Environmental pollution big data real-time monitoring and early warning system and method

Abstract

The present application relates to the technical field of environmental monitoring, in particular to an environmental pollution big data real-time monitoring and early warning system and method, wherein a data acquisition and access module realizes real-time acquisition of multi-source heterogeneous environmental data, covers fixed monitoring, mobile monitoring and third-party interface data, and ensures comprehensive data sources; a space-time fusion and simulation module realizes fusion of discrete data and surface source data by coupling a spatial interpolation algorithm and an atmospheric diffusion model, generates a pollutant concentration field and a diffusion trend field with space-time continuity, and improves data usability; a dynamic intelligent early warning decision module dynamically calculates early warning thresholds containing concentration thresholds and gradient thresholds through machine learning, simulates and deduces in combination with the diffusion trend field, generates structured early warning events, and realizes accurate early warning; a closed-loop response and optimization module automatically matches emergency plans, issues differentiated control instructions, collects feedback data for performance evaluation and feeds back an optimized threshold calculation model, and forms a complete closed loop.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, specifically to a real-time monitoring and early warning system and method for environmental pollution big data. Background Technology

[0002] With the acceleration of industrialization and urbanization, environmental pollution problems are becoming increasingly complex, and sudden and complex pollution incidents are occurring frequently, placing higher demands on real-time and accurate monitoring and early warning. Existing environmental monitoring and early warning systems suffer from fragmented technological paths and poor system coordination: data collection, processing, analysis, decision-making, and execution are often completed by independent systems, resulting in disconnected technological pathways and the formation of "data silos" and "decision silos." There is a lack of a complete technological closed loop from perception to cognition to action.

[0003] Therefore, there is an urgent need for a new environmental pollution monitoring and early warning technology that can deeply integrate multi-source data, achieve dynamic intelligent early warning, and enable closed-loop optimization and iteration. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time monitoring and early warning system and method for environmental pollution big data, so as to solve the technical problems mentioned in the background.

[0005] To achieve the above objectives, the first technical solution provided by this invention is as follows: Real-time monitoring and early warning system and methods for environmental pollution big data, including The data acquisition and access module is used to acquire multi-source heterogeneous environmental data in real time from fixed monitoring sensor networks, mobile monitoring units and third-party data interfaces. The environmental data includes pollutant concentration data, pollution source operating data, meteorological parameters and geographic information. The spatiotemporal fusion and simulation module is used to clean and spatiotemporally align multi-source heterogeneous environmental data, and to fuse discrete point monitoring data and area source meteorological data through coupled spatial interpolation algorithms and atmospheric diffusion models to generate a pollutant concentration field and diffusion trend field with spatiotemporal continuity within the target area. The dynamic intelligent early warning decision-making module includes a dynamic threshold management unit and an early warning analysis and generation unit. The dynamic threshold management unit is used to dynamically calculate the early warning thresholds for different spatial locations within a preset time period based on machine learning and diffusion trend fields. The early warning thresholds include a concentration threshold reflecting instantaneous exceedance and a gradient threshold reflecting deterioration trends. The early warning analysis and generation unit is used to compare the pollutant concentration field with the dynamic early warning thresholds in real time and introduce the diffusion trend field to simulate and extrapolate pollution events. When the threshold triggering conditions are met and the extrapolation results conform to the preset diffusion pattern, a structured early warning event is generated, which includes the early warning level, dominant pollutant, core impact area, expected evolution, and source tracing direction. The closed-loop response and optimization module is used to automatically match emergency plans according to the structured early warning events, issue differentiated control instructions to designated terminals, collect environmental feedback data and handling reports after the instructions are executed, compare and analyze the feedback data with the early warning simulation results, generate early warning effectiveness evaluation results, and feed the evaluation results back to the dynamic threshold management unit for adaptive optimization of the threshold calculation model.

[0006] Preferably, the data acquisition and access module includes a ubiquitous sensing layer, a wide-area access layer, and an edge preprocessing unit. The ubiquitous sensing layer consists of a fixed monitoring sensor network deployed within the monitoring area and a mobile monitoring unit deployed on a mobile basis. The wide-area access layer is used to access and aggregate multi-source external data through a secure data gateway protocol. The multi-source external data includes refined grid weather forecasts and real-time data from meteorological departments' APIs, real-time traffic flow and congestion data from traffic management departments' road networks, online monitoring and operating data of key pollution sources from ecological and environmental departments' ecological and environmental departments' online monitoring and operating data, and satellite remote sensing inversion atmospheric parameters and water spectral data from remote sensing platforms' remote sensing platforms. The edge preprocessing unit is built into the key nodes of the ubiquitous sensing layer and the wide-area access layer, respectively. It is used to perform localized preprocessing before data is uploaded, including timestamp calibration, unit unification, real-time calibration based on device calibration curves, and preliminary outlier filtering based on rule engine, to generate a standardized data stream with quality identifiers.

