A method and system for tracing air pollution sources based on numerical simulation target area pointing

Abstract

The present invention discloses a method and system for tracing the source of air pollution based on numerical simulation target area pointing, the method comprising: obtaining an initial basic data set and determining the area to be detected; simulating the air quality of the area to be detected through a block air quality model to determine the pollution target area; determining the preset flight path information of the drone and performing real-time flow field monitoring and random particle simulation tracking to construct the airflow trajectory of the pollution target area; obtaining the target pollutant concentration contour and correcting the preset flight path information of the drone in combination with the airflow trajectory of the pollution target area; determining the location of the emission source area of ​​the target pollutant according to the corrected preset flight path information of the drone, and generating an air pollution source tracing report. The present invention can quickly track and locate the precise location of the emission source area of ​​the target pollutant. As a method and system for tracing the source of air pollution based on numerical simulation target area pointing, the present invention can be widely used in the field of air pollution prevention and control technology.

Description

Technical Field

[0001] The present invention relates to the technical field of air pollution prevention and control, and in particular to an air pollution source tracing method and system based on numerical simulation target area pointing. Background Art

[0002] Traditional air pollution source monitoring methods often rely on data collected by ground-based fixed monitoring stations. These data have obvious limitations in spatial coverage and temporal resolution, making it difficult to quickly and accurately locate pollution sources, and they cannot meet the business needs and daily management needs of environmental protection supervision areas such as counties, townships, and streets. In addition, related technologies often use empirical judgments or simple mathematical models to track pollution sources, lacking efficient and accurate positioning methods. Summary of the invention

[0003] In order to solve the above technical problems, the purpose of the present invention is to provide an atmospheric pollution source tracing method and system based on numerical simulation target area pointing, which can quickly track and locate the precise position of the emission source area of ​​the target pollutant.

[0004] The first technical solution adopted by the present invention is: a method for tracing the source of air pollution based on numerical simulation target area pointing, comprising the following steps:

[0005] Acquire an initial basic data set and determine a region to be detected based on the initial basic data set;

[0006] Use the block air quality model to simulate the air quality of the area to be tested and determine the pollution target area;

[0007] Determining preset flight path information of a drone based on the pollution target area, wherein the drone has a sensor-type air quality monitor;

[0008] According to the preset flight path information, small-scale wind field construction and random particle simulation tracking are carried out to construct the airflow trajectory of the pollution target area;

[0009] Obtain the target pollutant concentration contour and correct the preset flight path information of the UAV in combination with the airflow trajectory of the polluted target area to obtain the corrected preset flight path information of the UAV;

[0010] The location of the emission source area of ​​the target pollutant is determined based on the corrected drone preset flight path information, and an air pollution source tracing report is generated.

[0011] Furthermore, the step of obtaining the initial basic data set and determining the area to be detected according to the initial basic data set specifically includes:

[0012] Acquire an initial basic data set and determine the center of the pollution area to be detected, wherein the initial basic data set includes initial meteorological forecast driving data, high-precision spatial data and a block scene map;

[0013] Based on the center of the pollution area to be detected, the forecast grid boundary and the forecast sub-grid size are constructed to the four sides to determine the area to be detected.

[0014] Furthermore, the block air quality model includes a mesoscale air quality numerical prediction model and a block scale air quality numerical prediction model. The step of simulating the air quality of the area to be detected by using the block air quality model to determine the pollution target area specifically includes:

[0015] The initial meteorological forecast driving data and high-precision spatial data are input into the block-scale air quality numerical prediction model for calculation to obtain the spatiotemporal characteristic information of the air quality forecast in the area to be tested;

[0016] The block-scale extremely high-resolution emission inventory data in the high-precision spatial data is input into the mesoscale air quality numerical prediction model and then distributed to the forecast subgrids. The pollution concentration is calculated using the Gaussian diffusion formula to obtain the preliminary forecast subgrid pollutant concentration.

[0017] Based on the preliminary forecast sub-grid pollutant concentration, the contribution of meteorological factors and atmospheric chemical reaction factors in the forecast sub-grid is eliminated to obtain the forecast sub-grid pollutant concentration;

[0018] According to the predicted sub-grid pollutant concentration, the pollutant distribution and concentration changes are identified and processed to determine the area to be inspected.

