1177 results about "Atmospheric pollution" patented technology

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

The invention belongs to the technical field of environmental science, and relates to a multi-source satellite remote sensing cooperative atmospheric pollution monitoring and dynamic tracing method and system. The method comprises the following steps: collecting, processing and screening multi-source data; constructing a pollutant diffusion model, and generating pollutant concentration distribution data; dynamically adjusting the diffusion coefficient and attenuation rate of the pollutant diffusion model, and generating a pollutant concentration distribution thermodynamic diagram; performing space matching, and iteratively updating the diffusion coefficient and the attenuation rate of the pollutant diffusion model until matching parameters meet set conditions; and verifying pollutant concentration distribution data in the pollutant concentration distribution thermodynamic diagram. According to the invention, parameters of the pollutant diffusion model can be dynamically adjusted and optimized based on real-time meteorological data, the accuracy of pollutant concentration distribution data generated by the pollutant diffusion model is ensured, and real-time tracking and accurate positioning of a pollution source are realized; the monitoring precision is obviously improved; the cooperative monitoring efficiency is improved, and comprehensive monitoring of atmospheric pollutants is realized.

The invention relates to an air pollution monitoring method based on unmanned aerial vehicle remote sensing and machine learning. According to the method, atmospheric pollutant concentration is monitored in real time by acquiring fixed monitoring stations and satellite remote sensing data, a pollution abnormal signal is generated when the concentration exceeds a preset threshold value, an unmanned aerial vehicle group is scheduled according to the signal to perform three-dimensional pollution data acquisition according to a preset path, and initial pollution distribution data is obtained; and dynamically adjusting a monitoring path and sensor configuration parameters of the unmanned aerial vehicle group by using a reinforcement learning algorithm, collecting real-time monitoring data, performing pollution traceability calculation in combination with a space-time diagram neural network model, generating a pollution source positioning result, then performing a pollution regulation and control instruction, and executing feedback control. By adopting the method, the accuracy of pollution source positioning and the timeliness of regulation and control can be effectively improved, and a scientific basis and technical support are provided for air pollution control.

The invention belongs to the technical field of atmospheric pollution monitoring, and discloses a method for realizing VOCS inversion of satellite remote sensing column concentration by using machine learning. Comprising the following steps: preprocessing multi-source data to obtain a multi-source feature data set; performing feature hierarchical extraction and association analysis based on the multi-source feature data set to obtain a feature association graph; performing dynamic feature embedding and relation modeling based on the feature association graph to obtain a component feature space; based on the component feature space, constructing a multi-task deep learning inversion model, and performing weight optimization to obtain an optimized inversion model; based on the optimized inversion model, space-time interpolation and uncertainty analysis are carried out on satellite remote sensing column concentration data, and a high-resolution VOCs component inversion graph is obtained; and multi-scale verification and dynamic correction are carried out based on the high-resolution VOCs component inversion graph and the ground VOCs component monitoring data, and a VOCs component inversion result is finally obtained, so that the refinement level of regional atmosphere quality management is improved.

The invention relates to the technical field of atmospheric environment monitoring, in particular to an atmospheric pollution source tracking and early warning system based on deep learning, which comprises a multi-source data acquisition layer for acquiring multi-dimensional data of atmospheric pollutant concentration, weather, geography and the like through multi-element equipment such as satellite remote sensing and ground sensors; the data preprocessing layer completes data cleaning by using a statistical method and a machine learning algorithm, and realizes heterogeneous data standardization processing through feature extraction, multi-source integration and space-time alignment, and the deep learning model layer realizes heterogeneous data standardization processing by means of a space-time convolutional neural network, a graph neural network and a long-short term memory network in combination with a space-time attention mechanism. The traceability analysis layer is combined with a back diffusion algorithm and a geographic information system to position a pollution source and generate a thermodynamic diagram. And the early warning decision-making layer sets threshold early warning through transfer learning of the adaptive area, and generates an optimal pollution control scheme based on reinforcement learning.

The invention discloses an atmospheric ecological environment data synchronous monitoring method and system, and relates to the technical field of atmospheric environment monitoring, and the method comprises the steps: building a sky-air-ground global monitoring network, and achieving the unification of global data collection and space-time reference; on the basis of a hybrid interpolation model, performing hybrid interpolation and residual analysis on the atmospheric ecological environment monitoring data collected in a global manner, identifying a monitoring weak region, and performing positioning and labeling; based on monitoring weak area position distribution, starting an unmanned aerial vehicle to execute a weak area inspection task, and collecting weak area monitoring data through field monitoring to realize monitoring-prediction closed loop verification; actual measurement and prediction data are fused to reconstruct a global monitoring network, a digital twin platform is constructed through three-dimensional situation mapping, and global sensing and synchronous monitoring of the atmospheric ecological environment are achieved. According to the invention, by constructing a space-air-ground collaborative three-dimensional monitoring network, total element synchronous acquisition and dynamic integration of atmospheric pollution data are realized, and a seamlessly connected data base is provided for pollution traceability and trend prediction.

