Unmanned aerial vehicle take-off and landing signal field simulation method based on multi-factor constraint

Abstract

The invention relates to the technical field of unmanned aerial vehicle take-off and landing data processing, in particular to an unmanned aerial vehicle take-off and landing signal field simulation method based on multi-factor constraints, and the method comprises the steps: extracting key ground detection points with shielded signals in an unmanned aerial vehicle take-off and landing signal field; determining the signal delay rate of the unmanned aerial vehicle at each key ground detection point; determining a multipath fading factor of each key ground detection point; determining a potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point; calculating the signal distortion rate of the unmanned aerial vehicle at each key ground detection point; and taking the ground building height at each key ground detection point as key input data, taking topographic data and meteorological data as multi-factor constraint data, and taking the signal distortion rate of the unmanned aerial vehicle as output data to construct a simulation model of the unmanned aerial vehicle take-off and landing signal field. A simulation model of an unmanned aerial vehicle take-off and landing signal field with higher accuracy can be constructed, so that the high-precision simulation requirement is met.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) take-off and landing data processing, and in particular to a UAV take-off and landing signal field simulation method based on multi-factor constraints. Background Art

[0002] With the rapid development of drone technology, drones have been widely used in agriculture, logistics, disaster relief, geographical survey, environmental monitoring and other fields. The take-off and landing of drones is one of the key links in their use. Especially in complex environments, how to ensure the smooth and safe take-off and landing of drones is an important part of improving the efficiency and safety of drone applications. In practical applications, the simulation of drone take-off and landing signal fields is an important technical means to simulate and optimize drone take-off and landing signal fields.

[0003] In the related technologies, the simulation link of the UAV take-off and landing signal field is mainly based on simulation technology and signal propagation models to simulate and optimize the propagation and reception of signals during the UAV take-off and landing process. However, in the traditional simulation scheme, since the simulation model of the UAV take-off and landing signal field is constructed only based on static data such as the height of ground buildings, the simulation model cannot accurately simulate the influence of multiple constraint factors during the actual flight process, and there is a problem of low simulation accuracy. Summary of the invention

[0004] In order to solve the technical problem that the traditional simulation scheme cannot accurately simulate the influence of multiple constraint factors in the actual flight process and the simulation accuracy is low, the purpose of the present invention is to provide a UAV take-off and landing signal field simulation method based on multi-factor constraints. The technical scheme adopted is as follows: A method for simulating a take-off and landing signal field of an unmanned aerial vehicle based on multi-factor constraints, the method comprising: Based on the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field, the key ground detection points where the signal is blocked in the UAV take-off and landing signal field are extracted; Determine the signal delay rate of the UAV at each of the key ground detection points; Acquire meteorological data and terrain data in the detection area where each of the key ground detection points is located, and determine the multipath fading factor of each of the key ground detection points based on the meteorological data and terrain data; Determining the potential influence coefficient between the signal delay rate and the multipath fading factor of each of the key ground detection points; Based on the multipath fading factor and potential impact coefficient corresponding to each of the key ground detection points, the signal distortion rate of the UAV at each of the key ground detection points is calculated; The ground building height at each of the key ground detection points is used as the key input data, the terrain data and meteorological data are used as the multi-factor constraint data, and the signal distortion rate of the UAV is used as the output data to construct a simulation model of the UAV take-off and landing signal field.

[0005] According to a UAV take-off and landing signal field simulation method based on multi-factor constraints provided by the present invention, the signal delay rate of the UAV at each of the key ground detection points is determined, including: For each of the key ground detection points, the time when the flight command is issued and the time when the flight attitude changes corresponding to each flight attitude of the UAV in each flight mission are obtained; Based on the time when the flight instruction corresponding to each flight attitude is issued and the time when the flight attitude changes, the probability of signal obstruction corresponding to each flight mission is calculated; Based on the probability of signal obstruction corresponding to each flight mission at each of the key ground detection points, the signal delay rate of the UAV at each of the key ground detection points is calculated.

[0006] According to a method for simulating a take-off and landing signal field of a UAV based on multi-factor constraints provided by the present invention, based on the time when the flight instruction corresponding to each flight attitude is issued and the time when the flight attitude changes, the probability of signal being blocked corresponding to each flight mission is calculated, including: Subtract the flight attitude change time corresponding to each flight attitude from the flight instruction issuance time and calculate the absolute value to obtain the time difference corresponding to each flight attitude; The time differences corresponding to all flight postures in each flight mission are averaged to calculate the probability of signal obstruction corresponding to each flight mission.

[0007] According to a method for simulating a take-off and landing signal field of a UAV based on multi-factor constraints provided by the present invention, based on the probability of signal obstruction corresponding to each flight mission at each key ground detection point, the signal delay rate of the UAV at each key ground detection point is calculated, including: The signal obstruction probabilities corresponding to all flight missions at each of the key ground detection points are averaged to obtain the average obstruction probability corresponding to each of the key ground detection points; Based on the mean of the occlusion probability, the signal delay rate of the UAV at each of the key ground detection points is determined.

