Long endurance optimization control method and system for vertical take-off and landing fixed-wing unmanned aerial vehicle

Abstract

The invention provides a long endurance optimization control method and system for a vertical take-off and landing fixed-wing unmanned aerial vehicle. The method comprises the steps of monitoring a target unmanned aerial vehicle in real time, collecting a real-time unmanned aerial vehicle state parameter and a real-time environment characteristic parameter, obtaining a preset initial power switching threshold value, performing processing in combination with the real-time unmanned aerial vehicle state parameter and the real-time environment characteristic parameter, obtaining a matched power switching threshold value, and performing power switching according to the matched power switching threshold value. Power switching parameters are obtained by combining the state parameters of the unmanned aerial vehicle and the real-time environment characteristic parameters, a staged power distribution strategy is executed, multi-working-condition simulation testing is carried out on the target unmanned aerial vehicle, test data are recorded, processing is carried out according to the test data and the reference data, a performance optimization quantification coefficient is obtained, and the optimization control effect is evaluated. Therefore, the long-endurance optimization control technology of the vertical take-off and landing fixed-wing unmanned aerial vehicle is realized.

Description

Technical Field

[0001] This application relates to the field of fixed-wing unmanned aerial vehicle (UAV) technology, and more specifically, to a long-endurance optimized control method and system for vertical take-off and landing (VTOL) fixed-wing UAVs. Background Technology

[0002] Vertical takeoff and landing (VTOL) fixed-wing UAVs combine the advantages of multi-rotor VTOL and fixed-wing high-speed cruise, and are widely used in surveying, inspection and other fields. However, their short flight time has always restricted their operational efficiency. Existing control technologies mostly adopt fixed power switching thresholds and single power distribution strategies, which cannot adapt to complex and ever-changing flight states and environmental conditions.

[0003] In actual flight, the state of the UAV and the environment change dynamically. Fixed thresholds can easily lead to deviations in the timing of power switching, resulting in additional energy consumption. The phased power distribution lacks flexibility and makes it difficult to balance energy consumption and performance during vertical take-off and landing, transition, and cruise phases. At the same time, the optimization effect lacks a quantitative evaluation mechanism, making it impossible to accurately verify the effectiveness of the control strategy.

[0004] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a long-endurance optimized control method and system for vertical take-off and landing fixed-wing unmanned aerial vehicles (UAVs). This method involves real-time monitoring of the target UAV, collecting real-time UAV state parameters and real-time environmental characteristic parameters, obtaining a preset initial power switching threshold, processing the real-time UAV state parameters and real-time environmental characteristic parameters to obtain a matching power switching threshold, obtaining power switching parameters based on the matching power switching threshold and the UAV state parameters and real-time environmental characteristic parameters, executing a phased power allocation strategy, conducting multi-condition simulation tests on the target UAV, recording the test data, processing the test data in conjunction with benchmark data to obtain performance optimization quantification coefficients, and evaluating the optimization control effect. This achieves the technology for long-endurance optimized control of vertical take-off and landing fixed-wing UAVs.

[0006] This application also provides a long-endurance optimized control method for a vertical takeoff and landing fixed-wing unmanned aerial vehicle, including the following steps: Real-time monitoring of target drones, collecting real-time drone status parameters and real-time environmental characteristic parameters; A preset initial power switching threshold is obtained, and then processed in combination with the real-time UAV state parameters and real-time environmental feature parameters to obtain a matching power switching threshold. Based on the matching power switching threshold, power switching parameters are obtained by combining the UAV state parameters and real-time environmental characteristic parameters, and a phased power allocation strategy is executed. The target UAV was subjected to multi-condition simulation tests, and the test data was recorded. The test data is processed in conjunction with benchmark data to obtain performance optimization quantification coefficients and to evaluate the optimization control effect.

[0007] Optionally, in the long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV described in this application, the real-time monitoring of the target UAV and the collection of real-time UAV state parameters and real-time environmental characteristic parameters include: Real-time monitoring of the target drone, collecting real-time drone status parameters, including remaining battery power, real-time flight speed, real-time flight altitude, and payload weight; Simultaneously acquire real-time environmental characteristic parameters of the area where the target UAV is located, including real-time temperature and real-time wind speed.

[0008] Optionally, in the long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV described in this application, the step of obtaining a preset initial power switching threshold and processing it in conjunction with the real-time UAV state parameters and real-time environmental characteristic parameters to obtain a matching power switching threshold includes: Based on the remaining battery power, real-time flight speed, real-time flight altitude, and payload weight, combined with the real-time temperature and real-time wind speed, a preset power adjustment correction model is used to obtain the power adjustment correction coefficient. Obtain the initial performance parameters of the target UAV, including minimum level flight speed, stall speed, output power, and output torque; Based on the minimum level flight speed, stall speed, output power, and output torque, a preset initial power switching threshold is matched using a preset performance parameter table. The matching power switching threshold is obtained by performing a correction process based on the power adjustment correction coefficient and the preset initial power switching threshold.

[0009] Optionally, in the long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV described in this application, the step of obtaining power switching parameters based on the matching power switching threshold, combined with the real-time UAV state parameters and real-time environmental characteristic parameters, and executing a phased power allocation strategy includes: The initial flight phase of the target UAV is obtained, including the initial vertical takeoff and landing phase, the initial transition phase, and the initial cruise phase; The initial flight phase is adjusted according to the matching power switching threshold to obtain a matching flight phase, including a matching vertical takeoff and landing phase, a matching transition phase, and a matching cruise phase. The corresponding power switching parameters, including velocity gradient, attitude change rate, and switching duration, are obtained based on the matching transition phase. The corresponding phased power distribution strategy is executed according to the matched flight phase.

[0010] Optionally, in the long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV described in this application, the step of performing multi-condition simulation tests on the target UAV and recording the test data includes: Set multiple operating conditions, including multiple sets of different wind conditions and multiple sets of different loads; The target UAV was simulated and tested according to the multiple sets of working conditions, and the corresponding test data was recorded. The test data includes flight time, energy consumption for each matched flight phase, attitude error, and trajectory deviation.

