102 results about "Pollution dispersion" patented technology

The invention discloses a clean workshop production environment quality control method and system, and relates to the technical field of environment control, and the method comprises the steps: constructing a multi-layer sensing network, deploying temperature and humidity, particle concentration, pressure difference and VOC gas sensors, and carrying out the preprocessing data of each node through edge calculation; fusing the data based on a dynamic weight distribution algorithm, adjusting the weight according to the confidence score, and generating an environment quality comprehensive index; an LSTM pollution diffusion prediction model is established, a diffusion path is calculated in combination with airflow field simulation when pollution suddenly occurs, and an emergency response partition strategy is generated; fresh air system control parameters are optimized through reinforcement learning, a dynamic ventilation frequency adjusting model is established based on a real-time environment quality index and historical energy consumption data, and energy consumption is minimized through a Q-learning algorithm on the premise that cleanliness is guaranteed; a double-threshold early warning mechanism is set, local supercharging purification is started when a comprehensive index exceeds a first-level threshold, a second-level threshold is linked with adjacent areas to form a dynamic isolation barrier, and an intervention scheme effect is simulated through a digital twin system.

The invention relates to the technical field of soil pollution detection, in particular to a soil heavy metal pollution intelligent detection method and system.The method comprises the steps that soil multi-source information and regional environment parameters of to-be-detected soil are collected, and soil pollution characteristics are recognized according to the soil multi-source information; establishing a pollution identification analysis model according to the soil pollution characteristics and the regional environment parameters, and analyzing whether the to-be-detected soil contains heavy metal pollutants or not; if the heavy metal pollutants are contained, pollution diffusion paths of the heavy metal pollutants in time and space are predicted according to a pollution identification analysis model; and if the soil does not contain the heavy metal pollutants, performing time sequence analysis on soil pollution characteristics, monitoring and analyzing future concentration distribution of the heavy metal pollutants, and establishing pollution risk early warning. According to the invention, the problems of long sampling analysis time consumption, incapability of dynamically monitoring pollutant concentration change in real time, insufficient diffusion prediction accuracy and difficulty in comprehensively evaluating high-concentration and potential risk areas in a traditional soil heavy metal detection method are effectively solved.

The invention discloses a cleaning path planning method and a cleaning device, and belongs to the technical field of automatic control, and the method comprises the steps: collecting the point cloud data of a to-be-cleaned region, constructing an initial composite map, setting a perception marking method, dividing the point cloud data into sub-regions, and marking a sudden change region after the sub-regions are matched with historical map features. Locally updating the composite map by using map optimization; constructing a safety buffer zone at the edge of the to-be-cleaned area, setting a buffer adjustment method, performing trajectory prediction on the abrupt change area, and dynamically adjusting a safety buffer distance according to a prediction result; calling a basic cleaning path, generating a layered path, setting a planning adjustment method, updating the layered path according to the abrupt change area in the cleaning process, calculating a pollution index of the to-be-cleaned area, constructing a time sequence prediction model, pre-judging a pollution diffusion trend, and performing dynamic adjustment; and after the to-be-cleaned area is cleaned, decision iteration is carried out to adapt to user habits.

The invention discloses a regional heavy pollution discriminating and forecasting system and method based on big data, and relates to the technical field of environmental pollution monitoring. Comprising a multi-source data acquisition module, an adaptive weight distribution module, a feature extraction module, a heavy pollution discrimination module, a pollution diffusion path simulation module, an intelligent early warning and protection suggestion module, an event-driven dynamic monitoring module and a multi-scale meteorological prediction optimization module. And the pollution diffusion path simulation module performs prediction analysis on a pollution diffusion path and an influence range by using a diffusion path simulation algorithm. By designing the pollution diffusion path simulation module and the intelligent early warning and protection suggestion module, the pollutant diffusion path prediction and early warning functions are realized, and the problem that early warning lags due to the fact that the pollution diffusion path simulation and early warning module is separated and cannot realize timely linkage of prediction results and protection suggestions is solved; and a more timely and effective prevention and control strategy is provided, and the capability of coping with heavy pollution events is enhanced.

