Urban low-altitude gridding management process control method and system based on unmanned aerial vehicle analysis

Abstract

The invention discloses an urban low-altitude gridding management process control method and system based on unmanned aerial vehicle analysis, and belongs to the technical field of unmanned aerial vehicle flight management.The urban low-altitude gridding management process control method comprises the steps that urban three-dimensional data are obtained, an urban area is divided into multiple grid units, meteorological data are obtained, and a building interference evaluation model is constructed; importing the open angle and the building surrounding rate of each grid unit into a building interference evaluation model to calculate the building intensity, evaluating the reflection interference on the unmanned aerial vehicle through the number and intensity of reflection paths, constructing a meteorological interference model, obtaining the cloud cluster humidity distribution, raindrop particle size and wind parameters of the grid units, and calculating the building intensity. The method comprises the following steps: importing meteorological data into a meteorological interference model to evaluate a rainfall interference condition and a wind disturbance condition, constructing a flight risk evaluation model, and importing building density, a multi-path reflection error index, a rainfall interference index and wind disturbance intensity into the flight risk evaluation model to evaluate the flight risk degree of the unmanned aerial vehicle. And the safety of the flight path of the unmanned aerial vehicle is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicle flight management, and in particular to a city low-altitude grid management process control method and system based on unmanned aerial vehicle analysis. BACKGROUND

[0002] With the gradual opening of city low-altitude airspace and the development of unmanned aerial vehicle technology, unmanned aerial vehicles are increasingly widely used in fields such as logistics distribution, city inspection, emergency response, and environmental monitoring. However, the city low-altitude environment has characteristics such as dense buildings, severe weather changes, and complex signal interference. Traditional path planning methods are mostly based on two-dimensional maps or simple obstacle avoidance models, and it is difficult to fully consider the influence of city spatial structure and environmental interference on flight safety. In the prior art, most path planning or airspace management systems ignore the following key factors: the influence of city three-dimensional building structure on signal propagation and flight feasibility is not modeled in detail, especially in high-density areas, GPS signal reflection and shielding are prone to occur, causing positioning drift and flight deviation; real-time weather conditions (such as wind shear, gusts, and rain clouds) cannot be dynamically fused and evaluated with flight paths, making it difficult to identify potential flight accident risks caused by adverse weather; there is a lack of fine low-altitude grid division and local risk prediction mechanism, making it difficult to identify and avoid high-risk areas in the entire flight path in advance.

[0003] The application introduces city canyon shielding and reflection error modeling to improve positioning error evaluation accuracy, integrates weather factor interference, predicts flight stability risks, and comprehensively considers building structure, signal error, weather disturbance, and flight speed to comprehensively evaluate the danger of unmanned aerial vehicle flight, thereby improving the safety of city unmanned aerial vehicle flight paths. SUMMARY

[0004] To overcome the deficiencies of the prior art, the application provides a city low-altitude grid management process control method and system based on unmanned aerial vehicle analysis.

[0005] To achieve the above-mentioned purpose, the application provides the following technical solutions:

[0006] The city low-altitude grid management process control method and system based on unmanned aerial vehicle analysis include the following specific steps:

[0007] Obtain city three-dimensional data, divide the city area into multiple grid units, and obtain weather data;

[0008] Build a building interference evaluation model, import the open angle and building enclosure rate of each grid unit into the building interference evaluation model to calculate the building density, and evaluate the reflection interference on the unmanned aerial vehicle through the number and intensity of reflection paths;

[0009] A weather interference model is constructed to obtain the cloud cluster humidity distribution, raindrop particle size and wind parameters of the grid unit, and the weather data is imported into the weather interference model to evaluate the rainfall interference and wind disturbance;

[0010] A flight risk evaluation model is constructed, and the building density, multipath reflection error index, rainfall interference index and wind disturbance intensity are imported into the flight risk evaluation model to evaluate the flight risk degree of the unmanned aerial vehicle.

