Unmanned aerial vehicle distribution network autonomous inspection method and system

Abstract

The invention provides an unmanned aerial vehicle distribution network autonomous inspection method and system, and the method comprises the steps: S1, enabling an unmanned aerial vehicle to move according to an inspection task, and enabling the inspection task to comprise a flight path, a plurality of waypoints, and a shooting task; s2, when the unmanned aerial vehicle moves to any waypoint, the unmanned aerial vehicle and the ground reference station receive satellite signals at the same time, and a moving signal and a reference signal are obtained respectively; s3, the ground reference station generates differential correction data according to the reference signal and the coordinates of the ground reference station, and sends the differential correction data to the unmanned aerial vehicle; s4, the unmanned aerial vehicle eliminates the common error according to the differential correction data and the mobile signal, corrects the coordinates of the unmanned aerial vehicle to obtain fine coordinates, adjusts the position of the unmanned aerial vehicle according to the accurate positioning and shooting task, and shoots the distribution network line at the waypoint; and S5, repeating the steps S2-S4 until the shooting task is completed. The problem that in the prior art, the positioning precision of the position of an unmanned aerial vehicle is not high in the shooting process of routing inspection of a distribution network is solved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) automatic inspection technology, and in particular to a method and system for autonomous inspection of power distribution networks using UAVs. Background Technology

[0002] When conducting drone-based power distribution network inspections, current technology primarily relies on inspectors manually controlling the drones to inspect the distribution lines (manual flight), which places high demands on the inspectors' skills. In manual inspections, inspectors need to use their experience and expertise to maneuver the drone to the appropriate location and adjust the gimbal to capture images of the distribution lines at the correct angle. This method, dependent on traditional manual or semi-automated methods, suffers from low efficiency, large coverage blind spots, incomplete data collection, and insufficient real-time response capabilities.

[0003] In recent years, with the rapid development of drone technology, sensor technology, and data communication technology, drone intelligent inspection systems have become an important way to solve the above-mentioned problems. While existing technologies include data acquisition and inspection platforms based on multi-sensor fusion, integrating new technologies such as neural network algorithms, edge computing, AI visual recognition, and autonomous driving, allowing drones to automatically determine the route and perform multi-angle autonomous zoom photography inspections of power distribution towers, drones typically use a multi-system fusion positioning method combining GPS, BeiDou (BDS), GLONASS, and Galileo during automatic navigation. While this fusion positioning method generally meets the positioning accuracy requirements during long-distance flights, it becomes difficult to achieve the required accuracy when the drone is moving and needs to fine-tune its position for photography. Furthermore, dynamic changes in the environment and site conditions, such as weather changes, sudden obstacles, or temporary changes in power equipment, can significantly affect positioning accuracy, preventing the drone from accurately locating itself and taking pictures at predetermined positions around the power distribution towers, resulting in incomplete data collection. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for autonomous inspection of power distribution networks by unmanned aerial vehicles (UAVs), which solves the problem of low positioning accuracy of the UAV itself during the UAV's inspection and shooting process.

[0005] According to an embodiment of the present invention, a method for autonomous inspection of power distribution networks by unmanned aerial vehicles (UAVs) includes: S1: The drone moves according to the inspection task, which includes flight route, multiple waypoints and shooting task; S2: When the UAV moves to any waypoint along the flight path, the UAV and the ground base station simultaneously receive satellite signals, and obtain the movement signal and the reference signal respectively. S3: The ground reference station generates differential correction data based on the reference signal and its own coordinates, and broadcasts the differential correction data to the UAV. S4: After eliminating common errors based on differential correction data and mobile signals, the UAV corrects its own coordinates to obtain fine coordinates. Then, based on precise positioning and shooting tasks, it adjusts its own position and shoots the power distribution lines at the waypoints. S5: Repeat steps S2-S4 until the shooting task is completed and the inspection ends.

[0006] Preferably, the method for a ground reference station to generate differential correction data based on a reference signal and its own coordinates includes: The reference pseudorange and reference carrier phase observations between the ground reference station and the satellite are calculated based on the reference signal and the ground reference station's own coordinates. The reference pseudorange observations, reference carrier phase observations, and coordinates of the ground reference station are used as differential correction data.

