An atmospheric environmental pollution incident emergency monitoring and early warning method and system

Abstract

The present application discloses an emergency monitoring and early warning method and system for atmospheric environmental pollution incidents, and the present application belongs to the field of environmental management technology. The method includes: obtaining multi-scale meteorological field data and real-time data on chlorine concentration from at least five monitoring stations, extracting the wind field direction and concentration change rate, and determining the location and wind direction of potential pollution hotspots. This information is sent to a drone, which automatically plans a path and collects supplementary concentration data from three angles. The monitoring data and supplementary sampling data are integrated, combined with the meteorological field input pollutant three-dimensional concentration distribution model to generate three-dimensional distribution data. This is then input into the CALPUFF inverse model, and the specific location and emission intensity of the pollution source are inverted. This solution improves the accuracy and timeliness of pollution source tracing, and also has high spatial resolution and flexible response capabilities, providing efficient and intelligent technical support for intelligent early warning, rapid tracing and scientific response to sudden toxic gas leakage incidents.

Description

Technical Field

[0001] The present application belongs to the field of environmental management technology, and specifically relates to an emergency monitoring and early warning method and system for atmospheric environmental pollution events. Background Art

[0002] Rapid and accurate monitoring and early warning of atmospheric pollution incidents, especially chlorine gas leakage accidents, are of vital importance for taking emergency measures in a timely manner and reducing accident losses.

[0003] Currently, emergency monitoring and early warning for atmospheric pollution incidents primarily utilizes a combination of fixed monitoring stations and manual inspections. Specifically, fixed monitoring stations deploy various high-precision sensors, such as gas analyzers and particulate matter monitors, to continuously and in real time monitor the concentrations of various atmospheric pollutants (such as sulfur dioxide, nitrogen oxides, particulate matter, volatile organic compounds, and certain toxic and hazardous gases like chlorine). These data are then transmitted to a central monitoring platform for analysis and processing, enabling timely detection of pollution anomalies and the issuance of early warning signals.

[0004] However, fixed monitoring sites are limited by their preset locations and coverage, making it difficult to fully capture the dynamic boundaries of pollution diffusion and anomalies in remote areas, and the equipment maintenance costs are high. Although manual inspections can supplement blind spot monitoring, they are limited by manpower efficiency and adaptability to extreme environments, and there is a response lag. The data fusion between the two is insufficient, and there is a lack of collaborative analysis capabilities for multi-source heterogeneous data, resulting in low pollution source positioning accuracy and large deviations in diffusion trend predictions, making it difficult to support efficient and accurate emergency decision-making. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for emergency monitoring and early warning of atmospheric environmental pollution events, which solves the problems that the use of fixed monitoring stations for emergency monitoring and early warning of atmospheric environmental pollution events is limited by the preset location and coverage range, making it difficult to fully capture the dynamic boundaries of pollution diffusion and anomalies in remote areas, and the equipment maintenance cost is high; although manual inspections can supplement blind spot monitoring, they are limited by manpower efficiency and adaptability to extreme environments, and there is a response lag; the data fusion between the two is insufficient, and there is a lack of collaborative analysis capabilities for multi-source heterogeneous data, resulting in low pollution source positioning accuracy, large deviations in diffusion trend predictions, and difficulty in supporting efficient and accurate emergency decision-making.

[0006] In a first aspect, an embodiment of the present application provides an atmospheric environmental pollution incident emergency monitoring and early warning method, the method comprising:

[0007] Acquiring multi-scale meteorological field data and real-time chlorine concentration data, determining wind field direction data from the multi-scale meteorological field data, and determining a concentration change rate of the real-time chlorine concentration data, and determining potential pollution hotspots based on the wind field direction data and the concentration change rate; wherein the real-time chlorine concentration data is obtained from at least five fixed monitoring stations;

[0008] Determining the location data and wind direction data of the potential pollution hotspot area, and sending the location data and wind direction data to a drone, so that the drone automatically plans a flight path based on the location data and wind direction data, and obtains supplementary chlorine concentration sampling data from at least three angles;

[0009] Obtaining supplementary chlorine concentration sampling data from at least three angles transmitted by a drone, inputting the real-time chlorine concentration data, the supplementary chlorine concentration sampling data, and the multi-scale meteorological field data into a preset three-dimensional pollutant concentration distribution model to obtain three-dimensional pollutant concentration distribution data;

[0010] The three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data are input into the CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity.

[0011] In a second aspect, an embodiment of the present application provides an atmospheric environmental pollution incident emergency monitoring and early warning system, the system comprising:

[0012] A potential pollution hotspot identification module is configured to obtain multi-scale meteorological field data and real-time chlorine concentration data, determine wind field direction data from the multi-scale meteorological field data, and determine the concentration change rate of the real-time chlorine concentration data, and determine potential pollution hotspots based on the wind field direction data and the concentration change rate; wherein the real-time chlorine concentration data is obtained from at least five fixed monitoring stations;

[0013] A drone-assisted sampling module is used to determine the location data and wind direction data of the potential pollution hotspot area, and send the location data and wind direction data to the drone, so that the drone can automatically plan a flight path based on the location data and wind direction data, and obtain supplementary chlorine concentration sampling data from at least three angles;

[0014] A three-dimensional concentration modeling module is used to obtain supplementary chlorine concentration sampling data from at least three angles transmitted by the drone, input the real-time chlorine concentration data, the supplementary chlorine concentration sampling data, and the multi-scale meteorological field data into a preset three-dimensional pollutant concentration distribution model to obtain three-dimensional pollutant concentration distribution data;

[0015] The pollution source inversion module is used to input the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into the CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity.

[0016] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0017] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0018] In an embodiment of the present application, multi-scale meteorological field data and real-time chlorine concentration data are obtained, wind field direction data of the multi-scale meteorological field data are determined, and the concentration change rate of the real-time chlorine concentration data is determined, and potential pollution hotspot areas are determined based on the wind field direction data and the concentration change rate; wherein, the real-time chlorine concentration data comes from at least five fixed monitoring stations; the position data and wind direction data of the potential pollution hotspot areas are determined, and the position data and the wind direction data are sent to a drone, so that the drone automatically plans a flight path based on the position data and the wind direction data, and obtains supplementary chlorine concentration sampling data from at least three angles; the supplementary chlorine concentration sampling data obtained from at least three angles and transmitted by the drone is obtained, and the real-time chlorine concentration data, the supplementary chlorine concentration sampling data and the multi-scale meteorological field data are input into a preset three-dimensional pollutant concentration distribution model to obtain three-dimensional pollutant concentration distribution data; the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data are input into a CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity. The above-mentioned emergency monitoring and early warning method for atmospheric environmental pollution incidents not only improves the accuracy and timeliness of pollution source tracing, but also has high spatial resolution and flexible response capabilities, providing efficient and intelligent technical support for intelligent early warning, rapid tracing and scientific response to sudden toxic gas leakage incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the method for emergency monitoring and early warning of atmospheric environmental pollution incidents provided in Example 1 of the present application;

[0020] Figure 2 This is a flow chart of an emergency monitoring and early warning method for atmospheric environmental pollution incidents provided in Example 2 of the present application;

[0021] Figure 3This is a schematic diagram of the structure of the atmospheric environmental pollution incident emergency monitoring and early warning system provided in Example 3 of the present application;

[0022] Figure 4 This is a schematic diagram of the structure of the electronic device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0023] To further clarify the objectives, technical solutions, and advantages of this application, specific embodiments of the present application are described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are intended only to illustrate this application and are not intended to limit it. It should also be noted that, for ease of description, the drawings only illustrate portions relevant to this application, not all of them. Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts depict the various operations (or steps) as sequential processes, many of the operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process may terminate upon completion of its operations, but may also include additional steps not shown in the accompanying drawings. The process may correspond to a method, function, procedure, subroutine, subprogram, or the like.

