Power distribution network tower coordinate dynamic verification method based on satellite and unmanned aerial vehicle cooperation

Abstract

The application relates to the technical field of high-precision positioning, in particular to a power distribution network tower coordinate dynamic verification method based on cooperation of satellites and unmanned aerial vehicles, which comprises the following steps: an unmanned aerial vehicle formation receives a satellite navigation signal, and synchronously collects relative ranging data in the formation and observation data of towers, a unified time stamp is added to all the data, time and space correlation deviation between different data sources is solved, an absolute delay compensation vector for time synchronization is calculated, original time and space data are corrected in time synchronization, accurate positions and postures of the unmanned aerial vehicle formation are calculated based on fused data after correction, and the corrected tower coordinates are output. The purpose of the application is to accurately obtain the spatial positions of power distribution network towers through cooperation of satellites and unmanned aerial vehicles.

Description

Technical Field

[0001] This application relates to the field of high-precision positioning technology, specifically to a method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration. Background Technology

[0002] Distribution network poles are steel or concrete structures used to support and erect medium- and low-voltage distribution lines in a distribution network. They carry conductors and insulators that transmit power from substations to users. Each pole has unique three-dimensional spatial coordinates in a Geographic Information System (GIS) to identify its precise geographical location. Because poles may shift due to geological disasters, nearby construction activities, or their own aging, the recorded coordinates may be inaccurate, leading to incorrect fault location based on these coordinates, thus affecting operation and maintenance efficiency and repair speed.

[0003] Existing methods for verifying tower coordinates first plan the flight path of UAV formations based on coarse coordinates provided by satellites. During flight, each UAV acquires positioning and observation data through a Global Navigation Satellite System (GNSS) receiver and its own sensors, and shares this data with a ground station. The ground station then fuses this data to calculate the high-precision position of the entire formation, and subsequently, the tower coordinates are derived. However, GNSS data may lag during UAV flight. Directly using data with lag would disrupt the temporal consistency of state estimation, causing the algorithm to mistake outdated information for current information, thus introducing errors and resulting in random deviations in the output UAV pose and tower coordinates from the true values. Summary of the Invention

[0004] In view of the above, it is necessary to provide a dynamic verification method for power distribution network tower coordinates based on satellite and UAV collaboration to solve the above problems.

[0005] One embodiment of this application provides a method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration, the method comprising: Collect satellite navigation data for each drone, ranging data between drones in the formation, and observation data for the tower, and add local timestamps; Based on the obtained satellite navigation data and the ranging data between UAVs in the formation, a relative time difference observation model between each pair of UAVs in the formation is constructed, and the absolute delay compensation vector of each UAV relative to the formation time reference is calculated by the whole network adjustment algorithm. The local timestamps of all UAV data are uniformly corrected using an absolute delay compensation vector. The time-aligned multi-source data is then input into a fusion filter to calculate the precise pose of each UAV in the formation. Based on the obtained precise pose and the observation data of the tower, the three-dimensional coordinates of the tower are determined by coordinate transformation.

[0006] Specifically, the construction of the relative time difference observation model between each pair of UAVs within the formation is as follows: The time from when drone A sends a signal to when drone B receives the signal is recorded as the first time; the time from when drone B receives the signal and sends a feedback signal to when drone A receives the feedback signal is recorded as the second time. Half of the difference between the first and second durations is taken as the instantaneous deviation of UAV B's clock relative to UAV A's clock; the formula for calculating the instantaneous deviation is used as the observation model for the relative time difference between UAV A and UAV B.

[0007] Specifically, the process of calculating the absolute delay compensation vector of each drone relative to the formation time reference using the full-network adjustment algorithm is as follows: By using the designated leader drone in the formation as the reference, and combining the instantaneous deviations obtained by pairing all drones in the formation, the time delay of each drone's clock relative to the reference clock is obtained after performing a network-wide adjustment algorithm. The vector composed of the time delays of all drones is then used as the absolute delay compensation vector.

