UAV Takeoff and Landing Signal Field Simulation Method Based on Multi-Factor Constraints

Abstract

The present invention relates to the technical field of UAV takeoff and landing data processing, and specifically relates to a simulation method for UAV takeoff and landing signal fields based on multi-factor constraints. The method includes: extracting key ground detection points where signals in the UAV takeoff and landing signal field are blocked; determining the signal delay rate of the UAV at each key ground detection point; determining the multipath fading factor of each key ground detection point; determining the 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 UAV at each key ground detection point; using the ground building height at each key ground detection point as key input data, using terrain data and meteorological data as multi-factor constraint data, and using the signal distortion rate of the UAV as output data to construct a simulation model of the UAV takeoff and landing signal field. A more accurate simulation model of the UAV takeoff and landing signal field can be constructed, thereby meeting the high-precision simulation requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV takeoff and landing data processing, and particularly relates to a simulation method for UAV takeoff and landing signal fields based on multi-factor constraints. Background Art

[0002] With the rapid development of UAV technology, UAVs have been widely used in many fields such as agriculture, logistics, disaster relief, geographical survey, environmental monitoring, etc. The takeoff and landing of UAVs is one of the key links in their use. Especially in complex environments, how to ensure the smooth and safe takeoff and landing process of UAVs is an important content for improving the application efficiency and safety of UAVs. In practical applications, UAV takeoff and landing signal field simulation is an important technical means for simulating and optimizing UAV takeoff and landing signal fields.

[0003] In related technologies, the UAV takeoff and landing signal field simulation link mainly simulates and optimizes the signal propagation and reception during the UAV takeoff and landing process based on simulation technology and signal propagation models. However, in traditional simulation schemes, since the simulation model of the UAV takeoff and landing signal field is constructed only based on static data such as ground building heights, this simulation model cannot accurately simulate the influence of multi-constraint factors in the actual flight process, resulting in a problem of low simulation accuracy. Summary of the Invention

[0004] In order to solve the technical problems that traditional simulation schemes cannot accurately simulate the influence of multi-constraint factors in the actual flight process and have low simulation accuracy, the purpose of the present invention is to provide a simulation method for UAV takeoff and landing signal fields based on multi-factor constraints, and the specific technical solutions adopted are as follows:

[0005] A simulation method for UAV takeoff and landing signal fields based on multi-factor constraints, the method includes:

[0006] Based on the ground building height corresponding to each ground detection point in the UAV takeoff and landing signal field, extract the key ground detection points where the signals in the UAV takeoff and landing signal field are blocked;

[0007] Determine the signal delay rate of the UAV at each of the key ground detection points;

[0008] Obtain the meteorological data and terrain data in the detection area where each of the key ground detection points is located, and based on the meteorological data and terrain data, determine the multipath fading factor of each of the key ground detection points;

[0009] Determine the potential influence coefficient between the signal delay rate and the multipath fading factor of each of the key ground detection points;

[0010] Based on the multipath fading factor and potential influence coefficient corresponding to each of the key ground detection points, calculate the signal distortion rate of the UAV at each of the key ground detection points;

[0011] Taking the ground building height at each of the key ground detection points as key input data, taking terrain data and meteorological data as multi-factor constraint data, and taking the signal distortion rate of the unmanned aerial vehicle as output data, a simulation model of the takeoff and landing signal field of the unmanned aerial vehicle is constructed.

[0012] According to a method for simulating the takeoff and landing signal field of an unmanned aerial vehicle based on multi-factor constraints provided by the present invention, determining the signal delay rate of the unmanned aerial vehicle at each of the key ground detection points includes:

[0013] For each of the key ground detection points, obtaining the flight instruction issuance time and the flight attitude change time corresponding to each flight attitude during each flight mission of the unmanned aerial vehicle;

[0014] Based on the flight instruction issuance time and the flight attitude change time corresponding to each flight attitude, calculating the signal occlusion probability corresponding to each flight mission;

[0015] Based on the signal occlusion probability corresponding to each flight mission at each of the key ground detection points, calculating the signal delay rate of the unmanned aerial vehicle at each of the key ground detection points.

