in particular to a distribution line UAV autonomous inspection method and system based on GNSS positioning. Background Art

[0002] With the expansion of the scale of power grids and the complexity of distribution networks, the importance of distribution line inspection in the safe operation of power grids is increasing. Traditional manual inspection methods are time-consuming, inefficient, and have high safety risks. It is difficult to meet the needs of modern power operation and maintenance. UAV technology has gradually become the main tool for distribution line inspection due to its flexibility and automation capabilities. In particular, UAV positioning and navigation technology based on the global navigation satellite system GNSS has achieved efficient inspection of distribution lines by providing high-precision location information and path planning. This technology can not only cover areas that are difficult to reach manually, but also show significant advantages in scenarios such as tower inspections, conductor inspections, and insulator fault detection. However, the actual scenarios of distribution line inspections often have problems such as strong signal interference, many obstructions, and complex terrain, which puts higher requirements on the positioning accuracy, path planning capabilities, and obstacle avoidance performance of UAVs.

[0003] Although the existing GNSS-based UAV inspection technology has made certain progress, it still has problems such as insufficient positioning accuracy, weak anti-interference ability and imperfect path planning in practical applications. GNSS signals are susceptible to multipath effects and electromagnetic interference, especially in mountainous areas, woodlands and other environments. The positioning accuracy is significantly reduced, affecting the reliability of inspection tasks. Although some technologies attempt to integrate the inertial navigation system INS and GNSS, the positioning error problem has not been fundamentally solved due to the failure to effectively combine real-time anti-interference processing and differential correction. In addition, the existing path planning technology is mainly based on a two-dimensional static environment and fails to fully consider the dynamic obstacle distribution in three-dimensional space, resulting in insufficient optimization of the inspection path. The dynamic changes in the tower position are also not updated in time, further reducing the inspection efficiency and data accuracy. Therefore, how to achieve high-precision positioning, real-time anti-interference processing and path optimization in a complex environment has become a core problem to be solved by existing technologies. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a distribution line drone autonomous inspection method based on GNSS positioning to solve the problem that GNSS signals are susceptible to multipath effects and electromagnetic interference, especially in mountainous areas, woodlands and other environments, the positioning accuracy is significantly reduced, affecting the reliability of inspection tasks.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for autonomous inspection of power distribution lines by drones based on GNSS positioning, which comprises:

[0008] The drone receives multi-band GNSS signals to obtain the initial position, and the inertial navigation technology INS is turned on to record the acceleration and angular velocity of the drone. The GNSS signal and the inertial navigation system INS data are fused through the Kalman filter algorithm to obtain high-precision position coordinates.

[0009] Real-time monitoring of the signal-to-noise ratio (SNR) of GNSS signals around the tower. When interference is detected, anti-interference processing is performed and fusion positioning data is output;

[0010] The fused positioning data after anti-interference processing is combined with the differential data of the ground base station to perform high-precision correction and calculate the final positioning coordinates;

[0011] According to the final positioning coordinates and the theoretical coordinates of the tower, the offset of the tower is calculated and the coordinates of the tower are updated;

[0012] The inspection path is planned based on the corrected tower coordinates and the current position of the drone. The intelligent obstacle avoidance algorithm is used to detect obstacles on the flight path according to the inspection path, calculate the safety distance, and dynamically adjust the inspection path according to the position and size of the obstacles to obtain the obstacle avoidance path.

[0013] Determine the key shooting points around each tower, generate a mission point sequence, and the drone will fly to the mission points one by one according to the mission point sequence to complete the inspection mission.

[0014] As a preferred solution of the autonomous inspection method of distribution lines by drones based on GNSS positioning described in the present invention, wherein: the drone receives multi-band GNSS signals to obtain the initial position, and at the same time turns on the inertial navigation technology INS to record the acceleration and angular velocity of the drone, the specific steps are as follows:

[0015] The drone receives GNSS multi-band signals and uses real-time kinematic positioning (RTK) to calculate the initial position and obtain the initial three-dimensional coordinates (x GNSS ,y GNSS , z GNSS ) and timestamp t GNSS , where x GNSS Indicates the initial lateral coordinate of the drone calculated by GNSS signal, y GNSS Indicates the initial longitudinal coordinate of the drone calculated by GNSS signal, z GNSS Indicates that the initial altitude coordinates of the drone are calculated through GNSS signals;

[0016] Activate the Inertial Navigation System (INS) on the drone, and collect the linear acceleration and angular velocity of the drone through the accelerometer and gyroscope respectively;

