Real-time monitoring and early warning management system based on ecological environment

Abstract

The invention, which relates to the technical field of ecological environment monitoring and intelligent management, discloses a real-time monitoring and early warning management system based on an ecological environment, comprising a multi-modal data acquisition module for acquiring and preprocessing multi-medium environment data, a multi-medium risk conduction modeling module for constructing a directed graph, and a dynamic modeling risk conduction process. The cross-medium risk conduction chain effect evaluation system has the advantages that the problem of cross-medium risk conduction chain effect evaluation deficiency is effectively solved, multi-modal data provides a data basis, the modeling module constructs a directed conduction relation graph, and the model construction module constructs a three-dimensional model and simulates pollutant migration and transformation and risk effects, and the intelligent early warning module generates early warnings, so that the cross-medium risk conduction chain effect evaluation system has the advantages that the cross-medium risk conduction chain effect evaluation deficiency problem is effectively solved; a graph neural network is used for dynamic modeling, a conduction path is presented, a digital twin engine simulates pollutant migration and transformation and a risk conduction effect, an intelligent early warning module carries out multi-level evaluation and timely early warning, a decision intervention module generates an optimal intervention scheme, and the system realizes accurate evaluation and effective response of cross-medium risk conduction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological environment monitoring and intelligent management, in particular to a real-time monitoring and early warning management system based on ecological environment. BACKGROUND

[0002] With the intensification of global climate change, the acceleration of industrialization process and the increasing influence of human activities on the natural environment, the complexity and vulnerability of ecological environment continue to increase, and problems such as air pollution, water eutrophication, soil heavy metal exceeding standard and sharp reduction of biological diversity pose serious challenges to ecological system safety and human sustainable development. In this context, establishing a comprehensive, real-time and accurate ecological environment monitoring and early warning system has become the core requirement for realizing fine management, risk prevention and control and scientific decision-making of ecological environment. The prior art has certain defects. First, the prior art lacks multi-medium data integration and dynamic modeling of risk transmission, making it difficult to present cross-medium risk transmission paths and effects. Second, the prior art lacks cooperation among modules, and does not form a closed-loop control system, resulting in disconnection between risk assessment and management and lagging decision-making. Therefore, we propose a real-time monitoring and early warning management system based on ecological environment. SUMMARY

[0003] The purpose of the present application is to provide a real-time monitoring and early warning management system based on ecological environment.

[0004] To achieve the above purpose, the present application provides the following technical solution: a real-time monitoring and early warning management system based on ecological environment, the management system comprising a multi-modal data acquisition module, a multi-medium risk transmission modeling module, a digital twin engine module, an intelligent early warning module, a decision intervention module, a data interaction interface module and a space-time database module: The multi-modal data acquisition module is used to acquire multi-medium environmental data of atmosphere, water, soil, biology and human activities, including real-time sensor monitoring data, satellite remote sensing image data, unmanned aerial vehicle inspection video data and regional social and economic data, and to standardize and preprocess the collected multi-source heterogeneous data, and output to the multi-medium risk transmission modeling module; The multi-medium risk transmission modeling module is used to construct a directed transmission relationship graph containing environmental medium nodes, biological carrier nodes and human exposure nodes, and the nodes are connected by material migration path edges to form a risk transmission knowledge graph. Based on the graph neural network algorithm, the pollution migration, biological enrichment and exposure risk transmission process between nodes are dynamically modeled, and the cross-medium risk transmission path data is output to the digital twin engine module; The digital twin engine module constructs a three-dimensional digital twin model of the ecological environment system based on the received data, the model integrates an environmental migration simulation sub-module, a biological enrichment deduction sub-module and a human exposure evaluation sub-module, and through real-time data-driven model operation, simulates the migration and transformation process of pollutants among multiple media and the risk transmission effect, and outputs risk transmission dynamic data to the intelligent early warning module; The intelligent early warning module establishes a multi-level evaluation system including ecological health, human safety and economic impact based on the received data, sets dynamic elastic early warning thresholds, generates early warning information including risk level, influence range and transmission path when the monitored risk transmission intensity exceeds the corresponding threshold, and synchronously transmits the early warning information to the decision intervention module and the data interaction interface module; The decision intervention module receives the early warning information of the intelligent early warning module, generates an optimal intervention scheme for different risk transmission paths based on a preset intervention strategy library and a reinforcement learning algorithm, the scheme includes pollution source blocking measures, ecological restoration equipment scheduling strategies and cross-department linkage instructions, outputs the intervention scheme to the data interaction interface module, and receives intervention effect feedback data from the execution terminal to optimize the strategy library; The data interaction interface module receives the original monitoring data of the multi-modal data acquisition module and the intervention scheme of the decision intervention module, and pushes the monitoring data, early warning information and linkage instructions to the sensor network, ecological regulation equipment and environmental protection, agricultural, health management departments in real time, realizes cross-system data sharing and collaborative response; The spatio-temporal database module is used for storing multi-medium environment historical data, risk transmission modeling parameters, digital twin model configuration files and early warning decision records, and adopts spatio-temporal indexing technology to organize the data, supports spatio-temporal correlation query and long-term trend analysis of multi-medium data, and provides basic data support for the multi-medium risk transmission modeling module and the digital twin engine module.

[0005] As a further scheme of the present application: the multi-modal data acquisition module includes a sensor array for acquiring monitoring area data and a data preprocessing unit, the sensor array includes atmospheric pollutant sensors, water quality multi-parameter sensors, soil heavy metal sensors and biological physiological signal sensors, and the data preprocessing unit is used for format unification processing of analog signals, image data and video data output by different sensors, to generate standardized data stream including time stamp, spatial coordinate and data quality identification.

[0006] As a further solution of the present invention: the multi-media risk conduction modeling module realizes risk conduction process modeling by constructing a directed conduction relationship graph, including but not limited to atmospheric nodes, surface water nodes, groundwater nodes, soil nodes, plant nodes, animal nodes and human settlement nodes, and defines conduction path edges, including but not limited to dry and wet deposition edges, runoff erosion edges, root absorption edges, food chain transmission edges and respiratory exposure edges. Each edge is associated with substance migration rate parameters and bioconcentration coefficient parameters to form a complete risk conduction knowledge graph.

