A method and device for controlling a wire reel of a drone tow rope

Abstract

This application belongs to the field of UAV tow rope technology, and discloses a method and device for controlling the tow rope reel of a UAV. The method includes: real-time acquisition of UAV hovering altitude data and calculation of corresponding real-time elevation difference data; using a particle filtering algorithm to remove noise from the real-time elevation difference data to obtain a terrain elevation difference change sequence; calculating the sag depth of each elevation difference sampling point in the terrain elevation difference change sequence to obtain a sag depth distribution curve; obtaining a sequence of abrupt change positions including multiple abrupt change locations based on the sag depth distribution curve; calculating the tension increment or tension decrement at each abrupt change location to obtain a tension change rate distribution; obtaining dynamic characteristics of tension growth based on the tension change rate distribution and baseline trend line analysis at each abrupt change location; and generating a set of control commands for the tow rope reel based on the dynamic characteristics of tension growth, a preset safe tension range, and the rated parameters of the tow rope reel. This application can avoid the risk of sag loss of control in the tow rope.

Description

Technical Field

[0001] This application relates to the field of drone tow rope technology, and in particular to a method and device for controlling the spool of a drone tow rope. Background Technology

[0002] Drones carrying guide ropes play an irreplaceable role in complex terrain environments such as power line erection and material transportation in mountainous areas. When a drone carries a guide rope and flies in undulating terrain, the rope will form a sag under the action of gravity. This natural sag phenomenon is relatively easy to control in flat terrain, but it brings severe challenges in hilly and mountainous environments.

[0003] When a drone hovers at a constant altitude while the ground elevation changes, the effective sag length of the rope changes accordingly, leading to a significant alteration in the sag shape. Existing reel control systems generally employ constant speed or simple segmented speed regulation for rope deployment and retrieval, neglecting the dynamic impact of terrain changes on the rope's stress state. They fail to respond in real-time to these terrain-induced stress changes, often resulting in lag in rope tension control and triggering a series of chain reactions. When a drone flies from one side of a valley to the other, the effective rope length increases as the ground elevation decreases, the sag deepens, and the rope tension decreases accordingly. However, when the drone crosses a ridge, the ground elevation rises rapidly, the effective rope length shortens sharply, and the tension increases abruptly. This sudden change in tension not only imposes impact loads on the rope material but can also lead to uncontrolled sag, causing the rope to collide with or become entangled with ground obstacles. In steep ridge areas, even a small drone movement can cause drastic fluctuations in rope stress due to rapid changes in terrain elevation, far exceeding the response capabilities of traditional control systems.

[0004] Therefore, the technical problem solved by this application is how to design a terrain-adaptive acceleration and deceleration control curve for the spool of the drone's traction rope, so as to avoid rope stress and sag loss caused by sudden changes in terrain. Summary of the Invention

[0005] This application provides a method and device for controlling the reel of a drone's traction rope, which can ensure that the speed at which the reel retracts and extends the traction rope at the target time and position in the future can avoid the risk of the traction rope sag and going out of control.

[0006] In a first aspect, embodiments of this application provide a method for controlling the reel of a drone's tow rope, including:

[0007] Real-time acquisition of drone hovering altitude data and calculation of corresponding real-time elevation difference data;

[0008] The particle filter algorithm is used to remove noise from the real-time elevation difference data to obtain the topographic elevation difference change sequence;

[0009] Calculate the sag depth at each elevation sampling point in the topographic elevation change sequence to obtain the sag depth distribution curve; based on the sag depth distribution curve, obtain the abrupt change location sequence including multiple abrupt change locations;

[0010] Calculate the tension increment or tension decrease at each abrupt change location to obtain the tension change rate distribution; based on the tension change rate distribution and the baseline trend line analysis at each abrupt change location, obtain the dynamic characteristics of tension growth.

[0011] The control command set for the reel is generated based on the dynamic characteristics of tension growth, the preset safe tension range, and the rated parameters of the reel; the control command set includes the target time, target speed, and target position.

[0012] Furthermore, the sag depth of each elevation sampling point in the topographic elevation change sequence is calculated, including:

[0013] Obtain the unit weight, starting coordinates, and hovering coordinates of the elevation difference sampling point of the UAV tow rope, and input the catenary equation to obtain the boundary condition equation system; solve the boundary condition equation system to obtain the horizontal translation parameters and vertical translation parameters;

[0014] The catenary equation is constructed based on the unit weight, horizontal translation parameters, vertical translation parameters, and the initial horizontal tension value; the horizontal tension value is iterated until the catenary equation matches the arc length of the UAV traction rope.

[0015] Based on the successfully matched catenary equation, the vertical height corresponding to each rope point on the drone's traction rope when the drone hovers at the elevation difference sampling point is obtained; the target rope point corresponding to the lowest vertical height is obtained; the distance between the target rope point and the line connecting the starting coordinate and the hovering coordinate is calculated as the sag depth of the elevation difference sampling point.

[0016] Furthermore, the mutation location sequence obtained above based on the sag depth distribution curve includes multiple mutation locations, including:

[0017] The horizontal tension value at which the catenary equation is successfully matched is taken as the target horizontal tension value of the elevation difference sampling point.

[0018] The target vertical tension value of the elevation difference sampling point is calculated based on the integral method and the sag depth of the elevation difference sampling point.

[0019] Add the target's horizontal tension value and the target's vertical tension value to obtain the total tension value at the height difference sampling point;

[0020] Calculate the tension difference between the total tension value of the elevation difference sampling point and the previous elevation difference sampling point, and the average tension value of all elevation difference sampling points; if the ratio of the tension difference to the average tension value exceeds the preset mutation threshold, the geographical coordinates of the elevation difference sampling point corresponding to the tension difference are taken as the mutation location;

[0021] Calculate the elevation gradient between the elevation difference sampling point and the previous elevation difference sampling point; determine the terrain type of the geographical location coordinates of the elevation difference sampling point based on the elevation gradient value; terrain types include ridges and valleys;

[0022] Each mutation location and its corresponding terrain type are output as a mutation location sequence.

[0023] Furthermore, the above calculation of tension increments or decreases at each abrupt change location yields the tension change rate distribution, including:

[0024] Acquire two target sampling points at a preset distance before and after the mutation location;

[0025] Calculate the tension reduction at abrupt locations with a valley topography based on the total tension value of the two target sampling points;

[0026] Calculate the tension increment at abrupt changes in terrain type ridge based on the total tension value of the two target sampling points;

[0027] The tension increments or decreases at each abrupt change location are summarized into a tension change rate distribution.

[0028] Furthermore, based on the analysis of the tension change rate distribution and the baseline trend line at each abrupt change location, the dynamic characteristics of tension growth are obtained, including:

[0029] The tension increment or tension decrease with the largest absolute value in the tension change rate distribution is taken as the maximum change rate.

[0030] The moving average method was used to extract the baseline trend line of the total tension value sequence at each abrupt change location;

[0031] Calculate the total tension value sequence and the deviation sequence of the baseline trend line at each abrupt change location;

[0032] Based on the Garhanning window Fourier transform, the deviation sequence was identified, and the maximum amplitude and dominant period of tension fluctuation were obtained.

