21627 results about "Pollutant" patented technology

The invention discloses a water quality prediction method based on graph neural network and spatial-temporal feature fusion, and the method comprises the steps: obtaining hydrological data and water quality data of a plurality of monitoring stations in a preset drainage basin in a preset time period, representing the monitoring stations as nodes of a graph, and representing the adjacency relation between the monitoring stations as edges of the graph, a graph topology network mapping multiple monitoring station pollutant data is constructed, a spatial-temporal feature fusion module is adopted to perform feature extraction and fusion on the graph topology network to obtain water pollution spatial-temporal feature data, and the spatial-temporal feature fusion module is obtained by training a graph convolutional network, a gating circulation unit and a self-attention mechanism. And performing fusion processing on the extracted feature data to form a time sequence data set of a graph topology structure, and finally predicting the data set through a graph time sequence prediction model to obtain water quality prediction results of the plurality of monitoring stations in a target time period. The method can effectively deal with the spatial and temporal change process of water pollution, reduce the water quality prediction cost and improve the water quality prediction precision.

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

The invention provides an intelligent regulation and control method for a three-stage constructed wetland recirculating aquaculture system, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, continuously collecting water quality data and operation control data of each control unit, and constructing a multi-source heterogeneous data set under a unified time scale; then, constructing a pollution evolution trend prediction model, and capturing a dynamic evolution trend of water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key control parameters which influence the water quality change of the system at present, and reasoning the dynamic adjustable boundary of the key control parameters; and finally, constructing a reinforcement learning strategy network fusing state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among pollutant removal efficiency, a water quality standard-reaching rate and operation energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the wetland system can be realized.

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention provides a drainage basin water quality pollution traceability analysis method and system fused with space-time big data, and relates to the technical field of pollution analysis, and the method comprises the steps: collecting water quality, weather, surface runoff and other data, and generating a data set through space-time label association; and on the basis of the data set, constructing a pollutant migration and diffusion model containing a pollutant concentration attenuation and flow direction prediction sub-model, and calculating a pollutant concentration change rate and a migration path. And finally, the pollution sources are divided into point source pollution, non-point source pollution and endogenous pollution through the pollution source identification model, the contribution rate of each pollution source is calculated, and a pollution traceability analysis report is generated. The method can comprehensively consider the influence of various factors on the water quality, realizes the accurate positioning and quantitative analysis of the pollution source, and improves the traceability efficiency and accuracy.

The invention discloses a photovoltaic panel automatic cleaning system based on an unmanned aerial vehicle and a method thereof, and relates to the technical field of solar photovoltaic. The system comprises an unmanned aerial vehicle, a pollutant cleaning device, a pollutant detection device and a remote control center, comprising the steps of planning a flight path of an unmanned aerial vehicle and obtaining a photovoltaic panel image to determine the type, position and cleaning time of pollutants; a cleaning mode is selected to carry out cleaning operation, and secondary detection is carried out on the cleaned photovoltaic panel; and recording each cleaning task and process and establishing a self-adaptive model. The unmanned aerial vehicle is used for monitoring the photovoltaic panel in real time, image analysis and pollutant identification are carried out through the remote control center, the cleaning device is used for cleaning the photovoltaic panel, and the pollutant detection device is used for carrying out secondary detection after cleaning; according to the cleaning device, multiple cleaning modes are combined, flexible selection is achieved according to the types of pollutants, the cleaning effect of the photovoltaic panel is guaranteed, the cleaning efficiency is improved, and meanwhile the energy-saving effect is achieved.

The invention relates to the technical field of environment monitoring, and discloses a water ecological pollution diffusion prediction method and system based on big data, and the method comprises the steps: constructing a sensing network of a water environment, obtaining real-time water environment data, carrying out the preprocessing of the data through an edge calculation node, and synchronously transmitting the data to a central platform; based on historical data and real-time water body environment data, establishing a dynamic prediction model of pollutant boundary condition parameters; according to the dynamic boundary condition parameters, constructing a pollutant propagation atlas, analyzing the spatial and temporal distribution trend of pollutant concentration and identifying the position of a pollution source; and according to a pollution source position identification result, executing aging prediction, and triggering a pollution early warning response in combination with a set threshold value. The space coverage rate and timeliness of pollution monitoring are improved, the intelligence and credibility of pollution tracking are enhanced, and the intelligent and scientific level of water ecological environment pollution prevention and control is improved.