[0007] As a further solution of the present invention, the environmental migration simulation submodule of the digital twin engine module uses a multi-media coupled migration dynamics model to simulate the cross-media migration of pollutants. The expression of the multi-media coupled migration dynamics model is as follows: ; in, It represents the net migration flux of pollutants per unit volume from medium i to medium j at time t, taking into account the time accumulation effect and spatial gradient diffusion. is the interfacial mass transfer coefficient that changes dynamically with time s, and is an adaptive function constructed through real-time meteorological data (wind speed, precipitation) and hydrological parameters (runoff velocity, tidal period). Calculation reflects the impact of environmental conditions on mass transfer efficiency, is the dynamic contact area between media i and j, which is applicable to seasonally changing interfaces such as surface water and soil, atmosphere and vegetation canopy, represents the concentration gradient operator, which is used to describe the spatial distribution difference of pollutants on both sides of the medium interface. is the distribution equilibrium constant of pollutants between media i and j, and is calculated through a multiple regression model of the physicochemical properties of the substance (octanol-water partition coefficient, vapor pressure) and the medium characteristic parameters (soil organic matter content, water salinity). Sure, is the volume density of medium i, is the cross-medium diffusion coefficient, which comes from the molecular dynamics simulation database, is the adsorption equilibrium time constant, which is used to quantify the delay effect of adsorption-desorption kinetics of pollutants on the surface of the medium. is the saturation concentration of medium i for the target pollutant, The target calculation volume for medium j is dynamically divided according to the spatial resolution of the monitoring area (e.g., 100m×100m grid volume) and is used to normalize the total migration amount to unit volume flux. and They are the real-time concentrations of pollutants in media i and j at time s, which are obtained through real-time monitoring by sensors of the multimodal data acquisition module, and the unit is kg / m 3 .

[0008] As a further scheme of the present application: the biological enrichment deduction submodule in the digital twin engine module calculates the pollutant concentration in the organism through a biological enrichment kinetics equation, and the biological enrichment kinetics equation is as follows: ; Wherein, represents the pollutant concentration in the organism at time t, is the pollutant concentration in the environmental medium, represents the absorption rate constant of the organism to the pollutant, which is related to the surface area of the organism and the metabolic rate, is the pollutant discharge rate constant, is the metabolic rate constant of the pollutant in the organism, and the equation comprehensively considers the absorption, discharge and metabolism of the organism to the pollutant, can accurately calculate the change of the pollutant in the organism with time, and further analyze the enrichment effect of the pollutant in the food chain.

[0009] As a further scheme of the present application: the multi-level evaluation system established by the intelligent early warning module includes an ecological health evaluation unit, a human safety evaluation unit and an economic impact evaluation unit, the ecological health evaluation unit calculates the species transmission vulnerability index based on the species sensitivity database and the Bayesian inference algorithm, and triggers the ecological early warning when the index exceeds 30%-35% of the local species tolerance median, the human safety evaluation unit calculates the exposure risk hazard quotient, that is, the ratio of daily pollutant intake to reference dose, and triggers the health early warning when the exposure risk hazard quotient is greater than or equal to 1, and the economic impact evaluation unit adopts the input-output model combined with Monte Carlo simulation to calculate the economic impact coefficient of the risk transmission path, and triggers the economic early warning when the expected loss exceeds 5% of the regional annual environmental protection budget.

[0010] As a further scheme of the present application: the decision intervention module trains the intervention scheme based on the reinforcement learning algorithm, specifically including: The action space is defined as five intervention measures, i.e., pollution source blocking, ecological restoration device scheduling, agricultural product sales prohibition area demarcation, emergency water source allocation and pollution isolation belt setting, each measure corresponding to a standardized execution parameter (such as blocking valve opening degree, device start-stop time, sales prohibition area boundary coordinates), and the reward function is defined as the ratio of risk transmission attenuation rate to intervention cost, wherein the risk transmission attenuation rate is the percentage of the initial risk value to the risk value reduction within 48 hours after the intervention, and the intervention cost includes the comprehensive conversion cost of device operation energy consumption, material consumption and manpower investment; the system outputs a scheme combination containing at least two measures within 10 minutes after receiving the early warning information through deep Q network training; in the reinforcement learning process, the strategy library iteration optimization is automatically triggered every 50-55 times of intervention effect feedback data updating, and the priority weight and parameter configuration of each measure are adjusted by comparing the actual risk attenuation effect with the model prediction value.

[0011] As a further scheme of the application: the cross-department linkage instruction of the data interaction interface module includes a risk transmission thermal map, an intervention measure priority list and an effect feedback mechanism, the risk transmission thermal map marks the risk level of each region with a 100m*100m grid precision, the intervention measure priority list specifies the execution order of the environmental protection department to cut off the pollution source, the agricultural department to start crop detection and the health department to carry out population health monitoring, and the effect feedback mechanism requires the execution terminal to return the pollution concentration change data after intervention and the transmission path coefficient adjustment suggestion every 15-20 minutes.

[0012] As a further scheme of the application: the spatio-temporal database module adopts a hierarchical storage architecture, including a real-time data layer, a historical data layer and a model parameter layer, the real-time data layer stores high-frequency monitoring data for 30-40 days, supports second-level query response, the historical data layer stores multi-medium environmental data for more than 3 years, adopts a spatio-temporal cube structure for organization, supports cross-year risk transmission trend analysis, the model parameter layer stores biological enrichment factor, medium distribution coefficient, mass transfer coefficient, adsorption equilibrium time constant and pollutant saturation concentration parameters required for risk transmission modeling, and is updated in real time with external authoritative databases, including the Environmental Science Data Center Database, the International Organization for Standardization Parameter Library and the standard parameter library published by industry associations.