[0033] Determine the peak interval and rise duration based on the maximum amplitude and dominant cycle;

[0034] The maximum rate of change, peak interval, and rise duration are used as dynamic characteristics of tension growth.

[0035] Furthermore, the rated parameters of the coil include rated acceleration data and mechanical inertia coefficient; the aforementioned set of control commands for generating the coil based on the dynamic characteristics of tension growth, the preset safe tension range, and the rated parameters of the coil includes:

[0036] The response rate of the coil motor is obtained by comparing the rated acceleration data with the mechanical inertia coefficient.

[0037] The first ratio is obtained by comparing the maximum rate of change with the response rate of the coil motor.

[0038] If the first ratio is less than the preset safety factor, the rated acceleration data is multiplied by the preset safety factor to obtain the upper limit of acceleration; the lower limit of acceleration is determined according to the preset safety tension range to obtain the acceleration boundary range.

[0039] Multiply the ascent duration by the drone's flight speed to obtain the terrain change distance;

[0040] Add the terrain change distance to the current position coordinates of the drone to obtain the terrain change coordinates;

[0041] The response delay time of the coil motor is obtained and multiplied by the flight speed of the UAV to obtain the pre-adjustment lead.

[0042] Determine the speed change trigger coordinates based on the pre-adjustment lead and the coordinates of the terrain change abruptly.

[0043] The predicted peak tension is obtained based on the maximum rate of change and the peak interval.

[0044] The target speed adjustment value is obtained based on the difference between the predicted peak tension and the upper limit of the preset safe tension range;

[0045] A trapezoidal velocity curve is generated based on the acceleration boundary range, the speed change trigger coordinates, and the target velocity adjustment value.

[0046] The trapezoidal velocity curve is sampled according to a preset sampling period to obtain a set of control commands.

[0047] Furthermore, the method also includes:

[0048] After obtaining the set of control commands, construct a simulation scenario of sag runaway;

[0049] Based on the catenary equation and dynamic equation, the maximum swing amplitude and horizontal offset distance of the UAV traction rope are calculated in the sag runaway simulation scenario, and the set of coordinate points of the rope spatial trajectory is obtained.

[0050] In the set of rope spatial trajectory coordinate points, the rope spatial trajectory coordinate points whose distance from the obstacle in the sag loss simulation scenario is less than the preset safety distance are marked as collision risk points;

[0051] If the number of collision risk points exceeds the preset optimization threshold, the acceleration boundary range and pre-adjustment lead are updated, and the control command set is recalculated until the number of collision risk points is less than the preset optimization threshold.

[0052] Secondly, embodiments of this application provide a reel control device for a drone traction rope, comprising:

[0053] The data acquisition module is used to collect the hovering altitude data of the UAV in real time and calculate the corresponding real-time elevation difference data.

[0054] The filtering module is used to remove noise from real-time elevation difference data using a particle filtering algorithm to obtain a sequence of topographic elevation difference changes.

[0055] The mutation detection module is used to calculate the sag depth of each elevation difference sampling point in the terrain elevation difference change sequence, and obtain the sag depth distribution curve; based on the sag depth distribution curve, a mutation location sequence including multiple mutation locations is obtained;

[0056] The feature module is used to calculate the tension increment or tension decrease at each abrupt change location to obtain the tension change rate distribution; based on the tension change rate distribution and the baseline trend line analysis at each abrupt change location, the dynamic characteristics of tension growth are obtained.

[0057] The control module is used to generate a set of control commands for the reel based on the dynamic characteristics of tension growth, the preset safe tension range, and the rated parameters of the reel; the set of control commands includes the target time, target speed, and target position.

[0058] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the steps of a spool control method for a drone tow rope as described in any of the above embodiments.

[0059] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a spool control method for a drone tow rope as described in any of the above embodiments.

[0060] In summary, compared with the prior art, the beneficial effects of the technical solution provided in this application include at least the following:

[0061] This application provides a method for controlling a spool of a drone's tow rope. First, a particle filtering algorithm is used to filter noise from real-time elevation data between the drone and the geographical environment, resulting in a smooth sequence of terrain elevation changes. Then, based on the terrain elevation change sequence, the method analyzes the changes in the sag depth of the tow rope caused by the changes in terrain elevation, accurately determining the location of abrupt tension changes. This step describes the risk of sag loss of control of the tow rope due to changes in drone hovering altitude and terrain. Finally, the method extracts dynamic features of tension growth based on the distribution of tension change rates at each abrupt change location. These dynamic features not only describe the trend of tension change in the tow rope caused by the terrain where the drone is currently operating, but also predict the impact of changes in the terrain ahead on the tension. Therefore, based on the dynamic features of tension growth, a preset safe tension range, and the rated parameters of the spool, a set of control commands for the spool is generated to ensure that the speed at which the spool retracts and extends the tow rope at the target time and position in the future can avoid the risk of sag loss of control of the tow rope. Attached Figure Description

[0062] Figure 1 A flowchart illustrating a method for controlling the reel of a drone traction rope, provided as an exemplary embodiment of this application.

[0063] Figure 2 This is a structural diagram of a reel control device for a drone traction rope, provided as an exemplary embodiment of this application. Detailed Implementation

[0064] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0065] Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] Please see Figure 1 This application provides a method for controlling the reel of a drone's traction rope, including:

[0067] Step S1: Collect the hovering altitude data of the UAV in real time and calculate the corresponding real-time elevation difference data.

[0068] Specifically, the system acquires atmospheric pressure values ​​measured by the UAV's barometric altimeter and ellipsoidal altitude data output by the GPS receiver. The ellipsoidal altitude data is then corrected in real time using pseudorange corrections sent by the differential GPS base station. The atmospheric pressure values ​​are converted into altitude according to the standard atmospheric pressure formula. Finally, the two altitude measurement results are weighted and fused to obtain the UAV's hovering altitude data.

[0069] In drone-borne tow rope operations, obtaining accurate hovering altitude data is fundamental to achieving precise control of rope tension. The barometric altimeter onboard the drone calculates flight altitude by measuring changes in atmospheric pressure, its working principle based on the physical law that standard atmospheric pressure decreases with altitude. When the drone operates in mountainous environments, local pressure disturbances and temperature changes can affect measurement accuracy, thus requiring correction using GPS ellipsoidal altitude data. The application of differential GPS technology significantly improves elevation measurement accuracy. The ground base station continuously receives satellite signals and calculates its own position error, transmitting pseudorange corrections to the drone in real time via a radio link. The drone receiver corrects its GPS measurements based on these corrections, reducing elevation errors from the meter level to the decimeter level. In one implementation, carrier phase differential technology is used to achieve centimeter-level positioning accuracy by resolving integer ambiguities.

