Emergency monitoring and early warning method and system for atmospheric environmental pollution event

Abstract

The invention discloses an atmospheric environmental pollution event emergency monitoring and early warning method and system, and belongs to the technical field of environmental management. The method comprises the following steps: acquiring multi-scale meteorological field data and chlorine concentration real-time data of at least five monitoring stations, extracting a wind field direction and a concentration change rate, and determining a position and a wind direction of a potential pollution hot spot area. The information is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle automatically plans a path and collects supplementary concentration data from three angles. And fusing the monitoring data and the supplementary sampling data, and combining with a meteorological field input pollutant three-dimensional concentration distribution model to generate three-dimensional distribution data. And inputting the CALPUFF reverse model, and carrying out inversion to obtain the specific position and the emission intensity of the pollution source. According to the scheme, the accuracy and timeliness of source tracing of the pollution source are improved, high spatial resolution and flexible response capability are achieved, and efficient and intelligent technical support is provided for intelligent early warning, rapid source tracing and scientific response of sudden toxic gas leakage events.

Description

Technical Field

[0001] This application belongs to the technical field of environmental management, and particularly relates to an emergency monitoring and early warning method and system for atmospheric environmental pollution incidents. Background Art

[0002] Quickly and accurately monitoring and early warning atmospheric environmental pollution incidents, especially chlorine leakage accidents, is of crucial significance for taking timely emergency measures and reducing accident losses.

[0003] Currently, the emergency monitoring and early warning for atmospheric environmental pollution incidents mainly adopt a mode combining fixed monitoring stations and manual inspections. Specifically, fixed monitoring stations deploy various high-precision sensor devices, such as gas analyzers, particulate matter monitors, etc., to continuously and real-time monitor the concentrations of various pollutants in the atmosphere (such as sulfur dioxide, nitrogen oxides, particulate matter, volatile organic compounds, and specific toxic and harmful gases such as chlorine), and transmit the data to the central monitoring platform for analysis and processing, so as to timely detect pollution anomalies and issue early warning signals.

[0004] However, fixed monitoring stations are limited by the preset positions and coverage ranges, making it difficult to comprehensively 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 area monitoring, they are limited by human efficiency and adaptability to extreme environments, resulting in response lags; the data fusion degree of the two is insufficient, lacking the collaborative analysis ability of multi-source heterogeneous data, leading to low accuracy in source location and large deviations in diffusion trend prediction, and it is difficult to support efficient and accurate emergency decision-making. Summary of the Invention

[0005] The embodiments of this application provide an emergency monitoring and early warning method and system for atmospheric environmental pollution incidents, which solve the problems that the emergency monitoring and early warning of atmospheric environmental pollution incidents using fixed monitoring stations are limited by the preset positions and coverage ranges, making it difficult to comprehensively 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 area monitoring, they are limited by human efficiency and adaptability to extreme environments, resulting in response lags; the data fusion degree of the two is insufficient, lacking the collaborative analysis ability of multi-source heterogeneous data, leading to low accuracy in source location and large deviations in diffusion trend prediction, and it is difficult to support efficient and accurate emergency decision-making.

[0006] In a first aspect, the embodiments of this application provide an emergency monitoring and early warning method for atmospheric environmental pollution incidents, and the method includes: Obtain multi-scale meteorological field data and real-time chlorine concentration data, determine the wind field direction data of the multi-scale meteorological field data, and determine the concentration change rate of the real-time chlorine concentration data, and determine potential pollution hot spots according to 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; Determine the location data and wind direction data of the potential pollution hot spot area, and send the location data and the wind direction data to the drone, so that the drone can automatically plan a flight path according to the location data and the wind direction data, and obtain supplementary chlorine concentration sampling data from at least three angles; Obtain the supplementary chlorine concentration sampling data obtained from at least three angles transmitted by the drone, and 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; 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.

[0007] In a second aspect, an embodiment of the present application provides an emergency monitoring and early warning system for atmospheric environmental pollution events, and the system includes: A potential pollution hot spot area identification module, configured to obtain multi-scale meteorological field data and real-time chlorine concentration data, determine the wind field direction data of the multi-scale meteorological field data, and determine the concentration change rate of the real-time chlorine concentration data, and determine potential pollution hot spots according to 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; A drone-assisted sampling module, configured to determine the location data and wind direction data of the potential pollution hot spot area, and send the location data and the wind direction data to the drone, so that the drone can automatically plan a flight path according to the location data and the wind direction data, and obtain supplementary chlorine concentration sampling data from at least three angles; A three-dimensional concentration modeling module, configured to obtain the supplementary chlorine concentration sampling data obtained from at least three angles transmitted by the drone, and 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, configured 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.

[0008] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0009] 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.

[0010] In an embodiment of the present application, multi-scale meteorological field data and real-time chlorine concentration data are acquired, the wind field direction data of the multi-scale meteorological field data is determined, and the concentration change rate of the real-time chlorine concentration data is determined. And a potential pollution hot spot area is determined according to 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 hot spot area 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 according to the position data and the wind direction data, and acquires supplementary chlorine concentration sampling data from at least three angles; the supplementary chlorine concentration sampling data acquired from at least three angles transmitted by the drone is acquired, 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 backward model to obtain the specific position of the pollution source and the pollution emission intensity. Through the above atmospheric environmental pollution event emergency monitoring and early warning method, not only the accuracy and timeliness of pollution source tracing are improved, but also high spatial resolution and flexible response capabilities are possessed, providing efficient and intelligent technical support for the intelligent early warning, rapid tracing and scientific response of sudden toxic gas leakage events. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a schematic flowchart of an atmospheric environmental pollution event emergency monitoring and early warning method provided by Embodiment 1 of the present application; Figure 2 is a schematic flowchart of an atmospheric environmental pollution event emergency monitoring and early warning method provided by Embodiment 2 of the present application; Figure 3 is a schematic structural diagram of an atmospheric environmental pollution event emergency monitoring and early warning system provided by Embodiment 3 of the present application; Figure 4 is a schematic structural diagram of an electronic device provided by Embodiment 4 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] In order to make the objectives, technical solutions, and advantages of the present application clearer, the following further describes the specific embodiments of the present application in detail with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Additionally, it should be noted that for ease of description, only parts related to the present application rather than all content are shown in the drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0013] The following will clearly describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application belong to the scope of protection of the present application.

[0014] The terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. generally belong to the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0015] The following further describes in detail a RSMC chip, a multi-stage chip startup method, and a Beidou communication and navigation device provided in the embodiments of the present application with reference to the accompanying drawings, through specific embodiments and their application scenarios.

