Space wind field and environment field evaluation system and method based on unmanned aerial vehicle cluster collaboration

Abstract

The invention discloses a space wind field and environment field evaluation system and method based on unmanned aerial vehicle cluster collaboration, and belongs to the technical field of regional wind field and environment field monitoring and evaluation. The system integrates a mooring unmanned aerial vehicle, a mobile unmanned aerial vehicle, a monitoring sensor, a hyperspectral camera and a data processing and control system. Dynamically planning a monitoring path through an ant colony algorithm, and correcting abnormal data in combination with a moving average method; constructing a landform, wind field and environment field multi-parameter coupling model based on a Bayesian network, and predicting the spatial distribution of the wind field and pollutants / health-care indexes by using a Kriging method; a multi-dimensional evaluation system is established by applying an interval number analytic hierarchy process and a cloud model, and risk grading early warning is realized by integrating wind field characteristics, pollutant concentration, diffusion characteristics and health-care indexes. The problems that traditional monitoring points are sparse, cost is high, and prediction and visualization in space are difficult are solved, and the method is suitable for accurate evaluation of wind fields and environment fields in complex areas such as industrial parks, construction sites and health and maintenance sites.

Description

Technical Field

[0001] This invention belongs to the field of regional wind field and environmental field monitoring and assessment technology, specifically involving a spatial wind field and environmental field assessment system and method based on UAV swarm collaboration. It is particularly suitable for multi-dimensional wind field and environmental field monitoring and risk assessment in complex terrain areas such as industrial parks, construction sites and health and wellness venues, as well as in the low-altitude economic field. Background Technology

[0002] With the accelerating pace of industrialization and urbanization, environmental problems such as air pollution and ecological damage are becoming increasingly severe. Accurate monitoring and assessment of wind and environmental fields are crucial prerequisites for air pollution prevention and control, as well as for the creation and evaluation of healthy living environments. Traditional wind and environmental field monitoring methods have the following limitations: Insufficient spatial resolution: Relying on fixed monitoring towers, the measurement points are sparse and spatially limited, making it difficult to capture the spatial distribution characteristics of wind fields, the spatial distribution and diffusion patterns of pollutants, and the spatial distribution characteristics of health and wellness indicators in complex terrain areas (such as industrial parks, construction sites, and health and wellness sites).

[0003] Poor timeliness and continuity: The observation data is not continuous in time and space, making it impossible to track the dynamic changes in concentration around the pollution source in real time.

[0004] Cost and scale limitations: Although meteorological satellites can monitor large areas, they are expensive and it is difficult to accurately measure and assess the fine wind field and environmental field parameters of small and medium-scale areas (such as industrial parks, construction sites and health care sites).

[0005] Multi-rotor drones possess advantages such as flexible control, convenient takeoff and landing, low cost, and the ability to hover in fixed positions. Equipped with monitoring sensors, they can acquire three-dimensional spatial measurement data and are gradually being applied in wind farm and environmental field monitoring. Meanwhile, tethered drones, connected to a ground-based mobile power source via a tethered cable, can achieve 24-hour continuous monitoring. However, current drone monitoring technology still faces challenges: Data interference issue: The rotor downwash can reduce the accuracy of wind field and environmental field data collected by monitoring sensors.

[0006] Insufficient dynamic coordination: The monitoring range of a single drone is limited and its efficiency is low. Furthermore, it lacks a dynamic task allocation mechanism for cluster coordination, making it difficult to achieve efficient coverage of complex areas.

[0007] Prediction and assessment bottlenecks: Traditional assessment methods rely too much on human experience and are difficult to deeply integrate multi-source data such as geomorphological features, wind field characteristics, and pollutant concentrations to build intelligent prediction models, resulting in poor accuracy in pollution source tracing, diffusion simulation, and environmental risk assessment.

[0008] Furthermore, with the rapid development of the low-altitude economy, logistics drones and electric vertical takeoff and landing (eVTOL) aircraft operating in low-altitude airspace face higher demands for flight safety, route planning, and operational risk management. However, traditional technologies struggle to provide low-altitude aircraft with high-resolution, real-time updated three-dimensional wind and environmental field information, leading to insufficient flight safety assessments and inaccurate route planning. Therefore, there is an urgent need for an integrated solution that combines high-precision monitoring, intelligent prediction, and multi-dimensional assessment to overcome technical bottlenecks in spatiotemporal resolution, real-time performance, and assessment accuracy, thereby meeting the application needs of complex site wind and environmental field monitoring and the low-altitude economy. Summary of the Invention