[0011] Optionally, in the long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV described in this application, the step of processing the test data in combination with benchmark data to obtain performance optimization quantification coefficients and evaluating the optimized control effect includes: Obtain benchmark data that matches the test data, perform statistical processing, and obtain performance optimization data; The performance optimization data includes flight time improvement rate, energy consumption reduction rate, attitude error reduction rate, and trajectory deviation reduction rate. The performance optimization quantification coefficient is obtained by weighting the flight time improvement rate, energy consumption reduction rate, attitude error reduction rate, and trajectory deviation reduction rate. The first threshold comparison result is obtained by comparing the performance optimization quantization coefficient with the preset performance optimization quantization threshold. The optimized control effect of the target UAV is evaluated based on the comparison results of the first threshold.

[0012] Secondly, this application provides a long-endurance optimized control system for a vertical takeoff and landing fixed-wing unmanned aerial vehicle (VTOL) aircraft. The system includes a memory and a processor. The memory contains a program for a long-endurance optimized control method for the VTOL fixed-wing aircraft. When the program for the long-endurance optimized control method for the VTOL fixed-wing aircraft is executed by the processor, it implements the following steps: A preset initial power switching threshold is obtained, and then processed in combination with the real-time UAV state parameters and real-time environmental feature parameters to obtain a matching power switching threshold. Based on the matching power switching threshold, power switching parameters are obtained by combining the UAV state parameters and real-time environmental characteristic parameters, and a phased power allocation strategy is executed. The target UAV was subjected to multi-condition simulation tests, and the test data was recorded. The test data is processed in conjunction with benchmark data to obtain performance optimization quantification coefficients and to evaluate the optimization control effect.

[0013] Optionally, in the long-endurance optimized control system for the vertical takeoff and landing fixed-wing UAV described in this application, the real-time monitoring of the target UAV and the collection of real-time UAV status parameters and real-time environmental characteristic parameters include: Real-time monitoring of the target drone, collecting real-time drone status parameters, including remaining battery power, real-time flight speed, real-time flight altitude, and payload weight; Simultaneously acquire real-time environmental characteristic parameters of the area where the target UAV is located, including real-time temperature and real-time wind speed.

[0014] Optionally, in the long-endurance optimized control system for the vertical takeoff and landing fixed-wing UAV described in this application, the step of obtaining a preset initial power switching threshold and processing it in conjunction with the real-time UAV state parameters and real-time environmental characteristic parameters to obtain a matching power switching threshold includes: Based on the remaining battery power, real-time flight speed, real-time flight altitude, and payload weight, combined with the real-time temperature and real-time wind speed, a preset power adjustment correction model is used to obtain the power adjustment correction coefficient. Obtain the initial performance parameters of the target UAV, including minimum level flight speed, stall speed, output power, and output torque; Based on the minimum level flight speed, stall speed, output power, and output torque, a preset initial power switching threshold is matched using a preset performance parameter table. The matching power switching threshold is obtained by performing a correction process based on the power adjustment correction coefficient and the preset initial power switching threshold.

[0015] Optionally, in the long-endurance optimized control system for the vertical takeoff and landing fixed-wing UAV described in this application, the step of obtaining power switching parameters based on the matching power switching threshold, combined with the real-time UAV state parameters and real-time environmental characteristic parameters, and executing a phased power allocation strategy includes: The initial flight phase of the target UAV is obtained, including the initial vertical takeoff and landing phase, the initial transition phase, and the initial cruise phase; The initial flight phase is adjusted according to the matching power switching threshold to obtain a matching flight phase, including a matching vertical takeoff and landing phase, a matching transition phase, and a matching cruise phase. The corresponding power switching parameters, including velocity gradient, attitude change rate, and switching duration, are obtained based on the matching transition phase. The corresponding phased power distribution strategy is executed according to the matched flight phase.

[0016] As can be seen from the above, the long-endurance optimized control method and system for vertical take-off and landing fixed-wing UAVs provided in this application achieves long-endurance optimized control technology for vertical take-off and landing fixed-wing UAVs by real-time monitoring of the target UAV, collecting real-time UAV state parameters and real-time environmental characteristic parameters, obtaining a preset initial power switching threshold, processing the real-time UAV state parameters and real-time environmental characteristic parameters to obtain a matching power switching threshold, obtaining power switching parameters based on the matching power switching threshold and the real-time UAV state parameters and real-time environmental characteristic parameters, executing a phased power distribution strategy, conducting multi-condition simulation tests on the target UAV, recording the test data, processing the test data in conjunction with benchmark data to obtain performance optimization quantification coefficients, and evaluating the optimization control effect.

[0017] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a long-endurance optimized control method for a vertical takeoff and landing fixed-wing unmanned aerial vehicle provided in an embodiment of this application; Figure 2 A flowchart illustrating the process of obtaining the matching power switching threshold in the long-endurance optimized control method for a vertical take-off and landing fixed-wing UAV provided in this application embodiment; Figure 3 A flowchart illustrating the phased power distribution strategy for the long-endurance optimized control method of the vertical takeoff and landing fixed-wing UAV provided in this application embodiment; Figure 4 A flowchart for evaluating the optimization control effect of the long-endurance optimization control method for the vertical take-off and landing fixed-wing UAV provided in the embodiments of this application.

[0020] Figure 5 This is a high-level flowchart of various embodiments of the methods in this application, which can be used for long-endurance optimized control methods for vertical take-off and landing fixed-wing unmanned aerial vehicles. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] Please refer to Figure 1 , Figure 1 This is a flowchart of a long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV according to some embodiments of this application. This long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV is used in terminal devices, such as computers and mobile terminals. The long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV includes the following steps: S11. Real-time monitoring of the target UAV, collecting real-time UAV status parameters and real-time environmental characteristic parameters; S12. Obtain a preset initial power switching threshold, and process it in conjunction with the real-time UAV status parameters and real-time environmental feature parameters to obtain a matching power switching threshold. S13. Based on the matching power switching threshold, the power switching parameters are obtained by combining the UAV state parameters and real-time environmental feature parameters, and a phased power allocation strategy is executed. S14. Perform multi-condition simulation tests on the target UAV and record the test data; S15. Process the test data and benchmark data to obtain the performance optimization quantification coefficient and evaluate the optimization control effect.

