Bulk cargo wharf pollution source rapid positioning and response system based on meteorological sensor network

Abstract

The invention discloses a meteorological sensor network-based bulk cargo terminal pollution source rapid positioning and response system. The system comprises a data acquisition module; a data transmission module; a pollution source positioning analysis module; a response control module; and a visual display module. Multi-source sensing equipment is integrated, comprehensive acquisition of three-dimensional space data of atmospheric particulates and various pollutant concentration meters is realized, meteorological data and pollutant concentration data can be comprehensively utilized by integrating a physical diffusion model and a machine learning algorithm, rapid source tracing and probability positioning of a pollution source are realized, and the method is simple and convenient to operate. According to the hybrid modeling method, the limitation of a traditional single model in a complex environment is overcome, and the accuracy and robustness of positioning are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of pollution location technology, and in particular to a rapid location and response system for pollution sources at bulk cargo terminals based on a meteorological sensor network. Background Technology

[0002] Bulk cargo terminals generate significant amounts of dust during the loading, unloading, transportation, and storage of materials such as coal and ore. Simultaneously, port transportation also generates various gaseous pollutants, posing a serious threat to the surrounding environment and the health of port workers. Monitoring port pollution typically relies on a limited number of fixed ground-based monitoring stations, combined with remote sensing data from meteorological satellites, to construct pollution distribution maps for macroscopic analysis. For tracing pollution sources, numerical simulation methods based on Gaussian diffusion models or Lagrange particle models are commonly used, combined with meteorological data for reverse engineering. However, these traditional methods have significant limitations. For example, patent application CN117092300A, which combines lidar with air quality monitoring instruments to analyze pollutant transport and diffusion, chemical reactions, and characteristic source spectra, primarily focuses on monitoring and post-event analysis, lacking in rapid response and proactive control of pollution. Furthermore, it often focuses on a single monitoring and tracing stage, failing to establish a complete closed-loop automated response system encompassing monitoring, tracing, early warning, and control. This prevents the tracing results from being quickly translated into effective pollution reduction measures. Summary of the Invention

[0003] This invention aims to address the shortcomings of existing technologies by providing a rapid location and response system for pollution sources at bulk cargo terminals based on a meteorological sensor network.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A rapid location and response system for pollution sources at bulk cargo terminals based on meteorological sensor networks includes:

[0006] Data acquisition module: Acquires meteorological data and pollutant concentration data through a pre-set meteorological sensor network, wherein the meteorological data includes at least wind speed, wind direction, temperature, humidity and atmospheric pressure;

[0007] Data transmission module: Formats meteorological data and pollutant concentration data into a unified data format according to preset format requirements, and stores them through the cloud or local database;

[0008] Pollution source location analysis module: It obtains unified data from the database and performs pollution source location and diffusion path simulation by combining physical diffusion models with machine learning algorithms;

[0009] Response control module: Generates control instructions based on pollution source location results, and triggers early warnings or executes pollution control operations;

[0010] Visualization module: Displays the location of pollution sources, diffusion paths, early warning information, and control results on the interface in real time.

[0011] The data acquisition module includes:

[0012] Laser scanning radar unit: used to acquire atmospheric particulate matter data and its spatiotemporal distribution characteristics;

[0013] Air quality monitoring unit: used to collect concentration data of one or more pollutants among PM2.5, PM10, SO2, NOx, and O3;

[0014] Meteorological sensing unit: Consists of one or more integrated meteorological stations, using ultrasonic anemometers, temperature sensors, relative humidity sensors, and atmospheric pressure sensors to collect data on wind speed, wind direction, temperature, humidity, and air pressure;

[0015] Unmanned aerial vehicle (UAV) remote sensing unit: used to collect three-dimensional spatial data when pollution anomalies are detected.

[0016] The pollution source location analysis module includes:

[0017] Physical diffusion model unit: Simulates pollutant transport paths based on Gaussian diffusion or inverse diffusion models;

[0018] Machine learning source tracing unit: Analyzes and probabilistically locates pollution source contribution through spatiotemporal graph neural networks or Bayesian networks;

[0019] The dynamic path optimization unit uses real-time meteorological data to correct the pollution diffusion path and generate accurate pollution source location information.

