Method and system for intelligently improving plateau performance of fixed-wing unmanned aerial vehicle

Abstract

The invention provides a method and a system for intelligently improving plateau performance of a fixed-wing unmanned aerial vehicle. The method comprises the following steps: preprocessing a plateau environment to obtain effective plateau environment data, normalizing the effective plateau environment data to obtain plateau environment relative data, calculating according to the plateau environment relative data in combination with a weighted average algorithm to obtain environment comprehensive data, performing threshold comparison on the environment comprehensive data, and calculating the plateau environment relative data. Obtaining an environment grade, and adjusting the power of the unmanned aerial vehicle according to the environment grade; therefore, through real-time acquisition of a plateau environment, calculation of environment comprehensive data and threshold comparison, an environment grade evaluation and power intelligent adjustment technology is realized.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) control technology, and more specifically, to a method and system for intelligently enhancing the high-altitude performance of a fixed-wing UAV. Background Technology

[0002] Fixed-wing drones, with their advantages of long endurance, high speed, and strong payload capacity, are widely used in surveying and exploration, environmental monitoring, and emergency rescue in plateau regions. However, the plateau environment is characterized by high altitude, low oxygen concentration, low air density, large temperature differences between day and night, and complex terrain, which bring many challenges to drone flight: low oxygen environment can lead to incomplete combustion in the engine of fuel-powered drones, resulting in reduced power output, and reduced battery activity and discharge efficiency in electric drones; low air density results in insufficient propeller lift, affecting flight stability; low temperature can easily cause difficulty in starting the engine and battery capacity decay; and changes in the slope of complex terrain can lead to overload of the drone during climb, increasing flight risks and excessive energy consumption.

[0003] Existing UAV high-altitude adaptation technologies mostly employ single-parameter adjustments, failing to fully consider the coupled influence of multiple factors in the high-altitude environment. This results in low adjustment precision and difficulty adapting to different levels of high-altitude conditions. Furthermore, the lack of coordinated optimization of key factors such as temperature, slope, and air density leads to UAVs' power performance, endurance, and flight safety failing to meet actual operational requirements in high-altitude environments. Therefore, there is an urgent need for a technical solution that can comprehensively perceive multi-dimensional data of the high-altitude environment, intelligently match power output and auxiliary strategies, and comprehensively improve the high-altitude adaptability of fixed-wing UAVs. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for intelligently improving the high-altitude performance of fixed-wing unmanned aerial vehicles (UAVs). This system can achieve environmental level assessment and intelligent power adjustment technology by real-time acquisition of high-altitude environment data, calculation of comprehensive environmental data, and threshold comparison.

[0005] This application also provides a method for intelligently enhancing the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV), comprising the following steps: After acquiring plateau environmental data and preprocessing it, effective plateau environmental data is obtained. After normalizing the effective plateau environmental data, relative plateau environmental data are obtained, and comprehensive environmental data are calculated. The environmental level is obtained by comparing the comprehensive environmental data with the preset environmental level assessment threshold. Adjust the drone's power according to the environmental level.

[0006] Optionally, in the method for intelligently improving the high-altitude performance of fixed-wing UAVs described in this application, the step of obtaining effective high-altitude environmental data after acquiring and preprocessing high-altitude environmental data specifically includes: High-altitude environmental data, including altitude, oxygen concentration, and wind speed, are collected by preset sensors at preset frequencies. The average value and standard deviation were calculated based on the collected altitude data, oxygen concentration data, and wind speed data. After filtering the plateau environmental data using the 3σ principle and then applying median filtering, effective plateau environmental data is obtained, including effective altitude data, effective oxygen concentration data, and effective wind speed data.

[0007] Optionally, in the method for intelligently improving the plateau performance of fixed-wing UAVs described in this application, the step of normalizing the effective plateau environment data to obtain relative plateau environment data and calculating comprehensive environmental data specifically includes: Relative data on the plateau environment include relative altitude data, relative oxygen concentration data, and relative wind speed data; Comprehensive environmental data is obtained by using a preset weighted average fusion algorithm to calculate relative altitude data, relative oxygen concentration data, and relative wind speed data.

[0008] Optionally, in the method for intelligently improving the high-altitude performance of fixed-wing UAVs described in this application, the step of comparing comprehensive environmental data with a preset environmental level assessment threshold to obtain the environmental level specifically includes: The environmental level is obtained by comparing comprehensive environmental data with preset environmental level assessment thresholds. The preset environmental level assessment thresholds include a first threshold and a second threshold, and the first threshold is greater than the second threshold; If the comprehensive environmental data is greater than or equal to the first threshold, the environmental level is a severe plateau environment. If the comprehensive environmental data is less than the first threshold and greater than or equal to the second threshold, the environmental level is moderate plateau environment. If the comprehensive environmental data is less than the second threshold, the environmental level is classified as mild plateau environment.