[0016] According to a method for simulating the takeoff and landing signal field of an unmanned aerial vehicle based on multi-factor constraints provided by the present invention, based on the flight instruction issuance time and the flight attitude change time corresponding to each flight attitude, calculating the signal occlusion probability corresponding to each flight mission includes:

[0017] Taking the absolute value of the difference between the flight attitude change time and the flight instruction issuance time corresponding to each flight attitude, and calculating the time difference corresponding to each flight attitude;

[0018] Calculating the average value of the time differences corresponding to all flight attitudes in each flight mission, and calculating the signal occlusion probability corresponding to each flight mission.

[0019] According to a method for simulating the takeoff and landing signal field of an unmanned aerial vehicle based on multi-factor constraints provided by the present invention, based on the signal occlusion probability corresponding to each flight mission at each of the key ground detection points, calculating the signal delay rate of the unmanned aerial vehicle at each of the key ground detection points includes:

[0020] Calculating the average value of the signal occlusion probabilities corresponding to all flight missions at each of the key ground detection points, and calculating the average occlusion probability corresponding to each of the key ground detection points;

[0021] Based on the average occlusion probability, determining the signal delay rate of the unmanned aerial vehicle at each of the key ground detection points.

[0022] A method for simulating an unmanned aerial vehicle (UAV) takeoff and landing signal field based on multi-factor constraints provided by the present invention, based on the meteorological data and terrain data, determines the multipath fading factor of each of the key ground detection points, including:

[0023] Extract the adjacent ground detection points with the same ground building height as each key ground detection point from all the key ground detection points;

[0024] Determine the information entropy of the terrain data between each key ground detection point and the adjacent ground detection points, and use the information entropy as the terrain complexity of each key ground detection point;

[0025] According to the meteorological data of each key ground detection point, determine the meteorological complexity of each key ground detection point;

[0026] Based on the terrain complexity and meteorological complexity of each key ground detection point, calculate the multipath fading factor of each key ground detection point.

[0027] A method for simulating an unmanned aerial vehicle (UAV) takeoff and landing signal field based on multi-factor constraints provided by the present invention, according to the meteorological data of each key ground detection point, determines the meteorological complexity of each key ground detection point, including:

[0028] Respectively fit various sub-data in the meteorological data of each key ground detection point to obtain a meteorological fitting curve corresponding to each sub-data;

[0029] Determine the data variance corresponding to the meteorological fitting curve of each sub-data;

[0030] Calculate the average value of the data variances corresponding to all sub-data in the meteorological data of each key ground detection point to obtain the meteorological complexity of each key ground detection point.

[0031] A method for simulating an unmanned aerial vehicle (UAV) takeoff and landing signal field based on multi-factor constraints provided by the present invention, based on the terrain complexity and meteorological complexity of each key ground detection point, calculates the multipath fading factor of each key ground detection point, including:

[0032] Multiply the terrain complexity and meteorological complexity of each key ground detection point to obtain a complexity product;

[0033] Perform normalization processing on the complexity product to obtain the multipath fading factor of each key ground detection point.

[0034] A method for simulating an unmanned aerial vehicle (UAV) takeoff and landing signal field based on multi-factor constraints provided by the present invention, determines the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point, including:

[0035] Fit the multipath fading factors corresponding to all key ground detection points to obtain a first fitting curve;

[0036] Fit the signal delay rates corresponding to all key ground detection points to obtain a second fitting curve;

[0037] Calculate the mean square error between the first fitting curve and the second fitting curve;

[0038] Take the mean square error as the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point.

[0039] According to a method for simulating an unmanned aerial vehicle (UAV) takeoff and landing signal field based on multi-factor constraints provided by the present invention, based on the multipath fading factor and the potential influence coefficient corresponding to each key ground detection point, calculate the signal distortion rate of the UAV at each key ground detection point, including:

[0040] Determine the adjacent ground detection points in the detection area with the same ground building height as each key ground detection point;

[0041] Calculate the absolute mean value of the difference between the multipath fading factor corresponding to each key ground detection point and the multipath fading factors corresponding to all adjacent ground detection points;

[0042] Divide the potential influence coefficient corresponding to each key ground detection point by the sum of the absolute mean value of the difference and a preset non-zero fixed value, and take the opposite number to calculate an intermediate distortion rate value;

[0043] Input the intermediate distortion rate value into the natural exponential function to calculate the signal distortion rate of the UAV at each key ground detection point.

