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

Abstract

The application discloses a kind of real-time monitoring and early warning management system based on ecological environment, and the application relates to the technical field of ecological environment monitoring and intelligent management, contain multimodal data acquisition module, obtain multi-medium environment data and pre-process, multi-medium risk conduction modeling module constructs directed graph, dynamically models risk conduction process, digital twin engine module constructs three-dimensional model, simulates pollutant migration transformation and risk effect, and intelligent early warning module generates early warning, the advantages of the application are that: effectively solve the problem of missing chain effect evaluation of cross-media risk conduction, multimodal data provide data basis, modeling module constructs directed conduction relationship diagram, dynamically models using graph neural network, presents conduction path, digital twin engine simulates pollutant migration transformation and risk conduction effect, and intelligent early warning module multi-level evaluation and timely early warning, decision intervention module generates optimal intervention scheme, and the system realizes accurate evaluation and effective response of cross-media 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. Air pollution, water eutrophication, soil heavy metal exceeding standard and sharp reduction of biodiversity pose serious challenges to ecological system safety and human sustainable development. In this context, it is a core requirement to establish a comprehensive, real-time and accurate ecological environment monitoring and early warning system for fine management, risk prevention and control and scientific decision-making.

[0003] 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 modules in the prior art are not well coordinated, and a closed-loop control system has not been formed, resulting in a 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

[0004] The purpose of the present application is to provide a real-time monitoring and early warning management system based on ecological environment.

[0005] 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:

[0006] 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;

[0007] 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. The nodes are connected by material migration paths 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;

[0008] The digital twin engine module constructs a three-dimensional digital twin model of the ecological environment system based on the received data, integrates an environmental migration simulation sub-module, a biological enrichment deduction sub-module and a human exposure assessment sub-module, and simulates the migration and transformation process of pollutants among multiple media and the risk transmission effect through real-time data-driven model operation, and outputs risk transmission dynamic data to the intelligent early warning module.

[0009] 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, impact 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.

[0010] 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.

[0011] 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.

[0012] 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 organizes the data by using a spatio-temporal indexing technology, 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.

[0013] 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 performing format unification processing on analog signals, image data and video data output by different sensors, and generating standardized data stream including time stamp, spatial coordinate and data quality identification.

[0014] As a further scheme 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 body 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 being associated with a substance migration rate parameter and a biological enrichment coefficient parameter, to form a complete risk transmission knowledge graph.

[0015] As a further scheme of the present application: the environment migration simulation submodule of the digital twin engine module simulates the cross-medium migration of pollutants by using a multi-medium coupling migration mechanics model, and the expression of the multi-medium coupling migration mechanics model is as follows:

[0016] ;

[0017] , wherein J represents the net migration flux per unit volume of pollutants from medium i to medium j at time t, considering the time accumulation effect and the spatial gradient diffusion, is an interface mass transfer coefficient that dynamically changes with time s, and is an adaptive function constructed by real-time meteorological data (wind speed, precipitation) and hydrological parameters (runoff velocity, tidal period) is calculated to reflect the influence of environmental conditions on mass transfer efficiency, is the dynamic contact area of medium i and j, which is suitable for the interface between surface water body and soil, atmosphere and vegetation canopy, etc. that changes with the seasons, represents a concentration gradient operator, which is used to describe the difference in spatial distribution of pollutants on both sides of the medium interface, is the distribution equilibrium constant of the pollutant between medium i and j, which is determined by a multiple regression model of material physicochemical properties (octanol-water partition coefficient, vapor pressure) and medium characteristic parameters (soil organic matter content, water salinity) , is the bulk density of medium i, is the cross-medium diffusion coefficient, which is derived from a molecular dynamics simulation database, is an adsorption equilibrium time constant, which is used to quantify the adsorption-desorption kinetic delay effect of pollutants on the surface of the medium, is the saturation concentration of medium i for the target pollutant, is the target calculation volume of medium j, which is dynamically divided according to the spatial resolution of the monitoring area (such as a 100m x 100m grid volume), and is used to normalize the total migration amount to a unit volume flux, and are the real-time concentrations of pollutants in medium i and j at time s, respectively, which are obtained by real-time monitoring of sensors in the multi-modal data acquisition module, and the unit is kg / m​3 .

[0018] As a further scheme of the 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:

[0019] ;

[0020] 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.

[0021] As a further scheme of the 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.