[0007] Preferably, the common errors include ionospheric errors, ephemeris errors, and satellite clock errors; Methods for UAVs to eliminate common errors in mobile signals based on differential correction data include: The pseudorange and carrier phase observations between the UAV and the satellite are calculated based on the mobile signal and the UAV's own coordinates. The dual-frequency observation method is used to eliminate ionospheric errors in the moving pseudorange observations and moving carrier phase observations, resulting in the first pseudorange observation and the first carrier phase observation. The satellite clock error in the first pseudorange observation and the first carrier phase observation is eliminated using the double difference method to obtain the second pseudorange observation and the second carrier phase observation. The satellite coordinates are corrected to obtain accurate satellite coordinates. Then, the satellite clock error is eliminated based on the accurate satellite coordinates, the second pseudorange observation value, and the second carrier phase observation value to obtain accurate pseudorange observation values ​​and accurate carrier phase observation values.

[0008] Preferably, the method for eliminating ionospheric errors in moving pseudorange and moving carrier phase observations using dual-frequency observation to obtain the first pseudorange and first carrier phase observations includes: The drone acquires satellite signals at a first frequency and a second frequency, respectively, and obtains the first frequency signal and the second frequency signal. The pseudorange observation value and the carrier phase observation value of the first frequency are calculated based on the first frequency signal and the UAV's own coordinates. The pseudorange observation value and the carrier phase observation value of the second frequency are calculated based on the second frequency signal and the UAV's own coordinates. A first-order ionospheric delay model is constructed. Based on the pseudorange observations at the first frequency, the carrier phase observations at the first frequency, the pseudorange observations at the second frequency, and the carrier phase observations at the second frequency, the first-order ionospheric delay model is used to calculate the geometrically independent pseudorange observations and the geometrically independent carrier phase observations. Based on the UAV's own coordinates, the geometrically independent pseudorange observations and geometrically independent carrier phase observations are corrected to obtain the first pseudorange observation and the first carrier phase observation, respectively.

[0009] Preferably, the method for eliminating satellite clock errors in the first pseudorange observation and the first carrier phase observation using the double-difference method to obtain the second pseudorange observation and the second carrier phase observation includes: The UAV calculates the broadcast clock error based on the differential correction data broadcast by the ground reference station, and corrects the broadcast clock error using the clock error correction provided by the SSR to obtain the corrected clock error; The first pseudorange observation and the first carrier phase observation are corrected based on the UAV’s own clock error and the corrected clock error to obtain the second pseudorange observation and the second carrier phase observation respectively. A double-difference model is constructed to eliminate satellite clock errors in the second pseudorange observation and the second carrier phase observation.

[0010] Preferably, the formula for calculating the precise pseudorange observation is as follows: The formula for calculating the precise carrier phase observation is as follows: in, This is the second pseudorange observation. This is the second carrier phase observation value. To accurately project the geometric distance between satellite coordinates and UAV coordinates.

[0011] Preferably, after obtaining the fine coordinates, a relative coordinate offset is added to the precise coordinates to obtain the shooting coordinates, and then the UAV performs the shooting task at the shooting coordinate position.

[0012] On the other hand, according to embodiments of the present invention, an unmanned aerial vehicle (UAV) autonomous power distribution network inspection system is also provided. This system uses the above-described UAV autonomous power distribution network inspection method, including: A configuration terminal is used to configure inspection tasks and send the inspection tasks to the UAV. The inspection tasks include flight routes, multiple waypoints, and shooting tasks. The ground reference station is used to receive satellite signals, obtain reference signals, generate differential correction data based on the reference signals and its own coordinates, and broadcast the differential correction data to the UAV. The drone includes a signal transceiver module, a positioning module, a flight module, and a mission execution module. The signal transceiver module receives inspection tasks, differential correction data, and satellite signals to obtain a motion signal. The positioning module corrects its own coordinates after eliminating common errors in the differential correction data and motion signal to obtain fine coordinates. The flight module controls the drone to move along a flight path or fine coordinates. The mission execution module controls the drone to complete the shooting task.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The drone moves along its flight path and performs shooting tasks at each waypoint. In this invention, when the drone moves to a waypoint, it receives satellite signals through a ground reference station with known precise coordinates, generates differential correction data, and then the drone also receives satellite signals. The differential correction data is used to correct the satellite signals received by the drone, and the drone's own coordinates are corrected based on the corrected satellite signals to obtain fine coordinates. Then, the drone adjusts its position according to the precise positioning and shooting tasks and shoots the power distribution lines at the waypoint to collect more comprehensive data. Attached Figure Description