[0008] The construction of the relative time difference observation model between each pair of UAVs within the formation includes: For UAV i, UAV j, and the same satellite p in the formation, the specific formula for the relative time difference observation model constructed at epoch t is: in, This represents the difference between the carrier phase observations of UAV i and UAV j on satellite p at epoch t. This represents the difference in geometric distance between satellite p and drone i and between satellite p and drone j; Indicates the wavelength of the carrier signal; This represents the difference in carrier phase single-difference integer ambiguity between UAV i and UAV j with respect to satellite p; This represents the receiver's relative clock error to be solved; This represents the residual error.

[0009] The method for obtaining the single-difference integer ambiguity is performed using the LAMBDA algorithm.

[0010] Specifically, the absolute delay compensation vector for each drone relative to the formation time reference is calculated using a full-network adjustment algorithm, which is as follows: By using the designated leader drone in the formation as a reference, and by pairing all drones in the formation with each other to obtain the relative clock difference of the receiver, the absolute delay of each drone relative to the formation reference is calculated using an adjustment algorithm. The ratio of the absolute delay of each drone to the carrier signal frequency is calculated to obtain the time delay of each drone. The vector composed of the time delays of all drones is used as the absolute delay compensation vector.

[0011] Specifically, the method of uniformly correcting the local timestamps of all UAV data using an absolute delay compensation vector involves: The corrected timestamp is obtained by subtracting the corresponding element in the absolute delay compensation vector from the local timestamp of each data point from each drone.

[0012] Specifically, the calculation of the precise pose of each UAV within the formation involves: Based on the IMU data corrected by the UAV timestamp, the predicted pose of the UAV at the current moment is calculated by integrating the motion model. Combined with the extended Kalman filter algorithm, the precise pose of each UAV in the formation is obtained.

[0013] The process of determining the three-dimensional coordinates of the tower is specifically as follows: The tower feature points are extracted from the lidar points collected by the UAV, and the UAV's body coordinates are transformed to the absolute geodetic coordinate system using the UAV's precise pose at the current moment to obtain the three-dimensional coordinates of the tower feature points.

[0014] If multiple drones or a single drone scans and detects the same point multiple times, the absolute coordinates of all calculated tower feature points are adjusted to obtain the final feature point coordinates.

[0015] This application has at least the following beneficial effects: (1) In existing technologies, observation data is usually synchronized by default or a fixed delay model is used during data processing. However, this leads to random delay errors being introduced into the positioning solution, resulting in cumulative errors and ultimately reducing the accuracy of the tower coordinates. This application effectively suppresses the positioning error caused by random delay by dynamically calculating the absolute delay compensation vector from the UAV operation data and performing time synchronization correction on all observation data. This makes the UAV's own positioning and its measurement results of the tower more accurate, thereby significantly improving the verification accuracy of the tower coordinates.

[0016] (2) Existing methods generally use fixed delay prediction models or hardware synchronization schemes, which are difficult to respond in real time to dynamic delay changes caused by complex environmental factors (such as signal blockage or electromagnetic interference), resulting in performance degradation or even failure in complex scenarios. This application uses multi-source data collected in real time during the task to dynamically calculate the delay, which can adaptively compensate for random fluctuations in delay and ensure the reliability and stability of collaborative positioning and verification in complex environments.

[0017] (3) To achieve high-precision synchronization, existing technologies typically require additional hardware such as high-precision timing modules and dedicated time synchronization networks, or the pre-establishment and maintenance of complex channel delay prediction models, which significantly increases system cost and technical complexity. This application generates absolute delay compensation vectors from existing observation data through an algorithm, without the need for additional dedicated synchronization hardware or the construction of offline prediction models. Based on existing common UAV sensor configurations, the accuracy can be improved simply through the algorithm, greatly reducing costs. Attached Figure Description

[0018] Figure 1 A flowchart of the dynamic verification method for power distribution network tower coordinates based on satellite and UAV collaboration provided in this application. Detailed Implementation

[0019] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0021] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0023] This application proposes a dynamic verification method for power distribution network tower coordinates based on satellite and UAV collaboration. The implementation process is as follows: Figure 1 As shown, the method includes the following steps: S1: Collect satellite navigation data for each drone, ranging data between drones in the formation, and observation data for the tower, and add a local timestamp.

