39 results about "Atmospheric diffusion" patented technology

The invention discloses a water environment dynamic pollution monitoring and atmospheric diffusion prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-source monitoring data of a water environment and an atmospheric environment, and constructing a pollution time series data set and an atmospheric meteorological data set; s2, constructing a variational auto-encoder model; s3, jointly optimizing a variational auto-encoder model structure and hyper-parameters by using a hosting crab optimization algorithm; s4, the optimized variational auto-encoder model is used for training, and potential variables of the pollution state are extracted; s5, constructing a diffusion modeling network, and outputting a diffusion prediction result; and S6, comparing a diffusion prediction result with a pollution threshold value, and triggering early warning information output when the diffusion prediction result exceeds the standard. According to the invention, intelligent monitoring of the water environment pollution state and accurate prediction of the diffusion behavior of pollutants to the atmosphere are realized, and efficient decision support is provided for environment early warning and treatment.

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 nuclear radiation diffusion analysis method based on a Gaussian mixture atmospheric diffusion model. The method comprises the steps that nuclear facility source item parameters are acquired; integrating meteorological, geography, remote sensing and radiation monitoring data by using a heterogeneous data fusion engine, extracting atmospheric turbulence, terrain and building interference characteristics and source item parameters, and forming a dynamic input parameter set; constructing an unsteady state diffusion model containing a multi-modal distribution function, processing features, obtaining a mixed distribution model, and correcting the mixed distribution model; predicting spatial and temporal distribution of radiation pollutants, and generating multi-scale diffusion field simulation; and optimizing a diffusion path, and generating a three-dimensional risk field. According to the method, multi-source data are integrated, an unsteady state diffusion model is constructed, Gaussian component weights are dynamically adjusted, the model is corrected by using real-time data, and a geographic information system is coupled to carry out spatial constraint optimization. And through multi-scale feature coupling, dynamic parameter adjustment and geographic space constraint correction, the depicting capability of the atmospheric diffusion model is remarkably improved, and the nuclear radiation diffusion prediction precision is greatly improved.

The present invention provides a method, device, and system for tracing the source of atmospheric pollutant emissions. The method comprises: obtaining tracer observed concentrations and pollutant observed concentrations at multiple observation points within a predetermined time range; determining the atmospheric diffusion coefficient and ground reflectance coefficient of a target Gaussian model based on the tracer observed concentrations at the multiple observation points; generating an initial chromosome population, and determining the fitness of each initial chromosome in the initial chromosome population based on the atmospheric diffusion coefficient, ground reflectance coefficient, the pollutant observed concentrations at the multiple observation points, and the target Gaussian model; determining a target chromosome population based on the fitness of each initial chromosome, and determining the emission source location and / or emission rate of the target pollutant based on the target chromosome population. Embodiments of the present invention can effectively improve the accuracy of tracing the source of atmospheric pollutant emissions.

The invention relates to the technical field of carbon emission source inversion calculation, in particular to a construction site carbon emission source inversion calculation method based on an atmospheric diffusion model. According to the technical scheme, the method comprises the following steps that a spatial diffusion model is constructed, meteorological parameters of a construction site are obtained through meteorological monitoring equipment, pollutant concentration distribution is calculated based on an atmospheric diffusion model, and the model corrects vertical diffusion by introducing a mirror image source item; performing dynamic noise reduction on the data: performing filtering processing on the original monitoring data, and balancing noise suppression and real fluctuation retention through a dynamic adjustment algorithm; performing iterative inversion calculation; and comprehensive verification: combining theoretical simulation with actual detection. Through the dynamic coupling meteorological-emission model, the adaptive noise reduction algorithm and the multi-source verification mechanism, accurate, efficient and low-cost inversion of the carbon emission source of the construction site is realized, and reliable technical support is provided for real-time carbon management, carbon transaction and green construction.

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 discloses a carbon dioxide column concentration prediction method and system based on a generative diffusion and atmospheric diffusion fusion model, and electronic equipment, and belongs to the technical field of environment prediction, and the method comprises the steps: obtaining the time dynamic characteristics of carbon dioxide column concentration data and meteorological data in a target region, according to the time dynamic characteristics and the time-space dynamic characteristics of the regional gridding meteorological data in the target region, fusing the time dynamic characteristics and the time-space dynamic characteristics; performing forward diffusion operation and reverse denoising on the fusion features to obtain a preliminary prediction result; smooth transition between adjacent moments in the prediction result is constrained through local smoothness loss, and the consistency of the change trend between the preliminary prediction result and the existing data is constrained through trend consistency loss. According to the method, spatio-temporal feature fusion is integrated, forward noise adding and reverse denoising are performed based on the diffusion model to reconstruct the prediction data, the moment sequence smooth continuity and the overall physical rationality of the prediction result are ensured through local and trend consistency loss, and the prediction precision is improved.