[0019] Furthermore, the step of determining the preset flight path information of the UAV based on the contaminated target area specifically includes:

[0020] Based on the contaminated target area, the coordinate information of the contaminated target area is obtained and converted into the coordinate range of the area of ​​interest of the UAV route;

[0021] Initialize the waypoint mission information of the drone, and send the overall information and waypoint information of the waypoint mission to the drone flight control system. The overall information of the waypoint mission includes the maximum flight speed, cruising speed, disconnection control and waypoint information. The waypoint information includes the waypoint altitude, waypoint longitude and latitude, buffer distance, heading angle and steering mode.

[0022] Integrate the coordinate range of the drone route area of ​​interest with the drone’s waypoint mission information to construct the drone route;

[0023] Add several routes that are approximately perpendicular to the drone route as control points at both ends of the drone route, connect the regional network model, and determine the preset flight path information of the drone.

[0024] Furthermore, the step of constructing a small-scale wind field and simulating and tracking random particles according to the preset flight path information to construct the airflow trajectory of the pollution target area specifically includes:

[0025] Set the simulated environment and atmospheric stability level under stable airflow conditions, control the drone to conduct real-time flow field monitoring and construct a small-scale wind field at the drone's location based on the preset flight path information;

[0026] Based on the small-scale wind field, the core area of ​​the pollution target area is set as the central position, and random particles are released to the receptor points of the pollution target area;

[0027] Track and count random particles to determine the number of random particles in the grid per unit time;

[0028] According to the number of random particles in the grid per unit time, the forward trajectory of the particle and the forward trajectory concentration of the particle are obtained;

[0029] The wind field results at the minute level are output through the block-scale large eddy model and read in reverse time order to plot the backward trajectory of particles and the concentration of backward trajectories of particles.

[0030] The airflow trajectory of the polluted target area is obtained by combining the forward trajectory of the particle, the forward trajectory concentration of the particle, the backward trajectory of the particle and the backward trajectory concentration of the particle.

[0031] Furthermore, the step of obtaining the target pollutant concentration contour and correcting the preset flight path information of the UAV in combination with the airflow trajectory of the pollution target area to obtain the corrected preset flight path information of the UAV specifically includes:

[0032] Build a perception window based on the block scene map to obtain the target pollutant concentration contours and patchy wind speed and direction, and evaluate the probability of the potential target pollution sources in all patches in real time;

[0033] Based on the airflow trajectory of the pollution target area, the UAV is controlled to conduct route survey and simulate several potential pollution diffusion paths. The offset and steering of the route are iterated to maximize the tracking weight index and obtain the corrected offset and steering.

[0034] Performing angle correction on the lines of the last two positions where the drone sensor previously crossed the boundary, reducing the boundary crossing angle, and obtaining a corrected boundary crossing angle;

[0035] The corrected offset steering and the corrected boundary intersection angle are combined to determine the minimum lockable range around the pollution source, and the corrected UAV preset flight path information is obtained.

[0036] Furthermore, the step of determining the location of the emission source area of ​​the target pollutant according to the corrected preset flight path information of the drone and generating an air pollution source tracing report specifically includes:

[0037] Based on the corrected preset flight path information of the drone, the suspected pollution sources in the high-value pollution area are determined and used as pollution exposure indicators, and thermal analysis is performed to construct a thermal map of the main pollutants in the survey area;

[0038] Obtain the spatiotemporal distribution characteristics, main diffusion paths and potential impacts of suspected pollution sources in high-pollution areas on the surrounding environment, and generate an air pollution source tracing report based on the heat map of major pollutants in the survey area.

[0039] The second technical solution adopted by the present invention is: an atmospheric pollution source tracing system based on numerical simulation target area pointing, comprising:

[0040] The first module is used to obtain an initial basic data set and determine the area to be detected based on the initial basic data set;

[0041] The second module is used to simulate the air quality of the area to be tested through the block air quality model and determine the pollution target area;

[0042] A third module is used to determine the preset flight path information of the UAV based on the pollution target area, and the UAV has a sensor-type air quality monitor;

[0043] The fourth module is used to construct a small-scale wind field and simulate and track random particles based on the preset flight path information to construct the airflow trajectory of the pollution target area;

[0044] The fifth module is used to obtain the target pollutant concentration contour and correct the preset flight path information of the UAV in combination with the airflow trajectory of the pollution target area to obtain the corrected preset flight path information of the UAV;

[0045] The sixth module is used to determine the location of the emission source area of ​​the target pollutant based on the corrected preset flight path information of the UAV and generate an air pollution source tracing report.

