High-altitude wind field detection method, device and system

Abstract

The invention discloses a high-altitude wind field detection method, device and system, and relates to the technical field of meteorological detection. The method comprises the steps that when it is detected that the unmanned aerial vehicle flies in a circling detection stage, flight information and first wind field data returned by the unmanned aerial vehicle are received; calculating the flight information through a second calculation method to obtain second wind field data; according to the first wind field data and the second wind field data, obtaining a wind field detection result of the unmanned aerial vehicle in a circling detection stage; when it is detected that the unmanned aerial vehicle flies in the variable-height detection stage, wind field detection results corresponding to two adjacent target circling detection stages are obtained; and according to the wind field detection results corresponding to the two target circling detection stages, obtaining a wind field detection result of the unmanned aerial vehicle in the variable height detection stage. The method can better adapt to altitude changes in the wind field detection process, and the accuracy of wind field measurement results at different altitudes is improved.

Description

Technical Field

[0001] This application relates to the field of meteorological detection technology, specifically to a method, device, and system for detecting upper-level wind fields. Background Technology

[0002] Accurate detection of upper-air wind fields is a core requirement for meteorological, aviation safety, and climate research. Currently, the main methods for upper-air wind measurement include: balloon wind measurement, wind profiler radar wind measurement, and UAV wind measurement. Among these, UAV wind measurement is gradually becoming a new direction in upper-air meteorological detection due to its advantages of flexibility, maneuverability, ability to fly and detect in hazardous environments, repeatability, and relatively low cost.

[0003] However, existing drone wind measurement technologies each have their own advantages and disadvantages, and the accuracy of wind field detection at different altitudes still needs to be improved. Summary of the Invention

[0004] This application provides a method, apparatus, and system for detecting high-altitude wind fields to solve the above-mentioned problems.

[0005] In a first aspect, embodiments of this application provide a method for detecting high-altitude wind fields, the method being applied to a ground control platform, the method comprising: When the drone is detected to be in the hovering and probing phase, the system receives flight information and first wind field data transmitted back by the drone; wherein, the first wind field data is obtained by calculating the collected environmental information using a first calculation method; The flight information is calculated using a second calculation method to obtain second wind field data; Based on the first wind field data and the second wind field data, the wind field detection results of the UAV during the hovering detection phase are obtained; When the UAV is detected to switch from a hovering detection phase to a variable altitude detection phase, the hovering detection phases of two targets adjacent to the variable altitude detection phase are identified, and the wind field detection results corresponding to the two target hovering detection phases are obtained respectively; wherein, the UAV flies in an alternating manner of variable altitude and hovering; Based on the wind field detection results corresponding to the two target hovering detection phases, the wind field detection results of the UAV in the variable altitude detection phase are obtained.

[0006] Secondly, embodiments of this application provide an upper-altitude wind field detection device, which is applied to a ground control platform and includes: The first calculation module is used to receive flight information and first wind field data transmitted back by the UAV when the UAV is detected to be in the hovering and detection phase of flight; wherein, the first wind field data is obtained by calculating the collected environmental information through a first calculation method; The second calculation module is used to calculate the flight information using a second calculation method to obtain second wind field data; The third calculation module is used to obtain the wind field detection results of the UAV in the hovering detection phase based on the first wind field data and the second wind field data. The determination module is used to determine two target hovering detection phases adjacent to the variable altitude detection phase when the UAV switches from a hovering detection phase to a variable altitude detection phase, and to obtain the wind field detection results corresponding to the two target hovering detection phases respectively; wherein the UAV flies in an alternating manner of variable altitude and hovering; The fourth calculation module is used to obtain the wind field detection results of the UAV in the variable altitude detection phase based on the wind field detection results corresponding to the two target hovering detection phases.

[0007] Thirdly, this application provides an upper-altitude wind field detection system, which includes: a ground control platform, meteorological data acquisition equipment, and a drone; The meteorological data acquisition device is mounted on the UAV and is used to collect environmental information and calculate the first wind field data using the first calculation method, and then send the first wind field data to the UAV. The ground control platform is used to remotely control the UAV and receive data transmitted back by the UAV. The ground control platform is used to execute the method provided in the first aspect of the embodiments of this application.

[0008] Fourthly, embodiments of this application provide an electronic device, characterized in that it includes a processor and a memory, the memory storing a computer program, and the processor executing the method provided in the first aspect of embodiments of this application by invoking the computer program.

[0009] Fifthly, embodiments of this application provide a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which, when run on a computer, causes the computer to execute the method provided in the first aspect of embodiments of this application.

[0010] The beneficial effects of the technical solutions provided by some embodiments of this application include at least the following: This application receives flight information and first wind field data transmitted by the UAV during the hovering detection phase, calculates second wind field data based on the flight information, and combines the first and second wind field data to calculate the wind field detection result during the hovering detection phase. This combination of first and second wind field data allows for real-time reflection of meteorological condition changes through the first wind field data and timely correction of the first wind field data through the second wind field data. The mutual verification between the first and second wind field data effectively reduces potential errors in individual data, improving the accuracy of the wind field detection results obtained by the UAV during the hovering detection phase. Furthermore, when the UAV is in the variable altitude detection phase, this application calculates the wind field detection result for the variable altitude phase by using the wind field detection results corresponding to the hovering detection phases of two adjacent targets. This helps avoid measurement errors caused by unstable UAV flight control during the variable altitude detection phase and the impact of altitude changes on measurement accuracy, further improving the accuracy of the wind field detection results during the variable altitude detection phase. Therefore, this application can better adapt to changes in altitude during wind field detection, ensuring the accuracy of measurement results at different altitudes and quickly obtaining real-time, high-precision wind direction and speed detection results at different altitudes. Furthermore, the combination of hovering detection and variable-altitude detection effectively utilizes the long-endurance detection advantage of UAVs, facilitating tiered detection at different altitudes in a short time. This makes the monitoring process more flexible, better able to cope with complex meteorological conditions, and allows for adjustments to detection strategies based on actual needs. On the other hand, hovering detection allows for flexible adaptation to local wind field changes, while variable-altitude detection systematically evaluates the vertical distribution of the wind field, enhancing the overall adaptability and reliability of wind field detection. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram illustrating an application scenario of an upper-altitude wind field detection system provided in an embodiment of this application. Figure 2 A schematic diagram illustrating data transmission between various hardware components in an upper-altitude wind field detection system provided in this application embodiment; Figure 3 A flowchart illustrating a method for detecting high-altitude wind fields provided in this application embodiment; Figure 4 This is a schematic diagram of the unmanned aerial vehicle (UAV) flight path provided in an embodiment of this application; Figure 5A flowchart illustrating another method for detecting high-altitude wind fields provided in this application embodiment; Figure 6 This is a schematic diagram illustrating the weighted fusion process of first wind field data and second wind field data provided in an embodiment of this application. Figure 7 A schematic diagram illustrating the fusion wind measurement principle using the Pitot-barometric gaussian method and the horizontal air velocity zeroing method, provided for embodiments of this application; Figure 8 A flowchart illustrating another method for detecting high-altitude wind fields provided in this application embodiment; Figure 9 A vector diagram of the first wind field data and the second wind field data provided in the embodiments of this application; Figure 10 A flowchart illustrating another method for detecting high-altitude wind fields provided in this application embodiment; Figure 11 A flowchart illustrating the weighted fusion of wind field detection results corresponding to two target hovering detection stages, as provided in this embodiment of the application; Figure 12 A flowchart illustrating another method for detecting high-altitude wind fields provided in this application embodiment; Figure 13 A schematic diagram of the structure of an upper-altitude wind field detection device provided in an embodiment of this application; Figure 14 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0014] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0015] Among existing UAV wind measurement methods, the horizontal airspeed zeroing method requires simple equipment, necessitates the UAV to hover in a horizontal plane with stable airspeed, and complete a full circle to calculate a valid value. However, it suffers from poor real-time performance and is unsuitable for long-endurance flight. Analytical wind measurement methods involve excessive computation and cannot detect wind field information in real time. Dead reckoning methods are easy to implement, simple in algorithm, and can achieve continuous measurements over large areas, but their wind measurement accuracy is relatively poor. The Pitot barometric anemometer method is low-cost, convenient and flexible, and can perform continuous measurements over large areas with a relatively high estimation frequency, capable of calculating small-scale changes in three-dimensional wind field information. However, its accuracy depends on high-precision inertial navigation, Pitot barometric, and angle measurement sensors, and as the detection altitude increases, the air becomes thinner, significantly reducing the accuracy of Pitot barometric anemometer data. Therefore, these UAV wind measurement methods struggle to obtain more high-precision wind field data at different altitudes while maintaining the advantages of long-endurance UAV detection.

[0016] The present application will now be described in detail with reference to specific embodiments.

[0017] The embodiments of this application provide a method, apparatus and system for detecting upper-altitude wind fields. The execution subject of the method is a ground control platform in the upper-altitude wind field detection system, or it can be the upper-altitude wind field detection device provided in the embodiments of this application, or an electronic device that integrates the upper-altitude wind field detection device.