[0008] According to a method for simulating a take-off and landing signal field of a UAV based on multi-factor constraints provided by the present invention, based on the meteorological data and terrain data, a multipath fading factor of each of the key ground detection points is determined, including: Extracting the close ground detection points that are consistent with the ground building height of each key ground detection point from all the key ground detection points; Determine the information entropy of the terrain data between each of the key ground detection points and the adjacent ground detection points, and use the information entropy as the terrain complexity of each of the key ground detection points; Determining the meteorological complexity of each of the key ground detection points based on the meteorological data of each of the key ground detection points; Based on the terrain complexity and meteorological complexity of each of the key ground detection points, the multipath fading factor of each of the key ground detection points is calculated.

[0009] According to a method for simulating a take-off and landing signal field of a UAV based on multi-factor constraints provided by the present invention, the meteorological complexity of each of the key ground detection points is determined according to the meteorological data of each of the key ground detection points, including: Fitting various sub-data in the meteorological data of each of the key ground detection points respectively to obtain a meteorological fitting curve corresponding to each sub-data; Determine the data variance corresponding to the meteorological fitting curve of each sub-data; The data variances corresponding to all seed data in the meteorological data of each of the key ground detection points are averaged to calculate the meteorological complexity of each of the key ground detection points.

[0010] According to a method for simulating a take-off and landing signal field of a UAV based on multi-factor constraints provided by the present invention, based on the terrain complexity and meteorological complexity of each of the key ground detection points, a multipath fading factor of each of the key ground detection points is calculated, including: Multiplying the terrain complexity and the meteorological complexity of each of the key ground detection points to obtain a complexity product; The complexity product is normalized to obtain the multipath fading factor of each of the key ground detection points.

[0011] According to a UAV take-off and landing signal field simulation method based on multi-factor constraints provided by the present invention, the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point is determined, including: Fitting the multipath fading factors corresponding to all key ground detection points to obtain a first fitting curve; Fitting the signal delay rates corresponding to all key ground detection points to obtain a second fitting curve; Calculating a mean square error between the first fitting curve and the second fitting curve; The mean square error is used as a potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point.

[0012] According to a method for simulating a take-off and landing signal field of a UAV based on multi-factor constraints provided by the present invention, based on the multipath fading factor and potential influence coefficient corresponding to each of the key ground detection points, the signal distortion rate of the UAV at each of the key ground detection points is calculated, including: Determine a nearby ground detection point in the detection area that has the same ground building height as each of the key ground detection points; Calculating the absolute mean of the difference between the multipath fading factor corresponding to each of the key ground detection points and the multipath fading factors corresponding to all the adjacent ground detection points; The potential influence coefficient corresponding to each of the key ground detection points is divided by the sum of the absolute mean of the difference and the preset non-zero fixed value and the reciprocal thereof to calculate the intermediate value of the distortion rate; The intermediate value of the distortion rate is input into the natural exponential function to calculate the signal distortion rate of the UAV at each of the key ground detection points.

[0013] According to a multi-factor constraint-based UAV take-off and landing signal field simulation method provided by the present invention, based on the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field, key ground detection points where signals are blocked in the UAV take-off and landing signal field are extracted, including: Compare the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field with the pre-obtained ground building height reference value to obtain a comparison result; The ground detection point where the comparison result shows that the ground building height is lower than the ground building height reference value is used as the key ground detection point where the signal is blocked in the UAV take-off and landing signal field.

[0014] The present invention has the following beneficial effects: By determining the signal delay rate of the UAV at the key ground detection points where the signal is blocked in the UAV take-off and landing signal field, obtaining the meteorological data and terrain data in the detection area where each key ground detection point is located, and determining the multipath fading factor of each key ground detection point based on the meteorological data and terrain data, and then determining the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point, based on the multipath fading factor and potential influence coefficient corresponding to each key ground detection point, the signal distortion rate of the UAV at each key ground detection point is calculated, and finally, the ground building height at each key ground detection point is used as the key input data, the terrain data and meteorological data are used as the multi-factor constraint data, and the signal distortion rate of the UAV is used as the output data to construct a simulation model of the UAV take-off and landing signal field. Since the UAV take-off and landing signal field simulation link introduces the multipath fading factor determined based on meteorological data and terrain data, by dynamically determining the potential influence coefficient between the signal delay rate and the multipath fading factor, a more accurate signal distortion rate is obtained based on the multipath fading factor and the potential influence coefficient, and then a simulation model of the UAV take-off and landing signal field with higher accuracy and reliability can be constructed, thereby meeting the high-precision simulation requirements of the UAV take-off and landing signal field. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A method flow chart of a method for simulating a take-off and landing signal field of a UAV based on multi-factor constraints provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the division of the inverted cone area; Figure 3 A schematic diagram showing the principle of a certain time delay between sending and receiving commands to control the flight of a drone; Figure 4 A schematic diagram of the position of the largest inscribed circle in a rectangular area formed by the nearest ground detection points in the neighborhood of a key ground detection point; Figure 5 It is a schematic diagram of the fitting forms of the first fitting curve and the second fitting curve; Figure 6 A system structure diagram of a UAV take-off and landing signal field simulation system based on multi-factor constraints provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a UAV take-off and landing signal field simulation method based on multi-factor constraints proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following is combined with Figure 1 To Attachment Figure 6 The specific scheme of the UAV take-off and landing signal field simulation method based on multi-factor constraints provided by the present invention is specifically described.