[0046] The beneficial effects of the method and system of the present invention are as follows: the present invention takes into account various aspects of locating pollution target areas, including real-time monitored flow field morphology and target pollutant concentration contour lines, etc. The UAV dynamically adjusts the flight route according to the real-time monitoring data to optimize the detection efficiency of pollution sources, identifies potential pollution target areas through high-precision air quality models, and uses sensors carried by the UAV for real-time monitoring and dynamic route adjustment. It can be widely used in various environments such as cities and industrial areas, and is particularly suitable for quickly locating and handling sudden environmental pollution incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of steps of a method for tracing the source of air pollution based on numerical simulation target area pointing of the present invention;

[0048] Figure 2 It is a structural block diagram of an atmospheric pollution source tracing system based on numerical simulation target area pointing of the present invention;

[0049] Figure 3 It is a composition and working principle diagram of the drone monitoring system provided by a specific embodiment of the present invention;

[0050] Figure 4 It is a flow chart of a UAV performing a task of dynamically adjusting a circle navigation positioning route provided by a specific embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only provided for the convenience of explanation and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0052] Reference Figure 1 and Figure 3 The present invention provides a method for tracing the source of air pollution based on numerical simulation target area pointing, the method comprising the following steps:

[0053] S100, obtaining an initial basic data set and determining a region to be detected according to the initial basic data set;

[0054] Specifically, an initial basic data set is obtained to determine the center of the polluted area to be detected. The initial basic data set includes initial meteorological forecast driving data, high-precision spatial data and block scene maps. Based on the center of the polluted area to be detected, the forecast grid boundary and the forecast sub-grid size are constructed on the four sides to determine the area to be detected.

[0055] In this embodiment, the initial weather forecast driving data uses the Global Forecast System product; the high-precision spatial data specifically includes: fine topographic data, fine land use data, medium-scale high-resolution emission inventory data, block-scale very high-resolution emission inventory data, wherein the block-scale very high-resolution emission inventory data specifically includes block-scale road mobile source data, block-scale population emission data, block-scale shipping emission data, and block-scale industrial source data; the block scene map includes road networks, emission enterprise location information, and flight control restricted area information.

[0056] S200, simulate the air quality of the area to be tested through the block air quality model to determine the pollution target area;

[0057] Specifically, the initial meteorological forecast driving data and high-precision spatial data are input into the block-scale air quality numerical prediction model for calculation to obtain the spatiotemporal characteristic information of the air quality forecast in the area to be detected; the block-scale extremely high-resolution emission inventory data in the high-precision spatial data is input into the mesoscale air quality numerical prediction model and then distributed to the forecast sub-grid, and the pollution concentration is calculated by the Gaussian diffusion formula to obtain the preliminary forecast sub-grid pollutant concentration; based on the preliminary forecast sub-grid pollutant concentration, the contribution of meteorological factors and atmospheric chemical reaction factors in the forecast sub-grid is eliminated to obtain the forecast sub-grid pollutant concentration; according to the forecast sub-grid pollutant concentration, the pollutant distribution and concentration change identification processing are carried out to determine the area to be detected.

[0058] In this embodiment, a block air quality model at a scale of 100 meters is used to simulate the air quality of the study area, potential pollution targets are identified, an initial forecast range is set with the expected pollution area as the center, and the four surrounding directions are preset as forecast grid boundaries; the forecast sub-grid size is set based on the preset location allocation needs according to the grid boundary; the spatiotemporal characteristic information of the mesoscale air quality forecast is combined with the block-scale emission data of the forecast range and input into the block-scale air quality forecast model for calculation, and the spatiotemporal characteristic information of the block-scale air quality forecast of the forecast range and forecast duration is obtained; in the block-scale air quality model of the 100-meter scale, the block-scale emission data of the forecast range is redistributed to the forecast sub-grid, and the Gaussian diffusion formula is used to recalculate the pollutant concentration of the forecast sub-grid, and the contribution of the mesoscale air quality model to these forecast sub-grids is eliminated, and the non-local transport volume is retained, and finally the forecast sub-grid concentration is obtained; the generated forecast sub-grid concentration data is used to analyze the distribution of pollutants and concentration fluctuations, and identify areas with higher concentrations or significant changes as potential pollution targets.

[0059] Among them, the mesoscale air quality numerical prediction model is the EMEP model, and the block-scale air quality numerical prediction model is the uEMEP model.