[0016] Embodiment 1 Figure 1 is a schematic flowchart of an emergency monitoring and early warning method for atmospheric environmental pollution events provided in Embodiment 1 of the present application. As Figure 1 shown, it specifically includes the following steps: S101. Obtain multi-scale meteorological field data and real-time chlorine concentration data, determine the wind field direction data of the multi-scale meteorological field data, and determine the concentration change rate of the real-time chlorine concentration data, and determine potential pollution hotspots according to 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.

[0017] Multi-scale meteorological field data can refer to a set of meteorological parameter data covering different spatial scales (such as regional level, urban level, local micro-region) and time scales (such as hourly level, minute level), and can include wind speed, wind direction (vector wind field), temperature, humidity, atmospheric pressure, boundary layer height, wet deposition, dry deposition coefficient, precipitation intensity, solar radiation, etc. The multi-scale is reflected in that the data granularity and accuracy can be dynamically switched according to different resolutions, ranges and application scenarios.

[0018] Real-time chlorine concentration data can be chlorine concentration measurement value data continuously obtained over time from fixed environmental monitoring stations.

[0019] Wind field direction data can be a spatial wind direction vector field extracted from multi-scale meteorological field data, representing the wind flow direction at different regions, different altitude layers, and different times. Specifically, it is usually two-dimensional or three-dimensional vector data (such as U, V components), and there is a wind direction (unit: °, for example, east wind is 90°) at each grid point or station location.

[0020] The concentration change rate can refer to the change rate of pollutant concentration per unit time, indicating the growth or decay trend of pollutants.

[0021] Potential pollution hotspots can be identified by analyzing wind field direction data and concentration change rate data, and are spatial regions where there may be pollution sources or pollutant accumulation phenomena.

[0022] Fixed monitoring stations can refer to pollutant concentration monitoring facilities deployed in preset positions in the target area for a long time, which can continuously and regularly collect real-time concentration data of specific pollutants (such as chlorine), and upload the data to the platform through a communication module for centralized analysis.

[0023] It is possible to access meteorological data interfaces (such as the Central Meteorological Observatory, WRF numerical forecast model output) to obtain multi-scale meteorological field data, and fixed monitoring stations regularly upload chlorine concentration data (such as sampling once per minute). At least 5 fixed stations are arranged, and the data is time-synchronized and reasonably spatially distributed. Extract horizontal and vertical wind vectors from the multi-scale meteorological field data (using U / V components) to obtain the wind field direction (in the form of angles / vectors) at each position and each time in each region. For each monitoring point, use a sliding time window (such as 5 minutes) to calculate the concentration change rate:

[0024] where \(i\) is the index of the fixed monitoring station; is the rate of change of concentration; is the real-time data of chlorine concentration at time \(t\); is the real-time data of chlorine concentration at time is the sliding time window.

[0025] When the chlorine concentration at some fixed monitoring stations continues to rise within a short period of time, and this rising trend is combined with the direction of the local wind field, it is possible to infer that the pollutants may come from a certain area upwind of these stations. By analyzing the concentration change trends of multiple stations and combining with the wind direction for path backtracking, the convergence area of these trends can be located in space, thus determining it as a potential pollution hot spot area. This area is very likely to be close to the location of the pollution source and can be used to guide the subsequent UAV sampling path planning and precise identification of the pollution source.

[0026] Based on the above technical solution, optionally, multi-scale meteorological field data is obtained, including: Based on the WRF model, meteorological satellite data and radar observation data are received in real time, the boundary conditions and initial fields of the WRF model are updated according to the meteorological satellite data and radar observation data, and the meteorological simulation process is driven to generate three-dimensional meteorological forecast results for a preset period; According to the CALMET adaptive downscaling method, the three-dimensional meteorological forecast results for a preset period are converted into multi-scale meteorological field data.

[0027] In this solution, the WRF model can be a numerical weather prediction model for weather forecasting and atmospheric research. It can simulate atmospheric processes at different spatial scales, including wind, temperature, humidity, precipitation, etc. The WRF model can nest multiple resolution regions, support real-time reception of multi-source data for updating boundary conditions and initial states, and is one of the most commonly used models in high-resolution meteorological simulations.

[0028] Meteorological satellite data can come from geostationary or polar-orbiting satellites and is used to monitor large-scale atmospheric features such as cloud images, radiation, temperature, and water vapor.

[0029] Radar observation data includes reflectivity, radial velocity, etc. obtained by weather radars, mainly reflecting near-surface atmospheric dynamic processes such as mesoscale convective systems and precipitation structures. These observation data, as external observation constraints, can be used to calibrate the WRF model and improve its simulation accuracy.

[0030] 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 simulation at a certain moment (such as \(t_0\)).

[0031] Boundary conditions can be external inputs at the edges of the model space throughout the entire simulation period, used to guide how the edges of the model area "exchange information with the external atmosphere" and maintain simulation continuity.

[0032] The preset period can refer to the time range predefined for a simulation or prediction task, such as "the next 3 hours" or "the next 12 hours", within which the model will generate meteorological prediction data.

[0033] Three-dimensional meteorological forecast results can refer to meteorological variable data with a three-dimensional structure (including horizontal dimension, vertical height, and time dimension) output by the model, such as wind fields, temperature fields, humidity fields, etc. These data can support subsequent applications such as pollutant dispersion simulation and air quality analysis.

[0034] CALMET can be a meteorological data processing module in the CALPUFF system, used to convert raw meteorological data into a high-resolution terrain-adapted meteorological field. Its adaptive downscaling method can "refine" the output data of relatively coarse resolutions such as WRF according to regional terrain, surface properties, and local observational data, generating multi-scale meteorological field data with higher spatial resolution and closer to the actual meteorological characteristics near the ground.

[0035] To achieve a high-precision and multi-scale adaptable meteorological driving data acquisition process, first, based on the WRF (Weather Research and Forecasting) model, observational data from meteorological satellites and ground-based radars are received in real time. Among them, meteorological satellite data include remote sensing information such as atmospheric temperature and humidity profiles, cloud images, and radiation fluxes, and radar observational data include ground precipitation intensity, radar reflectivity factor, and wind profile information, etc. Through data assimilation techniques (such as 3DVAR or EnKF), the above observational data are fused into the WRF model to dynamically correct and update its boundary conditions and initial fields, so as to improve the model's fitting ability to the actual atmospheric state. Subsequently, the updated initial fields and boundary conditions are used to drive the WRF numerical simulation module to conduct high-resolution three-dimensional meteorological element simulations for the set preset period (such as the next 1 - 6 hours), obtaining three-dimensional meteorological forecast results including elements such as wind speed, wind direction, temperature, and humidity at multiple vertical height levels. To make the three-dimensional meteorological results more conform to the local meteorological characteristics under complex terrain conditions, the CALMET adaptive downscaling method is introduced to process the WRF output results. This method integrates terrain factors (such as slope, height, surface type) and local observational data, adaptively adjusts key parameters such as wind fields and turbulent diffusion coefficients, downscales the original meteorological forecast data to higher spatial accuracy, and then generates multi-scale meteorological field data available for pollutant dispersion simulation.