[0020] See also Figure 1 , which shows a method flow chart of a method for simulating a UAV take-off and landing signal field based on multi-factor constraints provided by an embodiment of the present invention, such as Figure 1 As shown, the above-mentioned UAV take-off and landing signal field simulation method based on multi-factor constraints specifically includes the following steps: Step 110: Based on the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field, extract the key ground detection points where the signal is blocked in the UAV take-off and landing signal field.

[0021] It is understandable that in the process of simulating the UAV take-off and landing signal field, in order to accurately simulate the signal propagation and the changes in the signal during the UAV take-off and landing process, it is necessary to obtain in advance a series of original data related to signal propagation, aircraft movement, environment and weather, etc. These original data include: terrain data, meteorological data, ground building height and flight trajectory data, etc.

[0022] In practical applications, digital elevation models (DEM) and lidar can be used to obtain terrain data, and micro-images and images taken by drones can be used to extract the height of ground buildings, thereby generating accurate geographic information system (GIS) data.

[0023] Real-time data on wind speed, wind direction, temperature, humidity, etc. can be obtained through weather stations or weather servers. If weather data cannot be directly obtained in a local area, it can be predicted through a weather model, or recent historical weather data can be used.

[0024] The flight trajectory data may be acquired through the flight control system or simulator of the UAV. In this embodiment, the flight trajectory data may specifically include the flight path, speed, acceleration, etc.

[0025] In practical applications, the UAV take-off and landing signal field can be reasonably selected according to actual requirements. It is understandable that the flight altitude of a UAV is generally 200 meters and the flight distance is generally 3,000 meters. That is, during operation, no ground objects can appear in the inverted cone area with the UAV take-off and landing signal field as the center and the UAV take-off and landing signal field to a height of 200 meters above the sky, otherwise the signal will be blocked. Figure 2 The schematic diagram of the division of the inverted cone area is shown as an example. Figure 2 Among them, A represents the take-off and landing field, B represents the ground building, and C represents the mountain.

[0026] It is understandable that the ground detection point is a location point set on the ground for monitoring and detecting the drone and its surrounding environment.

[0027] In a specific implementation, based on the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field, the key ground detection points where the signal is blocked in the UAV take-off and landing signal field are extracted, specifically including: Firstly, the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field is compared with the pre-obtained ground building height reference value to obtain the comparison result.

[0028] It can be understood that the ground building height reference value refers to the highest height of the ground building at the ground detection point when the signal is not blocked.

[0029] Then, the ground detection points where the comparison result shows that the ground building height is lower than the ground building height reference value are used as the key ground detection points where the signals are blocked in the UAV take-off and landing signal field.

[0030] It can be seen from this that in the determination link of the key ground detection point, whether the signal is blocked is mainly judged based on the height difference between the ground building height (i.e., actual height) at the ground detection point and the highest height of the ground building at the ground detection point when the signal is not blocked. When the height of the ground building at the ground detection point is lower than the highest height of the ground building at the ground detection point when the signal is not blocked, it can be considered that the signal is blocked. This embodiment mainly analyzes the key ground detection points where the signal is blocked.

[0031] Step 120: Determine the signal delay rate of the UAV at each key ground detection point.

[0032] It can be understood that the signal delay rate can characterize the delay in signal propagation from the time the signal is sent to the time it is received by the drone at the current key ground detection point.

[0033] Step 130: Acquire meteorological data and terrain data in the detection area where each key ground detection point is located, and determine the multipath fading factor of each key ground detection point based on the meteorological data and terrain data.

[0034] It can be understood that the multipath fading factor can characterize the impact of reflection, refraction and heat dissipation on the ground and surrounding environment on the propagation of UAV signals during the take-off and landing communication of UAV.

[0035] Step 140: Determine the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point.

[0036] In this embodiment, the potential influence coefficient can characterize the mutual influence relationship between the signal delay rate and the multipath fading factor of each key ground detection point. The smaller the potential influence coefficient, the stronger the mutual influence between the signal delay rate and the multipath fading factor of the key ground detection point.