[0020] The pollution source location analysis module also includes:

[0021] A linear relationship model is established based on the pollutant concentration matrix and the source configuration matrix, and the contribution value of the pollution source is calculated by minimizing the error matrix.

[0022] By combining real-time meteorological and topographic data, the parameters of the diffusion model are corrected to improve the accuracy of source location.

[0023] The response control module includes:

[0024] Early warning triggering unit: generates a pollution anomaly signal when the pollutant concentration exceeds a preset threshold;

[0025] Instruction generation unit: Generates control instructions based on pollution source location results, including instructions for production restriction, production shutdown, or dust suppression by water spraying;

[0026] Feedback execution unit: controls sprinkler trucks, spray devices or drones to carry out dust suppression or pollution interception operations.

[0027] The visualization module supports at least one of the following display methods:

[0028] Spatial distribution heatmap of pollutant concentrations;

[0029] Pollution source location marking and diffusion path animation;

[0030] Real-time alerts and historical data query interface;

[0031] Icons representing the execution status and effectiveness evaluation of control commands.

[0032] A rapid location and response method for pollution sources at bulk cargo terminals based on meteorological sensor networks includes the following steps:

[0033] Step S1: Collect meteorological data and pollutant concentration data of the bulk cargo terminal area through a meteorological sensor network;

[0034] Step S2: Format the collected data into a uniform format, and transmit and store it in a local database via cloud or wired network;

[0035] Step S3: Read standardized pollution data from the database, perform source tracing analysis of pollutants based on physical models and machine learning algorithms, and simulate diffusion paths;

[0036] Step S4: Generate pollution control instructions based on the pollution source location results and execute response operations;

[0037] Step S5: Visualize the location of the pollution source, the diffusion path, the early warning information, and the control results.

[0038] The specific steps of step S2 are as follows:

[0039] Step P1, Data Standardization:

[0040] Multi-source data from laser scanning radar, air quality monitoring unit, meteorological sensing unit, and UAV remote sensing unit are uniformly converted into a preset standardized data format; the standardized data format includes data acquisition timestamp, device geographic coordinates, sensor type, numerical value, and unit fields;

[0041] Step P2, Data Quality Control:

[0042] The standardized data undergoes preprocessing, which includes: identifying and removing outliers using the Z-score method or interquartile range method; filling in missing data using time-series interpolation or correlation analysis based on meteorological elements; and performing smoothing filtering on the data to reduce random noise interference.

[0043] Step P3, Data Storage and Indexing:

[0044] The data that has passed quality control will be marked with quality tags and transmitted to the cloud platform or local database; a joint index based on timestamps and spatial coordinates will be built in the database to support efficient spatiotemporal queries in the subsequent traceability analysis module.

[0045] The specific steps of step S3 are as follows:

[0046] Step T1, data reading and feature extraction: Retrieve formatted pollutant detection data and related meteorological parameters from the database; specific steps are as follows:

[0047] Input data: Time series data of pollutant concentrations at each monitoring point, wind speed, wind direction, temperature, air pressure, and static data such as dock topography and building layout;

[0048] Feature extraction: Extracting statistical features of pollutant concentrations, such as mean, peak value, and gradient changes;

[0049] Convert wind speed and wind direction into wind vectors (u, v);

[0050] By combining geographic information system (GIS) data, monitoring points are spatially associated with potential pollution sources, such as loading and unloading machines, storage yards, and conveyor belts;

[0051] T2 step, physical diffusion simulation: This step uses physical laws to simulate the diffusion behavior of pollutants, providing physical constraints and prior knowledge for machine learning models. Specifically, it includes:

[0052] Core Model: A Gaussian plume model or a Gaussian smoke cloud model is adopted. For continuous point sources, the formula for the Gaussian plume model is as follows: ,in Let be the pollutant concentration at a location x meters downwind, y meters crosswind, and z meters high; Q be the emission velocity of the pollution source, i.e., the source strength; u be the average wind speed; and He be the effective emission height of the pollution source. , These are the diffusion parameters in the horizontal and vertical directions, respectively, and are functions of atmospheric stability and downwind distance x;

[0053] Concentration at multiple downstream monitoring points is known. In such cases, the most probable source location can be inferred by using the least squares method or an optimization algorithm. He Yuanqiang Q;

[0054] Step T3: Pollution Source Contribution Analysis and Probabilistic Localization Based on Machine Learning: This step utilizes data-driven methods to learn the spatiotemporal patterns of pollution propagation in complex environments, compensating for the shortcomings of purely physical models in dealing with non-ideal conditions, such as complex wind fields and building disturbances.