[0018] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario of an upper-altitude wind field detection system provided in an embodiment of this application. The upper-altitude wind field detection system includes a drone 1, a meteorological data acquisition device 2, and a ground control platform 3.

[0019] The UAV 1 can be a fixed-wing UAV, which can transmit data with the meteorological data acquisition equipment 2 and the ground control platform 3 respectively, and is used to receive and execute control commands sent by the ground control platform 3, and transmit data back to the ground control platform 3. Ground control platform 2 is used to remotely control UAV 1 and receive data transmitted back from UAV 1, and obtain high-altitude wind field detection results based on the transmitted data. Meteorological data acquisition device 3, mounted on drone 1, is used to acquire meteorological data and transmit it to the drone; the meteorological data includes wind field data such as wind speed and wind direction. It is understood that meteorological data acquisition device 3 can also acquire meteorological data such as temperature, humidity, and air pressure according to actual needs.

[0020] Please see Figure 2 , Figure 2 This is a schematic diagram of data transmission between various hardware components in an upper-altitude wind field detection system provided in an embodiment of this application.

[0021] Meteorological data acquisition equipment can collect data through various sensors and transmit the data to drones. For example, temperature and humidity data can be collected through temperature and humidity sensors, static pressure and total pressure data can be collected through Pitot tube airspeed sensors, and wind speed and direction can be calculated in real time.

[0022] The drone is equipped with a flight controller, a data transmission module, and a GPS module. It can receive control commands transmitted from the ground control platform and transmit the drone's airspeed, ground speed, attitude, and meteorological data back to the ground control platform for further analysis and processing.

[0023] Please see Figure 3 , Figure 3 This is a flowchart illustrating a method for detecting upper-altitude wind fields provided in an embodiment of this application. This method can be implemented using a computer program and can run on a ground control platform based on the von Neumann architecture. The computer program can be integrated into the application or run as a standalone utility application. The method is applied to a ground control platform, which can be an electronic device. This electronic device can be a smartphone, tablet, PDA, laptop, or desktop computer, etc. The method includes: S101. When the drone is detected to be in the hovering and probing phase, receive the flight information and first wind field data transmitted back by the drone.

[0024] Understandably, before the ground control platform controls the drone to conduct flight exploration, it can pre-plan the flight path within the target exploration area so that the drone can fly along the planned route.

[0025] In the embodiments of this application, the UAV can plan its flight path by hovering and probing at different altitudes. The trajectory during the hovering and probing phase is a circular trajectory centered around the hovering point.

[0026] The ground control platform controls the UAV to fly and explore the target detection area according to the planned route, so that the meteorological data acquisition equipment can obtain the first wind field data of the target detection area and receive the flight information and first wind field data transmitted back by the UAV in real time.

[0027] In the embodiments of this application, the flight information received by the ground control platform includes the airspeed, ground speed, and attitude information of the UAV. Airspeed refers to the speed of the UAV relative to the air; ground speed is the speed of the UAV relative to the ground, obtained by the GPS module on the UAV.

[0028] In the embodiments of this application, the first wind field data is obtained by calculating the collected environmental information using a first calculation method, wherein the first wind field data includes wind speed and wind direction.

[0029] For example, the first calculation method can be the Pitot-barrel wind measurement method, that is, the Pitot-barrel airspeed sensor is mounted on the UAV, and environmental information such as static pressure and total pressure is collected by the Pitot-barrel airspeed sensor to calculate the first wind field data.

[0030] Specifically, the Pitot-barometric air measurement method relies on the static pressure measured by the Pitot-barometric airspeed sensor. P Total pressure P t The airspeed was calculated using the data and a simplified Bernoulli equation. : (1) in, k The value is based on the ground speed of the UAV via the GPS module, and is a constant value determined after calibrating the airspeed during actual flight. The wind speed can be calculated using the vector relationship between airspeed, ground speed, and wind speed. and wind direction angle for: (2) in, For the track angle, The deflection angle caused by crosswinds, ,in Here, is the heading angle, which is the angle between the tangent to the flight path and the aircraft's longitudinal axis, with clockwise being positive; A is the angle between the wind speed vector and the ground speed vector, expressed as: (3) Using the first calculation method described above, we can obtain real-time first wind field data and transmit it back to the ground control platform via UAV.

[0031] It should be noted that when the UAV is in the hovering and detection phase of flight, the wind direction and wind speed values ​​measured by the Pitot barometric anemometer method often show certain regular changes, and multiple sets of wind field data can be calculated.

[0032] S102. The flight information is calculated using the second calculation method to obtain the second wind field data.

[0033] In the embodiments of this application, the data transmitted back by the UAV is processed on the ground control platform, and the second wind field data is calculated by the second calculation method.

[0034] For example, the second calculation method can be the horizontal airspeed zeroing method, that is, the second wind field data can be calculated by using the flight information such as airspeed and ground speed transmitted back by the UAV.

[0035] Specifically, during the hovering detection phase, the UAV controls its flight based on the detected airspeed. The airspeed value remains relatively stable and can be considered to have no significant change. When the UAV completes one circle, the sum of the horizontal airspeed vectors is zero, indicating that the average wind speed in that area equals the average ground speed. Based on this principle, the wind speed calculated by the horizontal airspeed zeroing method after the UAV completes one circle around the hovering point is obtained. for: (4) Where T represents the time it takes for the drone to circle once. , This indicates the ground speed of the drone obtained from the GPS module.

[0036] In actual calculations, ground speed is used. Component magnitudes in the x and y directions and Perform wind speed and wind direction calculations, wind speed Components in the x and y directions respectively for: (5) Wind speed Wind angle They are respectively: (6) Using the second calculation method described above, highly accurate second wind field data can be obtained from the ground control platform.

[0037] S103. Based on the first wind field data and the second wind field data, the wind field detection results of the UAV in the hovering detection phase are obtained.

[0038] In the embodiments of this application, the first wind field data transmitted back in real time by the UAV is combined with the second wind field data calculated by the ground control platform. The first wind field data can be corrected by the second wind field data, so as to achieve the complementary advantages of the first calculation method and the second calculation method and improve the accuracy of the wind field detection results during the hovering detection phase.

[0039] For example, when the first calculation method uses the Pitot-barrel anemometer method and the second calculation method uses the horizontal airspeed zeroing method, the Pitot-barrel anemometer method has good real-time performance and can perform long-range and large-area wind field measurements. However, in actual measurements, when the altitude changes significantly, the measurement of the airspeed value will be significantly affected, thus affecting the final wind direction and wind speed calculation results. The horizontal airspeed zeroing method is not suitable for long-range and large-area wind field measurements, but this method has the same physical meaning as the balloon anemometer method, has high wind measurement accuracy, and is not affected by altitude. By combining these two anemometer methods, the accuracy of wind field detection results during the hovering detection phase at different altitudes can be ensured while measuring wind fields over long distances and over large areas.

[0040] S104. When the UAV is detected to switch from the hovering detection phase to the variable altitude detection phase, determine the two target hovering detection phases adjacent to the variable altitude detection phase, and obtain the wind field detection results corresponding to the two target hovering detection phases respectively.

[0041] In the embodiments of this application, the UAV can also plan its flight path by alternating between variable altitude detection and hovering detection. Specifically, during the hovering detection phase, the UAV flies in a circular trajectory centered on a hovering point on a horizontal plane at a certain altitude. During the variable altitude detection phase, the UAV performs variable altitude flight on an arbitrary trajectory, that is, after the hovering detection phase at one altitude, it flies with either an increase or decrease in altitude until it reaches the hovering detection phase at the next altitude.

[0042] In one feasible implementation, during the variable altitude detection phase, the UAV can fly at varying altitudes, following a spiral trajectory, a serpentine trajectory, or a straight trajectory, etc. (See also...) Figure 4 , Figure 4 This diagram illustrates a flight path obtained by a drone using alternating spiral and hovering maneuvers, taking a spiral trajectory as an example. Spiral detection allows for rapid wind field detection at different altitudes.

[0043] In the embodiments of this application, the UAV flies in a manner that alternates between variable altitude and hovering. When the UAV is detected to switch from the hovering detection phase to the variable altitude detection phase, the hovering detection phases of two targets adjacent to the variable altitude detection phase are identified, and the wind field detection results corresponding to the two target hovering detection phases are obtained respectively.

[0044] Specifically, the previous hovering detection stage and the next hovering detection stage adjacent to the variable altitude detection stage are determined, resulting in two target hovering detection stages. The wind field detection results of the UAV in the hovering detection stage have been obtained through steps S101 to S103, so the wind field detection results corresponding to these two target hovering detection stages can be obtained directly.

[0045] S105. Based on the wind field detection results corresponding to the two target hovering detection phases, the wind field detection results of the UAV in the variable altitude detection phase are obtained.

[0046] During the variable altitude detection phase, the UAV's flight altitude constantly changes. When the altitude change is significant, the accuracy of sensor measurements may be affected, ultimately impacting the calculation results for wind speed and direction. Therefore, in the embodiments of this application, the wind field detection results corresponding to two adjacent target hovering detection phases are combined to obtain the wind field detection results for the UAV during the variable altitude detection phase.