[0022] As a further scheme of the application: the decision intervention module trains the intervention scheme based on the reinforcement learning algorithm, specifically including:

[0023] The action space is defined as five intervention measures, including pollution source blocking, ecological restoration device scheduling, agricultural product sales prohibition area delineation, emergency water source allocation and pollution isolation belt setting, each measure corresponding to a standardized execution parameter (such as blocking valve opening, 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 intervention, and the intervention cost includes the comprehensive conversion cost of device operation energy consumption, material consumption and labor input; 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 update, and the priority weight and parameter configuration of each measure are adjusted by comparing the actual risk attenuation effect with the model prediction value.

[0024] 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.

[0025] 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.

[0026] By using the above technical scheme, compared with the prior art, the application has the following beneficial effects:

[0027] 1. The present application effectively solves the problem of missing 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.

[0028] 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. The decision intervention module receives the warning information, generates the optimal scheme from standardized intervention measures such as pollution source blocking and ecological restoration equipment scheduling using reinforcement learning algorithm, executes through the data interaction interface module, and collects feedback data of intervention effect in real time to optimize the strategy library. 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

[0029] Figure 1 The system flowchart in the embodiments of the present application. DETAILED DESCRIPTION

[0030] 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 and does not constitute a limitation of the present application.

[0031] In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as there is no conflict between them.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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 early 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.

[0037] 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 including 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 an execution terminal to optimize the strategy library;

[0038] 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 a sensor network, ecological regulation equipment and environmental protection, agricultural and health management departments in real time, to realize cross-system data sharing and collaborative response;

[0039] 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, organizing data by using a spatio-temporal indexing technology, supporting spatio-temporal correlation query and long-term trend analysis of multi-medium data, and providing basic data support for the multi-medium risk transmission modeling module and the digital twin engine module.

[0040] 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 performing format unification processing on analog signals, image data and video data output by different sensors, to generate standardized data streams containing timestamps, spatial coordinates and data quality identifiers.

[0041] 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 body nodes, underground water 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 being associated with a material migration rate parameter and a biological enrichment coefficient parameter, to form a complete risk transmission knowledge graph;

[0042] There are 12 types of nodes in total, as follows:

[0043] ① Atmospheric node: representing an atmospheric environment medium, storing gaseous pollutant concentration data such as PM2.5, SO2 and NOx;

[0044] ② Surface water body node: representing surface water environments such as rivers, lakes and reservoirs, storing parameters such as pH value, dissolved oxygen and heavy metal ion concentration;

[0045] ③ Groundwater Node: Represents the groundwater environment, storing monitoring data of nitrate, sulfate, trace elements, etc.

[0046] ④ Soil Node: Represents the surface and profile soil, storing parameters such as moisture content, organic matter content, and pollutant adsorption concentration;

[0047] ⑤ Sediment Node: Represents river sediment and lake bed sediment, storing interstitial water pollutant concentration and sedimentation rate data;

[0048] ⑥ Plant Node: Represents the vegetation community, storing leaf pollutant enrichment, root absorption rate, and transpiration parameters;

[0049] ⑦ Animal Node: Represents wild animals and farmed organisms, storing body pollutant residue and food chain transmission efficiency parameters;

[0050] ⑧ Microorganism Node: Represents soil / water microorganism communities, storing pollutant degradation enzyme activity and metabolite data;

[0051] ⑨ Human Settlement Node: Represents urban / rural residential areas, storing population exposure frequency and indoor / outdoor pollutant concentration difference data;

[0052] ⑩ Industrial Pollution Source Node: Represents factories, mines, and other human pollution sources, storing pollutant emission rate and treatment facility efficiency data;

[0053] ⑪ Agricultural Activity Node: Represents agricultural areas such as farmland and breeding farms, storing fertilizer and pesticide usage and livestock manure pollutant output data;

[0054] ⑫ Nature Reserve Node: Represents ecologically sensitive areas, storing species diversity index and ecological vulnerability assessment parameters;

[0055] There are 28 path edges in total, as follows:

[0056] ① Atmosphere-Surface Water Conduction Edge

[0057] Wet Deposition Edge: The process of atmospheric pollutants entering surface water through rainfall / snowfall;

[0058] Dry Deposition Edge: The process of atmospheric particulate matter entering surface water through gravitational settling or inertial collision;

[0059] Gas-Liquid Interface Mass Transfer Edge: The process of gaseous pollutants diffusing bidirectionally at the atmosphere-water interface through Henry's Law;

[0060] ② Atmosphere-Soil Conduction Edge

[0061] Dust Deposition Edge: The process of atmospheric particulate matter (such as PM10) settling on the soil surface;

