High-altitude wind energy capturing method and system of multi-rotor mooring unmanned aerial vehicle

Abstract

The invention discloses a high-altitude wind energy capturing method of a multi-rotor mooring unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle control. The high-altitude wind energy capturing method for the multi-rotor mooring unmanned aerial vehicle comprises the following steps that S1, structural environment data and historical operation and maintenance data are preprocessed; s2, performing cable regulation and control and tension wind coupling catenary curve optimization according to a structural turbulent flow risk analysis result, and entering an operation window hierarchical scheduling process; s3, adjusting an included angle between a cable and a main wind direction according to a space coupling effect analysis result, and entering a working window hierarchical scheduling process; s4, executing a job window hierarchical scheduling process according to a risk amplification health suppression evaluation result; and S5, constructing a risk attribution and autonomous evolution optimization mechanism based on the multi-source risk quantitative index. According to the method, multi-stage real-time discrimination of the turbulence risk, the spatial effect and the operation window risk is realized through structural turbulence risk analysis and distributed operation risk analysis in combination with risk amplification health suppression evaluation.

Description

Technical Field

[0001] This invention relates to the field of intelligent highway inspection technology, and in particular to a method and system for capturing high-altitude wind energy from a multi-rotor tethered drone. Background Technology

[0002] With the rapid development of new energy technologies and intelligent unmanned aerial vehicle (UAV) systems, multi-rotor tethered UAVs have been widely used in high-altitude wind energy harvesting. Existing technologies typically employ a multi-rotor UAV platform, connecting the UAV to ground anchor points and a power supply system via a flexible tether line. This enables the UAV to continuously hover and harvest wind energy at fixed points and along dynamic paths at high altitudes. The tether line not only provides mechanical fixation but also integrates power transmission and data communication functions, allowing the UAV to receive continuous power from the ground and exchange information with high reliability, significantly extending its endurance and improving mission stability.

[0003] For example, invention patent CN113071674A discloses a tethered unmanned aerial vehicle (UAV) system, including a UAV body with multiple pairs of rotors, flexible tethering lines, and a dedicated adapter plate. The UAV uses diagonally arranged rotor groups. To address uneven load distribution when the tethering lines are not connected, the adapter plate and hooks precisely position the drooping section of the tethering lines near specific rotors, achieving balanced load distribution across all rotors. The adapter plate integrates multiple electrical interfaces, uses hooks to elastically clamp cables, and optimizes electrical connection paths, supporting efficient power supply between the UAV and ground power sources. The system also integrates various functional modules such as a lighting panel, sensors, and a downward-looking radar. The adapter plate has cable channels and clearance holes to accommodate the layout requirements of different functional components, improving the UAV's safety and spatial integration.

[0004] For example, invention patent CN113619806B discloses a multifunctional tethered drone and its recovery system, including the drone body, a ground recovery station, two tethering lines, a mounting assembly, and detachable electronic devices. The drone connects to the ground recovery station via the two tethering lines, enabling efficient power supply and stable flight. The mounting assembly can move along the tethering lines between the drone and the recovery station, facilitating the mounting, replacement, and recovery of electronic devices. The system features multi-level electrical interfaces and a magnetic plug-in structure, supporting the connection, disconnection, and secure locking of the mounted equipment. The mounting assembly integrates a guiding mechanism and a hollow motor, enabling autonomous movement and power supply of the equipment in the air. The ground recovery station has a storage cavity and a winding mechanism, facilitating the centralized storage of the drone, mounting assembly, and electronic devices, as well as cable recovery. It is widely applicable to aerial operations in lighting, warning, and communication scenarios.

[0005] The above-mentioned technology has at least the following technical problems: Existing methods and systems for capturing high-altitude wind energy from multi-rotor tethered drones mainly focus on the engineering implementation of tethered wire electrical connections, load balancing, movement of mounted components, and equipment storage. These methods enable continuous power supply to drones at high altitudes and the mounting of multiple mission devices. However, they generally lack systematic quantification and adaptive control of aerodynamic disturbances of the tethered wires, spatial layout, and structural health risks during high-altitude wind energy capture. They primarily focus on structural and interface optimization. When faced with complex operating conditions such as wind energy density fluctuations, extreme disturbances, and structural fatigue, existing technologies have significant shortcomings in terms of drone energy efficiency, continuous operation, and safety protection, making it difficult to meet the requirements for efficient, stable, and safe high-altitude wind energy capture operations. Summary of the Invention

[0006] This invention provides a method and system for capturing high-altitude wind energy from a multi-rotor tethered drone, which solves the problem in the prior art that the tether line disturbs the local airflow around the drone during the high-altitude wind energy capture process, leading to a decrease in energy capture efficiency, an increase in energy loss, and an exacerbation of fatigue in the overall structure. This invention improves energy capture efficiency and operational safety in complex wind fields.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solution: On the one hand, a method for capturing high-altitude wind energy from a multi-rotor tethered UAV is provided. This method includes: S1, collecting structural environment data and acquiring historical operation and maintenance data; preprocessing the structural environment data and historical operation and maintenance data; S2, performing structural disturbance risk analysis on the structural environment data and historical operation and maintenance data, and optimizing cable control and tension-wind coupling catenary curves based on the structural disturbance risk analysis results, then entering the hierarchical scheduling process for the operation window; S3, performing spatial coupling effect analysis on the structural environment data and historical operation and maintenance data, adjusting the angle between the cable and the prevailing wind direction based on the spatial coupling effect analysis results, then entering the hierarchical scheduling process for the operation window; S4, performing risk amplification and health suppression assessment on the structural environment data and historical operation and maintenance data, and executing the hierarchical scheduling process for the operation window based on the risk amplification and health suppression assessment results; S5, constructing a risk attribution and autonomous evolution optimization mechanism based on multi-source risk quantification indicators, integrating dynamic causal tracing, scenario contingency plan simulation, and optimal response path.

[0008] Optionally, structural environment data and historical operation and maintenance data are collected. The specific process for preprocessing the structural environment data and historical operation and maintenance data is as follows: Structural environment data is collected, and historical operation and maintenance data is obtained. The structural environment data includes: total cable mass, total cable length, current release length, swing amplitude data, acceleration signal, on-site wind speed data, cable diameter, material surface roughness, material elastic modulus, cable spatial attitude data, air density, energy capture area data, UAV output power, ground anchor point spatial coordinates, UAV spatial coordinates, prevailing wind direction data, cable tension value, rotor speed, and anchor point force value. The historical operation and maintenance data includes: historical mission runtime, historical output power, historical risk event time, historical wind speed data, historical angle between the main projection and the prevailing wind direction, historical cable swing amplitude data, historical turbulence energy efficiency data, historical risk event occurrence density, and historical risk... Event duration; multi-scale denoising and trend enhancement of acceleration signals, sway amplitude data, and cable tension values ​​using wavelet transform denoising algorithm; multi-dimensional feature clustering of cable tension values, anchor point force values, rotor speed, and cable spatial attitude data using health feature clustering algorithm based on K-means clustering and density clustering algorithm to identify abnormal fluctuation patterns in structural state; intelligent completion of short-term missing and discontinuous data in historical output power, historical task runtime, air density, and on-site wind speed using sliding window interpolation and exponential smoothing algorithm; high-risk interval location and score boundary extraction of historical risk event time, historical risk event occurrence density, and historical wind speed data using piecewise autoregression and risk score dynamic threshold extraction algorithm; standardization and normalization of structural environment data and historical operation and maintenance data using distribution standardization and linear normalization algorithms.