[0047] For example, interpolation can be used to process the wind field detection results corresponding to the two target hovering detection phases to obtain the wind field detection results during the variable altitude detection phase of the UAV. For instance, using the wind field detection results corresponding to adjacent target hovering detection phases, the wind field detection results during the variable altitude detection phase can be estimated using methods such as linear interpolation, bilinear interpolation, or spline interpolation. It should be noted that this method is generally suitable for situations where the wind field distribution changes relatively steadily.

[0048] For example, a weighted average method can be used to process the wind field detection results corresponding to the two target hovering detection phases to obtain the wind field detection results during the variable altitude detection phase of the UAV. For instance, weights can be assigned to the wind field detection results corresponding to the two target hovering detection phases based on factors such as altitude difference and time difference. Then, the wind field data from two adjacent hovering phases can be weighted and averaged to obtain the wind field detection results during the variable altitude detection phase. This method can reflect the impact of altitude changes on the wind field.

[0049] For example, a predictive model can be built based on algorithms such as machine learning and neural network models. This model can be trained to learn from the wind field detection results corresponding to the two target hovering detection phases, thereby predicting the wind field detection results when the UAV is in the variable altitude detection phase. It should be noted that this method is suitable for situations with large amounts of data and complex nonlinear relationships.

[0050] It should be noted that the high-altitude wind field detection method in this application embodiment can be used regardless of whether the UAV is ascending or descending. Specifically, taking the spiral trajectory as an example of the variable altitude detection phase, when the UAV is ascending, it flies in an alternating spiral ascent and hovering manner. During the hovering detection phase, the wind field detection results are obtained by following steps S101-S103. During the spiral ascent detection phase, the wind field detection results are obtained by following steps S104-S105. Similarly, when the UAV is descending, it flies in an alternating spiral descent and hovering manner. During the hovering detection phase, the wind field detection results are obtained by following steps S101-S103. During the spiral descent detection phase, the wind field detection results are obtained by following steps S104-S105.

[0051] This application receives flight information and first wind field data transmitted by the UAV during its hovering detection phase. Based on the flight information, it calculates second wind field data and combines the first and second wind field data to obtain the wind field detection result during the hovering detection phase. In other words, this application combines the first and second wind field data. The first wind field data reflects changes in meteorological conditions in real time, while the second wind field data corrects the first wind field data in a timely manner. Through mutual verification between the first and second wind field data, potential errors in individual data points are effectively reduced, improving the accuracy of the wind field detection results obtained by the UAV during the hovering detection phase.

[0052] Furthermore, when the UAV is in the variable altitude detection phase, the change in altitude can easily lead to unstable flight control and large measurement errors. Therefore, this application calculates the wind field detection results for the variable altitude detection phase by using the wind field detection results corresponding to the two adjacent target hovering detection phases, thereby improving the accuracy of the wind field detection results for the variable altitude detection phase.

[0053] This application uses a combination of hovering detection and variable altitude detection. On the one hand, it can effectively utilize the long-endurance detection advantage of UAVs, which helps to achieve layered detection of different altitudes in a short time, making the monitoring process more flexible, better able to cope with complex weather conditions, and convenient to adjust the detection strategy according to actual needs. On the other hand, hovering detection can flexibly adapt to local wind field changes, while variable altitude detection can systematically evaluate the vertical distribution of the wind field, thereby enhancing the adaptability and reliability of the overall wind field detection.

[0054] Please see Figure 5 , Figure 5 This is a flowchart illustrating another embodiment of the high-altitude wind field detection method proposed in this application, the method comprising: S201. When the drone is detected to be in the hovering and probing phase, receive the flight information and first wind field data transmitted back by the drone.

[0055] This step is the same as step S101, and will not be repeated here.

[0056] S202. The flight information is calculated using the second calculation method to obtain the second wind field data.

[0057] This step is the same as step S102, and will not be repeated here.

[0058] S203. The first wind field data and the second wind field data are weighted and fused to obtain the wind field detection results when the UAV is in the hovering detection phase.

[0059] In the embodiments of this application, weights can be assigned to the first wind field data and the second wind field data respectively, and then the first wind field data and the second wind field data can be fused by weighted summation to obtain the wind field detection results when the UAV is in the hovering detection phase. The weights of the first wind field data and the second wind field data are used to measure the importance of the first wind field data and the second wind field data respectively, and the weights can be adjusted as needed.

[0060] It should be noted that in the embodiments of this application, both the first wind field data and the second wind field data include wind speed and wind direction, and the wind field detection results during the UAV's hovering detection phase also include wind speed and wind direction. During the weighted fusion of the first and second wind field data, wind speed and wind direction can be processed separately. That is, the wind speed in the first wind field data and the wind speed in the second wind field data are weighted and fused to obtain the wind speed during the UAV's hovering detection phase; the wind direction in the first wind field data and the wind speed in the second wind field data are weighted and fused to obtain the wind direction during the UAV's hovering detection phase. It is understood that the weights for wind speed and wind direction can be different.

[0061] For example, the first wind field data and the second wind field data can be fused using a linear weighting method. For instance, if a first weight is assigned to the first wind field data and a second weight is assigned to the second wind field data, then the result of fusing the first wind field data and the second wind field data is the product of the first weight and the first wind field data plus the product of the second weight and the second wind field data.

[0062] For example, a decomposition and weighting method can also be used to fuse the first wind field data and the second wind field data. For instance, based on the wind direction in the first wind field data, the wind speed in the first wind field data is decomposed to obtain the horizontal component u1 and the vertical component v1 of the wind speed in the first wind field data; based on the wind direction in the second wind field data, the wind speed in the second wind field data is decomposed to obtain the horizontal component u2 and the vertical component v2 of the wind speed in the second wind field data. Then, the horizontal component u1 of the wind speed in the first wind field data and the horizontal component u2 of the wind speed in the second wind field data are weighted and summed to obtain the horizontal component U of the fused wind speed. 12 The vertical components v1 of wind speed in the first wind field data and v2 of wind speed in the second wind field data are weighted and summed to obtain the vertical component V of the merged wind speed. 12 Finally, the combined wind speed V is obtained by synthesizing the horizontal and vertical components of the combined wind speed. mix , The combined wind direction θ is obtained based on the horizontal and vertical components of the combined wind speed, where θ = arctan2(V 12 U 12The combined wind speed and wind direction can be used as the wind field detection results when the UAV is in the hovering detection phase.

[0063] In some possible embodiments, such as Figure 6 The diagram shown illustrates the weighted fusion process of the first wind field data and the second wind field data in this application. Step S203 specifically includes: S203-1. The wind speed in the first wind field data and the wind speed in the second wind field data are weighted and summed to obtain the hovering wind speed when the UAV is in the hovering detection phase.

[0064] S203-2 Calculate the wind direction difference between the wind direction in the second wind field data and the wind direction in the first wind field data.

[0065] S203-3. The wind direction and wind direction difference in the first wind field data are weighted and summed to obtain the circling wind direction when the UAV is in the circling detection phase.

[0066] Among them, the wind field detection results of the drone in the hovering detection phase include hovering wind speed and hovering wind direction.

[0067] Specifically, let the target weight corresponding to the first wind field data be . The target weight corresponding to the second wind field data is , Then the wind field detection result corresponding to the i-th swirling detection stage is: (7) in, and These represent the wind speed and direction, respectively, of the first wind field data corresponding to the i-th swirling detection phase. and These represent the wind speed and direction of the second wind field data corresponding to the i-th swirling detection phase, respectively. and These represent the swirling wind speed and swirling wind direction in the wind field detection results corresponding to the i-th swirling detection stage.

[0068] For example, such as Figure 7 The diagram illustrates the fusion wind measurement principle when the first calculation method uses the Pitot-Barrel barometric anemometer and the second calculation method uses the horizontal airspeed zeroing method. The Pitot-Barrel barometric anemometer calculates airspeed using total static pressure data collected by a Pitot-Barrel airspeed sensor, and then uses a drone-borne sensor to calculate wind direction based on airspeed and ground speed. and wind speed The horizontal airspeed zeroing method uses ground speed obtained from GPS sensors and the principle that the vector sum of the horizontal airspeed of a drone during one circle is zero to calculate the wind direction. Wind speed Finally, the wind speed and direction data calculated by the two methods are weighted and fused to obtain the estimated wind direction and wind speed values ​​during the swirling detection phase. .

[0069] It should be noted that in some possible embodiments, it is necessary to determine in advance whether the first wind field data contains multiple sets of wind field data; wherein, the wind field data includes wind speed and wind direction. For example, if the first calculation method adopts the Pitot-barometric anemometer method, it can obtain multiple solutions during the swirling detection phase, and in this case, the first wind field data contains multiple sets of wind field data. Therefore, in the embodiments of this application, when it is determined that the first wind field data contains multiple sets of wind field data, the average wind speed corresponding to multiple wind speeds in the first wind field data is calculated as the wind speed of the first wind field data, and the average wind direction corresponding to multiple wind directions in the first wind field data is calculated as the wind direction of the first wind field data, and the first wind field data and the second wind field data are weighted and fused based on this.