[0009] Optionally, in the method for intelligently enhancing the high-altitude performance of a fixed-wing UAV as described in this application, the step of adjusting the UAV's power according to the environmental level specifically includes: If the drone is fuel-powered, query the preset drone environment level power output mapping table to obtain the environment-adaptive power and fuel injection timing optimization data; If the drone is electrically powered, the real-time remaining battery power data is obtained, and the discharge current upper limit data is obtained by querying the preset drone current upper limit adjustment mapping table based on the real-time remaining battery power data and environmental level data.

[0010] Optionally, the method for intelligently improving the high-altitude performance of fixed-wing UAVs described in this application further includes: Acquire ambient temperature data of the drone and compare the ambient temperature data with a preset temperature threshold; When the ambient temperature is lower than the preset temperature threshold, power-assisted optimization measures are activated. If the drone is fuel-powered, the power assistance optimization measures are to start the engine intake preheating and fuel tank preheating. If the drone is electrically powered, the power assistance optimization measure is to heat the battery pack.

[0011] Optionally, it also includes: Acquire the current location of the drone and the location data of the places it will pass through after a preset time period in the planned trajectory; Obtain the slope ratio data based on the current location data and the location data along the route; The slope ratio data is compared with the preset slope ratio threshold to obtain the slope level, including high slope level or low slope level; For steep slopes, the climb rate will be dynamically adapted and segmented for climbing, with a pre-set timeframe. Optionally, this may also include: The system acquires the air density data of the drone's current location and compares it with a preset air density state assessment threshold to obtain the current air density state, including high density state, normal density state, or low density state. The corresponding propeller pitch setting is matched according to the air density conditions. Specifically, high density conditions correspond to small pitch settings, normal density conditions correspond to medium pitch settings, and low density conditions correspond to large pitch settings.

[0012] Secondly, this application provides an intelligent system for enhancing the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV). The system includes a memory and a processor. The memory includes a method program for intelligently enhancing the high-altitude performance of a fixed-wing UAV. When executed by the processor, the method program performs the following steps: After acquiring plateau environmental data and preprocessing it, effective plateau environmental data is obtained. After normalizing the effective plateau environmental data, relative plateau environmental data are obtained, and comprehensive environmental data are calculated. The environmental level is obtained by comparing the comprehensive environmental data with the preset environmental level assessment threshold. Adjust the drone's power according to the environmental level.

[0013] Optionally, in the intelligent plateau performance enhancement system for fixed-wing UAVs described in this application, the step of acquiring plateau environmental data and preprocessing it to obtain effective plateau environmental data specifically includes: High-altitude environmental data, including altitude, oxygen concentration, and wind speed, are collected by preset sensors at preset frequencies. The average value and standard deviation were calculated based on the collected altitude data, oxygen concentration data, and wind speed data. After filtering the plateau environmental data using the 3σ principle and then applying median filtering, effective plateau environmental data is obtained, including effective altitude data, effective oxygen concentration data, and effective wind speed data.

[0014] As described above, this application provides a method and system for intelligently improving the high-altitude performance of a fixed-wing UAV. This method obtains effective high-altitude environmental data by preprocessing the high-altitude environment, normalizes this data to obtain relative high-altitude environmental data, calculates comprehensive environmental data using a weighted average algorithm based on the relative data, compares the comprehensive environmental data against thresholds to obtain the environmental level, and adjusts the UAV's power according to the environmental level. Thus, through real-time acquisition of the high-altitude environment, calculation of comprehensive environmental data, and threshold comparison, it achieves environmental level assessment and intelligent power adjustment technology.

[0015] 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 the 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

[0016] 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.

[0017] Figure 1 A flowchart illustrating an intelligent method for enhancing the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV) as provided in this application embodiment; Figure 2 A flowchart illustrating the method for obtaining effective high-altitude environmental data in an intelligent method for improving the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV) according to an embodiment of this application. Figure 3 A flowchart illustrating the acquisition of comprehensive environmental data for a method of intelligently enhancing the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV) provided in this application embodiment; Figure 4 This is a schematic diagram of a system for intelligently enhancing the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV) according to an embodiment of this application. Detailed Implementation

[0018] 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 some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can 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.

[0019] 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, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0020] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for intelligently enhancing the high-altitude performance of a fixed-wing UAV according to some embodiments of this application. This method for intelligently enhancing the high-altitude performance of a fixed-wing UAV is used in a terminal device. A method for intelligently enhancing the high-altitude performance of a fixed-wing UAV includes the following steps: S11. Obtain plateau environmental data and perform preprocessing to obtain effective plateau environmental data; S12. After normalizing the effective plateau environmental data, the relative plateau environmental data is obtained and the comprehensive environmental data is calculated. S13. Compare the comprehensive environmental data with the preset environmental level assessment threshold to obtain the environmental level; S14. Adjust the drone's power according to the environmental level.