[0007] Preferably, the fixed monitoring sensor network includes a gridded monitoring system consisting of a high-precision reference station and a densely deployed network of air quality micro-stations, water quality monitoring buoys, noise sensors, and characteristic pollutant monitors; The mobile monitoring unit includes a drone equipped with multi-parameter detection equipment, a vehicle-mounted mobile monitoring platform, and a handheld inspection terminal, used for adaptive mobile monitoring and source tracing sampling in fixed monitoring blind spots, sudden pollution areas, and the vicinity of key pollution sources.

[0008] Preferably, the spatiotemporal fusion and simulation module includes a data preprocessing unit, a multi-source fusion computing unit, and a simulation and deduction unit; The data preprocessing unit is used to perform secondary cleaning and spatiotemporal gridding processing on the standardized data stream from the data acquisition and access module; The multi-source fusion computing unit is used to perform fusion calculations and couple a spatial interpolation algorithm with an atmospheric diffusion model. The spatial interpolation algorithm is used to generate an initial concentration distribution field of pollutants on a three-dimensional spatiotemporal grid based on measured data from a fixed monitoring sensor network. The atmospheric diffusion model uses refined grid meteorological data to simulate the advection, diffusion, and deposition processes of pollutants. The coupling process is as follows: the initial concentration distribution field is used as the initial field and source strength constraint of the atmospheric diffusion model, and the mobile monitoring unit and satellite remote sensing inversion data are iteratively assimilated to correct the simulation parameters, thereby generating a pollutant concentration field with high spatiotemporal resolution. The simulation and extrapolation unit is used to drive the atmospheric diffusion model to perform forward simulations for a future preset period based on the current pollutant concentration field, real-time meteorological field, and preset pollution source intensity scenario library, thereby generating a diffusion trend field that reflects the spatiotemporal evolution of pollutant concentration.

[0009] Preferably, the closed-loop response and optimization module includes a plan matching and instruction generation unit, a multi-channel instruction distribution unit, and a feedback learning unit; The contingency plan matching and instruction generation unit is used to analyze the warning level, core impact area and source tracing information in the structured early warning event, match the optimal response plan from the digital emergency plan library, and generate an executable differentiated control instruction set based on real-time road network data and law enforcement resource distribution data. The instruction set includes suggestions for production restriction and shutdown for suspected pollution sources, traffic diversion plans for the affected area, and health protection guidelines for the public. The multi-channel instruction distribution unit is used to synchronously distribute differentiated control instruction sets to the corresponding terminal execution units through a dedicated communication protocol. The terminal execution units include an enterprise operating condition monitoring system, a traffic signal control platform, a public information release platform, and a mobile law enforcement terminal. The feedback learning unit is used to continuously collect and aggregate instruction confirmation receipts, on-site handling reports, and environmental feedback data obtained by the data acquisition and access module after instruction execution from the terminal execution unit. By comparing the environmental feedback data with the expected evolution in the early warning simulation results, the accuracy of the early warning and the effectiveness of the handling measures are quantitatively evaluated, the early warning effectiveness evaluation result is generated, and the machine learning model parameters in the dynamic threshold management unit are incrementally trained and adjusted based on the result.

[0010] To achieve the above objectives, the second technical solution provided by the present invention is as follows: A real-time monitoring and early warning method for environmental pollution based on big data, which provides early warning based on a monitoring and early warning system, specifically includes the following steps: S1. Multi-source data synchronous acquisition and fusion field construction: Real-time acquisition of fixed-point monitoring data, mobile monitoring data, meteorological grid data and pollution source operating condition data of the target area; After spatiotemporal alignment and cleaning of the data, data fusion and assimilation are performed by coupling spatial interpolation algorithm and atmospheric diffusion physical model to generate pollutant concentration field at the current moment, and based on this, the model is driven to perform forward simulation of the future preset time period to generate diffusion trend field. S2. Dynamic early warning threshold calculation: Using the diffusion trend field and real-time meteorological field as the core inputs, combined with historical assimilation data, a machine learning model is used to predict the probability distribution of pollutant concentrations at different times and locations in the future; and an environmental capacity dynamic assessment model is introduced to correct the probability distribution, generating a dynamic early warning threshold set that adapts to meteorological conditions and spatial location. The threshold set includes a concentration threshold and a gradient threshold. S3, Intelligent Early Warning Event Generation: The pollutant concentration field is compared with the dynamic early warning threshold set in real time. When the threshold is exceeded, the pollution event simulation is activated immediately. S4. Closed-loop response and model optimization: Based on the structured early warning event, match the emergency plan, generate and execute differentiated control instructions; after the instructions are executed, collect actual environmental response data, compare the actual environmental response data with the expected evolution in the simulation, and calculate the early warning accuracy and handling effectiveness indicators; based on these indicators, iteratively optimize the parameters of the machine learning model and the dynamic environmental capacity assessment model in step S2, and use the optimized model for subsequent threshold calculations.