[0017] Through the Kalman filtering algorithm, fuse the GNSS signal and the data of the Inertial Navigation System (INS) to obtain high-precision position coordinates. The specific steps are as follows:

[0018] Use the Kalman filtering algorithm to fuse the GNSS signal and the data of the Inertial Navigation System (INS) to obtain high-precision position coordinates. The fusion expression is:

[0019]

[0020] where, (x f , y f , z f ) are the lateral coordinate, longitudinal coordinate and altitude coordinate of the drone in the three-dimensional space after fusion, (v x,INS , v y,INS , v z,INS ) are the angular velocity components of the drone in the three-dimensional space calculated through the Inertial Navigation System (INS), Δt represents the time interval, (a x,sync , a y,sync , a z,sync ) are the acceleration components of the drone in the three-dimensional space of the INS acceleration data after time synchronization, k1 represents the weight factor of the INS predicted position, k2 represents the acceleration correction weight factor, and e represents the base of the natural logarithm.

[0021] As a preferred solution of the method for autonomous inspection of distribution lines by drones based on GNSS positioning described in the present invention, wherein: the signal-to-noise ratio (SNR) of the GNSS signal around the pole tower is monitored in real time. When interference is detected, anti-interference processing is performed, and the fused positioning data is output. The specific steps are as follows:

[0022] During the flight of the drone, the GNSS multi-band signal is received in real time and the signal-to-noise ratio (SNR) of the signal is extracted. The SNR expression is:

[0023]

[0024] where, i is the index variable of the satellite signal, SNR i represents the signal-to-noise ratio of the i-th satellite signal, P signal,i represents the signal power of the i-th satellite signal, and P noise,i represents the noise power of the i-th satellite signal;

[0025] Set the signal-to-noise ratio threshold T1. When SNR i < T1, it is determined that the satellite signal is interfered; otherwise, it is determined that the satellite signal is not interfered;

[0026] When the GNSS signal is detected to be interfered, the weighted satellite selection method and the method of dynamically adjusting the positioning weight are used for anti-interference processing to obtain the three-dimensional position coordinates (x filtered ,y filtered 、z filtered );

[0027] The GNSS coordinates (x filtered ,y filtered 、z filtered ) is updated with the inertial navigation system INS data input fusion expression to obtain high-precision fusion positioning coordinates (x f ,y f ,z f ).

[0028] As a preferred solution of the autonomous inspection method of distribution line UAV based on GNSS positioning described in the present invention, the fusion positioning data after anti-interference processing is combined with the differential data of the ground base station to perform high-precision correction and calculate the final positioning coordinates. The specific steps are as follows:

[0029] Before data fusion, the GNSS signal timestamp t GNSS The timestamp of the differential data of the ground base station is synchronized and corrected to obtain the synchronized timestamp t sync ;

[0030] Use ground base station differential data to correct GNSS fusion positioning coordinates;

[0031] The calculation formula of the final positioning coordinates after differential correction is:

[0032]

[0033] Among them, (x final ,y final , z final ) represents the final three-dimensional coordinate after correction, (Δx base , Δy base , Δz base ) represents the correction value of the GNSS positioning error by the base station differential data, θ corr represents the direction angle of error correction, Represents the sensitivity factor of error correction, cos represents the cosine function, and sin represents the sine function.

[0034] As a preferred solution of the autonomous inspection method of distribution lines by drones based on GNSS positioning according to the present invention, the offset of the tower is calculated according to the final positioning coordinates and the theoretical coordinates of the tower, and the coordinates of the tower are updated. The specific steps are:

[0035] According to the corrected final three-dimensional coordinates and the tower target coordinates x tower ,y tower , z tower , calculate the actual position offset of the tower, the expression is:

[0036] Δx tower =x final -x tower ,Δy tower =y final -y tower ,Δz tower =z final -z tower ;

[0037] Among them, (Δx tower , Δy tower , Δz tower ) represents the actual position offset of the tower;

[0038] Update the corrected tower coordinates into the database.