[0021] It is understandable that the plateau environment is more complex than the ordinary environment and has a greater impact on the performance of drones. In order to better improve the performance of drones in the plateau environment, it is necessary to acquire plateau environment data, process it to obtain effective plateau environment data. Since different parameters have different units and different standards, normalization processing is required. The normalized data is the relative plateau environment data. The relative plateau environment data is weighted and summed to obtain comprehensive environmental data. The comprehensive environmental data is compared with the preset environmental level assessment threshold to obtain the environmental level. Different environmental levels have different impacts on drones. Therefore, the drone's power is adjusted differently for different environmental levels in order to better improve the drone's performance. In this embodiment, a fixed-wing drone refers to a fixed-wing drone that can take off and land vertically.

[0022] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the process of obtaining effective high-altitude environmental data in a method for intelligently enhancing the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV) according to an embodiment of this application. According to an embodiment of the present invention, obtaining effective high-altitude environmental data after acquiring and preprocessing the high-altitude environmental data specifically includes: S21. Collect and acquire plateau environmental data, including altitude data, oxygen concentration data, and wind speed data, through preset sensors at preset frequencies. S22. Calculate the corresponding average value and standard deviation based on the collected altitude data, oxygen concentration data, and wind speed data. S23. After filtering the plateau environmental data using the 3σ principle and then performing median filtering, effective plateau environmental data is obtained, including effective altitude data, effective oxygen concentration data, and effective wind speed data.

[0023] Understandably, the preset frequency can be set according to user needs and the requirements for improving the performance of the drone. In this embodiment, the preset frequency is set to 100ms. When calculating the average and standard deviation, the calculation is performed based on the data collected from the last 10 times for each parameter. Data exceeding the range of [μ-3σ, μ+3σ] are judged as outliers and removed. The median filter uses a third-order median filter to further eliminate sensor noise interference, obtain stable preprocessed data, and mark it as valid high-altitude environment data. The third-order median filter is a type of nonlinear digital filtering algorithm. The core logic is: after sorting the three consecutively collected adjacent data points, the median value is taken as the output result at the current moment, thereby suppressing impulse noise in the data.

[0024] Please refer to Figure 3 , Figure 3This is a flowchart illustrating the process of obtaining comprehensive environmental data in a method for intelligently enhancing the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV) according to an embodiment of this application. According to an embodiment of the present invention, the step of normalizing effective high-altitude environmental data to obtain relative high-altitude environmental data and calculating comprehensive environmental data specifically includes: S31. Relative data on the plateau environment include relative altitude data, relative oxygen concentration data, and relative wind speed data. S32. The relative altitude data, relative oxygen concentration data, and relative wind speed data are used to calculate the comprehensive environmental data through a preset weighted average fusion algorithm.

[0025] It is understandable that the normalization formula is X'=(X-X_min) / (X_max-X_min), where X is the original data, X_min is the minimum threshold of the parameter, and X_max is the maximum threshold; X' is the normalized value, and its value range is [0,1]. In this embodiment, the normalization range of altitude corresponds to 0 (3000m)-1 (6000m), oxygen concentration corresponds to 0 (20%VOL)-1 (10%VOL), and wind speed corresponds to 0 (0m / s)-1 (15m / s). The comprehensive environmental data refers to data that can comprehensively reflect the impact of the environment on the UAV. The weighted average fusion algorithm formula is: ;in, For comprehensive environmental data, , , These are relative altitude data, relative oxygen concentration data, and relative wind speed data, respectively. , , These are preset characteristic coefficients, which are obtained from historical data analysis.

[0026] According to an embodiment of the present invention, the step of comparing comprehensive environmental data with a preset environmental level assessment threshold to obtain an environmental level specifically includes: The environmental level is obtained by comparing comprehensive environmental data with preset environmental level assessment thresholds. The preset environmental level assessment thresholds include a first threshold and a second threshold, and the first threshold is greater than the second threshold; If the comprehensive environmental data is greater than or equal to the first threshold, the environmental level is a severe plateau environment. If the comprehensive environmental data is less than the first threshold and greater than or equal to the second threshold, the environmental level is moderate plateau environment. If the comprehensive environmental data is less than the second threshold, the environmental level is classified as mild plateau environment.

[0027] It is understandable that the environmental level can be obtained by comparing thresholds based on comprehensive environmental data. Different environmental levels have different effects on drones, so it is necessary to clarify the environmental level first. Severe plateau environment means high altitude, low oxygen concentration and high wind speed. In this embodiment, the first threshold is 0.6 and the second threshold is 0.3.

[0028] According to an embodiment of the present invention, adjusting the power of the UAV according to the environmental level specifically includes: If the drone is fuel-powered, query the preset drone environment level power output mapping table to obtain the environment-adaptive power and fuel injection timing optimization data; If the drone is electrically powered, the real-time remaining battery power data is obtained, and the discharge current upper limit data is obtained by querying the preset drone current upper limit adjustment mapping table based on the real-time remaining battery power data and environmental level data.