[0009] Optionally, the specific process for structural disturbance risk analysis based on structural environment data and historical operation and maintenance data is as follows: Acquire data on total cable mass, total cable length, current release length, sway amplitude, acceleration signal, on-site wind speed, cable diameter, material surface roughness, material elastic modulus, cable spatial attitude, real-time local wind speed, air density, energy capture area, UAV output power, historical mission runtime, historical output power, historical risk event time, and historical wind speed; obtain the mass per unit length by performing a ratio algorithm on the total cable mass and total cable length, and obtain the cable length mass value by performing a mass-length product algorithm on the mass per unit length and the current release length; collect sway amplitude data using a cable vibration sensor; obtain the cable swing amplitude using an extreme value statistical algorithm on the sway amplitude data; collect acceleration signals using an accelerometer; obtain the cable displacement time series data using an integration algorithm on the acceleration signals, and extract the cable sway angular frequency from the cable displacement time series data using a fast Fourier transform; collect cable spatial attitude data using a multi-point attitude sensor and laser ranging, and process the cable spatial attitude data using curve fitting... The combined algorithm obtains the deviation value of the tension wind coupled catenary curve; real-time local wind speed is obtained by filtering the field wind speed data through a time sliding window; the optimal output power is obtained by calculating the real-time local wind speed, air density, and energy capture area data using wind energy physics formulas; the efficiency loss power difference is obtained by calculating the difference between the UAV output power and the optimal output power; the energy efficiency assessment period is obtained by using spectrum analysis and adaptive clustering algorithms for historical mission runtime, historical output power, and historical risk event times; and the historical wind speed data and field wind speed data are processed using a long short-term memory network. The optimal wind energy output value is obtained by multiplying the cable length mass value by the square of the cable swing amplitude and the cable swing angular frequency, and then multiplying by half to obtain the cable vibration energy term; the turbulence aerodynamic coefficient, the deviation value of the tension wind coupled catenary curve, and the square of the real-time local wind speed are multiplied to obtain the turbulence sag risk term; the efficiency loss power difference is multiplied by the energy efficiency assessment period to obtain the energy efficiency loss risk term; the cable vibration energy term, the turbulence sag risk term, and the energy efficiency loss risk term are added together as the risk numerator, and then the risk numerator is divided by the optimal wind energy output value to obtain the turbulence risk value.

[0010] Optionally, based on the structural disturbance risk analysis results, cable regulation and tension-wind coupled catenary curve optimization are performed, and the specific process of entering the hierarchical scheduling process of the operation window is as follows: Real-time comparison of disturbance risk value and disturbance risk threshold, including primary risk threshold and secondary risk threshold: When the disturbance risk value is less than or equal to the primary risk threshold, the disturbance risk is determined to be low and the operation is stable; the current mooring tension, sag shape and spatial layout settings are maintained without active intervention; When the disturbance risk value is greater than the primary risk threshold and less than or equal to the secondary risk threshold, the disturbance risk is determined to be increased, with potential energy efficiency loss and structural fatigue; the mooring tension is increased and the cable release length is shortened to optimize the tension-wind coupled catenary curve; When the disturbance risk value is greater than the secondary risk threshold, the disturbance risk is determined to be significant, with serious energy efficiency loss and structural abnormality; physical control measures are implemented: the mooring tension is continuously increased and the tension-wind coupled catenary curve is tightened; if the disturbance risk value is still greater than the secondary risk threshold after the physical control measures are implemented, the hierarchical scheduling process of the operation window is entered.

[0011] Optionally, the specific process for analyzing the spatial coupling effect of structural environment data and historical operation and maintenance data is as follows: Obtain the disturbance risk value, ground anchor point spatial coordinates, UAV spatial coordinates, prevailing wind direction data, cable sway, historical main projection angle with prevailing wind direction, historical cable sway data, and historical disturbance energy efficiency data; calculate the main projection direction of the mooring line using geometric relationships based on the ground anchor point spatial coordinates and UAV spatial coordinates; obtain the angle between the main projection and prevailing wind direction using a vector angle algorithm based on the prevailing wind direction data and the main projection direction of the mooring line; multiply the disturbance risk value by the sum of the sum of the squared sine of the angle between the main projection and prevailing wind direction multiplied by an angle sensitivity coefficient, and then add this sum to the product of the sway energy amplification coefficient and the squared cable sway to obtain the spatially sensitive energy term; obtain the downwind suppression correction term by multiplying the sum of the downwind correction coefficient and the cosine of the angle between the main projection and prevailing wind direction; divide the spatially sensitive energy term by the downwind suppression correction term to obtain the spatial coupling effect value.

[0012] Optionally, adjusting the angle between the cable and the prevailing wind direction based on the spatial coupling effect analysis results and entering the hierarchical scheduling process within the operation window involves the following steps: Real-time comparison of the spatial coupling effect value and the spatial coupling effect threshold, which includes a primary effect threshold and a secondary effect threshold. When the spatial coupling effect value is less than or equal to the primary effect threshold, the current spatial layout and wind direction are deemed to be well-adapted, maintaining the current anchor point, UAV spatial orientation, and cable status without requiring active adjustment. When the spatial coupling effect value is greater than the primary effect threshold but less than or equal to the secondary effect threshold, the spatial configuration is deemed to have an adverse impact, reducing the cable release length and increasing tension, while ensuring the flight path and operational requirements are met. The system corrects the spatial coordinates of the ground anchor points and the relative position and altitude of the UAV in space, adjusts the main projection direction of the mooring line to align with the prevailing wind direction, reduces the angle between the cable and the prevailing wind direction, and dynamically aligns the main projection direction of the mooring line with the prevailing wind direction. When the spatial coupling effect value is greater than the secondary effect threshold, it is determined that there is an energy loss and structural safety risk, and physical control measures are implemented: switch the anchor point and UAV orientation, strictly align the main projection of the mooring line with the prevailing wind direction, continuously reduce the angle between the main projection of the mooring line and the prevailing wind direction until it approaches horizontal, and tighten the cable. If the spatial coupling effect value is still greater than the secondary effect threshold after the physical control measures are implemented, the system enters the hierarchical scheduling process of the operation window.

[0013] Optionally, the specific process for analyzing the spatial coupling effect of structural environment data and historical operation and maintenance data is as follows: Obtain the disturbance risk value, spatial coupling effect value, on-site wind speed data, UAV output power, optimal output power, cable tension value, sway amplitude data, rotor speed, and anchor point force value; obtain the short-term wind speed change rate from the on-site wind speed data using a difference algorithm; obtain the energy efficiency loss rate from the UAV output power and optimal output power using a difference normalization algorithm; obtain the structural health margin from the cable tension value, sway amplitude data, rotor speed, and anchor point force value using a health assessment algorithm; add the disturbance risk value and the spatial coupling effect value, multiply by the short-term wind speed change rate, and then multiply by the energy efficiency loss rate to obtain the numerator of the risk; multiply the structural health margin by a historical high-risk memory factor to obtain the denominator of the risk; finally, divide the numerator by the denominator to obtain the operational window risk value.

[0014] Optionally, the specific process of implementing the hierarchical scheduling procedure for the operation window based on the risk amplification and health suppression assessment results is as follows: Real-time comparison of the operation window risk value and the operation window risk threshold, and execution of the hierarchical scheduling procedure for the operation window: When the operation window risk value is less than or equal to the operation window risk threshold, the operation task is divided into multiple sub-tasks using dynamic programming, allowing deviation from the original path, and prioritizing the allocation of high-energy-consuming and critical tasks to the operation zone with tailwind and stable airflow to reduce the load on the UAV and its subsystems; When the operation window risk value is greater than the operation window risk threshold, the operation task is immediately suspended, the UAV enters safe standby, the cable release length is continuously shortened, the distance between the UAV and the anchor point is reduced to the operating load required to maintain flight, and an early warning reminder is issued for manual intervention.

[0015] Optionally, based on multi-source risk quantification indicators, the specific process of constructing a risk attribution and autonomous evolution optimization mechanism by integrating dynamic cause tracing, scenario contingency plan simulation, and optimal response path is as follows: Based on historical operational window risk values, disturbance risk values, spatial coupling effect values, and global risk response processes, a retrospective analysis is conducted on the entire process of each high-risk trigger and abnormal adjustment. The changing trends of structural environment data and historical operation and maintenance data are tracked, and the core causes behind risk events are dynamically traced and attributed. Sudden wind shear in the external environment, structural entry into a critical aging state, and misconfiguration issues in spatial layout are accurately identified and located. After completing the risk attribution... Based on the identified core causes, emergency response strategies are generated, including tension control, spatial anchor point adjustment, droop curve optimization, operation window switching, and task splitting and rearrangement. Based on structural environment data and historical operation and maintenance data, the emergency response strategies are simulated and ranked in order of merit. The changes in disturbance risk, energy efficiency improvement, and structural health improvement under each emergency response strategy are quantitatively analyzed. The emergency measures with the best risk reduction effect and the least energy efficiency loss are selected for implementation. A risk attribution and autonomous evolution optimization library is created, and all strategy simulation results and emergency measure implementation effects are recorded in the risk attribution and autonomous evolution optimization library and fed back in real time.