[0009] The purpose of this invention is to provide a spatial wind field and environmental field assessment system and method based on UAV swarm collaboration. Through swarm collaborative optimization, dynamic task planning, intelligent predictive modeling, and comprehensive evaluation, it achieves efficient and accurate monitoring and assessment of regional spatial wind and environmental fields. This allows for precise acquisition of three-dimensional spatial wind and environmental field parameters (pollution and health indicators), making it suitable for complex scenarios such as the measurement and prediction of spatial wind fields in complex terrain areas, real-time dynamic monitoring, source tracing, and diffusion prediction of air pollution in industrial parks and dust from construction sites, and multi-dimensional comprehensive assessment of the environment in health and wellness venues. Furthermore, this invention can also provide flight safety assessment, route planning, and operational risk warning support for low-altitude aircraft such as logistics UAVs and electric vertical take-off and landing (eVTOL) aircraft, further expanding the technical value of UAV swarm collaborative spatial wind and environmental field monitoring technology in emerging low-altitude economic application scenarios. This effectively solves the technical problems mentioned in the background art, such as insufficient spatiotemporal resolution, poor real-time performance, and inaccurate risk assessment.

[0010] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: This invention provides a method for assessing spatial wind fields and environmental fields based on UAV swarm collaboration, comprising the following steps: Step S1: Construct a multi-rotor UAV swarm collaborative monitoring system: integrate multi-rotor UAVs equipped with hyperspectral cameras, monitoring sensors, mounting brackets, and data processing and control systems; Step S2, System Calibration: Determine the installation positions of the mounting bracket and monitoring sensors through wind tunnel testing, and then install the monitoring sensors on the multi-rotor UAV using the mounting bracket; Step S3, Dynamic Task Planning: Divide the target area into a three-dimensional grid, plan the UAV flight path based on the ant colony algorithm combined with the environmental field gradient, and trigger encrypted sampling in areas where the pollutant concentration in the environmental field exceeds the threshold. Step S4, Data Acquisition and Real-time Correction: Regional spatial wind field and environmental field data are collected using a multi-rotor UAV swarm collaborative monitoring system; outliers in the wind field and environmental field data are identified using the absolute mean method, and the measured data are corrected using the moving average method; aerial images of the terrain are captured using a hyperspectral camera on a multi-rotor UAV, and the roughness element size in the terrain images is identified using YOLO series deep learning algorithms, and the ground roughness length of the corresponding area is calculated. z 0 and roughness index α Hyperspectral cameras are used to collect spectral information of pollutants in the area, which helps in the identification and concentration analysis of pollutants and improves the accuracy and diversity of environmental field monitoring. Step S5, Constructing a digital model: Based on measured data, a multi-parameter coupled model of landform features, wind field, temperature field, humidity field and environmental field is established through a Bayesian network; Step S6, Spatial 3D Wind Field and Environmental Field Prediction: Based on the Kriging method, spatial interpolation is performed on the measured data to predict the spatial distribution of the regional 3D wind field, pollutant diffusion field, and health and wellness indicators, and the results are analyzed using the root mean square error. RMSE Verify the accuracy of the prediction; Step S7, Multi-dimensional Assessment and Early Warning: Construct a three-level assessment system including pollutant concentration indicators, health and wellness environment indicators, and low-altitude flight environment safety indicators. Use the interval number hierarchical analysis method to determine the indicator weights, combine cloud models to handle data uncertainty, output four-color early warning levels, and generate early warning reports. Among them, the low-altitude flight environment safety indicators are based on three-dimensional wind field, meteorological conditions, and terrain obstacle parameters to calculate the flight environment suitability index, which is used for aircraft route optimization and operational risk classification early warning.

[0011] Optionally, in step S1, the multi-rotor drone includes a tethered drone and multiple mobile drones, wherein the tethered drone is connected to a ground-based mobile power source via a tether cable to achieve 24-hour continuous monitoring; the mobile drone has a flight time of ≥45 minutes; the multi-rotor drone has a positioning accuracy of ≤0.5m, a payload capacity of ≥5kg, and a wind resistance of level 7.

[0012] Optionally, in step S2, the mounting bracket is made of high-strength, lightweight aluminum alloy and is installed directly above the center of the UAV. Its dimensions are determined by wind tunnel testing.

[0013] Optionally, in step S3, the ant colony algorithm dynamically adjusts the monitoring priority based on the pollutant concentration; the higher the concentration, the denser the monitoring points are deployed.

[0014] Optionally, in step S4, the wind speed time history in the wind field is inverted based on the wind direction time history corrected by the moving average method: ; in, To invert the wind direction timeline; , These are the results after correction using the moving average method. x , y Time history of directional wind speed component.

[0015] Optionally, in step S6, the weighting coefficients of the Kriging method... λ i The solution is found by minimizing the variance of the estimation error, while satisfying the unbiased estimation condition. and constraints ,in z p For predicted values, z m These are measured values.

