27 results about "Atmospheric diffusion" patented technology

The invention relates to a territorial space planning data monitoring evaluation method and system, and the method comprises the steps: obtaining multi-modal data, constructing an atmospheric diffusion model based on the multi-modal data, reversely positioning a pollution source, and obtaining a pollutant responsibility matrix; carrying out space grid fusion calculation through the pollutant responsibility matrix and the carbon sink data to generate a carbon source-carbon sink distribution diagram representing a territorial space carbon balance pattern; based on the regional characteristics of the carbon source-carbon sink distribution diagram, generating a territorial space carbon planning scheme through a low-carbon guiding algorithm; constructing a disaster model based on the multi-modal data, and carrying out space coupling analysis on a disaster risk area and a high-carbon sink potential area output by the disaster model to obtain a land space carbon restoration scheme; a digital twinborn platform is constructed according to multi-modal data, an atmospheric diffusion model and a disaster model, an expected carbon sink increment value of a land space carbon planning scheme is deduced and verified through the digital twinborn platform, and iteration of the implementation scheme is carried out.

The invention provides a dynamic space-time air quality prediction method based on a physical constraint graph attention network, and the method comprises the steps: carrying out the collection and preprocessing of multi-source data, and obtaining the pollutant concentration and meteorological element historical sequence of a monitoring station; constructing a dynamic space-time diagram network fusing distance attenuation and a wind direction driving transmission path based on the longitude and latitude of the station and the real-time meteorological field; multi-cycle time sequence features are extracted in a self-adaptive mode through an enhanced time sequence network ETNet, and attention weight distribution is guided through physical priori; time sequence features are embedded into the dynamic graph, a physically constrained graph diffusion attention network PC-GDAN is input, and space-time diffusion modeling is achieved through a differential operator embedded into an atmospheric diffusion equation; time sequence and space features are fused, mass conservation and diffusion smoothness constraints are introduced in decoding prediction, and a concentration prediction result conforming to a physical rule is generated. The method effectively fuses physical mechanisms and data, has strong space-time modeling capability and physical interpretability, and significantly improves the precision and robustness of air quality prediction.

The invention provides an air quality data quality control method based on time series data, and relates to the technical field of air detection, and the method comprises the steps: obtaining multi-source atmosphere monitoring station data, constructing a spatial topological relation network of monitoring stations, and defining a dynamic hierarchical structure of atmosphere pollutant data under different spatial and temporal scales and a cross-hierarchical association rule of the dynamic hierarchical structure; carrying out anomaly detection and traceability analysis on the obtained monitoring data, and carrying out data correction and filling; performing credibility evaluation and grading on the corrected monitoring data by applying a space-time clustering algorithm and combining a site topological relation and a data association rule, and generating a data credibility grading result of a space-time dimension; when the data credibility is detected to be abnormal or the potential failure risk is detected, a graph neural network model integrated with atmospheric diffusion physical constraints is adopted; and generating a visual atmosphere data quality evaluation and control decision report, and automatically triggering an instrument calibration, maintenance or data review instruction for a specific site or region according to the risk level.

The invention provides a radiation dose evaluation method and system based on radiation environment monitoring data, and relates to the technical field of radiation environment monitoring. The method comprises the following steps: recording the release source intensity of each nuclide at a release point, drawing an equally-spaced concentric arc grid, calculating the release height by combining the ground and the release point air pressure, air density and gravitational acceleration, and assigning nuclide hazard factors to the outer edge of the grid; according to the distance between the release point and the outer edge of the grid, diffusion parameters are calculated by using an empirical formula, a Gaussian atmospheric diffusion model is constructed in combination with the release height and hazard factors, and nuclide air activity concentration is output; comparing the total effective dose with a preset threshold value to calibrate a diffusion range, quantifying the radiation dose per unit area, and dividing atmospheric stability coefficient grades; constructing three types of correction factors of release height, material state and weather condition, establishing a comprehensive dose evaluation formula by combining the unit area radiation dose and the correction factors, comparing a preset threshold value, judging a risk level, and performing periodic updating.