[0024] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0025] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0026] Below, in conjunction with the accompanying drawings, an RSMC chip, a chip multi-stage startup method, and a Beidou communication and navigation device provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0027] Example 1

[0028] Figure 1This is a flow chart of the method for emergency monitoring and early warning of atmospheric environmental pollution incidents provided in Example 1 of this application. Figure 1 As shown, the specific steps include:

[0029] S101, obtaining multi-scale meteorological field data and real-time chlorine concentration data, determining wind field direction data of the multi-scale meteorological field data, and determining the concentration change rate of the real-time chlorine concentration data, and determining potential pollution hotspot areas based on the wind field direction data and the concentration change rate; wherein the real-time chlorine concentration data comes from at least five fixed monitoring stations.

[0030] Multi-scale meteorological field data refers to a collection of meteorological parameter data covering different spatial scales (e.g., regional, city, and local micro-area) and temporal scales (e.g., hourly and minute-by-minute). These data may include wind speed, wind direction (vector wind field), temperature, humidity, atmospheric pressure, boundary layer height, wet deposition, dry deposition coefficient, precipitation intensity, and solar radiation. Multi-scale data is reflected in the ability to dynamically switch data granularity and accuracy based on different resolutions, scopes, and application scenarios.

[0031] The real-time chlorine concentration data may be chlorine concentration measurement data obtained continuously over time from a fixed environmental monitoring station.

[0032] Wind field direction data can be a spatial wind direction vector field extracted from multi-scale meteorological field data, which indicates the wind flow direction in different regions, different altitude layers, and different times. Specifically, it is usually two-dimensional or three-dimensional vector data (such as U and V components). There is a wind direction at each grid point or station location (unit: °, for example, east wind is 90°).

[0033] The concentration change rate can refer to the rate of change of pollutant concentration per unit time, indicating the growth or decay trend of the pollutant.

[0034] Potential pollution hotspots can be identified by analyzing wind field direction data and concentration change rate data, and are spatial areas where pollution sources or pollutant accumulation may exist.

[0035] Fixed monitoring sites refer to pollutant concentration monitoring facilities that are deployed long-term at preset locations within the target area. They can continuously and regularly collect real-time concentration data of specific pollutants (such as chlorine) and upload the data to the platform for centralized analysis through the communication module.

[0036] Meteorological data interfaces (such as those from the Central Meteorological Observatory and the WRF numerical forecast model) can be accessed to obtain multi-scale meteorological field data. Fixed monitoring stations can regularly upload chlorine concentration data (e.g., sampling once per minute). At least five fixed stations should be deployed to ensure data time synchronization and reasonable spatial distribution. Horizontal and vertical wind vectors (U / V components can be used) are extracted from the multi-scale meteorological field data to obtain the wind field direction (in angle / vector form) at each location and time in each region. For each monitoring point, a sliding time window (e.g., 5 minutes) is used to calculate the concentration change rate:

[0037]

[0038] Where i is the fixed monitoring site index; is the concentration change rate; is the real-time data of chlorine concentration at time t; for Real-time data of chlorine concentration at all times; is a sliding time window.

[0039] When chlorine concentrations at certain fixed monitoring stations rise continuously over a short period of time, and this upward trend correlates with local wind direction, it can be inferred that the pollutants likely originated from a specific area upwind of these stations. By analyzing concentration trends at multiple stations and tracing back the paths based on wind direction, it is possible to spatially locate the intersection of these trends, identifying them as potential pollution hotspots. This area is highly likely to be near the source of the pollution and can be used to guide subsequent drone sampling route planning and precise pollution source identification.

[0040] On the basis of the above technical solution, it is optional to obtain multi-scale meteorological field data, including:

[0041] Receive meteorological satellite data and radar observation data in real time based on the WRF model, update the boundary conditions and initial field of the WRF model according to the meteorological satellite data and radar observation data, drive the meteorological simulation process, and generate three-dimensional meteorological forecast results for a preset time period;

[0042] According to the CALMET adaptive downscaling method, the three-dimensional weather forecast results of the preset time period are converted into multi-scale meteorological field data.

[0043] In this scenario, the WRF model is a numerical weather prediction model used for weather forecasting and atmospheric research. It can simulate atmospheric processes at different spatial scales, including wind, temperature, humidity, and precipitation. The WRF model can nest multiple resolution regions and supports real-time multi-source data for updating boundary conditions and initial states. It is one of the most commonly used models in high-resolution meteorological simulations.

[0044] Meteorological satellite data can come from geostationary or polar-orbiting satellites and are used to monitor large-scale atmospheric characteristics such as cloud patterns, radiation, temperature, and water vapor.

[0045] Radar observations, including reflectivity and radial velocity data acquired by weather radar, primarily reflect near-surface atmospheric dynamics, such as mesoscale convective systems and precipitation structures. These observations, acting as external constraints, can be used to calibrate the WRF model and improve its simulation accuracy.

[0046] The initial field can be the atmospheric state data (such as the three-dimensional spatial distribution of temperature, humidity, wind speed, etc.) required for the WRF model to start simulating at a certain time (such as t0).

[0047] Boundary conditions can be external inputs at the edges of the model space throughout the simulation period, used to guide how the edges of the model area "exchange information with the external atmosphere" to maintain simulation continuity.

[0048] The preset period may refer to a time range predefined for a simulation or prediction task, such as "next 3 hours" or "next 12 hours", during which the model will generate weather forecast data.

[0049] Three-dimensional weather forecast results refer to meteorological variable data with a three-dimensional structure (including horizontal, vertical, and temporal dimensions) output by the model, such as wind, temperature, and humidity fields. This data can support subsequent applications such as pollutant dispersion simulation and air quality analysis.

[0050] CALMET is the meteorological data processing module within the CALPUFF system, used to convert raw meteorological data into high-resolution, terrain-adapted meteorological fields. Its adaptive downscaling method refines coarser-resolution output data from WRF and other datasets based on regional topography, surface properties, and local observations, generating multi-scale meteorological data with higher spatial resolution that more closely reflects actual ground-level meteorological characteristics.