[0014] Figure 1 This is a diagram illustrating the unmanned aerial vehicle (UAV) autonomous power distribution network inspection method according to an embodiment of the present invention.

[0015] Figure 2 This is a configuration diagram of the inspection task according to an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0017] like Figure 1 As shown in the figure, an embodiment of the present invention proposes a method for autonomous inspection of power distribution networks by unmanned aerial vehicles (UAVs), including: S1: The drone moves according to the preset inspection task, which includes flight route, multiple waypoints and shooting task; like Figure 2 As shown, before the drone begins its inspection, it is necessary to remotely configure the inspection task for the drone using a terminal device / system. This mainly includes the flight route, flight speed, flight altitude, multiple waypoints and their corresponding altitudes, and shooting tasks. All waypoints are on the flight route, and each waypoint corresponds to a pole. When the drone flies to a waypoint during the inspection, it needs to stop at the waypoint and perform a shooting task to collect data from the pole at that location.

[0018] The collected RTK latitude and longitude coordinates and altitude of each tower are used as the core parameters of the flight path. With the tower coordinates as the core reference point, all motion parameters of the UAV, such as flight path, hovering point, shooting point, gimbal angle, and focal length, are pre-set in the terminal equipment / system according to the inspection specifications. The inspection task is generated and sent to the UAV, which automatically performs the inspection according to the inspection task.

[0019] S2: When the UAV moves to any waypoint along the flight path, the UAV and the ground base station simultaneously receive satellite signals, and obtain the movement signal and the reference signal respectively. Because ground-based reference stations maintain a relatively constant location, their coordinates are quite accurate and precise (centimeter-level). However, UAVs, due to frequent changes and signal interference and errors, can only obtain approximate coordinates in meters, not precise centimeter-level coordinates. Therefore, this invention addresses this by having both the UAV and the ground-based reference station simultaneously receive satellite signals when the UAV reaches its waypoint, obtaining both a motion signal and a reference signal. The ground-based reference station then uses the reference signal and accurate, precise coordinates to process the received reference signal, calculating the interference and errors within it. This allows the UAV to eliminate interference and errors from the motion signal, obtaining more accurate and precise coordinates and accurately locating its own position.

[0020] S3: The ground reference station generates differential correction data based on the reference signal and its own coordinates, and broadcasts the differential correction data to the UAV. With ground reference station For satellite Taking the observation as an example (unit: meters), the reference pseudorange (code) observation value is calculated based on the reference signal: And the reference carrier phase observations (converted to meters in "phase × wavelength"): in The geometric distance between the satellite and the ground reference station is calculated using the coordinates of the satellite and the ground reference station, yielding the Euclidean distance between them. The speed of light; , These are the clock biases of the ground reference station and the satellite itself, respectively. , These are the tropospheric and ionospheric delays, respectively. The carrier wavelength; The ambiguity is an integer. , To measure residuals such as noise and multipath.

[0021] Since multiple drones may be conducting inspections near the ground reference station simultaneously, and other devices may require the ground reference station to provide reference signal data, it is not practical to establish a one-to-one connection between the ground reference station and a single drone or other device. Therefore, the ground reference station broadcasts data to all surrounding devices, sending the reference pseudorange observations, reference carrier phase observations, and the coordinates of the ground reference station as differential correction data to all surrounding drones.