[0024] It is worth noting that existing VTOL fixed-wing UAV control systems suffer from fixed power switching thresholds and rigid power distribution, making them unsuitable for dynamic operating conditions and lacking quantitative evaluation methods. To address these issues, firstly, the target VTOL fixed-wing UAV is monitored in real time, collecting real-time UAV status parameters, including remaining battery power, real-time flight speed, real-time flight altitude, and payload weight. Simultaneously, real-time environmental characteristic parameters of the target UAV's location are acquired, including real-time temperature and real-time wind speed. Then, based on the minimum level flight speed, stall speed, output power, and output torque, a preset initial power switching threshold is matched using a preset performance parameter table, combined with real-time UAV status parameters and real-time environmental parameters. The system processes environmental characteristic parameters to obtain a matching power switching threshold. Based on this threshold, it combines the UAV's state parameters and real-time environmental characteristic parameters to obtain power switching parameters, including velocity gradient, attitude change rate, and switching duration. A phased power allocation strategy is then implemented. On the other hand, multi-condition simulation tests are conducted on the target UAV, including multiple sets of tests under different wind conditions and different loads. Test data is recorded, including flight time, energy consumption in each matched flight phase, attitude error, and trajectory deviation. The test data is then processed in conjunction with baseline data to obtain performance optimization quantification coefficients and evaluate the optimization control effect. This enables the realization of long-endurance optimized control technology for vertical take-off and landing fixed-wing UAVs.

[0025] According to an embodiment of the present invention, the real-time monitoring of the target UAV and the collection of real-time UAV status parameters and real-time environmental characteristic parameters include: Real-time monitoring of the target drone, collecting real-time drone status parameters, including remaining battery power, real-time flight speed, real-time flight altitude, and payload weight; Simultaneously acquire real-time environmental characteristic parameters of the area where the target UAV is located, including real-time temperature and real-time wind speed.

[0026] It is particularly important to note that, in order to provide accurate data support for the optimized control of vertical take-off and landing fixed-wing UAVs, a multi-dimensional real-time monitoring system needs to be constructed to continuously perceive the status of the target UAV and monitor key parameters that directly affect flight performance and energy consumption, including the remaining battery power, which is the core basis for avoiding energy overdraft; real-time flight speed and altitude, which together determine flight attitude stability and payload weight, and their changes are directly related to power output requirements; while monitoring the airframe status, real-time environmental characteristic parameters of its area should be acquired simultaneously, among which real-time temperature affects battery activity and aerodynamic drag, while real-time wind speed is related to wind disturbance resistance power distribution.

[0027] Please refer to Figure 2 , Figure 2This is a flowchart illustrating the process of obtaining a matching power switching threshold in a long-endurance optimized control method for a vertical take-off and landing fixed-wing unmanned aerial vehicle (UAV) according to some embodiments of this application. According to an embodiment of the present invention, obtaining a preset initial power switching threshold and processing it in conjunction with the real-time UAV state parameters and real-time environmental characteristic parameters to obtain a matching power switching threshold includes: S21. Based on the remaining battery power, real-time flight speed, real-time flight altitude, and payload weight, combined with the real-time temperature and real-time wind speed, the data is processed through a preset power adjustment correction model to obtain the power adjustment correction coefficient. S22. Obtain the initial performance parameters of the target UAV, including minimum level flight speed, stall speed, output power, and output torque; S23. Match a preset initial power switching threshold with a preset performance parameter table based on the minimum level flight speed, stall speed, output power and output torque. S24. Based on the power adjustment correction coefficient and the preset initial power switching threshold, a correction process is performed to obtain the matching power switching threshold.

[0028] It is particularly important to note that, to achieve precise adaptation of power switching for vertical take-off and landing fixed-wing UAVs, the correction criteria must first be calculated. Real-time data on remaining battery power, flight speed, altitude, and payload weight, along with simultaneously acquired ambient temperature and wind speed data, are input into a preset power adjustment correction model. Based on the battery power level determining the upper limit of power output, flight parameters reflecting the current energy consumption state, and payload and environmental parameters relating to additional power requirements, the model quantifies the combined impact of these factors through a weighted algorithm, outputting a power adjustment correction coefficient. Subsequently, the UAV's initial performance parameters are retrieved, including the minimum level flight speed and stall speed to ensure flight safety, as well as the output power and output torque determining power supply capability. These parameters are the core basis for setting the basic power threshold. The system searches a preset performance parameter table based on these performance parameters. This table integrates the matching rules between the performance and power thresholds of different aircraft models, quickly locating the appropriate preset initial power switching threshold. Finally, the initial threshold is dynamically corrected using the power adjustment correction coefficient to eliminate deviations between the fixed threshold and real-time operating conditions, ultimately obtaining a matching power switching threshold that fits the current flight state.

[0029] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the execution of a phased power allocation strategy in a long-endurance optimized control method for a vertical take-off and landing fixed-wing unmanned aerial vehicle (UAV) according to some embodiments of this application. According to an embodiment of the present invention, the step of obtaining power switching parameters based on the matched power switching threshold, combined with the real-time UAV state parameters and real-time environmental characteristic parameters, and executing the phased power allocation strategy includes: S31. Obtain the initial flight phase of the target UAV, including the initial vertical take-off and landing phase, the initial transition phase, and the initial cruise phase; S32. Adjust the initial flight phase according to the matching power switching threshold to obtain a matching flight phase, including a matching vertical takeoff and landing phase, a matching transition phase, and a matching cruise phase. S33. Obtain the corresponding power switching parameters according to the matching transition phase, including velocity gradient, attitude change rate and switching duration; S34. Execute the corresponding phased power distribution strategy according to the matched flight phase.