[0051] To achieve high-precision, multi-scale adaptive meteorological data acquisition, the WRF (Weather Research and Forecasting) model first receives real-time observational data from meteorological satellites and ground-based radar. Satellite data include remote sensing information such as atmospheric temperature and humidity profiles, cloud maps, and radiation flux, while radar data include surface precipitation intensity, radar reflectivity, and wind profiles. These observational data are integrated into the WRF model using data assimilation techniques (such as 3DVAR or EnKF), dynamically modifying and updating its boundary conditions and initial fields to improve the model's ability to fit the actual atmospheric state. Subsequently, the updated initial fields and boundary conditions drive the WRF numerical simulation module, simulating high-resolution three-dimensional meteorological elements for a preset time period (e.g., 1-6 hours in the future). This generates three-dimensional forecasts for wind speed, wind direction, temperature, and humidity at multiple vertical levels. To ensure that these three-dimensional forecasts better reflect the local meteorological characteristics of complex terrain, the CALMET adaptive downscaling method is introduced to process the WRF output. This method integrates terrain factors (such as slope, height, and surface type) and local observation data, adaptively adjusts key parameters such as wind field and turbulent diffusion coefficient, downscales the original meteorological forecast data to higher spatial accuracy, and then generates multi-scale meteorological field data that can be used for pollutant diffusion simulation.

[0052] In this scheme, by combining the WRF model with real-time meteorological satellite and radar observation data, the boundary conditions and initial fields are dynamically updated, which can significantly improve the accuracy and timeliness of meteorological simulations. At the same time, the CALMET adaptive downscaling method is introduced to make meteorological field data more consistent with the terrain and local characteristics, ensuring that the downstream pollution diffusion simulation has higher spatial resolution and true reflection ability, thereby providing scientific and reliable meteorological support for pollution source location, concentration prediction and emergency response.

[0053] S102, determining the location data and wind direction data of the potential pollution hotspot area, and sending the location data and wind direction data to the drone, so that the drone automatically plans a flight path based on the location data and wind direction data, and obtains supplementary chlorine concentration sampling data from at least three angles.

[0054] Location data can be the specific geographic coordinates of potential pollution hotspots, typically including longitude, latitude, and possibly altitude ranges. This data is used to determine the spatial location of pollution hotspots and facilitate drone positioning and mission planning.

[0055] Wind direction data refers to the prevailing wind direction in a pollution hotspot, i.e., the direction from which the wind blows within the current or forecasted timeframe. This is crucial for drones to assess pollution spread trends and determine appropriate sampling angles.

[0056] A drone is an aircraft that flies without a pilot, controlled by remote control or automated systems. In this scenario, the drone provides environmental monitoring capabilities, receiving location and wind direction data for polluted areas, intelligently planning flight paths, and collecting chlorine concentration data at predetermined locations to supplement information in areas not fully covered by fixed monitoring stations.

[0057] Real-time chlorine concentration data (from fixed monitoring stations) and wind direction data are obtained. Based on wind direction and concentration change rate, pollutant dispersion trends are predicted. Wind direction determines the direction of pollutant spread, while the concentration change rate indicates the rate of change of pollutants in a specific area. By combining these data, the location of potential pollution hotspots can be estimated, typically expressed as longitude and latitude coordinates. Wind direction data typically comes from meteorological observations (e.g., satellites, radar data, or ground-based weather stations). This data provides the prevailing wind direction and may include information such as wind speed and air pressure. Once the location (latitude and longitude) and wind direction data (wind direction and other relevant meteorological information) of the pollution hotspot are determined, they are transmitted to the drone via wireless communication or a network. The drone typically receives this information via wireless networks or satellite communications. After receiving this information, the drone automatically plans a flight path based on pre-defined algorithms and flight rules. The goal of flight path planning is to accurately sample within the pollution hotspot, avoiding excessive overlap or missed areas to ensure comprehensive and effective sampling. The flight altitude and angle are adjusted based on the wind direction data to obtain the most accurate concentration data. Based on location and wind direction data, the drone plans a route that maximizes coverage of the pollution hotspot. Path planning needs to consider wind speed and direction to prevent wind interference or impact on the flight path. The flight path typically includes multiple points, and the drone collects chlorine concentration data at different altitudes or angles. To ensure comprehensive coverage, the flight path will include at least three sampling angles (for example, from different altitudes, flight angles, or offset angles). The drone flies within the pollution hotspot according to the planned flight path, acquiring additional chlorine concentration data from different angles using its onboard gas sensors or other sampling equipment. Specifically, upon reaching the predetermined location, the drone activates its sensors to collect chlorine concentration data. Sampling from at least three angles allows for a comprehensive assessment of chlorine concentrations in the pollution hotspot from multiple perspectives. The collected data is transmitted in real time to a ground station or data processing center for further analysis and decision support.

[0058] S103, obtain the supplementary chlorine concentration sampling data obtained from at least three angles transmitted by the drone, input the real-time chlorine concentration data, the supplementary chlorine concentration sampling data and the multi-scale meteorological field data into a preset three-dimensional pollutant concentration distribution model to obtain the three-dimensional pollutant concentration distribution data.

[0059] Supplemental chlorine concentration sampling data can be collected by drones from multiple angles (at least three). Because chlorine concentration data from ground-based monitoring stations may have blind spots or insufficient coverage, drones can supplement this data and provide more accurate information on pollutant concentration distribution. Supplemental data typically comes from drone-mounted sensors, with sampling points located at different flight angles or altitudes, enabling a comprehensive assessment of pollutant distribution from multiple dimensions.

[0060] The pre-defined three-dimensional pollutant concentration distribution model can be a mathematical or physical model used to predict or calculate the three-dimensional distribution of pollutants within a specific area and time. Based on a variety of input data (such as real-time chlorine concentration data, supplementary sampling data, and meteorological data), the model simulates the diffusion and concentration distribution of pollutants in the atmosphere. The model generally considers meteorological factors (such as wind speed, direction, temperature, and humidity), the intensity of pollution sources, and their emission characteristics, and calculates the spatial distribution of pollutants through numerical simulation methods.

[0061] Three-dimensional pollutant concentration distribution data can be used to describe the concentration distribution of pollutants in space (X, Y, and Z coordinates) and time (at time T). This data is typically presented as a three-dimensional grid, with each grid point containing a pollutant concentration value at a specific point in time and space. Three-dimensional data not only demonstrates the distribution of pollutants in a plane but also reveals their dispersion in height (e.g., vertically).

[0062] The drone is equipped with a wireless data transmission system, which transmits chlorine concentration data collected from each sampling point to the ground control system in real time. The control system processes and stores this real-time data, ensuring that the data can be synchronized for subsequent processing. The real-time data is synchronized with the data collected by the drone and spatially mapped as needed. Each data point is ensured to have corresponding spatial coordinates and timestamps. The real-time chlorine concentration data, supplementary chlorine concentration sampling data, and multi-scale meteorological field data are input into a pre-set three-dimensional pollutant concentration distribution model. The pre-set three-dimensional pollutant concentration distribution model calculates the three-dimensional spatial distribution of the pollutant based on the input data (real-time chlorine concentration data, supplementary sampling data, and multi-scale meteorological field data).