[0070] It should be noted that the two altitude measurement methods each have their own characteristics. Barometric altimeters have a fast response speed and good short-term stability, but are susceptible to systematic deviations caused by weather systems; GPS ellipsoidal altitude data has high absolute accuracy, but signal quality deteriorates in valley-obstructed environments. A weighted fusion algorithm combines the advantages of both, with the weighting coefficients dynamically adjusted based on the geometric accuracy factor of the GPS signal. When satellite distribution is good, the GPS weight is increased; conversely, the barometric altimeter weight is increased.

[0071] Then, the raster data of the digital elevation model of the UAV's flight area is read from the geographic information database. Based on the UAV's GPS coordinates, the corresponding terrain grid cell is located. A bilinear interpolation method is used to weighted calculate the elevation values ​​of the four vertices of the grid, obtaining the ground elevation value of the UAV's vertical projection point. The hovering altitude data is subtracted from the ground elevation value to obtain the real-time elevation difference data. In one embodiment, the digital elevation model uses a regular grid structure to store terrain data, with each grid node recording the ground elevation value at its corresponding location. When the UAV flies to a certain location, its GPS coordinates typically fall within a rectangular area formed by four grid nodes. The bilinear interpolation method first performs linear interpolation on two pairs of nodes in the X direction to obtain two intermediate elevation values, and then interpolates these two intermediate values ​​in the Y direction to obtain the precise ground elevation value of the UAV's projection point. This method is computationally simple and ensures the continuity of the elevation surface, making it particularly suitable for real-time processing requirements.

[0072] Step S2: Use a particle filter algorithm to remove noise from the real-time elevation difference data to obtain the topographic elevation difference change sequence.

[0073] Among them, the particle filter algorithm shows unique advantages in processing nonlinear, non-Gaussian noise elevation difference data.

[0074] In the specific implementation of the particle filtering algorithm, a state-space model is first established, using the real-time elevation difference value h and the elevation difference change rate v from the real-time elevation difference data as state variables. The state transition equation is constructed as h(k+1) = h(k) + v(k) * Δt + w(k), where Δt is the sampling interval and w(k) is the process noise. The observation equation directly uses the elevation difference value measured by the sensor as the observation. During algorithm initialization, N particles are randomly generated in the state space, each representing a possible elevation difference state. In the prediction phase, the state of each particle at the next time step is calculated based on the state transition equation and the process noise distribution. In the update phase, the observation likelihood of each particle is calculated, i.e., the degree of matching between the predicted value and the actual observed value. The higher the likelihood, the closer the particle is to the true state. After weight updates, resampling is performed, high-weight particles are copied and low-weight particles are removed, keeping the total number of particles unchanged. Through multiple iterations, the particle swarm gradually converges to the vicinity of the true state. The weighted average of all particles yields the final elevation difference estimate. This final elevation difference estimate is used as the result after filtering the real-time elevation difference value h, and the various elevation difference estimates are arranged to obtain the elevation difference estimate sequence.

[0075] It should be noted that when the UAV crosses a valley with a large depth (such as 80 meters), the corresponding terrain elevation changes drastically over a short distance. The original sensor data fluctuates violently due to measurement noise. Particle filtering effectively suppresses noise interference through a probabilistic framework. The output elevation difference estimation sequence maintains the main trend of terrain change while filtering out high-frequency disturbance components.

[0076] After obtaining the elevation difference estimation sequence, it is resampled at fixed time intervals to eliminate the differences in update frequency between different sensors. A sliding window is used to select continuous elevation difference estimation points, and a cubic spline function is applied to fit the discrete points within the window to output a continuous and smooth elevation difference change sequence. In one possible implementation, the sliding window can select the 20 most recent elevation difference estimation points, sliding forward over time to always keep an eye on the latest data. The cubic spline function ensures the continuity of the function value, first derivative, and second derivative at each data point, generating a curve that passes through all data points and has good smoothness. Compared to simple linear interpolation, cubic splines can more accurately describe the gradual process of terrain undulation, and the output elevation difference change sequence provides reliable input data for subsequent tension analysis.

[0077] It can be assumed that the data update frequencies of different sensors differ. Barometric altimeters typically output at a frequency of 50Hz, while differential GPS is limited to only 10Hz due to communication bandwidth limitations. This frequency inconsistency can cause timestamp alignment problems during data fusion. This application converts irregular data streams into equally spaced sequences by resampling at fixed time intervals, and then uses a cubic spline function to curve fit the equally spaced sequences. Ultimately, the original noisy sensor data is converted into a continuous and smooth sequence of terrain elevation changes, eliminating interference caused by measurement noise and data asynchrony.

[0078] Step S3: Calculate the sag depth of each elevation sampling point in the terrain elevation change sequence to obtain the sag depth distribution curve; based on the sag depth distribution curve, obtain the abrupt change position sequence including multiple abrupt change positions.

[0079] Among them, the elevation difference sampling point is the drone's position when the drone's hovering altitude data is collected in the above steps.

[0080] Specifically, the sag depth of each elevation sampling point in the above-mentioned topographic elevation change sequence includes:

[0081] Step S31: Obtain the unit weight of the UAV tow rope, the starting coordinates, and the hovering coordinates of the elevation difference sampling point, and input the catenary equation to obtain the boundary condition equation set; solve the boundary condition equation set to obtain the horizontal translation parameters and the vertical translation parameters.

[0082] Wherein, the starting coordinates are the position coordinates of the cable reel, and the starting coordinates (x1, y1) and the hovering coordinates (x2, y2) are the positions of the two ends of the traction rope, respectively; the equation of the catenary is:

[0083]

[0084] Where w is the unit weight of the drone's traction rope, x is the horizontal coordinate of each rope point on the traction rope, c is the horizontal translation parameter, which determines the position of the catenary curve in the horizontal direction; the cosh function is an even function symmetric about the y-axis, and the existence of the horizontal translation parameter c allows this symmetric curve to be translated left and right on the x-axis to match the horizontal position of the two actual suspension points of the rope; d is the vertical translation parameter, which determines the reference position of the catenary curve in the vertical direction; T is the horizontal tension value, representing the tension of the traction rope in the horizontal direction.

[0085] When constructing the boundary condition equations, T is assumed to be an initial value, such as T=1. The coordinates of the two suspension points (x1, y1) and (x2, y2) are input into the above formula to obtain the boundary condition equations. Solving the equations yields the values ​​of c and d.

[0086] Step S32: Construct the catenary equation based on unit weight, horizontal translation parameters, vertical translation parameters, and initialized horizontal tension value; iterate the horizontal tension value until the catenary equation matches the arc length of the UAV traction rope.

[0087] Substitute the values ​​of c and d obtained in the previous step into the equation. Now, x and y are variables. Let the horizontal tension value T iterate from its initial value. In each iteration, match the catenary equation with the arc length of the drone's traction cable. The arc length expression is:

[0088]

[0089] Where y'(x) is the first derivative of y(x), the first derivative of the catenary equation after iteration T is obtained and substituted into the arc length expression, and then the absolute value of the difference between L_calculated and the release length S of the traction rope is calculated. If |L_calculated - S| is less than a preset error threshold (such as 0.001 meters), the iteration is considered to have converged, and the T value at this time is the target horizontal tension value.