[0037] Step 150: Based on the multipath fading factor and potential impact coefficient corresponding to each key ground detection point, the signal distortion rate of the UAV at each key ground detection point is calculated.

[0038] It can be understood that the potential impact coefficient is a key factor in measuring the signal distortion rate of UAVs at key ground detection points. It can be used as an impact weight to calculate the signal distortion rate of UAVs flying in different observation areas, and then simulate the take-off and landing signal field of UAVs.

[0039] Step 160: The ground building height at each key ground detection point is used as the key input data, the terrain data and meteorological data are used as the multi-factor constraint data, and the signal distortion rate of the UAV is used as the output data to construct a simulation model of the UAV take-off and landing signal field.

[0040] It can be understood that the simulation model of the UAV take-off and landing signal field can characterize the correspondence between the signal distortion rate of the UAV at any ground detection point and the ground building height, terrain data and meteorological data. By inputting the ground building height, terrain data and meteorological data at a certain ground observation point into the simulation model, the signal distortion rate of the UAV at the ground detection point can be directly output.

[0041] The solution provided in this embodiment introduces dynamic influencing parameters such as terrain data and meteorological data in the simulation of the UAV take-off and landing signal field, so as to obtain a more accurate simulation model, thereby improving the simulation accuracy and simulation reliability of the UAV take-off and landing signal field.

[0042] When a physical obstacle appears in front of the drone during flight, signal transmission may be blocked or attenuated, resulting in interruption of the communication link or signal loss. For example, if the drone's flight distance is 300 meters and there is a physical obstacle at a distance of 100 meters, the communication between the drone and the ground terminal will be interrupted within the range of 100-300 meters.

[0043] It is understandable that the signal shielding situation of the drone relative to the ground detection point can only be preliminarily judged based on the height of the ground building at the ground detection point, which cannot accurately assess the actual state of signal shielding. That is, during the flight of the drone, even if signal shielding occurs, there may be a situation where the signal can be received by the ground detection point, and the signal reception of the ground detection point is not completely shielded. Therefore, this embodiment calculates the signal delay rate of the drone at each key ground detection point to further determine the propagation delay of the signal at the key ground detection point where signal shielding is preliminarily judged.

[0044] In one embodiment, determining the signal delay rate of the UAV at each key ground detection point specifically includes: The first step is to obtain the time when the flight command is issued and the time when the flight attitude changes for each flight attitude of the UAV during each flight mission for each key ground detection point.

[0045] In practical applications, during a flight, the drone sends flight attitude control instructions, i.e., flight instructions, to the drone through the ground drone control system, and records the timestamp of the flight instructions corresponding to each flight attitude, i.e., the time when the flight instructions are issued t; at the same time, the aircraft carried by the drone monitors the timestamp of the change in the drone's flight attitude, i.e., the time when the flight attitude changes . Figure 3 The example shows that there is a certain time delay between sending and receiving instructions during the flight control of the UAV.

[0046] In this embodiment, the flight attitude data that can characterize the change in flight attitude may specifically include various types of data such as roll angle, pitch angle, yaw angle, etc.

[0047] In the second step, based on the time when the flight command corresponding to each flight attitude is issued and the time when the flight attitude changes, the probability of signal obstruction corresponding to each flight mission is calculated.

[0048] In a specific implementation, based on the time when the flight command corresponding to each flight attitude is issued and the time when the flight attitude changes, the probability of signal obstruction corresponding to each flight mission is calculated, specifically including: Firstly, the time difference between the flight attitude change time corresponding to each flight attitude and the time when the flight instruction is issued is subtracted and the absolute value is calculated to obtain the time difference corresponding to each flight attitude.

[0049] Then, the time differences corresponding to all flight postures in each flight mission are averaged to calculate the signal blocking probability corresponding to each flight mission.

[0050] In this embodiment, the probability of signal obstruction corresponding to the i-th flight mission can be calculated as follows: (1) in, represents the probability of signal being blocked corresponding to the i-th flight mission, Indicates the time when the flight instruction is issued. Indicates the moment when the flight attitude changes. Indicates the time difference corresponding to the nth flight attitude, Represents the total number of flight attitudes in the i-th flight mission.

[0051] The third step is to calculate the signal delay rate of the UAV at each key ground detection point based on the signal blocking probability corresponding to each flight mission at each key ground detection point.

[0052] In a specific implementation, based on the signal blocking probability corresponding to each flight mission at each key ground detection point, the signal delay rate of the UAV at each key ground detection point is calculated, specifically including: Firstly, the signal obstruction probability corresponding to all flight missions at each key ground detection point is averaged, and the mean obstruction probability corresponding to each key ground detection point is calculated.

[0053] Then, based on the mean occlusion probability, the signal delay rate of the UAV at each key ground detection point is determined.