[0016] On the other hand, a high-altitude wind energy capture system for multi-rotor tethered drones is provided. This system applies a high-altitude wind energy capture method for multi-rotor tethered drones, including a data acquisition and preprocessing module for acquiring structural environment data and historical operation and maintenance data; preprocessing the structural environment data and historical operation and maintenance data; a tethering line control and sag optimization module for performing structural disturbance risk analysis on the structural environment data and historical operation and maintenance data, and optimizing the cable control and tension-wind coupling catenary curve based on the structural disturbance risk analysis results, before entering the hierarchical scheduling process of the operation window; and spatial layout and wind direction planning. The module is used to perform spatial coupling effect analysis on structural environment data and historical operation and maintenance data. Based on the spatial coupling effect analysis results, it adjusts the angle between the cable and the prevailing wind direction and enters the hierarchical scheduling process of the operation window. The operation window and avoidance operation module is used to perform risk amplification and health suppression assessment on structural environment data and historical operation and maintenance data. Based on the risk amplification and health suppression assessment results, it executes the hierarchical scheduling process of the operation window. The risk attribution and autonomous optimization module is used to construct a risk attribution and autonomous autonomous optimization mechanism based on multi-source risk quantification indicators, integrating dynamic cause tracing, scenario contingency plan simulation and optimal response path.

[0017] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. This invention, through the stratification criterion of disturbance risk, dynamically adjusts the cable tension and sag curve, which can adaptively suppress cable vibration and turbulence, thereby achieving the effect of reducing energy loss and structural fatigue risk, and effectively solving the problems of strong cable disturbance and difficulty in guaranteeing structural life in the prior art.

[0018] 2. This invention adopts a dynamic collaborative optimization strategy of spatial layout and prevailing wind direction to adjust the spatial position of anchor points and UAVs in a timely manner, so that the main projection direction of the cable is similar to the prevailing wind direction, thereby reducing the impact of crosswinds and improving the spatial adaptability of the system, and solving the problem that crosswind disturbances cannot be actively avoided in the prior art.

[0019] 3. This invention, through risk-based scheduling of the operation window, combined with dynamic task splitting and tailwind priority allocation mechanism, can flexibly switch the operation status and task execution order of the UAV, thereby achieving the effect of intelligent avoidance of high-risk sections and operation continuity, effectively solving the problem of difficult smooth scheduling of high-risk working conditions in the prior art.

[0020] 4. This invention integrates health margin and historical high-risk memory factors into global criteria, dynamically balances energy efficiency and structural safety, and thus achieves early warning of risk evolution and adaptive protection, effectively solving the problem of lack of full life cycle health management in existing technologies. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a high-altitude wind energy capture method for a multi-rotor tethered unmanned aerial vehicle provided in an embodiment of the present invention; Figure 2 This is a general overview diagram of a high-altitude wind energy capture system for a multi-rotor tethered unmanned aerial vehicle provided in an embodiment of the present invention; Figure 3 This is a spatial parameter correlation heat map of a high-altitude wind energy capture method and system for multi-rotor tethered drones provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or “connected,” and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0025] It should be noted that the terms "up", "down", "left", "right", "front", and "back" used in this invention are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0026] like Figure 1The flowchart shown in this application illustrates a method and system for capturing high-altitude wind energy from a multi-rotor tethered drone. The method and system include: S1, collecting structural environment data and acquiring historical operation and maintenance data; preprocessing the structural environment data and historical operation and maintenance data; S2, performing structural disturbance risk analysis on the structural environment data and historical operation and maintenance data, optimizing cable control and tension-wind coupling catenary curves based on the structural disturbance risk analysis results, and entering the hierarchical scheduling process for the operation window; S3, performing spatial coupling effect analysis on the structural environment data and historical operation and maintenance data, adjusting the angle between the cable and the prevailing wind direction based on the spatial coupling effect analysis results, and entering the hierarchical scheduling process for the operation window; S4, performing risk amplification and health suppression assessment on the structural environment data and historical operation and maintenance data, and executing the hierarchical scheduling process for the operation window based on the risk amplification and health suppression assessment results; S5, constructing a risk attribution and autonomous evolution optimization mechanism based on multi-source risk quantification indicators, integrating dynamic causal tracing, scenario contingency plan simulation, and optimal response path.

[0027] Preferably, structural environment data is collected and historical operation and maintenance data is obtained; the specific process of preprocessing structural environment data and historical operation and maintenance data is as follows: collect structural environment data and obtain historical operation and maintenance data; Through a multi-sensor network and intelligent data acquisition, comprehensive coverage of key structural and environmental parameters across the entire field is achieved. Structural environmental data includes: total cable mass, total cable length, current release length, swing amplitude data, acceleration signal, on-site wind speed data, cable diameter, material surface roughness, material elastic modulus, cable spatial attitude data, air density, energy capture area data, UAV output power, ground anchor point spatial coordinates, UAV spatial coordinates, prevailing wind direction data, cable tension value, rotor speed, and anchor point force value. This enables multi-dimensional real-time perception of UAV structural safety, aerodynamic performance, energy conversion efficiency, and operational space status, providing a solid data foundation for subsequent risk assessment and dynamic control.

[0028] Historical operation and maintenance data includes: historical task runtime, historical output power, historical risk event time, historical wind speed data, historical angle between the main projection and the main wind direction, historical cable sway data, historical turbulence energy efficiency data, historical risk event occurrence density, and historical risk event duration. Through in-depth mining of historical operation and maintenance data, the performance evolution, anomaly distribution, and environmental adaptation process of the entire equipment operation cycle can be accurately reviewed.

[0029] Wavelet transform denoising algorithm is used to perform multi-scale denoising and trend enhancement on acceleration signals, oscillation amplitude data, and cable tension values, effectively suppressing the impact of high-frequency noise and environmental disturbances on the signal. A health feature clustering algorithm based on K-means clustering and density clustering is used to perform multi-dimensional feature clustering on cable tension values, anchor point force values, rotor speed, and cable spatial attitude data, identifying abnormal fluctuation patterns in structural status and forming a high-confidence early warning basis for structural health. Sliding window interpolation and exponential smoothing algorithms are used to store historical output power, historical mission runtime, air density, and on-site wind speed data. The system intelligently fills in short-term missing and broken points, eliminating defects in sensor link interruptions and transient acquisition anomalies. Through piecewise autoregression and dynamic threshold extraction algorithms for risk scores, it locates high-risk intervals and extracts score boundaries for historical risk event time, occurrence density, and wind speed data, accurately defining high-risk operation periods and extreme wind field boundaries. Through distribution standardization and linear normalization algorithms, it standardizes and normalizes structural environment data and historical operation and maintenance data, achieving scale unification and feature interval mapping for multi-source heterogeneous data, enhancing the model's generalization ability and cross-condition adaptability.

[0030] In this embodiment, accurate multi-dimensional data acquisition, real-time perception, and high-quality preprocessing of the entire operation process of a multi-rotor tethered UAV can be achieved, effectively improving the characterization capabilities of key structural states, aerodynamic environment, and historical operational characteristics. Through full-field multi-sensor fusion and intelligent algorithm processing, not only are high-frequency noise and abnormal disturbances in the original signals significantly suppressed, but also structural health hazards and high-incidence areas of risk events are dynamically identified, short-term acquisition gaps are filled, and multi-source data scales are calibrated, ensuring the accuracy and timeliness of data in subsequent risk assessment, state criteria, and adaptive control. This provides a solid data foundation and technical support for intelligent risk prevention and control, energy efficiency optimization, and full life-cycle health management of UAV high-altitude wind energy capture operations.