[0039] As a preferred solution of the autonomous inspection method of distribution lines by drones based on GNSS positioning described in the present invention, the inspection path is planned based on the corrected tower coordinates and the current position of the drone, obstacles on the flight path are detected according to the inspection path using an intelligent obstacle avoidance algorithm, a safety distance is calculated, and the inspection path is dynamically adjusted according to the position and size of the obstacle to obtain an obstacle avoidance path. The specific steps are as follows:

[0040] According to the corrected tower coordinates and the current position of the drone (x final ,y final , z final ), using the A* algorithm to generate a global inspection path, generating a series of path point sequences P = P0, P1, ..., P n , where P represents the entire path point set, P0 represents the starting point coordinates of the path, and P n Represents the coordinates of the end point of the path, and n represents the maximum index value of the path point;

[0041] Each path point is a coordinate point planned in the grid map. Set the coordinates of the path point that the current drone is flying to as (x cur ,y cur , z cur );

[0042] The drone performs inspection tasks according to the global inspection path, detects obstacles on the flight path in real time, and dynamically adjusts the path in combination with RRT*;

[0043] When an obstacle is detected, the coordinates of the path points are dynamically adjusted using the following formula based on the obstacle's position, size, and flight safety distance to obtain the obstacle avoidance path:

[0044]

[0045] Among them, (x adj ,y adj , z adj ) represents the three-dimensional coordinates of the dynamically adjusted path point, (x obs ,y obs , z obs ) represents the three-dimensional coordinates of the center position of the obstacle, ￡ and § are the adjustment amplitude coefficients, φ represents the horizontal yaw angle of the drone adjusted around the obstacle, δ represents the pitch angle of the drone adjusted around the obstacle, and d min Indicates the minimum safe distance between the drone and obstacles;

[0046] When min When >0, it means the drone always maintains a safe distance from obstacles.

[0047] As a preferred solution of the autonomous inspection method of distribution lines by drones based on GNSS positioning described in the present invention, wherein: the key shooting points around each tower are determined, a task point sequence is generated, and the drones fly to the task points one by one according to the task point sequence to complete the inspection task. The specific steps are as follows:

[0048] Combine the global inspection path generated by A* with the obstacle avoidance path dynamically adjusted by RRT* to detect obstacles on the path in real time during flight;

[0049] When there are no obstacles on the current path point, the flight will follow the original global inspection path;

[0050] When there is an obstacle on the current path point, RRT* is called to dynamically adjust the path and generate a new obstacle avoidance path P adj , the new obstacle avoidance path P adj It is concatenated with the original global inspection path to form an updated inspection path P. new ;

[0051] The drone follows the updated inspection path P new Fly to the mission points one by one to complete the inspection mission.

[0052] In a second aspect, the present invention provides a distribution line UAV autonomous inspection system based on GNSS positioning, including a high-precision positioning module, a correction module, a path planning module, an inspection module and an analysis module.

[0053] The high-precision positioning module is responsible for obtaining the real-time high-precision position coordinates of the drone to provide accurate positioning for inspection tasks;

[0054] The correction module is responsible for correcting the actual position coordinates of the tower in real time, providing accurate target coordinates for the planning of the inspection path;

[0055] The path planning module is responsible for generating the optimal inspection path according to the corrected tower coordinates and the current position of the drone, and adjusting the path in combination with dynamic obstacle avoidance;

[0056] The inspection module is used to determine key inspection points, guide the drone to complete the inspection task point by point, and collect inspection data;

[0057] The analysis module is responsible for storing, analyzing and managing the inspection data.

[0058] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for autonomous inspection of distribution lines by drones based on GNSS positioning as described in the first aspect of the present invention is implemented.

[0059] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for autonomous inspection of distribution lines by drones based on GNSS positioning as described in the first aspect of the present invention.

[0060] The beneficial effects of the present invention are as follows: the present invention realizes high-precision positioning of UAVs in complex environments through the fusion positioning of multi-band GNSS signals and inertial navigation system INS, and solves the problem that traditional GNSS positioning is susceptible to interference and signal loss; the reliability of positioning data is significantly improved through anti-interference processing of real-time monitoring of signal-to-noise ratio SNR and dynamic weight adjustment; combined with the differential correction technology of ground base stations, the ionospheric delay and system error are further eliminated to achieve centimeter-level positioning accuracy, and the dynamic adaptability and accuracy of the inspection path are ensured by calculating the tower offset and updating the tower coordinates, and the A* algorithm is used for global path planning, and combined with the RRT* dynamic obstacle avoidance algorithm, it is ensured that the UAV can complete the inspection task safely and efficiently in complex terrain. Finally, through the generation of task point sequence and point-by-point flight, the UAV realizes a comprehensive inspection of key parts of the tower and ensures the integrity and accuracy of data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 This is a flow chart of the autonomous inspection method for distribution lines by drones based on GNSS positioning in Example 1.