[0011] Preferably, the obtaining of the urban three-dimensional data, dividing the urban area into a plurality of grid units, and obtaining weather data comprises the following specific steps:

[0012] S11, obtaining high-precision three-dimensional geographic information data of the urban area, including building height, contour and other spatial structures, limiting the urban space between the minimum flyable height and the maximum allowable height, dividing the three-dimensional space area according to a preset spatial resolution into regular three-dimensional flight grid points, generating a set of grid units, and recording the spatial height and surrounding obstacle information corresponding to each grid point;

[0013] S12, collecting real-time weather radar data, and obtaining future weather data in combination with weather forecasts.

[0014] Preferably, the construction of the building interference evaluation model, importing the open angle and building enclosure rate of each grid unit into the building interference evaluation model to calculate the building density, and evaluating the reflection interference on the unmanned aerial vehicle through the number and intensity of reflection paths comprises the following specific steps:

[0015] S21, the narrow channel formed by high-rise buildings in the city will cause signal reflection and multipath propagation of the unmanned aerial vehicle, causing positioning errors, the open angle and enclosure rate of each grid unit are evaluated through the city building model, wherein the open angle is the ratio of the unobstructed view angle above the grid unit, a plurality of rays are emitted from each grid point in different directions by using a ray casting algorithm, and the proportion of rays that are not blocked by buildings is counted, the enclosure rate is calculated by projecting rays from the grid point in all directions on the horizontal plane, and the average distance of the rays meeting the buildings and the ratio of the total number of directions are calculated, the open angle and the enclosure rate are substituted into the building density calculation formula to evaluate the building density, wherein the building density calculation formula is: D1=α1·(1-θ(x,y,z))+α2·E(x,y,z), wherein θ(x,y,z) is the open angle, E(x,y,z) is the building enclosure rate, and α1 and α2 are weights;

[0016] S22, based on the urban BIM modeling data, extract all building facades, construct a reflector library based on the building facade three-dimensional model, record the reflection coefficient and the azimuth angle, obtain the solar elevation angle and the unmanned aerial vehicle flight height, wherein each reflector index includes: reflector center coordinates, reflector coefficient and reflector orientation, obtain the unmanned aerial vehicle flight position and the reflection path formed by the signal emission point in the building facade, calculate the reflection weight for each reflection path, wherein the reflection weight is: w k = p k · cos (f k ), wherein p k is the reflection coefficient, f k is the reflection angle, and cos (f k ) is used as the signal energy attenuation, for each flight position, the weighted total of all reflection paths is counted and recorded as the total reflection intensity, and the total reflection intensity is substituted into the multi-path reflection error index calculation formula to evaluate the signal reflection situation, wherein the multi-path reflection error index calculation formula is: wherein S(x, y, z) is the weighted total of the reflection path, S max is the maximum total reflection intensity in the city;

[0017] Preferably, the weather interference model is constructed, the cloud humidity distribution, raindrop particle size and wind parameters of the grid unit are obtained, and the weather data is imported into the weather interference model to evaluate the rainfall interference and wind disturbance situation, which includes the following specific steps:

[0018] S31, identify the three-dimensional rainfall cloud volume by real-time prediction of weather radar data, determine whether each flight grid unit is in the cloud, obtain the cloud humidity distribution and rainfall particle size distribution, and substitute the cloud humidity and rainfall particle size into the rainfall interference index calculation formula to evaluate the rainfall interference, wherein the rainfall interference index calculation formula is: wherein H(x, y, z) is the cloud humidity, R(x, y, z) is the rainfall particle size, and h m and h m are weights, H m is the maximum cloud humidity, and R m is the maximum rainfall particle size that the unmanned aerial vehicle can withstand;

[0019] S32, obtain the wind field information through the weather model, extract the wind speed vector of each grid unit, the wind shear intensity is the wind speed variation gradient at the same height surface, which is measured by the partial derivative of the wind speed vector to the height, the gust disturbance intensity is the standard deviation of the wind speed change per unit time, and the wind disturbance intensity is evaluated based on the wind shear intensity and the gust disturbance intensity. The wind shear intensity and the gust disturbance intensity are normalized and weighted to obtain the wind disturbance intensity.