[0060] S300, based on the pollution target area, determining the preset flight path information of the UAV, the UAV having a sensor-type air quality monitor;

[0061] Specifically, based on the contaminated target area, the coordinate information of the contaminated target area is obtained and converted into the coordinate range of the UAV route interest area; the waypoint mission information of the UAV is initialized, and the overall information and waypoint information of the waypoint mission are sent to the UAV flight control system, wherein the overall information of the waypoint mission includes the maximum flight speed, cruising speed, disconnection control and waypoint information, and the waypoint information includes the waypoint altitude, waypoint longitude and latitude, buffer distance, heading angle and steering mode; the coordinate range of the UAV route interest area and the waypoint mission information of the UAV are integrated to construct the UAV route; several routes that are approximately perpendicular to the UAV route are added to the UAV route as control points at both ends of the UAV route, the regional network model is connected, and the preset flight path information of the UAV is determined.

[0062] In this embodiment, according to the simulation results, the flight path of the sensor-type air quality monitor carried by the UAV is planned to ensure that the route fully covers the potential target area, specifically including: converting the coordinate information of the pollution target area into the coordinate range of the route interest area; initializing the information of the waypoint mission, sending the overall information of the waypoint mission and the waypoint information to the UAV flight control system, wherein the overall information of the waypoint mission includes the maximum flight speed, cruising speed, disconnection control and waypoint information of the UAV, and the waypoint information includes the waypoint altitude, waypoint longitude and latitude, buffer distance, heading angle, steering mode, etc.; constructing the route, adding several routes that are approximately perpendicular to the mapping route within the survey area as control points at both ends of the basic route, connecting the regional network model, and hovering trajectory to increase the coverage of data collection.

[0063] S400, constructing a small-scale wind field and performing random particle simulation tracking according to preset flight path information to construct an airflow trajectory of the pollution target area;

[0064] Specifically, a simulated environment and an atmospheric stability level under stable airflow conditions are set, and the UAV is controlled to perform real-time flow field monitoring and construct a small-scale wind field at the location of the UAV according to the preset flight path information; based on the small-scale wind field, the core area of ​​the pollution target area is set as the central position, and random particles are released to the receptor points of the pollution target area; the random particles are tracked and counted to determine the number of random particles in the grid per unit time; the forward trajectory of the particles and the forward trajectory concentration of the particles are obtained according to the number of random particles in the grid per unit time; the wind field results at the minute level are output through a block-scale large eddy model and the wind field results are read in reverse time order to draw the backward trajectory of the particles and the backward trajectory concentration of the particles; the airflow trajectory of the pollution target area is obtained by combining the forward trajectory of the particles, the forward trajectory concentration of the particles, the backward trajectory of the particles and the backward trajectory concentration of the particles.

[0065] In this embodiment, the UAV constructs a small-scale wind field according to the flow field morphology monitored in real time, simulates the release of random particles in the wind field, and obtains the airflow trajectory by tracking the random particles, which specifically includes: establishing a two-dimensional block concentration field as a simulation environment under stable airflow conditions; in the steady-state model, the atmospheric stability level is set to a medium level; using CFD to construct a turbulent concentration field, selecting the polluted gas to be tracked as the gas type, and setting the core area of ​​the pollution target area at the center; releasing random particles at the receptor point in the pollution target area and tracking the particles; counting the number of particles per unit grid per unit time, determining the main airflow path grid according to the number of particles per unit grid per unit time, thereby determining the particle trajectory; by recording the position of each particle at each simulation time step, drawing the forward trajectory and the forward trajectory concentration of the particle according to the position; obtaining the wind field results of the block-scale large eddy model output at the minute level, and reading the wind field results in reverse time sequence, simulating and recording the position of the particle, and drawing the backward trajectory and the backward trajectory concentration of the particle according to the position.

[0066] Among them, the particle trajectory simulated using the forward time sequence is the forward trajectory, and the particle trajectory simulated using the reverse time sequence is the backward trajectory.

[0067] S500, obtaining target pollutant concentration contours and correcting the preset flight path information of the UAV in combination with the airflow trajectory of the pollution target area to obtain the corrected preset flight path information of the UAV;

[0068] Specifically, a perception window is constructed based on the block scene map to obtain the target pollutant concentration contour lines and patchy wind speed and direction, and the probability of occurrence of potential target pollution sources in all patches is evaluated in real time; based on the airflow trajectory of the pollution target area, the UAV is controlled to conduct route surveys and simulate several potential pollution diffusion paths, and the offset steering of the route is iterated to maximize the tracking weight index, and the corrected offset steering is obtained; the angle of the line of the last two positions where the UAV sensor previously crossed the boundary is corrected to reduce the boundary intersection angle and obtain the corrected boundary intersection angle; the minimum lockable range around the pollution source is determined by combining the corrected offset steering and the corrected boundary intersection angle, and the corrected UAV preset flight path information is obtained.