[0055] Pollution source contribution analysis and probabilistic localization are performed using spatiotemporal graph neural networks (STGNN) or Bayesian networks.

[0056] When constructing the graph structure using the Spatiotemporal Graph Neural Network (STGNN), each monitoring sensor is treated as a node in the graph. The STGNN simultaneously captures the spatial dependence and temporal evolution of node features, thereby inferring which node's upstream is most likely a pollution source and providing the source's contribution.

[0057] When using Bayesian inference, the location and intensity of the pollution source are treated as random variables, and their posterior probabilities are calculated based on Bayes' theorem. ,in This is the posterior probability, which is the probability of the pollution source given the known observation data. Let be the likelihood function, representing the probability of obtaining the current observation data given the pollution source; The prior probability represents prior knowledge about the likelihood of a pollution source before any data is available. This serves as evidence, specifically the marginal likelihood, which represents the average probability of observed data under all possible pollution source scenarios.

[0058] The T4 step, model fusion, and dynamic path optimization fuse the results of the physical model and the machine learning model, and use real-time data for dynamic correction to obtain the final positioning result and diffusion path.

[0059] Establish a linear system to quantify the contribution of multiple potential pollution sources to each monitoring point; Let C be an m×1 vector representing the pollutant concentration measurements at m monitoring points; S be an n×1 vector representing the emission intensity of n potential pollution sources, i.e., the source contribution value to be determined; and A be an m×n source configuration matrix, where the matrix elements are... The coefficient represents the contribution of the j-th pollution source to the concentration at the i-th monitoring point. This coefficient is usually calculated by a physical diffusion model and includes information on wind speed, wind direction, distance, and atmospheric stability; E is an m×1 error vector.

[0060] The source contribution value S is solved by minimizing the error matrix E using the least squares method. The solution is:

[0061] Then, by using real-time meteorological and terrain data, the parameters in the source configuration matrix A are dynamically updated, thereby realizing real-time correction of the diffusion path and the original positioning results and improving positioning accuracy.

[0062] The beneficial effects of this invention are: This invention integrates multi-source sensing devices to achieve comprehensive acquisition of three-dimensional spatial data of atmospheric particulate matter and various pollutant concentration meters. By integrating physical diffusion models and machine learning algorithms, it can comprehensively utilize meteorological data and pollutant concentration data to achieve rapid source tracing and probabilistic location of pollution sources. This hybrid modeling method overcomes the limitations of traditional single models in complex environments and significantly improves the accuracy and robustness of location. Attached Figure Description

[0063] Figure 1 This is a framework diagram of the rapid location and response system for pollution sources at bulk cargo terminals based on meteorological sensor networks, as described in this invention.

[0064] The following will describe in detail, with reference to the accompanying drawings, embodiments of the invention. Detailed Implementation

[0065] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0066] A rapid location and response system for pollution sources at bulk cargo terminals based on meteorological sensor networks includes:

[0067] Data acquisition module: Acquires meteorological data and pollutant concentration data through a pre-set meteorological sensor network, wherein the meteorological data includes at least wind speed, wind direction, temperature, humidity and atmospheric pressure;

[0068] The data acquisition module includes a laser scanning radar unit: used to acquire atmospheric particulate matter data and its spatiotemporal distribution characteristics;

[0069] Air quality monitoring unit: used to collect concentration data of one or more pollutants among PM2.5, PM10, SO2, NOx, and O3;

[0070] Meteorological sensing unit: Consists of one or more integrated meteorological stations, using ultrasonic anemometers, temperature sensors, relative humidity sensors, and atmospheric pressure sensors to collect data on wind speed, wind direction, temperature, humidity, and air pressure;

[0071] Unmanned aerial vehicle (UAV) remote sensing unit: used to collect three-dimensional spatial data when pollution anomalies are detected.