[0044] According to a method for simulating an unmanned aerial vehicle (UAV) takeoff and landing signal field based on multi-factor constraints provided by the present invention, based on the ground building height corresponding to each ground detection point in the UAV takeoff and landing signal field, extract the key ground detection points where the signals in the UAV takeoff and landing signal field are blocked, including:

[0045] Compare the ground building height corresponding to each ground detection point in the UAV takeoff and landing signal field with a pre-obtained ground building height reference value to obtain a comparison result;

[0046] Take the ground detection points with the comparison result that the ground building height is lower than the ground building height reference value as the key ground detection points where the signals in the UAV takeoff and landing signal field are blocked.

[0047] The present invention has the following beneficial effects:

[0048] By determining the signal delay rate of the unmanned aerial vehicle (UAV) at the key ground detection points where the signals in the UAV takeoff and landing signal field are blocked, obtaining the meteorological data and terrain data in the detection area where each key ground detection point is located, and based on the meteorological data and terrain data, determining the multipath fading factor of each key ground detection point, then determining the 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 UAV at each key ground detection point based on the multipath fading factor and the potential influence coefficient corresponding to each key ground detection point, and finally using the ground building height at each key ground detection point as the key input data, the terrain data and meteorological data as the multi-factor constraint data, and the signal distortion rate of the UAV as the output data, a simulation model of the UAV takeoff and landing signal field is constructed. Since the multipath fading factor determined based on the meteorological data and terrain data is introduced in the simulation of the UAV takeoff and landing signal field, 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 thus a simulation model of the UAV takeoff and landing signal field with higher accuracy and reliability can be constructed, so as to meet the high-precision simulation requirements for the UAV takeoff and landing signal field. Brief Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0050] Figure 1 It is a flowchart of a method for simulating a UAV takeoff and landing signal field based on multi-factor constraints provided by an embodiment of the present invention;

[0051] Figure 2 It is a schematic diagram of the division of an inverted conical area;

[0052] Figure 3 It is a schematic diagram of the principle that there is a certain time delay in sending and receiving instructions during the flight of the UAV;

[0053] Figure 4 It is a schematic diagram of the position of the largest inscribed circle in a rectangular area formed by the ground detection points closest to a key ground detection point in its neighborhood;

[0054] Figure 5 It is a schematic diagram of the fitting forms of the first fitting curve and the second fitting curve;

[0055] Figure 6The system structure diagram of a UAV takeoff and landing signal field simulation system based on multi-factor constraints provided by an embodiment of the present invention. Detailed implementation manners

[0056] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of a UAV takeoff and landing signal field simulation method based on multi-factor constraints proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0058] The following combines the attached Figure 1 to the attached Figure 6 Specifically illustrate the specific scheme of a UAV takeoff and landing signal field simulation method provided by the present invention.

[0059] Please refer to Figure 1 , which shows the method flow chart of a UAV takeoff and landing signal field simulation method provided by an embodiment of the present invention. As Figure 1 shown, the above-mentioned UAV takeoff and landing signal field simulation method based on multi-factor constraints specifically includes the following steps:

[0060] Step 110: Based on the ground building height corresponding to each ground detection point in the UAV takeoff and landing signal field, extract the key ground detection points where the signals in the UAV takeoff and landing signal field are blocked.

[0061] It can be understood that during the process of simulating the UAV takeoff and landing signal field, in order to accurately simulate the signal propagation situation and the changes in the signals during the UAV takeoff and landing process, a series of original data related to signal propagation, aircraft movement, environment, and meteorology need to be obtained in advance. These original data include: terrain data, meteorological data, ground building height, and flight trajectory data, etc.

[0062] In practical applications, digital elevation model (DEM) and lidar can be used to obtain terrain data, and micro-images and images taken by UAVs can be used to extract the ground building height, and then accurate geographic information system (GIS) data can be generated.

[0063] Obtain real-time data such as wind speed, wind direction, temperature, and humidity through a weather station or a weather server. If meteorological data cannot be directly obtained in a local area, it can be predicted through a meteorological model or historical meteorological data in the recent period can be used.

[0064] Flight trajectory data can be obtained through the flight control system or simulator of the unmanned aerial vehicle (UAV). In this embodiment, the flight trajectory data may specifically include flight path, speed, acceleration, etc.