[0069] In this embodiment, if Figure 4As shown, the UAV calculates the monitoring path weight in real time according to the airflow trajectory in the wind farm and the target pollutant concentration contour lines and dynamically adjusts the circle navigation route, which includes: creating a fixed-size perception window according to the scene map, obtaining the target pollutant concentration contour lines and the patchy wind speed and direction, and evaluating the probability of the occurrence of potential target pollution sources in all patches in real time; while the UAV is conducting aerial surveys along the initial route, it simulates several potential pollution diffusion paths, iterates the offset and steering of the route to maximize the tracking weight index; performs angle correction on the lines of the last two positions where the sensor previously crossed the boundary to reduce the boundary intersection angle; each time the sensor crosses the boundary, it performs angle correction, evaluates the next target in real time and moves until it reaches the minimum lockable range around the pollution source.

[0070] The pollution diffusion path estimation process is enhanced by an active perception navigation strategy to maximize the information obtained in each heading iteration.

[0071] S600: Determine the location of the emission source area of ​​the target pollutant according to the corrected preset flight path information of the UAV, and generate an air pollution source tracing report.

[0072] Specifically, based on the corrected preset flight path information of the drone, the suspected pollution sources in the high-pollution area are determined and used as pollution exposure indicators, and thermal analysis is carried out to construct a heat map of the main pollutants in the survey area. The spatiotemporal distribution characteristics, main diffusion paths and potential impacts of the suspected pollution sources in the high-pollution area are obtained, and combined with the heat map of the main pollutants in the survey area, an atmospheric pollution source tracing report is generated.

[0073] In this embodiment, through data analysis, the precise location of the emission source area of ​​the target pollutant is finally determined, and a detailed source tracing report is generated, which specifically includes: taking the suspected pollution source in the high-value pollution area as the pollution exposure indicator, conducting thermal analysis to construct a thermal map of the main pollutants in the survey area; describing the type of pollution source, emission substances, possible emission paths and quantified emission levels, including information such as the industry type and characteristics of the pollution source; the impact of the individual pollution source on the surrounding air quality, including the spatiotemporal distribution characteristics of pollutant concentrations, the main diffusion paths and their potential impact on the surrounding communities and environment; based on the nature and impact of the pollution source, assessing its risks to human health and the environment, and providing concentration distribution maps, propagation path schematics and on-site monitoring data reports.

[0074] In summary, a specific embodiment is used to illustrate. First, prepare and produce the topography, land use, urban canopy data, MEIC emission inventory data and block-scale extremely high-resolution emission inventory data of place A, and download the GFS forecast products with a time resolution of 6 hours (a total of 18) from May 18, 2023 to May 20, 2023 (a total of 3 days). The mesoscale meteorological numerical prediction model (hereinafter referred to as the WRF model) WRF and the mesoscale air quality numerical prediction model EMEP (hereinafter referred to as the EMEP model) simulate the location of place B, and the scale air quality numerical prediction model uEMEP (hereinafter referred to as the uEMEP model) of place B simulates the location of place A, and the forecast duration is 72 hours. The WRF model and the EMEP model are centered at 23.5°N, 113.7°E. The grid resolution of the WRF model and the EMEP model is set to 3 km, covering area B, with a grid number of 145×124. The subgrid resolution of the uEMEP model is set to 250m, covering area A, with a subgrid number of 480×480. The GFS forecast product is interpolated in the WRF model, the meteorological boundary field is initialized, and the meteorological field with a resolution of 3 km from May 18, 2023 to May 20, 2023 in area B is simulated to obtain the spatiotemporal characteristic information of the meteorological forecast with a resolution of 3 km. The spatiotemporal characteristic information of the meteorological forecast with a resolution of 3 km is used as the meteorological driving field of the EMEP model to simulate the air quality field with a resolution of 3 km from May 18, 2023 to May 20, 2023 in area B, and obtain the spatiotemporal characteristic information of the air quality forecast with a resolution of 3 km. The spatiotemporal characteristic information of air quality forecast with a resolution of 3 km is used as the air quality driving field of the uEMEP model to simulate the air quality field with a resolution of 250 m in place A from May 18, 2023 to May 20, 2023, and the air quality forecast parameters with a resolution of 250 m are obtained, including: 2m nitrogen oxide concentration, 2m carbon dioxide concentration, 2m ozone concentration, 2m particulate matter concentration (including PM 10 and PM 2.5 ). The concentration data of the obtained sub-grids are vectorized to determine several locations with high concentrations of polluted gases within the block scale, and are linked in sequence for linear fitting. The areas with high concentrations or significant changes are taken as pollution target areas. The drone pre-route planning is set according to the pollution target areas.