[0013] Compared with the prior art, the application has the following beneficial effects: 1. The present application effectively solves the problem of the absence of chain effect evaluation of cross-media risk transmission in the prior art by constructing a system including a multi-modal data acquisition module, a multi-medium risk transmission modeling module, a digital twin engine module, etc. The multi-modal data acquisition module widely collects multi-medium environmental data such as atmosphere, water, soil, etc. and pre-processes them to provide a comprehensive and accurate data basis for subsequent analysis. The multi-medium risk transmission modeling module constructs a directed transmission relationship graph and uses a graph neural network algorithm to dynamically model the pollution migration, biological enrichment and exposure risk transmission process between nodes, clearly presenting the path of cross-media risk transmission. The digital twin engine module further simulates the migration and transformation of pollutants between multi-media and the risk transmission effect, realizes the visualization and quantitative evaluation of the chain effect, and the intelligent early warning module performs multi-level evaluation and timely warning based on this. The decision intervention module can generate the optimal intervention scheme according to the evaluation results. The whole system realizes the accurate evaluation and effective response to the chain effect of cross-media risk transmission. 2. The present application constructs a "monitoring-modeling-early warning-intervention" whole-process closed-loop control system through the deep cooperation of the multi-modal data acquisition module, the multi-medium risk transmission modeling module, the intelligent early warning module and the decision intervention module, effectively solving the core problem of the disconnection between risk assessment and management in the prior art. The multi-modal data acquisition module acquires multi-medium environmental data such as atmosphere, water, soil, and organisms in real time, and provides full-factor data input for modeling after standardized preprocessing. The multi-medium risk transmission modeling module constructs a directed transmission relationship graph based on a graph neural network algorithm, dynamically analyzes the cross-media transmission path of pollution migration, biological enrichment, etc., and outputs risk transmission data to drive the digital twin engine module to simulate the risk transmission effect of the real ecological system. The intelligent early warning module establishes a multi-level evaluation system of ecological health, human safety and economic impact based on the simulation results, identifies the risk level in real time through dynamic elastic threshold and generates accurate early warning. After receiving the warning information, the decision intervention module generates the optimal scheme from standardized intervention measures such as pollution source blocking and ecological restoration equipment scheduling using reinforcement learning algorithm, drives cross-departmental linkage execution through the data interaction interface module, and collects intervention effect feedback data to optimize the strategy library in real time. This closed-loop system realizes the whole-process automation and intelligentization from environmental data collection to risk intervention effect feedback for the first time, upgrades the traditional passive monitoring to active risk prevention and control, significantly improves the response speed and decision-making scientificity of ecological environment management, and provides an integrated technical solution for sudden pollution incident response and long-term ecological security protection. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The system flowchart in the embodiments of the present application is shown. DETAILED DESCRIPTION

[0015] The specific embodiments of the present application will be further described below with reference to the drawings, and it should be noted that the description of these embodiments is used to help understand the present application, but does not constitute a limitation on the present application.

[0016] In addition, the technical features involved in each embodiment of the application described below can be combined with each other as long as there is no conflict between them.

[0017] Please refer to the accompanying Figure 1 The present application is a real-time monitoring and early warning management system based on ecological environment, which includes a multi-modal data acquisition module, a multi-medium risk transmission modeling module, a digital twin engine module, an intelligent early warning module, a decision intervention module, a data interaction interface module, and a space-time database module: The multi-modal data acquisition module is used to obtain multi-medium environmental data of atmosphere, water, soil, biology and human activities, including real-time sensor monitoring data, satellite remote sensing image data, unmanned aerial vehicle inspection video data and regional social and economic data, and to standardize and pretreat the collected multi-source heterogeneous data, and output to the multi-medium risk transmission modeling module; The multi-medium risk transmission modeling module is used to construct a directed transmission relationship graph containing environmental medium nodes, biological carrier nodes and human exposure nodes, and the nodes are connected by material migration path edges to form a risk transmission knowledge graph. Based on the graph neural network algorithm, the pollution migration, biological enrichment and exposure risk transmission process between nodes are dynamically modeled, and the cross-medium risk transmission path data is output to the digital twin engine module; The digital twin engine module constructs a three-dimensional digital twin model of the ecological environment system based on the received data. The model integrates environmental migration simulation submodules, biological enrichment deduction submodules and human exposure assessment submodules. Through real-time data-driven model operation, the migration and transformation process of pollutants between multi-media and the risk transmission effect are simulated, and the risk transmission dynamic data is output to the intelligent early warning module; The intelligent early warning module establishes a multi-level evaluation system containing ecological health, human safety and economic impact based on the received data, sets dynamic elastic warning thresholds, and when the monitored risk transmission intensity exceeds the corresponding threshold, generates early warning information containing risk level, impact range and transmission path, and synchronously transmits to the decision intervention module and the data interaction interface module; The decision intervention module receives the early warning information of the intelligent early warning module, generates the optimal intervention scheme for different risk transmission paths based on the preset intervention strategy library and reinforcement learning algorithm, the scheme includes pollution source blocking measures, ecological restoration equipment scheduling strategy and cross-department linkage instructions, outputs the intervention scheme to the data interaction interface module, and receives intervention effect feedback data from the execution terminal to optimize the strategy library; The data interaction interface module receives the original monitoring data of the multi-modal data acquisition module and the intervention scheme of the decision intervention module, and pushes the monitoring data, early warning information and linkage instructions to the sensor network, ecological regulation equipment and environmental protection, agricultural, health management departments in real time, so that cross-system data sharing and collaborative response are realized. The space-time database module is used for storing multi-medium environment historical data, risk transmission modeling parameters, digital twin model configuration files and early warning decision records, and adopts space-time index technology to organize data, supports space-time correlation query and long-term trend analysis of multi-medium data, and provides basic data support for the multi-medium risk transmission modeling module and the digital twin engine module.

[0018] In an embodiment of the present application: the multi-modal data acquisition module includes a sensor array for acquiring monitoring area data and a data preprocessing unit, the sensor array includes atmospheric pollutant sensors, water quality multi-parameter sensors, soil heavy metal sensors and biological physiological signal sensors, and the data preprocessing unit is used for format unification processing of analog signals, image data and video data output by different sensors, to generate standardized data stream containing time stamp, spatial coordinate and data quality identification.