[0031] Preferably, the specific process for structural disturbance risk analysis based on structural environmental data and historical operation and maintenance data is as follows: Acquire data on total cable mass, total cable length, current release length, sway amplitude, acceleration signal, on-site wind speed, cable diameter, material surface roughness, material elastic modulus, cable spatial attitude, real-time local wind speed, air density, energy capture area, UAV output power, historical mission runtime, historical output power, historical risk event time, and historical wind speed data. This ensures stable acquisition and complete archiving of multi-source real-time data, providing a strong foundation for analysis. A ratio algorithm is then applied to the total cable mass and total cable length. The cable length mass is obtained by multiplying the unit length mass and the current release length using a mass-length product algorithm. A cable vibration sensor is used to collect swing amplitude data. An extreme value statistical algorithm is used to obtain the cable swing amplitude, effectively characterizing the amplitude boundary of the cable's dynamic response under actual operating conditions, thus aiding in the rapid detection and prevention of abnormal vibrations. An accelerometer is used to collect acceleration signals. An integration algorithm is used to obtain the cable's displacement time series data. A Fast Fourier Transform is used to extract the cable's swing angular frequency from the displacement time series data, dynamically tracking changes in the structural response frequency band and promptly revealing wind-induced vibrations. The dominant dynamic mode and energy accumulation range were determined. Cable spatial attitude data were collected using multi-point attitude sensors and laser ranging. Classical parabolic and adaptive polynomial regression analysis, with 2nd-4th order regressions, were used as the theoretical tension-wind coupled catenary curve benchmark. The spatial curve was reconstructed from the actual collected cable spatial attitude data using a least-squares fitting algorithm. The fitting accuracy was measured using mean square error and maximum absolute deviation error to obtain the tension-wind coupled catenary curve deviation value. Real-time local wind speed was obtained by filtering the field wind speed data using a time sliding window to suppress the impact of environmental interference and sudden wind field changes on the evaluation data. Real-time local wind speed and air density were also analyzed. The optimal output power is calculated using wind energy physics formulas based on the energy capture area data. The efficiency loss power difference is calculated by subtracting the output power of the UAV from the optimal output power, providing a real-time quantitative basis for evaluating energy efficiency loss and energy capture potential. The energy efficiency assessment period is obtained by using spectrum analysis and adaptive clustering algorithms for historical mission runtime, historical output power, and historical risk event time, accurately locating key energy consumption change cycles and high-risk fluctuation areas. The optimal wind energy output value is obtained by using a long short-term memory network algorithm for historical wind speed data and on-site wind speed data, realizing dynamic self-learning and efficient prediction of optimal wind energy capture capability under complex wind field conditions.

[0032] Multiplying the cable length mass value by the squares of the cable swing amplitude and the cable swing angular frequency, and then multiplying by half, yields the cable vibration energy term, reflecting the impact of wind-induced vibration on the periodic performance loss and fatigue accumulation of the structure. Multiplying the turbulence aerodynamic coefficient, the deviation value of the tension-wind coupled catenary curve, and the square of the real-time local wind speed yields the turbulence sag risk term, characterizing the energy loss and aerodynamic risk level under the combined action of cable spatial attitude deviation and flow field disturbance, enhancing the early perception of instability risks under extreme wind fields. Multiplying the efficiency loss power difference by the energy efficiency assessment period yields the energy efficiency loss risk term, quantifying the performance loss caused by insufficient energy conversion and local anomalies during wind energy capture. The cable vibration energy term, turbulence sag risk term, and energy efficiency loss risk term are added together as the risk numerator, and then the risk numerator is divided by the optimal wind energy output value to obtain the turbulence risk value. The specific calculation method is as follows: ; In the formula, This indicates the disturbance risk value, reflecting the risk level and operational status of multi-rotor tethered drones during high-altitude operations; This represents the cable length mass value, characterizing the cable's sensitivity to wind-induced loads and structural response; This indicates the cable swing amplitude, reflecting the maximum dynamic displacement characteristics of the cable under wind excitation. It represents the angular frequency of the cable's oscillation, revealing the dominant vibration frequency band and energy distribution pattern; This represents the deviation value of the tension-wind coupled catenary curve, which measures the degree of abnormal spatial attitude of the cable and the deviation from the flow field. It indicates the real-time local wind speed, reflecting the current local aerodynamic environment changes in the drone operation area; It represents the power difference in efficiency loss, quantitatively reflecting the actual energy efficiency loss caused by structural anomalies, aerodynamic disturbances, and insufficient energy conversion; This indicates the time period for energy efficiency assessment, ensuring the accuracy and representativeness of the time period for energy efficiency risk analysis; This represents the optimal wind energy output value, providing a reference for the quantification and global comparison of disturbance risk values; The turbulence aerodynamic coefficient is determined by wind tunnel calibration based on on-site wind speed data, cable diameter, material surface roughness, and material elastic modulus. The turbulence aerodynamic coefficient is obtained through a real-time fitting algorithm and ranges from 0.8 to 2.0.

[0033] In this embodiment, a refined modeling and high-dimensional risk quantification of the aerodynamic structural coupling effect under all operating conditions of a multi-rotor tethered UAV are achieved. Multi-source data from structural environment, spatial attitude, and wind field dynamics are effectively integrated. Through multi-algorithm collaboration and in-depth analysis of physical characteristic parameters, the complex relationship between cable vibration, spatial coupling, and energy loss on operational risks is comprehensively revealed. Based on theoretical curve fitting, dynamic frequency domain analysis, and wind energy physics modeling, the real-time state and trend of high-altitude turbulence and structural fatigue can be accurately reflected. Instability risks and performance bottlenecks under extreme conditions are dynamically identified. The output of turbulence risk values ​​quantifies the aerodynamic risk level under different environments and structural conditions, providing scientific criteria for subsequent graded control, proactive prevention, and adaptive optimization. This significantly improves the intelligent assessment, global protection, and operational safety assurance capabilities of the UAV wind energy capture system.

[0034] Preferably, the specific process of optimizing cable control and tension-wind coupled catenary curves based on structural disturbance risk analysis results, and entering the hierarchical scheduling process within the work window, is as follows: Real-time comparison of disturbance risk values ​​and disturbance risk thresholds, including primary and secondary risk thresholds; dynamic linkage of control strategies for each execution unit based on the disturbance risk level to achieve multi-level proactive control and safety protection. When the disturbance risk value is less than or equal to the first-level risk threshold, the disturbance risk is determined to be low and the operation is stable; the current mooring tension, sag shape and spatial layout settings are maintained without active intervention, ensuring long-term stability of the structure and aerodynamic performance.

[0035] When the disturbance risk value is greater than the first-level risk threshold but less than or equal to the second-level risk threshold, the disturbance risk is considered to have increased, indicating potential energy efficiency loss and structural fatigue. The tension adjustment mechanism is instructed to increase the current mooring tension by 10%-20%, and the cable retraction / release drive assembly to tighten the cable release length by 5%-10%, gradually adjusting at a controlled speed of 0.03-0.05 m / s, so that the tension-wind coupling catenary curve converges towards the theoretical state, with the curve fitting residual not exceeding 0.5-0.7 m. 2 Allowable deviation.

[0036] When the disturbance risk value exceeds the secondary risk threshold, the disturbance risk is deemed significant, resulting in severe energy efficiency loss and structural abnormalities. Physical control measures are implemented: the cable tension is increased to 20%-30% of the current level, and the cable release length is simultaneously reduced by 10%-15%, adjusted rapidly at a safe speed of 0.8-0.1 m / s to ensure that the extreme working conditions are avoided, and the tension limit and cable release limit safety boundary are monitored simultaneously. If the disturbance risk value still exceeds the secondary risk threshold after the implementation of physical control measures, the work window hierarchical scheduling process is initiated.

[0037] In this embodiment, this step introduces graded comparison and refined control of disturbance risk values, enabling proactive adjustment and safety assurance of multi-rotor tethered UAVs based on cable aerodynamic structure coupling analysis. It can dynamically constrain the adjustment range, extension / retraction speed, and spatial attitude deviation according to the disturbance risk level, ensuring that structural vibration and aerodynamic disturbances remain within a controllable range. The control process continuously monitors key fitting indicators and ultimate safety boundaries, effectively improving the structural stability and energy efficiency of the UAV under wind field changes and load disturbance conditions. Faced with extreme risk conditions, it can switch to graded scheduling and safe standby processes, enabling timely suspension and early warning of high-risk operations, greatly enhancing the continuity, robustness, and adaptive operation capabilities of the UAV high-altitude wind energy capture system.