[0020] Preferably, the construction flight risk assessment model, the building density, the multi-path reflection error index, the rainfall interference index and the wind disturbance intensity are introduced into the flight risk assessment model to evaluate the flight risk degree of the unmanned aerial vehicle, which comprises the following specific steps:

[0021] S41, the building density, the multi-path reflection error index, the rainfall interference index and the wind disturbance intensity are substituted into the flight danger calculation formula to evaluate the flight danger of the grid unit, wherein the flight danger calculation formula is: Wherein, μ j is the weight of the risk factor, D j (x, y, z) includes the building density, the multi-path reflection error index, the rainfall interference index and the wind disturbance intensity, V t is the flight speed of the unmanned aerial vehicle, and σ is the flight speed weight, and Vs is the maximum value of the safe flight speed of the unmanned aerial vehicle in the city.

[0022] S42, the flight path is composed of a plurality of discrete grid points, the risk composition vector of each point is calculated for a plurality of discrete position points on the flight path, the path is divided into a plurality of sections, the average risk value of each section is calculated, and a heat map is generated.

[0023] The urban low-altitude grid management process control system based on unmanned aerial vehicle analysis is realized based on the urban low-altitude grid management process control method based on unmanned aerial vehicle analysis, and specifically comprises:

[0024] A data acquisition module is configured to acquire urban three-dimensional data and meteorological data.

[0025] A building interference evaluation module is configured to calculate the building density by the open angle and the building enclosure rate of each grid unit, and to evaluate the reflection interference on the unmanned aerial vehicle by the reflection path number and intensity.

[0026] A meteorological interference module is configured to evaluate the rainfall interference and wind disturbance by the humidity distribution, raindrop particle size and wind parameters of the grid unit.

[0027] A flight risk evaluation module is configured to evaluate the flight risk degree of the unmanned aerial vehicle by the building density, the multi-path reflection error index, the rainfall interference index and the wind disturbance intensity.

[0028] An electronic device comprises a processor and a memory, wherein the memory stores a computer program that can be called by the processor.

[0029] The processor executes the above-mentioned urban low-altitude grid management process control method based on unmanned aerial vehicle analysis by calling the computer program stored in the memory.

[0030] A computer readable storage medium, characterized in that, store instructions, when the instructions run on the computer, make the computer execute the above-mentioned city low-altitude grid management process control method based on unmanned aerial vehicle analysis.

[0031] Compared with the prior art, the beneficial effects of the present application are:

[0032] The application discloses a city low-altitude grid management process control method based on unmanned aerial vehicle analysis and a system thereof, and belongs to the technical field of unmanned aerial vehicle flight management. The application acquires city three-dimensional data, divides a city area into a plurality of grid units, acquires meteorological data, constructs a building interference evaluation model, introduces the open angle and building enclosure rate of each grid unit into the building interference evaluation model to calculate building density, simultaneously evaluates the reflection interference of the unmanned aerial vehicle through the number and intensity of reflection paths, constructs a meteorological interference model, acquires the cloud cluster humidity distribution, raindrop particle size and wind parameters of the grid unit, introduces the meteorological data into the meteorological interference model to evaluate the rainfall interference condition and wind disturbance condition, constructs a flight risk evaluation model, introduces the building density, multi-path reflection error index, rainfall interference index and wind disturbance intensity into the flight risk evaluation model to evaluate the flight risk degree of the unmanned aerial vehicle, and the safety of the flight path of the unmanned aerial vehicle is improved. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 The present application is a city low-altitude grid management process control method based on unmanned aerial vehicle analysis overall flowchart.

[0034] Figure 2 The present application is a flight risk calculation flowchart.

[0035] Figure 3 The present application is a city low-altitude grid management process control system based on unmanned aerial vehicle analysis overall framework diagram. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments.