[0036] In this solution, by combining the WRF model with real-time meteorological satellite and radar observation data to dynamically update the boundary conditions and initial fields, the accuracy and timeliness of meteorological simulations can be significantly improved. At the same time, the CALMET adaptive downscaling method is introduced to make the meteorological field data more conform to the terrain and local characteristics, ensuring that the downstream pollution dispersion simulation has a higher spatial resolution and a more realistic reflection ability, thereby providing scientific and reliable meteorological support for pollution source location, concentration prediction, and emergency response.

[0037] S102. Determine the location data and wind direction data of the potential pollution hotspot area, and send the location data and the wind direction data to the unmanned aerial vehicle (UAV), so that the UAV can automatically plan a flight path according to the location data and the wind direction data, and obtain supplementary chlorine concentration sampling data from at least three angles.

[0038] The location data can be the specific coordinate information of the potential pollution hotspot area in the geographical space, usually including longitude, latitude, and possibly the altitude range. These data are used to determine the spatial location of the pollution hotspot for the UAV to perform positioning and flight mission planning.

[0039] The wind direction data can refer to the dominant wind direction information of the location where the pollution hotspot area is located, that is, from which direction the wind blows in the current or predicted time period in this area. This is of great significance for the UAV to judge the pollution dispersion trend and determine reasonable sampling angles.

[0040] The UAV can be an aircraft that does not require a pilot to board and is flown through a remote control or an automatic control system. In this scenario, the UAV has an environmental monitoring function, can receive the location data and wind direction data of the pollution area, intelligently plan a flight path, and collect chlorine concentration data at predetermined points to supplement the information of areas that cannot be fully covered by fixed monitoring stations.

[0041] Obtain real-time chlorine gas concentration data (from fixed monitoring stations) and wind field direction data. According to the wind field direction and the concentration change rate, predict the diffusion trend of pollutants. The wind field direction determines the propagation direction of pollutants, while the concentration change rate represents the change rate of pollutants in a specific area. By combining these data, the location data of potential pollution hotspots can be estimated, usually represented by longitude and latitude coordinates. Wind direction data usually comes from meteorological observations (e.g., meteorological satellites, radar data, or ground weather stations). These data provide the dominant direction of the wind and may include information such as wind speed and air pressure. Once the location data (longitude and latitude) of the pollution hotspot area and the wind direction data (wind direction and other relevant meteorological information) are determined, these data will be sent to the drone via wireless communication or network. Drones can usually receive this information through wireless networks or satellite communication. After receiving this information, the drone will automatically plan its flight path according to a predetermined algorithm and flight rules. The goal of flight path planning is to: conduct precise sampling within the pollution hotspot area. Avoid over-overlapping or missing areas to ensure the comprehensiveness and effectiveness of sampling. Adjust the flight altitude and angle according to the wind direction data to obtain the most accurate concentration data. The drone will plan a path that can maximize the coverage of the pollution hotspot area based on the location data and wind direction data. Path planning needs to consider the wind speed and direction to avoid interference or influence of the wind on the flight path. The flight path usually includes multiple points, and the drone will collect chlorine gas concentration data at different heights or angles. To ensure multi-directional coverage, the flight path will include at least three sampling angles (e.g., from different heights, different flight angles, or different offset angles). The drone flies within the pollution hotspot area according to the planned flight path and obtains supplementary chlorine gas concentration data from different angles through the onboard gas sensor or other sampling devices. Specifically, after the drone reaches the predetermined position, it will activate the sensor to collect chlorine gas concentration data. Sampling is carried out from at least three angles, so that the chlorine gas concentration in the pollution hotspot area can be comprehensively evaluated from multiple directions. The collected data will be transmitted back to the ground station or data processing center in real time for further analysis and decision support.

[0042] S103. Obtain the supplementary chlorine gas concentration sampling data obtained from at least three angles transmitted by the drone, and input the real-time chlorine gas concentration data, the supplementary chlorine gas 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.

[0043] The supplementary chlorine concentration sampling data can be chlorine concentration data collected by a drone from multiple angles (at least three angles). Since there may be blind spots or insufficient coverage in the chlorine concentration data of ground monitoring stations, using a drone can supplement this data and obtain more accurate pollutant concentration distribution information. The supplementary data usually comes from sensors carried by the drone, and the sampling points are located at different flight angles or heights to comprehensively evaluate the distribution of pollutants from multiple dimensions.

[0044] The preset three-dimensional pollutant concentration distribution model can be a mathematical or physical model used to predict or calculate the three-dimensional distribution of pollutants in a specific area and time. This model is based on various input data (such as real-time chlorine concentration data, supplementary sampling data, meteorological data, etc.) to simulate the diffusion and concentration distribution of pollutants in the atmosphere. The model generally considers meteorological factors (such as wind speed, wind direction, temperature, humidity, etc.), the intensity of the pollution source and its emission characteristics, and calculates the spatial distribution of pollutants through numerical simulation methods.

[0045] The three-dimensional pollutant concentration distribution data can be the concentration distribution of pollutants in space (XYZ coordinates) and time (at time T). The data is usually presented in the form of a three-dimensional grid, and each grid point contains the pollutant concentration value at a specific time point and spatial position. The three-dimensional data can not only show the distribution of pollutants on a plane but also reflect their diffusion in height (such as the vertical direction).

[0046] The drone is equipped with a wireless data transmission system to transmit the chlorine concentration data obtained from each sampling point to the ground control system in real time. The control system processes and stores these real-time data to ensure that the data can be synchronously processed subsequently. Synchronize the real-time data with the data collected by the drone and perform spatial position mapping as needed. Ensure that each data point has a corresponding spatial coordinate and time stamp. Input the real-time chlorine concentration data, supplementary chlorine concentration sampling data, and multi-scale meteorological field data into the preset three-dimensional pollutant concentration distribution model, and the preset three-dimensional pollutant concentration distribution model will calculate the three-dimensional spatial distribution of pollutants according to the input data (real-time chlorine concentration data, supplementary sampling data, multi-scale meteorological field data).