[0019] In an embodiment of the present application: the multi-medium risk transmission modeling module realizes risk transmission process modeling by constructing a directed transmission relationship graph, including but not limited to atmospheric nodes, surface water nodes, groundwater nodes, soil nodes, plant nodes, animal nodes and human settlement nodes, and defining transmission path edges, including but not limited to dry and wet deposition edges, runoff scouring edges, root absorption edges, food chain transmission edges and respiratory exposure edges, each edge is associated with substance migration rate parameters and biological enrichment coefficient parameters, forming a complete risk transmission knowledge graph; There are 12 types of nodes in total, as follows: ① Atmospheric node: representing atmospheric environment medium, storing gaseous pollutant concentration data such as PM2.5, SO2 and NOx; ② Surface water node: representing surface water environment such as rivers, lakes and reservoirs, storing parameters such as pH value, dissolved oxygen and heavy metal ion concentration; ③ Groundwater node: representing groundwater environment, storing monitoring data such as nitrate, sulfate and trace elements; ④ Soil node: representing surface and profile soil, storing parameters such as water content, organic matter content and pollutant adsorption concentration; ⑤ Sediment node: representing river sediment and lake sediment, storing interstitial water pollutant concentration and deposition rate data; ⑥ Plant node: representing vegetation community, storing parameters such as leaf pollutant enrichment, root absorption rate and transpiration; ⑦Animal node: representing wild animals and farmed organisms, storing data of internal pollutant residues and food chain transfer efficiency parameters; ⑧Microorganism node: representing microbial communities in soil / water, storing data of pollutant-degrading enzyme activities and metabolite products; ⑨Human settlement node: representing urban / rural residential areas, storing data of human exposure frequency and indoor / outdoor pollutant concentration difference; ⑩Industrial pollution source node: representing artificial pollution sources such as factories and mines, storing data of pollutant emission rates and treatment facility efficiency; ⑪Agricultural activity node: representing agricultural areas such as farmlands and farms, storing data of fertilizer and pesticide usage amounts and livestock manure pollutant output; ⑫Nature reserve node: representing ecologically sensitive areas, storing data of species diversity indices and ecological vulnerability assessment parameters; There are 28 path edges in total, as follows: ①Atmosphere-surface water conduction edge Wet deposition edge: the process of atmospheric pollutants entering surface water through rainfall / snowfall; Dry deposition edge: the process of atmospheric particulate matter entering surface water through gravitational settling or inertial collision; Gas-liquid interface mass transfer edge: the process of gaseous pollutants diffusing bidirectionally at the atmosphere-water interface through Henry's law; ②Atmosphere-soil conduction edge Dust deposition edge: the process of atmospheric particulate matter (such as PM10) settling onto the soil surface; Gas adsorption edge: the process of soil organic matter adsorbing and fixing volatile organic compounds (VOCs) in the atmosphere; ③Surface water-soil conduction edge Runoff scouring edge: the process of surface runoff carrying pollutants into the soil surface layer; Leakage penetration edge: the process of surface water penetrating into groundwater through soil pores; ④Surface water-sediment conduction edge Particle deposition edge: the process of water-suspended matter carrying pollutants to deposit into sediments; Interstitial water exchange edge: the process of pollutant diffusion and exchange between sediment interstitial water and overlying water; ⑤Soil-groundwater conduction edge Solute migration edge: the process of dissolved pollutants in soil entering groundwater through convection-diffusion; Ion exchange edge: the process of pollutant adsorption / desorption equilibrium between soil colloids and ions in groundwater; ⑥Soil-plant conduction edge Root absorption edge: the process of plant roots absorbing pollutants in the soil through active transport or passive diffusion; Transpiration transfer edge: the process of pollutants migrating from root system to stem and leaf through plant transpiration; Plant-animal transfer edge Food chain transfer edge: the process of phytophagous animals ingesting plant tissues leading to the enrichment of pollutants; Predation transfer edge: the process of carnivorous animals causing the bioamplification of pollutants by preying on lower trophic level animals; Water body-animal transfer edge Gill absorption edge: the process of aquatic animals directly absorbing dissolved pollutants in water body through gill filaments; Body surface permeation edge: the process of pollutants penetrating into the body of aquatic animals through their skin mucous membranes; Human-environment transfer edge Respiratory exposure edge: the exposure process of human population through inhaling air pollutants (such as PM2.5, ozone); Dietary intake edge: the process of ingesting pollutants through eating contaminated agricultural products and aquatic products; Skin contact edge: the process of absorbing pollutants through the direct contact of human skin with contaminated water and soil; Pollution source-environment transfer edge Industrial emission edge: the process of pollutants being released from industrial waste gas / waste water / solid waste into the atmosphere, water body and soil; Agricultural non-point source edge: the process of pollution diffusion caused by the loss of fertilizers and pesticides in farmland and the leakage of manure in breeding farms; Biological-environmental feedback edge Plant adsorption edge: the process of vegetation absorbing atmospheric pollutants through leaf stomata and fixing them; Microbial degradation edge: the process of soil / water microorganisms degrading organic pollutants through metabolic activity; Algal absorption edge: the process of aquatic algae absorbing nitrogen and phosphorus nutrients in water body through photosynthesis; Cross-media circulation edge Evaporation condensation edge: the circulation process of volatile pollutants evaporating into the atmosphere from water body / soil and then condensing and settling; Freeze-thaw migration edge: the redistribution process of pollutants in solid ice phase and liquid water phase in cold region soil through freeze-thaw action; Tidal transport edge: the process of pollutants being transported back and forth between land and sea media through tidal movement in coastal areas.

[0020] In an embodiment of the present application: the environment migration simulation submodule of the digital twin engine module simulates the cross-media migration of pollutants by using a multi-media coupled migration mechanics model, and the expression of the multi-media coupled migration mechanics model is as follows: ; wherein, It represents the net migration flux of pollutants per unit volume from medium i to medium j at time t, taking into account the time accumulation effect and spatial gradient diffusion. is the interfacial mass transfer coefficient that changes dynamically with time s, and is an adaptive function constructed through real-time meteorological data (wind speed, precipitation) and hydrological parameters (runoff velocity, tidal period). Calculation reflects the impact of environmental conditions on mass transfer efficiency, is the dynamic contact area between media i and j, which is applicable to seasonally changing interfaces such as surface water and soil, atmosphere and vegetation canopy, represents the concentration gradient operator, which is used to describe the spatial distribution difference of pollutants on both sides of the medium interface. is the distribution equilibrium constant of pollutants between media i and j, and is calculated through a multiple regression model of the physicochemical properties of the substance (octanol-water partition coefficient, vapor pressure) and the medium characteristic parameters (soil organic matter content, water salinity). Sure, is the volume density of medium i, is the cross-medium diffusion coefficient, which comes from the molecular dynamics simulation database, is the adsorption equilibrium time constant, which is used to quantify the delay effect of adsorption-desorption kinetics of pollutants on the surface of the medium. is the saturation concentration of medium i for the target pollutant, The target calculation volume for medium j is dynamically divided according to the spatial resolution of the monitoring area (e.g., 100m×100m grid volume) and is used to normalize the total migration amount to unit volume flux. and They are the real-time concentrations of pollutants in media i and j at time s, which are obtained through real-time monitoring by sensors of the multimodal data acquisition module, and the unit is kg / m 3 .

[0021] The saturation concentration of medium i for the target pollutant refers to the maximum concentration of the target pollutant that a specific environmental medium (such as the atmosphere, water, soil, organisms, etc.) can accommodate under certain conditions (temperature, pressure, physical and chemical properties of the medium, etc.). When the pollutant concentration in the medium reaches or exceeds the saturation concentration, the excess pollutants will no longer be adsorbed or retained by the medium, but will enter other media through processes such as migration and diffusion (such as leaching from soil to groundwater, volatilization from water bodies to the atmosphere, etc.), thereby triggering cross-medium risk transmission.