[0062] Gas adsorption edge: the process of soil organic matter adsorbing and fixing volatile organic compounds (VOCs) in the atmosphere;

[0063] ③Surface water-soil conduction edge

[0064] Runoff flushing edge: the process of surface runoff carrying pollutants into the surface layer of soil;

[0065] Leakage penetration edge: the process of surface water penetrating into groundwater through soil pores;

[0066] ④Surface water-sediment conduction edge

[0067] Particle deposition edge: the process of water suspended matter carrying pollutants to deposit into sediment;

[0068] Interstitial water exchange edge: the process of pollutant diffusion exchange between sediment interstitial water and overlying water;

[0069] ⑤Soil-groundwater conduction edge

[0070] Solute migration edge: the process of dissolved pollutants in soil entering groundwater through seepage convection-diffusion;

[0071] Ion exchange edge: the process of pollutant adsorption / desorption equilibrium between soil colloids and ions in groundwater;

[0072] ⑥Soil-plant conduction edge

[0073] Root absorption edge: the process of plant roots absorbing pollutants in soil through active transport or passive diffusion;

[0074] Transpiration transfer edge: the process of pollutants migrating from roots to stems and leaves through plant transpiration;

[0075] ⑦Plant-animal conduction edge

[0076] Food chain transmission edge: the process of herbivorous animals ingesting plant tissues leading to pollutant enrichment;

[0077] Predation conduction edge: the process of carnivorous animals causing pollutant biomagnification by preying on lower trophic level animals;

[0078] ⑧Water-animal conduction edge

[0079] Gill absorption edge: the process of aquatic animals directly absorbing dissolved pollutants in water through gill filaments;

[0080] Surface permeation edge: the process of pollutants penetrating into the body of aquatic animals through their skin and mucous membranes;

[0081] ⑨Human-environment conduction edge

[0082] Respiratory exposure edge: the process of exposure of the population to air pollutants (such as PM2.5, ozone) by inhalation;

[0083] Dietary intake edge: the process of pollutant intake by eating contaminated agricultural products, aquatic products;

[0084] Skin contact edge: the process of pollutant absorption caused by the direct contact of human skin with contaminated water and soil;

[0085] Pollution source-environment transmission edge

[0086] Industrial emission edge: the process of pollutant release from industrial waste gas / waste water / solid waste to the atmosphere, water body and soil;

[0087] 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;

[0088] Biological-environmental feedback edge

[0089] Plant adsorption edge: the process of absorption and fixation of atmospheric pollutants by vegetation through leaf stomata;

[0090] Microbial degradation edge: the process of degradation of organic pollutants by soil / water microorganisms through metabolic activity;

[0091] Algal absorption edge: the process of absorption of nitrogen and phosphorus nutrients in water by aquatic algae through photosynthesis;

[0092] Cross-media circulation edge

[0093] Evaporation and condensation edge: the circulation process of volatile pollutants in water / soil evaporating into the atmosphere and then condensing and settling;

[0094] Freeze-thaw migration edge: the redistribution process of soil pollutants in solid ice phase and liquid water phase through freeze-thaw action in cold regions;

[0095] Tidal transport edge: the process of reciprocal transport of pollutants between land and sea media through tidal movement in coastal areas.

[0096] 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:

[0097] ;

[0098] wherein, represents the net migration flux per unit volume of pollutants from medium i to medium j at time t, which comprehensively considers the time accumulation effect and spatial gradient diffusion, The interfacial mass transfer coefficient, which varies dynamically with time s, is an adaptive function constructed using real-time meteorological data (wind speed, precipitation) and hydrological parameters (runoff velocity, tidal period). The calculation reflects the impact of environmental conditions on mass transfer efficiency. Let be the dynamic contact area between media i and j, applicable to interfaces that change with the seasons, such as surface water and soil, atmosphere and vegetation canopy. This represents the concentration gradient operator, used to describe the spatial distribution differences of pollutants on both sides of the medium interface. The distribution equilibrium constant of pollutants between media i and j is determined by a multiple regression model using the physicochemical properties of the materials (octanol-water partition coefficient, vapor pressure) and the characteristic parameters of the media (soil organic matter content, water salinity). Sure, Let i be the volume density of medium i. The transmedium diffusion coefficient is derived from a molecular dynamics simulation database. This is the adsorption equilibrium time constant, used to quantify the delayed effect of adsorption-desorption kinetics of pollutants on the medium surface. Let i be the saturation concentration of the target pollutant in medium i. The target volume of medium j is calculated and dynamically divided according to the spatial resolution of the monitoring area (e.g., a 100m × 100m grid volume). This is used to normalize the total migration amount to the flux per unit volume. and The values ​​represent the real-time pollutant concentrations in media i and j at time s, respectively, acquired in real-time by sensors in the multimodal data acquisition module, with units of kg / m³. 3 .