[0016] Optionally, in step S7, the pollutant concentration indicators include: Primary indicators: Comparison of PM2.5, PM10, SO2, CO, NO2, and O3 concentrations with national environmental quality standards; Secondary indicators: the rate and extent of pollutant diffusion; Level 3 indicator: Air Quality Index (AQI) calculated based on measured data.

[0017] Optionally, in step S7, the health and wellness environment indicators include: Primary indicator: Comparison of air negative oxygen ion concentration with health standards; Secondary indicator: Outdoor land exercise weather index; Level 3 indicators: health and wellness climate suitability calculated using random forest analysis, which combines negative oxygen ion concentration, climate comfort, air quality index (AQI), and outdoor land exercise meteorological index.

[0018] Optionally, in step S7, the low-altitude flight environment safety indicators include: Primary indicator: Wind speed field intensity distribution calculated based on three-dimensional wind field and terrain obstacle parameters; Secondary indicator: The impact coefficient of meteorological conditions (including temperature, humidity, and precipitation probability) on flight safety; Level 3 Indicators: Flight environment suitability index calculated by multi-factor weighting, combining aircraft model parameters, wind field suitability, and meteorological conditions, is used for aircraft route optimization and operational risk classification and early warning.

[0019] The present invention also provides a space wind field and environmental field assessment system based on UAV swarm collaboration for performing the method, comprising: Tethered drone teams: Deployed in key or core areas of the target region or pollution source, they are connected to a ground-based mobile power source via tethered cables to achieve 24-hour continuous monitoring; Mobile drone teams: Based on the monitoring path dynamically planned by the ant colony algorithm, they collect spatial data in multiple points, multiple layers and multiple dimensions within the target area, so as to achieve efficient coverage of pollutant concentration gradients, spatial distribution of health and wellness environment indicators and spatial distribution of wind fields. Monitoring sensors and hyperspectral cameras: mounted on tethered and mobile drones, these sensors collect multi-dimensional wind and environmental parameters, and calculate corresponding roughness parameters from topographic images acquired by the hyperspectral camera; they also collect spectral information of pollutants in the area using the hyperspectral camera to assist in pollutant identification and concentration analysis, thereby improving the accuracy and diversity of environmental field monitoring. Mounting bracket: Made of high-strength, lightweight aluminum alloy, it is installed directly above the center of the UAV. The size of the bracket was determined by wind tunnel testing to reduce the interference of the rotor downwash on the monitoring sensor measurements. Data processing and control system: Communicates with a cluster of drones equipped with monitoring sensors and hyperspectral cameras via radio stations. It integrates flight control, mission scheduling, data correction and processing, 3D spatial interpolation, Bayesian network modeling, prediction of wind field and pollutant diffusion / health and wellness index spatial distribution, calculation of low-altitude flight environment safety suitability, hierarchical analysis of intervals, and cloud model evaluation. It outputs four-color (green / yellow / orange / red) risk classification early warning results for pollutant concentration, health and wellness environment, and low-altitude flight environment safety indicators, where green indicates safety, yellow indicates warning, orange indicates alert, and red indicates danger, and generates corresponding visualized risk reports. Early warning module: Based on the comprehensive analysis results of the data processing and control system, it further provides pollution source tracing analysis, health and wellness environment analysis, prevention and control suggestions, and route optimization and risk early warning support for aircraft in the low-altitude economic field.

[0020] Compared with the prior art, the advantages of this invention are as follows: 1. This invention integrates high-precision sensors (wind speed ±0.5m / s, wind direction ±3°, temperature ±0.2℃, humidity ±2%RH) into a multi-rotor UAV swarm collaborative system (including tethered and mobile UAVs). Combined with wind tunnel testing to optimize the mounting bracket design, it significantly reduces rotor downwash interference and solves the problems of sparse locations, high cost, spatiotemporal discontinuity, and difficulty in spatial prediction and visualization of traditional fixed monitoring stations. This enables real-time monitoring of regional three-dimensional spatial wind field and environmental field, and improves prediction and assessment accuracy.

[0021] 2. This invention dynamically divides a three-dimensional monitoring grid based on the ant colony algorithm, adjusts the drone path in real time according to the environmental field gradient (such as pollutant concentration threshold), triggers encrypted sampling, and then combines a distributed optimization model to allocate tasks, thereby improving monitoring efficiency and spatial coverage.

[0022] 3. This invention uses the absolute mean method to identify outliers in measured wind field and environmental field data and the moving average method to correct measured data in real time, effectively eliminating measurement deviations caused by UAV jitter and transmission instability.

