85 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 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 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 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 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 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 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 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 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 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.

The present invention belongs to the technical field related to digital twin watershed water environment simulation and discloses a method and system for simulating arbitrary pollution scenarios in a digital twin watershed water environment. The method includes the following steps: setting the pollutant concentration and flow rate of each grid cell in the digital twin watershed water environment model at time t0; pollution occurs at time t1; setting a pollution treatment method for the location where pollution is input, and solving the pollutant concentration and flow rate at the location where pollution occurs based on different pollution treatment methods; for locations where pollution is not input, using a hydrodynamic pollutant migration model to update the pollutant concentration and flow rate at the location where pollution does not occur, thereby obtaining the pollutant concentration and flow rate at each location in the digital twin watershed water environment model after pollution occurs. Through this invention, the impact of not considering the pollution treatment method after pollution occurs in the watershed on the spread of pollution is solved.

The application discloses a kind of based on Ray distributed environment sudden pollution emergency decision support system and working method, belong to environmental emergency management technical field.The environmental sudden pollution emergency decision support system based on distributed architecture includes infrastructure module, distributed computing module, data processing module, model service module and application service module.System bottom layer uses Ray distributed computing framework, combines pollution diffusion simulation, multi-source data fusion and real-time computing analysis, realizes the rapid assessment and dynamic response to sudden environmental pollution event.The application can solve the problem of the limitations of traditional emergency decision support system in computing performance, response time and expansibility.Through modular design, cover pollution source tracking, diffusion path prediction, emergency scheme optimization and resource scheduling management and other functions, support multi-scenario application.

The invention relates to the technical field of computer modeling, and provides a pollution diffusion path modeling method and system based on multiple sensors, which are used for improving the accuracy and adaptability of pollution diffusion path modeling. The method comprises the following steps: synchronously acquiring original observation data through a multi-source sensor deployed in a pollution monitoring area, and establishing a data association index based on a spatial arrangement position and a data acquisition timestamp of the multi-source sensor; extracting spatio-temporal characteristics representing gas migration in combination with the data association index and the physical diffusion characteristics of the pollution gas; establishing a basic model structure of a gas migration model based on spatial-temporal characteristics, analyzing and determining a correlation weight in combination with characteristic responsivity, and generating a parameter self-learning strategy; starting a parameter self-learning strategy, and iteratively updating the parameters by using a particle swarm search space to obtain optimized parameters; and importing the optimized parameters into a gas migration model for inversion calculation, determining initial distribution of path nodes in combination with topographic features and meteorological conditions, performing iterative updating, and outputting a pollution diffusion inversion path.

The invention discloses an urban atmospheric environment monitoring method and system based on big data analysis. The method comprises the following steps: respectively selecting at least one target pollutant concentration data from each pollutant concentration data sequence; constructing target pollutant concentration data sequences based on the at least one target pollutant concentration data, and judging whether the number of the target pollutant concentration data sequences is greater than a preset threshold; if the target pollutant concentration data sequence is greater than the preset threshold value, performing sequence fusion on each target pollutant concentration data sequence to obtain a final target pollutant concentration data sequence, and determining a target real pollution area grid to which each target pollutant concentration data belongs in the final target pollutant concentration data sequence; and determining an atmospheric pollution transmission path and / or an atmospheric pollution source according to each target real pollution area grid. According to the invention, the transmission path or pollution source of atmospheric pollution can be accurately deduced, and a scientific basis is provided for dynamic tracking of pollution diffusion.

The invention provides an ecological environment monitoring system and method, and the method comprises the steps: building a three-dimensional digital twinborn model of a designated city in a target region, and enabling the three-dimensional digital twinborn model to comprise thermal environment dynamic data of different environment nodes; determining spatial structure characteristics of different environment nodes, and constructing a spatial communication relationship through the spatial structure characteristics; determining diffusion intensity indexes of each environment node in different thermal diffusion ranges, and further determining the heat island effect intensity of each environment node by combining all the diffusion intensity indexes with the spatial communication relationship; based on the heat island effect intensity, evaluating the influence of the heat island effect intensity on a pollution diffusion field, and obtaining the heat influence pollution diffusion risk of each environment node in different heat diffusion ranges; and generating a multi-scale thermal pollution report of the target area through all the thermal influence pollution diffusion risks. By adopting the scheme of the invention, multi-scale monitoring can be carried out on urban thermal pollution based on a space-time cooperation mechanism of digital twinning.