[0022] S4: After eliminating common errors based on differential correction data and mobile signals, the UAV corrects its own coordinates to obtain fine coordinates. Then, based on precise positioning and shooting tasks, it adjusts its own position and shoots the power distribution lines at the waypoints. Because drones are constantly changing and subject to signal interference and errors, only their approximate coordinates in meters can be obtained. It is not possible to obtain their more precise centimeter-level coordinates at any time. Therefore, it is not possible to calculate and eliminate errors as easily as with ground base stations.

[0023] The mobile signals received by a drone from satellites can be used to obtain its own rough original coordinates through fusion positioning. In addition, the received signals will also have some common errors due to environmental interference, including ionospheric error, ephemeris error and satellite clock error.

[0024] Ionospheric error occurs when satellite signals pass through the atmospheric ionosphere. Free electrons in the ionosphere refract the signal, causing changes in the signal propagation path and speed. The degree of this effect is closely related to the signal frequency, ionospheric electron density, and propagation path length.

[0025] Ephemeris error refers to the deviation between the satellite's spatial position provided by ephemeris data and its actual position in a Global Navigation Satellite System (GNSS). It belongs to the category of systematic errors and is caused by factors such as insufficient accuracy in predicting satellite orbital parameters and the influence of complex perturbations, such as uneven Earth mass and atmospheric drag.

[0026] Satellite clock bias refers to the deviation between the satellite clock and the GPS reference time. Although GPS satellites use high-precision atomic clocks, there are still deviations or drifts. These deviations may be within 1 millisecond, and the resulting equivalent distance error can be up to about 300 kilometers.

[0027] These common errors all affect the positioning accuracy of the UAV. Therefore, this invention needs to eliminate these errors in order to refine the original coordinates and obtain fine coordinates.

[0028] Similar to ground-based reference stations, the UAV u-type satellite Based on the observations, the pseudorange between the UAV and the satellite can be calculated using the motion signal. And mobile carrier phase observations: (1) Elimination of ionospheric error ① Dual-frequency data processing Drones use the first frequency Second frequency Satellite signals are acquired using two different frequencies, resulting in a first frequency signal and a second frequency signal. Based on the calculation formulas for the moving pseudorange observations and the moving carrier phase observations, the corresponding first frequency pseudorange observation value is calculated. First frequency carrier phase observation value Second frequency pseudorange observations Second frequency carrier phase observation .

[0029] ② Construct a first-order ionospheric delay model Total electron content along the path ( ), For frequency (Hz).

[0030] ③ Geometrically independent combinations (used for estimation / quality control, star-by-star analysis) Observations based on geometrically independent pseudoranges It can estimate the path ionosphere or perform quality control on the network ionosphere correction field.

[0031] ④ Eliminate ionospheric errors Based on the drone's original coordinates Interpolation ionospheric correction is performed on the original coordinates. ,but: First pseudorange observation First carrier phase observation .

[0032] in The ionospheric mapping coefficients are related to frequency and satellite elevation angle. For function / raster set, It is a star-by-star scalar, stacked as a vector by stars.

[0033] (2) Satellite clock error elimination ① Broadcast clock bias correction (star-by-star scalar / time series) , in, , , For navigation message clock difference parameters, For clock difference reference time, Group delay; To correct relativity, For eccentricity, For the major half-axis, It is a near-point angle.

[0034] ②Precision / SSR clock bias enhancement (Star Chaser): in Clock error correction provided for precision or SSR systems.

[0035] ③ Observational Correction (Star-by-Star): Second pseudorange observation: Second carrier phase observation: At the speed of light, The clock bias is the UAV's own (which can be estimated in the filtered state or eliminated by double difference).

[0036] ④ The double-difference method simultaneously eliminates clock errors between satellites and UAVs. Reference satellite Constructing a double-difference (UAV) Base stations ): Double difference and The public items were eliminated.

[0037] (3) Ephemeris error ① Satellite position correction (vector) By correcting the satellite coordinates, accurate satellite coordinates are obtained. : in, Satellite ECEF coordinates calculated for broadcast ephemeris; This is the precision / SSR trajectory correction vector.