[0011] Preferably, the simulation in step S3 includes: S31. Simulate the spatiotemporal evolution of pollutants based on the diffusion trend field, and conduct reverse source tracing analysis by combining pollution source conditions and geographical information. S32. Based on the comprehensive threshold breakthrough results, simulated evolution path and source tracing, a structured early warning event is generated, which includes quantitative risk level, core impact range, expected evolution trend and potential source contribution.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: The early warning system of this invention features collaborative operation among its modules, effectively solving the problems of single data sources, rigid early warning systems, and lack of optimization in traditional systems. It significantly improves the comprehensiveness, accuracy, and continuity of monitoring and early warning. Specifically, the data acquisition and access module enables real-time acquisition of multi-source heterogeneous environmental data, covering fixed monitoring, mobile monitoring, and third-party interface data, ensuring comprehensive data sources. The spatiotemporal fusion and simulation module, through coupling spatial interpolation algorithms and atmospheric diffusion models, achieves the fusion of discrete and area source data, generating a pollutant concentration field and diffusion trend field with spatiotemporal continuity, improving data usability. The dynamic intelligent early warning decision module uses machine learning to dynamically calculate early warning thresholds including concentration and gradient thresholds, and combines this with the diffusion trend field for simulation and deduction, generating structured early warning events for accurate early warning. The closed-loop response and optimization module automatically matches emergency plans, issues differentiated control instructions, collects feedback data for effectiveness evaluation, and feeds back optimized threshold calculation models, forming a complete closed loop. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the tunnel structure after the completion of the construction of this invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] like Figure 1 As shown: Embodiment 1 of the present invention is as follows: A real-time monitoring and early warning system and method for environmental pollution big data includes a data acquisition and access module for acquiring multi-source heterogeneous environmental data from fixed monitoring sensor networks, mobile monitoring units and third-party data interfaces in real time. The environmental data includes pollutant concentration data, pollution source operating condition data, meteorological parameters and geographic information. The spatiotemporal fusion and simulation module is used to clean and spatiotemporally align multi-source heterogeneous environmental data, and to fuse discrete point monitoring data and area source meteorological data through coupled spatial interpolation algorithms and atmospheric diffusion models to generate a pollutant concentration field and diffusion trend field with spatiotemporal continuity within the target area. The dynamic intelligent early warning decision-making module includes a dynamic threshold management unit and an early warning analysis and generation unit. The dynamic threshold management unit is used to dynamically calculate the early warning thresholds for different spatial locations within a preset time period based on machine learning and diffusion trend fields. The early warning thresholds include a concentration threshold reflecting instantaneous exceedance and a gradient threshold reflecting deterioration trends. The early warning analysis and generation unit is used to compare the pollutant concentration field with the dynamic early warning thresholds in real time and introduce the diffusion trend field to simulate and extrapolate pollution events. When the threshold triggering conditions are met and the extrapolation results conform to the preset diffusion pattern, a structured early warning event is generated, which includes the early warning level, dominant pollutant, core impact area, expected evolution, and source tracing direction. The closed-loop response and optimization module is used to automatically match emergency plans according to the structured early warning events, issue differentiated control instructions to designated terminals, collect environmental feedback data and handling reports after the instructions are executed, compare and analyze the feedback data with the early warning simulation results, generate early warning effectiveness evaluation results, and feed the evaluation results back to the dynamic threshold management unit for adaptive optimization of the threshold calculation model.

[0017] Specifically, the data acquisition and access module includes a ubiquitous sensing layer, a wide-area access layer, and an edge preprocessing unit. The ubiquitous sensing layer consists of a fixed monitoring sensor network deployed within the monitoring area and a mobile monitoring unit deployed on a mobile basis. The wide-area access layer is used to access and aggregate multi-source external data through a secure data gateway protocol. The multi-source external data includes refined grid weather forecasts and real-time data from meteorological departments' APIs, real-time traffic flow and congestion data from traffic management departments' road networks, online monitoring and operating data of key pollution sources from ecological and environmental departments' ecological and environmental departments' online monitoring and operating data, and satellite remote sensing inversion atmospheric parameters and water spectral data from remote sensing platforms' remote sensing platforms. The edge preprocessing unit is built into the key nodes of the ubiquitous sensing layer and the wide-area access layer, respectively. It is used to perform localized preprocessing before data is uploaded, including timestamp calibration, unit unification, real-time calibration based on device calibration curves, and preliminary outlier filtering based on rule engine, to generate a standardized data stream with quality identifiers.

[0018] As described above, the ubiquitous sensing layer consists of a fixed monitoring sensor network and mobile monitoring units. The wide-area access layer accesses multi-source external data, significantly expanding the data sources and improving the spatiotemporal coverage of the data. The edge preprocessing unit performs localized preprocessing before data upload, including timestamp calibration, unit unification, real-time calibration based on device calibration curves, and preliminary outlier filtering based on a rule engine. This generates a standardized data stream with quality identifiers, reducing the pressure on subsequent data processing, improving data quality, and laying a solid foundation for subsequent spatiotemporal fusion and early warning analysis. At the same time, the secure data gateway protocol ensures the security of external data access, preventing data leakage or tampering.