The invention relates to a mobile air purifier control method, system, equipment and medium, and relates to the technical field of air purification, the method comprises the following steps: according to the scene information of the space, obtaining the distribution position of each key article in the space and the diffusion range of the corresponding pollution gas in the space in real time, and dividing the space into a plurality of subspaces; based on the gas sensing data of each subspace, generating a dynamic three-dimensional pollution thermodynamic diagram of the space; and calling a purification strategy executed by the purifier according to the pollution thermodynamic diagram of the space, the user behavior mode and the crowd distribution information. According to the method, air purification is carried out according to the object pollution diffusion mode of the space where the purifier is located and the activity behavior of the user, and the dynamic response accuracy and scene self-adaption collaboration of operation of the air purifier are improved and adjusted.

The invention discloses a casting workshop waste gas efficient purification treatment system, which comprises the following modules: a pollution state sensing module, which is used for constructing a pollution state map; the pollution diffusion prediction module is used for inputting the pollution state atlas into a three-dimensional space time prediction network constructed based on a light cone convolution structure to generate a pollution diffusion prediction atlas; the pollution residue mapping module is used for generating a pollution repayment map; the strategy parameter generation module is used for inputting the pollution diffusion prediction atlas and the pollution repayment atlas into the purification scheduling model to generate a purification strategy parameter set; the response inertia modeling module is used for constructing a response inertia curve; the start-stop time sequence correction module is used for correcting the start-stop time sequence based on the response inertia curve; and the equipment control module is used for generating an equipment control instruction and controlling the operation of the corresponding purification equipment. By combining the three-dimensional space time prediction network, the purification scheduling model and response compensation, efficient purification treatment of the waste gas of the casting workshop is realized.

The application discloses a land pollution monitoring, predicting and early warning system and method, relates to the technical field of environmental monitoring and pollution early warning, and comprises the following steps: acquiring multi-source data of a target land; pre-processing, feature extraction and fusion of the multi-source data; inputting the multi-source data into a dynamic pollution diffusion model to obtain a pollutant concentration field of the target land; inputting the fused data and the pollutant concentration field into a land pollutant concentration prediction model for prediction to obtain a pollutant concentration prediction value of the target land in a future target time period; comparing the pollutant concentration prediction value of the target land in the future target time period with early warning threshold values of different levels, and generating early warning instructions of corresponding levels according to a comparison result; generating corresponding early warning information according to the early warning instructions; and executing corresponding early warning measures according to the early warning information; and the application can realize scientific evaluation and hierarchical control of pollution risks through multi-dimensional data integration and model optimization.

The application discloses a water environment pollution monitoring system based on data analysis and belongs to the technical field of environment monitoring and data analysis. The system comprises the following modules: a spectrum collection module, which collects multi-band water body optical signals in real time through a distributed spectrum sensing node array to form a primary monitoring data set; a phase space reconstruction module, which reconstructs the phase space of time series spectrum signals by using a chaotic mapping theory to generate a high-dimensional attractor trajectory matrix representing the pollution diffusion state; a fractal feature extraction module, which extracts a singularity index sequence reflecting the pollution source intensity change from the high-dimensional attractor trajectory matrix by using an adaptive fractal dimension calculation method; and a pollution identification module, which dynamically identifies a pollution event triggering threshold and outputs a pollution source positioning and diffusion path prediction result based on the correlation integral analysis of the singularity index sequence and a historical pollution sample database.

This invention relates to the field of environmental science and technology, and discloses a simulation system for atmospheric pollution diffusion, including a data acquisition module, a data preprocessing module, a dynamic simulation model module, a simulation calculation module, a visualization module, and a decision support module. The data acquisition module collects data from multiple sources; the data preprocessing module cleans, fuses, and optimizes the collected data; the dynamic simulation model module employs a dynamic hybrid diffusion model and combines deep learning to optimize model parameters; the simulation calculation module performs efficient simulation of pollutant diffusion; the visualization module dynamically displays the pollutant concentration distribution and diffusion path in three dimensions; and the decision support module generates pollution control suggestions and risk warning information based on the simulation results. In this invention, by introducing a dynamic hybrid diffusion model, intelligent switching between Gaussian plume models and CFD models is achieved, enabling adaptation to various scenarios ranging from simple to complex terrains.