[0054] In this embodiment, the signal delay rate of the UAV at the u-th key ground detection point can be calculated as follows: (2) in, represents the signal delay rate of the UAV at the u-th key ground detection point, represents the probability of signal being blocked corresponding to the i-th flight mission, represents the total number of flight missions at the u-th key ground detection point, ( ) represents the hyperbolic tangent function, which can convert the input data into dimensionless data between 0 and 1.

[0055] Specifically, since the signal corresponding to the i-th flight mission is blocked with probability is a dimensioned parameter, while the signal delay rate that needs to be obtained in the end is a dimensionless parameter. Therefore, this embodiment implements dimensionless conversion through a dimensionless conversion function. For example, dimensionless conversion can be achieved by dividing the currently obtained mean occlusion probability by a preset occlusion probability reference value.

[0056] It is understandable that the signal delay rate may also be affected by the multipath effect during the actual flight process. If the UAV take-off and landing signal field is simulated based only on the ground building height of the ground detection point and the signal delay rate of the UAV, the simulation method cannot achieve a good simulation effect because it ignores the impact of the multipath effect on the signal distortion during the flight of the UAV. For the simulation of the UAV take-off and landing signal field, there is a lack of observation and analysis of dynamic factors in the signal field. For this reason, this embodiment introduces the calculation link of the multipath fading factor.

[0057] In one embodiment, based on meteorological data and terrain data, determining the multipath fading factor of each key ground detection point specifically includes: In the first step, the close ground detection points which are consistent with the ground building height of each key ground detection point are extracted from all the key ground detection points.

[0058] It can be understood that the multipath effect refers to the phenomenon that the same signal reaches the receiver along different paths due to reflection or refraction by obstacles (such as buildings, hills, ground, etc.) or changes in the refraction angle of the signal introduced by meteorological factors during the propagation process. The signals on these different paths will be superimposed on each other at the receiving end, forming a multipath effect.

[0059] In this embodiment, when defining the detection area where any key ground detection point is located, the position of the key ground detection point is taken as the center of the circle, and the maximum inscribed circle in the rectangular area formed by the ground detection points closest to the key ground detection point in the neighborhood is obtained as the detection area. The maximum inscribed circle can be found in Figure 4 As shown, Figure 4 The circular area D is indicated in the figure. Meteorological data and terrain data can be obtained in the detection area later.

[0060] The second step is to determine the information entropy of the terrain data between each key ground detection point and the nearby ground detection points, and use the information entropy as the terrain complexity of each key ground detection point.

[0061] It is understandable that in the process of determining the terrain complexity, the differences in terrain data of each key ground detection point and the adjacent ground detection points related to the key ground detection point are comprehensively considered, among which the information entropy can measure the uncertainty or randomness of the terrain data. The larger the entropy value of the information entropy, the greater the uncertainty of the terrain data, that is, the more complex the terrain, that is, the higher the terrain complexity, the more likely it is to affect the propagation route of the drone signal, which may increase the decoding error after the receiving end receives the signal, thereby increasing the signal distortion rate of the drone.

[0062] The third step is to determine the meteorological complexity of each key ground detection point based on the meteorological data of each key ground detection point.

[0063] In a specific implementation, the meteorological complexity of each key ground detection point is determined according to the meteorological data of each key ground detection point, specifically including: Firstly, various sub-data in the meteorological data of each key ground detection point are fitted respectively to obtain the meteorological fitting curve corresponding to each sub-data.

[0064] In this embodiment, the meteorological data includes multiple types of sub-data such as temperature, humidity, wind speed, etc. By fitting each sub-data separately, a meteorological fitting curve corresponding to each sub-data can be obtained, such as a temperature fitting curve, a humidity fitting curve, and a wind speed fitting curve.

[0065] Then, the data variance corresponding to the meteorological fitting curve of each sub-data is determined.

[0066] In the data variance calculation stage, multiple key data points in each meteorological fitting curve can be extracted first, and then the data means of all key data points in each meteorological fitting curve can be calculated respectively. Then, the data variance of all key data points is calculated based on the calculated data means, and this is used as the data variance of the meteorological fitting curve.

[0067] Finally, the data variance corresponding to all seed data in the meteorological data of each key ground detection point is averaged to calculate the meteorological complexity of each key ground detection point.

[0068] In this embodiment, the meteorological complexity can represent the complexity of various sub-data in the meteorological data.

[0069] The fourth step is to calculate the multipath fading factor of each key ground detection point based on the terrain complexity and meteorological complexity of each key ground detection point.

[0070] In a specific implementation, based on the terrain complexity and meteorological complexity of each key ground detection point, the multipath fading factor of each key ground detection point is calculated, specifically including: First, the terrain complexity and meteorological complexity of each key ground detection point are multiplied to obtain the complexity product.

[0071] Then, the complexity product is normalized to obtain the multipath fading factor of each key ground detection point.

[0072] In this embodiment, the multipath fading factor corresponding to the u-th key ground detection point can be calculated as follows: (3) in, represents the multipath fading factor corresponding to the u-th key ground detection point, represents the complexity of the terrain, represents the meteorological complexity, ( ) represents the normalization function.