[0075] Afterwards, the drone took off according to the preset route, carrying air quality sensors to monitor the concentration of pollutants in the air in real time.

[0076] The following boundary constraints must be met during route pre-planning, and their expressions are:

[0077] ;

[0078] In the above formula, is the position of the UAV relative to the origin in the inertial coordinate system, It is the boundary value of the area where the drone can fly.

[0079] During the flight of the drone, the following speed constraints should be met, and their expressions are:

[0080] ;

[0081] In the above formula, is the flight speed of the drone, are the minimum and maximum flight speeds that the drone can achieve, respectively.

[0082] From the perspective of UAV flight efficiency, the performance index of the route pre-planning problem is to pass through several pollutant concentration maximum points and reach the destination with the shortest distance, which can be specifically expressed as:

[0083] ;

[0084] In the above formula, F is the total cost, For the cost of the voyage, is the number of route points between two destination points, For the cost of threats.

[0085] In order to further improve the solution accuracy, obtain the flight reference instructions, and re-plan the UAV route. Establish the mathematical relationship between the state variables and the control variables, and obtain the UAV flight route by solving the optimal value of the objective function. Use the general optimal control method to mathematically describe it, expressed as a Bolza-type performance index function, and its expression is:

[0086] ;

[0087] In the above formula, is the state variable, is the initial moment, For the end moment, is a dynamic performance indicator, is the terminal performance index, then it satisfies:

[0088] 1) State equation constraint, its expression is:

[0089] ;

[0090] 2) Boundary conditions, which are expressed as:

[0091] ;

[0092] 3) Route constraints, which are expressed as:

[0093] ;

[0094] in is the control quantity and satisfies .

[0095] Taking the quad-rotor UAV dynamics model as an example, we further consider the constraints of the UAV speed, position, attitude, etc.:

[0096] 1) Basic information;

[0097] Location coordinates , speed along the axis , Axis posture and the angular velocity around the axis Respectively expressed as:

[0098] ;

[0099] In the above formula, The distances of the three axes of the drone relative to the origin of the inertial coordinate system; is the component of the UAV velocity in the three axis directions of the inertial coordinate system; For drone quality; is the acceleration due to gravity; is the vertical lift; is the torque; is the moment of inertia of the UAV relative to the inertial coordinate system.

[0100] 2) Performance constraints;

[0101] The performance of a quadcopter is determined by its own capabilities. To ensure the safety of the drone, the flight speed, attitude angle and angular velocity of the drone are constrained as follows:

[0102] ;

[0103] In the above formula, is the flight speed of the UAV in the inertial coordinate system, is the attitude angle of the drone in the fuselage coordinate system relative to the inertial coordinate system, is the angular velocity of the drone itself, is the boundary value of velocity, is the boundary value of the attitude angle, is the boundary value of the angular velocity.

[0104] During the flight, the drone adjusts its route based on real-time data, gradually focusing on areas with higher pollutant concentrations.

[0105] According to the route and planning results of the pollution target area, the monitoring route weight is calculated in real time by the airflow trajectory in the wind farm. The steps for solving the optimal pollution monitoring route by combining graph theory are as follows:

[0106] 1) The environment of the drone monitoring mission is abstracted into a weighted undirected graph model. In this model, drones, suspected pollution sources, key monitoring points, and other important locations are considered nodes in the graph. The connection between nodes, that is, the potential flight path of the drone, is represented by undirected edges. The weight of each edge represents the cost or distance of the drone flying between two nodes. These weights comprehensively consider the timeliness of flight monitoring.

[0107] 2) Use the particle swarm algorithm to pre-plan the itinerary and construct the adjacency matrix, which is the weight of the graph. The adjacency matrix is ​​established to represent the connection relationship and weights between nodes in the form of an adjacency matrix. The weights can be pre-planned according to actual needs or calculated in real time using the particle swarm algorithm for pre-planning.

[0108] 3) Determine the mission execution plan. Combine graph theory with the depth-first search algorithm to solve the shortest path problem and quickly obtain the UAV mission execution plan.

[0109] Eventually, the drone successfully located an unreported chemical plant discharge outlet.