[0029] Understandably, the UAV environmental level power output mapping table is obtained after analyzing historical data. The table includes the environmentally adapted power and fuel injection timing optimization corresponding to each environmental level. In this embodiment, the environmentally adapted power for a mild high-altitude environment is 65%-70% of the rated power, and the corresponding fuel injection timing optimization is an advance injection angle of 3-5ms. The environmentally adapted power for a moderate high-altitude environment is 70%-75% of the rated power, and the corresponding fuel injection timing optimization is an advance injection angle of 5-6ms. The environmentally adapted power for a severe environment is 75%-80% of the rated power, and the corresponding fuel injection timing optimization is... The injection timing is optimized to advance the injection angle by 6-8ms; the real-time remaining power data is the percentage of remaining power relative to the total battery capacity. When the real-time remaining power data is ≥60%, the upper limit of discharge is 0.8C-1.0C in mild high-altitude environments, 1.0C-1.2C in moderate high-altitude environments, and 1.2C-1.5C in severe high-altitude environments (C is the battery capacity ratio, such as 1C=10A for a 10Ah battery); when the real-time remaining power data is ∈ [30%, 60%), the upper limit of discharge for each level is reduced by 0.1C; when the real-time remaining power data is <30%, the upper limit of discharge for each level is reduced by 0.2C to ensure the safety of the battery under high load discharge.

[0030] According to an embodiment of the present invention, it further includes: Acquire ambient temperature data of the drone and compare the ambient temperature data with a preset temperature threshold; When the ambient temperature is lower than the preset temperature threshold, power-assisted optimization measures are activated. If the drone is fuel-powered, the power assistance optimization measures are to start the engine intake preheating and fuel tank preheating. If the drone is electrically powered, the power assistance optimization measure is to heat the battery pack.

[0031] It's understandable that high-altitude environments often have low temperatures, which can negatively impact drone performance. To improve drone performance, ambient temperature needs to be monitored and compared with preset temperature thresholds. If the ambient temperature is lower than the threshold, it indicates that the temperature is affecting drone performance, requiring the activation of power-assisted optimization measures. For fuel efficiency, this involves preheating the engine intake and fuel tank. Preheating methods include electric heating and engine waste heat recovery: when the remaining fuel level is above 30%, the electric heating unit (50-100W) is activated; when it's below 30%, the engine waste heat recovery unit is switched on, using fuel lines to cool the engine exhaust gases. The heat is transferred to the fuel tank, reducing additional energy consumption; intake air preheating can reduce fuel atomization particle size and improve combustion completeness; fuel tank preheating can ensure a continuous and stable fuel supply to the engine; when powered by electric motor, the lithium battery has the best discharge efficiency and capacity retention rate in the 5-10℃ range. In this embodiment, battery heating is achieved through heating elements, and the power of the heating elements is dynamically adjusted to maintain within the preset temperature range, thus avoiding energy waste caused by overheating. For example, when the initial battery temperature is -10℃, 8W power is used for heating. After 5 minutes, the temperature rises to 5℃, and the power is switched to 2W for heat preservation, saving more than 60% of energy consumption compared to constant 8W heating; the distributed arrangement of temperature sensors can ensure the accuracy of temperature monitoring and avoid local overheating.

[0032] According to an embodiment of the present invention, it further includes: Acquire the current location of the drone and the location data of the places it will pass through after a preset time period in the planned trajectory; Obtain the slope ratio data based on the current location data and the location data along the route; The slope ratio data is compared with the preset slope ratio threshold to obtain the slope level, including high slope level or low slope level; For steep slopes, the climbing rate will be dynamically adapted and segmented for climbing at a pre-set time.

[0033] It is understandable that the location data along the route is obtained through a satellite positioning module and the destination navigation system. Combined with the current location data, the gradient ratio of the path can be obtained in advance. A high gradient level refers to a gradient ratio greater than a certain value, indicating a steep slope requiring urgent climbing. Excessive climbing speed can cause a sudden surge in the load on the power system. Therefore, the climbing operation can be dynamically adapted according to the gradient level. In this embodiment, for high gradient levels, the climbing path needs to be pre-planned with a preset time, and the climbing is performed in segments. The segment height is set based on... Environmental Classification: In mild high-altitude environments, the air density is relatively high and the power reserve is sufficient, so the climbing altitude for each segment is set at 100m; in severe high-altitude environments, the air density is low and the power attenuation is significant, so the climbing altitude for each segment is set at 50m to reduce the power load of each climbing segment; a 5-10 second level flight transition phase is set between segments to allow the power system time to dissipate heat and adjust its state, avoiding performance degradation caused by continuous high-load operation; the maximum permissible climbing rate is set according to the environmental class: ≤3m / s in mild high-altitude environments, ≤2.5m / s in moderate high-altitude environments, and ≤2m / s in severe high-altitude environments.

[0034] According to an embodiment of the present invention, it further includes: The system acquires the air density data of the drone's current location and compares it with a preset air density state assessment threshold to obtain the current air density state, including high density state, normal density state, or low density state. The corresponding propeller pitch setting is matched according to the air density conditions. Specifically, high density conditions correspond to small pitch settings, normal density conditions correspond to medium pitch settings, and low density conditions correspond to large pitch settings.