[0063] Figure 2 This is a schematic diagram of the final positioning coordinates in Example 1. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a distribution line UAV autonomous inspection method based on GNSS positioning, comprising the following steps:

[0068] S1. Receive multi-band GNSS signals through the drone to obtain the initial position, and turn on the inertial navigation technology INS to record the acceleration and angular velocity of the drone; use the Kalman filter algorithm to fuse the GNSS signal and the inertial navigation system INS data to obtain high-precision position coordinates;

[0069] The drone receives GNSS multi-band signals (L1, L2, L5) and uses real-time kinematic positioning (RTK) to calculate the initial position and obtain the initial three-dimensional coordinates (x GNSS ,y GNSS , z GNSS ) and timestamp t GNSS , where x GNSS Indicates the initial lateral coordinate of the drone calculated by GNSS signal, y GNSS Indicates the initial longitudinal coordinate of the drone calculated by GNSS signal, z GNSS Indicates that the initial altitude coordinates of the drone are calculated through GNSS signals;

[0070] Multi-band GNSS signals can effectively reduce ionospheric delay errors and improve positioning accuracy, especially in complex environments. RTK technology can achieve centimeter-level positioning accuracy through differential data from ground base stations, which is suitable for the high-precision requirements of drone inspection tasks.

[0071] Activate the inertial navigation system INS on the drone, and collect the linear acceleration and angular velocity of the drone through the accelerometer and gyroscope respectively;

[0072] Linear acceleration is used to calculate the motion trend of the drone in three-dimensional space, and angular velocity is used to infer the attitude changes of the drone (pitch angle, roll angle, yaw angle);

[0073] INS has the advantage of not relying on external signals and can provide continuous motion data when GNSS signals are blocked (such as in mountainous areas and buildings). By recording acceleration and angular velocity, the position and attitude of the drone at any time can be effectively calculated.

[0074] Through the Kalman filter algorithm, the GNSS signal and the inertial navigation system INS data are fused to obtain high-precision position coordinates. The specific steps are as follows:

[0075] The Kalman filter algorithm is used to fuse the GNSS signal with the inertial navigation system INS data to obtain high-precision position coordinates. The fusion expression is:

[0076]

[0077] Among them, (x f ,y f ,z f ) are the lateral coordinates, longitudinal coordinates and height coordinates of the UAV in three-dimensional space after fusion, (v x,INS , v y,INS , v z,INS ) is the angular velocity component of the UAV in three-dimensional space calculated by the inertial navigation system INS, Δt represents the time interval, (a x,sync , a y,sync , a z,sync ) is the acceleration component of the UAV in three-dimensional space after time synchronization of INS acceleration data, k1 represents the weight factor of INS predicted position, k2 represents the acceleration correction weight factor, and e represents the base of the natural logarithm;

[0078] The value range of k1 is [0,1], which is used to adjust the influence of INS on the fusion result. When the GNSS signal is weak, the value of k1 will increase, so that the contribution of INS data to the result will be enhanced.

[0079] The value range of k2 is [0, 1], which is used to balance the influence of the acceleration correction term on the fusion result. When the acceleration changes greatly, the value of k2 will increase, enhancing the contribution of the acceleration data to error correction.

[0080] S2. Real-time monitor the signal-to-noise ratio SNR of the GNSS signal around the tower. When interference is detected, perform anti-interference processing and output the fused positioning data.

[0081] During the flight of the UAV, it receives GNSS multi-frequency signals (L1, L2, L5) in real time and extracts the signal-to-noise ratio SNR of the signals. The SNR expression is:

[0082]

[0083] where i is the index variable of the satellite signal, and SNR i represents the signal-to-noise ratio of the i-th satellite signal, and P signal,i represents the signal power of the i-th satellite signal, and P noise,i represents the noise power of the i-th satellite signal;

[0084] SNR is an important parameter that directly reflects the quality of the GNSS signal. By real-time monitoring SNR, signal interference and multipath effects can be quickly identified. Using multi-frequency signals (L1, L2, L5) can effectively reduce the error of single-frequency signals and improve the anti-interference ability in complex environments.

[0085] Set the signal-to-noise ratio threshold T1. When SNR i < T1, it is determined that the satellite signal is interfered; otherwise, it is determined that the satellite signal is not interfered.