[0073] In one embodiment, determining the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point specifically includes: In the first step, the multipath fading factors corresponding to all key ground detection points are fitted to obtain the first fitting curve.

[0074] The third step is to fit the signal delay rates corresponding to all key ground detection points to obtain the second fitting curve.

[0075] The fourth step is to calculate the mean square error between the first fitting curve and the second fitting curve.

[0076] In the fifth step, the mean square error is used as the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point.

[0077] In this embodiment, the corresponding observation position of each key ground detection point is used as the horizontal coordinate data, and the multipath fading factor and the signal delay rate are used as the vertical coordinate data, respectively, and the first fitting curve and the second fitting curve can be fitted respectively. Figure 5 The first fitting curve E and the second fitting curve R are shown as examples.

[0078] The mean square error between the first fitting curve and the second fitting curve can be expressed as follows: (4) Where, E represents the first fitting curve, R represents the second fitting curve, represents the mean square error function, It represents the potential influence coefficient between the signal delay rate and the multipath fading factor, that is, the mean square error between the first fitting curve and the second fitting curve. The smaller the mean square error, the stronger the mutual influence relationship between the signal delay rate and the multipath fading factor.

[0079] In this embodiment, the mean square error between the first fitting curve and the second fitting curve is calculated, that is, the execution process of the mean square error function is as follows: First, multiple groups of discrete points with consistent horizontal coordinate values ​​are obtained from the first fitting curve and the second fitting curve, that is, the signal delay rate and the multipath fading factor at the same observation position are obtained to obtain multiple groups of discrete point pairs.

[0080] Then, the square value of the difference between the two function values ​​at the same observation position in each set of discrete point pairs is calculated, that is, the square value of the difference between the signal delay rate and the multipath fading factor at the same observation position, to obtain the square error.

[0081] Finally, all the calculated error squares are averaged to obtain the mean square error between the first fitting curve and the second fitting curve.

[0082] It can be understood that the potential influence coefficient between the signal delay rate and the multipath fading factor is a key factor in measuring the signal distortion rate of the UAV at different ground detection points. In this embodiment, the potential influence coefficient is used as a weight value to calculate the signal distortion rate of the UAV flying in different observation areas, and then the take-off and landing signal field of the UAV is simulated.

[0083] In one embodiment, based on the multipath fading factor and the potential impact coefficient corresponding to each key ground detection point, the signal distortion rate of the drone at each key ground detection point is calculated, specifically including: The first step is to determine the nearby ground detection points that are consistent with the ground building height of each key ground detection point.

[0084] The second step is to calculate the absolute mean of the difference between the multipath fading factor corresponding to each key ground detection point and the multipath fading factors corresponding to all nearby ground detection points.

[0085] The third step is to calculate the potential impact coefficient corresponding to each key ground detection point and the sum of the absolute mean of the difference and the preset non-zero fixed value, and take the opposite number to calculate the intermediate value of the distortion rate.

[0086] In the fourth step, the intermediate value of the distortion rate is input into the natural exponential function to calculate the signal distortion rate of the UAV at each key ground detection point.

[0087] In this embodiment, the signal distortion rate of the UAV at the u-th key ground detection point can be expressed as follows: (5) in, represents the signal distortion rate of the UAV at the u-th key ground detection point, represents the potential impact coefficient, represents the multipath fading factor corresponding to the u-th key ground detection point. Assuming that there are M similar ground observation points in the current observation area except the u-th key ground detection point, M represents the number of similar ground observation points in the current observation area except the u-th key ground detection point. It represents the multipath fading factor of the Nth closest ground detection point among M similar ground observation points. h represents a constant value that is not 0, that is, a preset non-zero constant value is used to prevent the problem that the function with a denominator of 0 is meaningless. ( ) represents the natural exponential function.

[0088] In practical applications, based on the signal distortion rates of the UAV at different key ground observation points obtained above, a simulation model of the UAV take-off and landing signal field can be further constructed. Specifically, the signal distortion rates of different key ground observation points can be mapped to the point coordinates of the corresponding positions, that is, each position point includes the ground building height, terrain data, environmental data and signal distortion rate of the key ground detection points.

[0089] After constructing the simulation model of the UAV take-off and landing signal field, during the flight of the UAV, by inputting dynamic factors such as the ground building height, terrain data, and meteorological data at a certain key ground observation point, the signal distortion rate at the key ground observation point can be output.

[0090] In practical applications, the simulation model of the above-mentioned UAV take-off and landing signal field can be used to select a suitable UAV take-off and landing signal field. It can be understood that the UAV take-off and landing signal field should be set up on government public land first, followed by land with corporate and individual use rights. Based on this, the proposed layout range of the UAV take-off and landing signal field can be first selected, and multiple ground detection simulation points can be determined within the proposed layout range. The ground building heights, terrain data and environmental data of the multiple ground detection simulation points are used as input data, and are respectively input into the simulation model of the above-mentioned UAV take-off and landing signal field to obtain the signal distortion rate corresponding to each ground detection simulation point, and the location of the suitable UAV take-off and landing signal field is determined based on the ground detection simulation point with the smallest signal distortion rate.