The invention provides an industrial park soil and groundwater pollution early warning grade evaluation method and system.The method comprises the steps that production raw material components of pollution source enterprises, waste discharge data and soil heavy metal concentration evolution data in historical pollution events are integrated, optical sensing data of earth surface and underground monitoring points are combined, and the early warning grade of soil and groundwater pollution is evaluated; constructing a dynamic characteristic factor library reflecting pollution source characteristics; carrying out spatial concentration gradient calculation on the optical sensing data based on the characteristic factor library, and generating a dynamic heat map containing heavy metal distribution and migration trends at the same time; performing cross-modal analysis on the pollution source characteristics and the dynamic heat map, and establishing a spatio-temporal evolution trajectory of a pollution diffusion path; according to the diffusion rate and direction in the track, differentiated pollution risk early warning intervals are divided, and a targeted grading prevention and control strategy is generated. According to the invention, the timeliness of pollution early warning and the targeting of prevention and control measures are improved.

The invention discloses a plot pollution monitoring, predicting and early warning system and method, and relates to the technical field of environment monitoring and pollution early warning, and the method comprises the steps: obtaining multi-source data of a target plot; performing preprocessing, feature extraction and fusion on the multi-source data; inputting the multi-source data into the dynamic pollution diffusion model to obtain a pollutant concentration field of the target plot; jointly inputting the fusion data and the pollutant concentration field into a land parcel pollutant concentration prediction model for prediction to obtain a pollutant concentration prediction value of the target land parcel in a future target time period; comparing the pollutant concentration predicted value of the target land parcel in the future target time period with the early warning threshold values of all levels, and generating an early warning instruction of the corresponding level according to a comparison result; generating corresponding early warning information according to the early warning instruction; and executing corresponding early warning measures according to the early warning information. According to the invention, through multi-dimensional data integration and model optimization, scientific assessment and hierarchical management and control of pollution risks can be realized.

The invention relates to the technical field of environmental monitoring, in particular to an ecological environment and environmental pollution monitoring system and method, and a multi-source heterogeneous data acquisition module comprehensively acquires water quality, weather and space monitoring data. The system comprises a topological mapping data preprocessing module which is used for abnormal data detection and data vacancy filling; the state evolution analysis module is used for constructing an environment state transition network and analyzing an environment state evolution rule; the environment comprehensive evaluation module is used for calculating a water safety level index and a water pollution condition index and generating an environment quality evaluation report; the pollution diffusion prediction module is used for predicting a pollutant diffusion path and concentration distribution and identifying a high-risk area; the intelligent early warning and visualization module is used for generating graded early warning information and visually displaying the environment state and the pollution diffusion condition through multi-dimensional data, so that the coverage and the data integrity of environment monitoring are remarkably improved, the environmental pollution problem can be timely found and dealt with, and the ecological environment safety is guaranteed.

The invention relates to the technical field of big data monitoring, in particular to a highway environmental protection construction monitoring method and system based on big data, and the method comprises the following steps: calculating the spatial distance between sensors based on the geographic position information of a data collection point and the raised dust concentration distribution and particulate matter monitoring value of a road construction environment, and calculating the spatial distance between sensors; and connecting sensor nodes with a direct adjacency relation through difference calculation and normalization processing. According to the invention, through probability density calculation and time sequence analysis, abnormal edge screening, abnormal change detection precision improvement, misjudgment reduction, abnormal edge connection node position and space density calculation, high-density area and cluster center determination, accurate positioning of pollution points is realized, and the method is suitable for the real-time monitoring of the pollution points in combination with monitoring data change trend and diffusion path tracing. The method comprises the following steps: reconstructing a pollution diffusion chain, determining a pollution propagation range, optimizing pollution treatment, accurately identifying a pollution source through weight association degree and gradient analysis between path nodes, reducing monitoring blind areas, and providing comprehensive support for construction monitoring.