[0063] The training process of the preset pollutant three-dimensional concentration distribution model is as follows:

[0064] First, data related to pollutant concentrations is collected, including real-time chlorine concentration data, supplemental chlorine concentration sampling data from at least three angles, and multi-scale meteorological data. These data provide a rich set of input features for the model. Second, data preprocessing and cleaning are performed to ensure data quality and construct a feature set suitable for model training. For example, meteorological data is converted into parameters such as wind speed, wind direction, temperature, and humidity, and combined with pollution source emission data to provide detailed input information. When selecting a model, numerical or machine learning models suitable for simulating pollutant diffusion and concentration changes are typically chosen, such as regression models, deep learning models (such as convolutional neural networks or recurrent neural networks), or physical models based on meteorological diffusion theory (such as the CALPUFF model). These models can simulate the spatial and temporal distribution of pollutants and predict pollution concentrations under different conditions. During model training, the collected input data (such as meteorological conditions, pollution source locations, and concentration sampling data) are compared with actual concentration observations to calculate errors and adjust model parameters accordingly. The training process utilizes optimization algorithms such as backpropagation or error minimization to enable the model to more accurately capture pollutant diffusion patterns. After training is completed, cross-validation is used to ensure the generalization ability of the model, and the final result is a model that can accurately predict the distribution of pollutants in three-dimensional space based on input real-time meteorological data, chlorine concentration data, etc.

[0065] S104: Input the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into the CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity.

[0066] The CALPUFF inverse model is a pollutant dispersion simulation method used to infer the specific location of pollution sources and the intensity of pollutant emissions by analyzing air quality monitoring data. The CALPUFF model is a physics-based pollutant dispersion model that predicts the atmospheric diffusion of pollutants by considering meteorological conditions, topography, and other environmental factors. The inverse model is a reverse application of the CALPUFF model, primarily inferring the location and intensity of pollution sources using actual pollutant concentration data.

[0067] The specific location of a pollution source can refer to the geographic location of the source of the pollutant. By analyzing the diffusion patterns of pollutants in the air and combining them with pollution concentration data from air quality monitoring stations, inverse models can estimate the likely location of the pollution source. Typically, this process is based on sampling data, wind field data, meteorological conditions, and changes in pollutant concentrations, using reverse propagation simulations to determine the location of the pollution source.

[0068] Pollution emission intensity refers to the amount of pollutants released by a pollution source per unit time. It is typically expressed in units of mass (such as kilograms or tons) per hour. Inverse models can estimate pollution source emission intensity by combining pollutant concentration distribution data with meteorological data. The calculation takes into account environmental factors that influence pollutant dispersion (such as wind speed, temperature, and humidity) and estimates the source's emissions through reverse reasoning.

[0069] The three-dimensional pollutant concentration distribution data and the multi-scale meteorological data are input into the CALPUFF inverse model. After the data is input, model parameters must be set: The target area is determined, i.e., the area to be simulated by the model. This is typically determined based on the coverage of the pollutant concentration and meteorological data. The pollution source type is set: The inverse model assumes the location and emission pattern of the pollution source, allowing the specific location of the pollution source to be inferred based on the pollutant concentration distribution. The CALPUFF inverse model uses physical and chemical models to infer the pollutant diffusion path based on actual pollutant concentration data (such as data from monitoring stations) and surrounding meteorological data. Based on the pollutant concentration data and the inferred diffusion path, the inverse model determines the possible location of the pollution source. During the calculation process, the model takes into account wind speed, wind direction, meteorological conditions, and the changing trends of pollutant concentrations. After inferring the pollution source location, the model proceeds to calculate the emission intensity of the pollution source. Emission intensity is inferred based on the changes in pollutant concentration and meteorological conditions (such as wind speed and temperature), and is generally related to the size, emission volume, and emission method of the pollution source. The CALPUFF inverse model estimates the emission intensity of pollution sources based on the correlation between changes in pollutant concentrations and meteorological data, combined with the physical laws of pollutant diffusion in the environment. After the model completes, the CALPUFF inverse model outputs the following: The specific location of the pollution source: the possible geographic coordinates of the pollution source, usually expressed as coordinate points. The emission intensity of the pollution source: the amount of emissions from the pollution source over a specific period of time, usually expressed in units of mass (such as kilograms per hour or tons per hour).

[0070] In an embodiment of the present application, multi-scale meteorological field data and real-time chlorine concentration data are obtained, wind field direction data of the multi-scale meteorological field data are determined, and the concentration change rate of the real-time chlorine concentration data is determined, and potential pollution hotspot areas are determined based on the wind field direction data and the concentration change rate; wherein, the real-time chlorine concentration data comes from at least five fixed monitoring stations; the position data and wind direction data of the potential pollution hotspot areas are determined, and the position data and the wind direction data are sent to a drone, so that the drone automatically plans a flight path based on the position data and the wind direction data, and obtains supplementary chlorine concentration sampling data from at least three angles; the supplementary chlorine concentration sampling data obtained from at least three angles and transmitted by the drone is obtained, and the real-time chlorine concentration data, the supplementary chlorine concentration sampling data and the multi-scale meteorological field data are input into a preset three-dimensional pollutant concentration distribution model to obtain three-dimensional pollutant concentration distribution data; the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data are input into a CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity. The above-mentioned emergency monitoring and early warning method for atmospheric environmental pollution incidents not only improves the accuracy and timeliness of pollution source tracing, but also has high spatial resolution and flexible response capabilities, providing efficient and intelligent technical support for intelligent early warning, rapid tracing and scientific response to sudden toxic gas leakage incidents.

[0071] Example 2

[0072] Figure 2 This is a flow chart of the method for emergency monitoring and early warning of atmospheric environmental pollution incidents provided in Example 2 of this application. Figure 2 As shown, the specific steps include:

[0073] S201, obtaining multi-scale meteorological field data and real-time chlorine concentration data, determining wind field direction data of the multi-scale meteorological field data, and determining the concentration change rate of the real-time chlorine concentration data, and determining potential pollution hotspots based on the wind field direction data and the concentration change rate; wherein the real-time chlorine concentration data comes from at least five fixed monitoring stations.

[0074] S202, determining the location data and wind direction data of the potential pollution hotspot area, and sending the location data and wind direction data to the drone, so that the drone automatically plans a flight path based on the location data and wind direction data, and obtains supplementary chlorine concentration sampling data from at least three angles.

[0075] S203, obtaining supplementary chlorine concentration sampling data from at least three angles transmitted by the drone, inputting the real-time chlorine concentration data, the supplementary chlorine concentration sampling data, and the multi-scale meteorological field data into a preset three-dimensional pollutant concentration distribution model to obtain three-dimensional pollutant concentration distribution data.

[0076] S204: Input the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into the CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity.

[0077] S205 , using a confidence ellipse to obtain pollution source positioning error range data according to the specific location of the pollution source and the pollution emission intensity, and performing spatial optimization and updating on the specific location of the pollution source according to the pollution source positioning error range data.