The invention provides an industrial park environmental protection quality assessment method and system based on STGNN, and relates to the technical field of environmental protection quality assessment, and the method achieves the precise modeling of a complex pollution diffusion process of an industrial park through fusing multi-source heterogeneous data and constructing a space-time dynamic graph containing internal subregions and peripheral sensitive targets of the park; by introducing a space-time-physics fused STGNN model, a graph attention mechanism is utilized to dynamically capture a spatial dependency relationship affected by multiple factors such as geography, process, logistics and meteorology between regions, a time sequence model is combined to describe a pollution evolution rule, and an atmospheric diffusion physical mechanism is embedded to enhance the prediction reliability of the model under extreme working conditions; finally, environmental protection quality trend prediction, regional pollution traceability visualization and graded emergency scheduling scheme automatic generation are realized based on high-dimensional spatial-temporal characteristics, and the accuracy, interpretability and decision support capability of evaluation are improved.

This invention relates to the field of atmospheric environmental risk early warning technology, and in particular to an atmospheric environmental simulation diffusion method and system based on GIS and virtual reality. In simulating atmospheric environmental diffusion, this invention first acquires multi-source data for the simulation, and then processes the multi-source data. During the data processing step, when simulating atmospheric diffusion of pollution sources, a connection is established between the resolution of the geographic grid and the resolution of the pollution source data grid. This reduces information loss due to data mismatch during data fusion and improves the reliability of the atmospheric environmental diffusion simulation results.

This application discloses a method and system for determining pollution causes based on the correlation between atmospheric diffusion conditions and emission contributions, relating to the field of atmospheric pollution cause identification. The method includes: aligning meteorological, emission, and receptor observations to a unified spatiotemporal reference to obtain multi-source baseline data; calculating a diffusion stability index characterizing vertical mixing and horizontal transport using meteorological monitoring, generating time-varying diffusion condition weights; generating a time-response diffusion kernel from the emission source to the receptor point using a diffusion model under unit emission conditions, and constructing diffusion-contribution correlation information with observed concentrations to characterize the temporal coupling strength; establishing a diffusion-contribution dynamic optimization model, using diffusion condition weights to constrain the coupling relationship, and iteratively solving for time-varying contribution coefficients based on the convergence target of simulated and observed concentrations, outputting the dynamic contribution of each source and the pollution cause. Thus, time-varying diffusion conditions are explicitly introduced into the source contribution inversion framework, achieving stable separation and reliable attribution of source contributions.

The present application relates to carbon emission source inversion calculation technical field, especially to a kind of construction site carbon emission source inversion calculation method based on atmospheric diffusion model.The technical scheme includes the following steps: constructing space diffusion model: the meteorological parameter of construction site is obtained by meteorological monitoring equipment, pollutant concentration distribution is calculated based on atmospheric diffusion model, the model is corrected vertical diffusion by introducing mirror source term;Data dynamic noise reduction: the original monitoring data are filtered, and the balance between noise suppression and real fluctuation reservation is balanced by dynamic adjustment algorithm;Iterative inversion calculation;Comprehensive verification: by theory simulation and actual detection combination.The present application realizes the accurate, efficient and low-cost inversion of construction site carbon emission source by dynamically coupling meteorological-emission model, adaptive noise reduction algorithm and multi-source verification mechanism, which provides reliable technical support for real-time carbon management, carbon trading and green construction.

The invention discloses a greenhouse gas and atmospheric pollutant collaborative emission reduction benefit evaluation method based on energy activity horizontal dynamic coupling, and relates to the technical field of environmental protection and climate change coping intersection. According to the method, real-time accurate accounting of the emission amount is realized by constructing a high-temporal-spatial-resolution energy activity dynamic matrix and a dynamic emission factor model library. And designing different policy scenes, simulating a linkage effect by applying an integrated energy system model, and calculating the dynamic emission reduction amount. The carbon emission reduction monetization value and the health benefit value based on the atmospheric diffusion model are weighted and integrated, and the total policy cost is combined, so that a cooperative emission reduction comprehensive benefit index (SRI) is created for the first time as a core evaluation index. According to the method, the problems of static accounting distortion and multi-dimensional benefit splitting in a traditional method are solved, a complete technical path from dynamic process simulation to comprehensive benefit quantitative comparison is realized, and a scientific decision basis is provided for cost-benefit optimization of a cooperative control policy.