[0038] Preferably, the specific process for analyzing the spatial coupling effect of structural environment data and historical operation and maintenance data is as follows: Obtain the disturbance risk value, ground anchor point spatial coordinates, UAV spatial coordinates, prevailing wind direction data, and core parameters of cable sway. Combine this with historical main projection angle with prevailing wind direction, historical cable sway data, and historical disturbance energy efficiency data to deeply mine the spatial coupling and wind field response characteristics during operation, laying a solid data foundation for risk assessment and strategy output. Calculate the main projection direction of the mooring line using geometric relationships from the ground anchor point spatial coordinates and UAV spatial coordinates. Obtain the angle between the main projection and prevailing wind direction from the prevailing wind direction data and the main projection direction of the mooring line using a vector angle algorithm. Use three-dimensional spatial geometry and automated algorithms to achieve dynamic reconstruction and tracking of the spatial positions of the anchor points and UAVs. The spatial sensitive energy term is obtained by multiplying the eddy current risk value by the OnePlus angle sensitivity coefficient and the sum of the squared terms of the angle between the main projection and the main wind direction, and then adding the product of the swing amplitude energy amplification coefficient and the square of the cable swing amplitude. This effectively integrates the multiple influences of spatial eddy current and structural response, enabling focused and sensitive identification of high-risk areas. The downwind suppression correction term is obtained by multiplying the OnePlus downwind correction coefficient by the cosine term of the angle between the main projection and the main wind direction, dynamically coupling the risk suppression capability under downwind conditions. The spatial coupling effect value is obtained by dividing the spatial sensitive energy term by the downwind suppression correction term. The specific calculation formula is as follows: ; In the formula, This represents the spatial coupling effect value, which comprehensively measures the multi-faceted impacts of spatial layout, aerodynamic disturbance, and structural response in the tethered UAV operating area. This indicates the disturbance risk value, reflecting the risk level and operational status of multi-rotor tethered drones during high-altitude operations; It indicates the angle between the main projection and the main wind direction, accurately reflecting the adaptability and interaction mechanism between the spatial layout and the wind field environment; This indicates the cable swing amplitude, reflecting the maximum dynamic displacement characteristics of the cable under wind excitation. The angle sensitivity coefficient is obtained by fitting the sensitivity of the angle between the historical master projection and the prevailing wind direction and the turbulence risk value through regression analysis. The value ranges from 1.2 to 1.6. This represents the swing amplitude energy amplification factor, which is obtained by using an optimal fitting algorithm based on historical cable swing amplitude data and turbulence risk values. The value ranges from 0.7 to 1.2. The downwind correction factor is obtained by using a sensitivity analysis algorithm on historical turbulence energy efficiency data, the angle between the historical principal projection and the prevailing wind direction, and turbulence risk values. The value ranges from 0.5 to 0.8.

[0039] Table 1 shows the correlation between spatial parameters and response values, detailing real-time monitoring data for core parameters such as turbulence risk value, angle between the main projection and the prevailing wind direction, cable sway, angle sensitivity coefficient, sway energy amplification coefficient, downwind correction coefficient, and spatial coupling effect value under various typical operating conditions. For example, in test number 1, the turbulence risk value is 0.18, the angle between the main projection and the prevailing wind direction is 10 degrees, and the cable sway is 0.06, resulting in a spatial coupling effect value of 0.199. However, in test number 5, the turbulence risk value increases to 0.63, the angle increases to 85 degrees, the cable sway reaches 0.25, and the spatial coupling effect value significantly increases to 0.816, indicating that the combination of high angle, high amplitude, and high risk value leads to a significant enhancement in spatial response. The changes in various sensitivity coefficients further reflect the modulating effect of spatial parameters on the effect value under different operating conditions. Overall, as the angle between the main projection and the prevailing wind direction, cable sway, and turbulence risk value gradually increase, the spatial coupling effect value shows a stable upward trend. This table provides crucial data support for spatial layout safety management and high-risk identification, laying a data foundation for subsequent risk classification and control.

[0040] Table 1. Correlation between Spatial Parameters and Response Values

[0041] like Figure 3 The image shows a spatial parameter correlation heatmap provided in an embodiment of this application. (Refer to Table 1 and...) Figure 3 It can be seen that the correlation between the disturbance risk value, the angle between the main projection and the prevailing wind direction, and the cable sway amplitude and the spatial coupling effect value are the most significant. Specifically, as the angle increases from 10 degrees to 85 degrees, the spatial coupling effect value increases from 0.199 to 0.816, indicating that the change in the angle is one of the key factors affecting spatial risk. The heat map also shows that the angle sensitivity coefficient, the sway amplitude energy amplification coefficient, and the downwind correction coefficient all play an important role in regulating the spatial coupling effect value under different test numbers. The heat map provides a clear overview of the correlation distribution between various parameters and the spatial risk response, offering intuitive data support and a scientific basis for spatial adaptive regulation and risk factor optimization.

[0042] In this embodiment, by integrating multi-source structural environmental data and historical operation and maintenance data, a spatial distribution risk assessment and intelligent control system for multi-rotor tethered UAVs operating in complex high-altitude wind fields is constructed. Based on real-time data acquisition and 3D spatial reconstruction, it accurately captures the spatial dynamics of anchor points and UAVs, changes in prevailing wind direction, and dynamic responses of cables. Using spatially coupled sensitive energy criteria as the core, it dynamically quantifies the combined effects of multiple risk sources, including wind field disturbance, spatial angle, and cable sway. Through a downwind suppression correction algorithm, it enhances spatial adaptability and risk mitigation capabilities, enabling early warning of high-risk operating areas and intelligent avoidance of energy efficiency loss risks. This provides global dynamic support for the continuous energy harvesting, efficient operation, and structural safety of UAVs in complex spatial environments.

[0043] Preferably, the specific process of adjusting the angle between the cable and the prevailing wind direction based on the spatial coupling effect analysis results and entering the hierarchical scheduling process within the operation window is as follows: Real-time comparison of the spatial coupling effect value and the spatial coupling effect threshold, whereby the spatial coupling effect threshold includes a primary effect threshold and a secondary effect threshold. When the spatial coupling effect value is less than or equal to the first-level effect threshold, it is determined that the current spatial layout and wind direction are well adapted. The current anchor point, UAV spatial orientation and cable status are maintained without active adjustment, ensuring the stability of the overall structure and the continuity of the operation path.

[0044] When the spatial coupling effect value is greater than the first-level effect threshold and less than or equal to the second-level effect threshold, it is determined that the spatial configuration has an adverse impact. The cable release length is reduced to increase tension. Under the premise of ensuring the flight path and operation requirements, multiple anchor points are deployed and the UAV is autonomously positioned. By driving the ground multi-point electric anchor base, the spatial coordinates of the ground anchor points and the relative position and altitude of the UAV in space are corrected. The main projection direction of the mooring line is adjusted to be aligned with the prevailing wind direction. The angle between the cable and the prevailing wind direction is dynamically converged to ensure that the main projection direction of the mooring line quickly adapts to the prevailing wind direction and the direction dynamically converges.

[0045] When the spatial coupling effect value exceeds the second-order effect threshold, it is determined that there is energy efficiency loss and structural safety risk, and physical control measures are implemented: multi-directional movement of anchor points and autonomous heading adjustment of UAVs are carried out, the ground main control anchor point is switched to the downwind area and feasible safety zone, and azimuth commands are pushed in real time to guide UAVs to adjust to the optimal spatial path, so that the main projection of the mooring line is strictly aligned with the main wind direction within the minimum angle range of 3°-5° defined by the algorithm. The cable release length is continuously tightened and the tension is increased to the optimal state of structural safety through a high-response electric winch and tension controller, ensuring structural and spatial safety redundancy. If the spatial coupling effect value is still greater than the second-order effect threshold after the implementation of physical control measures, the operation window hierarchical scheduling process is entered.

[0046] In this embodiment, the spatial adaptability and structural safety capabilities of multi-rotor tethered drones in complex high-altitude wind fields are enhanced by achieving multi-level dynamic optimization of drone anchor points, spatial positions, and cable status. Through hierarchical determination of spatial coupling effect values, the anchor points and drone orientation can be flexibly adjusted when the risk level changes, and the main projection direction of the cable and the prevailing wind direction can be dynamically converged. This achieves windward spatial arrangement and enhanced structural redundancy, ensuring the continuity of the operational path, energy efficiency, and high safety in complex aerodynamic environments. It provides full-process spatial coordination and risk adaptability support for high-altitude wind energy capture operations.