[0099] The saturation concentration of a target pollutant in medium i refers to the maximum concentration of a 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 this saturation concentration, the excess pollutants will no longer be adsorbed or retained by the medium, but will enter other media through migration, diffusion and other processes (such as leaching from soil to groundwater, volatilization from water bodies to the atmosphere, etc.), thereby triggering cross-media risk transmission.

[0100] In one embodiment of the present invention: the bioaccumulation inference submodule in the digital twin engine module calculates the concentration of pollutants in the organism through a bioaccumulation kinetic equation, which is as follows:

[0101] ;

[0102] in, This represents the concentration of pollutants in the organism at time t. a concentration of a pollutant in an environmental medium, a rate constant of absorption of the pollutant by the organism, determined by experimentally calibrated values of the surface area of the organism and the metabolic rate, related to the surface area of the organism and the metabolic rate, a rate constant of excretion of the pollutant, a rate constant of metabolism of the pollutant in the organism, the equation comprehensively considers the absorption, excretion and metabolism of the pollutant by the organism, and can accurately calculate the change of the pollutant in the organism over time, and further analyze the enrichment effect of the pollutant in the food chain.

[0103] 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 a species transmission vulnerability index based on a species sensitivity database and a 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 an exposure risk hazard quotient, i.e. 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 an economic impact coefficient of the risk transmission path using an 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.

[0104] In an embodiment of the present application: the decision intervention module trains intervention schemes based on a reinforcement learning algorithm, specifically including:

[0105] defining the action space as 5 kinds of 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 to the risk value reduction 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 2 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, and by comparing the actual risk attenuation effect with the model prediction value, the priority weight and parameter configuration of each measure are adjusted.

[0106] 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 region 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.

[0107] 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, supporting second-level query response. The historical data layer stores more than 3 years of multi-medium environmental data, organized in a spatio-temporal cube structure, supporting cross-year risk transmission trend analysis. The model parameter layer stores the parameters required for risk transmission modeling, including biological enrichment factor, medium distribution coefficient, mass transfer coefficient, adsorption equilibrium time constant, and pollutant saturation concentration, which are 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.

[0108] Example One, please refer to the attached Figure 1 , watershed ecological environment monitoring and risk prevention and control scene:

[0109] Select a tributary watershed in the middle reaches of the Yangtze River (area about 200km 2 ), 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;

[0110] Two, system deployment parameters

[0111] 1. Multi-modal data acquisition module

[0112] Sensor array (deployment density: every 5km² / set):

[0113] Atmospheric sensor: TSI9350 PM2.5 / PM10 sensor (accuracy ±1μg / m³), API200 SO2 / NOx sensor (accuracy ±0.1ppb), deployed at 10 meteorological towers (height 15m) in the watershed;

[0114] Water quality sensor: YSI EXO2 multi-parameter probe (measurement parameters: pH, dissolved oxygen, ammonia nitrogen, total phosphorus, accuracy ±0.5%FS), deployed at 20 monitoring points in the main stream of the tributary and the mouths of 5 tributaries;

[0115] Soil sensors: METER EC-5 soil moisture sensors (accuracy ± 3%), XOS-2000 heavy metal detectors (detect Cd, Pb, Hg, accuracy ± 0.1 mg / kg), deployed in farmland area in 100m x 100m grid, a total of 150 measuring points;

[0116] Biological sensors: implantable fish electrical signal sensors (monitoring crucian carp and carp, sampling frequency 1 Hz), deployed in wetland shoal area (50 tails per hectare);

[0117] 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 be verified);

[0118] 2. Multi-medium risk transmission modeling module

[0119] Node definition (12 categories in total, core nodes in this scenario):

[0120] Environmental medium nodes: atmospheric nodes (N1), surface water body nodes (N2), farmland soil nodes (N3), wetland soil nodes (N4);

[0121] Biological carrier nodes: rice plant nodes (B1), crucian carp nodes (B2);

[0122] Human exposure nodes: residential area nodes (H1);

[0123] Conduction path edge (key path in this scenario):