The application provides a method and system for evaluating the influence of volcanic ash on urban air quality, relating to the technical field of environmental atmospheric pollutant evaluation and volcanic disaster monitoring, which comprises taking the concentration of sulfate in particulate matter as a key indicator, obtaining air quality monitoring data before, during and after volcanic eruption, automatically determining the influence period in combination with a dynamic threshold, simulating the diffusion path and concentration distribution of volcanic ash by using an atmospheric diffusion model, and predicting the influence range. A visual geographic information map is generated by Kriging spatial interpolation, and a characteristic index (sulfate / concentration ratio) is calculated to divide the influence level of the station, and a regional comprehensive influence index is calculated in combination with the weight of population density, so as to realize high, medium and low influence classification. The application can accurately quantify the contribution of volcanic eruption to urban air quality, and provide a scientific basis for air quality early warning and prevention and control.

The application provides a kind of nuclear power plant severe accident outside radiation consequence probability statistical evaluation method, including S1, probability is carried out to meteorological sequence;S2, consequence data collection of object of attention: on the basis of existing mainstream object of attention, it is formed to object of attention set by refinement and expansion, for each object of attention, obtain consequence result {D1, D2,..., DN} obtained by atmospheric diffusion calculation and analysis corresponding to all meteorological sequences;S3, on the basis of a series of consequence results of attention associated with different meteorological sequences obtained in the step S2, the probability of each consequence result of attention occurs is determined;S4, based on the consequence result of attention obtained in the step S2 and the corresponding probability determined in the step S3, generate dynamic matrix;S5, for the dynamic matrix generated in the step S4, carry out beyond probability calculation.The application covers wide consequence order of magnitude, adapts multi-scale risk expression, can ensure that in low value area is not too sparse, high value area is not blindly refined.

The invention belongs to the technical field of atmospheric environment simulation and nuclear emergency management, and particularly relates to a nuclear emergency response and decision analysis method based on a Gaussian mixture atmospheric diffusion model, and the method comprises the following steps: S1, obtaining the spatial-temporal characteristics of a pollution source item and corresponding environmental parameters, processing the spatial-temporal characteristics, and obtaining the environmental parameters of the pollution source item; obtaining standardized input data containing time dimension information; s2, constructing a Gaussian mixture atmospheric diffusion model; s3, inputting the standardized input data obtained in the S1 and the corresponding environmental parameters into the Gaussian mixture atmospheric diffusion model, and iteratively updating the model parameters based on the diffusion consistency constraint loss function until a preset termination condition is met; and S4, after a nuclear or radioactive emergency event occurs, inputting the standardized input data and the corresponding current environmental parameters into the trained Gaussian mixture atmospheric diffusion model to obtain a prediction result of the spatial and temporal distribution of the pollutant concentration. According to the method, the non-uniform diffusion behavior under the complex weather and terrain can be accurately described.

The application discloses a gas leakage source tracing detection method with additional confidence space fault tolerance rate. On the basis of an atmospheric diffusion model, the application adds confidence space fault tolerance rate to the estimated positions of multiple gas leakage sources detected within a small interval time, combines a radio positioning method, establishes a fault tolerance space of a leakage point within continuous detection time, then carries out clustering to form a fault tolerance circle, carries out intersection processing on the fault tolerance circles formed at continuous detection time, obtains a fault tolerance space of an initial estimated leakage point, and finally carries out leakage point fault tolerance space filtering through environmental information to obtain a final leakage point estimated area. The reliability and scientificity of the leakage source positioning result are greatly improved.