[0047] Preferably, the specific process for analyzing the spatial coupling effect of structural environment data and historical operation and maintenance data is as follows: Obtain disturbance risk values, spatial coupling effect values, on-site wind speed data, UAV output power, optimal output power, cable tension values, oscillation amplitude data, rotor speed, and anchor point force values ​​to comprehensively cover key physical quantities affecting the stability of UAV high-altitude wind energy capture, ensuring high spatiotemporal resolution and real-time performance of multi-source data; obtain the short-term wind speed change rate from the on-site wind speed data using a differential algorithm; obtain the energy efficiency loss rate from the UAV output power and optimal output power using a difference normalization algorithm to quantify the real-time impact of structural and aerodynamic state changes on energy conversion efficiency; obtain the structural health margin from the cable tension values, oscillation amplitude data, rotor speed, and anchor point force values ​​using a health assessment algorithm to evaluate the structural safety margin and fatigue risk of the UAV and key components in complex wind field environments.

[0048] The risk value is calculated by adding the disturbance risk value to the spatial coupling effect value, multiplying it by the short-term wind speed change rate, and then multiplying it by the energy efficiency loss rate. This serves as the numerator of the risk calculation, providing a high-dimensional aggregation that reflects the energy consumption, risk, and synergistic effect of external disturbances under the current multi-physics coupling, thus enhancing the accurate characterization of high-risk situations. The structural health margin is multiplied by a historical high-risk memory factor to form the denominator of the risk calculation. This introduces the equipment's historical high-risk exposure memory into the risk quantification, improving the dynamic assessment capability of cumulative fatigue and resilience. Finally, the risk numerator is divided by the risk denominator to obtain the operational window risk value. The specific calculation formula is as follows: ; In the formula, This represents the risk value of the work window, which is a comprehensive criterion for judging the risk level under different work periods and spatial environments; This indicates the disturbance risk value, reflecting the risk level and operational status of multi-rotor tethered drones during high-altitude operations; This represents the spatial coupling effect value, which comprehensively measures the multi-faceted impacts of spatial layout, aerodynamic disturbance, and structural response in the tethered UAV operating area. It represents the short-term wind speed change rate, quantifying the instantaneous fluctuation intensity of the wind field during the operation; It represents the energy efficiency loss rate and dynamically reveals the impact of structural anomalies and aerodynamic instability on energy conversion efficiency. It represents the structural health margin, measuring the remaining life and safety margin of critical structural components; The historical high-risk memory factor is obtained by using a risk memory decay algorithm to evaluate the timing, density, and duration of historical risk events. The value ranges from 0 to 1.

[0049] In this embodiment, the safety, stability, and intelligence of high-altitude wind energy capture operations by multi-rotor tethered UAVs are comprehensively improved. Through multi-source dynamic comparison of risk numerators and denominators, rapid identification and early warning of high-risk operating conditions, structural fatigue hazards, and energy efficiency losses are achieved. The output operational window risk value effectively enhances the global identification capability of the superimposed effects of extreme turbulence, spatial mismatch, and historical high risks, ensuring refined management and operational continuity of task scheduling in complex aerodynamic environments. This provides a solid data foundation and real-time risk quantification support for the safe and efficient capture and autonomous decision-making of high-altitude wind energy.

[0050] Preferably, the specific process of implementing the hierarchical scheduling procedure for work windows based on the risk amplification and health suppression assessment results is as follows: Real-time comparison of the risk value and risk threshold of the work window, and execution of the hierarchical scheduling procedure for the work window: When the risk value of the operation window is less than or equal to the risk threshold of the operation window, the ground intelligent scheduling unit and the airborne mission management work together to call a dynamic programming algorithm with space wind field adaptability to efficiently divide the overall operation task. This allows each sub-task to deviate from its original path. Combined with meteorological forecasts and structural health data constraints, high-energy-consuming and critical tasks are dynamically matched to the operation range with downwind and stable airflow to improve energy efficiency output and system redundancy, reduce the risk of continuous load on a single unit, and the paths of all task sub-ranges are generated by constraints and dynamically corrected in real time.

[0051] When the risk value of the operation window exceeds the risk threshold, a pause command is issued through the task management and safety control unit. The drone immediately switches to safe standby mode and continuously retrieves the cable through the ground winch. The spatial position of the drone and anchor point is adjusted, and the drone is smoothly returned to a safe flight altitude boundary greater than or equal to 20m. The safety tension is strictly limited to 1.1 times the tension, the minimum safe power is greater than or equal to 1.2 times the hovering power, and the minimum flight altitude and maximum retraction speed are within the parameter boundaries of 0.08-1.0m / s. This ensures that no high-risk states of structural overload, low-altitude collision, or insufficient power occur during the entire retraction and standby period, reducing the operating load to the level required to maintain flight, and initiating an early warning to remind manual intervention.

[0052] In this embodiment, dynamic hierarchical scheduling and precise safety management of multi-rotor tethered drones under different risk conditions are achieved, improving the energy efficiency and mission continuity of high-altitude wind energy capture operations. By efficiently segmenting and dynamically allocating sub-tasks, the risk of fatigue accumulation and performance degradation under continuous high loads on a single drone is reduced. At the same time, switching to a safe standby mode during high-risk periods and accurately retrieving the drone to the safety boundary ensures structural safety and power redundancy throughout the entire operation process. This greatly enhances the adaptive adjustment capability to complex wind field changes and sudden operating conditions, achieving efficient energy capture in high-altitude operation scenarios and global safety and reliability under extreme conditions.

[0053] Preferably, the specific process of constructing a risk attribution and autonomous evolution optimization mechanism based on multi-source risk quantification indicators and integrating dynamic cause tracing, scenario contingency plan simulation and optimal response path is as follows: Based on historical operation window risk values, disturbance risk values, spatial coupling effect values ​​and global risk response processes, a review and analysis of the entire process of each high-risk trigger and abnormal adjustment is conducted. Combining long-term trend tracking and multi-variable collaborative analysis of structural environment data and historical operation and maintenance data, the core causes behind risk events are dynamically traced and attributed. This enables intelligent detection and positioning of high-risk scenarios such as sudden external wind shear, critical structures entering a critical aging state and spatial layout misconfiguration, providing a solid data foundation and causal link support for subsequent decision-making.

[0054] Based on risk attribution, a modular emergency response generation and intelligent simulation mechanism is adopted. For the identified core causes, emergency response strategies including tension control, spatial anchor point adjustment, droop curve optimization, operation window switching, and task splitting and rearrangement are generated. Based on structural environment data and historical operation and maintenance data, the emergency response strategies are simulated and ranked in order of superiority. The changes in disturbance risk, energy efficiency improvement, and structural health improvement under each emergency response strategy are quantitatively analyzed. The emergency measures with the best risk reduction effect and the least energy efficiency loss are selected for implementation. A risk attribution and autonomous evolution optimization library is created, and all strategy simulation results and implementation effects are dynamically archived in the risk attribution and autonomous evolution optimization library. The feedback is continuously used to optimize the model and response mechanism, realizing efficient autonomous evolution and safety closed loop across cycles and multiple scenarios.

[0055] In this embodiment, the entire process of tracing and multi-dimensional attribution of high-risk triggers is achieved, enabling efficient identification of multiple risk scenarios caused by sudden wind field changes, structural fatigue, and spatial misconfiguration, providing precise support for UAV risk prevention and control. Through a built-in modular emergency response strategy generation and intelligent simulation mechanism, dynamic optimization of tension control, spatial anchor point adjustment, droop curve morphology, operation window switching, and task rearrangement measures is implemented to achieve optimal selection and implementation of risk response paths. All risk attributions, strategy simulations, and their execution effects are archived to an optimization library in real time, promoting adaptive learning and closed-loop evolution, and enhancing autonomous safety management capabilities and lifecycle stability under complex wind fields and extreme operating conditions.