[0037] Embodiment 1

[0038] Please refer to Figures 1-2 An embodiment provided by the present application is a city low-altitude grid management process control method based on unmanned aerial vehicle analysis, which comprises the following specific steps:

[0039] Acquire city three-dimensional data, divide a city area into a plurality of grid units, and acquire meteorological data.

[0040] The open angle and building enclosure rate of each grid unit are introduced into the building interference evaluation model to calculate the building density, and the reflection interference on the UAV is evaluated through the number and intensity of reflection paths;

[0041] A weather interference model is constructed, the cloud cluster humidity distribution, raindrop particle size and wind parameters of the grid unit are obtained, and the weather data are introduced into the weather interference model to evaluate the rainfall interference and wind disturbance;

[0042] A flight risk evaluation model is constructed, the building density, multi-path reflection error index, rainfall interference index and wind disturbance intensity are introduced into the flight risk evaluation model to evaluate the flight risk degree of the UAV.

[0043] In the embodiment, it needs to be specifically explained that the urban three-dimensional data is obtained, the urban area is divided into a plurality of grid units, and the weather data are obtained, including the following specific steps:

[0044] S11, obtaining high-precision three-dimensional geographic information data of the urban area, including building height, contour and other spatial structures, limiting the urban space between the minimum flyable height and the highest permissible height, dividing the three-dimensional space area according to a preset spatial resolution, dividing into regular three-dimensional flight grid points, generating a grid unit set, and recording the spatial height and surrounding obstacle information corresponding to each grid point;

[0045] S12, collecting real-time weather radar data, and obtaining future weather data in combination with weather forecasts.

[0046] In the embodiment, it needs to be specifically explained that the building interference evaluation model is constructed, the open angle and building enclosure rate of each grid unit are introduced into the building interference evaluation model to calculate the building density, and the reflection interference on the UAV is evaluated through the number and intensity of reflection paths, including the following specific steps:

[0047] S21, the narrow channel formed by high-rise buildings in the city can cause the GPS signal of the UAV to be reflected and multipath propagated, the open angle and the enclosure rate of each grid unit are evaluated through the city building model, wherein the open angle is the ratio of the unobstructed view angle above the grid unit, a plurality of rays are emitted from each grid point upward at different angles through the ray projection algorithm, and the proportion of the rays that are not blocked by the building is counted, the enclosure rate is the ratio of the average distance of the rays encountering the building to the total number of directions calculated by projecting rays from the grid point in all directions, the open angle and the enclosure rate jointly determine the degree of closure of the urban canyon of the grid unit, the open angle and the enclosure rate are substituted into the building density calculation formula to evaluate the building density, wherein the building density calculation formula is: D1 = a1-(1-0(x, y, z))+a2E(x, y, z), wherein 0(x, y, z) is the open angle, E(x, y, z) is the building enclosure rate, a1 and a2 are weights, the quantitative analysis of the urban canyon effect is realized, the recognition ability of the signal unstable area in the flight path is improved, the open angle and the enclosure rate are calculated through the ray projection algorithm, which can accurately describe the degree of closure of the urban canyon of the grid unit from different angles (vertical and horizontal). The open angle reflects the unobstructed view angle above the grid unit, and the enclosure rate reflects the distribution of buildings around the grid point. Substituting the two into the building density calculation formula realizes the quantitative analysis of the urban canyon effect, making the evaluation of the building density more scientific and accurate, and the narrow channel formed by high-rise buildings in the city can cause the GPS signal of the UAV to be reflected and multipath propagated, and the building density is closely related to the signal instability. By accurately evaluating the building density, the area in the flight path where the signal may be unstable can be more accurately identified, providing an important basis for UAV flight path planning, and helping to take measures in advance to avoid the risk of signal interference;