[0091] In some embodiments, by acquiring multiple ground detection simulation points in the area where the flight route is to be deployed, the ground building heights, terrain data, and environmental data of the multiple ground detection simulation points in the area where the flight route is to be deployed are used as input data, and are respectively input into the simulation model of the above-mentioned UAV take-off and landing signal field to obtain the signal distortion rate corresponding to each ground detection simulation point, and then, based on the ground detection simulation points with smaller signal distortion rates in the area where the flight route is to be deployed, reliable solution support can be provided for the flight path planning of the UAV.

[0092] The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints provided by the present invention screens the detection area and key ground detection points where signals are blocked, calculates the signal delay rate of the UAV according to the time difference between the change of flight attitude and the issuance of control instructions at the key ground detection points, subsequently obtains terrain data and meteorological data in the observation area and integrates them into multipath fading factors that can describe multi-constraint factors, analyzes the potential influence coefficient between the signal delay rate and the multipath fading factor as the influence weight for calculating the signal distortion rate, and finally calculates the signal distortion rate of the UAV at different key ground detection points according to the multi-constraint factor differences between all key ground detection points and the differences between the signal delay rates, and then constructs a simulation model of the take-off and landing signal field of the UAV through the signal distortion rate, thereby realizing the simulation of the take-off and landing signal field of the UAV based on multi-factor constraints, improving the simulation accuracy of the take-off and landing signal field based on multi-factor constraints, and also providing reliable data support for signal safety and flight path optimization during the take-off and landing of the UAV.

[0093] Based on the same general inventive concept, the present invention also protects a UAV take-off and landing signal field simulation system based on multi-factor constraints. The UAV take-off and landing signal field simulation system based on multi-factor constraints provided by the present invention is described below. The UAV take-off and landing signal field simulation system based on multi-factor constraints described below and the UAV take-off and landing signal field simulation method based on multi-factor constraints described above can be referenced to each other.

[0094] See also Figure 6 , which shows a system structure diagram of a UAV take-off and landing signal field simulation system based on multi-factor constraints provided by an embodiment of the present invention, such as Figure 6 As shown, the above-mentioned UAV take-off and landing signal field simulation system based on multi-factor constraints specifically includes: The extraction module 210 is used to extract key ground detection points where signals are blocked in the UAV take-off and landing signal field based on the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field.

[0095] The first processing module 220 is used to determine the signal delay rate of the UAV at each key ground detection point.

[0096] The acquisition module 230 is used to acquire the meteorological data and terrain data in the detection area where each key ground detection point is located, and determine the multipath fading factor of each key ground detection point based on the meteorological data and terrain data.

[0097] The second processing module 240 is used to determine the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point.

[0098] The calculation module 250 is used to calculate the signal distortion rate of the UAV at each key ground detection point based on the multipath fading factor and potential influence coefficient corresponding to each key ground detection point.

[0099] The simulation module 260 is used to construct a simulation model of the UAV take-off and landing signal field by taking the ground building height at each key ground detection point as the key input data, taking the terrain data and meteorological data as the multi-factor constraint data, and taking the signal distortion rate of the UAV as the output data.

[0100] The UAV take-off and landing signal field simulation system based on multi-factor constraints provided by the present invention introduces a multipath fading factor determined based on meteorological data and terrain data into the UAV take-off and landing signal field simulation link, and dynamically determines the potential influence coefficient between the signal delay rate and the multipath fading factor. A more accurate signal distortion rate is obtained according to the multipath fading factor and the potential influence coefficient, thereby constructing a simulation model of the UAV take-off and landing signal field with higher accuracy and reliability.

[0101] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0102] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for simulating the take-off and landing signal field of unmanned aerial vehicles based on multi-factor constraints, characterized in that: The method comprises: Based on the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field, the key ground detection points where the signal is blocked in the UAV take-off and landing signal field are extracted; Determine the signal delay rate of the UAV at each of the key ground detection points; Acquire meteorological data and terrain data in the detection area where each of the key ground detection points is located, and determine the multipath fading factor of each of the key ground detection points based on the meteorological data and terrain data; Determining the potential influence coefficient between the signal delay rate and the multipath fading factor of each of the key ground detection points; Based on the multipath fading factor and potential impact coefficient corresponding to each of the key ground detection points, the signal distortion rate of the UAV at each of the key ground detection points is calculated; The ground building height at each of the key ground detection points is used as the key input data, the terrain data and meteorological data are used as the multi-factor constraint data, and the signal distortion rate of the UAV is used as the output data to construct a simulation model of the UAV take-off and landing signal field.