[0024] Satellite navigation signals (from GNSS systems, such as BeiDou and GPS) are used to provide an absolute spatiotemporal reference benchmark. By obtaining the approximate position, velocity, and time (PVT) information of each UAV through satellite navigation signals, a high-precision time benchmark is provided for all locally collected data from power distribution towers.

[0025] Each UAV carries a GNSS receiver that continuously receives signals from multiple navigation satellites. It decodes these signals to obtain raw observations (such as pseudorange) for UAV positioning and extracts navigation messages containing time information. The pulse-of-seconds (PPS) signal output by the receiver, along with the calculated PVT information, together constitute the external time reference source for the UAV system.

[0026] However, in the dynamic environment of highly maneuverable UAV flight, GNSS signal reception may be affected by interference such as obstruction and multipath effects, leading to momentary jitter or loss of lock in the PPS signal. The data acquisition triggering time of each sensor (such as lidar, IMU, and data link module), internal bus transmission delays, and clock domain conversions between different devices all introduce non-negligible, random soft time synchronization errors. Therefore, while adding a local timestamp to each sensor's data using GNSS time as a reference is statistically accurate in the long term, unpredictable, minute random deviations still exist at the absolute time level of individual data packets. This application aims to dynamically eliminate such time deviations before data fusion.

[0027] Satellite signals are not only used to provide a precise time reference, but also to solve the problem of the relative geometric relationship between UAVs in the formation and the precise measurement of the target tower by the UAVs. The purpose of simultaneously collecting these two types of data is to obtain the precise spatial relative relationship between UAVs and between UAVs and towers, so as to calculate random delays and determine the coordinates of the towers.

[0028] During flight, each member of the formation periodically (ten times per minute in this embodiment) performs distance measurements between themselves or with the lead drone according to a predetermined multiple access communication protocol (time division multiple access in this embodiment) to obtain the real-time slant distance between the drones. This process is carried out in parallel with the reception of satellite signals. The drones use lidar to scan the power grid towers along the predetermined flight path and receive the echoes to generate point clouds.

[0029] Each drone maintains a local high-precision clock based on a time base. When any sensor generates a valid data point, a timestamp based on the local clock is appended to the data and it is encapsulated into a raw spatiotemporal data packet.

[0030] S2: Based on the obtained satellite navigation data and the ranging data between UAVs in the formation, construct a relative time difference observation model between each pair of UAVs in the formation, and use the whole network adjustment algorithm to calculate the absolute delay compensation vector of each UAV relative to the formation time reference.

[0031] Although the drone swarm has added local timestamps based on unified time synchronization to all collected satellite signals, ranging data between drones, and tower observation data, and generated raw spatiotemporal data packets, the different data recording the same physical event in these data packets are not synchronized at the actual time of occurrence due to the different processing times of the sensor hardware of each drone, the different transmission paths of the data in the swarm network, and the random interference that the signals may encounter.

[0032] To accurately measure and quantify these actual time deviations, an absolute delay compensation vector is generated. This vector is then used in subsequent steps to compensate and align the timestamps of all observed data, correcting them to the same true time reference plane, ultimately achieving high-precision tower coordinate calculation. This scheme compares the recorded timestamps of the same physical event under different node local clocks and calculates their differences to obtain the absolute delay compensation vector. This vector represents the systematic time deviation of each UAV sensor data stream relative to the cooperative reference time reference. This deviation consists of two parts: the clock difference between the local clock and the reference clock, and the processing and transmission delay of the data link. The magnitude of each value in the absolute delay compensation vector is proportional to the deviation of the corresponding UAV's local time reference relative to the cooperative reference time reference established by the system; the larger the deviation, the larger the required compensation value (absolute value).