[0030] It is particularly important to note that the power requirements of vertical takeoff and landing (VTOL) fixed-wing UAVs vary drastically with the flight phase. Therefore, it is necessary to first clearly define the initial flight phases: the initial VTOL phase responsible for takeoff and landing, the initial transition phase for converting from multi-rotor to fixed-wing mode, and the initial cruise phase to ensure long-endurance operations. These initial phases are based on general operating conditions and are difficult to adapt to real-time dynamic scenarios. Therefore, targeted adjustments are needed based on the obtained matching power switching thresholds. By precisely matching the thresholds with the power requirements of each phase, the initial phases can be optimized into matching flight phases that align with the current operating conditions: matching VTOL. The system focuses on low-energy takeoff and landing control during the initial phase, emphasizes high efficiency and energy saving during the cruise phase, and requires clear definition of key parameters during the matching transition phase, which is the core of power switching. The system extracts the speed gradient (to ensure a smooth speed transition during mode change), attitude change rate (to avoid violent fuselage shaking), and switching duration (to ensure smooth power connection) from this phase to form the core power switching parameters. Finally, based on the optimized matching flight phase, a phased power distribution strategy is implemented: the vertical takeoff and landing phase prioritizes lift output, the transition phase dynamically adjusts power according to the switching parameters, and the cruise phase maintains the optimal flight state with the lowest energy consumption.

[0031] According to an embodiment of the present invention, the step of performing multi-condition simulation testing on the target UAV and recording test data includes: Set multiple operating conditions, including multiple sets of different wind conditions and multiple sets of different loads; The target UAV was simulated and tested according to the multiple sets of working conditions, and the corresponding test data was recorded. The test data includes flight time, energy consumption for each matched flight phase, attitude error, and trajectory deviation.

[0032] It is particularly important to note that, in order to fully verify the adaptability and reliability of the optimized control strategy for vertical take-off and landing fixed-wing UAVs, multiple sets of simulation conditions covering complex real-world scenarios need to be constructed. These include wind conditions encompassing different levels such as light wind, medium-speed wind, strong wind, and gusts, simulating various wind disturbances that may be encountered during operations; and load conditions with gradients such as no load, standard load, and maximum load to meet the requirements of different operational tasks. Then, based on these multiple sets of conditions, simulated flight tests are conducted on the target UAV on a professional UAV simulation platform, with core test data accurately recorded throughout. The data includes key evaluation indicators of the optimization effect: flight time directly reflects the achievement of long-endurance optimization goals; energy consumption in each matched flight phase can pinpoint the optimization space for power distribution; and attitude error and trajectory deviation measure flight stability and control accuracy.

[0033] Please refer to Figure 4 , Figure 4 This is a flowchart illustrating the evaluation of the optimized control effect of a long-endurance optimized control method for a vertical takeoff and landing fixed-wing unmanned aerial vehicle (UAV) according to some embodiments of this application. According to embodiments of the present invention, the step of processing the test data in conjunction with benchmark data to obtain performance optimization quantification coefficients and evaluating the optimized control effect includes: S41. Obtain benchmark data that matches the test data, and perform statistical processing to obtain performance optimization data; S42, The performance optimization data includes flight time improvement rate, energy consumption reduction rate, attitude error reduction rate, and trajectory deviation reduction rate; S43. The performance optimization quantification coefficient is obtained by weighting the flight time improvement rate, energy consumption reduction rate, attitude error reduction rate and trajectory deviation reduction rate. S44. Compare the performance optimization quantization coefficient with the preset performance optimization quantization threshold to obtain the first threshold comparison result; S45. Evaluate the optimized control effect of the target UAV based on the comparison result of the first threshold.

[0034] It is particularly important to note that, in order to objectively quantify the effectiveness of the optimized control strategy for vertical takeoff and landing fixed-wing UAVs, it is necessary to first retrieve benchmark data that matches the simulation test data. This data represents the core performance indicators of the UAV under the same operating conditions without the optimized strategy, ensuring the comparability of the evaluation. Statistical processing, such as difference and ratio calculations, is then performed on the test data and benchmark data to obtain performance optimization data that intuitively reflects the optimization effect, including flight time improvement rate, energy consumption reduction rate, attitude error reduction rate, and trajectory deviation reduction rate. Among these, the flight time improvement rate directly reflects the achievement of long-endurance goals, the energy consumption reduction rate reflects optimized energy utilization efficiency, and the attitude error and trajectory deviation reduction rates measure the improvement in flight control accuracy. Considering that flight time and energy consumption are core objectives, the four indicators are weighted (flight time and energy consumption have higher weights) to calculate a comprehensive performance optimization quantification coefficient. This coefficient is then compared with a preset threshold to obtain the first threshold comparison result: if the coefficient is higher than the threshold, the optimization effect is excellent and meets expectations; otherwise, the control parameters need to be adjusted, ultimately completing a scientific evaluation of the optimized control effect.

[0035] Please refer to Figure 5 , Figure 5 This is a high-level flowchart of the methods of various embodiments of this application, which can be used for long-endurance optimized control of vertical takeoff and landing fixed-wing unmanned aerial vehicles (UAVs). According to embodiments of the present invention, for example, the initial flight phase of the target UAV is obtained, including an initial vertical takeoff and landing phase, an initial transition phase, and an initial cruise phase. Then, each initial flight phase is adjusted accordingly based on a matching power switching threshold to obtain a matched flight phase, including a matched vertical takeoff and landing phase, a matched transition phase, and a matched cruise phase. This technical solution enables vertical takeoff and landing fixed-wing UAVs to better adapt to real-time dynamic scenarios.

[0036] According to an embodiment of the present invention, it further includes: The operation of the target UAV is monitored in real time and the remaining mission resources are extracted, including remaining flight time, remaining range, and remaining payload requirements. The remaining flight time, remaining flight range, and remaining payload requirements are weighted to obtain the mission remaining ratio. The task remaining ratio is compared with a preset task remaining threshold to obtain a second threshold comparison result. The upper limit of power output is adjusted accordingly based on the comparison results of the second threshold.