[0110] Reference Figure 2 , an atmospheric pollution source tracing system based on numerical simulation target area pointing, including:

[0111] The first module 201 is used to obtain an initial basic data set and determine a region to be detected according to the initial basic data set;

[0112] The second module 202 is used to simulate the air quality of the area to be detected through the block air quality model to determine the pollution target area;

[0113] The third module 203 is used to determine the preset flight path information of the UAV based on the pollution target area, and the UAV has a sensor-type air quality monitor;

[0114] The fourth module 204 is used to construct a small-scale wind field and simulate and track random particles according to the preset flight path information to construct an airflow trajectory in the pollution target area;

[0115] The fifth module 205 is used to obtain the target pollutant concentration contour and correct the preset flight path information of the UAV in combination with the airflow trajectory of the pollution target area to obtain the corrected preset flight path information of the UAV;

[0116] The sixth module 206 is used to determine the location of the emission source area of ​​the target pollutant based on the corrected preset flight path information of the UAV and generate an air pollution source tracing report.

[0117] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0118] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for tracing the source of air pollution based on numerical simulation target area pointing, characterized in that: The following steps are involved: Acquire an initial basic data set and determine the center of the pollution area to be detected, wherein the initial basic data set includes initial meteorological forecast driving data, high-precision spatial data and a block scene map; The initial meteorological forecast driving data and high-precision spatial data are input into the block-scale air quality numerical prediction model for calculation to obtain the spatiotemporal characteristic information of the air quality forecast in the area to be tested; The block-scale extremely high-resolution emission inventory data in the high-precision spatial data is input into the mesoscale air quality numerical prediction model and then distributed to the forecast subgrids. The pollution concentration is calculated using the Gaussian diffusion formula to obtain the preliminary forecast subgrid pollutant concentration. Based on the preliminary forecast sub-grid pollutant concentration, the contribution of meteorological factors and atmospheric chemical reaction factors in the forecast sub-grid is eliminated to obtain the forecast sub-grid pollutant concentration; According to the pollutant concentration of the forecast sub-grid, the pollutant distribution and concentration change are identified and processed to determine the area to be tested; Use the block air quality model to simulate the air quality of the area to be tested and determine the pollution target area; Based on the contaminated target area, the coordinate information of the contaminated target area is obtained and converted into the coordinate range of the area of ​​interest of the UAV route; Initialize the waypoint mission information of the drone, and send the overall information and waypoint information of the waypoint mission to the drone flight control system. The overall information of the waypoint mission includes the maximum flight speed, cruising speed and disconnection control, and the waypoint information includes the waypoint altitude, waypoint longitude and latitude, buffer distance, heading angle and steering mode; Integrate the coordinate range of the drone route area of ​​interest with the drone’s waypoint mission information to construct the drone route; Adding several routes approximately perpendicular to the drone route as control points at both ends of the drone route, connecting the regional network model, and determining the preset flight path information of the drone, wherein the drone has a sensor-type air quality monitor; Set the simulated environment and atmospheric stability level under stable airflow conditions, control the drone to conduct real-time flow field monitoring and construct a small-scale wind field at the drone's location based on the preset flight path information; Based on the small-scale wind field, the core area of ​​the pollution target area is set as the central position, and random particles are released to the receptor points of the pollution target area; Track and count random particles to determine the number of random particles in the grid per unit time; According to the number of random particles in the grid per unit time, the forward trajectory of the particle and the forward trajectory concentration of the particle are obtained; The wind field results at the minute level are output through the block-scale large eddy model and read in reverse time order to plot the backward trajectory of particles and the concentration of backward trajectories of particles. The airflow trajectory of the polluted target area is obtained by combining the forward trajectory of the particle, the forward trajectory concentration of the particle, the backward trajectory of the particle and the backward trajectory concentration of the particle; Build a perception window based on the block scene map to obtain the target pollutant concentration contours and patchy wind speed and direction, and evaluate the probability of the potential target pollution sources in all patches in real time; Based on the airflow trajectory of the pollution target area, the UAV is controlled to conduct route survey and simulate several potential pollution diffusion paths. The offset and steering of the route are iterated to maximize the tracking weight index and obtain the corrected offset and steering. Performing angle correction on the lines of the last two positions where the drone sensor previously crossed the boundary, reducing the boundary crossing angle, and obtaining a corrected boundary crossing angle; Combine the corrected offset steering and the corrected boundary intersection angle to determine the minimum lockable range around the pollution source, and obtain the corrected preset flight path information of the UAV; Based on the corrected preset flight path information of the drone, the suspected pollution sources in the high-value pollution area are determined and used as pollution exposure indicators, and thermal analysis is performed to construct a thermal map of the main pollutants in the survey area; Obtain the spatiotemporal distribution characteristics, main diffusion paths and potential impacts of suspected pollution sources in high-pollution areas on the surrounding environment, and generate an air pollution source tracing report based on the heat map of major pollutants in the survey area.