[0033] In one embodiment, the relative clock offset between any two drones is directly calculated using ranging signals that are periodically exchanged between each drone within the formation and contain precise transmission and reception times. Finally, by integrating all measurement data, an absolute clock offset compensation value relative to the formation's unified time reference is calculated for each drone, and all compensation values ​​constitute an absolute delay compensation vector.

[0034] For a complete two-way ranging operation, which is a signal round initiated and completed by two UAVs (e.g., A and B) in a formation to measure the clock deviation between each other, the core of absolute delay compensation vector generation is calculating the clock deviation. The formula for the relative time difference observation model is: in, This represents the instantaneous deviation of drone B's clock relative to drone A's clock during a specific interaction. A positive value indicates that B's clock reading is slower than A's clock reading. Second. , These represent the precise times when drone A and drone B, as recorded by their local clocks, emitted their own wireless ranging signals. , These represent the precise times when UAV A and UAV B successfully received the ranging signal sent by the other, as recorded by their local clocks; where, Record this as the first duration. This is recorded as the second duration; This indicates the difference between the first and second durations.

[0035] The absolute delay compensation vector is then... By designating a leader drone in the formation as a baseline and pairing all drones within the formation (e.g., three drones A, B, and C, specifically in the combination of AB, AC, and BC), multiple calculations are performed. After the observed values ​​are processed using the least squares adjustment algorithm to solve the entire network, the absolute deviation of UAV i's clock relative to the formation reference clock is estimated. This is the time delay amount, which is used to perform consistency correction on the timestamps of all data.

[0036] Although the absolute transmission time of the signal between drones A and B is unknown and may vary, in a short ranging interaction, the paths the signal takes from A to B and from B to A can be considered symmetrical, meaning the transmission times are equal. By subtracting the time measurements of the two one-way transmissions, the identical transmission delays are canceled out, leaving only the clock discrepancies between the two drones in the result. The constituent items (i.e.) This allows for the direct calculation of the clock skew. By repeating this process throughout the entire formation network, a redundant clock skew observation network is obtained. Then, an adjustment algorithm is used to eliminate noise from individual measurements, ultimately estimating the stable and reliable absolute clock skew for each UAV. This forms a compensation vector that can be applied to data synchronization.

[0037] In another embodiment: using GNSS signals that can be received by all UAVs, the relative receiver clock difference between UAVs in the formation is directly estimated by constructing an inter-station single-difference observation equation. This clock difference reflects the relative hardware delay of the entire link from signal reception to data timestamp generation. Finally, the time delay of each UAV's GNSS data stream relative to the formation reference is calculated, and the set of these delays constitutes the absolute delay compensation vector.

[0038] First, for UAV i, UAV j, and the same satellite p in the formation, the specific formula for the relative time difference observation model constructed at epoch t is: in, This represents the difference between the carrier phase observations of UAV i and UAV j on satellite p at epoch t. This represents the difference in geometric distance between satellite p and UAV i and between satellite p and UAV j, calculated based on satellite ephemeris and approximate coordinates of the UAVs. Indicates the wavelength of the carrier signal; This represents the difference in carrier phase single-difference integer ambiguity between UAV i and UAV j with respect to satellite p; The relative clock error of the receiver to be solved is represented by the constant deviation in time synchronization between the GNSS receiving channels of UAV i and UAV j. This represents residual errors (such as tropospheric and ionospheric delay residuals, multipath effects, and observation noise).

[0039] The core function of single-difference operation is to eliminate the clock error of satellite p, but retain the relative clock error between UAV i and UAV j, which is the key observation used in this scheme to estimate time synchronization error. By having all UAVs in the formation synchronously observe multiple identical satellites, a series of single-difference equations as described above can be established. These equations share the same relative clock error parameter but have different geometric distance differences and single-difference integer ambiguities.

[0040] In practical processing, the integer ambiguity of each single difference is first determined by conventional means (in this embodiment, the LAMBDA algorithm is used to search based on the known approximate location). Then, the fixed ambiguity is substituted into the equation system as a known value. The relative time delay of UAV i relative to UAV j can be calculated with high accuracy by using the parameter estimation method of least squares or Kalman filtering.