[0070] S204. When the UAV is detected to switch from the hovering detection phase to the variable altitude detection phase, determine the two target hovering detection phases adjacent to the variable altitude detection phase, and obtain the wind field detection results corresponding to the two target hovering detection phases respectively.

[0071] The drones fly by alternating between varying altitudes and hovering.

[0072] This step is the same as step S104, and will not be repeated here.

[0073] S205. Based on the wind field detection results corresponding to the two target hovering detection phases, the wind field detection results of the UAV in the variable altitude detection phase are obtained.

[0074] This step is the same as step S105, and will not be repeated here.

[0075] This application performs weighted fusion of first and second wind field data to obtain wind field detection results during the hovering detection phase of the UAV. Specifically, by weighted summing of the wind speeds in the first and second wind field data, the hovering wind speed during the hovering detection phase is obtained; the wind direction difference between the wind direction in the second and first wind field data is calculated, and the wind direction and wind direction difference in the first wind field data are weighted and summed to obtain the hovering wind direction during the UAV's hovering detection phase. This allows for timely correction of the wind speed and direction in the first wind field data using the more accurate wind speed and direction data from the second wind field data. This effectively reduces systematic and random errors that may arise from a single method, while preventing the failure or generation of abnormal data under specific conditions, ensuring the stability and robustness of the overall results. It better adapts to complex meteorological conditions and ensures the accuracy of wind speed and direction detection within a certain radius of the hovering detection range.

[0076] Please see Figure 8 , Figure 8 This is a flowchart illustrating another embodiment of the high-altitude wind field detection method proposed in this application, the method comprising: S301. When the drone is detected to be in the hovering and probing phase, receive the flight information and first wind field data transmitted back by the drone.

[0077] This step is the same as step S101, and will not be repeated here.

[0078] S302. The flight information is calculated using the second calculation method to obtain the second wind field data.

[0079] This step is the same as step S102, and will not be repeated here.

[0080] S303 assigns initial weights to the first and second wind field data respectively.

[0081] In the embodiments of this application, a weighted method is used to fuse the first wind field data and the second wind field data. This requires assigning weights to the first and second wind field data respectively. However, the choice of weight directly affects the calculated wind field detection results. Therefore, in the embodiments of this application, initial weights are assigned to the first and second wind field data respectively to facilitate subsequent weight adjustment.

[0082] S304. Calculate the wind speed difference between the wind speed in the second wind field data and the wind speed in the first wind field data.

[0083] In some possible embodiments, it is necessary to determine in advance whether the first wind field data contains multiple sets of wind field data; wherein, the wind field data includes wind speed and wind direction.

[0084] When it is determined that the first wind field data includes multiple sets of wind field data, the average wind speed corresponding to multiple wind speeds in the first wind field data is calculated as the wind speed of the first wind field data. Then, the wind speed difference between the wind speed in the second wind field data and the wind speed in the first wind field data is calculated.

[0085] S305. Calculate the wind direction difference between the wind direction in the second wind field data and the wind direction in the first wind field data.

[0086] Similar to step S304, it is determined in advance whether the first wind field data contains multiple sets of wind field data. If it is determined that the first wind field data contains multiple sets of wind field data, the average wind direction corresponding to multiple wind directions in the first wind field data is calculated as the wind direction of the first wind field data. Then, the wind direction difference between the wind direction in the second wind field data and the wind direction in the first wind field data is calculated.

[0087] S306. Based on the wind speed difference and wind direction difference, adjust the initial weights of the first wind field data and the second wind field data respectively to obtain the target weights of the first wind field data and the second wind field data.

[0088] Understandably, to obtain more accurate wind field detection results, it is necessary to analyze the relative importance of the first and second wind field data. Generally, the first wind field data is real-time data transmitted back by the UAV, which may be affected by factors such as altitude and sensor accuracy, leading to larger measurement errors. The second wind field data, on the other hand, is calculated on a ground control platform, and a more reliable second calculation method can be selected. Therefore, the differences between the second and first wind field data can be compared to determine the reliability of the first wind field data, thereby adjusting the weights to obtain the target weights for the first and second wind field data.

[0089] For example, such as Figure 9 The diagram shows a vector image of first and second wind field data provided in a certain embodiment of this application. The solid black arrows represent the first wind field data, and the dashed black arrows represent the second wind field data. The arrow direction indicates the wind direction, and the arrow length indicates the wind speed. During the hovering detection phase, if methods such as the Pitot barometric anemometer are used to calculate the first wind field data, the first wind field data contains multiple sets of wind field data, such as... Figure 9 As shown, if the second wind field data falls within the fan-shaped area formed by the vector diagram of the first wind field data and the wind speed of the second wind field data is close to the average wind speed of the first wind field data, it indicates that the reliability of the first wind field data is high, and the target weight of the first wind field data can be increased; otherwise, the target weight of the first wind field data should be decreased.

[0090] In the embodiments of this application, the wind speed difference and wind direction difference between the second wind field data and the first wind field data are calculated respectively, and the initial weights of the first wind field data and the second wind field data are adjusted according to the wind speed difference and wind direction difference respectively.

[0091] In some possible embodiments, the weight adjustment method of step S306 specifically includes: Based on the wind speed difference and wind direction difference, determine whether the wind speed difference and wind direction difference meet the preset conditions; wherein, the preset conditions are that the wind speed difference is less than the first preset threshold and the wind direction difference is less than the second preset threshold. If the wind speed and direction differences meet preset conditions, increase the initial weight of the first wind field data and decrease the initial weight of the second wind field data to obtain the target weights of the first and second wind field data. Similarly, if the wind speed and direction differences do not meet the preset conditions, decrease the initial weight of the first wind field data and increase the initial weight of the second wind field data to obtain the target weights of the first and second wind field data.

[0092] For example, if the first preset threshold is set to 2 m / s and the second preset threshold is set to 20 degrees, then when the wind speed difference is less than 2 m / s and the wind direction difference is less than 20 degrees, the initial weight of the first wind field data is increased to obtain the target weight γ=0.3 for the first wind field data and the target weight of the second wind field data is 1-γ=0.7.

[0093] S307. Based on the target weight of the first wind field data and the target weight of the second wind field data, the first wind field data and the second wind field data are weighted and fused to obtain the wind field detection results when the UAV is in the hovering detection phase.

[0094] In the embodiments of this application, the first wind field data and the second wind field data are weighted and fused according to the target weight of the first wind field data and the target weight of the second wind field data. The first wind field data and the second wind field data can be fused in the same weighted summation method as in step S203 to obtain the circling wind speed and circling wind direction of the UAV in the circling detection phase, which will not be elaborated here.

[0095] S308. When the UAV is detected to switch from the hovering detection phase to the variable altitude detection phase, the hovering detection phase of the two targets adjacent to the variable altitude detection phase is determined, and the wind field detection results corresponding to the two target hovering detection phases are obtained respectively; wherein, the UAV flies in an alternating manner of variable altitude and hovering.

[0096] This step is the same as step S104, and will not be repeated here.

[0097] S309. Based on the wind field detection results corresponding to the two target hovering detection phases, the wind field detection results of the UAV in the variable altitude detection phase are obtained.

[0098] This step is the same as step S105, and will not be repeated here.

[0099] In the hovering detection phase, this application calculates the wind speed and direction differences between the second and first wind field data by comparing the differences between them. Based on these differences, the initial weights of the first and second wind field data are adjusted to obtain target weights for both data. This allows for timely weight adjustments based on different measurement conditions, increasing the weight of more reliable wind field data. Finally, the first and second wind field data are weighted and fused according to their respective target weights to obtain the wind field detection results during the hovering detection phase. This weight adjustment effectively reduces the negative impact of abnormal data on the detection results, enhances the robustness of the high-altitude wind field detection system, and further improves the accuracy of wind field detection results during the hovering detection phase.

[0100] Please see Figure 10 , Figure 10 This is a flowchart illustrating another embodiment of the high-altitude wind field detection method proposed in this application. The method includes: S401. When the drone is detected to be in the hovering and probing phase, receive the flight information and first wind field data transmitted back by the drone.

[0101] The first wind field data is obtained by calculating the collected environmental information using the first calculation method.

[0102] This step is the same as step S101, and will not be repeated here.

[0103] S402. The flight information is calculated using the second calculation method to obtain the second wind field data.

[0104] This step is the same as step S102, and will not be repeated here.

[0105] S403. Based on the first wind field data and the second wind field data, the wind field detection results of the UAV in the hovering detection phase are obtained.

[0106] This step is the same as step S103, and will not be repeated here.

[0107] S404. When the UAV is detected to switch from the hovering detection phase to the variable altitude detection phase, determine the two target hovering detection phases adjacent to the variable altitude detection phase, and obtain the wind field detection results corresponding to the two target hovering detection phases respectively.

[0108] The drones fly by alternating between varying altitudes and hovering.

[0109] This step is the same as step S104, and will not be repeated here.

[0110] S405. The wind field detection results corresponding to the two target hovering detection phases are weighted and fused to obtain the wind field detection results when the UAV is in the variable altitude detection phase.