The invention discloses a comprehensive evaluation model construction method for pollution discharge right distribution of a stationary pollution source. The method comprises the steps of collecting air pollution discharge distribution data and monitoring data of a preset space region; performing grid environment capacity measurement and calculation on the monitoring data based on an ecological sensitivity threshold value to obtain an environment capacity upper limit, performing atmospheric diffusion deduction on the air pollution emission distribution data based on the collaborative capacity of a precursor to obtain predicted ozone data, and performing dynamic ozone measurement and calculation on the monitoring data to obtain measurement and calculation data, taking a difference value between the predicted ozone data and the measurement and calculation data as an ozone difference value; performing ecological impact analysis according to the measurement and calculation data to obtain ecological impact factors, and constructing an ozone comprehensive evaluation function according to the ecological impact factors, the environment capacity upper limit and the ozone difference; and according to the ozone comprehensive evaluation function, constructing a pollution emission and ozone distribution comprehensive evaluation model, inputting to-be-evaluated data into the pollution emission and ozone distribution comprehensive evaluation model, and outputting a result.

The invention provides a GIS (Geographic Information System)-based organic pollutant concentration boundary simulation method. The method comprises the following steps: acquiring multi-source data and preprocessing the multi-source data; constructing a multi-model fused GIS space simulation system and simulating concentration distribution of organic pollutants in soil, water and atmosphere; and analyzing the simulation result and generating a static map. According to the method, by coupling the EFDC hydrodynamic model, the CALPUFF atmospheric diffusion model and the Kriging interpolation model, the migration and diffusion characteristics of organic pollutants in multiple media of water, atmosphere and soil can be comprehensively reflected; according to multi-source data of geography, environment monitoring, weather and pollution sources, model parameters are optimized through a machine learning algorithm, and it is ensured that R of a simulation result is larger than or equal to 0.85, and the spatial resolution is not lower than 1 km; according to the invention, the spatial visualization of the simulation result is realized based on the GIS platform, the concentration thermodynamic diagram and the contour boundary are generated, and the concentration distribution of new pollutants is visually presented.

Embodiments of the present application disclose a gas leakage risk simulation method and device based on laser remote sensing and a medium. The method comprises: dividing a region to be simulated into a plurality of two-dimensional geographic grids; using a laser remote sensing device to monitor the concentration of gas pollutants in a plurality of monitoring grids in the region to be simulated in real time, wherein the laser remote sensing device is arranged at a high position of a leakage risk region boundary in the region to be simulated, and a monitoring angle covers a leakage risk device and surrounding grids in the leakage risk region; monitoring grids with an instantaneous increment of pollutant concentration greater than a set positive threshold are identified as suspected leakage grids and added as leakage sources in an atmospheric diffusion simulation model, and a minute-level or higher time-precision simulation is performed on the pollutant leakage process of the leakage sources. The embodiments can capture leakage events in a larger range in real time and dynamically simulate diffusion paths.

The invention provides an ozone pollution assessment method based on SHAP-IPSO-CNN. The ozone pollution assessment method is used for assessing high accuracy and high interpretability of ozone pollution in a chemical industrial park. The method comprises the following steps: firstly, predicting the distribution concentration of volatile organic compounds discharged by enterprises in a park in a target monitoring station by using an atmospheric diffusion model, and forming model input characteristics by the distribution concentration, the concentration of other pollutants in the target monitoring station and meteorological factors; then, three different machine learning models are considered for deep comparison and preferential SHAP analysis, and a feature importance queue is established by using an SHAP analysis result. Then, an improved particle swarm optimization algorithm is combined with the feature importance of SHAP analysis, and training features are dynamically adjusted to optimize the performance of the CNN model. According to the integrated model, by combining the efficient feature extraction capability of a convolutional neural network (CNN), the global search optimization characteristic of an improved particle swarm optimization (IPSO) algorithm and the explanation capability of SHAP analysis, the influence evaluation on ozone formation is realized. The method provided by the invention has high reliability and application prospect under complex conditions.