[0019] Specifically, the fixed monitoring sensor network includes a gridded monitoring system consisting of high-precision reference stations and densely deployed air quality micro-stations, water quality monitoring buoys, noise sensors, and characteristic pollutant monitors; The mobile monitoring unit includes a drone equipped with multi-parameter detection equipment, a vehicle-mounted mobile monitoring platform, and a handheld inspection terminal, used for adaptive mobile monitoring and source tracing sampling in fixed monitoring blind spots, sudden pollution areas, and the vicinity of key pollution sources.

[0020] As described above, the fixed monitoring sensor network consists of high-precision reference stations and densely gridded micro-stations, achieving comprehensive coverage and accurate monitoring of the target area. The mobile monitoring unit includes drones, vehicle-mounted platforms, and handheld terminals, enabling mobile monitoring and source tracing sampling of fixed monitoring blind spots, sudden pollution areas, and areas surrounding key pollution sources. The combination of fixed and mobile monitoring eliminates monitoring blind spots, improves the response speed to sudden pollution events, and enables pollution source tracing, providing support for precise handling.

[0021] Specifically, the spatiotemporal fusion and simulation module includes a data preprocessing unit, a multi-source fusion computing unit, and a simulation and deduction unit; The data preprocessing unit is used to perform secondary cleaning and spatiotemporal gridding processing on the standardized data stream from the data acquisition and access module; The multi-source fusion computing unit is used to perform fusion calculations and couple a spatial interpolation algorithm with an atmospheric diffusion model. The spatial interpolation algorithm is used to generate an initial concentration distribution field of pollutants on a three-dimensional spatiotemporal grid based on measured data from a fixed monitoring sensor network. The atmospheric diffusion model uses refined grid meteorological data to simulate the advection, diffusion, and deposition processes of pollutants. The coupling process is as follows: the initial concentration distribution field is used as the initial field and source strength constraint of the atmospheric diffusion model, and the mobile monitoring unit and satellite remote sensing inversion data are iteratively assimilated to correct the simulation parameters, thereby generating a pollutant concentration field with high spatiotemporal resolution. The simulation and extrapolation unit is used to drive the atmospheric diffusion model to perform forward simulations for a future preset period based on the current pollutant concentration field, real-time meteorological field, and preset pollution source intensity scenario library, thereby generating a diffusion trend field that reflects the spatiotemporal evolution of pollutant concentration.

[0022] As described above, the data preprocessing unit performs secondary cleaning and spatiotemporal gridding on the standardized data stream, further improving data quality. The multi-source fusion computing unit couples spatial interpolation algorithms with atmospheric diffusion models to generate an initial concentration distribution field from fixed monitoring data. It then combines meteorological data to simulate the pollutant diffusion process, iteratively assimilating parameters from mobile monitoring and remote sensing data to generate a high spatiotemporal resolution pollutant concentration field, improving its accuracy and refinement. The simulation and extrapolation unit, based on the current concentration field, meteorological field, and pollution source intensity scenario database, drives the model to perform forward simulation, generating a diffusion trend field and providing reliable data support for dynamic early warning and simulation extrapolation.

[0023] Specifically, the closed-loop response and optimization module includes a plan matching and instruction generation unit, a multi-channel instruction distribution unit, and a feedback learning unit; The contingency plan matching and instruction generation unit is used to analyze the warning level, core impact area and source tracing information in the structured early warning event, match the optimal response plan from the digital emergency plan library, and generate an executable differentiated control instruction set based on real-time road network data and law enforcement resource distribution data. The instruction set includes suggestions for production restriction and shutdown for suspected pollution sources, traffic diversion plans for the affected area, and health protection guidelines for the public. The multi-channel instruction distribution unit is used to synchronously distribute differentiated control instruction sets to the corresponding terminal execution units through a dedicated communication protocol. The terminal execution units include an enterprise operating condition monitoring system, a traffic signal control platform, a public information release platform, and a mobile law enforcement terminal. The feedback learning unit is used to continuously collect and aggregate instruction confirmation receipts, on-site handling reports, and environmental feedback data obtained by the data acquisition and access module after instruction execution from the terminal execution unit. By comparing the environmental feedback data with the expected evolution in the early warning simulation results, the accuracy of the early warning and the effectiveness of the handling measures are quantitatively evaluated, the early warning effectiveness evaluation result is generated, and the machine learning model parameters in the dynamic threshold management unit are incrementally trained and adjusted based on the result.

[0024] As described above, the contingency plan matching and instruction generation unit parses structured early warning event information, matches the optimal emergency plan, and generates differentiated control instruction sets based on road network data and law enforcement resource distribution to ensure the accuracy and executability of the instructions. The multi-channel instruction distribution unit synchronously distributes instructions to multiple terminal execution units via a dedicated communication protocol, ensuring rapid instruction delivery and improving response efficiency. The feedback learning unit collects instruction receipts, response reports, and environmental feedback data, compares and analyzes them to generate early warning effectiveness evaluation results, and incrementally trains and adjusts the machine learning model parameters of the dynamic threshold management unit to achieve adaptive optimization of the early warning model, forming a closed loop of "monitoring-early warning-response-optimization" to continuously improve system performance.