[0111] During the variable altitude detection phase, the first wind field data transmitted by the UAV may have some errors due to the continuous change in altitude. In order to improve the wind field detection accuracy during the variable altitude detection phase, in the embodiments of this application, after obtaining the wind field detection results corresponding to the two target hovering detection phases respectively, weights are assigned to the two target hovering detection phases respectively, and the wind field detection results corresponding to the two target hovering detection phases are fused by weighted summation. The fused result is then used as the wind field detection result of the UAV during the variable altitude detection phase.

[0112] For example, when the wind field changes are not significant, a linear weighting method can be directly used to fuse the wind field detection results corresponding to the two target swirling detection stages. For instance, a third weight is assigned to the wind field detection results of the first target swirling detection stage, and a fourth weight is assigned to the wind field detection results of the second target swirling detection stage. Then, the result of fusing the wind field detection results corresponding to the two target swirling detection stages is the product of the third weight and the wind field detection results of the first target swirling detection stage plus the product of the fourth weight and the wind field detection results of the second target swirling detection stage.

[0113] It should be noted that the wind field detection results for each target hovering detection phase include both wind speed and wind direction. During the weighted fusion process, wind speed and wind direction can be fused separately. For example, the wind speed fusion result is: the product of the third weight and the wind speed from the previous target hovering detection phase, plus the product of the fourth weight and the wind speed from the subsequent target hovering detection phase. This wind speed fusion result represents the variable altitude wind speed from the wind field detection results during the UAV's variable altitude detection phase. Similarly, the wind direction fusion result is: the product of the third weight and the wind direction from the previous target hovering detection phase, plus the product of the fourth weight and the wind direction from the subsequent target hovering detection phase. This wind direction fusion result represents the variable altitude wind direction from the wind field detection results during the UAV's variable altitude detection phase.

[0114] For example, when wind field data varies significantly, a nonlinear weighting method can be used to fuse the wind field detection results corresponding to the two target hovering detection phases. For instance, factors affecting the wind field detection results can be selected as influencing factors (such as time difference, altitude difference, azimuth, and / or climatic conditions), and a nonlinear function (such as an exponential or logarithmic function) can be used to establish a nonlinear relationship between the weights and these influencing factors. This allows the weights for the two target hovering detection phases to be adjusted separately, ensuring that the wind field detection results during the variable altitude detection phase of the UAV can adapt to the changes in these influencing factors and guarantee the accuracy of wind field detection.

[0115] In some possible embodiments, please refer to Figure 11 Step S405 specifically includes: S405-1. The wind speeds in the wind field detection results corresponding to the two target hovering detection phases are weighted and summed to obtain the variable altitude wind speed when the UAV is in the variable altitude detection phase.

[0116] S405-2. Calculate the difference between wind directions in the wind field detection results corresponding to the two target circling detection stages.

[0117] Specifically, the difference between the wind direction in the wind field detection results of the latter target circling detection phase and the wind direction in the wind field detection results of the latter target circling detection phase is calculated. The former target circling detection phase is the first of the two target circling detection phases executed, and the latter target circling detection phase is the last of the two target circling detection phases executed.

[0118] S405-3. The difference between wind directions in the wind field detection results corresponding to the previous target hovering detection phase is weighted and summed to obtain the variable altitude wind direction when the UAV is in the variable altitude detection phase.

[0119] In the embodiments of this application, the wind speed and wind direction at different altitudes obtained by weighted summation of the wind field detection results corresponding to the two target hovering detection phases are the wind field detection results of the UAV in the different altitude detection phase.

[0120] Specifically, let the two target hovering detection stages be numbered i and i+1, where i≥1, and the target weight of the wind field detection result corresponding to the i-th target hovering detection stage is: The target weight of the wind field detection result corresponding to the (i+1)th target swirling detection stage is: , Then, in step S405, the wind field detection results corresponding to the two target hovering detection stages are weighted and fused to obtain the expression for the wind field detection result of the i-th variable height detection stage: (8) in, Let be the wind speed in the wind field detection results corresponding to the i-th target hovering detection stage. This refers to the wind speed in the wind field detection results corresponding to the (i+1)th target hovering detection phase. The wind direction is the wind direction in the wind field detection results corresponding to the i-th target hovering detection stage. The wind direction is the wind direction in the wind field detection results corresponding to the (i+1)th target hovering detection stage. Let be the variable-altitude wind speed in the wind field detection results of the i-th variable-altitude detection stage. The variable altitude wind direction is the wind direction at the variable altitude in the wind field detection results of the i-th variable altitude detection stage.

[0121] In the variable altitude detection phase, this application acquires the wind field detection results corresponding to two adjacent target hovering detection phases. The wind speeds in the wind field detection results from the two target hovering detection phases are weighted and summed to obtain the variable altitude wind speed. The difference between the wind directions in the wind field detection results from the two target hovering detection phases is calculated, and the difference between the wind directions in the wind field detection results from the previous target hovering detection phase is weighted and summed to obtain the variable altitude wind direction. In other words, this application estimates the wind field detection results for the variable altitude detection phase using the wind field detection results from two consecutive hovering detection phases. This helps avoid large measurement errors caused by unstable aircraft flight control during this phase, thus improving the accuracy of the wind field detection results for the variable altitude detection phase.

[0122] Please see Figure 12 , Figure 12 This is a flowchart illustrating another embodiment of the high-altitude wind field detection method proposed in this application. The method includes: S501: When the drone is detected to be in the hovering and probing phase, receive the flight information and first wind field data transmitted back by the drone.

[0123] The first wind field data is obtained by calculating the collected environmental information using the first calculation method.

[0124] This step is the same as step S101, and will not be repeated here.

[0125] S502. The flight information is calculated using the second calculation method to obtain the second wind field data.

[0126] This step is the same as step S102, and will not be repeated here.

[0127] S503. Based on the first wind field data and the second wind field data, the wind field detection results of the UAV in the hovering detection phase are obtained.

[0128] This step is the same as step S103, and will not be repeated here.

[0129] S504. When the UAV is detected to switch from the hovering detection phase to the variable altitude detection phase, determine the two target hovering detection phases adjacent to the variable altitude detection phase, and obtain the wind field detection results corresponding to the two target hovering detection phases respectively.

[0130] The drones fly by alternating between varying altitudes and hovering.

[0131] This step is the same as step S104, and will not be repeated here.

[0132] S505 assigns initial weights to the wind field detection results corresponding to the two target circling detection phases, respectively.

[0133] Understandably, to ensure the accuracy of wind field detection results during the variable altitude detection phase, after obtaining the wind field detection results corresponding to the two target hovering detection phases adjacent to the variable altitude detection phase, it is necessary to assess the importance of the wind field detection results corresponding to the two target hovering detection phases. Therefore, an initial weight is assigned to the wind field detection results corresponding to the two target hovering detection phases for subsequent weight adjustment. For example, the initial weight of the wind field detection results corresponding to the two target hovering detection phases can be set to 0.5.

[0134] S506. Calculate the two height differences between the altitude of the target detection point and the corresponding circling heights of the two target circling detection stages.

[0135] Specifically, when the UAV is in the variable altitude detection phase, it needs to continuously acquire wind field detection results at target detection points at different altitudes. In order to assess the importance of the wind field detection results corresponding to the two target hovering detection phases to the wind field detection results at the target detection point, this application calculates two altitude differences between the altitude of the target detection point and the hovering altitude corresponding to the two target hovering detection phases, as the basis for the importance assessment.

[0136] S507. Based on these two height differences, adjust the initial weights of the wind field detection results corresponding to the two target circling detection stages respectively, and obtain the target weights corresponding to the two target circling detection stages respectively.

[0137] In some possible embodiments, step S507 includes: Based on these two height differences, the target hovering and detection phase that is closer in height to the target detection point is selected from the two target hovering and detection phases. Increase the initial weight of the wind field detection results corresponding to the target circling detection stage that is closer to the target detection point, and decrease the initial weight of the wind field detection results corresponding to the target circling detection stage that is farther away from the target detection point, to obtain the target weights corresponding to the two target circling detection stages respectively.

[0138] For example, let the target weight of the wind field detection result corresponding to the i-th target hovering detection stage be . λ The target weight of the wind field detection result corresponding to the (i+1)th target circling detection stage is 1- λ ,in As the UAV changes altitude from the hovering height corresponding to the i-th target hovering detection phase to the hovering height corresponding to the (i+1)-th target hovering detection phase, λ It gradually changes from 1 to 0.

[0139] Specifically, the height difference Δh between the target detection point and the circling height corresponding to the i-th target circling detection stage can be used as the independent variable and the target weight γ as the dependent variable to establish a linear or nonlinear relationship between the target weight γ and the height difference Δh, thereby realizing the adjustment of the target weight corresponding to the two target circling detection stages respectively.

[0140] S508. Based on the target weights corresponding to the two target hovering detection phases, the wind field detection results corresponding to the two target hovering detection phases are weighted and fused to obtain the wind field detection results when the UAV is in the variable altitude detection phase.

[0141] The specific weighted fusion method in this step is the same as in step S405, and will not be repeated here.