[0047] The training process of the preset three-dimensional pollutant concentration distribution model is as follows: First, collect data related to pollutant concentrations, including real-time chlorine concentration data, supplementary chlorine concentration sampling data obtained from at least three angles, multi-scale meteorological field data, etc. These data provide rich input features for the model. Secondly, through data preprocessing and cleaning, ensure the quality of the data, and construct a feature set suitable for model training. For example, convert meteorological data into parameters such as wind speed, wind direction, temperature, and humidity, and combine with pollutant emission data to provide detailed input information. When selecting a model, a numerical model or machine learning model suitable for simulating pollutant diffusion and concentration changes is usually chosen, such as a regression model, a deep learning model (such as a convolutional neural network or a recurrent neural network), or a physical model based on meteorological diffusion theory (such as the CALPUFF model). Through these models, the distribution of pollutants in space and time can be simulated, and the pollution concentration under different conditions can be predicted. During the model training process, by comparing the collected input data (meteorological conditions, pollutant source locations, concentration sampling data, etc.) with the actual concentration observation values, calculate the error, and adjust the model parameters according to the error. The training process uses optimization algorithms such as backpropagation or minimizing the error, enabling the model to more accurately capture the pollutant diffusion law. After training, through cross-validation to ensure the generalization ability of the model, and finally obtain a model that can accurately predict the distribution of pollutants in three-dimensional space based on the input real-time meteorological data, chlorine concentration data, etc.

[0048] 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 pollutant source and the pollution emission intensity.

[0049] The CALPUFF inverse model can be a model for simulating pollutant diffusion, mainly used to infer the specific location of the pollutant source and the pollutant emission intensity by analyzing air quality monitoring data. The CALPUFF model is a physics-based pollutant diffusion model that predicts the diffusion process of pollutants in the atmosphere by considering meteorological conditions, terrain, and other environmental factors. The inverse model is the reverse application of the CALPUFF model, mainly inferring the location and intensity of the pollutant source from the actual pollutant concentration data in reverse.

[0050] The specific location of the pollutant source can refer to the geographical location of the source that generates pollutants. By analyzing the diffusion pattern of pollutants in the air and combining with the pollution concentration data of air quality monitoring stations, the inverse model can estimate the possible location of the pollutant source. Usually, this process is based on sampling data, wind field data, meteorological conditions, and changes in pollutant concentrations, and determines the location of the pollutant source through simulated backpropagation.

[0051] Pollution emission intensity can refer to the amount of pollutants released by a pollution source per unit time. It is usually expressed in mass units (such as kilograms or tons) per hour, representing the emission intensity of the pollution source. By combining the pollutant concentration distribution data and meteorological data, the inverse model can estimate the emission intensity of the pollution source. During the calculation process, environmental factors affecting pollutant dispersion (such as wind speed, temperature, humidity, etc.) are considered, and the emission amount of the pollution source is estimated through reverse reasoning.

[0052] Input the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into the CALPUFF inverse model. After inputting the data, model parameter settings must be carried out: Determine the target area: that is, the area to be simulated by the model. This is usually determined according to the coverage of pollutant concentration data and meteorological data. Set the pollution source type: The inverse model assumes the location and emission pattern of the pollution source in order to infer the specific location of the pollution source based on the pollutant concentration distribution. The CALPUFF inverse model uses the actually sampled pollutant concentration data (such as data from monitoring stations) and the surrounding meteorological field data, and uses physical and chemical models to reverse calculate the diffusion path of pollutants. Based on the pollutant concentration data, the inverse model determines the possible location of the pollution source according to the calculated diffusion path. During the calculation process, the model takes into account the changes in wind speed, wind direction, meteorological conditions, and pollutant concentration. After reverse calculating the location of the pollution source, the model will continue to calculate the emission intensity of the pollution source. The emission intensity is calculated based on the changes in pollutant concentration and meteorological conditions (such as wind speed, temperature, etc.), and is usually related to the size, emission amount, and emission method of the pollution source. The CALPUFF inverse model will reverse estimate the emission intensity of the pollution source by combining the relationship between the changes in pollutant concentration and the meteorological field data, and the physical laws of pollutant dispersion in the environment. After the model runs to completion, the CALPUFF inverse model will output the following results: The specific location of the pollution source: that is, the possible geographical coordinates of the pollution source, usually represented in the form of coordinate points. The emission intensity of the pollution source: the emission amount of the pollution source within a specific time, usually expressed in mass units (such as kilograms per hour or tons per hour).

[0053] In the embodiments of the present application, multi-scale meteorological field data and real-time chlorine concentration data are obtained, the wind field direction data of the multi-scale meteorological field data is determined, and the concentration change rate of the real-time chlorine concentration data is determined. And a potential pollution hot spot area is determined according to 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 the wind direction data of the potential pollution hot spot area are determined, and the position data and the wind direction data are sent to a drone for the drone to automatically plan a flight path according to the position data and the wind direction data, and obtain supplementary chlorine concentration sampling data from at least three angles; the supplementary chlorine concentration sampling data obtained from at least three angles 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 backward model to obtain the specific position of the pollution source and the pollution emission intensity. Through the above atmospheric environmental pollution event emergency monitoring and early warning method, not only the accuracy and timeliness of pollution source tracing are improved, but also high spatial resolution and flexible response capabilities are possessed, providing efficient and intelligent technical support for the intelligent early warning, rapid tracing and scientific response of sudden toxic gas leakage events.

[0054] Embodiment 2 Figure 2 is a schematic flowchart of the atmospheric environmental pollution event emergency monitoring and early warning method provided in Embodiment 2 of the present application. As Figure 2 shown, it specifically includes the following steps: S201, Obtain multi-scale meteorological field data and real-time chlorine concentration data, determine the wind field direction data of the multi-scale meteorological field data, and determine the concentration change rate of the real-time chlorine concentration data, and determine a potential pollution hot spot area according to 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.

[0055] S202, Determine the position data and the wind direction data of the potential pollution hot spot area, and send the position data and the wind direction data to a drone for the drone to automatically plan a flight path according to the position data and the wind direction data, and obtain supplementary chlorine concentration sampling data from at least three angles.

[0056] S203, Obtain the supplementary chlorine concentration sampling data obtained from at least three angles transmitted by the drone, and 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.

[0057] 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.

[0058] S205. Use a confidence ellipse to obtain the pollution source location error range data based on the specific location of the pollution source and the pollution emission intensity, and perform spatial optimization and update on the specific location of the pollution source according to the pollution source location error range data.