[0025] A real-time monitoring and early warning method for environmental pollution based on big data, which provides early warning based on a monitoring and early warning system, specifically includes the following steps: S1. Multi-source data synchronous acquisition and fusion field construction: Real-time acquisition of fixed-point monitoring data, mobile monitoring data, meteorological grid data and pollution source operating condition data of the target area; After spatiotemporal alignment and cleaning of the data, data fusion and assimilation are performed by coupling spatial interpolation algorithm and atmospheric diffusion physical model to generate pollutant concentration field at the current moment, and based on this, the model is driven to perform forward simulation of the future preset time period to generate diffusion trend field. S2. Dynamic early warning threshold calculation: Using the diffusion trend field and real-time meteorological field as the core inputs, combined with historical assimilation data, a machine learning model is used to predict the probability distribution of pollutant concentrations at different times and locations in the future; and an environmental capacity dynamic assessment model is introduced to correct the probability distribution, generating a dynamic early warning threshold set that adapts to meteorological conditions and spatial location. The threshold set includes a concentration threshold and a gradient threshold. S3, Intelligent Early Warning Event Generation: The pollutant concentration field is compared with the dynamic early warning threshold set in real time. When the threshold is exceeded, the pollution event simulation is activated immediately. S4. Closed-loop response and model optimization: Based on the structured early warning event, match the emergency plan, generate and execute differentiated control instructions; after the instructions are executed, collect actual environmental response data, compare the actual environmental response data with the expected evolution in the simulation, and calculate the early warning accuracy and handling effectiveness indicators; based on these indicators, iteratively optimize the parameters of the machine learning model and the dynamic environmental capacity assessment model in step S2, and use the optimized model for subsequent threshold calculations.

[0026] Specifically, the simulation in step S3 includes: S31. Simulate the spatiotemporal evolution of pollutants based on the diffusion trend field, and conduct reverse source tracing analysis by combining pollution source conditions and geographical information. S32. Based on the comprehensive threshold breakthrough results, simulated evolution path and source tracing, a structured early warning event is generated, which includes quantitative risk level, core impact range, expected evolution trend and potential source contribution.

[0027] As described above, step S31 simulates the spatiotemporal evolution of pollutants based on the diffusion trend field, and conducts reverse source tracing analysis by combining pollution source conditions and geographical information to clarify the pollution source; step S32 integrates the threshold breakthrough results, evolution path, and source tracing direction to generate a structured early warning event that includes quantitative risk level, core impact range, expected evolution trend, and potential source contribution. This makes the early warning information more comprehensive and accurate, providing detailed basis for subsequent differentiated control and precise handling, and enhancing the practical value of the early warning event.

[0028] The actual application examples of the above-mentioned early warning system are as follows: A Case Study of a Real-Time Monitoring and Early Warning System for Regional Complex Atmospheric Pollution Assuming this system is applied to an industrial park and surrounding urban area (target area), the main monitoring and early warning targets are atmospheric compound pollutants such as PM2.5, O3, and VOCs.

[0029] I. System Overall Architecture and Data Flow This system is deployed on a cloud platform and adopts a microservice architecture. Its core workflow is as follows: Data inflow: Various types of data are continuously fed in through the data acquisition and access module.

[0030] Core processing: The data enters the spatiotemporal fusion and simulation module to generate the "current pollutant concentration field" and the "diffusion trend field for the next 6 hours".

[0031] Intelligent decision-making: The dynamic intelligent early warning decision-making module uses the above-mentioned "field" data to perform dynamic threshold calculation and analysis, and generate early warning events.

[0032] Response optimization: The closed-loop response and optimization module executes response instructions, collects feedback, and optimizes the model.

[0033] II. Detailed Implementation Methods of Each Module 1. Specific implementation of the data acquisition and access module: Ubiquitous sensing layer: Deploy approximately 50 air quality micro-stations (grid-based monitoring system) at the park boundary, internal roads, and sensitive points (schools, residential areas) to form a basic monitoring network.

[0034] Two high-precision reference stations were set up upwind and downwind of the prevailing wind direction in the park to calibrate the micro-station data.

[0035] Equipped with three vehicle-mounted mobile monitoring platforms, it patrols along preset and dynamic routes to monitor traffic pollution and abnormal emissions.

[0036] Equipped with two drones and a portable mass spectrometer, the facility conducts vertical profile monitoring and source tracing sampling over the factory area and around the chimneys when it receives instructions or detects anomalies.

[0037] Wide Area Access Layer: Every 10 minutes, the meteorological bureau provides 1 km × 1 km grid meteorological data (wind, temperature, humidity, pressure, and mixed layer height) through the government data gateway.

[0038] Access traffic control center data to obtain real-time average vehicle speed and traffic flow on major roads.

[0039] Access the CEMS (Continuous Emission Monitoring System) data of key polluting enterprises and the operating status signals of major production equipment (such as fan load and reactor temperature).

[0040] Access remote sensing data from the Satellite Environment Application Center of the Ministry of Ecology and Environment to obtain products such as aerosol optical depth (AOD).