The invention discloses an environmental impact assessment method based on an atmospheric diffusion model, and relates to the technical field of environmental impact assessment, and the method comprises the steps: obtaining a production plan of a target factory, and generating a pollution gas emission time sequence; setting a target monitoring area by taking the target factory as a pollution source center, and determining a plurality of environment quality control nodes in the target monitoring area; obtaining weather forecast data in a preset time window, and establishing an atmospheric diffusion model of the target monitoring area; based on the atmospheric diffusion model, obtaining a plurality of pollution gas diffusion sub-models of the plurality of environment quality control nodes; obtaining a plurality of pollution gas accumulation concentration time sequences; and when the pollution gas accumulation concentration of any environment quality control node in the plurality of pollution gas accumulation concentration time sequences at any time point exceeds a preset pollution gas threshold value, generating a production plan adjustment instruction. According to the invention, the technical problem of hysteresis of pollutant environmental impact assessment in the prior art is solved.

The invention provides a probability statistical evaluation method for off-site radiation consequences in a severe accident of a nuclear power plant. The method comprises the following steps: S1, probabilizing a meteorological sequence; s2, consequence data acquisition of the concerned objects: on the basis of existing mainstream concerned objects, refining and expanding to form a concerned object set, and for each concerned object, obtaining consequence results {D1, D2,..., DN} obtained by atmospheric diffusion calculation and analysis corresponding to all meteorological sequences; s3, on the basis of a series of attention consequence results associated with different meteorological sequences obtained in the step S2, determining the occurrence probability of each attention consequence result; s4, generating a dynamic matrix based on the attention consequence result obtained in the step S2 and the corresponding probability determined in the step S3; and S5, performing surpassing probability calculation on the dynamic matrix generated in the step S4. The method covers a wide consequence order of magnitude, adapts to multi-scale risk expression, and can ensure that a low-value region is not too sparse and a high-value region is not blindly refined.

The present application relates to a kind of nuclear accident dose evaluation method based on radiation environment monitoring data.The present application is directed to the problems that nuclear accident releases for a short time, radionuclide activity concentration in ambient air changes fast with time and space, and radiation environment monitoring data cannot characterize the change of radionuclide in space and time, based on the time integral concentration calculation method of radionuclide in ambient air under accident conditions, generalized least squares method and regression kriging interpolation method, using radiation environment monitoring data and atmospheric diffusion model prediction results, the radionuclide activity concentration in ambient air changing with time and space based on radiation environment monitoring data is obtained, and the dose of personnel is accurately evaluated by calculation result, thereby establishing a nuclear accident dose accurate evaluation method based on radiation environment monitoring data.

The application provides a method and system for simulating the physical and chemical coupling of atmospheric diffusion under a nuclear and chemical integrated accident scene. The method first constructs a parameterized model of a nuclear and chemical integrated source term, determines the distribution ratio of nuclides on particles of different chemical properties according to the physical characteristics of nuclides and the release rate of chemical components; then runs the aerosol dynamics coupling evolution operator to monitor the concentration of hazardous chemicals and the relative humidity in the simulation space in real time, calculates the particle size change and density change of the nuclide carrier aerosol in the diffusion process; based on the evolved physical parameters, the settling term in the atmospheric diffusion equation is corrected in real time, and the dynamic diffusion distribution of radioactive nuclides in three-dimensional space is deduced by using the iterative step method; finally, the ground pollution correction map and the synergistic damage evaluation results under the nuclear and chemical integrated accident are output in combination with the chemical and radiation synergistic factors. The application solves the problem of diffusion prediction distortion caused by the neglect of the physical and chemical interaction in the traditional model, and improves the accuracy of complex disaster consequence evaluation.

The application discloses a fog grading dataset construction method and system based on an atmospheric diffusion model and a diffusion generation network, relates to the field of computer vision and image generation, and discloses the following technical scheme: a diffusion generation network is used to generate a clear image according to a text prompt; a depth estimation network is used to obtain a corresponding depth map; a transmittance is calculated based on an atmospheric scattering physical model and the depth map to synthesize an initial fog image; the clear image, the depth map and the transmittance map are input into a conditional control diffusion generation network as a condition to generate a target fog image; a fog grading network is used to evaluate the visibility grade of the target fog image, and a scattering coefficient is fed back and corrected; multi-level fog images are generated based on the corrected parameters, and are paired and labeled with the clear image to construct a grading dataset. The technical scheme can automatically generate a high-quality grading fog dataset which is physically real and has controllable concentration.