[0078] A confidence ellipse is a spatial statistical analysis method used to represent the uncertainty range of an estimated point in two-dimensional space (e.g., the location of a pollution source). It is drawn based on the covariance matrix of the estimated location and is similar to a two-dimensional version of a "confidence interval" in statistics. The center point is the initial estimated location of the pollution source. The major and minor axes reflect the distribution of location errors in different directions (usually related to wind direction and diffusion trends). Confidence level: For example, a 95% confidence ellipse indicates that there is a 95% probability that the true location of the pollution source will fall within the ellipse.

[0079] Pollution source location error range data refers to a calculated set of spatial data used to quantify the error in pollution source location estimates. Specifically, it includes: the geometric parameters of the confidence ellipse (center point, major axis, minor axis, and azimuth angle), the confidence level represented by the ellipse (e.g., 90%, 95%), the directional distribution of the location error (e.g., due to wind field influence, the error is greater in a certain direction), and the spatial offset used for error correction (used to update the location point).

[0080] After obtaining the preliminary positioning results of the pollution source (the specific location of the pollution source) and the corresponding pollution emission intensity, the system calculates the uncertainty distribution of the pollution source location estimate based on the existing three-dimensional pollutant concentration distribution data and multi-scale meteorological field data. By statistically modeling factors such as pollution diffusion paths and wind field disturbances, the spatial error covariance of the pollution source location is obtained, and a confidence ellipse is constructed to represent the spatial range where the true pollution source location may exist. This confidence ellipse can clearly indicate the distribution trend and confidence probability of the positioning error in different directions. Next, the system superimposes this confidence ellipse on the original pollution source estimation point, performs weight correction on the specific location of the pollution source in the main direction of the error, and combines the emission intensity influencing factors with actual monitoring data feedback to perform spatial optimization and update of the specific location of the pollution source to improve the source positioning accuracy and traceability credibility. This process can be iterated continuously until the error between the prediction and the actual monitoring results converges to an acceptable range.

[0081] S206 , using the probability cloud map to obtain pollutant diffusion credible area data according to the specific location of the pollution source and the pollution emission intensity, and correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible area data.

[0082] A probability cloud map is a "credible probability map" of pollutant diffusion in space, created by integrating multiple factors, including the specific location of pollution sources, uncertainty ranges, emission intensity, and meteorological disturbances. Based on statistical simulation and propagation modeling, the map creates a "credible probability region" for pollutant diffusion. Different colors or transparency levels indicate the probability of a pollutant's presence at that location, reflecting the trend and credibility of pollutant diffusion driven by factors like wind and terrain.

[0083] Credible pollutant dispersion area data can be extracted from probability cloud maps and are highly reliable areas of possible pollutant distribution. These areas represent the range of locations where pollutants are most likely to reach or accumulate under given conditions. Probability thresholds (e.g., 95% credible areas) are often used to define boundaries and assist in determining pollution risk areas and decision-making response scopes.

[0084] The visible range refers to the spatial area ultimately rendered and displayed to the user during the visualization of three-dimensional pollutant concentration distribution. Typically, the visible range takes into account factors such as pollutant concentration thresholds (i.e., display only occurs when concentrations exceed a certain value), user perspective, and map layer restrictions. Correcting the visible range using trusted region data can make the display more accurate to the actual distribution of pollutants, avoid misleading displays, and help optimize resource scheduling and emergency response scope.

[0085] The CALPUFF inverse model uses the specific location of pollution sources, emission intensity, and multi-scale meteorological data (including wind speed, wind direction, temperature, and boundary layer height) as the basic parameters for pollutant dispersion simulation. Combining Monte Carlo simulation with stochastic perturbation wind field modeling, multiple simulations of pollutant dispersion paths are conducted near the pollution source locations, accounting for variables such as emission intensity errors, wind field uncertainty, and topographic influences. The resulting multiple pollution dispersion paths are statistically superimposed to obtain the probability of pollutant occurrence at different locations. These probabilities are then mapped into a two- or three-dimensional spatial image, forming a pollutant probability cloud map. Based on a set confidence probability threshold (e.g., 90% or 95%), the spatial range covering the high probability distribution of pollutants is extracted from the probability cloud map to form a credible pollutant dispersion region. This region represents the set of locations with a high probability of pollutant presence. The original three-dimensional pollutant concentration distribution data is spatially aligned with the credible region. Pollutant distribution data with low confidence outside the credible region is masked, down-weighted, or visually diluted, so that only the credible region is fully displayed.

[0086] This example incorporates a scientific error control mechanism into the pollution source location and pollutant dispersion visualization process by integrating spatial uncertainty modeling using confidence ellipses and probability cloud maps. The confidence ellipses are used to characterize the spatial error range of pollution source locations and emission intensity estimates, thereby optimizing and updating pollution source locations. The probability cloud map, based on the probability distribution of pollution dispersion, identifies highly reliable pollutant distribution areas, thereby correcting the three-dimensional pollutant concentration visualization range. This not only improves the accuracy and credibility of pollution source tracing and pollution visualization results, but also enhances scientific decision-making support for responding to environmental emergencies.

[0087] Based on the above technical solution, optionally, after correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible area data, the method further includes:

[0088] Based on the specific location of the pollution source after spatial optimization and update, and the three-dimensional pollutant concentration distribution data after correcting the visible range, a pollution warning area layer and an evacuation route layer are generated.

[0089] In this solution, the pollution alert zone map can be defined as a spatial map of areas with potential health risks, based on optimized pollution source locations and revised three-dimensional pollutant concentration distribution data, combined with factors such as pollutant type, toxicity level, and meteorological conditions (such as wind speed and direction). This map is typically dynamically configurable, with varying risk levels (e.g., high, medium, and low). Based on standard concentration thresholds, for example, a "red alert zone" is designated when chlorine gas reaches a certain concentration.

[0090] An evacuation route layer refers to a layer of recommended evacuation routes automatically or semi-automatically generated based on factors such as pollution diffusion trends, topography, accessibility, and wind direction avoidance strategies, combined with the scope of the warning area. Multiple route options (such as shortest distance, least risk, and fastest access) can be integrated; routes can be optimized by combining data such as building density, traffic conditions, and the distribution of susceptible populations. The output format is typically a 3D route map or a 2D path trajectory diagram, which can be used for command and dispatch or public evacuation navigation.

[0091] Based on the updated specific locations of pollution sources after spatial optimization and the three-dimensional pollutant concentration distribution data after correcting the visible range, the pollution source can be used as the center point. Combined with the diffusion trends of pollutants in different directions and heights, and using preset concentration threshold classification standards, a pollution warning area layer corresponding to the risk level can be dynamically generated. This layer can be superimposed on the GIS map to show the spatial boundaries of high-, medium-, and low-risk areas. Subsequently, combining multi-source data such as topography, building distribution, road network, wind direction avoidance principles, and population density, a path optimization algorithm (such as A* or Dijkstra algorithm) is used to automatically plan the optimal evacuation path from high-risk areas to safe areas, forming an evacuation path layer to support emergency management departments in visual command and real-time dispatch.