[0072] Data transmission module: Formats meteorological data and pollutant concentration data into a unified data format according to preset format requirements, and stores them through the cloud or local database;

[0073] Pollution source location analysis module: It obtains unified data from the database and performs pollution source location and diffusion path simulation by combining physical diffusion models with machine learning algorithms;

[0074] The pollution source location analysis module includes:

[0075] Physical diffusion model unit: Simulates pollutant transport paths based on Gaussian diffusion or inverse diffusion models;

[0076] Machine learning source tracing unit: Analyzes and probabilistically locates pollution source contribution through spatiotemporal graph neural networks or Bayesian networks;

[0077] The dynamic path optimization unit uses real-time meteorological data to correct the pollution diffusion path and generate accurate pollution source location information.

[0078] A linear relationship model is established based on the pollutant concentration matrix and the source configuration matrix, and the contribution value of the pollution source is calculated by minimizing the error matrix.

[0079] By combining real-time meteorological and topographic data, the parameters of the diffusion model are corrected to improve the accuracy of source location.

[0080] Response control module: Generates control instructions based on pollution source location results, and triggers early warnings or executes pollution control operations;

[0081] The response control module includes:

[0082] Early warning triggering unit: generates a pollution anomaly signal when the pollutant concentration exceeds a preset threshold;

[0083] Instruction generation unit: Generates control instructions based on pollution source location results, including instructions for production restriction, production shutdown, or dust suppression by water spraying;

[0084] Feedback execution unit: controls sprinkler trucks, spray devices or drones to carry out dust suppression or pollution interception operations.

[0085] Visualization module: Displays the location of pollution sources, diffusion paths, early warning information, and control results on the interface in real time.

[0086] The visualization module supports at least one of the following display methods:

[0087] Spatial distribution heatmap of pollutant concentrations;

[0088] Pollution source location marking and diffusion path animation;

[0089] Real-time alerts and historical data query interface;

[0090] Icons representing the execution status and effectiveness evaluation of control commands.

[0091] A rapid location and response method for pollution sources at bulk cargo terminals based on meteorological sensor networks includes the following steps:

[0092] Step S1: Collect meteorological data and pollutant concentration data of the bulk cargo terminal area through a meteorological sensor network;

[0093] Step S2: Format the collected data into a uniform format and transmit it via cloud or wired network and store it in a local database; Step P1: Data standardization:

[0094] Multi-source data from laser scanning radar, air quality monitoring unit, meteorological sensing unit, and UAV remote sensing unit are uniformly converted into a preset standardized data format; the standardized data format includes data acquisition timestamp, device geographic coordinates, sensor type, numerical value, and unit fields;

[0095] Step P2, Data Quality Control:

[0096] The standardized data undergoes preprocessing, which includes: identifying and removing outliers using the Z-score method or interquartile range method; filling in missing data using time-series interpolation or correlation analysis based on meteorological elements; and performing smoothing filtering on the data to reduce random noise interference.

[0097] Step P3, Data Storage and Indexing:

[0098] The data that has passed quality control will be marked with quality tags and transmitted to the cloud platform or local database; a joint index based on timestamps and spatial coordinates will be built in the database to support efficient spatiotemporal queries in the subsequent traceability analysis module.

[0099] Step S3: Read standardized pollution data from the database, perform source tracing analysis of pollutants based on physical models and machine learning algorithms, and simulate diffusion paths;

[0100] The specific steps of step S3 are as follows:

[0101] Step T1, data reading and feature extraction: Retrieve formatted pollutant detection data and related meteorological parameters from the database; specific steps are as follows:

[0102] Input data: Time series data of pollutant concentrations at each monitoring point, wind speed, wind direction, temperature, air pressure, and static data such as dock topography and building layout;

[0103] Feature extraction: Extracting statistical features of pollutant concentrations, such as mean, peak value, and gradient changes;

[0104] Convert wind speed and wind direction into wind vectors (u, v);

[0105] By combining geographic information system (GIS) data, monitoring points are spatially associated with potential pollution sources, such as loading and unloading machines, storage yards, and conveyor belts;

[0106] T2 step, physical diffusion simulation: This step uses physical laws to simulate the diffusion behavior of pollutants, providing physical constraints and prior knowledge for machine learning models. Specifically, it includes:

[0107] Core Model: A Gaussian plume model or a Gaussian smoke cloud model is adopted. For continuous point sources, the formula for the Gaussian plume model is as follows: ,in Let be the pollutant concentration at a location x meters downwind, y meters crosswind, and z meters high; Q be the emission velocity of the pollution source, i.e., the source strength; u be the average wind speed; and He be the effective emission height of the pollution source. , These are the diffusion parameters in the horizontal and vertical directions, respectively, and are functions of atmospheric stability and downwind distance x;

[0108] Concentration at multiple downstream monitoring points is known. In such cases, the most probable source location can be inferred by using the least squares method or an optimization algorithm. He Yuanqiang Q;

[0109] Step T3: Pollution Source Contribution Analysis and Probabilistic Localization Based on Machine Learning: This step utilizes data-driven methods to learn the spatiotemporal patterns of pollution propagation in complex environments, compensating for the shortcomings of purely physical models in dealing with non-ideal conditions, such as complex wind fields and building disturbances.

[0110] Pollution source contribution analysis and probabilistic localization are performed using spatiotemporal graph neural networks (STGNN) or Bayesian networks.

[0111] When constructing the graph structure using the Spatiotemporal Graph Neural Network (STGNN), each monitoring sensor is treated as a node in the graph. The STGNN simultaneously captures the spatial dependence and temporal evolution of node features, thereby inferring which node's upstream is most likely a pollution source and providing the source's contribution.

[0112] When using Bayesian inference, the location and intensity of the pollution source are treated as random variables, and their posterior probabilities are calculated based on Bayes' theorem. ,in This is the posterior probability, which is the probability of the pollution source given the known observation data. Let be the likelihood function, representing the probability of obtaining the current observation data given the pollution source; The prior probability represents prior knowledge about the likelihood of a pollution source before any data is available. This serves as evidence, specifically the marginal likelihood, which represents the average probability of observed data under all possible pollution source scenarios.

[0113] The T4 step, model fusion, and dynamic path optimization fuse the results of the physical model and the machine learning model, and use real-time data for dynamic correction to obtain the final positioning result and diffusion path.

[0114] Establish a linear system to quantify the contribution of multiple potential pollution sources to each monitoring point; Let C be an m×1 vector representing the pollutant concentration measurements at m monitoring points; S be an n×1 vector representing the emission intensity of n potential pollution sources, i.e., the source contribution value to be determined; and A be an m×n source configuration matrix, where the matrix elements are... The coefficient represents the contribution of the j-th pollution source to the concentration at the i-th monitoring point. This coefficient is usually calculated by a physical diffusion model and includes information on wind speed, wind direction, distance, and atmospheric stability; E is an m×1 error vector.

[0115] The source contribution value S is solved by minimizing the error matrix E using the least squares method. The solution is:

[0116] Then, by using real-time meteorological and terrain data, the parameters in the source configuration matrix A are dynamically updated, thereby realizing real-time correction of the diffusion path and the original positioning results and improving positioning accuracy.

[0117] Step S4: Generate pollution control instructions based on the pollution source location results and execute response operations;

[0118] Step S5: Visualize the location of the pollution source, the diffusion path, the early warning information, and the control results.

[0119] Example 1

[0120] Step S1 involves deploying the necessary meteorological sensor network, air quality monitoring network, and mobile monitoring units for pollution monitoring at the bulk cargo terminal.

[0121] Deployment of meteorological sensor network: Install at least four integrated meteorological stations in key areas such as the bulk cargo terminal's storage yard, loading and unloading area, wharf front and port boundary.

[0122] Air quality monitoring network deployment: Miniature air quality monitoring stations will be deployed upwind and downwind of the prevailing wind direction at sensitive locations along the dock to monitor PM2.5, PM10, SO2, and NO in real time. X O3 concentration. An atmospheric particulate matter laser scanning radar will be installed at the highest point of the wharf, with a scanning range that can cover the entire port area and major storage yards, to obtain data on the vertical distribution and spatiotemporal migration of particulate matter.

[0123] Configuration of the mobile monitoring unit: An automatic take-off and landing hangar for unmanned aerial vehicles (UAVs) is set up in the central area of ​​the dock.