[0090] The release length S of the traction rope can be directly obtained from the recording of the reel control system. If the iteration does not converge, the T value is iterated again (e.g., using the Newton-Raphson method for iteration), and c, d, and L_calculated are recalculated until convergence.

[0091] Step S33: Based on the successfully matched catenary equation, obtain the vertical height corresponding to each rope point on the drone's traction rope when the drone hovers at the elevation difference sampling point; obtain the target rope point corresponding to the lowest vertical height; calculate the distance between the target rope point and the line connecting the starting coordinate and the hovering coordinate, as the sag depth of the elevation difference sampling point.

[0092] Specifically, after successful matching and obtaining the target horizontal tension value, the catenary equation can calculate the vertical height of each rope point; obtain the lowest vertical height, and calculate the vertical distance between the rope point corresponding to the lowest vertical height and the line connecting the suspension points at both ends of the traction rope, which is the sag depth corresponding to the current two suspension point positions or the current height difference sampling point.

[0093] When the terrain elevation changes, even if the rope length remains constant, the change in the relative height difference between the two ends will cause the catenary shape to redistribute, and the sag depth will adjust accordingly. This dynamic change process is tracked by continuously solving the catenary equation at different elevation sampling points, thereby obtaining the sag depth values ​​corresponding to different terrain locations and forming a sag depth distribution curve.

[0094] Specifically, the mutation location sequence obtained above based on the sag depth distribution curve, which includes multiple mutation locations, includes:

[0095] Step S34: The horizontal tension value when the catenary equation is successfully matched is taken as the target horizontal tension value of the elevation difference sampling point.

[0096] Step S35: Calculate the target vertical tension value of the elevation difference sampling point based on the integral method and the sag depth of the elevation difference sampling point.

[0097] Step S36: Add the target horizontal tension value and the target vertical tension value to obtain the total tension value of the height difference sampling point.

[0098] Specifically, based on the principle of static equilibrium, each micro-segment of the rope remains in equilibrium under the action of gravity and the tension at both ends. Therefore, the weight of the rope from the lowest point (i.e., the target rope point) to any position can be calculated by integration, thereby determining the target vertical tension value of the target rope point. Then, the target horizontal tension value is superimposed to obtain the total tension value of the target rope point. The total tension value of the target rope point is used as the total tension value of the elevation difference sampling point corresponding to the target rope point.

[0099] Step S37: Calculate the tension difference between the total tension value of the elevation difference sampling point and the previous elevation difference sampling point, and the average tension value of all elevation difference sampling points; if the ratio of the tension difference to the average tension value exceeds the preset mutation threshold, the geographical coordinates of the elevation difference sampling point corresponding to the tension difference are taken as the mutation location.

[0100] It can be assumed that the judgment of tension abrupt change adopts the relative rate of change criterion. When the ratio of the tension difference between two adjacent elevation difference sampling points to the average tension of all sampling points exceeds the preset abrupt change threshold, the area is considered to have a risk of tension abrupt change. For example, near the ridgeline, the rapid uplift of the terrain causes the effective length of the rope to shorten, and the total tension value corresponding to the elevation difference sampling point increases rapidly over a short distance. Therefore, the geographical coordinates corresponding to the elevation difference sampling point are marked as the abrupt change location.

[0101] Step S38: Calculate the elevation gradient value between the elevation difference sampling point and the previous elevation difference sampling point; determine the terrain type of the geographical location coordinates of the elevation difference sampling point based on the elevation gradient value; terrain types include ridges and valleys.

[0102] The formula for calculating the elevation gradient is Δh / Δx, where Δh is the height difference between two adjacent elevation sampling points, and Δx is the horizontal distance between two adjacent elevation sampling points. As the UAV flies along a predetermined route, it records real-time elevation data at fixed intervals. The gradient between adjacent elevation sampling points reflects the severity of terrain undulations.

[0103] The characteristic of ridge regions is a rapid increase in ground elevation over a short distance, causing a rapid shortening of the effective suspension length of the rope and a sharp increase in tension. Conversely, in valley regions, a decrease in ground elevation increases the suspension length of the rope, and the tension gradually decreases. Therefore, the sign of the elevation gradient value can be used to determine whether the current abrupt change location belongs to a ridge or a valley.

[0104] Step S39: Output each mutation location and its corresponding terrain type as a mutation location sequence.

[0105] The terrain type (positive for ridge, negative for valley) is determined by combining the elevation gradient of the mutation location. Each mutation location is assigned a terrain type attribute, and the output is a sequence of mutation locations containing location coordinates and terrain type.

[0106] Furthermore, for each mutation location in the mutation location sequence, the second derivative of the sag depth distribution curve at that mutation location is calculated to obtain the corresponding rate of curvature change. At the same time, the vertical height of the target rope point corresponding to the mutation location is obtained. If the rate of curvature change exceeds a preset material bending limit threshold or the vertical height is less than a preset safe height threshold, it is determined that there is a risk of sag loss of control, and the corresponding mutation location is marked with a loss of control risk indicator.

[0107] Step S4: Calculate the tension increment or tension decrease at each abrupt change location to obtain the tension change rate distribution; based on the tension change rate distribution and the baseline trend line analysis at each abrupt change location, obtain the dynamic characteristics of tension growth.

[0108] Specifically, the above calculation of tension increments or decreases at each abrupt change location yields the tension change rate distribution, including:

[0109] Step S41: Obtain two target sampling points at a preset distance before and after the mutation location.

[0110] Step S42: Calculate the tension reduction at the abrupt change location with terrain type valley based on the total tension value of the two target sampling points; calculate the tension increment at the abrupt change location with terrain type ridge based on the total tension value of the two target sampling points.

[0111] Specifically, the total tension value of the 10th elevation difference sampling point before and after each mutation location (i.e., before and after the sampling time) can be obtained, and the following can be calculated: (total tension value of the target sampling point after the target sampling point - total tension value of the target sampling point before the target sampling point) / horizontal distance between the two target sampling points to obtain the tension change rate of each mutation location. Based on the marked terrain type area in the mutation location sequence, it can be determined whether the mutation location is located in a ridge area or a valley area. The tension change rate of the mutation location in the ridge area is used as the tension increment, and the tension change rate of the mutation location in the valley area is used as the tension decrement.

[0112] Step S43: Summarize the tension increments or decreases at each abrupt change location into a tension change rate distribution.

[0113] It can be considered that this application uses a differential method to calculate the rate of tension change. In the ridge region, the point where the tension begins to rise and the point where it reaches its peak are selected, and the ratio of the tension difference between the two points to the horizontal distance is calculated to obtain the tension increment. The descent rate is calculated similarly in the valley region, forming a complete distribution of the rate of tension change under different terrain conditions.

[0114] In one embodiment, the above-described analysis based on the tension change rate distribution and baseline trend lines at each abrupt change location yields the dynamic characteristics of tension growth, which may specifically include:

[0115] Step S44: The tension increment or tension decrease with the largest absolute value in the tension change rate distribution is taken as the maximum change rate.