The invention discloses a vehicle-mounted laser gas pollution detection and prediction system, which comprises a mobile laser detection platform, a machine prediction system and a dynamic pollution map generation system, and is characterized in that the mobile laser detection platform is carried on a new energy vehicle and is integrated with a laser gas detector, a high-precision positioning module, an inertial navigation system and an environment sensor group; the machine prediction system receives real-time traffic flow data, weather forecast data and topographic data and outputs a pollution diffusion prediction result; the dynamic pollution map generation system comprises a space-time synchronization module, a data fusion module and a pollution event marking module. And the space-time synchronization module binds the gas pollution data with the positioning coordinates and the millisecond timestamp through hardware clock synchronization.

The present application provides a control method and system for tunnel energy-saving ventilation based on fuzzy control, which relates to the technical field of tunnel ventilation. The method comprises: obtaining vehicle flow data in the current tunnel; obtaining predicted pollutant concentrations corresponding to the vehicle flow data and predicted required air volume corresponding to the predicted pollutants based on a traffic flow prediction model and a pollution diffusion model; inputting the predicted pollutant concentrations and predicted required air volume into the fuzzy control model to obtain the number of fans required for the current tunnel, thereby improving ventilation efficiency, reducing resource loss, improving road safety, and reducing operation and maintenance costs.

The invention belongs to the technical field of pollution traceability, and provides a pollution traceability analysis method and system based on water quality sampling data. The method comprises the following steps: receiving multiple pieces of returned water quality monitoring data corresponding to a target sampling area, and obtaining geographic information, hydrological information and known pollution source information of the target sampling area; performing evaluation by adopting an analytic hierarchy process to obtain hydrological complexity of the target sampling area, and formulating a simulation strategy of the pollution diffusion simulation model; controlling the pollution diffusion simulation model to perform analogue simulation according to the simulation strategy to obtain a plurality of pollutant traceability path simulation results; and screening the plurality of pollutant traceability path simulation results by using a generative adversarial network to obtain an optimal pollutant traceability path simulation result. According to the invention, accurate and efficient pollution tracing can be realized, a reliable basis is provided for treatment, and the pertinence and effectiveness of pollution treatment can be remarkably improved.

The invention discloses an intelligent monitoring system for construction environment pollution, particularly relates to the field of tunnel construction environment pollution monitoring, is used for solving the problems of pollution diffusion and risk assessment, and accurately calculates an air flow vector field through an intelligent air pollution monitoring system in combination with a fluid dynamics principle and a Navier-Stokes equation. Air flow and pollutant diffusion paths in a tunnel are disclosed. And constructing a pressure gradient field, quantifying the pressure difference and air exchange capacity between regions, and providing driving force analysis of pollutant diffusion. Calculating a pollutant transmission coefficient through nonlinear coupling calculation in combination with an air flow correlation degree, a pressure gradient field and an air exchange capability coefficient, and quantifying the pollutant transmission capability of adjacent regions; and then real-time evaluation and grading are carried out on pollution risks, high-risk areas are identified in time, and early warning information is pushed through the intelligent terminal. Therefore, the ventilation strategy is optimized, the energy consumption is reduced, and the health of constructors and the construction safety are guaranteed.

The invention relates to the field of underground water pollution monitoring, in particular to a dynamic intelligent monitoring method and system for underground water pollution. The method comprises the following steps: constructing and updating an underground water solute transport model; generating a comprehensive pollution feature vector by using the obtained multi-source data which is related to the groundwater pollution and comprises a plurality of pollution indexes; respectively constructing a pollution source positioning model and a pollution diffusion model; triggering a pollution alarm according to the comprehensive pollution feature vector; according to the multi-source data and the current hydrogeological data, utilizing the pollution source positioning model to obtain a pollution source position; respectively utilizing the pollution diffusion model and the groundwater solute transport model to carry out prediction simulation to obtain first pollution distribution changing along with time and second pollution distribution changing along with time; and carrying out weighted fusion on the first pollution distribution and the second pollution distribution to generate predicted pollution distribution changing along with time. The problem that underground water pollution monitoring is not timely and accurate enough in the prior art is solved.