[0124] Response facilities: The terminal's intelligent sprinkler trucks, yard spray guns, conveyor belt spraying systems and other dust suppression equipment will be upgraded with the Internet of Things, and PLC control modules and communication modules will be added to enable them to receive remote commands.

[0125] Communication and computing infrastructure: Deploy 5G / 4G or industrial Wi-Fi within the port area to ensure low-latency, high-reliability connectivity for all sensing devices and actuators; deploy high-performance servers locally at the dock or in the cloud to run databases, physical models, and machine learning algorithms.

[0126] S2 steps, data processing and transmission:

[0127] Write a data formatting program to add a uniform timestamp, device ID, and geographic coordinate label to all data, and convert it into the system-defined JSON format.

[0128] A time-series database is used to store real-time data streams from sensors, facilitating quick queries of time-series changes. A relational database is used to store structured data such as device information, model parameters, and historical events, and PostGIS extensions are used to support spatial queries.

[0129] Step S3 involves source tracing analysis of pollutants and simulation of diffusion pathways:

[0130] The physical diffusion model configuration incorporates a Gaussian plume model within the system, setting underlying surface parameters based on GIS data from the wharf. Driven by monitored wind speed, direction, and atmospheric stability, the model pre-determines all possible work points within the wharf as potential pollution sources. Based on the current northwest wind of 3.5 m / s, the physical diffusion model unit initially simulates that the pollution may originate from area A of the storage yard. A spatiotemporal graph neural network (STGNN) is developed and trained, defining each monitoring point as a node in the graph. Node features include current pollutant concentration and meteorological data. Historical pollution event data from the wharf are collected to train the network, enabling it to infer the pollution source location from complex spatiotemporal data patterns. After analyzing the data, the STGNN model provides a high-probability point, also indicating the reclaimer work point in area A of the storage yard. The "probability distribution map" output by the STGNN and the "source strength contribution value" output by the physical model are weighted and fused to generate a final, more accurate pollution source location result. This confirms the pollution source as reclaimer No. 3 in area A of the storage yard and simulates the path of dust diffusion downwind towards the wharf office area.

[0131] Step S4: Generate pollution control instructions based on the pollution source location results and execute response operations:

[0132] Anomaly Trigger: A fixed monitoring station located downwind of the storage yard detected a PM10 concentration that increased from 80 μg / m³ within 5 minutes. 3 It continued to rise to 150 μg / m 3 Exceeding the preset threshold of 120 μg / m 3 The response control module's early warning trigger unit immediately generates a "pollution anomaly signal," along with the time, location, and exceedance data.

[0133] Intelligent Response: Based on the positioning results, the command generation unit automatically generates two control commands:

[0134] Command 1: Send a command to the PLC of material handling machine No. 3 to immediately start its built-in spray dust suppression system.

[0135] Command 2: Issue a task to the nearest water truck No. 1, planning a route to spray water on the downwind road of area A of the storage yard to suppress dust and prevent its spread.

[0136] The feedback execution unit issues instructions and monitors the execution status of the sprinkler truck and spray system in real time.

[0137] S5 Steps, Visual Display: A red warning prompt box automatically pops up on the monitoring screen of the visualization display module. On the electronic map, the location of the pollution source (No. 3 material reclaimer) is highlighted with a red icon. A dynamic pollution diffusion path animation begins to play, pointing to the office area. The icon of the sprinkler truck begins to move and displays the "Executing" status.

[0138] The invention has been described above with reference to the accompanying drawings. Obviously, the specific implementation of the invention is not limited to the above-described manner. Any improvements made using the inventive concept and technical solution, or direct application to other situations without modification, are all within the scope of protection of the invention.

Claims

1. A rapid location and response system for pollution sources at bulk cargo terminals based on meteorological sensor networks, characterized in that, include: Data acquisition module: Acquires meteorological data and pollutant concentration data through a pre-set meteorological sensor network, wherein the meteorological data includes at least wind speed, wind direction, temperature, humidity and atmospheric pressure; Data transmission module: Formats meteorological data and pollutant concentration data into a unified data format according to preset format requirements, and stores them through the cloud or local database; Pollution source location analysis module: It obtains unified data from the database and performs pollution source location and diffusion path simulation by combining physical diffusion models with machine learning algorithms; Response control module: Generates control instructions based on pollution source location results, and triggers early warnings or executes pollution control operations; Visualization module: Displays the location of pollution sources, diffusion paths, early warning information, and control results on the interface in real time.