[0023] 4. The hyperspectral camera integrated in this invention is not only used for aerial photography of terrain images, but also combines the YOLO deep learning algorithm to identify roughness element dimensions and extract the ground roughness length z0 and roughness index. α It supports precise wind field modeling; at the same time, it can analyze the surface reflectance spectrum of specific bands to achieve rapid identification and supplementary verification of pollutant and surface cover distribution, and improve the accuracy of pollution source analysis and tracing.

[0024] 5. This invention constructs a multi-parameter coupled model of landform, wind field, temperature, humidity, and environmental field based on Bayesian networks, suitable for impact assessment under small sample data conditions. It then uses the Kriging spatial interpolation algorithm to predict the spatial distribution of three-dimensional wind field and pollutant diffusion / health and wellness indicators, and optimizes the weights and sums through a semi-variogram function. RMSE Verify accuracy and achieve visualized output of unbiased, minimum variance estimates.

[0025] 6. This invention constructs a three-level assessment index system (pollutant concentration, diffusion range, AQI / negative oxygen ion concentration, health and wellness climate index, and low-altitude flight environment safety index), combines interval number hierarchical analysis method to determine weights, and solves the problem of assessment subjectivity; then, it applies cloud model to process data uncertainty, outputs four-color warning levels (green / yellow / orange / red), and automatically generates prevention and control suggestions.

[0026] 7. This invention constructs the influence of factors such as wind field, landform type, temperature field, and humidity field on the spatial diffusion and distribution of regional environmental field parameters, so as to realize the source tracing and diffusion prediction of pollutants.

[0027] 8. This invention can be widely applied to air pollution control scenarios such as industrial parks and construction sites, improving the environmental quality of health and wellness venues, and meeting the needs of multi-dimensional environmental monitoring and assessment. The three-dimensional spatial wind field characteristics obtained by this invention lay a theoretical foundation for regional wind environment assessment and wind-resistant design of buildings and structures. In addition, this invention can also be applied to the low-altitude economic field, especially providing high-resolution spatial wind field and environmental field data support for low-altitude aircraft (such as logistics drones and electric vertical take-off and landing aircraft), assisting in flight safety assessment, route optimization, and operational risk early warning, and improving the operational safety and airworthiness management level of low-altitude airspace aircraft. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a schematic diagram of the structure of a space wind field and environmental field assessment system based on UAV swarm collaboration provided in an embodiment of the present invention; Figure 2 Before and after correction of the five-point moving average method provided in the embodiments of the present invention x Time history diagram of directional wind speed components; Figure 3 Before and after correction of the five-point moving average method provided in the embodiments of the present invention y Time history diagram of directional wind speed components; Figure 4 The wind direction time history diagrams before and after correction using the five-point moving average method are provided in this embodiment of the invention. Figure 5 This is a diagram showing the results of Kriging method prediction of horizontal wind speed provided in an embodiment of the present invention; Figure 6 This is a logical framework diagram of the space wind field and environmental field assessment method based on UAV swarm collaboration provided in the embodiments of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0031] See Figure 1 and Figure 6As shown, this invention provides a space wind field and environmental field assessment system based on UAV swarm collaboration, comprising: Tethered drone teams: Deployed in key or core areas of the target region or pollution source, they are connected to a ground-based mobile power source via tethered cables to achieve 24-hour continuous monitoring; Mobile drone teams: Based on the monitoring path dynamically planned by the ant colony algorithm, they collect spatial data in multiple points, multiple layers and multiple dimensions within the target area, so as to achieve efficient coverage of pollutant concentration gradients, spatial distribution of health and wellness environment indicators and spatial distribution of wind fields. Monitoring sensors and hyperspectral cameras: mounted on tethered and mobile drones, these sensors collect multi-dimensional wind and environmental parameters, and calculate corresponding roughness parameters from topographic images acquired by the hyperspectral camera; they also collect spectral information of pollutants in the area using the hyperspectral camera to assist in pollutant identification and concentration analysis, thereby improving the accuracy and diversity of environmental field monitoring. Mounting bracket: Made of high-strength, lightweight aluminum alloy, it is installed directly above the center of the UAV. The size of the bracket was determined by wind tunnel testing to reduce the interference of the rotor downwash on the monitoring sensor measurements. Data processing and control system: Communicates with a cluster of drones equipped with monitoring sensors and hyperspectral cameras via radio stations. It integrates flight control, mission scheduling, data correction and processing, 3D spatial interpolation, Bayesian network modeling, prediction of wind field and pollutant diffusion / health and wellness index spatial distribution, calculation of low-altitude flight environment safety suitability, hierarchical analysis of intervals, and cloud model evaluation. It outputs four-color (green / yellow / orange / red) risk classification early warning results for pollutant concentration, health and wellness environment, and low-altitude flight environment safety indicators, where green indicates safety, yellow indicates warning, orange indicates alert, and red indicates danger, and generates corresponding visualized risk reports. Early warning module: Based on the comprehensive analysis results of the data processing and control system, it further provides pollution source tracing analysis, health and wellness environment analysis, prevention and control suggestions, and route optimization and risk early warning support for aircraft in the low-altitude economic field.