[0022] In one embodiment of the present invention, the bioaccumulation deduction submodule in the digital twin engine module calculates the concentration of pollutants in the organism through the bioaccumulation kinetic equation, which is as follows: ; in, represents the concentration of the pollutant in the organism at time t, is the concentration of the pollutant in the environmental medium, represents the absorption rate constant of the organism to the pollutant, is determined by the experimental calibration value of the surface area of the organism and the metabolic rate, and is related to the surface area of the organism and the metabolic rate, is the excretion rate constant of the pollutant, is the metabolic rate constant of the pollutant in the organism, which comprehensively considers the absorption, excretion and metabolism of the organism to the pollutant, and can accurately calculate the change of the pollutant in the organism with time, and further analyze the enrichment effect of the pollutant in the food chain.

[0023] In an embodiment of the present application: the multi-level evaluation system established by the intelligent early warning module includes an ecological health evaluation unit, a human safety evaluation unit and an economic impact evaluation unit, the ecological health evaluation unit calculates the species transmission vulnerability index based on the species sensitivity database and the Bayesian inference algorithm, and triggers an ecological early warning when the index exceeds 30%-35% of the local species tolerance median, the human safety evaluation unit calculates the exposure risk hazard quotient, that is, the ratio of daily pollutant intake to reference dose, and triggers a health early warning when the exposure risk hazard quotient is greater than or equal to 1, and the economic impact evaluation unit calculates the economic impact coefficient of the risk transmission path by using the input-output model combined with Monte Carlo simulation, and triggers an economic early warning when the expected loss exceeds 5% of the annual environmental protection budget of the region.

[0024] In an embodiment of the present application: the decision intervention module trains intervention schemes based on a reinforcement learning algorithm, specifically including: defining the action space as five intervention measures including pollution source blocking, ecological restoration equipment scheduling, agricultural product sales ban area delineation, emergency water source allocation and pollution isolation belt setting, each measure corresponding to standardized execution parameters (such as blocking valve opening degree, equipment start-stop time, sales ban area boundary coordinates), defining the reward function as the ratio of risk transmission attenuation rate to intervention cost, wherein the risk transmission attenuation rate is the percentage of the initial risk value within 48 hours after intervention, and the intervention cost includes the comprehensive conversion cost of equipment operation energy consumption, material consumption and labor input, through deep Q network training, the system outputs a scheme combination containing at least two measures within 10 minutes after receiving the early warning information, in the reinforcement learning process, every 50-55 times of intervention effect feedback data update automatically triggers strategy library iteration optimization, by comparing the actual risk attenuation effect with the model prediction value, the priority weight and parameter configuration of each measure are adjusted.

[0025] In an embodiment of the present application: the cross-department linkage instruction of the data interaction interface module includes a risk transmission heat map, an intervention measure priority list, and an effect feedback mechanism. The risk transmission heat map marks the risk level of each area with a 100m x 100m grid precision. The intervention measure priority list specifies the execution order of the environmental protection department to cut off the pollution source, the agricultural department to start crop detection, and the health department to carry out population health monitoring. The effect feedback mechanism requires the execution terminal to return the pollution concentration change data after intervention and the transmission path coefficient adjustment suggestion every 15-20 minutes.

[0026] In an embodiment of the present application: the spatio-temporal database module adopts a hierarchical storage architecture, including a real-time data layer, a historical data layer, and a model parameter layer. The real-time data layer stores 30-40 days of high-frequency monitoring data, supports second-level query response. The historical data layer stores more than 3 years of multi-medium environmental data, adopts a spatio-temporal cube structure for organization, supports cross-year risk transmission trend analysis. The model parameter layer stores the biological enrichment factor, medium distribution coefficient, mass transfer coefficient, adsorption equilibrium time constant, and pollutant saturation concentration parameters required for risk transmission modeling, and is updated in real time with external authoritative databases, including the Environmental Science Data Center Database, the International Organization for Standardization Parameter Library, and the standard parameter library published by industry associations.

[0027] Example One, please refer to the attached Figure 1 , watershed ecological environment monitoring and risk prevention and control scene: Select a tributary watershed (area about 200km 2 ) in the middle reaches of the Yangtze River, including farmland, wetland, residential area and small industrial agglomeration area, focusing on monitoring the cross-medium risk transmission (soil→water→human body) caused by pesticide residues and industrial wastewater discharge; Two, system deployment parameters 1. Multi-modal data acquisition module Sensor array (deployment density: every 5km² / set): Atmospheric sensor: TSI9350 type PM2.5 / PM10 sensor (precision ±1μg / m³), API200 type SO2 / NOx sensor (precision ±0.1ppb), deployed in 10 meteorological towers (height 15m) in the watershed; Water quality sensor: YSI EXO2 multi-parameter probe (measurement parameters: pH, dissolved oxygen, ammonia nitrogen, total phosphorus, precision ±0.5%FS), deployed at 20 monitoring points in the main stream of the tributary and the mouths of 5 tributaries; Soil sensor: METER EC-5 soil moisture sensor (precision ±3%), XOS-2000 type heavy metal detector (detects Cd, Pb, Hg, precision ±0.1mg / kg), deployed in farmland area with a 100m x 100m grid, a total of 150 measurement points; Biosensor: Implantable fish electric signal sensor (monitoring crucian carp, carp, sampling frequency 1 Hz), deployed in wetland shoal area (50 tails / acre); Data preprocessing: Unified data format is ISO19139 geographic information standard, timestamp accuracy to seconds, spatial coordinates use WGS84 coordinate system, data quality identification is divided into three levels A (high quality), B (qualified), C (need to verify); 2. Multi-medium risk transmission modeling module Node definition (12 categories in total, core nodes in this scenario): Environmental medium nodes: atmospheric node (N1), surface water body node (N2), farmland soil node (N3), wetland soil node (N4); Biological carrier nodes: rice plant node (B1), crucian carp node (B2); Human exposure node: residential node (H1); Conduction path edge (key path in this scenario): Soil→water: runoff flushing edge (E1, migration rate parameter =0.05m / h, automatically increased by 30% in rainy season); Water→biological: fish absorption edge (E2, biological concentration coefficient BCF=1000L / kg, for organophosphorus pesticides); Soil→crop: root absorption edge (E3, absorption rate parameter =0.2cm / h, positively correlated with soil moisture content); Graph neural network configuration: GATv2 architecture is used, 3 convolution layers, 8 attention heads per layer, input feature dimension 128, training data set is the past 3 years of historical data of the basin (total 10950 groups); 3. Digital twin engine module (1) Environmental migration simulation sub-module (take soil→water phosphorus migration as an example) Multi-medium coupled migration dynamics model parameters:

[0028] Branch river section calculation volume (divided by 100m river section, average water depth 2m, width 50m, =100×50×2=10 4 m 3 ); : Runoff flushing mass transfer coefficient, rainy season (s=rainy season period) k=0.08m / h, non-rainy season k=0.05m / h (adjusted dynamically according to real-time rainfall); : Soil-water contact area, 1500 m² in rainy season, 1000 m² in dry season 2 ; : Total soil phosphorus concentration (real-time monitoring value, peak value in rainy season 50 mg / kg, converted to volume concentration =0.05 kg / m 3 ); : Total water phosphorus concentration (real-time monitoring value, background value 0.02 mg / L, exceeding threshold 0.05 mg / L); : Phosphorus distribution equilibrium constant between soil and water, calculated by =1000×OM+50 (OM is soil organic matter content, OM=2% in this scenario, =1000×0.02+50=70); : Soil bulk density 1.3 g / cm³ =1300 kg / m 3 ; : Phosphorus diffusion coefficient across media 1×10−9 m 2 / s (from soil hydrodynamic database); : Adsorption equilibrium time constant 24 h (i.e. 24 hours for phosphorus to reach adsorption equilibrium on soil surface); : Soil phosphorus saturation concentration 500 mg / kg (laboratory measured value); Calculation result: Phosphorus migration flux Q=0.01 kg / m 3 / h per river section in rainy season, leading to an increase of 0.1 μg / L in total water phosphorus concentration per hour; (2) Biological enrichment deduction submodule (taking rice enrichment of cadmium as an example) Biological enrichment kinetics equation parameters: ; : Soil cadmium concentration 0.3 mg / kg (0.3 times exceeding, GB15618-2018 agricultural land standard); : Rice root absorption rate constant 0.1 kg / (kg·d) (positively correlated with root surface area 100 cm² / plant); : Cadmium excretion rate constant 0.05 kg / (kg·d); : Cadmium metabolic rate constant 0 (cadmium is difficult to metabolize in plants); Calculation result: After 45 days of planting, the cadmium concentration in rice is: =0.3×(0.1 / (0.05+0))×(1−e−0.05×45)=0.6×(1−0.105)=0.537mg / kg (exceeds the food limit of 0.2mg / kg); 4. Intelligent early warning module Ecological health assessment: The rice species transmission vulnerability index was calculated to be 40% (exceeding the local tolerance median by 30%, triggering a yellow alert); Human safety assessment: The average daily cadmium intake of residents through eating rice is 0.537 mg / kg×200g / d=0.107 mg / d, and the reference dose is =0.001mg / (kg・d) (for an adult weighing 60kg), HQ=0.107 / (0.001×60)=1.78>1, triggering an orange health alert; Economic impact assessment: 500 hectares of farmland are expected to be affected, with rice production losses of 1.5 million yuan, exceeding 5% of the region's annual environmental protection budget (5% of a 20 million yuan budget is 1 million yuan), triggering a yellow economic alert; 5. Decision Intervention Module Action Space: Pollution source blocking: closure of the sewage outlets of three phosphate fertilizer plants in the basin (valve opening 100% closed, response time 5 minutes); Ecological restoration equipment dispatch: start 10 river aeration devices (power 5kW / unit, operating time 24h / d); Designation of prohibited sales areas for agricultural products: a 5km radius around farmland with excessive cadmium levels will be designated as a temporary sales prohibition area (involving five natural villages); Reinforcement learning output plan: Prioritize blocking pollution sources and banning the sale of agricultural products (risk reduction rate 65%, cost 800,000 yuan), while simultaneously dispatching aeration devices to enhance water self-purification capacity; 6.Data interaction interface module Linkage instructions: Risk heat map: marked with a 100m×100m grid, with 23 red grids (cadmium concentration > 0.3mg / kg) and 58 yellow grids (0.2-0.3mg / kg); Priority list: ① Environmental protection departments cut off the pollution source within 60 minutes; ② Agricultural departments complete crop sampling and testing within 24 hours; ③ Health departments initiate urine cadmium screening for residents (target population: 5,000 people); 7. Spatiotemporal database module Storage parameters: Real-time data layer: stores the last 30 days of data, with an average daily data volume of 1.2GB, and supports 500 concurrent queries in seconds; Historical data layer: store data from 2018 to 2023, adopt space-time cube structure, spatial resolution 100m, temporal resolution 1 hour; Implementation effect

[0029] 48 hours after the pollution source is blocked, the total phosphorus concentration in the water body decreases by 25%, and the growth rate of cadmium concentration in rice is reduced by 40%; System response time: from early warning generation to intervention plan execution, total time consumption is 95 minutes, which is 60% shorter than traditional system.

[0030] Example two, please refer to the attached Figure 1 , soil-biological-human exposure risk prevention and control scene around the mining area: Select an area around a lead-zinc mine in southwest China (area 50 km²), focus on monitoring the risk of soil lead pollution enrichment through the food chain of vegetation → livestock → human body, and the direct exposure risk of residents to atmospheric lead dust; II. System deployment parameters 1. Multi-modal data acquisition module Sensor array (deployment density: every 2km 2 / set): Atmospheric sensor: Thermo Scientific 42i NOx sensor, 5030 lead dust detector (detection limit 0.1 μg / m³), 3 monitoring stations (height 20m) are deployed in the upwind and downwind directions of the mining area; Soil sensor: HACH soil lead detector (accuracy ±0.5mg / kg), EC-10 soil conductivity sensor, deployed in a 50m×50m grid around the mining area, a total of 100 measurement points; Biological sensor: ear tag physiological signal sensor (monitors body temperature and heart rate, sampling frequency 5Hz), pasture chlorophyll fluorescence sensor (monitors lead stress response, accuracy ±1%), 50 beef cattle and 10 pasture monitoring points are deployed in the pasture; Human activity data: obtain residents' dietary structure and outdoor activity duration through community questionnaire (200 valid samples per day); 2. Multi-medium risk transmission modeling module Node definition (core nodes in this scenario): Environmental medium nodes: atmospheric node (N1), mining area soil node (N2), farmland soil node (N3); Biological carrier nodes: pasture node (B1), beef cattle node (B2); Human exposure node: residential node (H1); Conduction path edge (key path in this scenario): Atmosphere → soil: dry deposition edge (E1, deposition rate 0.01 mg / (m²・h), positively correlated with wind speed); Soil→Pasture: Root uptake edge (E2, uptake rate parameter k = 0.15 cm / h, affected by soil pH, with the highest efficiency at pH = 6.5); Pasture→Cattle: Food chain transfer edge (E3, bioconcentration factor BCF = 50, enrichment factor of lead in cattle liver); 3. Digital twin engine module (1) Environmental migration simulation submodule (taking atmospheric lead dust deposition as an example) Multi-medium coupling migration dynamics model parameters:

[0031] : Calculated volume of mine soil (1 km2 area, soil depth 0.5 m, = 106 m2×0.5 m = 5×105 m3); : Dry deposition mass transfer coefficient, k = 0.02 m / h at wind speed 5 m / s, k = 0.05 m / h at wind speed 10 m / s (adjusted dynamically according to real-time weather data); : Atmospheric and soil contact area, i.e. ground surface area, fixed 1 km2 = 10 6 m²; : Atmospheric lead concentration (real-time monitoring value, peak value in downwind direction of mining area 5 μg / m³ = 5×10 -9 kg / m³); : Partition equilibrium constant of lead in atmosphere-soil, calculated by = 500×OM + 100 (OM = 1.5%, = 500×0.015 + 100 = 107.5); : Atmospheric density 1.2 kg / m³; : Lead trans-medium diffusion coefficient 2×10 -8 m² / s (from atmospheric particulate matter deposition model database); : Atmospheric lead saturation concentration 50 μg / m³ (wet deposition occurs when exceeded); Calculation result: Single-day dry deposition lead flux Q = 2×10 −7 kg / m³ / d, leading to a monthly increase of 0.1 mg / kg in soil lead concentration; (2) Biological enrichment deduction submodule (taking lead enrichment in cattle as an example) Biological enrichment kinetics equation parameters: ; : Lead concentration in forage 2 mg / kg (exceeding the safety threshold of 1 mg / kg); : Intestinal absorption rate constant of beef cattle 0.2 kg / (kg·d) (positively correlated with body weight 500 kg and daily intake 15 kg); : Lead excretion rate constant 0.1 kg / (kg·d); : Lead metabolism rate constant 0.02 kg / (kg·d) (limited liver metabolism capacity); Calculation result: After 60 days of breeding, the lead concentration in the liver of the cow is: = 2 x (0.2 / (0.1+0.02)) x (1-e-0.12x60) = 2 x 1.667 x (1-0.0006) = 3.332 mg / kg (exceeding the lead limit for meat by 0.5 mg / kg); 4. Intelligent early warning module Ecological health assessment: Forage species transmission vulnerability index 50% (exceeding the local tolerance median by 30%, triggering an orange warning); Human safety assessment: The average daily lead intake of residents through eating beef is 3.332 mg / kg x 100 g / d = 0.333 mg / d; HQ = 0.333 / (0.0035 mg / (kg·d) x 60 kg) = 1.59 > 1, triggering a red health warning; Economic impact assessment: It is estimated that 500 beef cattle in the farm need to be detected, resulting in a loss of 2 million yuan, which exceeds 5% of the regional annual environmental protection budget (budget 30 million yuan, 5% is 1.5 million yuan), triggering an orange economic warning.

[0032] 5. Decision intervention module Action space: Pollution source blocking: Require the mining enterprise to install a bag-type dust collector (dust removal efficiency 95%, complete within 3 days); Ecological restoration equipment scheduling: Unmanned aerial vehicle to spread lead passivation agent (dosage 20 kg / ha, covering an area of 20 km² around the mine); Pollution isolation belt setting: Planting Chinese brake (lead hyperaccumulator) in a range of 1 km around the mine (planting density 10 plants / m²); Reinforcement learning output scheme: Simultaneous implementation of pollution source blocking + passivation agent spreading + isolation belt planting (risk attenuation rate 75%, cost 1.2 million yuan), preferentially controlling atmospheric lead dust emissions and soil lead activity; 6. Data interaction interface module Linkage instructions: Risk heat map: 12 red grids (cow liver lead >0.5mg / kg) correspond to real-time positioning of pasture partitions; Priority list: ① Mine enterprises submit dust removal equipment installation plan within 24 hours; ② Agricultural department completes general survey of lead content in pasture within 3 days; ③ Health department starts blood lead screening of children (target population 1000 people); 7. Spatiotemporal database module Storage parameters: Model parameter layer: Synchronize the lead bioconcentration factor (BCF=30-80) published by the International Lead Zinc Study Group (ILZSG) and the soil lead migration model parameters of the United States EPA.

[0033] III. Implementation effect After the dust removal equipment is operated, the atmospheric lead concentration is reduced by 60%, and the soil lead bioavailability is reduced by 40% after the passivation agent is scattered.

[0034] The system automatically optimizes the intervention strategy through reinforcement learning, and after 3 months, the lead concentration in cow liver is reduced to 0.8mg / kg, close to the safety threshold.

[0035] According to the embodiments, the multi-medium environment data is acquired by the multi-modal data acquisition module and preprocessed to provide a data basis for the multi-medium risk conduction modeling module, the latter constructs a directed conduction relationship graph and dynamically models, the digital twin engine module simulates the risk conduction effect, the intelligent early warning module makes real-time early warning accordingly, and the decision intervention module generates an optimal scheme and drives execution and optimizes the strategy. The "monitoring-modeling-early warning-intervention" closed-loop system solves the problems of missing cross-medium risk conduction evaluation and disconnection between evaluation and management, greatly shortens the response time of the watershed scene system, significantly reduces the lead concentration in cow liver in the mining area after the strategy optimization, realizes full-process automation and intelligentization, improves the efficiency and scientificity of ecological environment management, provides an integrated solution for pollution response and ecological safety protection, and has obvious advantages and broad prospects. Although the present application is disclosed in a preferred embodiment as above, it is not intended to limit the present application, and any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, any modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, which does not deviate from the technical solutions of the present application, falls within the protection scope defined by the claims of the present application.

Claims

1. A real-time monitoring and early warning management system based on ecological environment, characterized in that: The management system includes: Multimodal data acquisition module. Multimodal data includes real-time sensor monitoring data, satellite remote sensing image data, drone inspection video data, and regional socioeconomic data. It is used to obtain multi-media environmental data on the atmosphere, water, soil, organisms, and human activities. The collected multi-source heterogeneous data is standardized and pre-processed and output to the multi-media risk transmission modeling module. The multi-media risk transmission modeling module is used to construct a directed transmission relationship graph including environmental media nodes, biological carrier nodes, and human exposure nodes. Based on the graph neural network algorithm, it dynamically models the pollution migration, biological enrichment, and exposure risk transmission processes between each node, and outputs cross-media risk transmission path data to the digital twin engine module. The digital twin engine module constructs a three-dimensional digital twin model of the ecological environment system based on the received data. The model integrates the environmental migration simulation submodule, the biological enrichment deduction submodule and the human exposure assessment submodule. It drives the model operation through real-time data, simulates the migration and transformation process of pollutants between multiple media and the risk transmission effect, and outputs the risk transmission dynamic data to the intelligent early warning module. The intelligent early warning module establishes a multi-level assessment system based on the received data, including ecological health, human safety and economic impact, and sets dynamic elastic early warning thresholds. When the monitored risk transmission intensity exceeds the corresponding threshold, it generates early warning information and transmits it synchronously to the decision-making intervention module and the data interaction interface module; The decision-making intervention module receives the intervention plan generated by the early warning information, outputs the intervention plan to the data interaction interface module, and receives the intervention effect feedback data from the execution terminal to optimize the strategy library. The decision-making intervention module generates the intervention plan based on the machine learning algorithm; The spatiotemporal database module is used to store historical data of multi-media environments, risk conduction modeling parameters, digital twin model configuration files, and early warning decision records, and uses spatiotemporal indexing technology to organize the data.