[0124] Soil to water body: runoff edge (E1, migration rate parameter =0.05m / h, automatically increased by 30% in rainy season);

[0125] Water body to biology: fish absorption edge (E2, biological enrichment coefficient BCF=1000L / kg, for organophosphorus pesticides);

[0126] Soil to crop: root absorption edge (E3, absorption rate parameter =0.2cm / h, positively correlated with soil moisture content);

[0127] 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 (a total of 10950 groups);

[0128] 3. Digital twin engine module

[0129] (1) Environmental migration simulation sub-module (take soil to water body phosphorus migration as an example)

[0130] Multi-media coupling migration mechanics model parameters:

[0131]

[0132] Branch river section calculation volume (divided by 100 m river section, average water depth 2 m, width 50 m, =100×50×2=10 4 m 3 );

[0133] : Runoff scouring mass transfer coefficient, rainy season (s = rainy season period) k = 0.08 m / h, non-rainy season k = 0.05 m / h (dynamically adjusted according to real-time rainfall);

[0134] : Soil and water contact area, runoff spreads to 1500 m² in rainy season, 1000 m 2 in non-rainy season;

[0135] Total phosphorus concentration of soil (real-time monitoring value, peak value in rainy season 50 mg / kg, converted to volume concentration =0.05kg / m 3 );

[0136] : Total phosphorus concentration of water (real-time monitoring value, background value 0.02 mg / L, over-limit threshold 0.05 mg / L);

[0137] : Phosphorus distribution equilibrium constant between soil and water, calculated by =1000×OM+50 (OM is the soil organic matter content, OM = 2% in this scenario, =1000×0.02+50=70);

[0138] : Soil bulk density 1.3 g / cm3=1300kg / m 3 ;

[0139] : Phosphorus cross-media diffusion coefficient 1×10−9m 2 / s (from soil hydrodynamic database);

[0140] : Adsorption equilibrium time constant 24 h (i.e. 24 hours are needed for phosphorus to reach adsorption equilibrium on the soil surface);

[0141] : Soil phosphorus saturation concentration 500 mg / kg (laboratory measured value)

[0142] Calculation result: Phosphorus migration flux Q = 0.01 kg / m 3 / h in a single river section during the rainy season, resulting in an increase of 0.1 μg / L in the total phosphorus concentration of the water body per hour;

[0143] (2) Biological enrichment deduction sub-module (taking rice enrichment of cadmium as an example)

[0144] Biological enrichment kinetics equation parameters: ;

[0145] : Soil cadmium concentration 0.3 mg / kg (0.3 times higher than the standard, GB15618-2018 agricultural land standard);

[0146] : Rice root absorption rate constant 0.1 kg / (kg·d) (positively correlated with root surface area 100 cm² / plant);

[0147] : Cadmium discharge rate constant 0.05 kg / (kg·d);

[0148] : Cadmium metabolic rate constant 0 (cadmium is difficult to metabolize in plants);

[0149] Calculation result: After 45 days of planting, the cadmium concentration in rice is:

[0150] = 0.3 × (0.1 / (0.05+0)) × (1-e-0.05×45) = 0.6 × (1-0.105) = 0.537 mg / kg (exceeding the food limit of 0.2 mg / kg);

[0151] 4. Intelligent early warning module

[0152] Ecological health assessment: The rice species transmission vulnerability index is calculated as 40% (exceeding the local tolerance median value of 30%, triggering a yellow warning);

[0153] Human safety assessment: The daily average cadmium intake of residents through the consumption of rice is 0.537 mg / kg × 200 g / d = 0.107 mg / d, and the reference dose = 0.001 mg / (kg·d) (for adults weighing 60 kg), HQ = 0.107 / (0.001×60) = 1.78 > 1, triggering an orange health warning;

[0154] Economic impact assessment: It is estimated that 500 hectares of farmland will be affected, with a loss of 1.5 million yuan in rice production, which is 5% more than the annual environmental protection budget (budget 20 million yuan, 5% is 1 million yuan), triggering a yellow economic warning;

[0155] 5. Decision intervention module

[0156] Action space:

[0157] Pollution source blocking: Close the sewage outlets of 3 phosphate fertilizer plants in the watershed (valve opening 100% closed, response time 5 minutes);

[0158] Ecological restoration equipment scheduling: Start 10 river aeration devices (power 5kW per unit, running time 24h / d);

[0159] Farmland delineation: Delineate a temporary sales ban area within 5km of the cadmium-exceeding farmland (5 natural villages involved);