[0032] Both the tethered drones of the tethered drone group and the mobile drones of the mobile drone group are multi-rotor drones 1. Furthermore, each multi-rotor drone 1 is equipped with a monitoring sensor 2 mounted on a support bracket 4, and a hyperspectral camera 3 is also mounted on its bottom. The hyperspectral camera is used to acquire topographical feature information and pollutant spectral feature information of the target area, supporting subsequent roughness parameter extraction and pollutant identification and spatial distribution analysis, thereby improving the accuracy of environmental field monitoring.

[0033] It should be noted that the tethered drone needs to be connected to a mobile power source 8 on the ground via the tether cable 5.

[0034] This invention provides a method for assessing spatial wind fields and environmental fields based on UAV swarm collaboration, comprising the following steps: Step S1: Construct a multi-rotor drone cluster collaborative monitoring system: integrate a multi-rotor drone 1 equipped with a hyperspectral camera 3, a monitoring sensor 2, a mounting bracket 4, and a data processing and control system 7; wherein, the hyperspectral camera is used for acquiring terrain information and identifying the spectral characteristics of pollutants, providing multi-dimensional monitoring data support.

[0035] Step S2, System calibration: Determine the installation positions of the mounting bracket 4 and the monitoring sensor 2 through wind tunnel testing, and then install the monitoring sensor 2 on the multi-rotor UAV 1 through the mounting bracket 4; Step S3, Dynamic Task Planning: Divide the target area into a three-dimensional grid, plan the UAV flight path based on the ant colony algorithm combined with the environmental field gradient, and trigger encrypted sampling in areas where the pollutant concentration in the environmental field exceeds the threshold. Step S4, Data Acquisition and Real-time Correction: Regional spatial wind field and environmental field data are collected using a multi-rotor UAV swarm collaborative monitoring system; outliers in the wind field and environmental field data are identified using the absolute mean method, and the measured data are corrected using the moving average method; aerial images of the terrain are captured using a hyperspectral camera on a multi-rotor UAV, and the roughness element size in the terrain images is identified using YOLO series deep learning algorithms, and the ground roughness length of the corresponding area is calculated. z 0 and roughness index α Hyperspectral cameras are used to collect spectral information of pollutants in the area, which helps in the identification and concentration analysis of pollutants and improves the accuracy and diversity of environmental field monitoring. Step S5, Construct a digital model: Based on measured data, establish terrain features using a Bayesian network (ground roughness length can be input). z 0 represents geomorphological features), wind field (wind profile, turbulence intensity profile, roughness index can be input). α A multi-parameter coupled model of wind field categories, temperature field, humidity field and environmental field under different landforms; Step S6, Spatial 3D Wind Field and Environmental Field Prediction: Based on the Kriging method, spatial interpolation is performed on the measured data to predict the spatial distribution of the regional 3D wind field, pollutant diffusion field, and health and wellness indicators, and the results are analyzed using the root mean square error. RMSE To verify the prediction accuracy, please refer to [link / reference]. Figure 5 As shown; Step S7 involves constructing a three-tiered assessment system encompassing pollutant concentration indicators, health and wellness environment indicators, and low-altitude flight environment safety indicators. Interval-based hierarchical analysis is used to determine indicator weights, and cloud models are employed to address data uncertainties. A four-color warning level is then output, and a warning report is generated. The pollutant spectral information acquired by hyperspectral cameras further enhances the reliability of pollutant emission source identification and distribution prediction, providing multi-dimensional data support for pollutant source tracing and risk warning. The low-altitude flight environment safety indicators, based on three-dimensional wind field, meteorological conditions, and terrain obstacle parameters, calculate a flight environment suitability index for low-altitude aircraft route optimization and operational risk classification and warning, ensuring flight safety in the low-altitude economic sector.

[0036] In step S1, the monitoring sensor includes: Wind field monitoring sensors: ultrasonic anemometer, wind speed ±0.5m / s, wind direction ±3°; Environmental monitoring sensors include: a negative oxygen ion sensor (10 ions / cm³ resolution); a PM2.5 sensor (detection limit 1 μg / m³); a temperature sensor (accuracy ±0.2℃); a humidity sensor (accuracy ±2%RH); and conventional air quality monitoring sensors. In addition, the multi-rotor UAV is equipped with a hyperspectral camera, which can acquire topographical information and pollutant spectral information of the monitoring area, providing multi-dimensional data support for subsequent data correction, model prediction, and pollutant source tracing.