[0142] In the embodiments of this application, wind field detection results corresponding to two target hovering detection phases adjacent to the variable altitude detection phase are obtained. Based on the altitude difference between the target detection point and the hovering altitudes corresponding to the two target hovering detection phases, the initial weights of the wind field detection results corresponding to the two target hovering detection phases are adjusted. This allows for real-time weight allocation adjustment based on the UAV's flight altitude, better adapting to altitude changes. Furthermore, dynamic weight adjustment better addresses rapidly changing wind fields, improving the real-time performance and accuracy of monitoring under complex meteorological conditions. This application also performs weighted fusion of the wind field detection results corresponding to the two target hovering detection phases based on their respective target weights, and uses this fusion as the wind field detection result for the UAV in the variable altitude detection phase, further improving the accuracy of wind field detection results at different altitudes during the variable altitude detection phase.

[0143] Please see Figure 13 , Figure 13This is a schematic diagram of the structure of an upper-altitude wind field detection device proposed in this application. This upper-altitude wind field detection device can be implemented as all or part of a device through software, hardware, or a combination of both. It should be noted that this device is applied to a ground control platform and includes a first calculation module 1301, a second calculation module 1302, a third calculation module 1303, a determination module 1304, and a fourth calculation module 1305. The first calculation module 1301 is used to receive flight information and first wind field data transmitted back by the UAV when the UAV is detected to be in the hovering and detection phase of flight; wherein, the first wind field data is obtained by calculating the collected environmental information through a first calculation method; The second calculation module 1302 is used to calculate the flight information using the second calculation method to obtain the second wind field data; The third calculation module 1303 is used to obtain the wind field detection results when the UAV is in the hovering detection phase based on the first wind field data and the second wind field data. The determination module 1304 is used to determine two target hovering detection phases adjacent to the variable altitude detection phase when the UAV switches from the hovering detection phase to the variable altitude detection phase, and to obtain the wind field detection results corresponding to the two target hovering detection phases respectively; wherein the UAV flies in an alternating manner of variable altitude and hovering; The fourth calculation module 1305 is used to obtain the wind field detection results of the UAV in the variable altitude detection phase based on the wind field detection results corresponding to the two target hovering detection phases.

[0144] In some possible embodiments, the third calculation module 1303 is specifically used to perform weighted fusion of the first wind field data and the second wind field data to obtain the wind field detection results when the UAV is in the hovering detection phase.

[0145] In some possible embodiments, the third computing module 1303 includes: The first weighted summation unit is used to weight and sum the wind speed in the first wind field data and the wind speed in the second wind field data to obtain the hovering wind speed when the UAV is in the hovering detection phase. The first difference calculation unit is used to calculate the wind direction difference between the wind direction in the second wind field data and the wind direction in the first wind field data; The second weighted summation unit is used to perform weighted summation on the wind direction and wind direction difference in the first wind field data to obtain the circling wind direction when the UAV is in the circling detection phase; wherein, the wind field detection results of the UAV in the circling detection phase include circling wind speed and circling wind direction.

[0146] In some possible embodiments, the third computing module 1303 further includes: The first data judgment unit is used to determine whether the first wind field data contains multiple sets of wind field data; wherein, the wind field data includes wind speed and wind direction; The first average calculation unit is used to calculate the average wind speed corresponding to multiple wind speeds in the first wind field data as the wind speed of the first wind field data, and to calculate the average wind direction corresponding to multiple wind directions in the first wind field data as the wind direction of the first wind field data when it is determined that the first wind field data contains multiple sets of wind field data.

[0147] In some possible embodiments, the third computing module 1303 further includes: The first weight allocation unit is used to allocate initial weights to the first wind field data and the second wind field data respectively. The second difference calculation unit is used to calculate the wind speed difference between the wind speed in the second wind field data and the wind speed in the first wind field data. The third difference calculation unit is used to calculate the wind direction difference between the wind direction in the second wind field data and the wind direction in the first wind field data; The first weight adjustment unit is used to adjust the initial weights of the first wind field data and the second wind field data according to the wind speed difference and wind direction difference, respectively, to obtain the target weights of the first wind field data and the second wind field data. The third calculation module 1303 is specifically used to perform weighted fusion of the first wind field data and the second wind field data according to the target weight of the first wind field data and the target weight of the second wind field data, so as to obtain the wind field detection results when the UAV is in the hovering detection phase.

[0148] In some possible embodiments, the first weight adjustment unit includes: The judgment subunit is used to determine whether the wind speed difference and wind direction difference meet the preset conditions based on the wind speed difference and wind direction difference; wherein, the preset conditions are that the wind speed difference is less than the first preset threshold and the wind direction difference is less than the second preset threshold. The first adjustment subunit is used to increase the initial weight of the first wind field data and decrease the initial weight of the second wind field data when the wind speed difference and wind direction difference meet the preset conditions, so as to obtain the target weight of the first wind field data and the target weight of the second wind field data.

[0149] In some possible embodiments, the fourth calculation module 1305 is specifically used to perform weighted fusion of the wind field detection results corresponding to the two target hovering detection phases to obtain the wind field detection results when the UAV is in the variable altitude detection phase.

[0150] In some possible embodiments, the fourth computing module 1305 includes: The fourth weighted summation unit is used to weight and sum the wind speeds of the wind field detection results corresponding to the two target hovering detection phases, respectively, to obtain the variable altitude wind speed when the UAV is in the variable altitude detection phase. The fourth difference calculation unit is used to calculate the difference between wind directions in the wind field detection results corresponding to the two target circling detection stages. The fifth weighted summation unit performs a weighted summation of the differences between wind directions in the wind field detection results corresponding to the previous target hovering detection phase, to obtain the variable altitude wind direction when the UAV is in the variable altitude detection phase; wherein, the previous target hovering detection phase is the target hovering detection phase executed earlier among the two target hovering detection phases, and the wind field detection results when the UAV is in the variable altitude detection phase include variable altitude wind speed and variable altitude wind direction.

[0151] In some possible embodiments, the fourth computing module 1305 further includes: The second weight allocation unit is used to assign initial weights to the wind field detection results corresponding to the two target circling detection phases, respectively. The elevation difference calculation unit is used to calculate the two elevation differences between the altitude of the target detection point and the corresponding circling heights of the two target circling detection stages, respectively. The second weighting adjustment unit is used to adjust the initial weights of the wind field detection results corresponding to the two target circling detection stages according to the two height differences, so as to obtain the target weights corresponding to the two target circling detection stages respectively. The fourth calculation module 1305 is specifically used to perform weighted fusion of the wind field detection results corresponding to the two target hovering detection stages according to the target weights corresponding to the two target hovering detection stages, so as to obtain the wind field detection results of the UAV in the variable altitude detection stage.

[0152] In some possible embodiments, the second weighting adjustment unit includes: The selection sub-unit is used to select the target hovering detection stage with a higher altitude than the target detection point from the two target hovering detection stages based on the two altitude differences. The second adjustment subunit is used to increase the initial weight of the wind field detection results corresponding to the target circling detection stage that is closer to the target detection point, while decreasing the initial weight of the wind field detection results corresponding to the target circling detection stage that is farther away from the target detection point, so as to obtain the target weights corresponding to the two target circling detection stages respectively.

[0153] This application receives flight information and first wind field data transmitted by the UAV during the hovering detection phase, calculates second wind field data based on the flight information, and combines the first and second wind field data to calculate the wind field detection result during the hovering detection phase. This combination of first and second wind field data allows for real-time reflection of meteorological condition changes through the first wind field data and timely correction of the first wind field data through the second wind field data. The mutual verification between the first and second wind field data effectively reduces potential errors in individual data, improving the accuracy of the wind field detection results obtained by the UAV during the hovering detection phase. Furthermore, when the UAV is in the variable altitude detection phase, this application calculates the wind field detection result for the variable altitude phase by using the wind field detection results corresponding to the hovering detection phases of two adjacent targets. This helps avoid measurement errors caused by unstable UAV flight control during the variable altitude detection phase and the impact of altitude changes on measurement accuracy, further improving the accuracy of the wind field detection results during the variable altitude detection phase. Therefore, this application can better adapt to changes in altitude during wind field detection, ensuring the accuracy of measurement results at different altitudes and quickly obtaining real-time, high-precision wind direction and speed detection results at different altitudes. Furthermore, the combination of hovering detection and variable-altitude detection effectively utilizes the long-endurance detection advantage of UAVs, facilitating tiered detection at different altitudes in a short time. This makes the monitoring process more flexible, better able to cope with complex meteorological conditions, and allows for adjustments to detection strategies based on actual needs. On the other hand, hovering detection allows for flexible adaptation to local wind field changes, while variable-altitude detection systematically evaluates the vertical distribution of the wind field, enhancing the overall adaptability and reliability of wind field detection.

[0154] It should be noted that the above device embodiments correspond to the method embodiments of this application. For the parts of the device embodiments not described in detail, please refer to the method embodiments of this application.

[0155] The division of modules or functional units in the above-described device is for illustrative purposes only. In other embodiments, the device can be divided into different modules as needed to complete all or part of its functions. The implementation of each module in the device provided in this application embodiment can be in the form of a computer program. This computer program can run on a terminal or server. The program modules constituted by this computer program can be stored in the memory of the terminal or server. When the computer program is executed by a processor, it implements all or part of the steps of the data erasure method described in the embodiments of this application.