[0116] Step S45: Use the moving average method to extract the baseline trend line of the total tension value sequence at each mutation location.

[0117] Step S46: Calculate the total tension value sequence and the deviation sequence of the baseline trend line at each abrupt change location.

[0118] Step S47: Based on the Hanning window Fourier transform, identify the deviation sequence to obtain the maximum amplitude and dominant period of tension fluctuation.

[0119] Specifically, this application uses the moving average method to extract the baseline trend line of tension change, calculates the deviation sequence between the actual total tension value sequence and the baseline trend line, identifies the main frequency components in the deviation sequence by adding a Hanning window Fourier transform, takes the frequency corresponding to the peak of the power spectrum as the dominant frequency, and its reciprocal as the dominant period, thereby determining the maximum amplitude and dominant period of tension fluctuation.

[0120] It should be noted that the moving average method smooths the total tension value sequence at each abrupt change location by setting a sliding window. The window width is determined based on the typical cycle of terrain change; too narrow a window will retain too much noise, while too wide a window will smooth out important local features. The baseline trend line reflects the overall trend of tension change, eliminating interference from short-term fluctuations. The deviation sequence of the total tension value sequence at each abrupt change location from the baseline trend line contains information on periodic fluctuations caused by terrain undulations. The Fourier transform converts the deviation sequence from the time domain to the frequency domain, and the frequency points with concentrated energy are identified by analyzing the spectrum. These frequencies correspond to the spatial cycle of terrain undulations. The dominant frequency usually corresponds to the average spacing between ridges and valleys, and its reciprocal is the dominant period. The maximum amplitude is obtained by finding the extreme points of the deviation sequence, reflecting the intensity of tension fluctuations.

[0121] Step S48: Determine the peak interval and rise duration based on the maximum amplitude and dominant cycle.

[0122] Specifically, based on the maximum amplitude and dominant period of the tension fluctuation, the horizontal distance between adjacent tension peaks is statistically analyzed to obtain the peak interval, and the time from the start of the tension rise to the peak value is calculated to obtain the rise duration.

[0123] Preferably, the statistical analysis of peak intervals requires first identifying all local maxima in the deviation sequence, determining the extreme value locations through the sign change of the first derivative, then calculating the horizontal distance between adjacent maxima, and taking the average value. The ascent duration reflects the length of the process from the initial increase in tension to reaching the peak. Under uniform flight conditions of the UAV, this time is proportional to the corresponding horizontal distance, reflecting the degree of terrain change. Steep ridges have shorter ascent times, while gentle slopes have longer ascent times.

[0124] Step S49: The maximum rate of change, peak interval, and rise duration are used as dynamic characteristics of tension growth.

[0125] Specifically, the three characteristic parameters are combined in vector form, with the maximum rate of change as the first dimension, the peak interval as the second dimension, and the rise duration as the third dimension, to form a three-dimensional characteristic vector, providing a quantitative basis for speed regulation in coil control.

[0126] Step S5: Generate a set of control commands for the reel based on the dynamic characteristics of tension growth, the preset safe tension range, and the rated parameters of the reel; the set of control commands includes the target time, target speed, and target position.

[0127] The rated parameters of the coil include rated acceleration data and mechanical inertia coefficient; specifically, the control command set for the coil generated based on the dynamic characteristics of tension growth, the preset safe tension range, and the rated parameters of the coil includes:

[0128] Step S51: Compare the rated acceleration data with the mechanical inertia coefficient to obtain the coil motor response rate; compare the maximum rate of change with the coil motor response rate to obtain the first ratio.

[0129] Step S52: If the first ratio is less than the preset safety factor, multiply the rated acceleration data and the preset safety factor to obtain the upper limit of acceleration; determine the lower limit of acceleration based on the preset safety tension range to obtain the acceleration boundary range.

[0130] In practical implementation, determining the acceleration boundary range requires comprehensive consideration of both mechanical capability and safety constraints. The rated acceleration data of the coil motor represents the physical limit of the drive system. This parameter is usually calibrated at the motor factory and is directly related to the motor power and moment of inertia. The mechanical inertia coefficient reflects the comprehensive inertial characteristics of the entire transmission system, including the coil, reducer, and coupling. When the tension change rate is too fast, even if the motor has sufficient acceleration capability, the inertia of the mechanical system will cause a response lag. The ratio of the maximum change rate to the motor response rate determines the controllability of the system. The first ratio essentially reflects the relative relationship between the disturbance speed and the control speed. When the first ratio is close to 1, it indicates that the response speed of the control system can barely keep up with the tension change. At this time, a larger safety margin needs to be set. Therefore, this application introduces a preset safety factor, usually between 0.6 and 0.8, to ensure that the control system has sufficient adjustment margin.

[0131] Specifically, the upper limit of acceleration is obtained by multiplying the rated acceleration data of the motor by a preset safety factor, while the lower limit of acceleration is set based on the mechanical properties of the rope material. When the tension approaches the yield strength of the material, a minimum speed regulation capability must be ensured to avoid rope breakage. Therefore, this lower limit of acceleration is usually set to 0.1 to 0.2 times the upper limit, which ensures both the flexibility of speed regulation and the ability to maintain control effectiveness even if the acceleration is too small.

[0132] Step S53: Multiply the ascent duration by the drone's flight speed to obtain the terrain change distance; add the terrain change distance to the drone's current position coordinates to obtain the terrain change coordinates.

[0133] Specifically, the coordinates of the terrain change are determined by distance estimation. As can be seen from the above embodiment, the characteristic parameter of the rise duration represents the time from the start of the rise to the peak value. Multiplying this time by the flight speed of the UAV gives the distance of the terrain change. Adding the distance of the terrain change to the current position coordinates gives the geographical coordinates of the terrain change point.

[0134] Step S54: Obtain the response delay time of the coil motor and multiply it by the flight speed of the UAV to obtain the pre-adjustment lead; determine the speed change trigger coordinates based on the pre-adjustment lead and the terrain change coordinates.

[0135] Specifically, this application considers the response delay characteristics of the coil control system by calculating the pre-adjustment lead. The motor response delay includes three parts: communication delay, controller processing delay, and mechanical response delay. The total delay time is usually on the order of hundreds of milliseconds. Multiplying this delay time by the flight speed of the UAV, we obtain the distance that needs to be adjusted in advance, i.e., the pre-adjustment lead. By moving the terrain change coordinates forward by the pre-adjustment lead, we obtain the speed change trigger coordinates.

[0136] Step S55: Obtain the predicted tension peak value based on the maximum rate of change and the peak interval; obtain the target speed adjustment value based on the difference between the predicted tension peak value and the upper limit of the preset safe tension range.

[0137] The predicted tension peak is obtained by multiplying the maximum rate of change by the peak interval, which is the maximum tension that can be achieved after passing the peak interval at the maximum rate of change. The calculation of the target speed adjustment value is based on the reverse derivation of tension control. The difference between the predicted tension peak and the upper limit of the preset safe tension range reflects the tension force that needs to be reduced. According to the square relationship between tension and the coil rotation speed, the amount of speed that needs to be reduced corresponding to the tension force that needs to be reduced can be calculated, which is the target speed adjustment value.