[0038] ② Projection of geometric distance (scalar) Projection of the geometric distance between precise satellite coordinates and UAV coordinates: , in, This is the unit vector for the line of sight.

[0039] ③ Observational Correction (Star-by-Star): After completing the clock error correction , Further reduce orbital errors based on this: Precise pseudorange observations: ; Precise carrier phase observations: .

[0040] Finally, after eliminating the aforementioned common errors, more accurate data can be obtained. Then, the original coordinates can be adjusted based on this accurate data. Make corrections.

[0041] The correction method is as follows: ① Corrected observation vector For the epoch All visible satellites (except the reference satellite) ),form: Each component has been adjusted according to the aforementioned ionospheric error ( or ), satellite clock bias ( ), ephemeris ( Error elimination is accomplished using the error elimination method.

[0042] ②State Vectors and Models , , Linearization: in , Composed of the cosines of each satellite direction, The carrier wavelength; It is the identity matrix; This is the noise vector.

[0043] ③ Solve the following formulas using least squares or Kalman spectroscopy: Subsequently By fixing the entire circumference (such as using the LAMBDA method), precise coordinates down to the centimeter level can be obtained. After obtaining the fine coordinates, a relative coordinate offset is added to the precise coordinates, such as altitude (Z-axis) + 1 meter, to obtain the shooting coordinates. Then, the position is adjusted according to the shooting coordinates and the shooting task, and the network lines at the waypoint are photographed to collect more comprehensive data.

[0044] Finally, repeat steps S2-S4 until all waypoints are photographed, and the inspection ends.

[0045] On the other hand, embodiments of the present invention also provide an unmanned aerial vehicle (UAV) autonomous power distribution network inspection system, which uses the above-described UAV autonomous power distribution network inspection method, including: A configuration terminal is used to configure inspection tasks and send the inspection tasks to the UAV. The inspection tasks include flight routes, multiple waypoints, and shooting tasks. The ground reference station is used to receive satellite signals, obtain reference signals, generate differential correction data based on the reference signals and its own coordinates, and broadcast the differential correction data to the UAV. The drone includes a signal transceiver module, a positioning module, a flight module, and a mission execution module. The signal transceiver module receives inspection tasks, differential correction data, and satellite signals to obtain a motion signal. The positioning module corrects its own coordinates after eliminating common errors in the differential correction data and motion signal to obtain fine coordinates. The flight module controls the drone to move along a flight path or fine coordinates. The mission execution module controls the drone to complete the shooting task.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for autonomous inspection of power distribution networks using unmanned aerial vehicles (UAVs), characterized in that: include: S1: The drone moves according to the inspection task, which includes flight route, multiple waypoints and shooting task; S2: When the UAV moves to any waypoint along the flight path, the UAV and the ground base station simultaneously receive satellite signals, and obtain the movement signal and the reference signal respectively. S3: The ground reference station generates differential correction data based on the reference signal and its own coordinates, and broadcasts the differential correction data to the UAV. S4: After eliminating common errors based on differential correction data and mobile signals, the UAV corrects its own coordinates to obtain fine coordinates. Then, based on precise positioning and shooting tasks, it adjusts its own position and shoots the power distribution lines at the waypoints. S5: Repeat steps S2-S4 until the shooting task is completed and the inspection ends.

2. The method for autonomous inspection of power distribution networks by unmanned aerial vehicles as described in claim 1, characterized in that: Methods for ground-based reference stations to generate differential correction data based on reference signals and their own coordinates include: The reference pseudorange and reference carrier phase observations between the ground reference station and the satellite are calculated based on the reference signal and the ground reference station's own coordinates. The reference pseudorange observations, reference carrier phase observations, and coordinates of the ground reference station are used as differential correction data.