[0048] S22, based on the city BIM modeling data, extract all building facades, build a reflection surface library based on the three-dimensional model of the building facade, record the reflection coefficient and the azimuth angle, obtain the solar elevation angle and the UAV flight height, wherein each reflection surface index includes: the center coordinates of the reflection surface, the reflection coefficient and the orientation of the reflection surface, the building surface material is classified and valued through remote sensing images, the reflection path formed by the UAV flight position and the signal emission point on the building facade is obtained, and the reflection weight of each reflection path is calculated, wherein the reflection weight is: w k = p k ·cos( f k ), wherein p k is the reflection coefficient, f k is the reflection angle, and cos(f k) For signal energy attenuation, the material reflection characteristics and the reflection angle are combined to reflect the signal loss trend. The weighted total number of all reflection paths is calculated for each flight position, denoted as total reflection intensity. The total reflection intensity is substituted into the multi-path reflection error index calculation formula to evaluate the signal reflection situation, wherein the multi-path reflection error index calculation formula is: wherein S(x, y, z) is the weighted total number of reflection paths, S max is the maximum total reflection intensity appearing in the city. Based on the city BIM modeling data, all building facades are extracted, a reflection surface library is constructed, and the reflection coefficient and azimuth angle are recorded. This step provides a basis for accurately calculating the reflection path and reflection weight, comprehensively considers the reflection of various building surfaces in the city on the UAV GPS signal, and makes the reflection interference evaluation more comprehensive; the reflection weight calculation formula comprehensively considers the reflection coefficient, reflection angle and signal energy attenuation, etc. The material reflection characteristics and the reflection angle are combined to more accurately reflect the loss trend of the signal in the reflection process. By calculating the reflection weight of each reflection path, the degree of reflection interference can be more accurately evaluated; by calculating the total reflection intensity and substituting it into the multi-path reflection error index calculation formula, the quantitative evaluation of the signal reflection situation is realized. The multi-path reflection error index can directly reflect the degree of reflection interference of the UAV in the flight process, and provides specific quantitative basis for judging the safety of the flight environment.

[0049] In the present embodiment, it needs to be specifically pointed out that the weather interference model is constructed, the cloud cluster humidity distribution, raindrop particle size and wind parameters of the grid unit are obtained, the weather data is imported into the weather interference model to evaluate the rainfall interference situation and wind disturbance situation, including the following specific steps:

[0050] S31, the volume of the three-dimensional rainfall cloud cluster is identified by real-time prediction of weather radar data. It is judged whether each flight grid unit is in the cloud cluster. The cloud cluster humidity distribution and rainfall particle size distribution are obtained. The cloud cluster humidity distribution represents the local saturation humidity level in the cloud. The raindrop particle size and concentration are inversely calculated based on the weather model to reflect the shielding degree of the visual sensor. The cloud cluster humidity and rainfall particle size are substituted into the rainfall interference index calculation formula to evaluate the rainfall interference situation, wherein the rainfall interference index calculation formula is: wherein H(x, y, z) is the cloud cluster humidity, R(x, y, z) is the rainfall particle size, η1 and η2 are weights, H m is the maximum value of the cloud cluster humidity, R mThe maximum rainfall particle size that the UAV can withstand is obtained by identifying the volume of the three-dimensional rainfall cloud cluster through real-time prediction of weather radar data, and judging whether each flight grid unit is in the cloud cluster, obtaining the cloud humidity distribution and rainfall particle size distribution, which makes the identification of the rainfall environment more accurate, and can timely discover the rainfall area that the UAV flight path may encounter; the rainfall interference index calculation formula comprehensively considers the cloud humidity and rainfall particle size two factors, and introduces the cloud humidity maximum value and the maximum rainfall particle size that the UAV can withstand for normalization processing, in this way, the shielding degree of the rainfall to the visual sensor of the UAV can be accurately quantified, which provides specific quantitative indicators for evaluating the interference of rainfall on the flight of the UAV, and helps to take measures in advance;

[0051] S32, obtain the wind field information through the weather model, such as the city-level microclimate model, extract the wind speed vector of each grid unit, the wind shear intensity is the wind speed change gradient of the same height surface, which is measured by the partial derivative of the wind speed vector to the height, that is, the vertical gradient, which represents the interference of the sharp change of the wind speed in the vertical direction to the flight attitude control, and the wind shear is too large to cause the flight height to change suddenly or the aircraft to shake violently, the gust disturbance intensity is the standard deviation of the wind speed change in unit time, the instantaneous wind speed increases sharply in a short time, which will cause the UAV attitude to be unstable, the standard deviation of the wind speed change in unit time is used to represent the intensity of the gust, which can reflect the instantaneous volatility of the local wind speed, and the wind disturbance intensity is evaluated based on the wind shear intensity and the gust disturbance intensity. The wind shear intensity and the gust disturbance intensity are normalized and weighted to obtain the wind disturbance intensity.