[0086] When it is detected that the GNSS signal is interfered, use the weighted satellite selection method and the method of dynamically adjusting the positioning weight for anti-interference processing to obtain the three-dimensional position coordinates (x filtered 、y filtered 、z filtered ) after anti-interference processing. The expression is:

[0087]

[0088] where x filtered 、y filtered and z filtered are the three-dimensional position coordinates after anti-interference processing, x GNSS,i 、y GNSS,i and z GNSS,i represent the GNSS coordinates calculated from the i-th satellite signal, N represents the number of GNSS satellite signals, and w i represents the weight of the i-th satellite signal;

[0089] wi The calculation formula is:

[0090]

[0091] Among them, SNR max Indicates the maximum signal-to-noise ratio in the current satellite signal;

[0092] By dynamically adjusting the weights of satellite signals, the interfered satellite signals can be effectively filtered out, thus improving the reliability of positioning data. Compared with the method of simply discarding low SNR signals, the weight adjustment method can make full use of weaker signals and avoid information loss.

[0093] The GNSS coordinates (x filtered ,y filtered 、z filtered ) is updated with the inertial navigation system INS data input fusion expression to obtain high-precision fusion positioning coordinates (x f ,y f ,z f ) for subsequent tower position correction and inspection route planning.

[0094] S3, combining the fused positioning data after anti-interference processing with the differential data of the ground base station, performing high-precision correction, and calculating the final positioning coordinates;

[0095] Before data fusion, the GNSS signal timestamp t GNSS The timestamp of the differential data of the ground base station is synchronized and corrected to obtain the synchronized timestamp t sync ;

[0096] The synchronous correction expression is:

[0097] t sync =t GNSS -Δt;

[0098] Use ground base station differential data to correct GNSS fusion positioning coordinates to eliminate ionospheric delay and satellite orbit error errors and improve positioning accuracy;

[0099] The calculation formula of the final positioning coordinates after differential correction is:

[0100]

[0101] Among them, (x final ,y final , z final ) represents the final three-dimensional coordinate after correction, (Δx base , Δy base , Δz base) represents the correction value of the GNSS positioning error by the base station differential data, θ corr represents the direction angle of error correction, Indicates the sensitivity factor of the error correction, which is used to control the adjustment range of the differential correction amount. Its value range is [0,1]. The larger the value, the higher the sensitivity of the correction to the error. cos represents the cosine function, and sin represents the sine function.

[0102] θ corr It is calculated from the relative position between the base station and the drone, and the expression is:

[0103]

[0104] Here, arctan represents the inverse tangent function.

[0105] S4. Calculate the offset of the tower according to the final positioning coordinates and the theoretical coordinates of the tower, and update the coordinates of the tower;

[0106] According to the corrected final three-dimensional coordinates and the tower target coordinates x tower ,y tower , z tower , calculate the actual position offset of the tower, the expression is:

[0107] Δx tower =x final -x tower ,Δy tower =y final -y tower ,Δz tower =z final -z tower ;

[0108] Among them, (Δx tower , Δy tower , Δz tower ) represents the actual position offset of the tower;

[0109] The corrected tower coordinates are updated to the database as a reference for subsequent inspection route planning.

[0110] S5. Plan the inspection path based on the corrected tower coordinates and the current position of the drone. Use the intelligent obstacle avoidance algorithm to detect obstacles on the flight path according to the inspection path, calculate the safety distance, and dynamically adjust the inspection path according to the position and size of the obstacles to obtain the obstacle avoidance path.

[0111] According to the corrected tower coordinates and the current position of the drone (x final , f final , z final), using the A* algorithm to generate a global inspection path, generating a series of path point sequences P = P0, P1, ..., P n , where P represents the entire path point set, P0 represents the starting point coordinates of the path, and P n Represents the coordinates of the end point of the path, and n represents the maximum index value of the path point;

[0112] Each path point is a coordinate point planned in the grid map. Set the coordinates of the path point that the current drone is flying to as (x cur ,y cur , z cur );

[0113] Its goal is to plan an optimal path from the starting point (the current position of the drone) to the target point (the tower and its key shooting points);

[0114] Define the search space as a three-dimensional grid map (including obstacle areas);

[0115] Set the current position of the drone as the starting point and the corrected tower coordinates as the target point;

[0116] Search for the optimal path in the three-dimensional grid space, avoid the obstacle area in the grid, and output the initial inspection path;

[0117] The A* algorithm can quickly generate the global optimal path from the starting point to the target point, taking into account the distribution of obstacles in three-dimensional space, and is suitable for path initialization of inspection tasks;

[0118] The drone performs inspection tasks according to the global inspection path, detects obstacles on the flight path in real time, and dynamically adjusts the path in combination with RRT* to ensure that the drone can safely avoid obstacles and efficiently complete the inspection task;

[0119] When an obstacle is detected, the coordinates of the path points are dynamically adjusted using the following formula based on the obstacle's position, size, and flight safety distance to obtain the obstacle avoidance path:

[0120]

[0121] Among them, (x adj ,y adj , z adj ) represents the three-dimensional coordinates of the dynamically adjusted path point, (x obs ,y obs , z obs ) represents the three-dimensional coordinates of the center position of the obstacle, ￡ and § are adjustment amplitude coefficients, which control the adjustment strength of the drone in the horizontal and vertical directions respectively, and the range is [0,1]. φ represents the horizontal yaw angle of the drone adjusted around the obstacle, δ represents the pitch angle of the drone adjusted around the obstacle, and d minIndicates the minimum safe distance between the drone and obstacles;

[0122] When min When >0, it means the drone always maintains a safe distance from obstacles;

[0123] The value range of ￡ and § is from 0 to 1. The larger the value, the larger the adjustment range, which is suitable for environments with dense obstacles; the smaller the value, the smaller the adjustment range, which is suitable for open environments.

[0124] S6. Determine the key shooting points around each tower, generate a task point sequence, and the drone flies to the task points one by one according to the task point sequence to complete the inspection task;

[0125] Combine the global inspection path generated by A* with the obstacle avoidance path dynamically adjusted by RRT* to detect obstacles on the path in real time during flight;

[0126] When there are no obstacles on the current path point, the flight will follow the original global inspection path;

[0127] When there is an obstacle on the current path point, RRT* is called to dynamically adjust the path and generate a new obstacle avoidance path P adj , P adj =(x adj ,y adj , z adj ), the new obstacle avoidance path P adj It is concatenated with the original global inspection path to form an updated inspection path P. new ;

[0128] The path concatenation expression is:

[0129] P new = {P0,P1,...,P m-1 ,P adj ,P m+1 ,...,P n};

[0130] Where m represents the path point index of the currently detected obstacle;

[0131] The drone follows the updated inspection path P new Fly to the mission point by point, complete the inspection mission and record the following data: the captured image or video data of the mission point, the flight trajectory of the drone and its three-dimensional coordinates, the obstacle position and its obstacle avoidance path adjustment record.

[0132] This embodiment also provides a distribution line UAV autonomous inspection system based on GNSS positioning, including: a high-precision positioning module, a correction module, a path planning module, an inspection module and an analysis module.

[0133] The high-precision positioning module is responsible for obtaining the real-time high-precision position coordinates of the drone to provide accurate positioning for inspection tasks;

[0134] The correction module is responsible for real-time correction of the actual position coordinates of the tower and provides accurate target coordinates for inspection route planning;

[0135] The path planning module is responsible for generating the optimal inspection path based on the corrected tower coordinates and the current position of the drone, and adjusting the path in combination with dynamic obstacle avoidance;

[0136] The inspection module is used to determine key inspection points, guide the drone to complete the inspection task point by point, and collect inspection data;

[0137] The analysis module is responsible for storing, analyzing and managing inspection data.

[0138] This embodiment also provides a computer device, which is suitable for the case of an autonomous inspection method of distribution lines by drones based on GNSS positioning, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the autonomous inspection method of distribution lines by drones based on GNSS positioning as proposed in the above embodiment.

[0139] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0140] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for autonomous inspection of distribution lines by drones based on GNSS positioning as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0141] In summary, the present invention achieves high-precision positioning of UAVs in complex environments through: fusion positioning of multi-band GNSS signals and inertial navigation system INS, and solves the problem that traditional GNSS positioning is susceptible to interference and signal loss; anti-interference processing through real-time monitoring of signal-to-noise ratio SNR and dynamic weight adjustment significantly improves the reliability of positioning data; combined with ground base station differential correction technology, ionospheric delay and system error are further eliminated to achieve centimeter-level positioning accuracy, and the dynamic adaptability and accuracy of the inspection path are ensured by calculating the tower offset and updating the tower coordinates, and the A* algorithm is used for global path planning, combined with the RRT* dynamic obstacle avoidance algorithm, to ensure that the UAV can complete the inspection task safely and efficiently in complex terrain, and finally, through the generation of task point sequence and point-by-point flight, the UAV realizes a comprehensive inspection of key parts of the tower and ensures the integrity and accuracy of data collection.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for autonomous inspection of power distribution lines by drones based on GNSS positioning, characterized in that: include, Through the drone, receive multi-band GNSS signals to obtain the initial position, and turn on the inertial navigation technology INS to record the acceleration and angular velocity of the drone; Through the Kalman filter algorithm, the GNSS signal and the inertial navigation system INS data are fused to obtain high-precision position coordinates; Real-time monitoring of the signal-to-noise ratio (SNR) of GNSS signals around the tower. When interference is detected, anti-interference processing is performed and fusion positioning data is output; The fused positioning data after anti-interference processing is combined with the differential data of the ground base station to perform high-precision correction and calculate the final positioning coordinates; According to the final positioning coordinates and the theoretical coordinates of the tower, the offset of the tower is calculated and the coordinates of the tower are updated; The inspection path is planned based on the corrected tower coordinates and the current position of the drone. The intelligent obstacle avoidance algorithm is used to detect obstacles on the flight path according to the inspection path, calculate the safety distance, and dynamically adjust the inspection path according to the position and size of the obstacles to obtain the obstacle avoidance path. Determine the key shooting points around each tower, generate a mission point sequence, and the drone will fly to the mission points one by one according to the mission point sequence to complete the inspection mission.