[0035] It is understandable that propeller pitch is defined as the theoretical forward distance a propeller can propel during one revolution. Air density and propeller pitch affect the thrust and efficiency of the UAV. High density refers to a high air density. In this embodiment, a high density state is defined as an air density ≥ 1.0 kg / m³, which is used with a small propeller pitch setting; a normal density state is defined as an air density of 0.7-1.0 kg / m³, which is used with a medium propeller pitch setting; and a low density state is defined as an air density < 0.7 kg / m³, which is used with a large propeller pitch setting.

[0036] It is worth mentioning that it also includes: Acquire the drone's current altitude data and real-time ambient air pressure data; Based on the current altitude data and real-time environmental air pressure data, query the preset altitude boost pressure three-dimensional mapping database to obtain the target boost pressure data; The air compressor in the drone compresses gas based on the target pressure data and delivers the compressed gas to the area under the drone's rotor blades.

[0037] Understandably, air pressure data also affects the flight of drones, because locations with low air pressure generally have low temperatures and low gas density. To improve drone performance, the air pressure under the rotor blades can be increased. The air compressor output is sprayed along the tangent of the rotor rotation to avoid interfering with the rotor's aerodynamic flow field. The preset altitude boost pressure three-dimensional mapping database is obtained through analysis of historical data. This database contains altitude data and corresponding real-time ambient air pressure data and target boost pressure data. The real-time ambient air pressure data plus the target boost pressure data equals the desired pressure data.

[0038] It is worth mentioning that delivering the compressed gas to the area beneath the drone's rotor blades also includes: Acquire a preset amount of real-time air density data below the propeller according to a preset fixed time period; The average air density data is obtained based on the real-time air density. The average air density data is compared with the preset target density data to obtain the relative density data; The relative density data is compared with the preset density state assessment threshold to obtain the density state, including the state of exceeding the standard, the state of normal, or the state of not meeting the standard. The operating status of the air compressor is adjusted according to the density status. If the density exceeds the standard, the operating power of the air compressor is reduced according to the preset rules. If the density is normal, the operating status of the air compressor remains unchanged. If the density does not meet the standard, the operating power of the air compressor is increased.

[0039] Understandably, a preset fixed time period can be set by the user to acquire a preset amount of data for averaging, which better reflects the actual air density. Acquiring relative density is significant for assessing the density status. The status assessment thresholds include a third threshold and a fourth threshold, where the third threshold is greater than the fourth threshold. If the relative density data is greater than or equal to the third threshold, it's considered an out-of-standard state, indicating that the real-time air density is greater than the preset target density. If the relative density data is less than the third threshold but greater than or equal to the fourth threshold, it's considered a normal state, indicating that the density fluctuates within the normal range of the target density. If the relative density data is less than the fourth threshold, it's considered a substandard state, indicating that the density is less than the preset target density. Whether reducing or increasing the air compressor's operating power, the goal is to adjust the air compressor's output pressure. The adjusted pressure can be calculated based on the acquired real-time absolute temperature data. The formula for calculating the pressure reduction when reducing the air compressor's operating power is as follows: ; in, The data is: real-time air density, T is real-time absolute temperature, and R is a preset air gas constant. The working power of the air compressor is adjusted according to the reduced pressure.

[0040] This invention also discloses an intelligent system for enhancing the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV), comprising a memory and a processor. The memory stores a method program for intelligently enhancing the high-altitude performance of the fixed-wing UAV. When the processor executes the method program, the following steps are implemented: After acquiring plateau environmental data and preprocessing it, effective plateau environmental data is obtained. After normalizing the effective plateau environmental data, relative plateau environmental data are obtained, and comprehensive environmental data are calculated. The environmental level is obtained by comparing the comprehensive environmental data with the preset environmental level assessment threshold. Adjust the drone's power according to the environmental level.

[0041] It is understandable that the plateau environment is more complex than the ordinary environment and has a greater impact on the performance of drones. In order to better improve the performance of drones in the plateau environment, it is necessary to acquire plateau environment data, process it to obtain effective plateau environment data. Since different parameters have different units and different standards, normalization processing is required. The normalized data is the relative plateau environment data. The relative plateau environment data is weighted and summed to obtain comprehensive environmental data. The comprehensive environmental data is compared with the preset environmental level assessment threshold to obtain the environmental level. Different environmental levels have different impacts on drones. Therefore, the drone's power is adjusted differently for different environmental levels in order to better improve the drone's performance. In this embodiment, a fixed-wing drone refers to a fixed-wing drone that can take off and land vertically.

[0042] According to an embodiment of the present invention, the step of obtaining effective plateau environmental data after acquiring plateau environmental data and performing preprocessing specifically includes: High-altitude environmental data, including altitude, oxygen concentration, and wind speed, are collected by preset sensors at preset frequencies. The average value and standard deviation were calculated based on the collected altitude data, oxygen concentration data, and wind speed data. After filtering the plateau environmental data using the 3σ principle and then applying median filtering, effective plateau environmental data is obtained, including effective altitude data, effective oxygen concentration data, and effective wind speed data.