2. The rapid location and response system for pollution sources at bulk cargo terminals based on meteorological sensor networks according to claim 1, characterized in that, The data acquisition module includes: Laser scanning radar unit: used to acquire atmospheric particulate matter data and its spatiotemporal distribution characteristics; Air quality monitoring unit: used to collect concentration data of one or more pollutants among PM2.5, PM10, SO2, NOx, and O3; Meteorological sensing unit: Consists of one or more integrated meteorological stations, using ultrasonic anemometers, temperature sensors, relative humidity sensors, and atmospheric pressure sensors to collect data on wind speed, wind direction, temperature, humidity, and air pressure; Unmanned aerial vehicle (UAV) remote sensing unit: used to collect three-dimensional spatial data when pollution anomalies are detected.

3. The rapid location and response system for pollution sources at bulk cargo terminals based on meteorological sensor networks according to claim 1, characterized in that, The pollution source location analysis module includes: Physical diffusion model unit: Simulates pollutant transport paths based on Gaussian diffusion or inverse diffusion models; Machine learning source tracing unit: Analyzes and probabilistically locates pollution source contribution through spatiotemporal graph neural networks or Bayesian networks; The dynamic path optimization unit uses real-time meteorological data to correct the pollution diffusion path and generate accurate pollution source location information.

4. The rapid location and response system for pollution sources at bulk cargo terminals based on meteorological sensor networks according to claim 3, characterized in that, The pollution source location analysis module also includes: A linear relationship model is established based on the pollutant concentration matrix and the source configuration matrix, and the contribution value of the pollution source is calculated by minimizing the error matrix. By combining real-time meteorological and topographic data, the parameters of the diffusion model are corrected to improve the accuracy of source location.

5. The rapid location and response system for pollution sources at bulk cargo terminals based on meteorological sensor networks according to claim 1, characterized in that, The response control module includes: Early warning triggering unit: generates a pollution anomaly signal when the pollutant concentration exceeds a preset threshold; Instruction generation unit: Generates control instructions based on pollution source location results, including instructions for production restriction, production shutdown, or dust suppression by water spraying; Feedback execution unit: controls sprinkler trucks, spray devices or drones to carry out dust suppression or pollution interception operations.

6. The rapid location and response system for pollution sources at bulk cargo terminals based on meteorological sensor networks according to claim 1, characterized in that, The visualization module supports at least one of the following display methods: Spatial distribution heatmap of pollutant concentrations; Pollution source location marking and diffusion path animation; Real-time alerts and historical data query interface; Icons representing the execution status and effectiveness evaluation of control commands.

7. A method for rapid location and response to pollution sources at bulk cargo terminals based on meteorological sensor networks, characterized in that, Includes the following steps: Step S1: Collect meteorological data and pollutant concentration data of the bulk cargo terminal area through a meteorological sensor network; Step S2: Format the collected data into a uniform format, and transmit and store it in a local database via cloud or wired network; Step S3: Read standardized pollution data from the database, perform source tracing analysis of pollutants based on physical models and machine learning algorithms, and simulate diffusion paths; Step S4: Generate pollution control instructions based on the pollution source location results and execute response operations; Step S5: Visualize the location of the pollution source, the diffusion path, the early warning information, and the control results.

8. A method for rapid location and response to pollution sources at bulk cargo terminals based on meteorological sensor networks, as described in claim 7, is characterized in that... The specific steps of step S2 are as follows: Step P1, Data Standardization: Multi-source data from laser scanning radar, air quality monitoring unit, meteorological sensing unit, and UAV remote sensing unit are uniformly converted into a preset standardized data format; the standardized data format includes data acquisition timestamp, device geographic coordinates, sensor type, numerical value, and unit fields; Step P2, Data Quality Control: The standardized data is preprocessed, including identifying and removing outliers using the Z-score method or the interquartile range method. Missing data were filled using time-series interpolation or correlation analysis based on meteorological elements; the data were smoothed and filtered to reduce random noise interference. Step P3, Data Storage and Indexing: The data that has passed quality control will be marked with quality tags and transmitted to the cloud platform or local database; a joint index based on timestamps and spatial coordinates will be built in the database to support efficient spatiotemporal queries in the subsequent traceability analysis module.