The invention discloses an unmanned aerial vehicle inspection system for environmental state monitoring, which relates to the technical field of unmanned aerial vehicle control and comprises a pollutant monitoring data acquisition module, a pollutant monitoring data analysis module, a pollution diffusion point judgment module, a pollution diffusion trend line acquisition module, an inspection point coordinate acquisition module and an inspection route acquisition module. By generating an optimal or suboptimal inspection route, the unmanned aerial vehicle can efficiently pass through the predicted inspection points in sequence, accurate tracking and monitoring of the predicted pollution diffusion route are achieved, it is ensured that the unmanned aerial vehicle can be located at the front edge or a key area of pollution diffusion all the time, and by sequencing the prediction points and planning the route, the unmanned aerial vehicle can be accurately tracked and monitored. According to the invention, an efficient inspection route can be generated, unnecessary detour can be reduced, the unmanned aerial vehicle can adapt to the change of a pollution diffusion path in real time, the effectiveness and timeliness of monitoring can be ensured, and the inspection route can be rapidly adjusted and the pollution diffusion area can be emphatically monitored in an emergency pollution event of the unmanned aerial vehicle.

The invention relates to the technical field of water quality detection, and discloses a drinking water source intelligent monitoring method based on artificial intelligence, and the method comprises the steps: S1, a sensor arranged in a drinking water source region collects water quality parameters and hydrological and hydraulic information in real time; s2, on the basis of the collected hydrological and hydraulic information, establishing a pollution diffusion simulation model, when a sensor detects that water quality parameters of any monitoring point are abnormal, carrying out pollution diffusion simulation by taking the current monitoring point as an initial condition, and predicting a future diffusion trajectory and an influence area of pollution; and S3, the generated pollution diffusion trajectory and the influence area are acquired. According to the technical scheme of fusion of the pollution diffusion trend simulation module and the artificial intelligence identification process, the two-dimensional pollution diffusion model is constructed to dynamically simulate the space-time propagation trajectory of the pollutants, and the simulation result is used as a time sequence to assist in inputting the AI model, so that the technical effect of improving the prediction timeliness of the pollution event and the space coverage precision is achieved.

The invention provides a pollution source tracking and positioning method and system. The method comprises the following steps: acquiring sky-ground cooperation data of a set area; based on the sky-ground cooperation data, carrying out pollution concentration identification on a set area by utilizing a pollution concentration identification model to obtain a pollution concentration space-time distribution matrix; based on the obtained historical pollution event data of the set area, pollution source positioning is carried out by using a pollution source positioning model, and candidate pollution source position information is obtained; and based on the pollution concentration space-time distribution matrix and the candidate pollution source position information, pollution source tracking and positioning are carried out by using a particle swarm optimization algorithm to obtain optimal pollution source position information in a set area. According to the invention, multi-source information is integrated through sky-ground collaborative data, and an intelligent model and an optimization algorithm are combined, so that rapid and accurate positioning of a pollution source is realized, the tracking period is greatly shortened, the processing efficiency and accuracy are improved, and pollution diffusion is effectively suppressed.

The invention relates to a pollutant monitoring method, device and equipment based on multi-point source superposition diffusion, and belongs to the technical field of atmospheric pollutant diffusion analysis. The latitude and longitude coordinates of the emission source, the evaluation point and the evaluation area are represented by a geographic coordinate system, and the latitude and longitude coordinates are converted into plane coordinates under a projection coordinate system; a two-dimensional rectangular grid is adopted to cover an evaluation area, and a continuous space is discretized into a plurality of grid units; establishing a reference coordinate system and a diffusion coordinate system; the constructed Gaussian plume diffusion model is adopted to calculate pollutant concentration data of each emission source to each grid point, concentration contributions of the multiple emission sources are superposed, and total concentration data of the grid points are obtained; and enabling the pollutant concentration data of the evaluation point to be equivalent to the function output of the total concentration data of one or more grid points in the grid unit where the evaluation point is located, so as to obtain the pollutant concentration data of the target position. The invention provides a refined technical scheme for pollution diffusion simulation of complex multi-point sources.