[0160] Reinforcement learning output scheme: Prioritize pollution source blocking + agricultural product sales ban (risk attenuation rate 65%, cost 800,000 yuan), and simultaneously schedule aeration devices to improve water self-purification capacity;

[0161] 6. Data interaction interface module

[0162] Linkage instructions:

[0163] Risk heat map: Marked in 100m x 100m grid, red grid (cadmium concentration >0.3mg / kg) 23, yellow grid (0.2-0.3mg / kg) 58;

[0164] Priority list: ① Environmental protection department to cut off pollution source within 60 minutes; ② Agricultural department to complete crop sampling and testing within 24 hours; ③ Health department to start resident urine cadmium screening (target population 5000);

[0165] 7. Spatiotemporal database module

[0166] Storage parameters:

[0167] Real-time data layer: Store data for the past 30 days, with an average daily data volume of 1.2GB, supporting 500 concurrent second-level queries;

[0168] Historical data layer: Store data from 2018 to 2023, using a spatiotemporal cube structure, with a spatial resolution of 100m and a temporal resolution of 1 hour;

[0169] Implementation effect

[0170] 48 hours after pollution source blocking, water total phosphorus concentration decreased by 25%, and rice cadmium concentration growth rate slowed down by 40%;

[0171] System response time: from early warning generation to intervention plan execution, the total time is 95 minutes, which is 60% shorter than the traditional system.

[0172] Example two, please refer to the attached Figure 1 , the surrounding soil of the mining area-biological-human exposure risk prevention and control scene:

[0173] Select a lead-zinc mine in southwest China (area 50 km²), focus on monitoring the risk of soil lead pollution enrichment through vegetation→livestock→human food chain, and the direct exposure risk of residents to atmospheric lead dust;

[0174] II. System deployment parameters

[0175] 1. Multi-modal data acquisition module

[0176] Sensor array (deployment density: every 2km 2 / set):

[0177] Atmospheric sensor: Thermo Fisher 42i NOx sensor, 5030 lead dust detector (detection limit 0.1 μg / m³), 3 monitoring stations (height 20 m) are deployed in the upwind and downwind directions of the mining area;

[0178] Soil sensor: HACH soil lead detector (accuracy ±0.5 mg / kg), EC-10 soil conductivity sensor, deployed in a 50m×50m grid around the mining area, a total of 100 measurement points;

[0179] Biological sensor: ear tag physiological signal sensor (monitors body temperature, 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;

[0180] Human activity data: Obtain residents' dietary structure and outdoor activity duration through community questionnaire (200 valid samples per day);

[0181] 2. Multi-medium risk transmission modeling module

[0182] Node definition (core nodes in this scenario):

[0183] Environmental medium nodes: atmospheric node (N1), mining area soil node (N2), farmland soil node (N3);

[0184] Biological carrier nodes: pasture node (B1), beef cattle node (B2);

[0185] Human exposure node: residential node (H1);

[0186] Conduction path edge (key path in this scenario):

[0187] Atmosphere→ Soil: Dry deposition edge (E1, deposition rate 0.01 mg / (m²·h), positively correlated with wind speed);

[0188] 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);

[0189] Pasture→ Beef cattle: Food chain transfer edge (E3, bioconcentration factor BCF = 50, enrichment multiple of lead in cattle liver);

[0190] 3. Digital twin engine module

[0191] (1) Environmental migration simulation submodule (taking atmospheric lead dust deposition as an example)

[0192] Multimedia coupling migration dynamics model parameters:

[0193]

[0194] : Calculated volume of mine soil (1 km² area, soil depth 0.5 m, = 106 m²×0.5 m = 5×105 m³);

[0195] : 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 (dynamically adjusted according to real-time weather data);

[0196] : Atmospheric and soil contact area, i.e. ground surface area, fixed 1 km² = 10 6 m²;

[0197] : Atmospheric lead concentration (real-time monitoring value, peak value in downwind direction of mining area 5 μg / m³ = 5×10 -9 kg / m³);

[0198] : Partition equilibrium constant of lead between atmosphere-soil, calculated by = 500×OM + 100 (OM = 1.5%, = 500×0.015 + 100 = 107.5);

[0199] : Atmospheric density 1.2 kg / m³;

[0200] : Lead transmedia diffusion coefficient 2×10 -8 m² / s (from atmospheric particulate matter deposition model database);

[0201] : atmospheric lead saturation concentration 50 μg / m³ (wet deposition occurs after exceeding);

[0202] 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;