[0041] Edge Preprocessing Unit: Within the processor embedded in each microstation, a preprocessing program runs: converting the sensor's raw voltage / current signal into concentration values ​​using the latest calibration curve; synchronizing with the built-in GPS clock using timestamps; marking suspicious data according to rules (e.g., five consecutive values ​​exceeding three times the standard deviation) and attaching a device status code. Finally, a standardized data packet with {time, location, PM2.5 concentration, O3 concentration, quality indicator} is generated and uploaded.

[0042] On the data aggregation server (wide area access layer), external data from the API is formatted and spatiotemporally interpolated to match the spatiotemporal grid of this system.

[0043] 2. Specific implementation of the spatiotemporal fusion and simulation module: Data preprocessing unit: Receives all standardized data streams. First, it performs secondary cleaning, for example, using spatial consistency checks: if the data from a microstation deviates by more than 50% from the average of its eight surrounding stations, and its own status code is abnormal, then spatial interpolation is used to temporarily replace that value. Subsequently, the target area is divided into a 500m × 500m × 10-layer (vertical) three-dimensional grid with a time resolution of 10 minutes.

[0044] Multi-source fusion computing unit: Initial field generation: Using the measured PM2.5 concentrations from all fixed stations and mobile vehicles within 10 minutes, the inverse distance weighted (IDW) spatial interpolation algorithm is used to fill all horizontal grid points to form the initial concentration distribution field.

[0045] Model Coupling and Assimilation: The initial field described above is used as the initial concentration field for the CALPUFF atmospheric diffusion model. Meteorological data from a 1km grid are interpolated into the model grid to drive CALPUFF. The model simulates a concentration field. At this point, the concentration measured by a UAV at a certain altitude and the AOD retrieved from satellites (converted to a near-surface concentration reference by an algorithm) are used as "observations" and compared with the model's simulated values. An optimal interpolation (OI) assimilation algorithm is employed to back-calculate and correct the model's emission source strength inventory parameters based on the difference between the observed and simulated values. After several iterations of assimilation, the model's simulated field achieves a statistically optimal fit with various observation fields, ultimately outputting a highly reliable "current-moment pollutant concentration field" after multi-source data correction.

[0046] Simulation and extrapolation unit: Starting with the assimilated concentration field described above, and combining it with the latest weather forecast field (for the next 6 hours), the CALPUFF model is driven again for "forward simulation". The model will calculate the pollutant concentration change for each grid at each time step in the future (e.g., 1 hour), thereby generating a "diffusion trend field". This field not only contains concentration information, but also the direction, speed, and diffusion range of the pollution plume.

[0047] 3. Specific implementation of the dynamic intelligent early warning decision-making module: Dynamic Threshold Management Unit: Deploys a Long Short-Term Memory (LSTM) network model. Its input features include: the current diffusion trend field (the next 6 hours' sequence for each grid point), real-time weather data, historical concentration data from the past 24 hours, and date type (weekday / holiday). The output is the probability distribution (e.g., mean, 90th quantile) of PM2.5 concentration for each grid point over the next 1-6 hours.

[0048] Simultaneously, a dynamic environmental capacity assessment sub-model is run. This model calculates the pollutant ventilation coefficient and residence time for each grid point based on real-time wind speed, wind direction, mixing layer height, and topographic data.

[0049] Dynamic threshold generation: The 90th quantile concentration predicted by LSTM is divided by the real-time ventilation coefficient of the grid (normalized) to obtain the "dynamic concentration threshold considering diffusion capacity". For example, for areas with the same predicted concentration but lower wind speed, the threshold will be stricter (lower value). The gradient threshold is set based on the rate of change of the concentration field over time, combined with the evolution intensity of the diffusion trend field.

[0050] Early warning analysis and generation unit: Real-time comparison: Every 10 minutes, the latest "pollutant concentration field" is compared with the "dynamic early warning threshold set" grid by grid.

[0051] Event Activation and Simulation: Suppose that the PM2.5 concentration in a certain area downwind of the park exceeds the dynamic concentration threshold for two consecutive time periods (20 minutes). The system immediately "activates" a deep simulation and simulation of this event.

[0052] The simulation involves using a "diffusion trend field" to accurately simulate the diffusion path and impact range of the pollution plume over the next two hours. Simultaneously, a reverse trajectory model is activated, using the point exceeding the standard as the endpoint to trace the origin of the air mass over the past three hours. Areas where trajectory lines converge are identified as "potential source areas." Combined with real-time operating data from enterprises in the area (e.g., a sudden increase in VOCs emission concentration at a particular enterprise), a source tracing is generated.

[0053] Event Generation: Based on all the above information, a structured early warning event is generated: {Warning Level: Yellow; Dominant Pollutant: PM2.5; Core Impact Area: Grid Coordinate Set; Expected Evolution: Spreads southeastward in the next 1 hour, affecting a certain community; Source Tracing: Company A (70% contribution rate), Main Traffic Road (30% contribution rate)}.

[0054] 4. Specific implementation of the closed-loop response and optimization module: Contingency Plan Matching and Instruction Generation Unit: Upon receiving the aforementioned early warning event, the system matches the "Industrial Park PM2.5 Yellow Alert Contingency Plan" in the contingency plan database. The contingency plan template includes: notifying the Environmental Protection Bureau to inspect Company A, suggesting that the transportation department strengthen traffic control in the area, and issuing protection tips to residents of affected communities via the APP. Based on real-time traffic conditions, the system generates specific inspection routes and traffic control plans, forming an executable set of instructions.