[0052] In the present embodiment, it needs to be specifically pointed out that the flight risk assessment model is constructed, the building density, the multi-path reflection error index, the rainfall interference index and the wind disturbance intensity are introduced into the flight risk assessment model to evaluate the flight risk degree of the UAV, which includes the following specific steps:

[0053] S41, the building density, the multi-path reflection error index, the rainfall interference index and the wind disturbance intensity are substituted into the flight danger calculation formula to evaluate the flight danger of the grid unit, wherein the flight danger calculation formula is: Wherein, μ j is the weight of the risk factor, D j (x,y,z) includes the building density, the multi-path reflection error index, the rainfall interference index and the wind disturbance intensity, V t is the flight speed of the UAV, and σ is the flight speed weight. Vs is the maximum value of the safe flight speed of the UAV in the city, which represents the influence of speed on the impact risk. By comprehensively considering the building density, the multi-path reflection error, the rainfall interference, the wind disturbance intensity and the flight speed, the flight danger of the UAV in different environments can be more accurately evaluated;

[0054] S42, the flight path is composed of a plurality of discrete grid points, for a plurality of discrete position points on the flight path, the risk composition vector of each point is calculated respectively, the path is divided into a plurality of sections (such as every 50m), the average risk value of each section is calculated, a heat map is generated, and the safety change trend of the whole path is presented in a visual form to assist path planning to avoid high-risk areas, adjust the path, or give real-time warnings.

[0055] It should be noted that the value of the various set parameters in the embodiment is obtained by: obtaining representative historical surrounding building data in unmanned aerial vehicle flight, obtaining historical environmental data, hiring experts to manually judge the risk of unmanned aerial vehicle flight path, and inputting the calculation results and judgment results of each step in the embodiment into the fitting software to output the values of various set parameters with the highest judgment accuracy.

[0056] The embodiment has the following advantages over the prior art:

[0057] The application discloses a city low-altitude gridding management process control method and system based on unmanned aerial vehicle analysis, and belongs to the technical field of unmanned aerial vehicle flight management. The city three-dimensional data is obtained, the city area is divided into a plurality of grid units, and meteorological data is obtained. A building interference evaluation model is constructed. The open angle and building enclosure rate of each grid unit are introduced into the building interference evaluation model to calculate the building density. The reflection interference on the unmanned aerial vehicle is evaluated through the number and intensity of reflection paths. A meteorological interference model is constructed. The cloud cluster humidity distribution, raindrop particle size and wind parameters of the grid unit are obtained. The meteorological data is introduced into the meteorological interference model to evaluate the rainfall interference and wind disturbance. A flight risk evaluation model is constructed. The building density, multi-path reflection error index, rainfall interference index and wind disturbance intensity are introduced into the flight risk evaluation model to evaluate the flight risk degree of the unmanned aerial vehicle. The safety of the unmanned aerial vehicle flight path is improved.

[0058] Embodiment 2

[0059] As shown in Figure 3 The city low-altitude gridding management process control system based on unmanned aerial vehicle analysis is implemented based on the city low-altitude gridding management process control method based on unmanned aerial vehicle analysis. It specifically includes a data acquisition module, a building interference evaluation module, a meteorological interference module and a flight risk evaluation module. The data acquisition module is used to obtain city three-dimensional data and meteorological data. The building interference evaluation module is used to calculate the building density through the open angle and building enclosure rate of each grid unit, and evaluate the reflection interference on the unmanned aerial vehicle through the number and intensity of reflection paths. The meteorological interference module is used to evaluate the rainfall interference and wind disturbance through the humidity distribution, raindrop particle size and wind parameters of the grid unit. The flight risk evaluation module is used to evaluate the flight risk degree of the unmanned aerial vehicle through the building density, multi-path reflection error index, rainfall interference index and wind disturbance intensity.