2. The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints according to claim 1 is characterized in that: Determining the signal delay rate of the UAV at each of the key ground detection points includes: For each of the key ground detection points, the time when the flight command is issued and the time when the flight attitude changes corresponding to each flight attitude of the UAV in each flight mission are obtained; Based on the time when the flight instruction corresponding to each flight attitude is issued and the time when the flight attitude changes, the probability of signal obstruction corresponding to each flight mission is calculated; Based on the probability of signal obstruction corresponding to each flight mission at each of the key ground detection points, the signal delay rate of the UAV at each of the key ground detection points is calculated.

3. The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints according to claim 2 is characterized in that: Based on the flight instruction issuance time and the flight attitude change time corresponding to each flight attitude, the signal blocking probability corresponding to each flight mission is calculated, including: Subtract the flight attitude change time corresponding to each flight attitude from the flight instruction issuance time and calculate the absolute value to obtain the time difference corresponding to each flight attitude; The time differences corresponding to all flight postures in each flight mission are averaged to calculate the probability of signal obstruction corresponding to each flight mission.

4. The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints according to claim 2 is characterized in that: Based on the probability of signal obstruction corresponding to each flight mission at each of the key ground detection points, the signal delay rate of the UAV at each of the key ground detection points is calculated, including: The signal obstruction probabilities corresponding to all flight missions at each of the key ground detection points are averaged to obtain the average obstruction probability corresponding to each of the key ground detection points; Based on the mean of the occlusion probability, the signal delay rate of the UAV at each of the key ground detection points is determined.

5. The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints according to claim 1 is characterized in that: Determining the multipath fading factor of each of the key ground detection points based on the meteorological data and the terrain data includes: Extracting the close ground detection points that are consistent with the ground building height of each key ground detection point from all the key ground detection points; Determine the information entropy of the terrain data between each of the key ground detection points and the adjacent ground detection points, and use the information entropy as the terrain complexity of each of the key ground detection points; Determining the meteorological complexity of each of the key ground detection points based on the meteorological data of each of the key ground detection points; Based on the terrain complexity and meteorological complexity of each of the key ground detection points, the multipath fading factor of each of the key ground detection points is calculated.

6. The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints according to claim 5 is characterized in that: Determining the meteorological complexity of each of the key ground detection points based on the meteorological data of each of the key ground detection points includes: Fitting various sub-data in the meteorological data of each of the key ground detection points respectively to obtain a meteorological fitting curve corresponding to each sub-data; Determine the data variance corresponding to the meteorological fitting curve of each sub-data; The data variances corresponding to all seed data in the meteorological data of each of the key ground detection points are averaged to calculate the meteorological complexity of each of the key ground detection points.

7. The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints according to claim 5 is characterized in that: Based on the terrain complexity and meteorological complexity of each of the key ground detection points, the multipath fading factor of each of the key ground detection points is calculated, including: Multiplying the terrain complexity and the meteorological complexity of each of the key ground detection points to obtain a complexity product; The complexity product is normalized to obtain the multipath fading factor of each of the key ground detection points.

8. The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints according to claim 1 is characterized in that: Determining the potential influence coefficient between the signal delay rate and the multipath fading factor of each of the key ground detection points includes: Fitting the multipath fading factors corresponding to all key ground detection points to obtain a first fitting curve; Fitting the signal delay rates corresponding to all key ground detection points to obtain a second fitting curve; Calculating a mean square error between the first fitting curve and the second fitting curve; The mean square error is used as a potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point.

9. The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints according to claim 1 is characterized in that: Based on the multipath fading factor and potential impact coefficient corresponding to each of the key ground detection points, the signal distortion rate of the UAV at each of the key ground detection points is calculated, including: Determine a nearby ground detection point in the detection area that has the same ground building height as each of the key ground detection points; Calculating the absolute mean of the difference between the multipath fading factor corresponding to each of the key ground detection points and the multipath fading factors corresponding to all the adjacent ground detection points; The potential influence coefficient corresponding to each of the key ground detection points is divided by the sum of the absolute mean of the difference and the preset non-zero fixed value and the reciprocal thereof to calculate the intermediate value of the distortion rate; The intermediate value of the distortion rate is input into the natural exponential function to calculate the signal distortion rate of the UAV at each of the key ground detection points.

10. The method for simulating the take-off and landing signal field of a UAV based on multi-factor constraints according to claim 1, characterized in that: Based on the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field, the key ground detection points where the signal is blocked in the UAV take-off and landing signal field are extracted, including: Compare the ground building height corresponding to each ground detection point in the UAV take-off and landing signal field with the pre-obtained ground building height reference value to obtain a comparison result; The ground detection point where the comparison result shows that the ground building height is lower than the ground building height reference value is used as the key ground detection point where the signal is blocked in the UAV take-off and landing signal field.

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