[0065] In practical applications, the UAV takeoff and landing signal field can be reasonably selected according to actual requirements. It can be understood that the general flight altitude of the UAV is 200 meters and the general flight distance is 3000 meters, that is, no ground objects can appear within an inverted cone area centered on the UAV takeoff and landing signal field and with a height from the UAV takeoff and landing signal field to 200 meters above the ground, otherwise the signal will be blocked. Figure 2 An exemplary schematic diagram of the division of the inverted cone area is shown. Figure 2 In which A represents the takeoff and landing field, B represents the ground building, and C represents the mountain.

[0066] It can be understood that the ground detection point is a position point set on the ground for monitoring and detecting the UAV and its surrounding environment.

[0067] In a specific implementation, based on the ground building height corresponding to each ground detection point in the UAV takeoff and landing signal field, the key ground detection points where the signal in the UAV takeoff and landing signal field is blocked are extracted, specifically including:

[0068] First, compare the ground building height corresponding to each ground detection point in the UAV takeoff and landing signal field with the pre-obtained reference value of the ground building height to obtain a comparison result.

[0069] It can be understood that the reference value of the ground building height refers to the maximum height of the ground building at this ground detection point when the signal is not blocked.

[0070] Then, take the ground detection points with the comparison result that the ground building height is lower than the reference value of the ground building height as the key ground detection points where the signal in the UAV takeoff and landing signal field is blocked.

[0071] From this, it can be known that in the determination link of the key ground detection points, whether the signal is blocked is mainly judged according to the height difference between the ground building height (i.e., the actual height) at the ground detection point and the maximum height of the ground building at this ground detection point when the signal is not blocked. When the ground building height at the ground detection point is lower than the maximum height of the ground building at this ground detection point when it 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.

[0072] Step 120: Determine the signal delay rate of the UAV at each key ground detection point.

[0073] It can be understood that the signal delay rate can characterize the delay situation of signal propagation during the process of the signal being emitted and received by the UAV at the current key ground detection point.

[0074] Step 130: Obtain 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.

[0075] It can be understood that the multipath fading factor can characterize the influence on the UAV signal propagation caused by reflections, refractions, heat dissipation, etc. that occur on the ground and in the surrounding environment during the UAV takeoff and landing communication process.

[0076] Step 140: Determine 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 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.

[0078] Step 150: Calculate the signal distortion rate of the UAV at each key ground detection point based on the multipath fading factor and the potential influence coefficient corresponding to each key ground detection point.

[0079] It can be understood that the potential influence coefficient is a key factor for measuring the signal distortion rate of the UAV at the key ground detection point. Subsequently, it can be used as an influence weight to calculate the signal distortion rate of the UAV flying in different observation areas, so as to simulate the takeoff and landing signal field of the UAV.

[0080] Step 160: Use the ground building height at each key ground detection point as the key input data, the terrain data and meteorological data as multi-factor constraint data, and the signal distortion rate of the UAV as the output data to construct a simulation model of the UAV takeoff and landing signal field.

[0081] It can be understood that the simulation model of the UAV takeoff and landing signal field can characterize the corresponding relationship between the signal distortion rate of the UAV at any ground detection point and the ground building height, terrain data, and meteorological data. Inputting the ground building height, terrain data, and meteorological data at a certain ground observation point into the simulation model can directly output the signal distortion rate of the UAV at the ground detection point.

[0082] The solution provided in this embodiment introduces dynamic influence parameters such as terrain data and meteorological data in the simulation link of the UAV takeoff and landing signal field, enabling a more accurate simulation model to be obtained, thereby improving the simulation accuracy and reliability of the UAV takeoff and landing signal field.

[0083] When a physical obstacle appears in front of the UAV during flight, signal transmission may be blocked or attenuated, resulting in the interruption of the communication link or the loss of the signal. For example, assume the flight distance of the UAV is 300 meters, and there is a physical obstacle at the 100-meter position. In the range of 100 - 300 meters, the communication between the UAV and the ground terminal will be interrupted.