A method and apparatus for simulating and predicting ozone concentration in industrial parks using a coupled diffusion model and a zero-dimensional chemical box model are disclosed. The method includes: using upper-air and surface meteorological data and land use data as inputs to the AERMET model; using the AERMET output file and the emission inventory of pollution sources in the petrochemical park as input data to the AERMED model; running the AERMED model to obtain hourly concentration variation data of ozone precursors; and then using the model output results to input into the AtChem box model, combining the MCM mechanism to achieve ozone concentration prediction. This invention significantly improves the efficiency of ozone concentration prediction by coupling the atmospheric diffusion model and the box model, reducing the pressure of acquiring complex meteorological data using the three-dimensional model method and the pressure of measuring VOCs precursor components using the chemical box model observation method.

The present invention relates to a method for determining, at any point in a geographical area and at any time, the evolution in time and space of a concentration (Cp) of at least one pollutant emitted by a source (SE) and dispersed by the wind. In the present invention, the pollutants are considered to be transported by the wind, via the solution of an advection-diffusion equation, and the wind is characterized by Laplace flow (irrotational and incompressible flow), using a wind velocity potential model (WVPM). Furthermore, the present invention defines boundary conditions (BC) of the Stokes equations to account for complex obstacle geometries and non-uniform wind conditions within the wind velocity field model (WVPM). Figure 1 to be published

The invention discloses a fog grading data set construction method and system based on an atmospheric diffusion model and a diffusion generation network, relates to the field of computer vision and image generation, and aims to solve the problems that the existing fog image synthesis is unreal, the fog concentration is uncontrollable, and a high-quality grading pairing data set is difficult to construct automatically. A clear image is generated according to a text prompt by utilizing a diffusion generation network; obtaining a corresponding depth map through a depth estimation network; calculating transmissivity based on an atmospheric scattering physical model and the depth map, and synthesizing an initial fog image; inputting the clear image, the depth image and the transmissivity image as conditions into a condition control diffusion generation network to generate a target fog image; the visibility grade is evaluated by using a fog grading network, and a scattering coefficient is fed back and corrected; and generating a multi-stage fog image based on the corrected parameters, and pairing and labeling the multi-stage fog image with the clear image to construct a hierarchical data set. According to the technical scheme, the physically real and concentration-controllable high-quality graded fog data set can be automatically generated.

The invention relates to a CNN-LSTM-based radionuclide atmospheric diffusion prediction method and device. The method comprises the following steps: acquiring a test set and a training set of radionuclide atmospheric diffusion prediction; a CNN-LSTM model is trained according to the training set, and a trained CNN-LSTM model for radionuclide atmospheric diffusion prediction is obtained; the trained CNN-LSTM model is used to predict the test set, and a radionuclide concentration prediction value of radionuclide atmospheric diffusion prediction is obtained; and carrying out reverse normalization operation on the radionuclide concentration prediction value, and determining a final prediction value of radionuclide atmospheric diffusion prediction. The trained CNN-LSTM model is high in prediction accuracy and high in prediction speed.

The invention provides a method and system for evaluating the influence of volcanic ash on urban air quality, and relates to the technical field of environmental atmospheric pollutant evaluation and volcanic disaster monitoring, and the method comprises the steps: taking the sulfate concentration in particulate matters as a key index, and obtaining the air quality monitoring data before, in and in the later period of volcanic eruption; the influence time period is automatically judged in combination with a dynamic threshold value, the volcanic ash diffusion path and concentration distribution are simulated through an atmospheric diffusion model, and the influence range is predicted. A visual geographic information graph is generated through Kriging spatial interpolation, characteristic indexes (sulfate / concentration ratio) are calculated to perform site influence grade division, regional comprehensive influence indexes are calculated in combination with weights such as population density and the like, and high, medium and low influence grading is realized. According to the method, the contribution degree of volcanic eruption to urban air quality can be accurately quantified, and a scientific basis is provided for air quality early warning and prevention and control.