[0059] A confidence ellipse can be a spatial statistical analysis method used to represent the range of location uncertainty of an estimated point (such as the location of a pollution source) in a two-dimensional space. It is drawn based on the covariance matrix of the estimated location and is similar to the two-dimensional version of the "confidence interval" in statistics. Center point: The initial estimated location of the pollution source. Major axis and minor axis: Reflect the distribution degree of the location error in different directions (usually related to the wind direction and diffusion trend). Confidence level: For example, a 95% confidence ellipse means that statistically, there is a 95% probability that the true pollution source location will fall within this ellipse.

[0060] The pollution source location error range data can refer to a set of spatial data calculated to quantify the error in the estimated location of the pollution source. Specifically, it includes: the geometric parameters of the confidence ellipse (center point, major axis, minor axis, direction angle), the confidence level represented by the ellipse (such as 90%, 95%), the directional distribution of the location error (for example, due to the influence of the wind field, the error is larger in a certain direction), and the spatial offset for error correction (used to update the location point).

[0061] After obtaining the preliminary location result (specific location of the pollution source) and the corresponding pollution emission intensity of the pollution source, the system calculates the uncertainty distribution of the estimated location of this pollution source based on the existing three-dimensional pollutant concentration distribution data and multi-scale meteorological field data. By statistically modeling factors such as the pollution diffusion path and wind field perturbation, the error covariance of the pollution source location in space is obtained, and then a confidence ellipse is constructed to represent the possible spatial range where the true pollution source location may exist. This confidence ellipse can clearly indicate the distribution trend and confidence probability of the location error in different directions. Then, the system superimposes this confidence ellipse on the original pollution source estimated point, corrects the weight in the main error direction of the specific location of the pollution source, and combines the emission intensity influence factor and the actual monitoring data feedback to perform spatial optimization and update on the specific location of the pollution source to improve the source location accuracy and traceability credibility. This process can be continuously iterated until the error between the prediction and the actual monitoring result converges to an acceptable range.

[0062] S206. Use a probability cloud map to obtain the pollutant diffusion credible region data based on the specific location of the pollution source and the pollution emission intensity, and correct the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible region data.

[0063] A probability cloud map can refer to a "credible probability region map" of pollutant diffusion in space, drawn by integrating multiple factors such as the specific location of the pollution source, uncertainty range, emission intensity, and meteorological field disturbances, based on statistical simulation and propagation modeling methods. Different colors or transparencies in the map represent the probability levels of the pollutant's presence at that location, reflecting the trend and credibility of the pollutant's diffusion with factors such as the wind field and terrain.

[0064] Pollutant diffusion credible region data can be the regions of possible pollutant distribution extracted from the probability cloud map with high credibility. These regions represent the range of locations where the pollutant is most likely to reach or accumulate under given conditions, often defining the boundaries with a probability threshold (such as a 95% credible region), and are used to assist in judging the pollution risk area and the decision-making response range.

[0065] The visible range can refer to the spatial region range that is finally rendered and displayed to the user during the visualization of the three-dimensional pollutant concentration distribution. Usually, the visible range takes into account factors such as the pollutant concentration threshold (i.e., only displayed when exceeding a certain concentration value), the user's perspective, and the map level limit. By correcting the visible range with the credible region data, the display result can be made closer to the actual pollutant distribution, avoiding misleading displays and also helping to optimize resource scheduling and the emergency response range.

[0066] Use the specific location of the pollution source, emission intensity, and multi-scale meteorological field data (including wind speed, wind direction, temperature, boundary layer height, etc.) obtained from the CALPUFF inverse model as the basic parameters for pollutant diffusion simulation. Combine methods such as Monte Carlo simulation and stochastic perturbed wind field modeling to conduct multiple pollutant diffusion path simulations near the pollution source location, considering variable factors such as emission intensity error, wind field uncertainty, and terrain influence. Superimpose and statistically analyze the multiple pollution diffusion paths generated by the simulation to obtain the probability values of the pollutant's appearance at different locations, and map these probability values into two-dimensional or three-dimensional spatial images to form a pollutant probability cloud map. According to the set credible probability threshold (such as 90% or 95%), extract the spatial range covering the high-probability distribution of the pollutant in the probability cloud map to form pollutant diffusion credible region data. This region represents the set of locations where the pollutant has a high probability of existing. Match the original three-dimensional pollutant concentration distribution data with the credible region, and perform shielding, down-weighting, or visual fading on the pollutant distribution data outside the credible region and with low credibility, and only fully display the concentration distribution within the credible region.

[0067] In this embodiment, a scientific error control mechanism is introduced in the process of pollution source location and pollutant diffusion visualization by integrating the spatial uncertainty modeling method of confidence ellipse and probability cloud map. Among them, the confidence ellipse is used to characterize the spatial error range of pollution source location and emission intensity estimation, thereby realizing the optimization update of pollution source location; the probability cloud map is based on the probability distribution of pollution diffusion, identifying the pollutant distribution area with high credibility, thereby correcting the three-dimensional pollutant concentration visual range. It not only improves the accuracy and credibility of pollution source tracing and pollution visualization results, but also enhances the scientific decision-making support capability for responding to sudden environmental events.

[0068] 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: 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, the pollution warning area layer and the evacuation route layer are generated.

[0069] In this scheme, the pollution warning area layer can refer to the spatial area layer with potential health risks delineated based on the optimized specific location of the pollution source and the corrected three-dimensional pollutant concentration distribution data, combined with pollutant type, toxicity level, meteorological conditions (such as wind speed, wind direction) and other factors. It is usually a dynamically variable zoning map, including different risk levels (such as high risk, medium risk, and low risk); based on the standard concentration threshold, if chlorine reaches a certain concentration, it will be designated as a "red warning zone."

[0070] The evacuation route layer can refer to a recommended route layer for crowd evacuation that is automatically or semi-automatically generated based on factors such as pollution diffusion trends, geographical terrain, traffic accessibility, and wind direction avoidance strategies, combined with the scope of the warning area. It can integrate multiple path options (such as the shortest distance, the least risk, and the fastest access); it can optimize the path by combining data such as building density, traffic conditions, and distribution of susceptible populations; the output format is usually a three-dimensional route map or a path trajectory map on a two-dimensional map, which is used for command and dispatch or public evacuation navigation.

[0071] Based on the specific location of the pollution source after spatial optimization and the three-dimensional pollutant concentration distribution data after correcting the visible range, first, taking the pollution source as the center point, combining the diffusion trends of pollutants in different directions and heights, and using the preset concentration threshold classification standard, a pollution warning area layer corresponding to the risk level is dynamically generated; this layer can be superimposed on the GIS map to display the spatial boundaries of high, medium, and low-risk areas. Subsequently, combining multi-source data such as terrain and landform, building distribution, road network, wind direction avoidance principle, and population density, using path optimization algorithms (such as A* or Dijkstra algorithm) to automatically plan the optimal evacuation path from high-risk areas to safe areas, forming an evacuation path layer to support the emergency management department for visual command and real-time scheduling.