[0037] It is particularly important to note that, to ensure the efficient completion of predetermined missions by vertical takeoff and landing fixed-wing UAVs, their operational status must be monitored in real time during flight. Key indicators of remaining mission parameters should be extracted, including remaining flight time reflecting energy support capabilities, remaining range reflecting the operational scope, and remaining payload requirements related to mission objectives. Then, these three indicators are weighted according to mission priority: if the mission emphasizes endurance, remaining flight time has a higher weight; if wide-area coverage is required, remaining range has a higher weight. Finally, a mission remaining ratio that comprehensively reflects mission completion is calculated. This ratio is then compared with a preset mission remaining threshold to obtain a second threshold comparison result: a high ratio indicates sufficient mission remaining resources, allowing for a slight reduction in the power output limit to conserve energy; a low ratio requires increasing the power output limit to ensure mission completion, achieving dynamic adaptation of power output.

[0038] According to an embodiment of the present invention, it further includes: The system monitors the operation of the target drone in real time and acquires battery voltage, battery current, and battery temperature. Based on the battery voltage, battery current, and battery temperature, combined with the remaining battery capacity, the battery state assessment model is used to obtain the battery state definition factor. The third threshold comparison result is obtained by comparing the battery state definition factor with the preset battery state definition threshold. The corresponding charging and discharging strategy is matched based on the comparison results of the third threshold.

[0039] It is particularly important to note that the battery is the core power source of a vertical takeoff and landing fixed-wing UAV, and its condition directly affects flight safety and endurance. Therefore, it is necessary to monitor key battery parameters during the operation of the target UAV in real time, focusing on acquiring battery voltage, battery current, and battery temperature. Among these, voltage reflects the battery's energy storage level, current relates to real-time energy consumption intensity, and temperature determines the battery's activity and safety boundaries. These three parameters, along with the known remaining battery capacity data, are input into a preset battery condition assessment model. This model integrates the battery charge-discharge characteristic curve and failure risk threshold, and through multi-parameter coupling analysis, quantifies the battery's current health and operating status, outputting a precise battery condition definition factor. Then, this factor is compared with the preset battery condition definition threshold to obtain a third threshold comparison result: if the factor is within the safe range, a conventional charge-discharge strategy is matched; when it approaches the warning threshold, a protection strategy is switched to avoid overcharging and over-discharging, achieving dynamic control and safety assurance of the battery condition.

[0040] Secondly, the present invention also discloses a long-endurance optimized control system for a vertical takeoff and landing fixed-wing unmanned aerial vehicle (UAV), comprising a memory and a processor. The memory includes a long-endurance optimized control method program for the UAV, which, when executed by the processor, performs the following steps: Real-time monitoring of target drones, collecting real-time drone status parameters and real-time environmental characteristic parameters; A preset initial power switching threshold is obtained, and then processed in combination with the real-time UAV state parameters and real-time environmental feature parameters to obtain a matching power switching threshold. Based on the matching power switching threshold, power switching parameters are obtained by combining the UAV state parameters and real-time environmental characteristic parameters, and a phased power allocation strategy is executed. The target UAV was subjected to multi-condition simulation tests, and the test data was recorded. The test data is processed in conjunction with benchmark data to obtain performance optimization quantification coefficients and to evaluate the optimization control effect.

[0041] It is worth noting that existing VTOL fixed-wing UAV control systems suffer from fixed power switching thresholds and rigid power distribution, making them unsuitable for dynamic operating conditions and lacking quantitative evaluation methods. To address these issues, firstly, the target VTOL fixed-wing UAV is monitored in real time, collecting real-time UAV status parameters, including remaining battery power, real-time flight speed, real-time flight altitude, and payload weight. Simultaneously, real-time environmental characteristic parameters of the target UAV's location are acquired, including real-time temperature and real-time wind speed. Then, based on the minimum level flight speed, stall speed, output power, and output torque, a preset initial power switching threshold is matched using a preset performance parameter table, combined with real-time UAV status parameters and real-time environmental parameters. The system processes environmental characteristic parameters to obtain a matching power switching threshold. Based on this threshold, it combines the UAV's state parameters and real-time environmental characteristic parameters to obtain power switching parameters, including velocity gradient, attitude change rate, and switching duration. A phased power allocation strategy is then implemented. On the other hand, multi-condition simulation tests are conducted on the target UAV, including multiple sets of tests under different wind conditions and different loads. Test data is recorded, including flight time, energy consumption in each matched flight phase, attitude error, and trajectory deviation. The test data is then processed in conjunction with baseline data to obtain performance optimization quantification coefficients and evaluate the optimization control effect. This enables the realization of long-endurance optimized control technology for vertical take-off and landing fixed-wing UAVs.

[0042] According to an embodiment of the present invention, the real-time monitoring of the target UAV and the collection of real-time UAV status parameters and real-time environmental characteristic parameters include: Real-time monitoring of the target drone, collecting real-time drone status parameters, including remaining battery power, real-time flight speed, real-time flight altitude, and payload weight; Simultaneously acquire real-time environmental characteristic parameters of the area where the target UAV is located, including real-time temperature and real-time wind speed.

[0043] It is particularly important to note that, in order to provide accurate data support for the optimized control of vertical take-off and landing fixed-wing UAVs, a multi-dimensional real-time monitoring system needs to be constructed to continuously perceive the status of the target UAV and monitor key parameters that directly affect flight performance and energy consumption, including the remaining battery power, which is the core basis for avoiding energy overdraft; real-time flight speed and altitude, which together determine flight attitude stability and payload weight, and their changes are directly related to power output requirements; while monitoring the airframe status, real-time environmental characteristic parameters of its area should be acquired simultaneously, among which real-time temperature affects battery activity and aerodynamic drag, while real-time wind speed is related to wind disturbance resistance power distribution.

[0044] According to an embodiment of the present invention, obtaining a preset initial power switching threshold and processing it in conjunction with the real-time UAV state parameters and real-time environmental feature parameters to obtain a matching power switching threshold includes: Based on the remaining battery power, real-time flight speed, real-time flight altitude, and payload weight, combined with the real-time temperature and real-time wind speed, a preset power adjustment correction model is used to obtain the power adjustment correction coefficient. Obtain the initial performance parameters of the target UAV, including minimum level flight speed, stall speed, output power, and output torque; Based on the minimum level flight speed, stall speed, output power, and output torque, a preset initial power switching threshold is matched using a preset performance parameter table. The matching power switching threshold is obtained by performing a correction process based on the power adjustment correction coefficient and the preset initial power switching threshold.