3. The method for autonomous inspection of power distribution networks by unmanned aerial vehicles as described in claim 2, characterized in that: The common errors include ionospheric errors, ephemeris errors, and satellite clock errors; Methods for UAVs to eliminate common errors in mobile signals based on differential correction data include: The pseudorange and carrier phase observations between the UAV and the satellite are calculated based on the mobile signal and the UAV's own coordinates. The dual-frequency observation method is used to eliminate ionospheric errors in the moving pseudorange observations and moving carrier phase observations, resulting in the first pseudorange observation and the first carrier phase observation. The satellite clock error in the first pseudorange observation and the first carrier phase observation is eliminated using the double difference method to obtain the second pseudorange observation and the second carrier phase observation. The satellite coordinates are corrected to obtain accurate satellite coordinates. Then, the satellite clock error is eliminated based on the accurate satellite coordinates, the second pseudorange observation value, and the second carrier phase observation value to obtain accurate pseudorange observation values ​​and accurate carrier phase observation values.

4. The method for autonomous inspection of power distribution networks by unmanned aerial vehicles as described in claim 3, characterized in that: The methods for eliminating ionospheric errors in moving pseudorange and moving carrier phase observations using dual-frequency observations to obtain the first pseudorange and first carrier phase observations include: The drone acquires satellite signals at a first frequency and a second frequency, respectively, and obtains the first frequency signal and the second frequency signal. The pseudorange observation value and the carrier phase observation value of the first frequency are calculated based on the first frequency signal and the UAV's own coordinates. The pseudorange observation value and the carrier phase observation value of the second frequency are calculated based on the second frequency signal and the UAV's own coordinates. A first-order ionospheric delay model is constructed. Based on the pseudorange observations at the first frequency, the carrier phase observations at the first frequency, the pseudorange observations at the second frequency, and the carrier phase observations at the second frequency, the geometrically independent pseudorange observations and geometrically independent carrier phase observations are calculated using the first-order ionospheric delay model. Based on the UAV's own coordinates, the geometrically independent pseudorange observations and geometrically independent carrier phase observations are corrected to obtain the first pseudorange observation and the first carrier phase observation, respectively.

5. The method for autonomous inspection of power distribution networks by unmanned aerial vehicles as described in claim 3, characterized in that: Methods for eliminating satellite clock errors in the first pseudorange and first carrier phase observations using the double-difference method to obtain the second pseudorange and second carrier phase observations include: The UAV calculates the broadcast clock error based on the differential correction data broadcast by the ground reference station, and corrects the broadcast clock error using the clock error correction provided by the SSR to obtain the corrected clock error; The first pseudorange observation and the first carrier phase observation are corrected based on the UAV’s own clock error and the corrected clock error to obtain the second pseudorange observation and the second carrier phase observation respectively. A double-difference model is constructed to eliminate satellite clock errors in the second pseudorange observation and the second carrier phase observation.

6. The method for autonomous inspection of power distribution networks by unmanned aerial vehicles as described in claim 3, characterized in that: The formula for calculating precise pseudorange observations is as follows: The formula for calculating the precise carrier phase observation is as follows: in, This is the second pseudorange observation. This is the second carrier phase observation value. To accurately project the geometric distance between satellite coordinates and UAV coordinates.

7. The method for autonomous inspection of power distribution networks by unmanned aerial vehicles as described in claim 1, characterized in that: After obtaining the fine coordinates, a relative coordinate offset is added to the precise coordinates to obtain the shooting coordinates. Then, the drone performs the shooting task at the shooting coordinate position.

8. An unmanned aerial vehicle (UAV) autonomous power distribution network inspection system, characterized in that: The system uses a drone-based autonomous power distribution network inspection method as described in any one of claims 1-7, comprising: A configuration terminal is used to configure inspection tasks and send the inspection tasks to the UAV. The inspection tasks include flight routes, multiple waypoints, and shooting tasks. The ground reference station is used to receive satellite signals, obtain reference signals, generate differential correction data based on the reference signals and its own coordinates, and broadcast the differential correction data to the UAV. The drone includes a signal transceiver module, a positioning module, a flight module, and a mission execution module. The signal transceiver module receives inspection tasks, differential correction data, and satellite signals to obtain a motion signal. The positioning module corrects its own coordinates based on the differential correction data and the motion signal to obtain fine coordinates after eliminating common errors. The flight module controls the drone to move along a flight path or fine coordinates. The mission execution module controls the drone to complete the shooting task.

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