[0072] In this solution, by fusing the location of the pollution source after spatial optimization and the corrected three-dimensional pollutant concentration distribution data to generate a pollution warning area layer and an evacuation path layer, it can achieve accurate identification and dynamic warning of pollution risk areas, and at the same time provide a scientific and efficient evacuation route for personnel. The benefits are as follows: It not only improves the accuracy and timeliness of the emergency response to pollution accidents, but also enhances the operability and safety of public evacuation guidance, significantly reducing the risk of personal injury and property loss, and contributing to the realization of visual decision support and intelligent emergency management for pollution incidents facing actual combat.

[0073] On the basis of 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: Inputting the specific location of the pollution source after spatial optimization update, pollution emission intensity, and multi-scale meteorological field data into the CALPUFF forward simulation model to obtain PM2.5 concentration field data.

[0074] In this solution, the CALPUFF forward simulation model can be a key component in an air pollutant diffusion modeling system, used to simulate the whole process of pollutant diffusion, transportation, sedimentation, and transformation over time and space after being released from the pollution source. "Forward" means starting from the known pollution source information (location, emission intensity, emission height, emission rate, etc.), combining atmospheric meteorological data (such as wind speed, wind direction, temperature, turbulence intensity, etc.), and predicting the concentration distribution of pollutants over time in the target area.

[0075] PM2.5 concentration field data refers to the concentration distribution of fine particulate matter (with a diameter less than or equal to 2.5 micrometers) in the air within a given time and space range, usually in units of µg / m³. "Concentration field" indicates that this is a kind of spatial distribution data, which can be presented in the form of a two-dimensional map or a three-dimensional volume grid, reflecting the high and low PM2.5 concentrations in different locations.

[0076] 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 spatio-temporal resolution generated by the CALMET module (including wind speed, wind direction, temperature, turbulent diffusion parameters, etc.), these information are jointly used as input conditions and configured into the CALPUFF forward simulation model. During the simulation process, CALPUFF uses the Lagrangian particle tracking method to dynamically simulate the processes of pollutant particle transport, diffusion, dry and wet deposition, and chemical reactions, and then calculates the mass concentration distribution of PM2.5 at different time and space positions. Finally, the output PM2.5 concentration field data are displayed in a grid form to show the three-dimensional distribution characteristics of the fine particle concentration over time in the target area, which can be used in scenarios such as pollution situation awareness, risk assessment, and early warning response.

[0077] In this solution, the specific locations of the pollution sources optimized in space, the pollution emission intensity, and the multi-scale meteorological field data are input into the CALPUFF forward simulation model, which can accurately simulate the spatio-temporal diffusion process of pollutants (such as PM2.5) under real meteorological conditions, and effectively improve the scientificity and credibility of the concentration field prediction. This process helps to timely master the influence range, concentration change trend, and high-risk areas of pollutants, support the release of pollution warnings, public health protection, and emergency response decision-making, and improve the refinement and intelligence level of the overall atmospheric environment management.

[0078] On the basis of the above technical solution, optionally, after correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible region data, the method further includes: If a prediction instruction is received, determine the prediction area according to the prediction instruction, perform interpolation processing on the multi-scale meteorological field data of the prediction area and the three-dimensional pollutant concentration distribution data after correcting the visible range in time series, and construct a dynamic data set of pollution diffusion evolution over time; Obtain the topographic and geomorphic data of the prediction area, and fuse the dynamic data set and the topographic and geomorphic data to generate a visual dynamic layer.

[0079] In this solution, the prediction instruction can be an operation command issued by the system, user, or management platform, intending to perform a predictive analysis of the pollution trend in a specific area or time period. This instruction usually contains parameters, such as: the prediction area range, the start and end times of the prediction, the types of pollutants of concern (such as chlorine or PM2.5), the required output form (layer, chart, etc.), which are used to trigger the subsequent data processing and modeling processes.

[0080] The prediction area can refer to the spatial range determined by the system for conducting pollution diffusion trend analysis after receiving a prediction instruction, usually a geographical area (such as a certain industrial park in a city, the area around a port, a densely populated area, etc.). This area will serve as the core spatial domain for data processing and pollution simulation.

[0081] The dynamic data set can be a series of time-varying data sets generated by time series interpolation based on the three-dimensional pollutant concentration distribution data and multi-scale meteorological field data after correcting the visible range. It records the continuous state of pollutant diffusion, concentration change, and migration path over time, reflects the dynamic evolution process of the pollution field in a future period of time, and is the core supporting data for pollution trend simulation.

[0082] The topographic and geomorphic data can refer to the geographical spatial information within the prediction area, including terrain undulation (elevation), surface cover types (such as water bodies, buildings, forests), wind duct directions, mountain barriers, etc. These data have a significant impact on the pollutant diffusion process. For example, complex terrain can affect the wind field and pollutant transmission path. Therefore, topographic and geomorphic data need to be integrated in pollution simulation to improve accuracy.

[0083] The visualized dynamic layer can be a layer generated after integrating the dynamic data set with the topographic and geomorphic data, which can be dynamically displayed on a GIS platform or simulation system. It shows the whole process of pollutant concentration diffusion over time in the form of animations or time series graphs, intuitively presenting key information such as pollution development trends, influence ranges, and high-concentration zones, and assisting in emergency management and public warning.