[0041] Based on the designated leader aircraft in the formation (let its... ), and the relative delay between all pairs Once transmitted across the entire network, the absolute delay of each drone relative to this unified benchmark can be calculated. The ratio of the absolute delay of each drone to the carrier signal frequency is calculated to obtain the time delay of each drone, and finally an absolute delay compensation vector is formed. .

[0042] S3: Use the absolute delay compensation vector to uniformly correct the local timestamps of all UAV data, input the time-aligned multi-source data into the fusion filter, and calculate the precise pose of each UAV in the formation; based on the obtained precise pose and the observation data of the tower, combined with coordinate transformation, determine the three-dimensional coordinates of the tower.

[0043] Time synchronization correction: Based on the UAV number associated with each data point in the original spatiotemporal data packet, select the corresponding value from the absolute delay compensation vector. (or Then, timestamp rewriting is performed. For each piece of data from UAV i (whether it is GNSS observation, IMU data, relative ranging value or tower scanning data), the original timestamp is subtracted from the compensation value of the UAV to obtain the corrected timestamp. Finally, all data are time-series aligned and reassembled according to the corrected timestamp to generate a time-synchronized corrected dataset.

[0044] Motion compensation: The time synchronization correction step corrects the time reference of the observation data, but the UAV is in continuous motion, and its position and attitude change rapidly over time. To obtain each corrected timestamp... The spatial state of the corresponding drone needs to be compensated for motion.

[0045] Specifically, for any given UAV, its high-frequency inertial measurement unit (IMU) data provides continuous angular velocity and specific force information. Based on this UAV's... Known pose state near the time frame (e.g., output of the previous fusion cycle) By utilizing IMU data and performing inertial navigation calculations or motion model integration, the precise time of the UAV can be calculated through forward propagation (or interpolation if necessary). Predicted pose .

[0046] Data fusion: The corrected dataset is input into the error state extended Kalman filter, and the pose, velocity, and sensor bias of each UAV are used as state variables for estimation. In each filtering cycle, the algorithm synchronously processes the following time-aligned observation information: (1) GNSS pseudorange and carrier phase observations processed by differential or precise point positioning technology to provide absolute position constraints; (2) angular velocity and specific force information provided by the inertial measurement unit (IMU), which provides high-frequency relative motion prediction through integration; (3) relative ranging between UAVs in the formation to provide accurate relative geometric constraints. The filter optimally fuses all this information through the dynamic model and the observation model, and outputs the position and attitude angles of each UAV in the geocentric coordinate system or local projected coordinate system in real time, i.e., the precise pose sequence of the formation.

[0047] Tower coordinate verification: Based on the precise pose sequence, the position and attitude of the UAV at the precise moment of acquiring tower observation data are determined. Then, the original tower observation data acquired by the UAV at the same timestamp is loaded. For the lidar point cloud, a point cloud segmentation and fitting algorithm is used to automatically extract the three-dimensional coordinates of the feature points at the tower base and top. These coordinates are located in the sensor coordinate system with the UAV lidar as the origin. Then, using the precise pose of the UAV at that moment, the point cloud coordinates in the sensor coordinate system are transformed to the absolute geodetic coordinate system through three-dimensional coordinate rotation and translation transformation to obtain the absolute coordinates of the tower feature points. If multiple UAVs or multiple scans have measured the same point, the calculated absolute coordinates of these tower feature points are averaged or adjusted to obtain the final tower feature point coordinates.

[0048] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0049] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration, characterized in that, The method includes the following steps: Collect satellite navigation data for each drone, ranging data between drones in the formation, and observation data for the tower, and add local timestamps; Based on the obtained satellite navigation data and the ranging data between UAVs in the formation, a relative time difference observation model between each pair of UAVs in the formation is constructed, and the absolute delay compensation vector of each UAV relative to the formation time reference is calculated by the whole network adjustment algorithm. The local timestamps of all UAV data are uniformly corrected using an absolute delay compensation vector. The time-aligned multi-source data is then input into a fusion filter to calculate the precise pose of each UAV in the formation. Based on the obtained precise pose and the observation data of the tower, the three-dimensional coordinates of the tower are determined by coordinate transformation.