[0056] Preferred, such as Figure 2 As shown, the second aspect of this invention provides a high-altitude wind energy capture system for a multi-rotor tethered unmanned aerial vehicle (UAV), comprising: a data acquisition and preprocessing module for acquiring structural environment data and historical operation and maintenance data; preprocessing the structural environment data and historical operation and maintenance data; a tether line control and sag optimization module for performing structural disturbance risk analysis on the structural environment data and historical operation and maintenance data, and optimizing the cable and tension-wind coupling catenary curve based on the structural disturbance risk analysis results, and entering the hierarchical scheduling process of the operation window; a spatial layout and wind direction planning module for performing spatial coupling effect analysis on the structural environment data and historical operation and maintenance data, adjusting the angle between the cable and the prevailing wind direction based on the spatial coupling effect analysis results, and entering the hierarchical scheduling process of the operation window; an operation window and avoidance operation module for performing risk amplification and health suppression assessment on the structural environment data and historical operation and maintenance data, and executing the hierarchical scheduling process of the operation window based on the risk amplification and health suppression assessment results; and a risk attribution and autonomous optimization module for constructing a risk attribution and autonomous evolution optimization mechanism based on multi-source risk quantification indicators, integrating dynamic cause tracing, scenario contingency plan deduction, and optimal response path.

[0057] In this embodiment, through modular division of labor and deep fusion of multi-source data, intelligent control and dynamic risk optimization of the entire process of high-altitude wind energy capture by multi-rotor tethered UAVs are achieved. The various functional modules work together to improve structural safety, spatial adaptability, and operational continuity: the data acquisition and preprocessing module provides high-precision global perception and robust anomaly data purification; the tether line control and sag optimization module achieves adaptive adjustment of tension and sag curves, significantly suppressing turbulence and fatigue risks; the spatial layout and wind direction planning module improves the intelligent adaptation of spatial layout to the prevailing wind direction, dynamically avoiding high-risk spatial scenarios; the operation window and avoidance operation module supports hierarchical scheduling of operation tasks and risk health suppression, ensuring the overall safety and energy efficiency of UAV operations in complex wind fields; and the risk attribution and autonomous optimization module realizes full-process attribution deduction of high-risk factors and autonomous evolution of the optimal response path. This solution enhances the operational resilience of UAVs and the efficiency of high-altitude wind energy capture, providing a solid guarantee for safe operation and continuous high-efficiency output in complex scenarios.

[0058] The following points need to be explained: (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0059] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0060] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0061] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for capturing high-altitude wind energy from a multi-rotor tethered unmanned aerial vehicle, characterized in that, include: S1. Collect structural environment data and obtain historical operation and maintenance data; preprocess the structural environment data and historical operation and maintenance data. S2, perform structural disturbance risk analysis on structural environmental data and historical operation and maintenance data, optimize cable control and tension-wind coupling catenary curve based on the structural disturbance risk analysis results, and enter the hierarchical scheduling process of the operation window; S3, perform spatial coupling effect analysis on structural environment data and historical operation and maintenance data, adjust the angle between the cable and the prevailing wind direction based on the spatial coupling effect analysis results, and enter the hierarchical scheduling process of the operation window; S4 performs a risk amplification and health suppression assessment on structural environment data and historical operation and maintenance data, and executes a hierarchical scheduling process for work windows based on the risk amplification and health suppression assessment results. S5, based on multi-source risk quantification indicators, integrates dynamic cause tracing, scenario contingency plan simulation and optimal response path to construct a risk attribution and autonomous evolution optimization mechanism.

2. The method for capturing high-altitude wind energy of a multi-rotor tethered unmanned aerial vehicle according to claim 1, characterized in that: The process of collecting structural environment data and obtaining historical operation and maintenance data, and preprocessing the structural environment data and historical operation and maintenance data, is as follows: Collect structural environment data and obtain historical operation and maintenance data; Structural environmental data includes: total cable mass, total cable length, current release length, swing amplitude data, acceleration signal, on-site wind speed data, cable diameter, material surface roughness, material elastic modulus, cable spatial attitude data, air density, energy capture area data, UAV output power, ground anchor point spatial coordinates, UAV spatial coordinates, prevailing wind direction data, cable tension value, rotor speed, and anchor point force value. Historical operation and maintenance data includes: historical task runtime, historical output power, historical risk event duration, historical wind speed data, historical angle between the main projection and the main wind direction, historical cable sway data, historical turbulence energy efficiency data, historical risk event occurrence density, and historical risk event duration. Wavelet transform denoising algorithm is used to perform multi-scale denoising and trend enhancement on acceleration signals, sway amplitude data, and cable tension values; health feature clustering algorithm based on K-means clustering and density clustering algorithm is used to perform multi-dimensional feature clustering on cable tension values, anchor point force values, rotor speed, and cable spatial attitude data to identify abnormal fluctuation patterns in structural status; sliding window interpolation and exponential smoothing algorithm are used to intelligently complete short-term missing and discontinuous data in historical output power, historical task runtime, air density, and on-site wind speed data; piecewise autoregression and risk score dynamic threshold extraction algorithm are used to locate high-risk intervals and extract score boundaries for historical risk event time, historical risk event occurrence density, and historical wind speed data; distribution standardization and linear normalization algorithm are used to standardize and normalize structural environment data and historical operation and maintenance data.

3. The method for capturing high-altitude wind energy of a multi-rotor tethered unmanned aerial vehicle according to claim 1, characterized in that: The specific process for performing structural disturbance risk analysis on structural environmental data and historical operation and maintenance data is as follows: Acquire data on the total mass of the cable, total cable length, current release length, swing amplitude, acceleration signal, on-site wind speed, cable diameter, material surface roughness, material elastic modulus, cable spatial attitude, real-time local wind speed, air density, energy capture area, UAV output power, historical mission runtime, historical output power, historical risk event time, and historical wind speed; calculate the mass per unit length by performing a ratio algorithm between the total cable mass and total cable length, and calculate the cable length mass value by performing a mass-length product algorithm between the mass per unit length and the current release length; The cable vibration sensor collects swing amplitude data; the extreme value statistical algorithm is used to obtain the cable swing amplitude; the accelerometer collects acceleration signal; the acceleration signal is integrated to obtain the cable displacement time series data; the cable swing angular frequency is extracted from the cable displacement time series data through fast Fourier transform; the multi-point attitude sensor and laser ranging collect the cable spatial attitude data; the cable spatial attitude data is obtained through curve fitting algorithm to obtain the tension-wind coupled catenary curve deviation value. Real-time local wind speed is obtained by filtering the on-site wind speed data through a time sliding window. The optimal output power is calculated using wind energy physics formulas based on real-time local wind speed, air density, and energy capture area data. The efficiency loss power difference is calculated by subtracting the output power of the UAV from the optimal output power. The energy efficiency assessment period is obtained by using spectrum analysis and adaptive clustering algorithms based on historical mission runtime, historical output power, and historical risk event time. The optimal wind energy output value is obtained by using a long short-term memory network algorithm based on historical wind speed data and on-site wind speed data. The cable vibration energy term is obtained by multiplying the cable length mass value by the squares of the cable swing amplitude and the cable swing angular frequency, and then multiplying by half. The turbulence sag risk term is obtained by multiplying the turbulence aerodynamic coefficient, the deviation value of the tension wind coupled catenary curve, and the square of the real-time local wind speed. The energy efficiency loss risk term is obtained by multiplying the efficiency loss power difference with the energy efficiency assessment period. The cable vibration energy term, the turbulence sag risk term, and the energy efficiency loss risk term are added together as the risk numerator, and then the risk numerator is divided by the optimal wind energy output value to obtain the turbulence risk value.