The invention relates to the technical field of environment monitoring, in particular to an underground water pollution source traceability identification method and system, and the method comprises the following steps: S1, collecting the flow velocity, flow direction and water level change data of underground water in a polluted region in real time; s2, constructing a preliminary underground water flow model; s3, simulating a migration path of pollutants in underground water, and predicting a potential pollution source position; s4, processing the collected underground water flow velocity and pollutant diffusion path data, and removing abnormal data interference; s5, generating a spatial distribution map of the potential pollution source in combination with a geographic information system; and S6, verifying pollutant components and concentration distribution thereof, and confirming the accuracy of a traceability result. According to the invention, by combining a multi-pollutant traceability algorithm, an underground water flow model and a geographic information system technology, a pollutant migration path is accurately simulated and a pollution source spatial distribution map is generated, so that the precision and reliability of underground water pollution source traceability are greatly improved.

The invention relates to the field of environment monitoring, and discloses an efficient and accurate pollution source monitoring quality control system and method, and the system comprises a multi-modal data collection module, a high-dimensional data modeling module, a time-space correlation analysis module, a time sequence prediction module, a multi-task learning module and an anomaly detection feedback module. The method comprises the following steps: collecting data of pollutants such as air, water quality and noise, performing high-dimensional modeling and optimization on the data, and capturing space-time propagation paths of the pollutants among different sensor nodes; the system predicts the short-term change and the long-term trend of the pollutant concentration in real time; the multi-task learning model is used for predicting concentration changes of multiple pollutants at the same time, multi-scale decomposition is carried out on a time sequence through wavelet transform, abnormal changes in data are detected, and a feedback control strategy is generated. According to the invention, the precision and real-time performance of pollution source monitoring are improved, the dynamic change of multiple pollutants can be effectively handled, and the high efficiency and reliability of environment quality monitoring are ensured.

The invention relates to the technical field of pollutant tracing, and discloses a method and a system for tracing new pollutants in various environmental media based on machine learning. The method comprises the following steps: collecting multivariate environment data to construct a pollutant fingerprint database; performing space-time correlation analysis on the fingerprint database and the environment data, and reconstructing a transmission path; converting the data, the fingerprint database and the path diagram into source node fusion to construct an analytical model, and forming a contribution rate matrix; and positioning the pollution source through the three-dimensional features based on the matrix. According to the method and the device, an intelligent decision-making system based on machine learning can be constructed by integrating multi-source heterogeneous data under a complex environment background, automatic, standardized and precise traceability of new pollutants is realized, and the traceability efficiency and accuracy are improved.

The invention relates to the technical field of data processing, and discloses a hazardous chemical substance leakage and diffusion prediction method and system based on an unmanned ship, and a storage medium. The method comprises the following steps: acquiring water quality, weather and geographic data through an unmanned ship sensor, and processing to obtain an initial data set; calculating a diffusion range according to the data, optimizing a monitoring area, and generating a cooperative monitoring track; performing exception elimination and smoothing processing on the data, and constructing spatial distribution; solving a convection diffusion equation, and establishing a three-dimensional flow field model; analyzing a hazardous chemical substance diffusion path, and predicting pollutant distribution; and establishing a risk assessment system, and generating emergency disposal suggestions and decision-making schemes. According to the application, rapid and accurate monitoring of the polluted area is realized through the multi-parameter sensor array carried by the unmanned ship, and accurate prediction of the hazardous chemical substance pollutant diffusion process is realized in combination with multi-source data fusion processing and a three-dimensional flow field simulation technology.

The invention discloses an intelligent monitoring and early warning system and method for agricultural non-point source pollution, and relates to the technical field of environmental monitoring, accurate prediction of water pollutant concentration is realized through multi-source heterogeneous data acquisition and fusion, a dynamic attention mechanism and a PINN-Transformer coupling model, the system extracts data spatial and temporal characteristics by using an optimized Transformer model, and the method is applied to the intelligent monitoring and early warning of agricultural non-point source pollution. A water pollution diffusion physical constraint is embedded, it is ensured that a prediction result conforms to an actual hydrodynamic law, and based on high-precision spatial-temporal distribution data, an intelligent algorithm is adopted to track a pollution diffusion path and rapidly lock a pollution source; meanwhile, the cellular automaton model simulates pollution risk dynamic diffusion and assists regional risk assessment, the system also combines a block chain technology to carry out credible evidence storage on key monitoring data, and real-time data processing and early warning pushing are realized through a cloud edge collaborative architecture. And an efficient and reliable technical solution is provided for agricultural water environment management and pollution prevention and control.