[0092] This solution generates pollution warning area and evacuation route layers by integrating spatially optimized pollution source locations with corrected three-dimensional pollutant concentration distribution data. This allows for accurate identification and dynamic early warning of pollution risk areas, while also providing scientific and efficient evacuation routes. This approach not only improves the accuracy and timeliness of emergency responses to pollution incidents, but also enhances the operability and safety of public evacuation guidance, significantly reducing the risk of personal injury and property damage. It also facilitates practical visualization of pollution incident decision support and intelligent emergency management.

[0093] Based on the above technical solution, optionally, after correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible area data, the method further includes:

[0094] The specific location of pollution sources, pollution emission intensity and multi-scale meteorological field data after spatial optimization and update are input into the CALPUFF forward simulation model to obtain PM2.5 concentration field data.

[0095] In this approach, the CALPUFF forward simulation model can be a key component of an atmospheric pollutant dispersion modeling system, simulating the diffusion, transport, deposition, and transformation of pollutants over time and space after they are released from a pollution source. "Forward" refers to using known pollution source information (location, emission intensity, emission height, emission rate, etc.) combined with atmospheric meteorological data (such as wind speed, direction, temperature, and turbulence intensity) to predict the concentration distribution of pollutants over time within a target area.

[0096] PM2.5 concentration field data refers to the distribution of fine particulate matter (less than or equal to 2.5 microns in diameter) in the air over a given time and space, typically measured in µg / m³. "Concentration field" refers to this spatial distribution data, which can be displayed as a two-dimensional map or a three-dimensional volume grid, reflecting the highs and lows of PM2.5 concentrations across various locations.

[0097] Based on the optimized specific locations of pollution sources and the corresponding pollution emission intensity parameters, combined with the multi-scale meteorological field data with high temporal and spatial resolution generated by the CALMET module (including wind speed, wind direction, temperature, turbulent diffusion parameters, etc.), this information is configured as input conditions into the CALPUFF forward simulation model. During the simulation process, CALPUFF uses the Lagrangian particle tracking method to dynamically simulate the transport, diffusion, dry and wet deposition, and chemical reactions of pollutant particles, and then calculates the mass concentration distribution of PM2.5 at different time and spatial locations. Ultimately, the output PM2.5 concentration field data displays the three-dimensional distribution characteristics of fine particulate matter concentration in the target area over time in a gridded form, which can be used in scenarios such as pollution situation awareness, risk assessment, and early warning response.

[0098] In this solution, spatially optimized pollution source locations, pollution emission intensities, and multi-scale meteorological data are fed into the CALPUFF forward simulation model. This accurately simulates the spatiotemporal diffusion of pollutants (such as PM2.5) under real-world meteorological conditions, effectively improving the scientificity and credibility of concentration field predictions. This process helps to timely understand the impact range of pollutants, concentration trends, and high-risk areas, supporting pollution warning issuance, public health protection, and emergency response decision-making, and enhancing the overall refinement and intelligence of atmospheric environmental management.

[0099] Based on the above technical solution, optionally, after correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible area data, the method further includes:

[0100] If a prediction instruction is received, a prediction area is determined according to the prediction instruction, and multi-scale meteorological field data of the prediction area and three-dimensional pollutant concentration distribution data after correcting the visible range are interpolated in time series to construct a dynamic data set of pollution diffusion evolving over time;

[0101] Acquire the topographic data of the predicted area, fuse the dynamic data set and the topographic data, and generate a visual dynamic layer.

[0102] In this solution, a prediction instruction can be an operational command issued by the system, user, or management platform to estimate and analyze pollution trends in a specific area or time period. This instruction typically includes parameters such as the prediction area, the start and end time of the prediction, the type of pollutant of interest (such as chlorine or PM2.5), and the desired output format (layers, charts, etc.), which are used to trigger subsequent data processing and modeling processes.

[0103] The prediction area refers to the spatial scope for pollution diffusion trend analysis determined by the system after receiving the prediction instruction. It is usually a geographical area (such as an industrial park in a city, the vicinity of a port, a densely populated area, etc.). This area will serve as the core spatial domain for data processing and pollution simulation.

[0104] Dynamic datasets are a collection of time-varying data generated through time series interpolation, based on three-dimensional pollutant concentration distribution data after correcting for visible ranges and multi-scale meteorological data. They record the continuous state of pollutant diffusion, concentration changes, and migration paths over time, reflecting the dynamic evolution of pollution fields over time and serving as the core data for pollution trend simulations.

[0105] Topographic data refers to the geographic spatial information within the prediction area, including terrain relief (elevation), land cover types (such as water bodies, buildings, and forests), wind direction, and mountain barriers. This data significantly influences the diffusion of pollutants. For example, complex terrain can affect wind patterns and pollutant transmission paths. Therefore, topographic data must be integrated into pollution simulations to improve accuracy.

[0106] Dynamic visualization layers are generated by fusing dynamic datasets with topographic data, allowing for dynamic display on GIS platforms or simulation systems. They visualize the diffusion of pollutant concentrations over time through animations or time-series graphs, visually presenting key information such as pollution trends, impact areas, and peak concentration zones, assisting with emergency management and public warnings.

[0107] When the system receives a forecast command, it first parses the command content. The forecast command includes the forecast time range, forecast area, and pollutant types of interest. Based on the regional parameters in the command, the system determines the spatial range for the forecast. The forecast area can be user-provided geographic coordinates (e.g., a city or industrial area) or automatically derived by the system (e.g., a traffic-intensive area or the area surrounding a pollution source). Next, the system obtains real-time or historical meteorological data for the forecast area using meteorological models (such as the WRF model) or from meteorological databases. This data includes information such as wind speed, wind direction, temperature, humidity, and air pressure, which are crucial for predicting pollutant dispersion and concentration changes. Specifically, the system requires multi-scale meteorological data—local, regional, and larger-scale meteorological information—to fully simulate the spread of pollutants. Furthermore, the system requires pollutant concentration distribution data, which is derived from pollutant dispersion models (such as the CALPUFF model) or from actual monitoring and simulation results. To ensure more accurate pollutant concentration distributions, the system adjusts for topographic and meteorological factors to generate three-dimensional pollutant concentration distributions, representing pollutant concentrations at different times, altitudes, and geographic locations. After obtaining this data, the system interpolates the pollutant concentration data in a time series. The purpose of interpolation is to convert discrete time point data into continuous data, ensuring an accurate representation of the temporal evolution of pollutant concentrations throughout the forecast period. Using interpolation algorithms (such as linear and spline interpolation), the system predicts pollution concentrations at different time points and spatial locations, generating a dynamic dataset. This dynamic dataset integrates the temporal evolution of pollutant concentrations into a continuous data stream, reflecting the concentration changes from the source to the dispersion process. This dataset not only includes pollutant concentration data at different times and spaces but also considers the temporal and spatial dependence of pollutant dispersion, making the simulation of pollution dispersion more accurate. The system also requires topographic data for the forecast area, typically from a Geographic Information System (GIS) database. Topographic data includes the region's altitude, the distribution of mountains and rivers, and urban and rural land use. This data is crucial for modeling pollutant dispersion, as topography significantly affects pollutant transport. For example, mountains can block pollutant flow, or wind tunnels can concentrate pollutant dispersion. The system fuses multi-scale meteorological data and corrected three-dimensional pollutant concentration distribution data with topographic data. This step aims to optimize pollutant dispersion simulations by taking into account the impact of topography on pollutant dispersion paths. For example, wind tunnels and high ground may alter the direction of pollutant spread, or pollutants may accumulate in certain low-lying areas. By fusing these data, the system can generate more realistic pollution dispersion predictions.Finally, the system generates a dynamic visualization layer based on this fused data. This dynamic layer displays the evolution of pollutant concentrations within a GIS platform or dedicated visualization software. Users can use interactive features (such as a time slider and geographic coordinates) to view changes in pollution concentrations over time and at different locations. This allows users to monitor the dynamic spread of pollutants in real time, identify pollution hotspots, and initiate timely response measures.