[0055] Multi-channel instruction distribution unit: Through message queue, patrol instructions are pushed to the mobile enforcement terminal App of environmental law enforcement personnel in real time; traffic diversion plans are sent to the traffic signal control platform via dedicated line; and health tips are distributed through government SMS platform and environmental protection WeChat official account.

[0056] Feedback Learning Unit: Data collection: Collect on-site inspection reports uploaded by law enforcement officers (company A was found to have abnormal emissions and has been ordered to rectify), traffic flow data returned by the traffic platform after traffic diversion, and PM2.5 concentration data (environmental feedback data) transmitted by micro-stations and mobile monitoring vehicles in the area in the following 1-2 hours.

[0057] Effectiveness assessment: Compare the actual concentration decline curve with the predicted curve of the "expected evolution" in the early warning event. Calculate the accuracy of the early warning (e.g., whether the concentration decreased as expected) and the effectiveness of the response measures (e.g., whether the rate of decline was faster than expected).

[0058] Model optimization: The complete data sequence of this event (from threshold breach to post-management recovery) is used as a training sample and fed into the LSTM model of the dynamic threshold management unit for incremental training. Simultaneously, the parameters in the environmental capacity assessment sub-model are fine-tuned based on the difference between the actual diffusion situation and the model predictions. Through continuous learning, the system's early warnings for similar meteorological conditions and similar source areas will become increasingly accurate.

[0059] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that variations may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring and early warning system and method for environmental pollution using big data, characterized in that: include The data acquisition and access module is used to acquire multi-source heterogeneous environmental data in real time from fixed monitoring sensor networks, mobile monitoring units and third-party data interfaces. The environmental data includes pollutant concentration data, pollution source operating data, meteorological parameters and geographic information. The spatiotemporal fusion and simulation module is used to clean and spatiotemporally align multi-source heterogeneous environmental data, and to fuse discrete point monitoring data and area source meteorological data through coupled spatial interpolation algorithms and atmospheric diffusion models to generate a pollutant concentration field and diffusion trend field with spatiotemporal continuity within the target area. The dynamic intelligent early warning decision-making module includes a dynamic threshold management unit and an early warning analysis and generation unit. The dynamic threshold management unit is used to dynamically calculate the early warning thresholds for different spatial locations within a preset time period based on machine learning and diffusion trend fields. The early warning thresholds include a concentration threshold reflecting instantaneous exceedance and a gradient threshold reflecting deterioration trends. The early warning analysis and generation unit is used to compare the pollutant concentration field with the dynamic early warning thresholds in real time and introduce the diffusion trend field to simulate and extrapolate pollution events. When the threshold triggering conditions are met and the extrapolation results conform to the preset diffusion pattern, a structured early warning event is generated, which includes the early warning level, dominant pollutant, core impact area, expected evolution, and source tracing direction. The closed-loop response and optimization module is used to automatically match emergency plans according to the structured early warning events, issue differentiated control instructions to designated terminals, collect environmental feedback data and handling reports after the instructions are executed, compare and analyze the feedback data with the early warning simulation results, generate early warning effectiveness evaluation results, and feed the evaluation results back to the dynamic threshold management unit for adaptive optimization of the threshold calculation model.

2. The real-time monitoring and early warning system and method for environmental pollution big data according to claim 1, characterized in that: The data acquisition and access module includes a ubiquitous sensing layer, a wide-area access layer, and an edge preprocessing unit. The ubiquitous sensing layer consists of a fixed monitoring sensor network deployed within the monitoring area and a mobile monitoring unit deployed on a mobile basis. The wide-area access layer is used to access and aggregate multi-source external data through a secure data gateway protocol. The multi-source external data includes refined grid weather forecasts and real-time data from meteorological departments' APIs, real-time traffic flow and congestion data from traffic management departments' road networks, online monitoring and operating data of key pollution sources from ecological and environmental departments' ecological and environmental departments' online monitoring and operating data, and satellite remote sensing inversion atmospheric parameters and water spectral data from remote sensing platforms' remote sensing platforms. The edge preprocessing unit is built into the key nodes of the ubiquitous sensing layer and the wide-area access layer, respectively. It is used to perform localized preprocessing before data is uploaded, including timestamp calibration, unit unification, real-time calibration based on device calibration curves, and preliminary outlier filtering based on rule engine, to generate a standardized data stream with quality identifiers.

3. The real-time monitoring and early warning system and method for environmental pollution big data according to claim 2, characterized in that: The fixed monitoring sensor network includes a gridded monitoring system consisting of high-precision reference stations and densely deployed air quality micro-stations, water quality monitoring buoys, noise sensors, and characteristic pollutant monitors; The mobile monitoring unit includes a drone equipped with multi-parameter detection equipment, a vehicle-mounted mobile monitoring platform, and a handheld inspection terminal, used for adaptive mobile monitoring and source tracing sampling in fixed monitoring blind spots, sudden pollution areas, and the vicinity of key pollution sources.