The invention provides a marine pollution tracing and monitoring method based on satellite remote sensing and an unmanned aerial vehicle, and relates to the technical field of marine pollution tracing and monitoring, and the method comprises the steps: configuring a pollution diffusion path prediction model and a data driving model based on range pollution data, precise pollution data and deepwater data after multi-modal data fusion, and obtaining a pollution prediction result and pollution error data, carrying out hybrid drive fusion based on the pollution prediction result and the pollution error data, and tracking an initial source of pollutants to obtain pollution source tracing data. Through preprocessing and fusion of multi-modal data, in combination with range pollution data, precise pollution data and deep water data, a pollution diffusion path prediction model and a data driving model are dynamically adjusted, so that a more precise pollution prediction result and pollution error data are generated, and meanwhile, through reverse diffusion simulation and pollution traceability analysis, a more precise pollution prediction result is obtained. The high efficiency of pollution source positioning and decision support is improved, and an accurate and decision basis is provided for coping with marine pollution events.

The invention provides an underground water pollution source tracing and evaluation method for a riverside water source in a karst area, and the method comprises the following steps: collecting and preprocessing multi-source information, and obtaining a multi-source data set; a water quality sample is collected and analyzed, the surface water-underground water interaction process and pollution characteristics are comprehensively analyzed, and a pollution source type is obtained; performing space-time analysis on the multi-source data set to obtain a pollution activity time period and a potential pollution source area; constructing a karst area hydrological model, simulating the hydrological characteristics of the karst area, constructing a pollutant migration model, and simulating the migration process of pollutants; and constructing a traceability algorithm, tracking pollutants, confirming a pollution source, carrying out risk partitioning and risk assessment, and carrying out visual display of a result. According to the invention, interaction between surface water and underground water can be effectively assessed, accurate traceability of pollutants and multi-angle accurate identification of pollution sources can be realized, and management efficiency can be optimized through reliable risk assessment prediction.

The invention relates to the field of unmanned ship path planning, in particular to an autonomous water quality monitoring unmanned ship navigation control method and system based on multimode communication. The method comprises the following steps: collecting regional water quality monitoring parameters based on a shipborne water quality monitoring sensor, carrying out space pollution distribution excavation, and constructing a regional pollution concentration distribution diagram; obtaining a regional satellite remote sensing map, carrying out water flow distribution dynamic evolution, and constructing a water flow path distribution network; carrying out pollutant migration logic evolution and multi-region gradient diffusion prediction on the water flow path distribution network according to the regional pollution concentration distribution map, and constructing a water quality pollution diffusion prediction situation map; and according to the water quality pollution diffusion prediction situation map, carrying out highest diffusion progressive gradient identification, carrying out optimal navigation monitoring sequence analysis, and constructing an optimal navigation monitoring sequence. According to the invention, through dynamic and real-time unmanned ship water quality cruise path adjustment, the accuracy and efficiency of water quality monitoring are improved.

The invention relates to the technical field of data processing, and discloses a water quality and water quantity accurate simulation method and system based on an SWAT model and a storage medium. The method comprises the following steps: analyzing rainfall and evapotranspiration data through a two-parameter trend test, and generating standard hydrological data by adopting a triple configuration algorithm; dividing sub-basins by combining topographic information, and calculating a hydrological response unit; performing water balance operation and water conservation evaluation; pollutant diffusion is analyzed, and a diffusion equation is solved; running state monitoring is implemented, and an early warning system is designed; and comparing and correcting measured data, and constructing a hierarchical monitoring mechanism. According to the invention, the whole-process precise management from hydrological data acquisition, water quality evaluation to early warning monitoring is realized, and the accuracy and reliability of water quality and water quantity monitoring are remarkably improved.