The invention relates to the related technical field of soil pollution remediation, and discloses a soil pollution treatment system and method and application thereof, and the system comprises a multi-dimensional dynamic monitoring module which is used for collecting soil temperature, humidity, pH value, organic pollutant concentration and heavy metal concentration in real time; the data analysis and trend prediction module is used for dynamically predicting the pollution diffusion trend based on the pollution diffusion and reaction kinetic model; the repairing agent intelligent regulation and control module dynamically adjusts the type and proportion of a repairing agent according to the pollution type and concentration; the precise remediation module adopts a negative pressure extraction technology and a high-pressure injection technology and is used for realizing remediation of soil at different depths; the intelligent feedback and optimization module is used for monitoring a repair process and adjusting a repair strategy in real time; the following effects can be realized: pollution distribution and diffusion paths are evaluated in real time; dynamically optimizing the dosage and proportion of the repairing agent; synchronous restoration of organic matters and heavy metals is realized; secondary pollution is reduced, and the sustainable utilization capability of the soil is improved.

The invention provides a data model dual-drive-based water pollution prediction method and device, and relates to the technical field of water pollution monitoring, and the method comprises the steps: obtaining multi-source information reflecting an incidence relation between a pollution source position and water quality abnormity; training a preset random forest regression model by using the multi-source information and a particle swarm optimization algorithm to obtain a target random forest regression model, inputting real-time data of which the type is the same as that of the multi-source information into the target random forest regression model, and determining pollution source position information; constructing a one-dimensional pollutant diffusion model based on the spatial-temporal characteristic information, and expanding the one-dimensional pollutant diffusion model to a two-dimensional cellular model in combination with a cellular automaton simulation method; and inputting pollution source position information and environmental influence information into the two-dimensional cellular model to generate a water pollution diffusion prediction result. Through comprehensive application of the algorithm and the model, the accuracy and the real-time performance of water pollution prediction are improved.

The invention provides an unmanned aerial vehicle intelligent positioning cleaning method and system for photovoltaic pollution, and relates to the technical field of photovoltaic array cleaning maintenance, and the method comprises the steps: firstly obtaining a pollution related image set, flight parameters and power generation efficiency related data collected when an unmanned aerial vehicle flies over a photovoltaic array; pollution area positioning processing is carried out based on the data, and space positioning information is generated; planning a pollution cleaning path in combination with the spatial positioning information, the pollution diffusion trend data and the power generation priority data; controlling an unmanned aerial vehicle carried cleaning device to execute cleaning operation according to the path, and synchronously acquiring a real-time pollution change image and power generation efficiency change data to generate an operation result; and finally, carrying out cleaning effect evaluation based on an operation result and related data, generating pollution cleaning evaluation information containing supplementary cleaning suggestions, and realizing intelligentization and high efficiency of photovoltaic array cleaning.

The invention relates to the technical field of environmental science, in particular to an air quality space-time prediction method and device based on a hierarchical dynamic graph neural network. The method comprises the steps that a modeling framework of a layered dynamic graph neural network is introduced, the framework dynamically learns an adjacent matrix of a variable graph and a site graph, a system captures nonlinear interaction between multiple variables in a site and space-time dependence between sites, multi-level dynamic modeling of the pollution diffusion process is achieved, and the pollution diffusion efficiency is improved. Therefore, the accuracy and generalization ability of air quality prediction are remarkably improved. According to the method, the synergistic effect and potential influence among multiple variables are effectively captured, and the expression ability and modeling precision of single-site features are remarkably improved; the method can adapt to the dynamic characteristic that the spatial dependency relationship between pollution propagation paths and stations changes along with time, and the perception and response capability of the model to spatial and temporal changes is enhanced, so that the prediction flexibility and accuracy are improved; and the capability of the model in capturing the complex space-time dependency relationship is improved.