[0037] The multi-rotor UAV mainly includes a frame, battery, propellers, motor, electronic speed controller, navigation system, and remote control system. Specifically, it includes two types: tethered UAV and mobile UAV. The tethered UAV is connected to a ground-based mobile power source via a tether cable to achieve 24-hour continuous monitoring. The mobile UAV has a flight time of ≥45 minutes. The multi-rotor UAV has a positioning accuracy of ≤0.5m, a payload capacity of ≥5kg, and a wind resistance of up to level 7 winds.

[0038] The data processing and control system integrates a flight mission planning module, a data processing and analysis module, and a prediction, evaluation, and early warning module. It achieves collaborative control of the UAV and data transmission, storage, and processing through real-time interaction between the radio station and the multi-rotor UAV.

[0039] In step S2, in one specific embodiment, the mounting bracket is made of high-strength, lightweight aluminum alloy and is installed 20.2 cm directly above the center of the UAV. Its size is determined by wind tunnel testing to be 0.53 times the rotor diameter, so as to reduce the interference of the rotor downwash on the monitoring data.

[0040] In step S3, specifically, the grid of monitoring points is divided according to the building distribution and topography of the target study area, and the monitoring density is set based on existing historical data (pollutant concentration, wind field data, etc.). The monitoring point density is increased in areas with high pollutant / negative oxygen ion concentrations.

[0041] The ant colony algorithm dynamically adjusts the monitoring priority based on the pollutant concentration and calculates the optimal sampling point for the drone in real time. The higher the concentration, the denser the monitoring points are deployed.

[0042] The ground control system allocates monitoring tasks based on the UAV's battery level, location, and environmental data through a distributed optimization model.

[0043] In step S4, the outlier values ​​are measurement deviations caused by UAV vibration, fuselage tilt, and data transmission instability. The moving average method is a simple algorithm with low computational cost for smooth prediction.

[0044] Based on the wind speed time history corrected by the moving average method, the corresponding wind direction time history is retrieved: ; in, To invert the wind direction timeline; , These are the results after correction using the moving average method. x , y Time history of directional wind speed component.

[0045] Combination Figures 2 to 4 It can be seen that after correction by the five-point moving average method, the fluctuation of wind speed time history is significantly reduced, and the fluctuation of wind direction time history is also reduced. The moving average method has no effect on the average wind speed, average wind direction, and mean value of wind speed components, but it can significantly weaken the turbulence intensity and effectively eliminate the influence of outliers in the wind speed time history sample, thereby improving the reliability of the measured wind speed time history data.

[0046] Data correction is performed in real time, and the correction coefficients and algorithms are determined in advance during the system calibration phase.

[0047] Deep learning, using the YOLO series of algorithms, identifies the size information of various roughness elements (buildings, structures, trees, etc.) in the measured terrain, and calculates the corresponding surface roughness length. z 0 and roughness index α The correspondence.

[0048] At the same time, by combining the spectral data collected by the hyperspectral camera, abnormal pollutant readings can be cross-validated and supplemented to improve the reliability of environmental field data.

[0049] In step S6, the weighting coefficients of the Kriging method λ i The solution is found by minimizing the variance of the estimation error, while satisfying the unbiased estimation condition. and constraints ,in zp For predicted values, z m These are measured values.

[0050] Specifically, based on real-time corrected monitoring data, the Kriging method is used to predict the spatial distribution of three-dimensional wind field and pollution diffusion field / health and wellness indicators in the region. First, a suitable semi-variogram fitting model is determined in the Kriging method. Then, this model is used to estimate the wind field and environmental field parameters at unknown points. Finally, a visualized three-dimensional wind field and environmental field distribution map is drawn, and corresponding data is generated. RMSE (Root Mean Square Error) contour plots are used to assess prediction accuracy. The semivariogram formula is as follows: ; In the formula, It is a semi-variogram; n For the number of measurements; z i , z j They are respectively i , j Measured value at the location.

[0051] A semivariogram function is established based on the data from the measured points, assuming that other attribute values ​​within the space also satisfy this function. The distance between the unknown and known measured points is calculated, and the corresponding semivariogram is obtained. The optimal weighting coefficients for the unbiased estimate are obtained using the Lagrange multiplier method. The measured values ​​of the known measured points are then weighted and summed using the optimal weighting coefficients to obtain the predicted value for the unknown measured point. Finally, the error of a single measured point and the overall root mean square error (RMSE) can be used. RMSE To test the predictive reliability of the Kriging method, the corresponding evaluation formula is: ; In the formula, z m , z p These are the measured values. 、 Predicted value.

[0052] In step S7, the pollutant concentration indicators include: Primary indicators: Comparison of PM2.5, PM10, SO2, CO, NO2, and O3 concentrations with national environmental quality standards; Secondary indicators: the rate and extent of pollutant diffusion; Level 3 indicator: Air Quality Index (AQI) calculated based on measured data.