The invention discloses an intelligent water quality monitoring system and method based on a turbidity sensor, and relates to the technical field of water quality monitoring. Data acquisition nodes are arranged in a water area to be monitored, data are acquired by using the turbidity sensor and an intelligent water quality monitoring equipment group, an environment data set S is constructed, and preprocessing is performed; acquiring a turbidity change coefficient # imgabs0 # according to a turbidity data set in the preprocessed environment data set S, performing primary evaluation, marking abnormal data acquisition nodes, acquiring a particulate matter dynamic index KL and a pollution index WR according to a particulate matter data set and an organic pollutant data set in the environment data set S, and performing secondary evaluation according to the turbidity change coefficient # imgabs1 # and the turbidity change coefficient # imgabs0 # in combination with the particulate matter dynamic index KL and the pollution index WR. Acquiring a comprehensive water quality score Q, performing secondary evaluation, and constructing a water quality prediction model to predict the water quality change and regulate and control the water quality for the data acquisition nodes which are normally evaluated for the second time; the intelligent level of water quality monitoring is improved, the accuracy of water quality monitoring is enhanced, and the safety of water quality is ensured.

The invention discloses a stationary pollution source data acquisition and calibration method and system, a medium and a program product, and the method comprises the steps: carrying out the segmentation processing of a plurality of concentration change data, and obtaining a plurality of data segments; determining the concentration change trend of various pollutants, and calculating a correlation coefficient matrix; the Euclidean distance value between the correlation coefficient matrix of the current data segment and each correlation coefficient matrix before sorting is calculated; when the Euclidean distance is greater than a preset threshold value, marking the corresponding data segment as an abnormal data segment; determining the pollutant as a target pollutant, and establishing a material balance equation; based on the material balance equation, generating a theoretical concentration change curve of the target pollutant; calculating a deviation value, and when the deviation value exceeds a preset dynamic error limit value, determining a sensor correction coefficient according to the process parameters; and carrying out calibration processing on the acquired data of the target pollutant according to the sensor correction coefficient. According to the invention, the accuracy of data acquisition and calibration of the stationary pollution source is improved, so that the influence on formulation and execution of pollution prevention and control measures is reduced.

The invention relates to a regional pollution process real-time monitoring, regulation and control system and method based on artificial intelligence. The system is composed of a data acquisition module, a communication module, an artificial intelligence analysis module, an intelligent regulation and control module, an execution terminal and a user interaction module. The data acquisition module acquires multi-source data such as pollutant concentration and meteorological parameters by means of a multi-modal sensor, the data are processed and transmitted by the data communication module, and the artificial intelligence analysis module realizes pollution source tracing and accurate prediction by using technologies such as a space-time diagram convolutional network and an LSTM-Transform hybrid neural network. The intelligent regulation and control module generates a regulation and control strategy based on an NSGA-II algorithm, and the execution terminal is responsible for implementation. The user interaction module provides a visual interface and a manual intervention channel. The method comprises the steps of data acquisition and processing, model construction and prediction, regulation and control strategy formulation and execution and feedback optimization closed-loop operation. According to the invention, comprehensive real-time monitoring and accurate regulation and control of regional pollution are realized, the prediction accuracy is improved, the environmental, economic and social benefits are balanced, and an efficient technical means is provided for regional pollution treatment.

The present application relates to the field of environmental monitoring and governance, and discloses a supervision system and method for agricultural non-point source pollution scenarios. The system includes a perception module, a data management module, an intelligent analysis module, and an application service module. The perception module collects pollutant concentration, hydrological characteristics, and abnormal behavior data through water quality sensors, high-position video monitoring equipment, and drones; the data management module dynamically integrates, stores, and standardizes the data collected by the perception module; the intelligent analysis module predicts the diffusion path of pollutants in the water body based on the pollution diffusion model, and inverts the spatial location and intensity of the pollution source by combining the video monitoring abnormal behavior records with the diffusion model; the application service module displays the analysis results, triggers warnings, and supports remote governance operations through an intelligent early warning system, a GIS visualization platform, and mobile devices. The present invention can accurately monitor agricultural non-point source pollution, improve the efficiency of pollution source positioning and supervision, and provide technical support for the governance of agricultural non-point source pollution.