9. A method for rapid location and response to pollution sources at bulk cargo terminals based on meteorological sensor networks, as described in claim 8, is characterized in that... The specific steps of step S3 are as follows: Step T1, data reading and feature extraction: Retrieve formatted pollutant detection data and related meteorological parameters from the database; specific steps are as follows: Input data: Time series data of pollutant concentrations at each monitoring point, wind speed, wind direction, temperature, air pressure, and static data such as dock topography and building layout; Feature extraction: Extracting statistical features of pollutant concentrations, such as mean, peak value, and gradient changes; Convert wind speed and wind direction into wind vectors (u, v); By combining geographic information system (GIS) data, monitoring points are spatially associated with potential pollution sources, such as loading and unloading machines, storage yards, and conveyor belts; T2 step, physical diffusion simulation: This step uses physical laws to simulate the diffusion behavior of pollutants, providing physical constraints and prior knowledge for machine learning models. Specifically, it includes: Core Model: A Gaussian plume model or a Gaussian smoke cloud model is adopted. For continuous point sources, the formula for the Gaussian plume model is as follows: ,in Let be the pollutant concentration at a location x meters downwind, y meters crosswind, and z meters high; Q be the emission velocity of the pollution source, i.e., the source strength; u be the average wind speed; and He be the effective emission height of the pollution source. , These are the diffusion parameters in the horizontal and vertical directions, respectively, and are functions of atmospheric stability and downwind distance x; Concentration at multiple downstream monitoring points is known. In such cases, the most probable source location can be inferred by using the least squares method or an optimization algorithm. He Yuanqiang Q; Step T3: Pollution Source Contribution Analysis and Probabilistic Localization Based on Machine Learning: This step utilizes data-driven methods to learn the spatiotemporal patterns of pollution propagation in complex environments, compensating for the shortcomings of purely physical models in dealing with non-ideal conditions, such as complex wind fields and building disturbances. Pollution source contribution analysis and probabilistic localization are performed using spatiotemporal graph neural networks (STGNN) or Bayesian networks. When constructing the graph structure using the Spatiotemporal Graph Neural Network (STGNN), each monitoring sensor is treated as a node in the graph. The STGNN simultaneously captures the spatial dependence and temporal evolution of node features, thereby inferring which node's upstream is most likely a pollution source and providing the source's contribution. When using Bayesian inference, the location and intensity of the pollution source are treated as random variables, and their posterior probabilities are calculated based on Bayes' theorem. ,in This is the posterior probability, which is the probability of the pollution source given the known observation data. Let be the likelihood function, representing the probability of obtaining the current observation data given the pollution source; The prior probability represents prior knowledge about the likelihood of a pollution source before any data is available. This serves as evidence, specifically the marginal likelihood, which represents the average probability of observed data under all possible pollution source scenarios. The T4 step, model fusion, and dynamic path optimization fuse the results of the physical model and the machine learning model, and use real-time data for dynamic correction to obtain the final positioning result and diffusion path. Establish a linear system to quantify the contribution of multiple potential pollution sources to each monitoring point; Let C be an m×1 vector representing the pollutant concentration measurements at m monitoring points; S be an n×1 vector representing the emission intensity of n potential pollution sources, i.e., the source contribution value to be determined; and A be an m×n source configuration matrix, where the matrix elements are... The coefficient represents the contribution of the j-th pollution source to the concentration at the i-th monitoring point. This coefficient is usually calculated by a physical diffusion model and includes information on wind speed, wind direction, distance, and atmospheric stability; E is an m×1 error vector. The source contribution value S is solved by minimizing the error matrix E using the least squares method. The solution is: Then, by using real-time meteorological and terrain data, the parameters in the source configuration matrix A are dynamically updated, thereby realizing real-time correction of the diffusion path and the original positioning results and improving positioning accuracy.

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