[0045] It is particularly important to note that, to achieve precise adaptation of power switching for vertical take-off and landing fixed-wing UAVs, the correction criteria must first be calculated. Real-time data on remaining battery power, flight speed, altitude, and payload weight, along with simultaneously acquired ambient temperature and wind speed data, are input into a preset power adjustment correction model. Based on the battery power level determining the upper limit of power output, flight parameters reflecting the current energy consumption state, and payload and environmental parameters relating to additional power requirements, the model quantifies the combined impact of these factors through a weighted algorithm, outputting a power adjustment correction coefficient. Subsequently, the UAV's initial performance parameters are retrieved, including the minimum level flight speed and stall speed to ensure flight safety, as well as the output power and output torque determining power supply capability. These parameters are the core basis for setting the basic power threshold. The system searches a preset performance parameter table based on these performance parameters. This table integrates the matching rules between the performance and power thresholds of different aircraft models, quickly locating the appropriate preset initial power switching threshold. Finally, the initial threshold is dynamically corrected using the power adjustment correction coefficient to eliminate deviations between the fixed threshold and real-time operating conditions, ultimately obtaining a matching power switching threshold that fits the current flight state.

[0046] According to an embodiment of the present invention, the step of obtaining power switching parameters based on the matching power switching threshold, combined with the real-time UAV state parameters and real-time environmental feature parameters, and executing a phased power allocation strategy includes: The initial flight phase of the target UAV is obtained, including the initial vertical takeoff and landing phase, the initial transition phase, and the initial cruise phase; The initial flight phase is adjusted according to the matching power switching threshold to obtain a matching flight phase, including a matching vertical takeoff and landing phase, a matching transition phase, and a matching cruise phase. The corresponding power switching parameters, including velocity gradient, attitude change rate, and switching duration, are obtained based on the matching transition phase. The corresponding phased power distribution strategy is executed according to the matched flight phase.

[0047] It is particularly important to note that the power requirements of vertical takeoff and landing (VTOL) fixed-wing UAVs vary drastically with the flight phase. Therefore, it is necessary to first clearly define the initial flight phases: the initial VTOL phase responsible for takeoff and landing, the initial transition phase for converting from multi-rotor to fixed-wing mode, and the initial cruise phase to ensure long-endurance operations. These initial phases are based on general operating conditions and are difficult to adapt to real-time dynamic scenarios. Therefore, targeted adjustments are needed based on the obtained matching power switching thresholds. By precisely matching the thresholds with the power requirements of each phase, the initial phases can be optimized into matching flight phases that align with the current operating conditions: matching VTOL. The system focuses on low-energy takeoff and landing control during the initial phase, emphasizes high efficiency and energy saving during the cruise phase, and requires clear definition of key parameters during the matching transition phase, which is the core of power switching. The system extracts the speed gradient (to ensure a smooth speed transition during mode change), attitude change rate (to avoid violent fuselage shaking), and switching duration (to ensure smooth power connection) from this phase to form the core power switching parameters. Finally, based on the optimized matching flight phase, a phased power distribution strategy is implemented: the vertical takeoff and landing phase prioritizes lift output, the transition phase dynamically adjusts power according to the switching parameters, and the cruise phase maintains the optimal flight state with the lowest energy consumption.

[0048] According to an embodiment of the present invention, the step of performing multi-condition simulation testing on the target UAV and recording test data includes: Set multiple operating conditions, including multiple sets of different wind conditions and multiple sets of different loads; The target UAV was simulated and tested according to the multiple sets of working conditions, and the corresponding test data was recorded. The test data includes flight time, energy consumption for each matched flight phase, attitude error, and trajectory deviation.

[0049] It is particularly important to note that, in order to fully verify the adaptability and reliability of the optimized control strategy for vertical take-off and landing fixed-wing UAVs, multiple sets of simulation conditions covering complex real-world scenarios need to be constructed. These include wind conditions encompassing different levels such as light wind, medium-speed wind, strong wind, and gusts, simulating various wind disturbances that may be encountered during operations; and load conditions with gradients such as no load, standard load, and maximum load to meet the requirements of different operational tasks. Then, based on these multiple sets of conditions, simulated flight tests are conducted on the target UAV on a professional UAV simulation platform, with core test data accurately recorded throughout. The data includes key evaluation indicators of the optimization effect: flight time directly reflects the achievement of long-endurance optimization goals; energy consumption in each matched flight phase can pinpoint the optimization space for power distribution; and attitude error and trajectory deviation measure flight stability and control accuracy.

[0050] According to an embodiment of the present invention, the step of processing the test data in combination with benchmark data to obtain performance optimization quantification coefficients and evaluating the optimization control effect includes: Obtain benchmark data that matches the test data, perform statistical processing, and obtain performance optimization data; The performance optimization data includes flight time improvement rate, energy consumption reduction rate, attitude error reduction rate, and trajectory deviation reduction rate. The performance optimization quantification coefficient is obtained by weighting the flight time improvement rate, energy consumption reduction rate, attitude error reduction rate, and trajectory deviation reduction rate. The first threshold comparison result is obtained by comparing the performance optimization quantization coefficient with the preset performance optimization quantization threshold. The optimized control effect of the target UAV is evaluated based on the comparison results of the first threshold.