The invention provides an intelligent sewage treatment process optimization method and system. The method comprises the following steps: continuously collecting the flux attenuation rate, transmembrane pressure difference and pollutant spectrum data of a ceramic membrane, carrying out time sequence analysis, constructing a pollution accumulation dynamic model, and predicting a critical threshold value; when a threshold value is reached, triggering an ultrasonic cavitation device, adjusting high-frequency vibration to generate a microbubble group, stripping pollutants by utilizing shock waves, and calculating membrane flux recovery data; fusing the spectral data and the recovery data to extract an actual pollution stripping efficiency value, and comparing a theoretical value to obtain a difference proportion; if the actual value is lower than the theoretical value, the cleaning period is shortened, the fluid pressure is increased to strengthen the stripping effect, and meanwhile, dynamic model parameters are reversely corrected based on the pressure adjustment amplitude to form closed-loop regulation and control. According to the invention, intelligent prediction, self-adaptive cleaning and closed-loop optimization of ceramic membrane pollution are realized, the sewage filtering efficiency is improved, and the energy consumption and cleaning cost are reduced.

The invention relates to the technical field of water quality monitoring, in particular to a mining area groundwater environment monitoring system and method, and the system comprises a mining area groundwater partition recognition module, a groundwater pollution risk assessment module, a groundwater monitoring point optimization module, a groundwater pollution path analysis module and a groundwater dynamic monitoring planning module. According to the invention, through data-driven zoning and risk assessment processes, fine and dynamic management is realized in underground water environment monitoring, functional zone zoning boundaries are accurately calibrated, understanding of an underground water flowing mode is enhanced, pollution source control is more targeted, diffusion risk intensity is assessed in combination with flow velocity and pollutant release intensity, and the risk assessment accuracy is improved. The risk management strategy is optimized, the pollution diffusion range is predicted and identified more efficiently, the emergency response accuracy is improved, monitoring point arrangement is optimized through risk data, reasonable monitoring resource allocation is ensured, the sampling task weight and the time interval are dynamically adjusted, and resource optimization and monitoring efficiency improvement are achieved.

The invention provides a particulate matter and ozone source monitoring method and system based on a distributed sensor, and relates to the technical field of pollution treatment. According to the invention, sensor nodes with geographic perception capability are deployed in a monitoring area in a high-density manner, pollutant concentration and meteorological parameters are collected in real time, and data are uploaded to a cloud platform for preprocessing and dynamic calibration; a machine learning model is constructed based on the combined features of the pollutants and the meteorological factors, a driving relation is mined, and pollutant influence factors are extracted; further performing joint modeling on the influence factors and regional emission source data, identifying the coupling strength between pollutants and emission sources by adopting a classification or clustering method, and judging the categories of main sources; backward trajectory simulation, source fingerprint analysis and multi-source regression decomposition are combined to realize pollution path inversion and source contribution rate quantification; and finally, constructing a geographic information visualization platform, displaying a pollution thermodynamic diagram, a contribution change diagram and an evolution path diagram, and providing support for multi-source pollution traceability and scientific management and control.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.

The invention discloses a tunnel fan energy-saving control method and system, a storage medium and a program product, and relates to the field of general control or regulation systems.The method comprises the steps that historical train flow data are obtained, and the predicted train flow of the next time period is obtained through calculation according to the historical train flow data; collecting real-time gas concentration data and wind speed and direction data; calculating a theoretical gas concentration increment based on an aerodynamic model, the predicted train flow, the real-time gas concentration data and the wind speed and direction data; calculating according to the theoretical gas concentration increment and a pollutant diffusion model to obtain a pollutant diffusion trend; determining a target section according to the pollutant diffusion trend and a preset gas concentration threshold value; according to the position information of the target section and the pollutant diffusion trend, the minimum starting number of tunnel fans and the corresponding minimum rotating speed are obtained through calculation; and generating a fan control instruction according to the minimum number of started fans and the minimum rotating speed. According to the method, the effect of reducing the energy consumption of the tunnel fan while the ventilation effect of the tunnel is maintained is achieved.