4. The real-time monitoring and early warning system and method for environmental pollution big data according to claim 1, characterized in that: The spatiotemporal fusion and simulation module includes a data preprocessing unit, a multi-source fusion computing unit, and a simulation and deduction unit. The data preprocessing unit is used to perform secondary cleaning and spatiotemporal gridding processing on the standardized data stream from the data acquisition and access module; The multi-source fusion computing unit is used to perform fusion calculations and couple a spatial interpolation algorithm with an atmospheric diffusion model. The spatial interpolation algorithm is used to generate an initial concentration distribution field of pollutants on a three-dimensional spatiotemporal grid based on measured data from a fixed monitoring sensor network. The atmospheric diffusion model uses refined grid meteorological data to simulate the advection, diffusion, and deposition processes of pollutants. The coupling process is as follows: the initial concentration distribution field is used as the initial field and source strength constraint of the atmospheric diffusion model, and the mobile monitoring unit and satellite remote sensing inversion data are iteratively assimilated to correct the simulation parameters, thereby generating a pollutant concentration field with high spatiotemporal resolution. The simulation and extrapolation unit is used to drive the atmospheric diffusion model to perform forward simulations for a future preset period based on the current pollutant concentration field, real-time meteorological field, and preset pollution source intensity scenario library, thereby generating a diffusion trend field that reflects the spatiotemporal evolution of pollutant concentration.

5. The real-time monitoring and early warning system and method for environmental pollution big data according to claim 1, characterized in that: The closed-loop response and optimization module includes a plan matching and instruction generation unit, a multi-channel instruction distribution unit, and a feedback learning unit. The contingency plan matching and instruction generation unit is used to analyze the warning level, core impact area and source tracing information in the structured early warning event, match the optimal response plan from the digital emergency plan library, and generate an executable differentiated control instruction set based on real-time road network data and law enforcement resource distribution data. The instruction set includes suggestions for production restriction and shutdown for suspected pollution sources, traffic diversion plans for the affected area, and health protection guidelines for the public. The multi-channel instruction distribution unit is used to synchronously distribute differentiated control instruction sets to the corresponding terminal execution units through a dedicated communication protocol. The terminal execution units include an enterprise operating condition monitoring system, a traffic signal control platform, a public information release platform, and a mobile law enforcement terminal. The feedback learning unit is used to continuously collect and aggregate instruction confirmation receipts, on-site handling reports, and environmental feedback data obtained by the data acquisition and access module after instruction execution from the terminal execution unit. By comparing the environmental feedback data with the expected evolution in the early warning simulation results, the accuracy of the early warning and the effectiveness of the handling measures are quantitatively evaluated, the early warning effectiveness evaluation result is generated, and the machine learning model parameters in the dynamic threshold management unit are incrementally trained and adjusted based on the result.

6. A method for real-time monitoring and early warning of environmental pollution using big data, characterized in that: The early warning system based on any one of claims 1-5 specifically includes the following steps: S1. Multi-source data synchronous acquisition and fusion field construction: Real-time acquisition of fixed-point monitoring data, mobile monitoring data, meteorological grid data and pollution source operating condition data of the target area; After spatiotemporal alignment and cleaning of the data, data fusion and assimilation are performed by coupling spatial interpolation algorithm and atmospheric diffusion physical model to generate pollutant concentration field at the current moment, and based on this, the model is driven to perform forward simulation of the future preset time period to generate diffusion trend field. S2. Dynamic early warning threshold calculation: Using the diffusion trend field and real-time meteorological field as the core inputs, combined with historical assimilation data, a machine learning model is used to predict the probability distribution of pollutant concentrations at different times and locations in the future; and an environmental capacity dynamic assessment model is introduced to correct the probability distribution, generating a dynamic early warning threshold set that adapts to meteorological conditions and spatial location. The threshold set includes a concentration threshold and a gradient threshold. S3, Intelligent Early Warning Event Generation: The pollutant concentration field is compared with the dynamic early warning threshold set in real time. When the threshold is exceeded, the pollution event simulation is activated immediately. S4. Closed-loop response and model optimization: Based on the structured early warning event, match the emergency plan, generate and execute differentiated control instructions; after the instructions are executed, collect actual environmental response data, compare the actual environmental response data with the expected evolution in the simulation, and calculate the early warning accuracy and handling effectiveness indicators; based on these indicators, iteratively optimize the parameters of the machine learning model and the dynamic environmental capacity assessment model in step S2, and use the optimized model for subsequent threshold calculations.

7. The real-time monitoring and early warning system and method for environmental pollution big data according to claim 6, characterized in that: The simulation in step S3 includes: S31. Simulate the spatiotemporal evolution of pollutants based on the diffusion trend field, and conduct reverse source tracing analysis by combining pollution source conditions and geographical information. S32. Based on the comprehensive threshold breakthrough results, simulated evolution path and source tracing, a structured early warning event is generated, which includes quantitative risk level, core impact range, expected evolution trend and potential source contribution.

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