The invention provides a multi-source data fused water quality pollution monitoring method and device and a storage medium, and relates to the technical field of water quality monitoring. According to the method, through the key steps of data preprocessing, cross-modal feature fusion, pollution concentration estimation, graph neural network traceability and the like, multi-source water quality data and monitoring point space topology information are deeply integrated, and a whole-process automatic monitoring system is constructed. According to the method, end-to-end processing from data standardization to pollution emission source positioning is realized, a multi-source heterogeneous data barrier is effectively broken, the complementary value and the time-space association rule of different modal data are fully mined, the integrity, accuracy and efficiency of water quality pollution monitoring are improved, the emission source position and the pollution diffusion path can be accurately positioned, and the water quality pollution monitoring efficiency is improved. Full-link and targeted technical support is provided for water quality pollution control and treatment decision, and the problems that traditional monitoring data is low in utilization rate and insufficient in traceability accuracy are solved.

The invention provides a Gaussian pollution diffusion source intensity quantification method and system based on adaptive parameter correction, and relates to the technical field of environmental monitoring, and the method comprises the following steps: S1, obtaining monitoring data at a moment K through a fixed sensor in a target area subjected to grid processing; s2, performing early warning judgment on the station state of the fixed sensor according to the monitoring data, when the station state is judged to be an alarm state, recording the station position of the fixed sensor as an alarm point position, performing range screening around the alarm point position to obtain a plurality of point positions to be estimated, and turning to the step S3; s3, obtaining monitoring data of the alarm point location before the moment K, and performing Gaussian simulation diffusion according to the monitoring data of the moment K corresponding to the alarm point location, the monitoring data before the moment K, the site location and the plurality of to-be-estimated point locations; and S4, according to the monitoring data of all the fixed sensors and the pollutant diffusion concentration at the moment K corresponding to the alarm point location, the diffusion parameter is corrected.

The invention provides an ecological environment monitoring system. The ecological environment monitoring system comprises a monitoring terminal integrated with various environment monitoring devices, a computing terminal deployed close to the monitoring devices, and a cloud platform for performing pollution tracing and diffusion trend analysis by using a big data analysis method, according to the invention, the edge computing terminal is utilized to locally process the pollutant data and climate data collected by the monitoring terminal, and the processing result is compressed and uploaded, so that the delay of data transmission is reduced, and the real-time performance of pollutant monitoring is improved. A pollution source identification model of a cloud platform is combined with sensor data and neural network modeling, pollution components and concentration change trends are rapidly identified, a pollution diffusion path is constructed, and decision support is provided for pollution source tracing and treatment measures. Meanwhile, by integrating a short-term prediction model and a long-term prediction model and utilizing data characteristics of different time scales, the system can flexibly adjust a prediction strategy according to environmental changes, and the stability of pollution monitoring is improved.

The invention provides a regional purification quality evaluation method and device of an air purification unit. According to the method, target area division information and concentration data of pollutants such as fine particulate matters, carbon dioxide and volatile organic compounds are acquired, multi-pollutant concentration fusion analysis is performed based on a preset air quality standard, and a fusion concentration index reflecting spatial distribution characteristics is generated. And extracting the pollution accumulation rate of each partition according to the time change trend of the index, identifying the pollution propagation direction between adjacent partitions, and generating a regional purification quality evaluation result by cooperatively associating the two. And further combining the partition purification urgency degree and the pollution diffusion intensity in the evaluation result to formulate a regional collaborative operation strategy, and dynamically adjusting the air speed gear and the operation parameter configuration of the air purification unit corresponding to each partition. According to the technical scheme provided by the invention, the efficiency and precision of regional purification quality evaluation can be improved.