[0138] Step S56: Generate a trapezoidal velocity curve based on the acceleration boundary range, the speed change trigger coordinates, and the target velocity adjustment value.

[0139] The trapezoidal speed curve, a classic speed planning method in industrial control, involves three precise design stages in its construction: The acceleration phase starts from the current speed and accelerates to a predetermined upper acceleration limit until a transition speed is reached. Determining the transition speed requires considering both the target speed adjustment value and the available adjustment time. If sufficient adjustment time exists, the transition speed can be set higher to fully utilize the speed regulation capability of the coil. If time is limited, the transition speed needs to be reduced to shorten the acceleration / deceleration time. The constant speed phase maintains the transition speed, and its duration is calculated based on the total adjustment distance and the distance consumed in the acceleration / deceleration phases. The deceleration phase starts from the transition speed and decelerates at a negative value of the lower acceleration limit. The time integral of the entire speed curve equals the distance to the speed change trigger coordinate, and the integral of the speed change equals the target speed adjustment value. This trapezoidal curve avoids the shock caused by sudden speed changes while fully utilizing the system's speed regulation capability, achieving a balance between time and smoothness.

[0140] Step S57: Sample the trapezoidal velocity curve according to the preset sampling period to obtain a set of control commands.

[0141] Specifically, the control cycle of the coil control system is used as the preset sampling cycle, which is usually 10-50 milliseconds. The continuous trapezoidal speed curve is sampled at equal time intervals. The target time and target speed are recorded at each sampling point. At the same time, the target position corresponding to the target time is obtained by accumulating the displacement within each control cycle (the displacement is equal to the average speed within the cycle multiplied by the cycle length). A set of control commands is constructed, and the target position corresponding to the last target time should be the speed change trigger coordinate.

[0142] In practice, when the drone approaches the top of the ridge, the predicted tension peak based on the dynamic characteristics of tension growth indicates that a sharp drop in terrain ahead will cause a sudden increase in tension. By reducing the speed of the cable reel in advance using the above method, the tension is kept within a safe range when actually crossing the ridge, thus avoiding the risk of rope overload and breakage.

[0143] It is worth noting that the target speed in the above set of control commands is the rotational speed of the coil motor. In the actual coil control process, the target speed in the set of control commands will be converted into the corresponding pulse frequency value. The upper and lower limits of the acceleration boundary range are converted into the frequency change rate to prevent mechanical shock caused by excessive speed adjustment. The pulse sequence length is determined according to the time interval between target moments to form a digital control signal sequence. A low-pass filter is used to smooth the digital control signal sequence. The filter cutoff frequency is set to a preset ratio of the maximum response frequency of the coil motor to eliminate high-frequency components and step changes, resulting in a gradually smoothed control signal. This signal is then transmitted to the variable speed coil controller through the serial communication interface at a predetermined baud rate.

[0144] Furthermore, since the function of a low-pass filter is to eliminate abrupt changes in the control signal, the selection of the filter cutoff frequency needs to balance response speed and smoothness. An excessively high cutoff frequency will retain too much high-frequency noise, while an excessively low cutoff frequency will lead to a sluggish response. By setting the cutoff frequency to 0.3 to 0.5 times the motor's maximum response frequency, harmful high-frequency components can be filtered out while maintaining sufficient dynamic response capability. The serial communication interface adopts a standard industrial communication protocol, and the predetermined baud rate is determined based on the control cycle and data volume, typically set to 9600 or 19200 bps to ensure real-time transmission of control commands.

[0145] Furthermore, the corresponding target tension can be determined based on the mapping of each set of target speed, target time, and target position in the control command set. During actual control, the actual rotational speed fed back by the coil encoder and the real-time tension measured by the tension sensor are acquired. The deviation between the target tension and the real-time tension is calculated. If the deviation exceeds a preset tension threshold, the target speed for the next control cycle is corrected by multiplying the deviation by the preset proportional gain coefficient and integrating the result. This suppression response against sudden changes in rope tension is achieved through the adjustment of the variable-speed coil's rotational speed. In essence, this closed-loop feedback control mechanism achieves precise tension adjustment. By comparing the deviation between the target tension and the real-time tension, a correction procedure is initiated when the deviation exceeds the preset tension threshold. The correction amount is calculated using a proportional-integral control algorithm. The deviation multiplied by the proportional gain yields the immediate correction component, and the accumulated deviation multiplied by the integral gain yields the steady-state correction component. The two components are superimposed to form the total rotational speed correction command, which updates the target speed in the next control cycle. This closed-loop adjustment mechanism can dynamically compensate for external disturbances and predict deviations, effectively suppressing sudden changes in rope tension.

[0146] The above embodiment provides a spool control method for a drone tow rope. First, a particle filter algorithm is used to filter noise from the real-time elevation difference data between the drone and the geographical environment, resulting in a smooth sequence of terrain elevation changes. Then, based on the terrain elevation change sequence, the sag depth change of the tow rope caused by the terrain elevation change is analyzed to accurately determine the location of abrupt tension changes. This step describes the risk of sag loss of control of the tow rope due to drone hovering altitude and terrain changes. Finally, the dynamic features of tension growth are extracted based on the tension change rate distribution at each abrupt change location. These dynamic features not only describe the trend of tow rope tension changes caused by the terrain where the drone is currently operating, but also predict the impact of terrain changes ahead on the tension. Therefore, based on the dynamic features of tension growth, a preset safe tension range, and the rated parameters of the spool, a set of control commands for the spool is generated to ensure that the speed at which the spool retracts and extends the tow rope at the target time and position in the future can avoid the risk of sag loss of control of the tow rope.

[0147] In some embodiments, the method further includes:

[0148] Step S61: After obtaining the set of control commands, construct a sag runaway simulation scenario.

[0149] Furthermore, after obtaining the control command set, the difference in target velocity corresponding to adjacent target moments is calculated as the velocity adjustment value. Simultaneously, the tensile strength limit value of the rope material is read, and the safety threshold for the rate of tension change is set to 80% of the material's ultimate strength, providing sufficient safety margin for the traction rope under dynamic load conditions. If the rate of tension change generated by any velocity adjustment value exceeds the safety threshold, it is determined that the speed change coil control input does not meet the rope impact avoidance condition, meaning the current control command set poses a risk of rope breakage. The process directly jumps to step S64 to update the acceleration boundary range and pre-adjustment lead, thereby updating the control command set for further evaluation. If the rope impact avoidance condition is met, the current control command set's effectiveness in suppressing the risk of collision between the traction rope and ground obstacles is verified through a sag runaway simulation scenario.

[0150] Step S62: Based on the catenary equation and dynamic equation, calculate the maximum swing amplitude and horizontal offset distance of the UAV traction rope in the sag runaway simulation scenario, and obtain the set of coordinate points of the rope spatial trajectory.