[0084] It can be understood that only based on the ground building height of the ground detection point to preliminarily judge the signal occlusion situation of the UAV relative to the ground detection point, it is impossible to accurately evaluate the true state of signal occlusion. That is, during the flight of the UAV, even if signal occlusion occurs, there may still be a situation where the signal can be received by the ground detection point, rather than completely blocking the signal reception of the ground detection point. Therefore, in this embodiment, by calculating the signal delay rate of the UAV at each key ground detection point, the propagation delay situation of the signal at the key ground detection points where signal occlusion is preliminarily judged is further determined.

[0085] In one embodiment, determining the signal delay rate of the UAV at each key ground detection point specifically includes:

[0086] First step, for each key ground detection point, obtain the flight instruction issuance moment and the flight attitude change moment corresponding to each flight attitude of the UAV during each flight mission.

[0087] In practical applications, during a flight of the UAV, the ground UAV control system is used to send control instructions for the flight attitude of the UAV, that is, flight instructions, and record the time stamp of the flight instruction sent corresponding to each flight attitude, that is, the flight instruction issuance moment t; at the same time, the flight attitude change time stamp of the UAV is monitored through the aircraft carried on the UAV, that is, the flight attitude change moment 。 Figure 3 Exemplarily shows the situation that there is a certain time delay in sending and receiving instructions during the flight control of the UAV.

[0088] In this embodiment, the flight attitude data that can characterize the flight attitude change can specifically include various types of data such as roll angle, pitch angle, and yaw angle.

[0089] Second step, based on the flight instruction issuance moment and the flight attitude change moment corresponding to each flight attitude, calculate the signal occlusion probability corresponding to each flight mission.

[0090] In a specific implementation, based on the flight instruction issuance time and the flight attitude change time corresponding to each flight attitude, the signal occlusion probability corresponding to each flight mission is calculated, specifically including:

[0091] First, the absolute value of the difference between the flight attitude change time and the flight instruction issuance time corresponding to each flight attitude is calculated to obtain the time difference corresponding to each flight attitude.

[0092] Then, the average value of the time differences corresponding to all flight attitudes in each flight mission is calculated to obtain the signal occlusion probability corresponding to each flight mission.

[0093] In this embodiment, the signal occlusion probability corresponding to the i-th flight mission can be calculated as follows:

[0094] (1)

[0095] Wherein, represents the signal occlusion probability corresponding to the i-th flight mission, represents the flight instruction issuance time, represents the flight attitude change time, represents the time difference corresponding to the n-th flight attitude, represents the total number of flight attitudes in the i-th flight mission.

[0096] Thirdly, based on the signal occlusion 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.

[0097] In a specific implementation, based on the signal occlusion 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:

[0098] First, the average value of the signal occlusion probabilities corresponding to all flight missions at each key ground detection point is calculated to obtain the average occlusion probability corresponding to each key ground detection point.

[0099] Then, based on the average occlusion probability, the signal delay rate of the UAV at each key ground detection point is determined.

[0100] In this embodiment, the signal delay rate of the UAV at the u-th key ground detection point can be calculated as follows:

[0101] (2)

[0102] Wherein, represents the signal delay rate of the UAV at the u-th key ground detection point, Indicates the probability that the signal corresponding to the i-th flight mission is blocked. Indicates 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.

[0103] Specifically, since the probability that the signal corresponding to the i-th flight mission is blocked is a dimensional parameter, while the finally obtained signal delay rate is a dimensionless parameter. Therefore, in this embodiment, dimensionless conversion is achieved through a dimensionless conversion function. For example, dimensionless conversion can be achieved by dividing the currently obtained average blocked probability by the preset reference blocked probability value.

[0104] It can be understood that the signal delay rate may also be affected by the multipath effect during actual flight. If only the ground building height of the ground detection point and the signal delay rate of the UAV are used to simulate the UAV takeoff and landing signal field, this simulation method cannot achieve a good simulation effect because it ignores the influence of the multipath effect during the UAV flight on the signal distortion situation, and lacks the observation and analysis of dynamic factors in the signal field for the simulation of the UAV takeoff and landing signal field. Therefore, this embodiment introduces a calculation link for the multipath fading factor.

[0105] In one embodiment, based on meteorological data and terrain data, the multipath fading factor of each key ground detection point is determined, specifically including:

[0106] First step, extract the adjacent ground detection points with the same ground building height as each key ground detection point from all key ground detection points.

