16752 results about "Pollutant" patented technology

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 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 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 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 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 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 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 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 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 relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

The invention discloses a pollution space analysis method and system based on atmospheric pollution concentration data, and particularly relates to the technical field of data analysis. A high-precision pollutant concentration spatial distribution model is constructed by combining pollutant diffusion characteristics and geographic information, and regional pollution differences are revealed; identifying a pollution source emission mode and analyzing the evolution trends of pollutants in different time and space ranges by matching spatial and temporal change analysis with a pollution source emission list; the influence of real-time meteorological data on pollution diffusion is analyzed, and a pollutant spatial distribution model is adjusted, so that the prediction precision is improved; pollution concentration distribution, pollution source emission and meteorological influence are combined, pollution hot spot areas are accurately identified, future pollution evolution conditions are analyzed and predicted based on historical trends, scientific and reasonable pollution treatment suggestions are provided, the limitation of traditional interpolation and statistical regression models is broken through, dynamic and accurate modeling of pollutant concentration is achieved, and the method is suitable for large-scale popularization and application. And the scientificity of pollution source tracing and treatment decision making is improved.

The invention discloses a water body new pollutant risk assessment method and system. Belongs to the technical field of environmental science. Comprising the steps of collecting multi-source heterogeneous data to generate an initial data set; performing data fusion in the initial data set by using a weighted Bayesian fusion algorithm and a space-time interpolation method to generate a target data set; importing the target data set into a deep learning model for prediction and obtaining a prediction result; and obtaining a preset early warning condition and combining with the prediction result to judge whether risk early warning needs to be carried out. Through constructing a pollutant data acquisition system and data fusion, the monitoring precision of the new pollutants in the water body is improved. The fusion algorithm is used for data preprocessing, and pollutant detection is comprehensive. And the space-time diffusion process of the pollutants can be accurately modeled.

The invention provides a river water quality parameter supervision method and system based on deep learning. The method comprises the steps of self-calibration multi-source data acquisition, diffusive water quality prediction, extreme water quality parameter simulation, reverse diffusion pollution positioning and water quality parameter intelligent supervision. The invention relates to the technical field of river water quality supervision, in particular to a river water quality parameter supervision method and system based on deep learning. By introducing a graph convolutional neural network and a physical diffusion constraint model, space-time diffusion trend modeling of pollutants in a river channel is realized; constructing an extreme pollution event simulation and attribution mechanism by combining a generative adversarial network and physical verification; further adopting a multi-modal Bayesian inversion model and a graph deconvolution structure to realize accurate source tracing of the pollution source; the system can dynamically sense hydrological changes, construct an adaptive threshold judgment mechanism, realize prediction, tracking and response to pollution risks, and provide efficient and intelligent technical support for river ecological safety management.

The invention relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

The invention provides a river pollutant traceability method and a river pollutant traceability system. The method comprises the following steps: constructing a potential pollution source feature fingerprint database fused with an LSTM time sequence feature extraction model, carrying out abnormal water quality fingerprint identification on a monitored river reach, if identification is abnormal, collecting an upstream water sample, detecting to obtain a water quality fingerprint, comparing the water quality fingerprint with the abnormal water quality fingerprint, determining a target river reach according to a comparison result, and determining the target river reach according to the comparison result. The method comprises the following steps: sampling all enterprises of a target river reach in real time, comparing detection result data with detection result data of an abnormal water sample, determining potential pollution sources according to a comparison result, determining high-matching candidate sources from the potential pollution sources by utilizing an LSTM (Long Short Term Memory) time sequence feature extraction model, carrying out space transition verification on the high-matching candidate sources, and carrying out space transition verification on the high-matching candidate sources. And determining the pollution source according to the verification result. According to the method, the accuracy, efficiency and result reliability of tracing the river pollutants can be effectively improved, and the scenes of multi-source pollution, intermittent emission and the like of complex rivers can be effectively handled.