[0051] It is particularly important to note that, in order to objectively quantify the effectiveness of the optimized control strategy for vertical takeoff and landing fixed-wing UAVs, it is necessary to first retrieve benchmark data that matches the simulation test data. This data represents the core performance indicators of the UAV under the same operating conditions without the optimized strategy, ensuring the comparability of the evaluation. Statistical processing, such as difference and ratio calculations, is then performed on the test data and benchmark data to obtain performance optimization data that intuitively reflects the optimization effect, including flight time improvement rate, energy consumption reduction rate, attitude error reduction rate, and trajectory deviation reduction rate. Among these, the flight time improvement rate directly reflects the achievement of long-endurance goals, the energy consumption reduction rate reflects optimized energy utilization efficiency, and the attitude error and trajectory deviation reduction rates measure the improvement in flight control accuracy. Considering that flight time and energy consumption are core objectives, the four indicators are weighted (flight time and energy consumption have higher weights) to calculate a comprehensive performance optimization quantification coefficient. This coefficient is then compared with a preset threshold to obtain the first threshold comparison result: if the coefficient is higher than the threshold, the optimization effect is excellent and meets expectations; otherwise, the control parameters need to be adjusted, ultimately completing a scientific evaluation of the optimized control effect.

[0052] Please refer to Figure 5 , Figure 5 This is a high-level flowchart of the methods of various embodiments of this application, which can be used for long-endurance optimized control of vertical takeoff and landing fixed-wing unmanned aerial vehicles (UAVs). According to embodiments of the present invention, for example, the initial flight phase of the target UAV is obtained, including an initial vertical takeoff and landing phase, an initial transition phase, and an initial cruise phase. Then, each initial flight phase is adjusted accordingly based on a matching power switching threshold to obtain a matched flight phase, including a matched vertical takeoff and landing phase, a matched transition phase, and a matched cruise phase. This technical solution enables vertical takeoff and landing fixed-wing UAVs to better adapt to real-time dynamic scenarios.

[0053] According to an embodiment of the present invention, it further includes: The operation of the target UAV is monitored in real time and the remaining mission resources are extracted, including remaining flight time, remaining range, and remaining payload requirements. The remaining flight time, remaining flight range, and remaining payload requirements are weighted to obtain the mission remaining ratio. The task remaining ratio is compared with a preset task remaining threshold to obtain a second threshold comparison result. The upper limit of power output is adjusted accordingly based on the comparison results of the second threshold.

[0054] It is particularly important to note that, to ensure the efficient completion of predetermined missions by vertical takeoff and landing fixed-wing UAVs, their operational status must be monitored in real time during flight. Key indicators of remaining mission parameters should be extracted, including remaining flight time reflecting energy support capabilities, remaining range reflecting the operational scope, and remaining payload requirements related to mission objectives. Then, these three indicators are weighted according to mission priority: if the mission emphasizes endurance, remaining flight time has a higher weight; if wide-area coverage is required, remaining range has a higher weight. Finally, a mission remaining ratio that comprehensively reflects mission completion is calculated. This ratio is then compared with a preset mission remaining threshold to obtain a second threshold comparison result: a high ratio indicates sufficient mission remaining resources, allowing for a slight reduction in the power output limit to conserve energy; a low ratio requires increasing the power output limit to ensure mission completion, achieving dynamic adaptation of power output.

[0055] According to an embodiment of the present invention, it further includes: The system monitors the operation of the target drone in real time and acquires battery voltage, battery current, and battery temperature. Based on the battery voltage, battery current, and battery temperature, combined with the remaining battery capacity, the battery state assessment model is used to obtain the battery state definition factor. The third threshold comparison result is obtained by comparing the battery state definition factor with the preset battery state definition threshold. The corresponding charging and discharging strategy is matched based on the comparison results of the third threshold.

[0056] It is particularly important to note that the battery is the core power source of a vertical takeoff and landing fixed-wing UAV, and its condition directly affects flight safety and endurance. Therefore, it is necessary to monitor key battery parameters during the operation of the target UAV in real time, focusing on acquiring battery voltage, battery current, and battery temperature. Among these, voltage reflects the battery's energy storage level, current relates to real-time energy consumption intensity, and temperature determines the battery's activity and safety boundaries. These three parameters, along with the known remaining battery capacity data, are input into a preset battery condition assessment model. This model integrates the battery charge-discharge characteristic curve and failure risk threshold, and through multi-parameter coupling analysis, quantifies the battery's current health and operating status, outputting a precise battery condition definition factor. Then, this factor is compared with the preset battery condition definition threshold to obtain a third threshold comparison result: if the factor is within the safe range, a conventional charge-discharge strategy is matched; when it approaches the warning threshold, a protection strategy is switched to avoid overcharging and over-discharging, achieving dynamic control and safety assurance of the battery condition.

[0057] The present invention discloses a long-endurance optimized control method and system for vertical take-off and landing fixed-wing unmanned aerial vehicles (UAVs). This method involves real-time monitoring of the target UAV, collecting real-time UAV state parameters and real-time environmental characteristic parameters, obtaining a preset initial power switching threshold, processing the real-time UAV state parameters and real-time environmental characteristic parameters to obtain a matching power switching threshold, obtaining power switching parameters based on the matching power switching threshold and the UAV state parameters and real-time environmental characteristic parameters, executing a phased power allocation strategy, conducting multi-condition simulation tests on the target UAV, recording the test data, processing the test data in conjunction with benchmark data to obtain performance optimization quantification coefficients, and evaluating the optimization control effect. This achieves the technology for long-endurance optimized control of vertical take-off and landing fixed-wing UAVs.

[0058] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0059] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0060] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0061] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A long-endurance optimized control method for a vertical takeoff and landing fixed-wing unmanned aerial vehicle (UAV), characterized in that, Includes the following steps: Real-time monitoring of target drones, collecting real-time drone status parameters and real-time environmental characteristic parameters; A preset initial power switching threshold is obtained, and then processed in combination with the real-time UAV state parameters and real-time environmental feature parameters to obtain a matching power switching threshold. Based on the matching power switching threshold, power switching parameters are obtained by combining the UAV state parameters and real-time environmental characteristic parameters, and a phased power allocation strategy is executed. The target UAV was subjected to multi-condition simulation tests, and the test data was recorded. The test data is processed in conjunction with benchmark data to obtain performance optimization quantification coefficients and to evaluate the optimization control effect.