[0107] It can be understood that the multipath effect refers to the phenomenon that during the propagation process, due to the reflection or refraction of obstacles (such as buildings, hills, ground, etc.), or the change in the refraction angle of the signal introduced by meteorological factors, the same signal arrives at the receiver along different paths, and the signals on these different paths will be superimposed together at the receiving end, forming the multipath effect.

[0108] In this embodiment, during the delimitation process of the detection area where any key ground detection point is located, specifically, with the position of this key ground detection point as the center, obtain the largest inscribed circle in the rectangular area composed of the nearest ground detection points in the neighborhood of this key ground detection point, and use this as the detection area. The largest inscribed circle can be seen in Figure 4 shown, that is Figure 4 the circular area marked in. Subsequently, meteorological data and terrain data can be obtained in this detection area.

[0109] In the second step, determine the information entropy of the terrain data between each key ground detection point and its neighboring ground detection points, and use the information entropy as the terrain complexity of each key ground detection point.

[0110] It can be understood that during the determination of the terrain complexity, the differences in the terrain data of each key ground detection point and its neighboring ground detection points related to this key ground detection point are comprehensively considered. Among them, 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, which means the higher the terrain complexity. The more complex the terrain, the higher the possibility of affecting the propagation route of the UAV signal, which may increase the decoding error after the receiving end receives the signal, and further increase the signal distortion rate of the UAV.

[0111] In the third step, determine the meteorological complexity of each key ground detection point according to the meteorological data of each key ground detection point.

[0112] In a specific implementation, determining the meteorological complexity of each key ground detection point according to the meteorological data of each key ground detection point specifically includes:

[0113] First, fit each type of sub-data in the meteorological data of each key ground detection point respectively to obtain the meteorological fitting curve corresponding to each type of sub-data.

[0114] In this embodiment, the meteorological data includes multiple types of sub-data such as temperature, humidity, and wind speed. By fitting each type of sub-data respectively, the meteorological fitting curve corresponding to each type of sub-data can be obtained, such as the temperature fitting curve, the humidity fitting curve, and the wind speed fitting curve.

[0115] Then, determine the data variance corresponding to the meteorological fitting curve of each type of sub-data.

[0116] In the data variance calculation process, multiple key data points in each meteorological fitting curve can be extracted first, and then the data mean of all key data points in each meteorological fitting curve can be calculated respectively. Then, according to the calculated data mean, the data variance of all key data points can be calculated, and this is used as the data variance of this meteorological fitting curve.

[0117] Finally, calculate the mean of the data variances corresponding to all types of sub-data in the meteorological data of each key ground detection point to obtain the meteorological complexity of each key ground detection point.

[0118] In this embodiment, the meteorological complexity can characterize the complex situation of various types of sub-data in the meteorological data.

[0119] In the fourth step, 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.

[0120] 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:

[0121] First, multiply the terrain complexity and meteorological complexity of each key ground detection point to obtain the complexity product.

[0122] Then, perform normalization processing on the complexity product to obtain the multipath fading factor of each key ground detection point.

[0123] In this embodiment, the multipath fading factor corresponding to the u-th key ground detection point can be calculated as follows:

[0124] (3)

[0125] Wherein, represents the multipath fading factor corresponding to the u-th key ground detection point, represents the terrain complexity, represents the meteorological complexity, ( ) represents the normalization function.

[0126] 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:

[0127] In the first step, fit the multipath fading factors corresponding to all key ground detection points to obtain the first fitting curve.

[0128] In the second step, fit the signal delay rates corresponding to all key ground detection points to obtain the second fitting curve.

[0129] In the third step, calculate the mean square error between the first fitting curve and the second fitting curve.

[0130] In the fourth step, use the mean square error as the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point.

[0131] In this embodiment, taking the observation position corresponding to each key ground detection point as the abscissa data, and taking the multipath fading factor and the signal delay rate as the ordinate data respectively, the first fitting curve and the second fitting curve can be respectively fitted, Figure 5 Exemplarily shows the first fitting curve E and the second fitting curve R.

[0132] The mean square error between the first fitting curve and the second fitting curve can be expressed as follows:

[0133] (4)

[0134] Among them, E represents the first fitting curve, and R represents the second fitting curve. represents the mean square error function. 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.