[0053] The health and wellness environment indicators include: Primary indicator: Comparison of air negative oxygen ion concentration with health standards; Secondary indicator: Outdoor land exercise weather index; Level 3 indicators: health and wellness climate suitability calculated using random forest analysis, which combines negative oxygen ion concentration, climate comfort, air quality index (AQI), and outdoor land exercise meteorological index.

[0054] The safety indicators for the low-altitude flight environment include: Primary indicator: Wind speed field intensity distribution calculated based on three-dimensional wind field and terrain obstacle parameters; Secondary indicator: The impact coefficient of meteorological conditions (including temperature, humidity, and precipitation probability) on flight safety; Level 3 Indicators: Flight environment suitability index calculated by multi-factor weighting, combining aircraft model parameters, wind field suitability, and meteorological conditions, is used for aircraft route optimization and operational risk classification and early warning.

[0055] The weights of each assessment indicator are determined using the interval number hierarchical analysis method, fully considering the uncertainty and subjectivity of the indicators to make the assessment results more scientific and reasonable. Cloud models are combined to handle data uncertainty, transforming uncertain data into specific assessment levels (e.g., four levels: safe, early warning, warning, and danger, corresponding to four colors: green, yellow, orange, and red, respectively), achieving accurate assessment of the environmental field. Furthermore, multi-band pollutant information provided by hyperspectral cameras can be used to enhance pollutant source tracing and diffusion path analysis, improving the reliability and multi-dimensional visualization level of the assessment results.

[0056] Based on the assessment results, the system automatically analyzes the assessment level and generates corresponding early warning reports. The reports include a detailed interpretation of the assessment results, an assessment of potential environmental risks, and targeted recommendations and measures, providing strong support for environmental management and decision-making.

[0057] Based on the assessment results, monitoring tasks will be dynamically adjusted. For key areas with high pollution risk, monitoring density and frequency will be increased; at the same time, drone flight paths will be optimized to avoid unnecessary overlapping flights, expand monitoring coverage, and improve the overall effectiveness of the monitoring system.

[0058] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0059] Furthermore, it should be noted that the scope of the methods and systems in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0060] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A method for assessing spatial wind field and environmental field based on UAV swarm collaboration, characterized in that, Includes the following steps: Step S1: Construct a multi-rotor UAV swarm collaborative monitoring system: integrate multi-rotor UAVs equipped with hyperspectral cameras, monitoring sensors, mounting brackets, and data processing and control systems; Step S2, System Calibration: Determine the installation positions of the mounting bracket and monitoring sensors through wind tunnel testing, and then install the monitoring sensors on the multi-rotor UAV using the mounting bracket; Step S3, Dynamic Task Planning: Divide the target area into a three-dimensional grid, plan the UAV flight path based on the ant colony algorithm combined with the environmental field gradient, and trigger encrypted sampling in areas where the pollutant concentration in the environmental field exceeds the threshold. Step S4, Data Acquisition and Real-time Correction: Regional spatial wind field and environmental field data are collected using a multi-rotor UAV swarm collaborative monitoring system; outliers in the wind field and environmental field data are identified using the absolute mean method, and the measured data are corrected using the moving average method; aerial images of the terrain are captured using a hyperspectral camera on a multi-rotor UAV, and the roughness element size in the terrain images is identified using YOLO series deep learning algorithms, and the ground roughness length of the corresponding area is calculated. z 0 and roughness index α Hyperspectral cameras are used to collect spectral information of pollutants in the area, which helps in the identification and concentration analysis of pollutants and improves the accuracy and diversity of environmental field monitoring. Step S5, Constructing a digital model: Based on measured data, a multi-parameter coupled model of landform features, wind field, temperature field, humidity field and environmental field is established through a Bayesian network; Step S6, Spatial 3D Wind Field and Environmental Field Prediction: Based on the Kriging method, spatial interpolation is performed on the measured data to predict the spatial distribution of the regional 3D wind field, pollutant diffusion field, and health and wellness indicators, and the results are analyzed using the root mean square error. RMSE Verify the accuracy of the prediction; Step S7, Multi-dimensional Assessment and Early Warning: Construct a three-level assessment system including pollutant concentration indicators, health and wellness environment indicators, and low-altitude flight environment safety indicators. Use the interval number hierarchical analysis method to determine the indicator weights, combine cloud models to handle data uncertainty, output four-color early warning levels, and generate early warning reports. Among them, the low-altitude flight environment safety indicators are based on three-dimensional wind field, meteorological conditions, and terrain obstacle parameters to calculate the flight environment suitability index, which is used for aircraft route optimization and operational risk classification early warning.