[0043] Understandably, the preset frequency can be set according to user needs and the requirements for improving the performance of the drone. In this embodiment, the preset frequency is set to 100ms. When calculating the average and standard deviation, the calculation is performed based on the data collected from the last 10 times for each parameter. Data exceeding the range of [μ-3σ, μ+3σ] are judged as outliers and removed. The median filter uses a third-order median filter to further eliminate sensor noise interference, obtain stable preprocessed data, and mark it as valid high-altitude environment data. The third-order median filter is a type of nonlinear digital filtering algorithm. The core logic is: after sorting the three consecutively collected adjacent data points, the median value is taken as the output result at the current moment, thereby suppressing impulse noise in the data.

[0044] According to an embodiment of the present invention, the step of normalizing the effective plateau environmental data to obtain relative plateau environmental data and calculating comprehensive environmental data specifically includes: Relative data on the plateau environment include relative altitude data, relative oxygen concentration data, and relative wind speed data; Comprehensive environmental data is obtained by using a preset weighted average fusion algorithm to calculate relative altitude data, relative oxygen concentration data, and relative wind speed data.

[0045] It is understandable that the normalization formula is X'=(X-X_min) / (X_max-X_min), where X is the original data, X_min is the minimum threshold of the parameter, and X_max is the maximum threshold; X' is the normalized value, and its value range is [0,1]. In this embodiment, the normalization range of altitude corresponds to 0 (3000m)-1 (6000m), oxygen concentration corresponds to 0 (20%VOL)-1 (10%VOL), and wind speed corresponds to 0 (0m / s)-1 (15m / s). The comprehensive environmental data refers to data that can comprehensively reflect the impact of the environment on the UAV. The weighted average fusion algorithm formula is: ;in, For comprehensive environmental data, , , These are relative altitude data, relative oxygen concentration data, and relative wind speed data, respectively. , , These are preset characteristic coefficients, which are obtained from historical data analysis.

[0046] According to an embodiment of the present invention, the step of comparing comprehensive environmental data with a preset environmental level assessment threshold to obtain an environmental level specifically includes: The environmental level is obtained by comparing comprehensive environmental data with preset environmental level assessment thresholds. The preset environmental level assessment thresholds include a first threshold and a second threshold, and the first threshold is greater than the second threshold; If the comprehensive environmental data is greater than or equal to the first threshold, the environmental level is a severe plateau environment. If the comprehensive environmental data is less than the first threshold and greater than or equal to the second threshold, the environmental level is moderate plateau environment. If the comprehensive environmental data is less than the second threshold, the environmental level is classified as mild plateau environment.

[0047] It is understandable that the environmental level can be obtained by comparing thresholds based on comprehensive environmental data. Different environmental levels have different effects on drones, so it is necessary to clarify the environmental level first. Severe plateau environment means high altitude, low oxygen concentration and high wind speed. In this embodiment, the first threshold is 0.6 and the second threshold is 0.3.

[0048] According to an embodiment of the present invention, adjusting the power of the UAV according to the environmental level specifically includes: If the drone is fuel-powered, query the preset drone environment level power output mapping table to obtain the environment-adaptive power and fuel injection timing optimization data; If the drone is electrically powered, the real-time remaining battery power data is obtained, and the discharge current upper limit data is obtained by querying the preset drone current upper limit adjustment mapping table based on the real-time remaining battery power data and environmental level data.

[0049] Understandably, the UAV environmental level power output mapping table is obtained after analyzing historical data. The table includes the environmentally adapted power and fuel injection timing optimization corresponding to each environmental level. In this embodiment, the environmentally adapted power for a mild high-altitude environment is 65%-70% of the rated power, and the corresponding fuel injection timing optimization is an advance injection angle of 3-5ms. The environmentally adapted power for a moderate high-altitude environment is 70%-75% of the rated power, and the corresponding fuel injection timing optimization is an advance injection angle of 5-6ms. The environmentally adapted power for a severe environment is 75%-80% of the rated power, and the corresponding fuel injection timing optimization is... The injection timing is optimized to advance the injection angle by 6-8ms; the real-time remaining power data is the percentage of remaining power relative to the total battery capacity. When the real-time remaining power data is ≥60%, the upper limit of discharge is 0.8C-1.0C in mild high-altitude environments, 1.0C-1.2C in moderate high-altitude environments, and 1.2C-1.5C in severe high-altitude environments (C is the battery capacity ratio, such as 1C=10A for a 10Ah battery); when the real-time remaining power data is ∈ [30%, 60%), the upper limit of discharge for each level is reduced by 0.1C; when the real-time remaining power data is <30%, the upper limit of discharge for each level is reduced by 0.2C to ensure the safety of the battery under high load discharge.

[0050] According to an embodiment of the present invention, it further includes: Acquire ambient temperature data of the drone and compare the ambient temperature data with a preset temperature threshold; When the ambient temperature is lower than the preset temperature threshold, power-assisted optimization measures are activated. If the drone is fuel-powered, the power assistance optimization measures are to start the engine intake preheating and fuel tank preheating. If the drone is electrically powered, the power assistance optimization measure is to heat the battery pack.