The invention relates to the technical field of water treatment intelligent control, in particular to an AI-based intelligent urban water purification and sewage treatment process optimization system and method, and the method comprises the steps: obtaining related sensor data information, and monitoring and recording the change of key data in real time; establishing a water quality change prediction model by using a multiple linear regression model, wherein the water quality change prediction model predicts the water quality change according to the key data change acquired in real time; the dynamic agent AI model adjusts the dosage, and the water treatment chemical / biological reaction kinetics AI model predicts the pollutant degradation condition; a local lightweight AI model optimizer and a multi-target genetic algorithm are combined with energy consumption, medicament cost, processing efficiency and carbon emission to obtain an optimal control model; the optimal control model optimizes the operation parameters of the sewage treatment process according to the pollutant degradation condition. According to the method, the operation parameters of the sewage treatment process are optimized through the optimal control model, the energy consumption, the agent cost and the carbon emission are reduced, and the treatment efficiency is improved.

The invention discloses an underground water pollution traceability analysis and ecological restoration decision support system, and relates to the technical field of computer models, the system comprises a data monitoring and graph network construction part, a pollution diffusion simulation analysis part and an ecological restoration part; the data monitoring and graph network construction part is used for setting a plurality of monitoring points in a target underground water area, and completing the construction of a graph network by taking the reciprocal of the distance between nodes as an edge weight; the pollution diffusion simulation analysis part is used for performing feature extraction on the graph network to obtain graph network features and pollutant concentration distribution of each position in the target underground water area; and the ecological restoration part is used for setting a target pollutant concentration distribution, and screening out an optimal ecological restoration operation by utilizing the knowledge graph according to the difference. According to the invention, the monitoring and repairing capabilities of underground water pollution are obviously improved, and the repairing efficiency is improved.

The invention discloses a machine learning-based soil pollution risk assessment method and a machine learning-based soil pollution risk assessment device, and belongs to the technical field of soil pollution risk assessment. The split biological effect and chemical indexes in traditional evaluation are converted into unified risk driving force measurement. A nonlinear interaction entropy calculation mechanism can adaptively capture an antagonism-collaborative balance relationship between microbial communities and pollutants in a soil environment, so that the degradation potential of functional flora on specific pollutants is reflected, and the dynamic regulation effect of environmental factor fluctuation on bioavailability is quantified. According to the cross-scale feature fusion technology, the evaluation model not only can identify the current pollution space distribution, but also can pre-judge the morphological transformation trend of pollutants in biogeochemical circulation, and provides a quantitative basis with ecological interpretation for risk early warning.

The invention discloses a traceable road surface runoff pollutant monitoring method and system, and relates to the technical field of water pollution monitoring. The method comprises the following steps: capturing environmental factors of precipitation, air temperature and traffic flow according to road surface basic monitoring data, extracting pollutant concentration data of sampling points, analyzing relevance of the environmental factors, identifying and classifying pollutant concentrations, and obtaining pollutant concentration change trend characteristics. According to the method, the relationship between the environmental factors and the pollutants is accurately analyzed by capturing the environmental factors and the pollutant concentration data, the understanding of the change trend of the pollutant concentration is improved, the pre-judgment precision of future changes is effectively improved, in real-time data comparison, error analysis helps to identify deviations and adjust model weights, the prediction accuracy is improved, and the prediction accuracy is improved. Through monitoring resource demand prediction, optimization of monitoring point layout and resource configuration and dynamic adjustment of a monitoring strategy, pollutant concentration monitoring is more flexible and accurate, and monitoring efficiency and response speed are improved.

The invention discloses a carbon emission intensity inversion and traceability method and system based on numerical simulation and a reduced-order model. The method comprises the following steps: constructing a full-size three-dimensional map; numerical simulation of pollutant diffusion is carried out according to map information and known environment conditions; reducing the order of the analog data through an intrinsic orthogonal decomposition method; establishing a carbon emission simulation database under the multi-working-condition variable environment based on the reduced-order model; the carbon dioxide concentration and meteorological data are measured in real time and on site, a simulation database is used as a supplement, and the intensity of a known emission source and the intensity and position of an unknown emission source are calculated by adopting a source item estimation method, a particle swarm algorithm and the like. According to the invention, full-size reduction of a limited area and high-precision small-scale carbon emission monitoring can be realized, and effective emission source tracing, intensity analysis, potential emission source prediction and the like can be carried out according to a small amount of field actual measurement data.