The invention relates to the technical field of offshore area monitoring, in particular to an offshore area pollutant diffusion real-time tracking and traceability monitoring system. The method comprises the following steps: a data perception and fusion unit collects and preprocesses data, carries out space-time calibration and multi-source fusion, and outputs a standardized fusion space-time sequence; the pollutant diffusion simulation and prediction unit calculates a flow velocity field, simulates a diffusion process and predicts a trend; the pollutant traceability positioning unit tracks a pollution path and inverts pollution source parameters; the emergency decision support unit determines a risk level and generates an emergency plan. And through a space-time alignment algorithm and a multi-source fusion mechanism, the data consistency and reliability are improved, a high-quality data basis is provided for subsequent processing, and the monitoring accuracy is guaranteed.

The invention discloses an industrial area atmospheric environment monitoring management system and method, and relates to the technical field of industrial Internet of Things environment monitoring, and the method comprises the steps: triggering polarization imaging to scan a target region based on the time-space information of an abnormal event signal, obtaining the polarization characteristic data of a pollution plume, and generating a spatial distribution characteristic parameter; inputting the sound pressure abnormal indexes and the spatial distribution characteristic parameters into an industrial equipment leakage causal model, calculating a pollution source confidence coefficient, and when the pollution source confidence coefficient reaches a process safety dynamic threshold value, obtaining an industrial equipment traceability result and a leakage level; according to the traceability result of the industrial equipment and the leakage level, a hierarchical control instruction is obtained, meanwhile, the actually measured attenuation rate of the industrial pollutants is calculated, and the dynamic response effect is verified; according to the method, cross-modal fusion modeling is carried out on acoustic abnormal information and pollutant spatial distribution characteristics, so that the technical limitation that pollution source positioning is carried out by depending on a static empirical formula traditionally is broken through.

The invention relates to the technical field of river channel desilting treatment, in particular to a dynamic simulation and optimization decision-making-based river channel silt treatment method, which comprises the following steps of: acquiring and treating historical and real-time multi-dimensional environmental data of a to-be-treated river channel and an influence area of the to-be-treated river channel; a river basin process simulation system capable of simulating river water power, sediment transportation, pollutant migration and transformation and ecological response processes is constructed, decision variables forming a river regulation scheme are defined in a parameterized mode, a multi-target optimization problem is defined and set in a formalized mode, a multi-target optimization engine is adopted, and the river regulation scheme is optimized. The method comprises the following steps: embedding a drainage basin process simulation system as a core evaluation module into an optimization iterative loop, implementing an optimization scheme, and continuously monitoring the environmental state change in the implementation process and after the implementation; the method can accurately predict the long-term and short-term influence of different treatment schemes in multiple aspects of hydrodynamic force, silt, pollutant migration and transformation, ecological response and the like, and solves the problem of comprehensive treatment of sludge.

The invention relates to the technical field of data processing, and discloses an air quality evaluation method and system based on multi-source data. The method comprises the following steps: collecting and correcting multi-source monitoring data; dividing grid fusion environment characteristics; performing weighted calculation to generate an air quality distribution diagram; predicting future concentration in combination with history and weather; tracking pollution transmission to determine source region contribution; and evaluating risks and generating early warning suggestions in combination with population distribution. According to the method, accurate interpolation of pollutant concentration, future trend prediction and quantitative analysis of pollution source contribution rate are realized in combination with environmental characteristics under the condition that the monitoring points are not uniformly distributed, so that accurate health risk early warning and management and control decision are supported.

The invention belongs to the technical field of water pollutant traceability, and particularly discloses a water quality fingerprint database analysis method based on feature recognition, which relates to the field of water pollutant traceability, and comprises the following steps: cross-comparing feature curved surface information of different upstream and downstream under feature peak whole database data and sorting to obtain pollutant features under different intensities; the pollution source type is identified according to the pollutant difference; sorting the characteristic curved surface information of the water body samples reflecting different nodes, calling a chemical characteristic analysis result, outputting a chemical characteristic sequence, and selecting a first sequence sampling point position of the chemical characteristic sequence corresponding to the spectral characteristics of the identified pollution source type as a pollution source; according to the method, whether the identification result is met or not is analyzed according to the biological characteristics of the pollution source, efficient source tracing is carried out on water pollutants, an accurate pollutant transmission path is output, meanwhile, the corresponding pollutant type is evaluated, the corresponding pollutant diffusion trend is given, and powerful data is provided for follow-up pollutant treatment.