[0151] Among them, the sag loss simulation scenario includes terrain elevation data of steep ascents over ridges and descents over valleys, thereby simulating the sag depth change of the traction rope under terrain elevation changes, calculating the maximum swing amplitude and horizontal offset distance of the rope end in the vertical direction, and generating a set of coordinate points of the rope spatial trajectory.

[0152] Specifically, the sag runaway simulation scenario reproduces the dynamic behavior of ropes under complex geographical conditions by establishing a three-dimensional terrain model. In the scenario of rapidly ascending over a ridge, the corresponding terrain elevation data includes slope angle, rate of change of altitude, and surface roughness coefficient. These parameters directly affect the flight trajectory of the aircraft and the stress state of the rope. The scenario of descent over a valley simulates the complex swinging of the rope due to gravity and airflow when the aircraft descends rapidly.

[0153] The calculation of rope sag depth variation is based on catenary theory and dynamic equations. The natural sag state of the traction rope under gravity follows the catenary equations. However, during aircraft maneuvers, the traction rope is also affected by inertial forces, aerodynamic drag, and load oscillation. Therefore, a rope micro-element force analysis model can be established to calculate the position coordinates of each point on the rope at each moment. The maximum oscillation amplitude at the rope end is obtained by tracking the extreme values ​​of load displacement in the vertical and horizontal directions. The generation of the rope spatial trajectory coordinate point set uses a time-stepping algorithm to sample the rope state at fixed time intervals, recording the three-dimensional coordinate information of key rope nodes. Each coordinate point contains position information, velocity vector, and acceleration data, forming a complete description of the motion state. By connecting the coordinate points of adjacent moments, the motion trajectory of the rope throughout the entire flight process can be reconstructed.

[0154] Step S63: Mark the rope spatial trajectory coordinate points in the set of rope spatial trajectory coordinate points whose distance from the obstacle in the sag loss simulation scenario is less than the preset safety distance as collision risk points.

[0155] Step S64: If the number of collision risk points exceeds the preset optimization threshold, update the acceleration boundary range and pre-adjustment lead, and recalculate the control command set until the number of collision risk points is less than the preset optimization threshold.

[0156] Specifically, by using the set of coordinate points of the rope's spatial trajectory, the height of trees and the outline coordinates of buildings below the current flight path are read. The vertical distance between the rope trajectory coordinate points and the surface of ground obstacles is calculated. When the distance is less than a preset safety distance, it is marked as a collision risk point, and the number of collision risk points is counted. If the number of collision risk points exceeds a preset optimization threshold, the acceleration boundary range and pre-adjustment lead are readjusted. Specifically, this can be done by lowering the upper limit of acceleration to reduce the variation in the reel's winding and unwinding speed and increasing the pre-adjustment lead to provide more sufficient response time, until the conditions for avoiding rope impact are met.

[0157] Please see Figure 2 Another embodiment of this application provides a reel control device for a drone traction rope, comprising:

[0158] The acquisition module 101 is used to acquire the hovering altitude data of the UAV in real time and calculate the corresponding real-time elevation difference data.

[0159] The filtering module 102 is used to remove noise from the real-time elevation difference data using a particle filtering algorithm to obtain a topographic elevation difference change sequence.

[0160] The mutation judgment module 103 is used to calculate the sag depth of each elevation difference sampling point in the terrain elevation difference change sequence and obtain the sag depth distribution curve; based on the sag depth distribution curve, a mutation position sequence including multiple mutation positions is obtained.

[0161] Feature module 104 is used to calculate the tension increment or tension decrease at each abrupt change location to obtain the tension change rate distribution; based on the tension change rate distribution and the baseline trend line analysis at each abrupt change location, the dynamic characteristics of tension growth are obtained.

[0162] The control module 105 is used to generate a set of control commands for the reel based on the dynamic characteristics of tension growth, the preset safe tension range, and the rated parameters of the reel; the set of control commands includes the target time, target speed, and target position.

[0163] The specific limitations of the reel control device for a drone tow rope provided in this embodiment can be found in the embodiment of the drone tow rope reel control method described above, and will not be repeated here. Each module in the above-described drone tow rope reel control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0164] This application provides a computer device that may include a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it causes the processor to perform the steps of a drone tow rope reel control method as described in any of the above embodiments.

[0165] The working process, working details, and technical effects of the computer equipment provided in this embodiment can be found in the embodiment above regarding a method for controlling the reel of a drone's traction rope, and will not be repeated here.

[0166] This application provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the steps of a drone tow rope reel control method as described in any of the above embodiments. The computer-readable storage medium refers to a data storage carrier, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The working process, details, and technical effects of the computer-readable storage medium provided in this embodiment can be found in the above embodiments regarding a drone tow rope reel control method, and will not be repeated here.

[0167] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0168] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0169] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for controlling the reel of a drone's traction rope, characterized in that, include: Real-time acquisition of drone hovering altitude data and calculation of corresponding real-time elevation difference data; The noise in the real-time elevation difference data is removed by a particle filter algorithm to obtain the topographic elevation difference change sequence. Calculate the sag depth at each elevation sampling point in the terrain elevation change sequence to obtain the sag depth distribution curve; The calculation of the sag depth at each elevation sampling point in the terrain elevation change sequence includes: Obtain the unit weight of the UAV tow rope, the starting coordinates, and the hovering coordinates of the elevation difference sampling point, and input the catenary equation to obtain the boundary condition equation set; solve the boundary condition equation set to obtain the horizontal translation parameters and the vertical translation parameters; A catenary equation is constructed based on the unit weight, the horizontal translation parameters, the vertical translation parameters, and the initialized horizontal tension value; the horizontal tension value is iterated until the catenary equation matches the arc length of the UAV traction rope; Based on the successfully matched catenary equation, the vertical height corresponding to each rope point on the drone's traction rope when the drone hovers at the elevation difference sampling point is obtained; the target rope point corresponding to the lowest vertical height is obtained; the distance between the target rope point and the line connecting the starting coordinate and the hovering coordinate is calculated as the sag depth of the elevation difference sampling point. A mutation location sequence including multiple mutation locations is obtained based on the sag depth distribution curve; wherein, obtaining the mutation location sequence including multiple mutation locations based on the sag depth distribution curve includes: The horizontal tension value at which the catenary equation is successfully matched is taken as the target horizontal tension value of the elevation difference sampling point. The target vertical tension value of the elevation difference sampling point is calculated based on the integral method and the sag depth of the elevation difference sampling point. Add the target horizontal tension value and the target vertical tension value to obtain the total tension value of the height difference sampling point; Calculate the tension difference between the total tension value of the elevation difference sampling point and the previous elevation difference sampling point, and the average tension value of all elevation difference sampling points; if the ratio of the tension difference to the average tension value exceeds a preset mutation threshold, then the geographical coordinates of the elevation difference sampling point corresponding to the tension difference are taken as the mutation location; Calculate the elevation gradient value between the elevation difference sampling point and the previous elevation difference sampling point; determine the terrain type of the geographical location coordinates of the elevation difference sampling point based on the elevation gradient value; the terrain type includes ridges and valleys; Each mutation location and its corresponding terrain type are output as a mutation location sequence. Calculate the tension increment or tension decrease at each of the aforementioned abrupt change locations to obtain the tension change rate distribution; based on the tension change rate distribution and the baseline trend line analysis at each of the aforementioned abrupt change locations, obtain the dynamic characteristics of tension growth; Based on the aforementioned dynamic characteristics of tension growth, the preset safe tension range, and the rated parameters of the reel, a set of control commands for the reel is generated; the set of control commands includes the target time, target speed, and target position.