[0135] In this embodiment, the mean square error between the first fitting curve and the second fitting curve, that is, the execution process of the mean square error function, is as follows:

[0136] First, obtain multiple groups of discrete points with the same abscissa values from the first fitting curve and the second fitting curve respectively, that is, obtain the signal delay rate and the multipath fading factor at the same observation position respectively, and obtain multiple groups of discrete point pairs.

[0137] Then, calculate the square value of the difference between the two function values at the same observation position in each group of discrete point pairs, 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 squared error.

[0138] Finally, take the average of all the calculated squared errors to calculate the mean square error between the first fitting curve and the second fitting curve.

[0139] It can be understood that the potential influence coefficient between the signal delay rate and the multipath fading factor is the key factor for measuring the signal distortion rate of the UAV at different ground detection points. In this embodiment, this potential influence coefficient is used as the weight value to calculate the signal distortion rate of the UAV flying in different observation areas, and then to simulate the takeoff and landing signal field of the UAV.

[0140] In one embodiment, based on the multipath fading factor and the 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, specifically including:

[0141] The first step is to determine the adjacent ground detection points with the same ground building height as each key ground detection point.

[0142] The second step is to calculate the absolute mean value of the difference between the multipath fading factor corresponding to each key ground detection point and the multipath fading factors corresponding to all adjacent ground detection points.

[0143] The third step is to divide the potential influence coefficient corresponding to each key ground detection point by the sum of the absolute mean value of the difference and a preset non-zero fixed value and take the opposite number to calculate the intermediate value of the distortion rate.

[0144] In the fourth step, input the median value of the distortion rate into the natural exponential function to calculate the signal distortion rate of the UAV at each key ground detection point.

[0145] In this embodiment, the signal distortion rate of the UAV at the u-th key ground detection point can be expressed as follows:

[0146] (5)

[0147] Where, represents the signal distortion rate of the UAV at the u-th key ground detection point, represents the potential influence coefficient, represents the multipath fading factor corresponding to the u-th key ground detection point. Assume that there are M adjacent ground observation points in the current observation area except the u-th key ground detection point, that is, M represents the number of adjacent ground observation points in the current observation area except the u-th key ground detection point, represents the multipath fading factor of the N-th adjacent ground detection point among the M adjacent ground observation points, h represents a non-zero fixed value, that is, a preset non-zero fixed value, used to prevent the problem that the function is meaningless due to the denominator being 0, ( ) represents the natural exponential function.

[0148] In practical applications, according to the signal distortion rates of the UAV at different key ground observation points obtained above, a simulation model of the UAV takeoff 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 at the corresponding positions, that is, at each position point, it includes the ground building height, terrain data, environmental data, and signal distortion rate of the key ground detection point.

[0149] After the simulation model of the UAV takeoff and landing signal field is constructed, during the flight of the UAV, after inputting dynamic factors such as the ground building height, terrain data, and meteorological data of a certain key ground observation point, the signal distortion rate at this key ground observation point can be output.

[0150] In practical applications, the above simulation model of the UAV takeoff and landing signal field can be used to select a suitable UAV takeoff and landing signal field. It can be understood that the UAV takeoff and landing signal field should be preferably set on government-owned public land, followed by land with the right of use of enterprises and individuals. Based on this, the proposed layout range of the UAV takeoff and landing signal field can be selected first, and multiple ground detection simulation points are determined within the proposed layout range. The ground building height, terrain data, and environmental data of the multiple ground detection simulation points are used as input data and input into the above simulation model of the UAV takeoff and landing signal field respectively to obtain the signal distortion rate corresponding to each ground detection simulation point. According to the ground detection simulation point with the smallest signal distortion rate, the location of the suitable UAV takeoff and landing signal field is determined.

[0151] In some embodiments, by obtaining a plurality of ground detection simulation points within the planned layout area of the flight route, and using the ground building height, terrain data, and environmental data of the plurality of ground detection simulation points within the planned layout area of the flight route as input data, and respectively inputting them into the simulation model of the above-mentioned UAV takeoff and landing signal field, the signal distortion rate corresponding to each ground detection simulation point can be obtained. Furthermore, based on the ground detection simulation points with a smaller signal distortion rate within the planned layout area of the flight route, a reliable scheme support can be provided for the flight path planning of the UAV.