[0108] In this plan, by integrating spatially optimized pollution source locations, multi-scale meteorological fields, topography and three-dimensional pollutant concentration data, a dynamic data set of pollution diffusion is constructed, and a visual dynamic layer is generated to achieve accurate simulation and intuitive display of the entire pollution diffusion process. This not only improves the spatiotemporal resolution and accuracy of the prediction, but also provides strong technical support for environmental supervision, emergency response and public warning, helps to achieve scientific decision-making and efficient resource scheduling, and significantly enhances pollution prevention and control capabilities.

[0109] Based on the above technical solution, optionally, after generating the visual dynamic layer, the method further includes:

[0110] A time control slider is integrated into the dynamic visualization layer. In response to the user's interactive operation on the time control slider, the target three-dimensional pollutant concentration distribution data, target meteorological field data and pollution level map of the prediction area corresponding to the time point selected by the user are output.

[0111] In this solution, a time control slider can be a graphical user interface (GUI) control, typically appearing as a linear slider within a dynamic visualization layer. It allows users to select a specific time point by dragging the slider. The slider corresponds to the timeline in pollution dispersion simulations or meteorological simulation results, such as time series data for the next 24 or 48 hours. Users can use it to browse pollution conditions at different time points, enabling dynamic playback or forward preview of pollution evolution.

[0112] Interactions can refer to actions between the user and the system interface, such as clicking, dragging, and selecting. In this scenario, these specifically refer to actions such as dragging the time slider, clicking a specific time point, and playing a time animation. These actions trigger a system backend response, loading and displaying pollution and weather data for the corresponding time point.

[0113] The selected time point can be a specific time location selected by the user using the slider. For example, if the user slides to "April 16, 2025, 14:00," this time point will be the "selected time point." The system will then extract the corresponding pollutant concentration and meteorological information from a pre-built dynamic dataset based on this time point.

[0114] The target three-dimensional pollutant concentration distribution data can represent the concentration distribution of pollutants (such as PM2.5, SO2, NOx, etc.) in three-dimensional space (latitude, longitude, altitude) and at a specific time point, and is derived from a pollutant dispersion model (such as CALPUFF). It exists in the form of a grid, reflecting the pollution levels at different geographical locations and altitudes, and is the core basic data for pollution analysis and visualization.

[0115] The target meteorological field data can refer to the three-dimensional distribution information of meteorological elements within the prediction area at the selected time point, such as wind speed, wind direction, air temperature, humidity, etc. These data are derived from meteorological simulation systems such as the WRF model, and are important inputs for driving pollution dispersion simulations, and can also be used to analyze the trends of pollutant dispersion.

[0116] The pollution level map can be a result layer calculated based on pollutant concentrations and national or regional air quality standards (such as the AQI index). It converts pollution concentrations into multiple levels (such as excellent, good, slightly polluted, moderately polluted, etc.), and is usually presented on the map in color coding, enabling users to clearly understand the air quality levels of each area at a glance, and assisting in decision-making and emergency response. In the visualization dynamic layer, the system has integrated three-dimensional concentration data (such as PM2.5, NOx, etc.) of pollutants evolving over time and terrain information, and can dynamically display the changes of pollutants in different times and spaces. The generation of the pollution level map is based on this, and the three-dimensional pollutant concentration data at a certain time point is divided into concentration levels. The system will refer to national or regional air quality grading standards (such as AQI, GB3095, etc.): for example, a PM2.5 concentration ≤ 35 μg / m³ is regarded as "excellent"; 35 < PM2.5 ≤ 75 μg / m³ is "good"; PM2.5 > 75 μg / m³ is "slightly or more severely polluted". The system converts the concentration values of each area into levels, and encodes them with colors (such as green, yellow, red, etc.), and superimposes them on the original dynamic layer to form a pollution level map.

[0117] To integrate a time slider into a dynamic visualization layer, the system first organizes pollutant concentration data, meteorological data, and pollution level information into a multidimensional time series dataset with clear time tags. The time slider, as part of the user interface, allows users to slide the slider or select a specific time point. When users perform interactive operations (such as sliding, clicking the slider, or quickly jumping to a specific time), the system captures the current slider point and automatically interprets it as a specific timestamp. It then retrieves the corresponding three-dimensional pollutant concentration distribution data and regional meteorological data (such as wind speed, wind direction, temperature, and humidity) from a preloaded or on-demand time series database. Based on the pollutant concentration values ​​and national or local pollution classification standards (such as PM2.5 classification), a pollution level map is automatically generated. For layer rendering, the system overlays the three-dimensional pollution concentration field on the geographic map using volume rendering or isosurfaces. Meteorological information is represented by arrow vectors or color layers, and the pollution level map uses colored areas to distinguish the spatial distribution of different pollution levels. All of this layer information will be updated synchronously with the operation of the time slider, enabling a visual display of pollution diffusion trends, meteorological evolution processes, and dynamic changes in risk levels over time, providing users with intuitive, real-time, and interactive pollution evolution insights and risk warning support.

[0118] In this solution, by integrating a time slider into the dynamic visualization layer, users can intuitively and flexibly view the three-dimensional concentration distribution of pollutants, meteorological data, and pollution level maps corresponding to any point in time, enabling dynamic monitoring of pollution diffusion trends and meteorological changes. This interactive display method not only improves the efficiency and accuracy of information acquisition, but also supports real-time assessment and prediction of pollution risks.