[0156] Please see Figure 14 , Figure 14 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0157] like Figure 14As shown, the electronic device 140 may include: at least one processor 1401, at least one network interface 1404, user interface 1403, memory 1405, touch screen 1406, and at least one communication bus 1402.

[0158] The communication bus 1402 can be used to realize the connection and communication of the above components.

[0159] The user interface 1403 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0160] The network interface 1404 may optionally include a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0161] The processor 1401 may include one or more processing cores. The processor 1401 connects to various parts within the electronic device 140 using various interfaces and lines. It performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1405, and by calling data stored in the memory 1405. Optionally, the processor 1401 may be implemented using at least one hardware form selected from DSP, FPGA, and PLA. The processor 1401 may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1401 and may be implemented as a separate chip.

[0162] The memory 1405 may include RAM or ROM. Optionally, the memory 1405 may include a non-transitory computer-readable medium. The memory 1405 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 1405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1405 may also be at least one storage device located remotely from the aforementioned processor 1401. Figure 14 As shown, the memory 1405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for detecting high-altitude wind fields.

[0163] Specifically, the processor 1401 can be used to call the high-altitude wind field detection application stored in the memory 1405, and specifically perform the following operations: When the drone is detected to be in the hovering and probing phase, the system receives flight information and first wind field data transmitted back by the drone; the first wind field data is obtained by calculating the collected environmental information using a first calculation method. The second calculation method is used to calculate the flight information to obtain the second wind field data; Based on the first and second wind field data, the wind field detection results were obtained during the hovering detection phase of the UAV. When the UAV is detected to switch from the hovering detection phase to the variable altitude detection phase, the hovering detection phase of the two targets adjacent to the variable altitude detection phase is identified, and the wind field detection results corresponding to the two target hovering detection phases are obtained respectively; wherein, the UAV flies in an alternating manner of variable altitude and hovering; Based on the wind field detection results corresponding to the two target hovering detection phases, the wind field detection results of the UAV during the variable altitude detection phase are obtained.

[0164] In one feasible implementation, the processor 1401 executes the following steps: obtaining wind field detection results for the UAV during its hovering detection phase based on first wind field data and second wind field data, including: The first and second wind field data are weighted and fused to obtain the wind field detection results when the UAV is in the hovering detection phase.

[0165] In one feasible implementation, the processor 1401 performs weighted fusion of the first wind field data and the second wind field data to obtain the wind field detection results when the UAV is in the hovering detection phase, including: The wind speeds in the first wind field data and the wind speeds in the second wind field data are weighted and summed to obtain the hovering wind speed during the hovering detection phase of the UAV. Calculate the wind direction difference between the wind direction in the second wind field data and the wind direction in the first wind field data; The wind direction and wind direction difference in the first wind field data are weighted and summed to obtain the circling wind direction when the UAV is in the circling detection phase; the wind field detection results of the UAV in the circling detection phase include circling wind speed and circling wind direction.

[0166] In one feasible implementation, before the processor 1401 performs weighted fusion of the first wind field data and the second wind field data to obtain the wind field detection results during the UAV's hovering detection phase, it further includes: Determine whether the first wind field data contains multiple sets of wind field data; the wind field data includes wind speed and wind direction; When it is determined that the first wind field data includes multiple sets of wind field data, the average wind speed corresponding to multiple wind speeds in the first wind field data is calculated as the wind speed of the first wind field data, and the average wind direction corresponding to multiple wind directions in the first wind field data is calculated as the wind direction of the first wind field data.

[0167] In one feasible implementation, before the processor 1401 performs weighted fusion of the first wind field data and the second wind field data to obtain the wind field detection results during the UAV's hovering detection phase, it further includes: Initial weights were assigned to the first and second wind field data, respectively. Calculate the wind speed difference between the wind speed in the second wind field data and the wind speed in the first wind field data; Calculate the wind direction difference between the wind direction in the second wind field data and the wind direction in the first wind field data; Based on the wind speed difference and wind direction difference, the initial weights of the first wind field data and the second wind field data are adjusted respectively to obtain the target weights of the first wind field data and the second wind field data. The data from the first and second wind fields are weighted and fused, including: Based on the target weights of the first and second wind field data, the first and second wind field data are weighted and fused to obtain the wind field detection results when the UAV is in the hovering detection phase.

[0168] In one feasible implementation, the processor 1401 performs the following steps: adjusting the initial weights of the first wind field data and the second wind field data according to the wind speed difference and wind direction difference, respectively, to obtain the target weights of the first wind field data and the second wind field data, including: Based on the wind speed difference and wind direction difference, determine whether the wind speed difference and wind direction difference meet the preset conditions; wherein, the preset conditions are that the wind speed difference is less than the first preset threshold and the wind direction difference is less than the second preset threshold. If the wind speed difference and wind direction difference meet the preset conditions, increase the initial weight of the first wind field data and decrease the initial weight of the second wind field data to obtain the target weight of the first wind field data and the target weight of the second wind field data.

[0169] In one feasible implementation, the processor 1401 executes wind field detection results based on the wind field detection results corresponding to the two target hovering detection phases, to obtain the wind field detection results of the UAV during the variable altitude detection phase, specifically including: The wind field detection results corresponding to the two target hovering detection phases are weighted and fused to obtain the wind field detection results of the UAV during the variable altitude detection phase.

[0170] In one feasible implementation, the processor 1401 performs a weighted fusion of the wind field detection results corresponding to the two target hovering detection phases to obtain the wind field detection results of the UAV during the variable altitude detection phase, specifically including: The wind speeds of the wind field detection results corresponding to the two target hovering detection phases are weighted and summed to obtain the variable altitude wind speed when the UAV is in the variable altitude detection phase. Calculate the difference between wind directions in the wind field detection results corresponding to the two target hovering detection phases; The wind direction difference between the wind field detection results corresponding to the previous target hovering detection phase is weighted and summed to obtain the variable altitude wind direction when the UAV is in the variable altitude detection phase. The previous target hovering detection phase is the target hovering detection phase executed earlier among the two target hovering detection phases. The wind field detection results of the UAV in the variable altitude detection phase include variable altitude wind speed and variable altitude wind direction.

[0171] In one feasible implementation, before the processor 1401 performs weighted fusion of the wind field detection results corresponding to the two target hovering detection phases to obtain the wind field detection results of the UAV in the variable altitude detection phase, it further includes: Initial weights were assigned to the wind field detection results corresponding to the two target hovering detection phases, respectively. Calculate the two height differences between the altitude of the target detection point and the corresponding circling heights of the two target circling detection phases; Based on the two height differences, the initial weights of the wind field detection results corresponding to the two target circling detection stages are adjusted to obtain the target weights corresponding to the two target circling detection stages. The wind field detection results corresponding to the two target hovering detection phases are weighted and fused, including: Based on the target weights corresponding to the two target hovering detection phases, the wind field detection results corresponding to the two target hovering detection phases are weighted and fused to obtain the wind field detection results when the UAV is in the variable altitude detection phase.

[0172] In one feasible implementation, the processor 1401 performs an operation to adjust the initial weights of the wind field detection results corresponding to the two target hovering detection stages based on the two height differences, thereby obtaining the target weights corresponding to the two target hovering detection stages, including: Based on the two height differences, select the target hovering and detection phase where the height is closer to the target detection point from the two target hovering and detection phases; Increase the initial weight of the wind field detection results corresponding to the target circling detection stage that is closer to the target detection point, and decrease the initial weight of the wind field detection results corresponding to the target circling detection stage that is farther away from the target detection point, to obtain the target weights corresponding to the two target circling detection stages respectively.

[0173] This application receives flight information and first wind field data transmitted by the UAV during the hovering detection phase, calculates second wind field data based on the flight information, and combines the first and second wind field data to calculate the wind field detection result during the hovering detection phase. This combination of first and second wind field data allows for real-time reflection of meteorological condition changes through the first wind field data and timely correction of the first wind field data through the second wind field data. The mutual verification between the first and second wind field data effectively reduces potential errors in individual data, improving the accuracy of the wind field detection results obtained by the UAV during the hovering detection phase. Furthermore, when the UAV is in the variable altitude detection phase, this application calculates the wind field detection result for the variable altitude phase by using the wind field detection results corresponding to the hovering detection phases of two adjacent targets. This helps avoid measurement errors caused by unstable UAV flight control during the variable altitude detection phase and the impact of altitude changes on measurement accuracy, further improving the accuracy of the wind field detection results during the variable altitude detection phase. Therefore, this application can better adapt to changes in altitude during wind field detection, ensuring the accuracy of measurement results at different altitudes and quickly obtaining real-time, high-precision wind direction and speed detection results at different altitudes. Furthermore, the combination of hovering detection and variable altitude detection effectively utilizes the long-endurance detection advantage of UAVs, facilitating tiered detection at different altitudes in a short time. This makes the monitoring process more flexible, better able to cope with complex meteorological conditions, and allows for adjustments to detection strategies based on actual needs. On the other hand, hovering detection allows for flexible adaptation to local wind field changes, while variable altitude detection systematically evaluates the vertical distribution of the wind field, enhancing the overall adaptability and reliability of the detection.