2. The real-time monitoring and early warning management system based on the ecological environment according to claim 1, characterized in that: The multimodal data acquisition module includes a sensor array for acquiring monitoring area data and a data preprocessing unit. The sensor array includes an atmospheric pollutant sensor, a water quality multi-parameter sensor, a soil heavy metal sensor, and a biophysiological signal sensor. The data preprocessing unit is used to unify the format of the analog signals, image data, and video data output by different sensors to generate a standardized data stream containing a timestamp, spatial coordinates, and data quality identification.

3. The real-time monitoring and early warning management system based on the ecological environment according to claim 1, characterized in that: The multi-media risk conduction modeling module realizes risk conduction process modeling by constructing a directed conduction relationship graph, including but not limited to atmospheric nodes, surface water nodes, groundwater nodes, soil nodes, plant nodes, animal nodes and human settlement nodes, and defines conduction path edges, including but not limited to dry and wet deposition edges, runoff erosion edges, root absorption edges, food chain transmission edges and respiratory exposure edges. Each edge is associated with substance migration rate parameters and bioconcentration coefficient parameters to form a complete risk conduction knowledge graph.

4. The real-time monitoring and early warning management system based on the ecological environment according to claim 1, characterized in that: The environmental migration simulation submodule of the digital twin engine module uses a multi-media coupled migration dynamics model to simulate the cross-media migration of pollutants. The expression of the multi-media coupled migration dynamics model is as follows: ； in, represents the net migration flux of pollutants per unit volume from medium i to medium j at time t, is the interfacial mass transfer coefficient that changes dynamically with time s, is the dynamic contact area between media i and j, represents the concentration gradient operator, is the distribution equilibrium constant of pollutants between media i and j, is the volume density of medium i, is the cross-medium diffusion coefficient, is the adsorption equilibrium time constant, is the saturation concentration of medium i for the target pollutant, Calculate the target volume for medium j, and are the real-time concentrations of pollutants in media i and j at time s respectively.

5. The real-time monitoring and early warning management system based on the ecological environment according to claim 1 is characterized in that: The bioaccumulation deduction submodule in the digital twin engine module calculates the concentration of pollutants in the organism through the bioaccumulation kinetic equation, which is as follows: ； in, represents the concentration of pollutants in the organism at time t, is the concentration of pollutants in the ambient medium, It represents the rate constant of biological absorption of pollutants, which is related to the surface area and metabolic rate of the organism. is the pollutant discharge rate constant, is the metabolic rate constant of pollutants in organisms.

6. The real-time monitoring and early warning management system based on ecological environment according to claim 1, characterized in that: The multi-level assessment system established by the intelligent early warning module includes an ecological health assessment unit, a human safety assessment unit and an economic impact assessment unit. The ecological health assessment unit calculates the species conduction vulnerability index based on the species sensitivity database and the Bayesian inference algorithm. When the index exceeds 30%-35% of the median tolerance of local species, an ecological warning is triggered. The human safety assessment unit calculates the exposure risk hazard quotient, that is, the ratio of the average daily pollutant intake to the reference dose. When the exposure risk hazard quotient is ≥1, a health warning is triggered. The economic impact assessment unit uses an input-output model combined with Monte Carlo simulation to calculate the economic impact coefficient of the risk conduction path. When the expected loss exceeds 5% of the regional annual environmental protection budget, an economic warning is triggered.

7. The real-time monitoring and early warning management system based on ecological environment according to claim 1, characterized in that: The decision intervention module is based on the reinforcement learning algorithm training process intervention program, specifically including: The action space is defined as five intervention measures, including pollution source blocking, ecological restoration equipment scheduling, agricultural product sales ban area demarcation, emergency water source allocation and pollution isolation zone establishment. Each measure corresponds to standardized execution parameters, and the reward function is defined as the ratio of risk conduction attenuation rate to intervention cost, where the risk conduction attenuation rate is the percentage of the decrease in the target path risk value within 48 hours after the intervention to the initial risk value. The intervention cost includes the comprehensive converted cost of equipment operating energy consumption, material consumption and manpower input. Through deep Q network training, the system can output a plan combination containing at least two measures within 10 minutes after receiving the early warning information. During the reinforcement learning process, the feedback data of the intervention effect is updated every 50-55 times, and the iterative optimization of the strategy library is automatically triggered. By comparing the actual risk attenuation effect with the model prediction value, the priority weight and parameter configuration of each measure are adjusted.

8. The real-time monitoring and early warning management system based on ecological environment according to claim 1, characterized in that: The data interaction interface module receives original monitoring data and intervention plans, and pushes monitoring data, early warning information and linkage instructions to the sensor network, ecological control equipment and management departments in real time. The cross-department linkage instructions of the data interaction interface module include a risk conduction heat map, an intervention measure priority list and an effect feedback mechanism. The risk conduction heat map marks the risk level of each area with a 100m×100m grid accuracy. The intervention measure priority list clearly defines the execution order of the environmental protection department cutting off the pollution source, the agricultural department starting crop testing and the health department conducting population health monitoring. The effect feedback mechanism requires the execution terminal to return the pollutant concentration change data and conduction path coefficient adjustment suggestions after the intervention every 15 minutes to 20 minutes.

9. The real-time monitoring and early warning management system based on ecological environment according to claim 1, characterized in that: The spatiotemporal database module adopts a hierarchical storage architecture, including a real-time data layer, a historical data layer and a model parameter layer. The real-time data layer stores 30-40 days of high-frequency monitoring data and supports query responses in seconds. The historical data layer stores multi-media environmental data for more than 3 years and adopts a spatiotemporal cube structure to support cross-year risk conduction trend analysis. The model parameter layer stores the bioaccumulation factors, medium distribution coefficients, mass transfer coefficients, adsorption equilibrium time constants, and pollutant saturation concentration parameters required for risk conduction modeling, and is updated in real time with external authoritative databases. The external authoritative databases include the Environmental Science Data Center database, the International Organization for Standardization parameter library, and the standard parameter library published by industry associations.

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