[0084] When the system receives a prediction instruction, it first parses the instruction content. The prediction instruction includes the prediction time range, the prediction area, and the types of pollutants of concern. According to the regional parameters in the instruction, the system determines the spatial range to be predicted. The prediction area can be specific geographical coordinates provided by the user (such as a certain city or industrial area), or automatically derived by the system (such as a traffic-intensive area or the area around a pollution source). Next, the system obtains real-time or historical meteorological data within the prediction area through a meteorological model (such as the WRF model) or from a meteorological database. These data include information such as wind speed, wind direction, temperature, humidity, and air pressure. These meteorological parameters are important bases for predicting the diffusion and concentration changes of pollutants. In particular, the system needs to obtain multi-scale meteorological field data, that is, meteorological information at local, regional, and larger scales, to fully simulate the propagation process of pollutants. In addition, the system also needs to obtain pollutant concentration distribution data, which are obtained through a pollutant diffusion model (such as the CALPUFF model) or through actual monitoring and simulation results. To make the pollutant concentration distribution more accurate, the system will correct it according to topographical and meteorological factors to form three-dimensional pollutant concentration distribution data, indicating the pollutant concentration at different times, different heights, and different geographical locations. After obtaining these data, the system performs interpolation processing on the pollutant concentration data in time series. The purpose of interpolation processing is to convert discrete time node data into continuous data to ensure that the time evolution process of pollutant concentration during the entire prediction period can be accurately displayed. By using interpolation algorithms (such as linear interpolation, spline interpolation, etc.), the system can predict the pollution concentration at different time points and different spatial positions, generating a dynamic data set. This dynamic data set integrates the changes in pollutant concentration over time into a continuous data stream, reflecting the concentration changes of pollutants from the source to the diffusion process. This data set not only contains the concentration data of pollutants at different times and spaces, but also considers the time dependence and spatial dependence of pollutant diffusion, making the simulation of pollution diffusion more accurate. At the same time, the system also needs to obtain topographical and geomorphic data of the prediction area, which usually come from a Geographic Information System (GIS) database. Topographical and geomorphic data include the altitude of the area, the distribution of mountains and rivers, and the land use of cities and villages. These data are crucial for simulating the pollutant diffusion process because topography has a significant impact on the propagation of pollutants. For example, mountains may block the flow of pollutants, or air ducts may concentrate the diffusion of pollutants. The system fuses the multi-scale meteorological field data and the corrected three-dimensional pollutant concentration distribution data with the topographical and geomorphic data. The purpose of this step is to consider the impact of topography on the pollutant diffusion path and optimize the simulation results of pollutant diffusion. For example, air ducts and highlands may change the propagation direction of pollutants, or pollutants may accumulate in some low-lying areas. By fusing these data, the system can generate more realistic pollution diffusion prediction results.Finally, the system will generate a visual dynamic layer based on this fused data. This dynamic layer will display the change process of pollutant concentration in a GIS platform or a dedicated visualization software. Users can view the pollution concentration changes at different times and locations through interactive functions (such as time sliders, geographical coordinate information, etc.). In this way, users can monitor the dynamic changes of pollutant diffusion in real time, identify pollution hotspots, and take timely countermeasures.

[0085] In this solution, by fusing the spatially optimized pollutant source locations, multi-scale meteorological fields, topographical features, and three-dimensional pollutant concentration data, a dynamic dataset of pollution diffusion is constructed, and a visual dynamic layer is generated to achieve accurate simulation and intuitive display of the entire process of pollution diffusion. This not only improves the spatio-temporal resolution and accuracy of predictions but also provides strong technical support for environmental supervision, emergency response, and public warning, contributing to scientific decision-making and efficient resource scheduling, and significantly enhancing the pollution prevention and control capabilities.

[0086] Based on the above technical solution, optionally, after generating the visual dynamic layer, the method further includes: Integrate a time control slider in the visual dynamic layer, and in response to the user's interactive operation on the time control slider, output the target three-dimensional pollutant concentration distribution data, target meteorological field data, and pollution level map of the predicted area corresponding to the time point selected by the user.

[0087] In this solution, the time control slider can be a graphical user interface (GUI) control, usually appearing in the visual dynamic layer in the form of a linear slider, allowing users to select a specific time point by dragging the slider. The slider corresponds to the time axis in the pollution diffusion simulation or meteorological simulation results, such as the time series data for the next 24 hours or 48 hours. Users can use it to browse the pollution situation at different time points and achieve dynamic playback or forward preview of the pollution evolution process.

[0088] The interactive operation can refer to the actions between the user and the system interface, such as clicking, dragging, selecting, etc. In this scenario, it specifically refers to operations such as dragging the time control slider, clicking on a specified time point, and playing the time animation by the user. These operations will trigger the system backend to respond, load, and display the pollution and meteorological data corresponding to the time point.

[0089] The selected time point can be a specific time position selected by the user through the slider. For example, when the user slides to "14:00 on April 16, 2025", this time point is the "selected time point". The system will extract the corresponding pollutant concentration and meteorological information from the pre-constructed dynamic dataset according to this time point.

[0090] 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 sourced from a pollutant dispersion model (such as CALPUFF). It exists in a grid form, reflecting the pollution levels at different geographical locations and altitudes, and is the core basic data for pollution analysis and visualization.

[0091] 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 sourced from meteorological simulation systems such as the WRF model, are an important input for driving pollution dispersion simulations, and can also be used to analyze the trend of pollutant dispersion.

[0092] 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, light pollution, moderate pollution, etc.), usually presented in color coding on a map, enabling users to clearly understand the air quality level 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 of pollutants evolving over time (such as PM2.5, NOx, etc.) with terrain information, and can dynamically display the change process of pollutants at different times and in different 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 "light or more serious pollution". 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.

[0093] In the process of integrating a time control slider into the visual dynamic layer, the system first organizes pollutant concentration data, meteorological field data, and pollution level information into a multi-dimensional time series dataset with clear time tags. As a part of the user interface, the time control slider allows users to slide the slider or select specific time nodes on the interface. When users perform interactive operations (such as sliding, clicking the slider, or quickly jumping to a specified time), the system captures the time point where the slider is currently located, automatically parses it into a specific timestamp, and then retrieves the corresponding three-dimensional pollutant concentration distribution data, regional meteorological field data (such as wind speed, wind direction, temperature, humidity, etc.) in the pre-loaded or on-demand loaded time series database, and automatically generates a pollution level map based on the pollutant concentration value combined with national or local pollution grading standards (such as the grading of PM2.5). In terms of layer rendering, the system superimposes the three-dimensional pollution concentration field on the geographical map in the form of volume rendering or isosurface, the meteorological field information is represented by arrow vectors or color layers, and the pollution level map distinguishes the spatial distribution of different pollution levels in the form of colored areas. All these layer information will be updated synchronously with the operation of the time slider to achieve a visual display of the dynamic changes in the pollution diffusion trend, meteorological evolution process, and risk level over time, providing users with intuitive, real-time, and interactive pollution evolution insights and risk warning support.

[0094] In this solution, by integrating a time control slider into the visual dynamic layer, users can intuitively and flexibly view the three-dimensional pollutant concentration distribution, meteorological field data, and pollution level map corresponding to any time point, and achieve a dynamic grasp of the pollution diffusion trend and meteorological changes. This interactive display method not only improves the efficiency and accuracy of information acquisition but also supports the real-time assessment and prediction of pollution risks.