[0203] (2) Biological enrichment deduction submodule (taking beef cattle enrichment of lead as an example)

[0204] Biological enrichment kinetics equation parameters: ;

[0205] : lead concentration in forage 2 mg / kg (exceeding the forage lead safety threshold of 1 mg / kg);

[0206] : intestinal absorption rate constant of beef cattle 0.2 kg / (kg·d) (positively correlated with beef cattle weight 500 kg and daily forage intake 15 kg);

[0207] : lead excretion rate constant 0.1 kg / (kg·d);

[0208] : lead metabolism rate constant 0.02 kg / (kg·d) (limited liver metabolism capacity);

[0209] Calculation result: after 60 days of breeding, the lead concentration in beef cattle liver:

[0210] = 2 × (0.2 / (0.1+0.02)) × (1-e-0.12×60) = 2 × 1.667 × (1-0.0006) = 3.332 mg / kg (exceeding the meat lead limit of 0.5 mg / kg);

[0211] 4. Intelligent early warning module

[0212] Ecological health assessment: forage species transmission vulnerability index 50% (exceeding the local tolerance median value of 30%, triggering an orange warning);

[0213] Human safety assessment: daily average lead intake of residents through consumption of beef 3.332 mg / kg × 100 g / d = 0.333 mg / d;

[0214] HQ = 0.333 / (0.0035 mg / (kg·d) × 60 kg) = 1.59 > 1, triggering a red health warning;

[0215] Economic impact assessment: It is estimated that all 500 head of cattle in the ranch need to be tested, resulting in a loss of 2 million yuan, which exceeds 5% of the annual environmental protection budget for the region (budget 30 million yuan, 5% is 1.5 million yuan), triggering an orange economic warning.

[0216] 5. Decision intervention module

[0217] Action space:

[0218] Pollution source blocking: Require mining enterprises to install bag dust collectors (dust removal efficiency 95%, complete within 3 days);

[0219] Ecological restoration equipment scheduling: Unmanned aerial vehicle sowing lead passivation agent (dosage 20 kg / ha, covering an area of 20 km² around the mine);

[0220] Pollution isolation zone setting: Planting Chinese brake (lead hyperaccumulator, planting density 10 plants / m²) within 1 km range around the mine;

[0221] Reinforcement learning output scheme: Simultaneous implementation of pollution source blocking + passivation agent sowing + isolation zone planting (risk attenuation rate 75%, cost 1.2 million yuan), priority control of atmospheric lead dust emission and soil lead activity;

[0222] 6. Data interaction interface module

[0223] Linkage instructions:

[0224] Risk heat map: There are 12 red grids (liver lead >0.5 mg / kg) corresponding to the real-time positioning of the ranch subarea;

[0225] Priority list: ① Mining enterprises submit dust removal equipment installation plan within 24 hours; ② Agricultural department complete general survey of lead content in pasture within 3 days; ③ Health department start children's blood lead screening (target population 1000 people);

[0226] 7. Spatiotemporal database module

[0227] Storage parameters:

[0228] Model parameter layer: Simultaneously update the lead bioconcentration factor (BCF=30-80) published by the International Lead Zinc Study Group (ILZSG) and the U.S. EPA soil lead migration model parameters.

[0229] III. Implementation effect

[0230] After the dust removal equipment is running, the atmospheric lead concentration decreases by 60%, and the soil lead bioavailability decreases by 40% after the passivation agent is sown.

[0231] The system automatically optimizes the intervention strategy through reinforcement learning, and after 3 months, the liver lead concentration of cattle decreases to 0.8 mg / kg, close to the safety threshold.

[0232] According to the embodiments, the multi-modal data acquisition module acquires multi-medium environment data and pre-processes, 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 real-time early warns accordingly, the decision intervention module generates an optimal scheme and drives execution and optimization strategy, the "monitoring-modeling-early warning-intervention" closed-loop system solves the problems of lack of cross-medium risk conduction evaluation and disconnection between evaluation and management, the response time of the basin scene system is greatly shortened, the lead concentration of the liver of the mine area scene is significantly reduced after the strategy optimization, the present application realizes full-process automation and intelligentization, improves the efficiency and scientificity of ecological environment management, provides an integrated scheme for pollution response and ecological safety guarantee, and has obvious advantages and broad prospects