2. The method according to claim 1, characterized in that, In step S1, the multi-rotor drone includes a tethered drone and multiple mobile drones. The tethered drone is connected to a ground-based mobile power source via a tether cable to achieve 24-hour continuous monitoring. The mobile drone has a flight time of ≥45 minutes. The multi-rotor drone has a positioning accuracy of ≤0.5m, a payload capacity of ≥5kg, and a wind resistance of level 7.

3. The method according to claim 1, characterized in that, In step S2, the mounting bracket is made of high-strength, lightweight aluminum alloy and is installed directly above the center of the UAV. Its dimensions are determined by wind tunnel testing.

4. The method according to claim 1, characterized in that, In step S3, the ant colony algorithm dynamically adjusts the monitoring priority based on the pollutant concentration; the higher the concentration, the denser the monitoring points are deployed.

5. The method according to claim 1, characterized in that, In step S4, the wind speed time history in the wind field is corrected using the moving average method, and the corresponding wind direction time history is retrieved inversely: ； in, To invert the wind direction timeline; , These are the results after correction using the moving average method. x , y Time history of directional wind speed component.

6. The method according to claim 5, characterized in that, In step S6, the weighting coefficients of the Kriging method λ i The solution is found by minimizing the variance of the estimation error, while satisfying the unbiased estimation condition. and constraints ,in z p For predicted values, z m These are measured values.

7. The method according to claim 1, characterized in that, In step S7, the pollutant concentration indicators include: Primary indicators: Comparison of PM2.5, PM10, SO2, CO, NO2, and O3 concentrations with national environmental quality standards; Secondary indicators: the rate and extent of pollutant diffusion; Level 3 indicator: Air Quality Index calculated based on measured data.

8. The method according to claim 7, characterized in that, In step S7, the health and wellness environment indicators include: Primary indicator: Comparison of air negative oxygen ion concentration with health standards; Secondary indicator: Outdoor land exercise weather index; Level 3 indicators: health and wellness climate suitability calculated using random forest analysis, which combines negative oxygen ion concentration, climate comfort, air quality index, and outdoor land exercise meteorological index.

9. The method according to claim 7, characterized in that, In step S7, the low-altitude flight environment safety indicators include: Primary indicator: Wind speed field intensity distribution calculated based on three-dimensional wind field and terrain obstacle parameters; Secondary indicator: The impact coefficient of meteorological conditions (including temperature, humidity, and precipitation probability) on flight safety; Level 3 Indicators: Flight environment suitability index calculated by multi-factor weighting, combining aircraft model parameters, wind field suitability, and meteorological conditions, is used for aircraft route optimization and operational risk classification and early warning.

10. A space wind field and environmental field assessment system based on UAV swarm collaboration for performing the methods of claims 1-9, characterized in that, include: Tethered drone teams: Deployed in key or core areas of the target region or pollution source, they are connected to a ground-based mobile power source via tethered cables to achieve 24-hour continuous monitoring; Mobile drone teams: Based on the monitoring path dynamically planned by the ant colony algorithm, they collect spatial data in multiple points, multiple layers and multiple dimensions within the target area, so as to achieve efficient coverage of pollutant concentration gradients, spatial distribution of health and wellness environment indicators and spatial distribution of wind fields. Monitoring sensors and hyperspectral cameras: mounted on tethered and mobile drones, these sensors collect multi-dimensional wind and environmental parameters, and calculate corresponding roughness parameters from topographic images acquired by the hyperspectral camera; they also collect spectral information of pollutants in the area using the hyperspectral camera to assist in pollutant identification and concentration analysis, thereby improving the accuracy and diversity of environmental field monitoring. Mounting bracket: Made of high-strength, lightweight aluminum alloy, it is installed directly above the center of the UAV. The size of the bracket was determined by wind tunnel testing to reduce the interference of the rotor downwash on the monitoring sensor measurements. Data processing and control system: Communicates with a cluster of drones equipped with monitoring sensors and hyperspectral cameras via radio stations. It integrates flight control, mission scheduling, data correction and processing, 3D spatial interpolation, Bayesian network modeling, prediction of wind field and pollutant diffusion / health and wellness index spatial distribution, calculation of low-altitude flight environment safety suitability, hierarchical analysis of intervals, and cloud model evaluation. It outputs four-color risk classification early warning results for pollutant concentration, health and wellness environment, and low-altitude flight environment safety indicators, where green indicates safety, yellow indicates warning, orange indicates alert, and red indicates danger, and generates corresponding visualized risk reports. Early warning module: Based on the comprehensive analysis results of the data processing and control system, it further provides pollution source tracing analysis, health and wellness environment analysis, prevention and control suggestions, and route optimization and risk early warning support for aircraft in the low-altitude economic field.

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