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.

The invention provides a method and a device for predicting atmospheric environmental pollution by using a CALPUFF atmospheric diffusion model, belongs to the technical field of atmospheric environmental pollution prediction and evaluation, and can better simulate the process of atmospheric meteorological change, thereby better realizing the simulation of the change process of pollution factors in the atmosphere and enabling the prediction result of the atmospheric environmental pollution to be more accurate. The method comprises the following steps: generating a meteorological field of a research area based on atmospheric environmental pollution data in the research area; and based on the simulation parameters and a CALPUFF atmospheric diffusion model, performing segmented simulation on time distribution on a meteorological field of the research area to obtain an atmospheric environmental pollution prediction result.

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 present application discloses a method, system and electronic device for predicting carbon dioxide column concentration based on a fusion model of generative diffusion and atmospheric diffusion, belonging to the technical field of environmental prediction. It includes obtaining the moment dynamic characteristics of carbon dioxide column concentration data and meteorological data in the target area, as well as the spatio-temporal dynamic characteristics of regional grid meteorological data in the target area, and fusing the moment dynamic characteristics and spatio-temporal dynamic characteristics; performing forward diffusion operation and reverse denoising on the fused characteristics to obtain a preliminary prediction result; and constraining the smooth transition between adjacent moments in the prediction result through local smoothness loss, and constraining the consistency of the change trend between the preliminary prediction result and the existing data through trend consistency loss. The present application integrates spatio-temporal feature fusion, performs forward noise addition and reverse denoising reconstruction on prediction data based on the diffusion model, and ensures the smooth continuity of the time series and the overall physical rationality of the prediction result through local and trend consistency losses, thereby improving the prediction accuracy.

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 present invention provides an urban fine particulate matter source tracing system and method based on multiple atmospheric super stations, which relates to the technical field of air pollution monitoring and source tracing. The system includes a data acquisition module for collecting observation data in real time through multiple atmospheric super stations; a data preprocessing module for preprocessing the observation data; a source tracing model construction module for constructing a source tracing model based on the positive definite matrix factorization receptor model and the random forest algorithm; a real-time collaborative source tracing module for inputting the preprocessed data into the source tracing model and outputting the source contribution ratio and regional emission weight through parallel computing, and introducing an atmospheric diffusion model to dynamically correct the source tracing result; and a result display and analysis module for an interactive visualization tool based on WebGL to display the source tracing result through dynamic pie charts and 3D bar charts. The present invention solves the deficiencies of traditional methods and provides efficient decision-making support for urban pollution control.

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 present invention discloses a method and system for tracing the diffusion path of air pollution based on meteorological data, which relates to the technical field of pollution path tracing. The method includes: establishing a high-density monitoring network; obtaining the time-series point monitoring data set of the high-density monitoring network and distributing pollution hotspots; configuring high-precision sensors and monitoring drones in the pollution hotspots, establishing three-dimensional data of the pollution field, and synchronously obtaining the wind force data set; establishing state variables; using an atmospheric diffusion model to perform prediction fitting of a set of initial parameters based on the observed variable wind force data set to generate the pollutant distribution and establish candidate solutions; performing a global particle search on the candidate solutions to establish the tracing result of the pollution diffusion path. The present invention solves the technical problems in the prior art that it is difficult to accurately identify pollution hotspots and the accuracy and reliability of tracing the diffusion path of air pollution are low, and achieves the technical effect of improving the accuracy and reliability of tracing the diffusion path of air pollution.

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 embodiment of the invention discloses a gas leakage risk simulation method and device based on laser remote measurement and a medium. The method comprises the following steps: dividing a region to be simulated into a plurality of two-dimensional geographic grids; laser telemetering equipment is used for monitoring the gas pollutant concentration of a plurality of monitoring grids in the to-be-simulated area in real time, the laser telemetering equipment is deployed at the high position of the boundary of a leakage risk area in the to-be-simulated area, and a monitoring view angle covers a leakage risk device in the leakage risk area and surrounding grids of the leakage risk device; and identifying the monitoring grid with the instantaneous increment of the pollutant concentration greater than a set positive threshold value as a suspected leakage grid, adding the suspected leakage grid as a leakage source in the atmospheric diffusion simulation model, and performing minute-level or higher-time-precision simulation on the pollutant leakage process of the leakage source. According to the embodiment, leakage events in a large range can be captured in real time, and the diffusion path can be dynamically simulated.

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.