2. The long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV according to claim 1, characterized in that, The real-time monitoring of the target UAV collects real-time UAV status parameters and real-time environmental characteristic parameters, including: Real-time monitoring of the target drone, collecting real-time drone status parameters, including remaining battery power, real-time flight speed, real-time flight altitude, and payload weight; Simultaneously acquire real-time environmental characteristic parameters of the area where the target UAV is located, including real-time temperature and real-time wind speed.

3. The long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV according to claim 2, characterized in that, The process of obtaining a preset initial power switching threshold, combined with the real-time UAV state parameters and real-time environmental feature parameters, to obtain a matching power switching threshold includes: Based on the remaining battery power, real-time flight speed, real-time flight altitude, and payload weight, combined with the real-time temperature and real-time wind speed, a preset power adjustment correction model is used to obtain the power adjustment correction coefficient. Obtain the initial performance parameters of the target UAV, including minimum level flight speed, stall speed, output power, and output torque; Based on the minimum level flight speed, stall speed, output power, and output torque, a preset initial power switching threshold is matched using a preset performance parameter table. The matching power switching threshold is obtained by performing a correction process based on the power adjustment correction coefficient and the preset initial power switching threshold.

4. The long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV according to claim 3, characterized in that, The step of obtaining power switching parameters based on the matching power switching threshold, combined with the real-time UAV state parameters and real-time environmental characteristic parameters, and executing a phased power allocation strategy includes: The initial flight phase of the target UAV is obtained, including the initial vertical takeoff and landing phase, the initial transition phase, and the initial cruise phase; The initial flight phase is adjusted according to the matching power switching threshold to obtain a matching flight phase, including a matching vertical takeoff and landing phase, a matching transition phase, and a matching cruise phase. The corresponding power switching parameters, including velocity gradient, attitude change rate, and switching duration, are obtained based on the matching transition phase. The corresponding phased power distribution strategy is executed according to the matched flight phase.

5. The long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV according to claim 1, characterized in that, The multi-condition simulation test of the target UAV and the recording of test data include: Set multiple operating conditions, including multiple sets of different wind conditions and multiple sets of different loads; The target UAV was simulated and tested according to the multiple sets of working conditions, and the corresponding test data was recorded. The test data includes flight time, energy consumption for each matched flight phase, attitude error, and trajectory deviation.

6. The long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV according to claim 5, characterized in that, The process of processing the test data in conjunction with benchmark data to obtain performance optimization quantification coefficients and evaluating the optimization control effect includes: Obtain benchmark data that matches the test data, perform statistical processing, and obtain performance optimization data; The performance optimization data includes flight time improvement rate, energy consumption reduction rate, attitude error reduction rate, and trajectory deviation reduction rate. The performance optimization quantification coefficient is obtained by weighting the flight time improvement rate, energy consumption reduction rate, attitude error reduction rate, and trajectory deviation reduction rate. The first threshold comparison result is obtained by comparing the performance optimization quantization coefficient with the preset performance optimization quantization threshold. The optimized control effect of the target UAV is evaluated based on the comparison results of the first threshold.

7. A long-endurance optimized control system for a vertical takeoff and landing fixed-wing unmanned aerial vehicle, characterized in that, The system includes a memory and a processor. The memory contains a program for a long-endurance optimized control method for a vertical takeoff and landing fixed-wing UAV. When the program for the long-endurance optimized control method for the vertical takeoff and landing fixed-wing UAV is executed by the processor, it performs the following steps: Real-time monitoring of target drones, collecting real-time drone status parameters and real-time environmental characteristic parameters; A preset initial power switching threshold is obtained, and then processed in combination with the real-time UAV state parameters and real-time environmental feature parameters to obtain a matching power switching threshold. Based on the matching power switching threshold, power switching parameters are obtained by combining the UAV state parameters and real-time environmental characteristic parameters, and a phased power allocation strategy is executed. The target UAV was subjected to multi-condition simulation tests, and the test data was recorded. The test data is processed in conjunction with benchmark data to obtain performance optimization quantification coefficients and to evaluate the optimization control effect.

8. The long-endurance optimized control system for a vertical takeoff and landing fixed-wing UAV according to claim 7, characterized in that, The real-time monitoring of the target UAV collects real-time UAV status parameters and real-time environmental characteristic parameters, including: Real-time monitoring of the target drone, collecting real-time drone status parameters, including remaining battery power, real-time flight speed, real-time flight altitude, and payload weight; Simultaneously acquire real-time environmental characteristic parameters of the area where the target UAV is located, including real-time temperature and real-time wind speed.

9. The long-endurance optimized control system for a vertical takeoff and landing fixed-wing unmanned aerial vehicle according to claim 8, characterized in that, The process of obtaining a preset initial power switching threshold, combined with the real-time UAV state parameters and real-time environmental feature parameters, to obtain a matching power switching threshold includes: Based on the remaining battery power, real-time flight speed, real-time flight altitude, and payload weight, combined with the real-time temperature and real-time wind speed, a preset power adjustment correction model is used to obtain the power adjustment correction coefficient. Obtain the initial performance parameters of the target UAV, including minimum level flight speed, stall speed, output power, and output torque; Based on the minimum level flight speed, stall speed, output power, and output torque, a preset initial power switching threshold is matched using a preset performance parameter table. The matching power switching threshold is obtained by performing a correction process based on the power adjustment correction coefficient and the preset initial power switching threshold.

10. The long-endurance optimized control system for a vertical takeoff and landing fixed-wing unmanned aerial vehicle according to claim 9, characterized in that, The step of obtaining power switching parameters based on the matching power switching threshold, combined with the real-time UAV state parameters and real-time environmental characteristic parameters, and executing a phased power allocation strategy includes: The initial flight phase of the target UAV is obtained, including the initial vertical takeoff and landing phase, the initial transition phase, and the initial cruise phase; The initial flight phase is adjusted according to the matching power switching threshold to obtain a matching flight phase, including a matching vertical takeoff and landing phase, a matching transition phase, and a matching cruise phase. The corresponding power switching parameters, including velocity gradient, attitude change rate, and switching duration, are obtained based on the matching transition phase. The corresponding phased power distribution strategy is executed according to the matched flight phase.

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