[0119] Example 3

[0120] Figure 3 This is a schematic diagram of the structure of the atmospheric environment pollution incident emergency monitoring and early warning system provided in Example 3 of this application. Figure 3 As shown, specifically including:

[0121] Potential pollution hotspot identification module 301 is configured to obtain multi-scale meteorological field data and real-time chlorine concentration data, determine wind field direction data from the multi-scale meteorological field data, and determine the concentration change rate of the real-time chlorine concentration data, and determine potential pollution hotspots based on the wind field direction data and the concentration change rate; wherein the real-time chlorine concentration data is obtained from at least five fixed monitoring stations;

[0122] The drone-assisted sampling module 302 is configured to determine the location data and wind direction data of the potential pollution hotspot area, and transmit the location data and wind direction data to the drone, so that the drone automatically plans a flight path based on the location data and wind direction data, and obtains supplementary chlorine concentration sampling data from at least three angles;

[0123] The three-dimensional concentration modeling module 303 is used to obtain the supplementary chlorine concentration sampling data obtained from at least three angles transmitted by the drone, input the real-time chlorine concentration data, the supplementary chlorine concentration sampling data, and the multi-scale meteorological field data into a preset three-dimensional pollutant concentration distribution model to obtain three-dimensional pollutant concentration distribution data;

[0124] The pollution source inversion module 304 is used to input the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into the CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity.

[0125] The atmospheric environment pollution incident emergency monitoring and early warning system provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0126] Example 4

[0127] like Figure 4 As shown, an embodiment of the present application also provides an electronic device 400, including a processor 401, a memory 402, and a program or instruction stored in the memory 402 and executable on the processor 401. When the program or instruction is executed by the processor 401, each process of the above-mentioned embodiment of the method for emergency monitoring and early warning of atmospheric environmental pollution events is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0128] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0129] Example 5

[0130] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned cable installation process based on the tension adaptive control system embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0131] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk.

[0132] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or system comprising the element. In addition, it should be noted that the scope of the methods and systems in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0133] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of this application.

[0134] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0135] The above are only preferred embodiments of the present application and the technical principles employed. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that are possible for those skilled in the art will not depart from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include more other equivalent embodiments without departing from the concept of the present application. The scope of the present application is determined by the scope of the claims.

Claims

1. An emergency monitoring and early warning method for atmospheric environmental pollution incidents, characterized in that: The method comprises: Acquiring multi-scale meteorological field data and real-time chlorine concentration data, determining wind field direction data from the multi-scale meteorological field data, and determining a concentration change rate of the real-time chlorine concentration data, and determining potential pollution hotspots based on the wind field direction data and the concentration change rate; wherein the real-time chlorine concentration data is obtained from at least five fixed monitoring stations; Determining the location data and wind direction data of the potential pollution hotspot area, and sending the location data and wind direction data to a drone, so that the drone automatically plans a flight path based on the location data and wind direction data, and obtains supplementary chlorine concentration sampling data from at least three angles; Obtaining supplementary chlorine concentration sampling data from at least three angles transmitted by a drone, inputting the real-time chlorine concentration data, the supplementary chlorine concentration sampling data, and the multi-scale meteorological field data into a preset three-dimensional pollutant concentration distribution model to obtain three-dimensional pollutant concentration distribution data; Input the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into the CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity; Using the confidence ellipse to obtain pollution source positioning error range data based on the specific location of the pollution source and the pollution emission intensity, and performing spatial optimization and updating of the specific location of the pollution source based on the pollution source positioning error range data; The probability cloud map is used to obtain pollutant diffusion credible area data according to the specific location of the pollution source and the pollution emission intensity, and the visible range of the three-dimensional pollutant concentration distribution data is corrected according to the pollutant diffusion credible area data.

2. The method according to claim 1, characterized in that in, After correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible area data, the method further includes: Based on the specific location of the pollution source after spatial optimization and update, and the three-dimensional pollutant concentration distribution data after correcting the visible range, a pollution warning area layer and an evacuation route layer are generated.

3. The method according to claim 1, characterized in that in, Acquire multi-scale meteorological field data, including: Receive meteorological satellite data and radar observation data in real time based on the WRF model, update the boundary conditions and initial field of the WRF model according to the meteorological satellite data and radar observation data, drive the meteorological simulation process, and generate three-dimensional meteorological forecast results for a preset time period; According to the CALMET adaptive downscaling method, the three-dimensional weather forecast results of the preset time period are converted into multi-scale meteorological field data.

4. The method according to claim 1, wherein in, After correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible area data, the method further includes: The specific location of pollution sources, pollution emission intensity and multi-scale meteorological field data after spatial optimization and update are input into the CALPUFF forward simulation model to obtain PM2.5 concentration field data.

5. The method according to claim 1, characterized in that in, After correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible area data, the method further includes: If a prediction instruction is received, a prediction area is determined according to the prediction instruction, and multi-scale meteorological field data of the prediction area and three-dimensional pollutant concentration distribution data after correcting the visible range are interpolated in time series to construct a dynamic data set of pollution diffusion evolving over time; Acquire the topographic data of the predicted area, fuse the dynamic data set and the topographic data, and generate a visual dynamic layer.

6. The method according to claim 5, characterized in that in, After generating the visual dynamic layer, the method further includes: A time control slider is integrated into the dynamic visualization layer. In response to the user's interactive operation on the time control slider, the target three-dimensional pollutant concentration distribution data, target meteorological field data and pollution level map of the prediction area corresponding to the time point selected by the user are output.

7. An atmospheric environmental pollution incident emergency monitoring and early warning system, characterized in that: The system comprises: A potential pollution hotspot identification module is configured to obtain multi-scale meteorological field data and real-time chlorine concentration data, determine wind field direction data from the multi-scale meteorological field data, and determine the concentration change rate of the real-time chlorine concentration data, and determine potential pollution hotspots based on the wind field direction data and the concentration change rate; wherein the real-time chlorine concentration data is obtained from at least five fixed monitoring stations; A drone-assisted sampling module is used to determine the location data and wind direction data of the potential pollution hotspot area, and send the location data and wind direction data to the drone, so that the drone can automatically plan a flight path based on the location data and wind direction data, and obtain supplementary chlorine concentration sampling data from at least three angles; A three-dimensional concentration modeling module is used to obtain supplementary chlorine concentration sampling data from at least three angles transmitted by the drone, input the real-time chlorine concentration data, the supplementary chlorine concentration sampling data, and the multi-scale meteorological field data into a preset three-dimensional pollutant concentration distribution model to obtain three-dimensional pollutant concentration distribution data; A pollution source inversion module is used to input the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into the CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity; The system is also used to: Using the confidence ellipse to obtain pollution source positioning error range data based on the specific location of the pollution source and the pollution emission intensity, and performing spatial optimization and updating of the specific location of the pollution source based on the pollution source positioning error range data; The probability cloud map is used to obtain pollutant diffusion credible area data according to the specific location of the pollution source and the pollution emission intensity, and the visible range of the three-dimensional pollutant concentration distribution data is corrected according to the pollutant diffusion credible area data.

8. An electronic device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method for emergency monitoring and early warning of atmospheric environmental pollution events as described in any one of claims 1 to 6 are implemented.

9. A readable storage medium, characterized in that The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the atmospheric environmental pollution event emergency monitoring and early warning method according to any one of claims 1 to 6 are implemented.

Images