[0152] The method for simulating the UAV takeoff and landing signal field based on multi-factor constraints provided by the present invention screens the detection areas where signals are blocked and key ground detection points, calculates the signal delay rate of the UAV according to the time difference between the change in flight attitude and the issuance of control instructions at the key ground detection points. Subsequently, by obtaining the terrain data and meteorological data in the observation area and integrating them into a multipath fading factor that can describe multi-constraint factors, the potential influence coefficient between the signal delay rate and the multipath fading factor is analyzed as the influence weight for calculating the signal distortion rate. Finally, based on the differences in multi-constraint factors and the differences in signal delay rates between all key ground detection points, the signal distortion rate of the UAV at different key ground detection points is calculated. Furthermore, a simulation model of the UAV takeoff and landing signal field is constructed through the signal distortion rate, thereby realizing the simulation of the UAV takeoff and landing signal field based on multi-factor constraints, improving the simulation accuracy of the takeoff and landing signal field based on multi-factor constraints, and providing reliable data support for signal safety and flight path optimization during the UAV takeoff and landing process.

[0153] Based on the same general inventive concept, the present invention also protects a system for simulating the UAV takeoff and landing signal field based on multi-factor constraints. The system for simulating the UAV takeoff and landing signal field based on multi-factor constraints provided by the present invention will be described below. The system for simulating the UAV takeoff and landing signal field based on multi-factor constraints described below can be mutually referred to corresponding to the method for simulating the UAV takeoff and landing signal field based on multi-factor constraints described above.

[0154] Please refer to Figure 6 , which shows the system structure diagram of a system for simulating the UAV takeoff and landing signal field based on multi-factor constraints provided by an embodiment of the present invention. As Figure 6 shown, the above-mentioned system for simulating the UAV takeoff and landing signal field based on multi-factor constraints specifically includes:

[0155] An extraction module 210, configured to extract key ground detection points where signals in the UAV takeoff and landing signal field are blocked based on the ground building height corresponding to each ground detection point in the UAV takeoff and landing signal field.

[0156] A first processing module 220, configured to determine the signal delay rate of the UAV at each key ground detection point.

[0157] An acquisition module 230, configured to 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 the terrain data.

[0158] A second processing module 240, configured to determine the potential influence coefficient between the signal delay rate and the multipath fading factor of each key ground detection point.

[0159] A calculation module 250, configured to calculate the signal distortion rate of the unmanned aerial vehicle (UAV) at each key ground detection point based on the multipath fading factor and the potential influence coefficient corresponding to each key ground detection point.

[0160] A simulation module 260, configured to construct a simulation model of the UAV takeoff and landing signal field by using the ground building height at each key ground detection point as key input data, the terrain data and the meteorological data as multi-factor constraint data, and the signal distortion rate of the UAV as output data.

[0161] In the UAV takeoff and landing signal field simulation system based on multi-factor constraints provided by the present invention, since the multipath fading factor determined based on the meteorological data and the terrain data is introduced in the UAV takeoff and landing signal field simulation link, by dynamically determining the potential influence coefficient between the signal delay rate and the multipath fading factor, a more accurate signal distortion rate can be obtained based on the multipath fading factor and the potential influence coefficient, and further, a simulation model of the UAV takeoff and landing signal field with higher accuracy and reliability can be constructed.

[0162] Regarding the system in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0163] It should be noted that: the above sequence of the embodiments of the present invention is only for description, and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific sequence or continuous sequence shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0164] Each embodiment in this specification is described in a progressive manner, and the same or similar parts among the embodiments can be referred to each other. 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 key input data, the terrain data and the meteorological data are used as multi-factor constraint data, and the signal distortion rate of the UAV is used as output data to construct a simulation model of the UAV take-off and landing signal field; 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, a multipath fading factor of each of the key ground detection points is calculated; 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 variance corresponding to all seed data in the meteorological data of each of the key ground detection points is averaged to calculate the meteorological complexity 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, including: Multiplying the terrain complexity and the meteorological complexity of each of the key ground detection points to obtain a complexity product; Normalizing the complexity product to obtain a multipath fading factor of each of the key ground detection points; 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; 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.

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 signal blocking probability 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: 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: 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 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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