[0060] Embodiment 3

[0061] The embodiment provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program that can be invoked by the processor.

[0062] The processor executes the above-mentioned process control method for urban low-altitude gridding management based on analysis of a UAV by invoking the computer program stored in the memory.

[0063] The electronic device can have great differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the process control method for urban low-altitude gridding management based on analysis of a UAV provided by the above-mentioned method embodiment. The electronic device can also include other components for realizing device functions, for example, the electronic device can also have a wired or wireless network interface and an input and output interface, and the like, so as to perform input and output of data. The embodiment will not be described here.

[0064] Embodiment 4

[0065] The embodiment provides a computer readable storage medium, which stores an erasable computer program.

[0066] When the computer program runs on the computer device, the computer device executes the above-mentioned process control method for urban low-altitude gridding management based on analysis of a UAV.

[0067] For example, the computer readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0068] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, or the like including a set of one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

Claims

1. A method for controlling the process of urban low-altitude grid-based management based on UAV analysis, characterized in that, It includes the following specific steps: Acquire 3D urban data, divide the urban area into multiple grid units, and acquire meteorological data; A building interference assessment model is constructed. The opening angle and building enclosure ratio of each grid cell are imported into the building interference assessment model to calculate the building density. At the same time, the reflection interference to UAVs is assessed by the number and intensity of reflection paths. A meteorological disturbance model was constructed to obtain the cloud humidity distribution, raindrop size and wind parameters of the grid cells. The meteorological data was then imported into the meteorological disturbance model to assess the rainfall disturbance and wind disturbance. A flight risk assessment model was constructed, and building density, multipath reflection error index, rainfall interference index and wind disturbance intensity were imported into the flight risk assessment model to evaluate the degree of flight risk of UAVs.

2. The urban low-altitude grid-based management process control method based on UAV analysis as described in claim 1, characterized in that, The construction of the building interference assessment model involves importing the opening angle and building enclosure ratio of each grid cell into the model to calculate building density. Simultaneously, the assessment of UAV reflection interference based on the number and intensity of reflection paths includes the following specific steps: The open angle and enclosure ratio of each grid cell are evaluated using an urban building model. The open angle is the proportion of unobstructed views above the grid cell. A ray casting algorithm is used to project rays from each grid point upwards at multiple angles, and the proportion of rays not obstructed by buildings is counted. The enclosure ratio is calculated by projecting rays from the grid point to all directions on a horizontal plane, and the ratio of the average distance of the rays to buildings to the total number of directions is calculated. The open angle and enclosure ratio are then substituted into the building density calculation formula to evaluate the building density. The building density calculation formula is: D1=α1·(1-θ(x,y,z))+α2·E(x,y,z), where θ(x,y,z) is the open angle, E(x,y,z) is the building enclosure ratio, and α1 and α2 are weights. Based on urban BIM modeling data, all building facades were extracted. A reflective surface library was constructed based on the 3D model of the building facades, recording the reflection coefficient and azimuth angle. The solar altitude angle and UAV flight altitude were obtained. Each reflective surface index includes: center coordinates, reflection coefficient, and orientation. The reflection path formed by the UAV flight position and signal emission point on the building facade was obtained. The reflection weight was calculated for each reflection path, where the reflection weight is: w k =ρ k ·cos(φ k ), where ρ k φ is the reflection coefficient. k Let be the reflection angle. For each flight position, the weighted total of all reflection paths is calculated and denoted as the total reflection intensity. The total reflection intensity is then substituted into the multipath reflection error index calculation formula to evaluate the signal reflection situation. The multipath reflection error index calculation formula is as follows: Where S(x,y,z) is the weighted total number of reflection paths, S max This represents the maximum total reflectance in the city.