4. The method for capturing high-altitude wind energy of a multi-rotor tethered unmanned aerial vehicle according to claim 1, characterized in that: The specific process of optimizing cable control and tension-wind coupling catenary curves based on structural disturbance risk analysis results, and then entering the hierarchical scheduling process within the work window, is as follows: Real-time comparison of disturbance risk values ​​and disturbance risk thresholds, including primary risk thresholds and secondary risk thresholds: When the disturbance risk value is less than or equal to the first-level risk threshold, the disturbance risk is determined to be low and the operation is stable. Maintain the current mooring tension, sag shape, and spatial layout without active intervention; When the disturbance risk value is greater than the first-level risk threshold and less than or equal to the second-level risk threshold, the disturbance risk is determined to be increased, with potential energy efficiency loss and structural fatigue. Increase mooring tension, shorten cable release length, and optimize tension-wind coupling catenary curve; When the disturbance risk value is greater than the secondary risk threshold, the disturbance risk is determined to be significant, resulting in serious energy efficiency loss and structural anomalies. Implement physical control measures: continuously increase the mooring tension and tighten the force-wind coupling catenary curve. If the disturbance risk value is still greater than the secondary risk threshold after the implementation of physical control measures, then enter the hierarchical scheduling process of the operation window.

5. The method for capturing high-altitude wind energy of a multi-rotor tethered unmanned aerial vehicle according to claim 1, characterized in that: The specific process for analyzing the spatial coupling effect of structural environment data and historical operation and maintenance data is as follows: Acquire disturbance risk values, ground anchor point spatial coordinates, UAV spatial coordinates, prevailing wind direction data, cable sway, historical main projection angle with prevailing wind direction, historical cable sway data, and historical disturbance energy efficiency data; calculate the main projection direction of the mooring line from the ground anchor point spatial coordinates and UAV spatial coordinates using geometric relationships, and obtain the angle between the main projection and prevailing wind direction from the prevailing wind direction data and the main projection direction of the mooring line using a vector angle algorithm; Multiply the turbulence risk value by the sum of the square of the sine of the angle between the main projection and the main wind direction, and then add the sum of the product of the swing energy amplification factor and the square of the cable swing to obtain the space sensitive energy term. The downwind suppression correction term is obtained by multiplying the downwind correction factor by the cosine of the angle between the main projection and the main wind direction. The space-sensitive energy term is then divided by the downwind suppression correction term to obtain the space coupling effect value.

6. The method for capturing high-altitude wind energy of a multi-rotor tethered unmanned aerial vehicle according to claim 1, characterized in that: The specific process of adjusting the angle between the cable and the prevailing wind direction based on the spatial coupling effect analysis results, and entering the hierarchical scheduling process within the work window, is as follows: Real-time comparison of spatial coupling effect values ​​and spatial coupling effect thresholds, including first-order effect thresholds and second-order effect thresholds: When the spatial coupling effect value is less than or equal to the first-level effect threshold, it is determined that the current spatial layout and wind direction are well adapted, and the current anchor point, UAV spatial orientation and cable status are maintained without active adjustment. When the spatial coupling effect value is greater than the first-level effect threshold and less than or equal to the second-level effect threshold, it is determined that the spatial configuration has an adverse effect. The cable release length is reduced to increase tension. Under the premise of ensuring the flight path and operation requirements, the spatial coordinates of the ground anchor point and the relative position and altitude of the UAV in space are corrected. The main projection direction of the mooring line is adjusted to be aligned with the prevailing wind direction. The angle between the cable and the prevailing wind direction is reduced so that the main projection direction of the mooring line dynamically conforms to and converges with the prevailing wind direction. When the spatial coupling effect value is greater than the secondary effect threshold, it is determined that there is energy efficiency loss and structural safety risk, and physical control measures are implemented: switch the anchor point and the drone's orientation, strictly align the main projection of the mooring line with the prevailing wind direction, continuously reduce the angle between the main projection of the mooring line and the prevailing wind direction until it approaches horizontal, and tighten the cable. If the spatial coupling effect value is still greater than the secondary effect threshold after the physical control measures are implemented, the operation window hierarchical scheduling process is entered.

7. The method for capturing high-altitude wind energy of a multi-rotor tethered unmanned aerial vehicle according to claim 1, characterized in that: The specific process for analyzing the spatial coupling effect of structural environment data and historical operation and maintenance data is as follows: Acquire turbulence risk values, spatial coupling effect values, on-site wind speed data, UAV output power, optimal output power, cable tension values, oscillation amplitude data, rotor speed, and anchor point force values; The short-term wind speed change rate is obtained from the on-site wind speed data using a differential algorithm; the energy efficiency loss rate is obtained from the UAV output power and optimal output power using a difference normalization algorithm; and the structural health margin is obtained from the cable tension value, swing amplitude data, rotor speed and anchor point force value using a health assessment algorithm. Add the turbulence risk value and the spatial coupling effect value, multiply by the short-term wind speed change rate, and then multiply by the energy efficiency loss rate to get the risk numerator. Multiply the structural health margin by a historical high-risk memory factor to obtain the risk denominator; finally, divide the risk numerator by the risk denominator to obtain the operational window risk value.

8. The method for capturing high-altitude wind energy of a multi-rotor tethered unmanned aerial vehicle according to claim 1, characterized in that: The specific process of implementing the hierarchical scheduling procedure for job windows based on the risk amplification and health suppression assessment results is as follows: Real-time comparison of job window risk values ​​and job window risk thresholds, and execution of job window hierarchical scheduling process: When the risk value of the operation window is less than or equal to the risk threshold of the operation window, the operation task is divided into multiple sub-tasks using dynamic programming, allowing deviations from the original path, and prioritizing the allocation of high-energy-consuming and critical tasks to the operation range with tailwind and stable airflow to reduce the load on the UAV and its subsystems. When the risk value of the operation window exceeds the risk threshold of the operation window, the operation task is immediately suspended, the drone enters safe standby, the cable release length is continuously shortened, the distance between the drone and the anchor point is reduced to the operating load to maintain flight, and an early warning is issued to remind manual intervention.

9. A method for capturing high-altitude wind energy from a multi-rotor tethered unmanned aerial vehicle according to claim 1, characterized in that: The specific process of constructing a risk attribution and autonomous evolution optimization mechanism based on multi-source risk quantification indicators, integrating dynamic cause tracing, scenario contingency planning, and optimal response paths is as follows: Based on historical operational window risk values, disturbance risk values, spatial coupling effect values, and global risk response processes, a retrospective analysis is conducted on the entire process of each high-risk trigger and abnormal adjustment. The changing trends of structural environment data and historical operation and maintenance data are tracked, and the core causes behind risk events are dynamically traced and attributed. Sudden wind shear in the external environment, structural entry into critical aging state, and misconfiguration problems in spatial layout are accurately identified and located. Based on the risk attribution, emergency response strategies are generated for the identified core causes, including tension control, spatial anchor point adjustment, droop curve optimization, work window switching, and task splitting and rearrangement. Based on structural environment data and historical operation and maintenance data, the emergency response strategies are simulated and ranked in order of merit. The changes in disturbance risk, energy efficiency improvement, and structural health improvement under each emergency response strategy are quantitatively analyzed. The emergency measures with the best risk reduction effect and the least energy efficiency loss are selected for implementation. A risk attribution and autonomous evolution optimization library is created, and all strategy simulation results and emergency measure implementation effects are recorded in the risk attribution and autonomous evolution optimization library and fed back in real time.

10. A high-altitude wind energy capture system for a multi-rotor tethered unmanned aerial vehicle (UAV), employing the high-altitude wind energy capture method for a multi-rotor tethered UAV as described in any one of claims 1-9, characterized in that, include: The data acquisition and preprocessing module is used to collect structural environment data and obtain historical operation and maintenance data. Preprocess the structural environment data and historical operation and maintenance data; The mooring line control and sag optimization module is used to perform structural disturbance risk analysis on structural environmental data and historical operation and maintenance data. Based on the structural disturbance risk analysis results, it performs cable control and tension-wind coupling catenary curve optimization, and enters the hierarchical scheduling process of the operation window. The spatial layout and wind direction planning module is used to perform spatial coupling effect analysis on structural environment data and historical operation and maintenance data. Based on the spatial coupling effect analysis results, the angle between the cable and the prevailing wind direction is adjusted, and the operation window is entered into a hierarchical scheduling process. The job window and avoidance operation module are used to perform risk amplification and health suppression assessment on structural environment data and historical operation and maintenance data, and execute the job window hierarchical scheduling process according to the risk amplification and health suppression assessment results. The risk attribution and autonomous optimization module is used to construct a risk attribution and autonomous optimization mechanism based on multi-source risk quantification indicators, integrating dynamic cause tracing, scenario contingency plan simulation and optimal response path.

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