The invention provides a mine water disaster monitoring and early warning method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting multi-source data of a mining area, carrying out the time-space alignment of the multi-source data, and generating a data set, the multi-source data comprising remote sensing data; preprocessing the data, inputting the preprocessed data into a multi-modal fusion network, and extracting surface water body distribution, lithologic permeability, underground water level and structural fracture characteristics to obtain a three-dimensional hydrogeological static model; the hydrological numerical model based on physical driving is coupled with the static model, and the dynamic model is used for simulating the dynamic change of an underground water flow field and a pollutant diffusion path; based on the dynamic model updated in real time, multi-target collaborative evaluation is carried out to evaluate the mining area water resource, ecological and social collaborative effect; and calling an unmanned aerial vehicle to inspect a leakage position or a settlement position in the hydrogeological risk map based on a multi-target collaborative evaluation result. According to the method, the prediction accuracy is improved through the multi-source data.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The invention discloses a risk assessment method based on soil heavy metal pollution pathway analysis, and particularly relates to the technical field of land risk assessment. Heavy metal pollutant data and geographical location information of a target area are collected to generate a basic data set; constructing a heavy metal migration model based on the basic data set, and predicting a pollutant migration path and concentration change in combination with the comprehensive characteristics of the regional environment to identify a first high-risk region; a second high-risk area is identified by analyzing retention and release behaviors of pollutants in the microfractures in combination with quantitative soil microfracture connectivity characteristics and pollution migration behaviors of a dynamic entropy model; union processing is carried out on the first high-risk area and the second high-risk area to generate a comprehensive high-risk area; heavy metal concentration and land utilization changes in different time periods of the high-risk area are analyzed and synthesized through a multi-dimensional data fusion algorithm, and cumulative risk distribution is generated; and comparing the accumulated risk distribution with enterprise land use planning, evaluating potential environmental responsibility and generating a risk evaluation report.

The invention discloses an atmospheric environmental pollution event emergency monitoring and early warning method and system, and belongs to the technical field of environmental management. The method comprises the following steps: acquiring multi-scale meteorological field data and chlorine concentration real-time data of at least five monitoring stations, extracting a wind field direction and a concentration change rate, and determining a position and a wind direction of a potential pollution hot spot area. The information is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle automatically plans a path and collects supplementary concentration data from three angles. And fusing the monitoring data and the supplementary sampling data, and combining with a meteorological field input pollutant three-dimensional concentration distribution model to generate three-dimensional distribution data. And inputting the CALPUFF reverse model, and carrying out inversion to obtain the specific position and the emission intensity of the pollution source. According to the scheme, the accuracy and timeliness of source tracing of the pollution source are improved, high spatial resolution and flexible response capability are achieved, and efficient and intelligent technical support is provided for intelligent early warning, rapid source tracing and scientific response of sudden toxic gas leakage events.

The invention discloses a water quality detection system for hydraulic engineering and a monitoring method, and belongs to the technical field of water quality monitoring. The system is composed of a distributed sensor array, an edge calculation data acquisition module, a LoRa wireless communication module, a cloud analysis platform and an early warning execution terminal. The distributed sensor array adopts an anti-interference protective shell and a self-adaptive calibration algorithm. The monitoring method comprises the steps that water quality data are collected in real time and transmitted through a LoRa wireless network, edge nodes conduct data cleaning and abnormal value correction, a cloud analysis platform constructs a water quality dynamic model through a multi-source information fusion algorithm, and the pollutant diffusion trend is predicted in combination with deep learning. When the parameters exceed the standard, a grading early warning mechanism is triggered, regulation and control suggestions are synchronously generated, and the water treatment equipment is linked. All-weather continuous monitoring of the water quality of the water conservancy project is achieved, the problems that a traditional method is high in hysteresis quality and poor in reliability are solved, and the water quality abnormal response speed and the comprehensive treatment efficiency are remarkably improved.