3. The urban low-altitude grid-based management process control method based on UAV analysis as described in claim 2, characterized in that, The process of constructing a meteorological disturbance model, obtaining cloud humidity distribution, raindrop size, and wind parameters for grid cells, and importing meteorological data into the model to assess rainfall and wind disturbance includes the following specific steps: The volume of three-dimensional rain clouds is identified by real-time predictive weather radar data. Each flight grid cell is then assessed to determine if it is within a cloud cluster. The cloud humidity distribution and rainfall particle size distribution are obtained. These data are then substituted into the rainfall interference index calculation formula to evaluate rainfall interference. The rainfall interference index calculation formula is as follows: Where H(x,y,z) is the cloud humidity, R(x,y,z) is the precipitation particle size, η1 and η2 are weights, and H m R represents the maximum humidity of the cloud cluster. m The maximum raindrop size that the drone can tolerate; Wind field information is obtained through a meteorological model, and the wind speed vector of each grid cell is extracted. The wind shear intensity is the gradient of wind speed change at the same height surface, which is measured by the partial derivative of the wind speed vector with respect to height. The gust disturbance intensity is the standard deviation of wind speed change per unit time. The wind disturbance intensity is evaluated based on the wind shear intensity and the gust disturbance intensity. The wind disturbance intensity is obtained by normalizing the wind shear intensity and the gust disturbance intensity and then weighted summing them.

4. The urban low-altitude grid-based management process control method based on UAV analysis as described in claim 3, characterized in that, The construction of the flight risk assessment model, which incorporates building density, multipath reflection error index, rainfall interference index, and wind disturbance intensity into the model to assess the flight risk level of the UAV, includes the following specific steps: The flight hazard of grid cells is assessed by substituting building density, multipath reflection error index, rainfall disturbance index, and wind disturbance intensity into the flight hazard calculation formula. The flight hazard calculation formula is as follows: Where, μ j D represents the weights of the risk components. j (x,y,z) includes building density, multipath reflection error index, rainfall disturbance index, and wind disturbance intensity, V t Let σ be the flight speed of the drone, σ be the flight speed weight, and Vs be the maximum safe flight speed for drones in the city. The flight path consists of multiple discrete grid points. For each discrete location point on the flight path, the risk composition vector of each point is calculated. The path is divided into several segments, and the average risk value of each segment is calculated to generate a heat map.

5. The urban low-altitude grid-based management process control method based on UAV analysis as described in claim 4, characterized in that, The process of acquiring urban 3D data, dividing the urban area into multiple grid units, and acquiring meteorological data includes the following specific steps: The system acquires high-precision three-dimensional geographic information data of urban areas, limits urban space to between the minimum and maximum permitted flight altitudes, divides the three-dimensional spatial area into regular three-dimensional flight grids according to a preset spatial resolution, generates a set of grid cells, collects real-time meteorological radar data, and combines weather forecasts to obtain future meteorological data.

6. A process control system for urban low-altitude grid-based management based on UAV analysis, implemented based on the urban low-altitude grid-based management process control method for UAV analysis as described in any one of claims 1-5, characterized in that, Specifically, it includes: The data acquisition module is used to acquire urban 3D data and meteorological data; The building interference assessment module is used to calculate building density by the opening angle and building enclosure ratio of each grid cell, and to assess the reflection interference to UAVs by the number and intensity of reflection paths. The meteorological disturbance module is used to assess rainfall disturbance and wind disturbance through the humidity distribution, raindrop size and wind parameters of the grid cells; The flight risk assessment module is used to assess the flight risk level of drones through building density, multipath reflection error index, rainfall interference index, and wind disturbance intensity.

7. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor is characterized in that it executes the urban low-altitude grid management process control method based on UAV analysis as described in any one of claims 1-5 by calling a computer program stored in the memory.

8. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the urban low-altitude grid management process control method based on UAV analysis as described in any one of claims 1-5.

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