2. The method for autonomous inspection of power distribution lines by drones based on GNSS positioning according to claim 1, characterized in that: The method of receiving multi-band GNSS signals through a drone to obtain an initial position and simultaneously starting the inertial navigation technology INS to record the acceleration and angular velocity of the drone is as follows: The drone receives GNSS multi-band signals and uses real-time kinematic positioning (RTK) to calculate the initial position and obtain the initial three-dimensional coordinates (x GNSS ,y GNSS , z GNSS ) and timestamp t GNSS , where x GNSS Indicates the initial lateral coordinate of the drone calculated by GNSS signal, y GNSS Indicates the initial longitudinal coordinate of the drone calculated by GNSS signal, z GNSS Indicates that the initial altitude coordinates of the drone are calculated through GNSS signals; Activate the inertial navigation system INS on the drone, and collect the linear acceleration and angular velocity of the drone through the accelerometer and gyroscope respectively; The Kalman filter algorithm is used to fuse the GNSS signal and the inertial navigation system INS data to obtain high-precision position coordinates. The specific steps are: The Kalman filter algorithm is used to fuse the GNSS signal with the inertial navigation system INS data to obtain high-precision position coordinates. The fusion expression is: Among them, (x f ,y f ,z f ) are the lateral coordinates, longitudinal coordinates and height coordinates of the UAV in three-dimensional space after fusion, (v x,INS , v y,INS , v z,INS ) is the angular velocity component of the UAV in three-dimensional space calculated by the inertial navigation system INS, Δt represents the time interval, (a x,sync , a y,sync , a z,sync ) is the acceleration component of the UAV in three-dimensional space after time synchronization with the INS acceleration data, k1 represents the weight factor of the INS predicted position, k2 represents the acceleration correction weight factor, and e represents the base of the natural logarithm.

3. The method for autonomous inspection of power distribution lines by drones based on GNSS positioning according to claim 2, characterized in that: The real-time monitoring of the signal-to-noise ratio (SNR) of the GNSS signal around the tower, when interference is detected, anti-interference processing is performed, and fusion positioning data is output. The specific steps are: During the flight, the drone receives GNSS multi-band signals in real time and extracts the signal-to-noise ratio (SNR). The SNR expression is: Among them, i is the index variable of the satellite signal, SNR i represents the signal-to-noise ratio of the i-th satellite signal, P signal,i represents the signal power of the i-th satellite signal, P noise,i represents the noise power of the i-th satellite signal; Set the signal-to-noise ratio threshold T1. When SNR i < T1, it is determined that the satellite signal is interfered; otherwise, it is determined that the satellite signal is not interfered; When the GNSS signal is detected to be interfered, the weighted satellite selection method and the method of dynamically adjusting the positioning weight are used for anti-interference processing to obtain the three-dimensional position coordinates (x filtered ,y filtered 、z filtered ); The GNSS coordinates (x filtered ,y filtered 、z filtered ) is updated with the fusion expression of the inertial navigation system INS data input to obtain the high-precision fusion positioning coordinates (x f ,y f ,z f ).

4. The method for autonomous inspection of power distribution lines by drones based on GNSS positioning as claimed in claim 3, characterized in that: The fusion positioning data after anti-interference processing is combined with the differential data of the ground base station to perform high-precision correction and calculate the final positioning coordinates. The specific steps are: Before data fusion, the GNSS signal timestamp t GNSS The timestamp of the differential data of the ground base station is synchronized and corrected to obtain the synchronized timestamp t sync ; Use ground base station differential data to correct GNSS fusion positioning coordinates; The calculation formula of the final positioning coordinates after differential correction is: Among them, (x final ,y final , z final ) represents the final three-dimensional coordinate after correction, (Δx base , Δy base , Δz base ) represents the correction value of the GNSS positioning error by the base station differential data, θ corr represents the direction angle of error correction, Represents the sensitivity factor of error correction, cos represents the cosine function, and sin represents the sine function.