The invention provides an atmospheric control monitoring method and system. The atmospheric control monitoring method comprises the following steps: collecting concentration information of different volatile organic compounds at all sampling moments at each monitoring point through an intelligent pollutant sensor; determining the pollution potential of each volatile organic compound according to the concentration information of each volatile organic compound and the pollution diffusivity of the corresponding volatile organic compound; pollution spectrum information of different pollution sources is collected through an intelligent spectrum sensor, and then concentration contribution ratios when the different pollution sources in the atmospheric environment are converted into ozone pollution are generated on the basis of a machine learning algorithm in combination with the pollution spectrum information of the different pollution sources; carrying out load prediction on ozone pollution of each monitoring point according to the pollution potential of each volatile organic compound and the contribution ratio of different concentrations to obtain a pollution load degree; and performing partition treatment based on all pollution load degrees. By adopting the scheme of the invention, multi-factor nonlinear modeling of the atmospheric environment can be realized.

The invention relates to the field of atmospheric pollution diffusion simulation, and particularly discloses an atmospheric pollution diffusion process dynamic simulation and influence evaluation system, which comprises: a) a multi-pollutant collaborative simulation module, which is used for simulating the process of nonlinear interaction between various pollutants through chemical reaction and photochemical reaction, and the diffusion condition of the pollutants in the atmosphere; b) a big data analysis environmental impact assessment module which integrates historical monitoring data and social and economic data information by using a big data analysis technology to construct an assessment model; through the multi-pollutant collaborative simulation module, the system can accurately simulate the nonlinear interaction and diffusion conditions of various pollutants in the atmosphere, the high-precision simulation is beneficial to more accurate understanding of the propagation and conversion process of the atmospheric pollutants, and a solid foundation is provided for subsequent evaluation and strategy formulation.

The invention discloses a multi-source spatio-temporal data fusion air quality prediction method and system, relates to the technical field of data fusion, and comprises the steps of performing interpolation processing on a data missing region, considering data comprehensiveness, performing complementation interpolation on data on a pollution map, and providing a reliable basis for subsequent pollution analysis and air quality prediction. And the pollution characteristic condition of a pollution source is evaluated to dynamically adapt to and adjust the resolution of space and time in a targeted manner, so that the accuracy of map description is ensured, and the requirements of resources and simulation are balanced. The comprehensive contribution degree of each type of pollution diffusion cause features is defined, the pollution diffusion cause features are fused by means of the comprehensive contribution degree, the fused features are described from the three scales of time, space and physics, and the comprehensiveness of analysis is guaranteed. A pollution diffusion-air quality causal model is constructed based on fusion features, and features in multi-source directions are effectively integrated, so that the precision of air quality prediction is improved, and complex and variable air quality prediction requirements are met.

The present invention relates to an A.I. based event source tracking system and a controlling method for the same, and may track a CBR pollution source which is quicker and more reliable by using pollution spread information data for each time zone calculated from a CBR pollution spread prediction modeling tool in a protection region through artificial intelligence technology rather than using real sensor data measured under various environmental conditions in a given zone when a CBR situation occurs, and predict an initial event occurrence source by learning pollution spread (pollution material concentration and deposition amount) information distributed for each time zone on a given space (map) in an image format by using an A.I. based video prediction or next frame prediction).

The application relates to the technical field of water quality detection, and discloses a water quality detection stability control feature extraction method, which comprises the following steps: collecting water area data to construct a regional water system network model; performing node importance analysis on the constructed regional water system network model to obtain a key node set; collecting sample data at the key node positions; if pollution components are detected in the sample data, then combining a water pollution diffusion model to perform reverse deduction on the pollution positions, and collecting stable sample data at the pollution positions to perform water quality detection. The water system node with higher importance is selected as the key node, sample data is collected in the key node, water system pollutant missing detection is avoided, according to the concentration change trend of the pollutant components in the sample data, the water pollution diffusion model is used to calculate the probability density function of different pollution positions and pollution intensities, the pollution positions are determined, sample data is collected, and sample data with higher stability is obtained.