2. The method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration as described in claim 1, characterized in that, The specific steps for constructing the relative time difference observation model between each pair of UAVs within the formation are as follows: The time from when drone A sends a signal to when drone B receives the signal is recorded as the first time; the time from when drone B receives the signal and sends a feedback signal to when drone A receives the feedback signal is recorded as the second time. Half of the difference between the first and second durations is taken as the instantaneous deviation of the UAV B clock relative to the UAV A clock. The formula for calculating the instantaneous deviation is used as the relative time difference observation model between UAV A and UAV B.

3. The method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration as described in claim 2, characterized in that, The process of calculating the absolute delay compensation vector of each drone relative to the formation time reference using the whole-network adjustment algorithm is as follows: By using the designated leader drone in the formation as the reference, and combining the instantaneous deviations obtained by pairing all drones in the formation, the time delay of each drone's clock relative to the reference clock is obtained after performing a network-wide adjustment algorithm. The vector composed of the time delays of all drones is then used as the absolute delay compensation vector.

4. The method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration as described in claim 1, characterized in that, The construction of the relative time difference observation model between each pair of UAVs within the formation includes: For UAV i, UAV j, and the same satellite p in the formation, the specific formula for the relative time difference observation model constructed at epoch t is: in, This represents the difference between the carrier phase observations of UAV i and UAV j on satellite p at epoch t. This represents the difference in geometric distance between satellite p and drone i and between satellite p and drone j; Indicates the wavelength of the carrier signal; This represents the difference in carrier phase single-difference integer ambiguity between UAV i and UAV j with respect to satellite p; This represents the receiver's relative clock error to be solved; This represents the residual error.

5. The method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration as described in claim 4, characterized in that, The method for obtaining single-difference integer ambiguity is performed using the LAMBDA algorithm.

6. The method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration as described in claim 4, characterized in that, The absolute delay compensation vector for each drone relative to the formation time reference is calculated using a full-network adjustment algorithm, specifically as follows: By using the designated leader drone in the formation as a reference, and by pairing all drones in the formation with each other to obtain the relative clock difference of the receiver, the absolute delay of each drone relative to the formation reference is calculated using an adjustment algorithm. The ratio of the absolute delay of each drone to the carrier signal frequency is calculated to obtain the time delay of each drone. The vector composed of the time delays of all drones is used as the absolute delay compensation vector.

7. The method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration as described in claim 1, characterized in that, The method of uniformly correcting the local timestamps of all UAV data using an absolute delay compensation vector is as follows: The corrected timestamp is obtained by subtracting the corresponding element in the absolute delay compensation vector from the local timestamp of each data point from each drone.

8. The method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration as described in claim 1, characterized in that, The calculation of the precise pose of each UAV within the formation is specifically as follows: Based on the IMU data corrected by the UAV timestamp, the predicted pose of the UAV at the current moment is calculated by integrating the motion model. Combined with the extended Kalman filter algorithm, the precise pose of each UAV in the formation is obtained.

9. The method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration as described in claim 1, characterized in that, The process of determining the three-dimensional coordinates of the tower is as follows: The tower feature points are extracted from the lidar points collected by the UAV, and the UAV's body coordinates are transformed to the absolute geodetic coordinate system using the UAV's precise pose at the current moment to obtain the three-dimensional coordinates of the tower feature points.

10. The method for dynamic verification of power distribution network tower coordinates based on satellite and UAV collaboration as described in claim 9, characterized in that, If multiple drones or a single drone scans and detects the same point multiple times, the absolute coordinates of all calculated tower feature points are adjusted to obtain the final feature point coordinates.

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