[0051] It's understandable that high-altitude environments often have low temperatures, which can negatively impact drone performance. To improve drone performance, ambient temperature needs to be monitored and compared with preset temperature thresholds. If the ambient temperature is lower than the threshold, it indicates that the temperature is affecting drone performance, requiring the activation of power-assisted optimization measures. For fuel efficiency, this involves preheating the engine intake and fuel tank. Preheating methods include electric heating and engine waste heat recovery: when the remaining fuel level is above 30%, the electric heating unit (50-100W) is activated; when it's below 30%, the engine waste heat recovery unit is switched on, using fuel lines to cool the engine exhaust gases. The heat is transferred to the fuel tank, reducing additional energy consumption; intake air preheating can reduce fuel atomization particle size and improve combustion completeness; fuel tank preheating can ensure a continuous and stable fuel supply to the engine; when powered by electric motor, the lithium battery has the best discharge efficiency and capacity retention rate in the 5-10℃ range. In this embodiment, battery heating is achieved through heating elements, and the power of the heating elements is dynamically adjusted to maintain within the preset temperature range, thus avoiding energy waste caused by overheating. For example, when the initial battery temperature is -10℃, 8W power is used for heating. After 5 minutes, the temperature rises to 5℃, and the power is switched to 2W for heat preservation, saving more than 60% of energy consumption compared to constant 8W heating; the distributed arrangement of temperature sensors can ensure the accuracy of temperature monitoring and avoid local overheating.

[0052] According to an embodiment of the present invention, it further includes: Acquire the current location of the drone and the location data of the places it will pass through after a preset time period in the planned trajectory; Obtain the slope ratio data based on the current location data and the location data along the route; The slope ratio data is compared with the preset slope ratio threshold to obtain the slope level, including high slope level or low slope level; For steep slopes, the climbing rate will be dynamically adapted and segmented for climbing at a pre-set time.

[0053] It is understandable that the location data along the route is obtained through a satellite positioning module and the destination navigation system. Combined with the current location data, the gradient ratio of the path can be obtained in advance. A high gradient level refers to a gradient ratio greater than a certain value, indicating a steep slope requiring urgent climbing. Excessive climbing speed can cause a sudden surge in the load on the power system. Therefore, the climbing operation can be dynamically adapted according to the gradient level. In this embodiment, for high gradient levels, the climbing path needs to be pre-planned with a preset time, and the climbing is performed in segments. The segment height is set based on... Environmental Classification: In mild high-altitude environments, the air density is relatively high and the power reserve is sufficient, so the climbing altitude for each segment is set at 100m; in severe high-altitude environments, the air density is low and the power attenuation is significant, so the climbing altitude for each segment is set at 50m to reduce the power load of each climbing segment; a 5-10 second level flight transition phase is set between segments to allow the power system time to dissipate heat and adjust its state, avoiding performance degradation caused by continuous high-load operation; the maximum permissible climbing rate is set according to the environmental class: ≤3m / s in mild high-altitude environments, ≤2.5m / s in moderate high-altitude environments, and ≤2m / s in severe high-altitude environments.

[0054] According to an embodiment of the present invention, it further includes: The system acquires the air density data of the drone's current location and compares it with a preset air density state assessment threshold to obtain the current air density state, including high density state, normal density state, or low density state. The corresponding propeller pitch setting is matched according to the air density conditions. Specifically, high density conditions correspond to small pitch settings, normal density conditions correspond to medium pitch settings, and low density conditions correspond to large pitch settings.

[0055] It is understandable that propeller pitch is defined as the theoretical forward distance a propeller can propel during one revolution. Air density and propeller pitch affect the thrust and efficiency of the UAV. High density refers to a high air density. In this embodiment, a high density state is defined as an air density ≥ 1.0 kg / m³, which is used with a small propeller pitch setting; a normal density state is defined as an air density of 0.7-1.0 kg / m³, which is used with a medium propeller pitch setting; and a low density state is defined as an air density < 0.7 kg / m³, which is used with a large propeller pitch setting.

[0056] It is worth mentioning that it also includes: Acquire the drone's current altitude data and real-time ambient air pressure data; Based on the current altitude data and real-time environmental air pressure data, query the preset altitude boost pressure three-dimensional mapping database to obtain the target boost pressure data; The air compressor in the drone compresses gas based on the target pressure data and delivers the compressed gas to the area under the drone's rotor blades.

[0057] Understandably, air pressure data also affects the flight of drones, because locations with low air pressure generally have low temperatures and low gas density. To improve drone performance, the air pressure under the rotor blades can be increased. The air compressor output is sprayed along the tangent of the rotor rotation to avoid interfering with the rotor's aerodynamic flow field. The preset altitude boost pressure three-dimensional mapping database is obtained through analysis of historical data. This database contains altitude data and corresponding real-time ambient air pressure data and target boost pressure data. The real-time ambient air pressure data plus the target boost pressure data equals the desired pressure data.