5. The method for autonomous inspection of power distribution lines by drones based on GNSS positioning according to claim 4, characterized in that: The method of calculating the offset of the pole tower and updating the coordinates of the pole tower according to the final positioning coordinates and the theoretical coordinates of the pole tower is as follows: According to the corrected final three-dimensional coordinates and the tower target coordinates x tower ,y tower , z tower , calculate the actual position offset of the tower, the expression is: Δx tower =x final -x tower ,Δy tower =y final -y tower ,Δz tower =z final -z tower ; Among them, (Δx tower , Δy tower , Δz tower ) represents the actual position offset of the tower; Update the corrected tower coordinates into the database.

6. The method for autonomous inspection of power distribution lines by drones based on GNSS positioning as claimed in claim 5, characterized in that: The inspection path is planned based on the corrected tower coordinates and the current position of the drone, and obstacles on the flight path are detected using an intelligent obstacle avoidance algorithm according to the inspection path, and a safety distance is calculated. The inspection path is dynamically adjusted according to the position and size of the obstacle to obtain an obstacle avoidance path. The specific steps are: According to the corrected tower coordinates and the current position of the drone (x final ,y final , z final ), using the A* algorithm to generate a global inspection path, generating a series of path point sequences P = P0, P1, ..., P n , where P represents the entire path point set, P0 represents the starting point coordinates of the path, and P n Represents the coordinates of the end point of the path, and n represents the maximum index value of the path point; Each path point is a coordinate point planned in the grid map. Set the coordinates of the path point that the current drone is flying to as (x cur ,y cur , z cur ); The drone performs inspection tasks according to the global inspection path, detects obstacles on the flight path in real time, and dynamically adjusts the path in combination with RRT*; When an obstacle is detected, the coordinates of the path points are dynamically adjusted using the following formula based on the obstacle's position, size, and flight safety distance to obtain the obstacle avoidance path: Among them, (x adj ,y adj , z adj ) represents the three-dimensional coordinates of the dynamically adjusted path point, (x obs ,y obs , z obs ) represents the three-dimensional coordinates of the center position of the obstacle, ￡ and § are the adjustment amplitude coefficients, φ represents the horizontal yaw angle of the drone adjusted around the obstacle, δ represents the pitch angle of the drone adjusted around the obstacle, and d min Indicates the minimum safe distance between the drone and obstacles; When min When >0, it means the drone always maintains a safe distance from obstacles.

7. The method for autonomous inspection of power distribution lines by unmanned aerial vehicles based on GNSS positioning according to claim 6, characterized in that: The key shooting points around each tower are determined, a task point sequence is generated, and the drone flies to the task points one by one according to the task point sequence to complete the inspection task. The specific steps are as follows: Combine the global inspection path generated by A* with the obstacle avoidance path dynamically adjusted by RRT* to detect obstacles on the path in real time during flight; When there are no obstacles on the current path point, the flight will follow the original global inspection path; When there is an obstacle on the current path point, RRT* is called to dynamically adjust the path and generate a new obstacle avoidance path P adj , the new obstacle avoidance path P adj It is concatenated with the original global inspection path to form an updated inspection path P. new ; The drone follows the updated inspection path P new Fly to the mission points one by one to complete the inspection mission.

8. A distribution line drone autonomous inspection system based on GNSS positioning, based on the distribution line drone autonomous inspection method based on GNSS positioning according to any one of claims 1 to 7, characterized in that: Including high-precision positioning module, correction module, path planning module, inspection module and analysis module, The high-precision positioning module is responsible for obtaining the real-time high-precision position coordinates of the drone to provide accurate positioning for inspection tasks; The correction module is responsible for correcting the actual position coordinates of the tower in real time, providing accurate target coordinates for the planning of the inspection path; The path planning module is responsible for generating the optimal inspection path according to the corrected tower coordinates and the current position of the drone, and adjusting the path in combination with dynamic obstacle avoidance; The inspection module is used to determine key inspection points, guide the drone to complete the inspection task point by point, and collect inspection data; The analysis module is responsible for storing, analyzing and managing the inspection data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for autonomous inspection of distribution lines by drone based on GNSS positioning according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for autonomous inspection of distribution lines by drones based on GNSS positioning according to any one of claims 1 to 7 are implemented.

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