[0233] Although the present application is disclosed in the preferred embodiments, 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. An ecological environment-based real-time monitoring and early warning management system, characterized in that, The management system comprises: A multi-modal data acquisition module, multi-modal data including real-time sensor monitoring data, satellite remote sensing image data, unmanned aerial vehicle inspection video data and regional social and economic data, for obtaining multi-medium environmental data of atmosphere, water, soil, biology and human activities, standardizing and preprocessing the collected multi-source heterogeneous data, and outputting to a multi-medium risk transmission modeling module; The multi-medium risk transmission modeling module is used for constructing a directed transmission relationship graph containing environmental medium nodes, biological carrier nodes and human exposure nodes, dynamically modeling the pollution migration, biological enrichment and exposure risk transmission process between nodes based on a graph neural network algorithm, and outputting cross-medium risk transmission path data to a 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, integrates an environmental migration simulation submodule, a biological enrichment deduction submodule and a human exposure evaluation submodule, drives the model by real-time data, simulates the migration and transformation process of pollutants between multi-media and the risk transmission effect, and outputs risk transmission dynamic data to an 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 when the monitored risk transmission intensity exceeds the corresponding threshold, and synchronously transmits the early warning information to a decision intervention module and a data interaction interface module; The decision intervention module receives the intervention scheme generated by the early warning information, outputs the intervention scheme to the data interaction interface module, and receives intervention effect feedback data from an execution terminal to optimize the strategy library, and the decision intervention module generates the intervention scheme based on a machine learning algorithm; A space-time database module is used for storing multi-medium environmental historical data, risk transmission modeling parameters, digital twin model configuration files and early warning decision records, and organizing the data by using a space-time indexing technology.

2. The real-time monitoring and early warning management system based on ecological environment according to claim 1, characterized in that: The multi-modal data acquisition module comprises a sensor array for acquiring monitoring area data and a data preprocessing unit, the sensor array comprises 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 uniformly processing analog signals, image data and video data output by different sensors to generate standardized data streams containing time stamps, spatial coordinates and data quality identifiers. 3.The real-time monitoring and early warning management system based on ecological environment according to claim 1, characterized in that: 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 body nodes, underground water 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 being associated with a material migration rate parameter and a biological enrichment coefficient parameter to form a complete risk transmission knowledge graph.

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

5. The real-time monitoring and early warning management system based on ecological environment according to claim 1, characterized in that: The biological enrichment deduction submodule in the digital twin engine module calculates the concentration of pollutants in the organism through a biological enrichment kinetics equation as follows: ； wherein, C(t) represents the concentration of the pollutant in the organism at time t, Cenv represents the concentration of the pollutant in the environmental medium, kabs represents the absorption rate constant of the pollutant by the organism, which is related to the surface area of the organism and the metabolic rate, kex represents the excretion rate constant of the pollutant, kmet represents the metabolic rate constant of the pollutant in the organism.

6. The real-time monitoring and early warning management system based on ecological environment according to claim 1, characterized in that: 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. When the index exceeds 30%-35% of the local species tolerance median, an ecological early warning is triggered. The human safety evaluation unit calculates the exposure risk hazard quotient, which is the ratio of daily pollutant intake to reference dose. When the exposure risk hazard quotient is greater than or equal to 1, a health early warning is triggered. The economic impact evaluation unit uses an input-output model combined with Monte Carlo simulation to calculate the economic impact coefficient of the risk transmission path. When the expected loss exceeds 5% of the annual environmental protection budget of the region, an economic early 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 trains intervention programs based on a reinforcement learning algorithm, specifically including: The action space is defined as five intervention measures, including pollution source blocking, ecological restoration device scheduling, agricultural product sales ban area delineation, emergency water source allocation and pollution isolation belt setting. Each measure corresponds to a standardized execution parameter. The reward function is defined as the ratio of risk transmission attenuation rate to intervention cost. The risk transmission attenuation rate is the percentage of the initial risk value within 48 hours after intervention. 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. During the reinforcement learning process, the strategy library iteration optimization is automatically triggered every 50-55 times of intervention effect feedback data update. 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 programs, 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 transmission heat map, an intervention measure priority list and an effect feedback mechanism. The risk transmission heat map marks the risk level of each region 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 human health monitoring. The effect feedback mechanism requires the execution terminal to return the pollutant concentration change data and the transmission path coefficient adjustment suggestion every 15-20 minutes after intervention. 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, supports second-level query response, the historical data layer stores more than 3 years of multi-medium environmental data, adopts a spatiotemporal cube structure organization, supports cross-year risk transmission trend analysis, the model parameter layer stores biological enrichment factors, medium distribution coefficients, mass transfer coefficients, adsorption equilibrium time constants 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.

Images