[0095] Embodiment III Figure 3 It is a schematic structural diagram of the emergency monitoring and warning system for atmospheric environmental pollution events provided in Embodiment III of the present application. As Figure 3 shown, it specifically includes: A potential pollution hot spot area identification module 301, configured to obtain multi-scale meteorological field data and real-time chlorine concentration data, determine the wind field direction data of the multi-scale meteorological field data, and determine the concentration change rate of the real-time chlorine concentration data, and determine a potential pollution hot spot area according to 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; A drone-assisted sampling module 302, configured to determine the location data and wind direction data of the potential pollution hot spot area, send the location data and the wind direction data to the drone, so that the drone automatically plans a flight path according to the location data and the wind direction data, and obtains supplementary chlorine concentration sampling data from at least three angles; A three-dimensional concentration modeling module 303 is configured to obtain supplementary chlorine concentration sampling data acquired from at least three angles transmitted by a drone, input real-time chlorine concentration data, the supplementary chlorine concentration sampling data, and multi-scale meteorological field data into a preset three-dimensional pollutant concentration distribution model, and obtain three-dimensional pollutant concentration distribution data; A pollution source inversion module 304 is configured to input the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into a CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity.

[0096] The emergency monitoring and early warning system for atmospheric environmental pollution events provided by the embodiments of the present application can implement Figure 1 each process implemented by the method embodiments. To avoid repetition, details are not described herein again.

[0097] Embodiment 4 As Figure 4 shown, the embodiments of the present application further provide an electronic device 400, including a processor 401, a memory 402, and a program or instruction stored on the memory 402 and executable on the processor 401. When the program or instruction is executed by the processor 401, it implements each process of the method embodiment of the above-mentioned emergency monitoring and early warning method for atmospheric environmental pollution events, and can achieve the same technical effects. To avoid repetition, details are not described herein again.

[0098] It should be noted that the electronic device in the embodiments of the present application includes the above-mentioned mobile electronic device and non-mobile electronic device.

[0099] Embodiment 5 The embodiments of the present application further provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the embodiment of the above-mentioned cable installation process-based adaptive control system based on tension, and can achieve the same technical effects. To avoid repetition, details are not described herein again.

[0100] Wherein, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disks, or optical discs, etc.

[0101] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or system comprising a series of elements not only includes those elements but also other elements not expressly listed, or elements inherent to such process, method, article or system. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or system comprising that element. In addition, it should be pointed out 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, but may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0102] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, 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 several instructions for causing a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0103] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

[0104] The above is only the preferred embodiment of the present application and the technical principles applied. The present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments and substitutions that can be made by those skilled in the art will not depart from the protection scope 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. Without departing from the concept of the present application, more other equivalent embodiments may be included, and 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 events, characterized in that, The method includes: Obtaining multi-scale meteorological field data and real-time chlorine concentration data, determining the 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; Determining the location data and wind direction data of the potential pollution hotspots, and sending the location data and the wind direction data to a drone, for the drone to automatically plan a flight path according to the location data and the wind direction data, and obtain supplementary chlorine concentration sampling data from at least three angles; Obtaining the supplementary chlorine concentration sampling data obtained from at least three angles transmitted by the drone, and 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; Inputting the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into the CALPUFF backward model to obtain the specific location of the pollution source and the pollution emission intensity.

2. The method according to claim 1, wherein Wherein, After obtaining the specific location of the pollution source and the pollution emission intensity, the method further includes: Using a 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 spatially optimizing and updating the specific location of the pollution source according to the pollution source positioning error range data; Using a probability cloud map to obtain pollutant diffusion credible region data based on 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 region data.

3. The method according to claim 2, characterized in that Wherein, After correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible region data, the method further includes: Generating a pollution warning area layer and an evacuation route layer based on the spatially optimized and updated specific location of the pollution source and the three-dimensional pollutant concentration distribution data with the corrected visible range.

4. The method according to claim 1, characterized in that Wherein, Obtaining multi-scale meteorological field data includes: Based on the WRF model, receiving meteorological satellite data and radar observation data in real time, updating the boundary conditions and initial fields of the WRF model according to the meteorological satellite data and radar observation data, driving the meteorological simulation process, and generating three-dimensional meteorological forecast results for a preset time period; Converting the three-dimensional meteorological forecast results for a preset time period into multi-scale meteorological field data according to the CALMET adaptive downscaling method.

5. The method according to claim 2, wherein Wherein, After correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible region data, the method further includes: Inputting the spatially optimized and updated specific location of the pollution source, the pollution emission intensity, and the multi-scale meteorological field data into the CALPUFF forward simulation model to obtain PM2.5 concentration field data.

6. The method according to claim 2, wherein Wherein, After correcting the visible range of the three-dimensional pollutant concentration distribution data according to the pollutant diffusion credible region data, the method further includes: If a prediction instruction is received, determine a prediction area according to the prediction instruction, perform interpolation processing on the multi-scale meteorological field data of the prediction area and the three-dimensional pollutant concentration distribution data after correcting the visible range in time series, and construct a dynamic data set for the evolution of pollution diffusion over time; Obtain topographic and geomorphic data of the prediction area, and fuse the dynamic data set and the topographic and geomorphic data to generate a visual dynamic layer.

7. The method according to claim 6, characterized in that, Wherein, After generating the visual dynamic layer, the method further includes: Integrate a time control slider in the visual dynamic layer, and in response to a user's interaction operation on the time control slider, output 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.

8. An emergency monitoring and early warning system for atmospheric environmental pollution events, characterized in that, The system includes: A potential pollution hot spot area identification module, configured to obtain multi-scale meteorological field data and real-time chlorine concentration data, determine the wind field direction data of the multi-scale meteorological field data, and determine the concentration change rate of the real-time chlorine concentration data, and determine a potential pollution hot spot area according to 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; An unmanned aerial vehicle (UAV) assisted sampling module, configured to determine the location data and wind direction data of the potential pollution hot spot area, and send the location data and the wind direction data to the UAV, so that the UAV automatically plans a flight path according to the location data and the wind direction data, and obtains supplementary chlorine concentration sampling data from at least three angles; A three-dimensional concentration modeling module, configured to obtain supplementary chlorine concentration sampling data obtained from at least three angles transmitted by the UAV, and 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, configured to input the three-dimensional pollutant concentration distribution data and the multi-scale meteorological field data into a CALPUFF inverse model to obtain the specific location of the pollution source and the pollution emission intensity.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the emergency monitoring and early warning method for atmospheric environmental pollution events according to any one of claims 1-7 are implemented.

10. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium. When the program or instruction is executed by the processor, the steps of the emergency monitoring and early warning method for atmospheric environmental pollution events according to any one of claims 1-7 are implemented.

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