[0058] It is worth mentioning that delivering the compressed gas to the area beneath the drone's rotor blades also includes: Acquire a preset amount of real-time air density data below the propeller according to a preset fixed time period; The average air density data is obtained based on the real-time air density. The average air density data is compared with the preset target density data to obtain the relative density data; The relative density data is compared with the preset density state assessment threshold to obtain the density state, including the state of exceeding the standard, the state of normal, or the state of not meeting the standard. The operating status of the air compressor is adjusted according to the density status. If the density exceeds the standard, the operating power of the air compressor is reduced according to the preset rules. If the density is normal, the operating status of the air compressor remains unchanged. If the density does not meet the standard, the operating power of the air compressor is increased.

[0059] Understandably, a preset fixed time period can be set by the user to acquire a preset amount of data for averaging, which better reflects the actual air density. Acquiring relative density is significant for assessing the density status. The status assessment thresholds include a third threshold and a fourth threshold, where the third threshold is greater than the fourth threshold. If the relative density data is greater than or equal to the third threshold, it's considered an out-of-standard state, indicating that the real-time air density is greater than the preset target density. If the relative density data is less than the third threshold but greater than or equal to the fourth threshold, it's considered a normal state, indicating that the density fluctuates within the normal range of the target density. If the relative density data is less than the fourth threshold, it's considered a substandard state, indicating that the density is less than the preset target density. Whether reducing or increasing the air compressor's operating power, the goal is to adjust the air compressor's output pressure. The adjusted pressure can be calculated based on the acquired real-time absolute temperature data. The formula for calculating the pressure reduction when reducing the air compressor's operating power is as follows: ; in, The data is: real-time air density, T is real-time absolute temperature, and R is a preset air gas constant. The working power of the air compressor is adjusted according to the reduced pressure.

[0060] This invention discloses a method and system for intelligently improving the high-altitude performance of a fixed-wing unmanned aerial vehicle (UAV). The method involves preprocessing the high-altitude environment to obtain effective high-altitude environmental data, normalizing this data to obtain relative high-altitude environmental data, calculating comprehensive environmental data based on the relative data and a weighted average algorithm, comparing the comprehensive environmental data against thresholds to obtain an environmental level, and adjusting the UAV's power accordingly. Thus, through real-time acquisition of the high-altitude environment, calculation of comprehensive environmental data, and threshold comparison, the method achieves environmental level assessment and intelligent power adjustment.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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 method for intelligently improving plateau performance of a fixed-wing unmanned aerial vehicle, characterized in that, The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. 2.The method for intelligently improving plateau performance of a fixed-wing UAV according to claim 1, characterized in that, The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. 3.The method for intelligently improving plateau performance of a fixed-wing UAV according to claim 2, characterized in that, The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device.

4. The method for intelligently improving plateau performance of a fixed-wing UAV according to claim 1, characterized in that, The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device.

5. The method for intelligently improving plateau performance of a fixed-wing UAV according to claim 1, wherein, The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device.

6. The method for intelligently improving plateau performance of a fixed-wing UAV according to claim 1, wherein, The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device. The application relates to a highland environment data processing method and device.

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The application relates to If it is high slope grade, the climbing speed rate dynamic adaptation is started in advance to perform segmented climbing. 8.The method for intelligently improving plateau performance of a fixed-wing UAV according to claim 1, characterized in that, Also include: Air density data of the current position of the unmanned aerial vehicle is acquired and compared with a preset air density state evaluation threshold to obtain a current air density state, including a high density state, a normal density state or a low density state; According to the air density state, a corresponding propeller pitch gear is matched, specifically including a small pitch gear corresponding to the high density state, a middle pitch gear corresponding to the normal density state, and a large pitch gear corresponding to the low density state.

9. A plateau performance intelligent improvement system of a fixed-wing unmanned aerial vehicle, characterized in that, A memory and a processor are included, the memory includes a plateau performance intelligent improvement method program of a fixed-wing unmanned aerial vehicle, and the plateau performance intelligent improvement method program of the fixed-wing unmanned aerial vehicle is executed by the processor to realize the following steps: Obtain plateau environment data and obtain effective plateau environment data after preprocessing; After normalizing the effective plateau environment data, obtain plateau environment relative data and calculate environment comprehensive data; Compare the environment comprehensive data with a preset environment grade evaluation threshold to obtain an environment grade; Adjust the power of the unmanned aerial vehicle according to the environment grade.

10. The plateau performance intelligent improvement system of the fixed-wing unmanned plane according to claim 9, characterized in that, The acquisition of the plateau environment data and the preprocessing to obtain the effective plateau environment data specifically include: The plateau environment data, including altitude data, oxygen concentration data and wind speed data, are acquired by a preset sensor at a preset frequency; According to the collected altitude data, oxygen concentration data and wind speed data, the corresponding average value and standard deviation are calculated; After filtering the plateau environment data by the 3σ principle and then performing median filtering, the effective plateau environment data, including effective altitude data, effective oxygen concentration data and effective wind speed data, are obtained.

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