[0174] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the above-described instructions. Figures 3 to 12 One or more steps in the high-altitude wind field detection method provided in the illustrated embodiment. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0175] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line, DSL) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs, DVDs), or semiconductor media (e.g., Solid State Disks, SSDs), etc.

[0176] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and its implementation can be combined arbitrarily.

[0177] The embodiments described above are merely preferred embodiments of this application and are not intended to limit the scope of this application. Any modifications and improvements made by those skilled in the art to the technical solutions of this application without departing from the spirit of this application should fall within the protection scope defined by the claims of this application.

Claims

1. A method for detecting upper-level wind fields, characterized in that, The method is applied to a ground control platform, and the method includes: When the drone is detected to be in the hovering and probing phase, the system receives flight information and first wind field data transmitted back by the drone; wherein, the first wind field data is obtained by calculating the collected environmental information using a first calculation method; The flight information is calculated using a second calculation method to obtain second wind field data; Based on the first wind field data and the second wind field data, the wind field detection results of the UAV during the hovering detection phase are obtained; When the UAV is detected to switch from a hovering detection phase to a variable altitude detection phase, the hovering detection phases of two targets adjacent to the variable altitude detection phase are identified, and the wind field detection results corresponding to the two target hovering detection phases are obtained respectively; wherein, the UAV flies in an alternating manner of variable altitude and hovering; Based on the wind field detection results corresponding to the two target hovering detection phases, the wind field detection results of the UAV in the variable altitude detection phase are obtained.

2. The method for detecting upper-altitude wind fields according to claim 1, characterized in that, The step of obtaining the wind field detection results of the UAV during its hovering detection phase based on the first wind field data and the second wind field data includes: The first wind field data and the second wind field data are weighted and fused to obtain the wind field detection results when the UAV is in the hovering detection phase.

3. The method for detecting upper-altitude wind fields according to claim 2, characterized in that, The weighted fusion of the first wind field data and the second wind field data to obtain the wind field detection results of the UAV during its hovering detection phase includes: The wind speeds in the first wind field data and the second wind field data are weighted and summed to obtain the hovering wind speed of the UAV during the hovering detection phase. Calculate the wind direction difference between the wind direction in the second wind field data and the wind direction in the first wind field data; The wind direction in the first wind field data and the wind direction difference are weighted and summed to obtain the circling wind direction when the UAV is in the circling detection phase; wherein, the wind field detection result when the UAV is in the circling detection phase includes the circling wind speed and the circling wind direction.

4. The method for detecting upper-altitude wind fields according to claim 3, characterized in that, Before performing weighted fusion of the first wind field data and the second wind field data to obtain the wind field detection results during the hovering detection phase of the UAV, the method further includes: Determine whether the first wind field data contains multiple sets of wind field data; wherein, the wind field data includes wind speed and wind direction; When it is determined that the first wind field data includes the multiple sets of wind field data, the average wind speed corresponding to multiple wind speeds in the first wind field data is calculated as the wind speed of the first wind field data, and the average wind direction corresponding to multiple wind directions in the first wind field data is calculated as the wind direction of the first wind field data.

5. The method for detecting upper-altitude wind fields according to claim 2, characterized in that, Before performing weighted fusion of the first wind field data and the second wind field data to obtain the wind field detection results during the hovering detection phase of the UAV, the method further includes: Initial weights are assigned to the first wind field data and the second wind field data, respectively. Calculate the wind speed difference between the wind speed in the second wind field data and the wind speed in the first wind field data; Calculate the wind direction difference between the wind direction in the second wind field data and the wind direction in the first wind field data; Based on the wind speed difference and the wind direction difference, the initial weights of the first wind field data and the second wind field data are adjusted respectively to obtain the target weights of the first wind field data and the target weights of the second wind field data. The weighted fusion of the first wind field data and the second wind field data includes: Based on the target weights of the first wind field data and the second wind field data, the first wind field data and the second wind field data are weighted and fused to obtain the wind field detection results when the UAV is in the hovering detection phase.

6. The method for detecting upper-altitude wind fields according to claim 5, characterized in that, The step of adjusting the initial weights of the first wind field data and the second wind field data according to the wind speed difference and the wind direction difference, respectively, to obtain the target weights of the first wind field data and the second wind field data, includes: Based on the wind speed difference and the wind direction difference, determine whether the wind speed difference and the wind direction difference meet preset conditions; wherein, the preset conditions are that the wind speed difference is less than a first preset threshold and the wind direction difference is less than a second preset threshold; When the wind speed difference and the wind direction difference meet the preset conditions, the initial weight of the first wind field data is increased, while the initial weight of the second wind field data is decreased, to obtain the target weight of the first wind field data and the target weight of the second wind field data.

7. The method for detecting upper-altitude wind fields according to claim 1, characterized in that, The step of obtaining the wind field detection results of the UAV during the variable altitude detection phase based on the wind field detection results corresponding to the two target hovering detection phases respectively includes: The wind field detection results corresponding to the two target hovering detection phases are weighted and fused to obtain the wind field detection results of the UAV in the variable altitude detection phase.

8. The method for detecting upper-altitude wind fields according to claim 7, characterized in that, The weighted fusion of the wind field detection results corresponding to the two target hovering detection phases to obtain the wind field detection results of the UAV in the variable altitude detection phase specifically includes: The wind speeds of the wind field detection results corresponding to the two target hovering detection phases are weighted and summed to obtain the variable altitude wind speed of the UAV during the variable altitude detection phase. Calculate the difference between wind directions in the wind field detection results corresponding to the two target circling detection phases respectively; The wind direction difference between the wind field detection result corresponding to the previous target hovering detection phase and the wind direction is weighted and summed to obtain the variable altitude wind direction of the UAV in the variable altitude detection phase; wherein, the previous target hovering detection phase is the target hovering detection phase executed earlier among the two target hovering detection phases, and the wind field detection result of the UAV in the variable altitude detection phase includes the variable altitude wind speed and the variable altitude wind direction.

9. The method for detecting upper-altitude wind fields according to claim 7, characterized in that, Before weightedly fusing the wind field detection results corresponding to the two target hovering detection phases to obtain the wind field detection results of the UAV in the variable altitude detection phase, the method further includes: Initial weights are assigned to the wind field detection results corresponding to the two target swirling detection phases, respectively; Calculate the two height differences between the altitude of the target detection point and the spiraling height corresponding to the two target spiraling detection phases, respectively; Based on the two height differences, the initial weights of the wind field detection results corresponding to the two target swirling detection stages are adjusted to obtain the target weights corresponding to the two target swirling detection stages. The weighted fusion of wind field detection results corresponding to the two target circling detection phases includes: Based on the target weights corresponding to the two target hovering detection phases, the wind field detection results corresponding to the two target hovering detection phases are weighted and fused to obtain the wind field detection results of the UAV in the variable altitude detection phase.

10. The method for detecting upper-altitude wind fields according to claim 9, characterized in that, The step of adjusting the initial weights of the wind field detection results corresponding to the two target circling detection stages based on the two height differences, to obtain the target weights corresponding to the two target circling detection stages, includes: Based on the two height differences, select the target hovering detection phase where the height is closer to the target detection point from the two target hovering detection phases; Increase the initial weight of the wind field detection results corresponding to the target circling detection stage that is closer to the target detection point, and decrease the initial weight of the wind field detection results corresponding to the target circling detection stage that is farther away from the target detection point, so as to obtain the target weights corresponding to the two target circling detection stages respectively.

11. A high-altitude wind field detection device, characterized in that, The device is used on a ground control platform, and the device includes: The first calculation module is used to receive flight information and first wind field data transmitted back by the UAV when the UAV is detected to be in the hovering and detection phase of flight; wherein, the first wind field data is obtained by calculating the collected environmental information through a first calculation method; The second calculation module is used to calculate the flight information using a second calculation method to obtain second wind field data; The third calculation module is used to obtain the wind field detection results of the UAV in the hovering detection phase based on the first wind field data and the second wind field data. The determination module is used to determine two target hovering detection phases adjacent to the variable altitude detection phase when the UAV switches from a hovering detection phase to a variable altitude detection phase, and to obtain the wind field detection results corresponding to the two target hovering detection phases respectively; wherein the UAV flies in an alternating manner of variable altitude and hovering; The fourth calculation module is used to obtain the wind field detection results of the UAV in the variable altitude detection phase based on the wind field detection results corresponding to the two target hovering detection phases.

12. A high-altitude wind field detection system, characterized in that, The system includes: a ground control platform, meteorological data acquisition equipment, and unmanned aerial vehicles (UAVs); The meteorological data acquisition device is mounted on the UAV and is used to collect environmental information and calculate the first wind field data using the first calculation method, and then send the first wind field data to the UAV. The ground control platform is used to remotely control the UAV and receive data transmitted back by the UAV. The ground control platform is used to execute the high-altitude wind field detection method as described in any one of claims 1 to 10.

13. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program, the processor executing the high-altitude wind field detection method as described in any one of claims 1 to 10 by calling the computer program.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the high-altitude wind field detection method as described in any one of claims 1 to 10.

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