2. The method of claim 1, wherein, The calculation of the tension increment or tension decrease at each of the abrupt change locations to obtain the tension change rate distribution includes: Acquire two target sampling points at a preset distance before and after the mutation location; Calculate the tension reduction at abrupt locations with a valley topography based on the total tension value of the two target sampling points; Calculate the tension increment at abrupt changes in terrain type ridge based on the total tension value of the two target sampling points; The tension increments or decreases at each abrupt change location are summarized into the tension change rate distribution.

3. The method of claim 2, wherein, The dynamic characteristics of tension growth are obtained based on the tension change rate distribution and the baseline trend line analysis at each of the abrupt change locations, including: The tension increment or tension decrease with the largest absolute value in the tension change rate distribution is taken as the maximum change rate. A baseline trend line for the total tension value sequence at each of the aforementioned abrupt change locations is extracted using the moving average method; Calculate the total tension value sequence at each of the abrupt change locations and the deviation sequence of the baseline trend line; The deviation sequence was identified based on the Garhanning window Fourier transform, and the maximum amplitude and dominant period of the tension fluctuation were obtained. The peak interval and rise duration are determined based on the maximum amplitude and the dominant cycle. The maximum rate of change, the peak interval, and the duration of the rise are used as the dynamic characteristics of the tension growth.

4. The method of claim 3, wherein, The rated parameters of the coil include rated acceleration data and mechanical inertia coefficient; the generation of the control command set for the coil based on the dynamic characteristics of tension growth, the preset safe tension range, and the rated parameters of the coil includes: The rated acceleration data is compared with the mechanical inertia coefficient to obtain the response rate of the coil motor; The first ratio is obtained by comparing the maximum rate of change with the response rate of the coil motor. If the first ratio is less than the preset safety factor, then the rated acceleration data and the preset safety factor are multiplied to obtain the upper limit of acceleration; the lower limit of acceleration is determined according to the preset safety tension range to obtain the acceleration boundary range. Multiplying the ascent duration by the UAV's flight speed yields the terrain change distance; Add the terrain change distance to the current position coordinates of the UAV to obtain the terrain change coordinates; The response delay time of the coil motor is obtained and multiplied by the flight speed of the UAV to obtain the pre-adjustment lead. The speed change trigger coordinates are determined based on the pre-adjustment lead amount and the terrain change coordinates. The predicted peak tension is obtained based on the maximum rate of change and the peak interval. The target speed adjustment value is obtained based on the difference between the predicted peak tension and the upper limit of the preset safe tension range; A trapezoidal velocity curve is generated based on the acceleration boundary range, the speed change trigger coordinates, and the target velocity adjustment value. The trapezoidal velocity curve is sampled according to a preset sampling period to obtain the set of control commands.

5. The method of claim 4, wherein, Also includes: After obtaining the set of control commands, a sag runaway simulation scenario is constructed; Based on the catenary equation and dynamic equation, the maximum swing amplitude and horizontal offset distance of the UAV traction rope are calculated under the sag runaway simulation scenario, and the set of rope spatial trajectory coordinate points is obtained. The rope spatial trajectory coordinate points in the set of rope spatial trajectory coordinate points whose distance from the obstacle in the sag loss simulation scenario is less than the preset safety distance are marked as collision risk points. If the number of collision risk points exceeds a preset optimization threshold, the acceleration boundary range and pre-adjustment lead are updated, and the control command set is recalculated until the number of collision risk points is less than the preset optimization threshold.

6. A spool control device for a drone towrope, characterized by, include: The data acquisition module is used to collect the hovering altitude data of the UAV in real time and calculate the corresponding real-time height difference data; The filtering module is used to remove noise from the real-time elevation difference data using a particle filtering algorithm to obtain a topographic elevation difference change sequence. A mutation detection module is used to calculate the sag depth of each elevation difference sampling point in the terrain elevation change sequence, and obtain a sag depth distribution curve. The calculation of the sag depth of each elevation difference sampling point in the terrain elevation change sequence includes: obtaining the unit weight of the UAV tow rope, the initial coordinates, and the hovering coordinates of the elevation difference sampling point, and inputting the catenary equation to obtain a set of boundary condition equations; solving the set of boundary condition equations to obtain horizontal and vertical translation parameters; constructing a catenary equation based on the unit weight, the horizontal translation parameters, the vertical translation parameters, and the initialized horizontal tension value; iterating the horizontal tension value until the catenary equation matches the arc length of the UAV tow rope; obtaining the vertical height corresponding to each rope point on the UAV tow rope when the UAV hovers at the elevation difference sampling point based on the successfully matched catenary equation; obtaining the target rope point corresponding to the lowest vertical height; calculating the distance between the target rope point and the line connecting the initial coordinates and the hovering coordinates as the sag depth of the elevation difference sampling point; and obtaining a mutation position sequence including multiple mutation locations based on the sag depth distribution curve. The step of obtaining a mutation location sequence including multiple abrupt change locations based on the sag depth distribution curve includes: taking the horizontal tension value when the catenary equation is successfully matched as the target horizontal tension value of the elevation difference sampling point; calculating the target vertical tension value of the elevation difference sampling point according to the integral method and the sag depth of the elevation difference sampling point; adding the target horizontal tension value and the target vertical tension value to obtain the total tension value of the elevation difference sampling point; calculating the tension difference between the total tension value of the elevation difference sampling point and the previous elevation difference sampling point, and the average tension value of all elevation difference sampling points; if the ratio of the tension difference value to the average tension value exceeds a preset mutation threshold, then taking the geographical coordinates of the elevation difference sampling point corresponding to the tension difference value as the mutation location; calculating the elevation gradient value between the elevation difference sampling point and the previous elevation difference sampling point; determining the terrain type of the geographical coordinates of the elevation difference sampling point based on the elevation gradient value; the terrain type includes ridges and valleys; and outputting each mutation location and its corresponding terrain type as the mutation location sequence. The feature module is used to calculate the tension increment or tension decrease at each of the abrupt change locations to obtain the tension change rate distribution; based on the tension change rate distribution and the baseline trend line analysis at each of the abrupt change locations, the dynamic characteristics of tension growth are obtained. The control module is used to generate a set of control commands for the reel based on the dynamic characteristics of tension growth, the preset safe tension range, and the rated parameters of the reel; the set of control commands includes the target time, target speed, and target position.

7. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the reel control method for the drone traction rope as described in any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the spool control method for the drone tow rope as described in any one of claims 1 to 5.

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