2. An atmospheric pollution source tracing system based on numerical simulation target area pointing, characterized in that: Includes the following modules: The first module is used to obtain an initial basic data set and determine the center of the pollution area to be detected. The initial basic data set includes initial meteorological forecast driving data, high-precision spatial data and street scene map; The initial meteorological forecast driving data and high-precision spatial data are input into the block-scale air quality numerical prediction model for calculation to obtain the spatiotemporal characteristic information of the air quality forecast in the area to be tested; The block-scale extremely high-resolution emission inventory data in the high-precision spatial data is input into the mesoscale air quality numerical prediction model and then distributed to the forecast subgrids. The pollution concentration is calculated using the Gaussian diffusion formula to obtain the preliminary forecast subgrid pollutant concentration. Based on the preliminary forecast sub-grid pollutant concentration, the contribution of meteorological factors and atmospheric chemical reaction factors in the forecast sub-grid is eliminated to obtain the forecast sub-grid pollutant concentration; According to the pollutant concentration of the forecast sub-grid, the pollutant distribution and concentration change are identified and processed to determine the area to be tested; The second module is used to simulate the air quality of the area to be tested through the block air quality model and determine the pollution target area; The third module is used to obtain the coordinate information of the contaminated target area based on the contaminated target area and convert it into the coordinate range of the area of ​​interest of the UAV route; Initialize the waypoint mission information of the drone, and send the overall information and waypoint information of the waypoint mission to the drone flight control system. The overall information of the waypoint mission includes the maximum flight speed, cruising speed and disconnection control, and the waypoint information includes the waypoint altitude, waypoint longitude and latitude, buffer distance, heading angle and steering mode; Integrate the coordinate range of the drone route area of ​​interest with the drone’s waypoint mission information to construct the drone route; Adding several routes approximately perpendicular to the drone route as control points at both ends of the drone route, connecting the regional network model, and determining the preset flight path information of the drone, wherein the drone has a sensor-type air quality monitor; The fourth module is used to set the simulated environment and atmospheric stability level under stable airflow conditions, control the drone to perform real-time flow field monitoring and construct a small-scale wind field at the drone's location according to the preset flight path information; Based on the small-scale wind field, the core area of ​​the pollution target area is set as the central position, and random particles are released to the receptor points of the pollution target area; Track and count random particles to determine the number of random particles in the grid per unit time; According to the number of random particles in the grid per unit time, the forward trajectory of the particle and the forward trajectory concentration of the particle are obtained; The wind field results at the minute level are output through the block-scale large eddy model and read in reverse time order to plot the backward trajectory of particles and the concentration of backward trajectories of particles. The airflow trajectory of the polluted target area is obtained by combining the forward trajectory of the particle, the forward trajectory concentration of the particle, the backward trajectory of the particle and the backward trajectory concentration of the particle; The fifth module is used to build a perception window based on the block scene map, obtain the target pollutant concentration contours and patchy wind speed and direction, and evaluate the probability of the occurrence of potential target pollution sources in all patches in real time; Based on the airflow trajectory of the pollution target area, the UAV is controlled to conduct route survey and simulate several potential pollution diffusion paths. The offset and steering of the route are iterated to maximize the tracking weight index and obtain the corrected offset and steering. Performing angle correction on the lines of the last two positions where the drone sensor previously crossed the boundary, reducing the boundary crossing angle, and obtaining a corrected boundary crossing angle; Combine the corrected offset steering and the corrected boundary intersection angle to determine the minimum lockable range around the pollution source, and obtain the corrected preset flight path information of the UAV; The sixth module is used to determine the suspected pollution source in the high-value pollution area based on the corrected preset flight path information of the drone and use it as a pollution exposure indicator, conduct thermal analysis and construct a thermal map of the main pollutants in the survey area; Obtain the spatiotemporal distribution characteristics, main diffusion paths and potential impacts of suspected pollution sources in high-pollution areas on the surrounding environment, and generate an air pollution source tracing report based on the heat map of major pollutants in the survey area.

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