The invention relates to the technical field of mining area pollution monitoring, and discloses an atmospheric pollution monitoring system based on a mining area and a monitoring method thereof.The atmospheric pollution monitoring system based on the mining area comprises the steps that firstly, geographic environment data, outdoor environment data and well area environment data are obtained through a network deployment module and stored; the real-time transmission module is used for preprocessing the acquired data and transmitting the preprocessed data to the comprehensive analysis module, and the comprehensive analysis module is used for analyzing the atmospheric pollution index, judging whether to send out pollution early warning or not according to the calculation result of the atmospheric pollution index, and sending out a pollution alarm signal under the condition that the pollution early warning needs to be sent out; the mining area air pollution monitoring system can comprehensively monitor the mining area air pollution in a multi-dimensional manner, accurately identify the main pollution source and the dynamic change of the main pollution source, break through the limitation of traditional fixed site monitoring, flexibly capture the spatial and temporal change of the mining area pollution, accurately identify the pollution source, improve the monitoring accuracy and improve the monitoring efficiency. And an alarm signal is sent timely.

The invention relates to the technical field of data processing, in particular to an abnormal data processing system for atmospheric pollution monitoring, which comprises the following steps: acquiring a concentration sequential sequence and a standard sequential sequence of gas; performing preliminary evaluation on the concentration time sequence to obtain a preliminary abnormal degree; sequentially carrying out segmentation and fitting on the concentration time sequence to determine subsequences and a target curve; dividing a segmentation sequence based on the target curve, and segmenting a matching sequence; based on the change of the correlation between the segmentation sequence and the standard time sequence along with time, analyzing the offset characteristic of the gas concentration change trend to determine the trend time sequence offset degree, and combining the normal distribution fitting result of the concentration time sequence to determine the detection influence weight; and acquiring abnormal data in the concentration data of each gas at the monitoring point according to the detection influence weight and the initial abnormal degree. According to the method, the decision weight of the isolated tree is adaptively determined, so that the detection accuracy of the abnormal data in the concentration data is improved.

The invention discloses an atmospheric pollution source intelligent detection system and method, and relates to the technical field of atmospheric pollution detection, and the method comprises the steps: 1, collecting atmospheric pollutant concentration data, space-time position information and meteorological conditions at different time, space and altitude levels; step 2, constructing a pollutant diffusion path prediction model based on the collected pollutant concentration data, space-time position information and meteorological conditions, outputting a pollutant diffusion path prediction value, and predicting the pollutant diffusion path prediction value at a to-be-predicted point; and step 3, constructing a three-dimensional pollution distribution model based on the atmospheric pollutant concentration data, the space-time position information, the meteorological conditions and the derived spatial distance parameters, outputting a pollution concentration prediction value, and predicting the pollution concentration prediction value at the spatial point. According to the method, the risk level of the pollution source is accurately divided, and the problem that the real-time traceability and the classification response capability of the pollution event are limited due to the lack of a systematic data modeling mechanism and a response strategy in the existing method is solved.

The invention discloses an atmospheric pollution remote sensing intelligent monitoring system and method based on multi-modal data, and belongs to the technical field of atmospheric pollution remote sensing monitoring. Comprising a multi-source heterogeneous data acquisition and preprocessing module, a dynamic adaptive modal fusion module, a sudden change scene adaptive module, a three-dimensional pollution inversion enhancement module, a space-time diagram convolution traceability module and a weak signal pollution source collaborative sensing module. According to the method, the concentration contribution values of a plurality of pollution sources are superposed, the three-dimensional distribution of pollutants in space and time is accurately simulated, a visual basis is provided for pollution source positioning and diffusion path analysis, the synchronization of an inversion result and a real-time environment state is ensured through dynamic parameter updating, and the spatial resolution and prediction precision of pollution monitoring are remarkably improved.

The invention discloses an atmospheric environment monitoring emergency early warning method, and particularly relates to the technical field of atmospheric environment monitoring and early warning, and the method comprises the steps: S1, constructing a multi-source data fusion three-dimensional monitoring system, S2, constructing a dynamic risk assessment and early warning triggering mechanism based on artificial intelligence, and S3, constructing an emergency response cooperation and feedback optimization mechanism. According to the invention, the monitoring comprehensiveness and accuracy are improved by constructing a multi-source data fusion three-dimensional monitoring system, more scientific and reasonable early warning and decision are realized by means of a dynamic risk assessment and early warning trigger mechanism based on artificial intelligence, emergency response is optimized by means of an emergency response collaboration and feedback optimization mechanism, and the disposal efficiency is improved. The accuracy of atmospheric environment monitoring, the timeliness of early warning and the high efficiency of emergency disposal are comprehensively guaranteed, so that the atmospheric pollution hazard is effectively reduced, and the public health, the urban operation, the ecological environment safety and the social stability are guaranteed.

The invention belongs to the field of air pollution prevention and control, and particularly discloses an urban air pollution transmission path and source contribution evaluation system and method based on machine learning and a numerical mode, and the evaluation system comprises a meteorological simulation module, a pollution emission list module, a numerical traceability module and a machine learning transmission path evaluation module. According to the method, machine learning and a numerical mode are combined, the automation degree is high, automatic downloading, automatic running and online data analysis can be achieved without manual data statistics and analysis, operation is convenient, data accuracy is high, the response speed is high, and important support is provided for work such as atmospheric pollutant cause tracing and atmospheric pollution control scheme making.

The invention relates to an atmospheric pollutant early warning method and system based on multi-source isomerism, and the method comprises the steps: obtaining multi-source isomerism atmospheric pollutant data, carrying out the data arrangement of the atmospheric pollutant data, and obtaining the arranged atmospheric pollutant data; carrying out unified representation on the sorted air pollutant data to obtain air pollutant data subjected to unified representation, and carrying out intelligent detection by adopting a gridding edge calculation method according to the air pollutant data subjected to unified representation to obtain an air pollutant detection result; according to the detection result of the atmospheric pollutants, performing forecasting and early warning by adopting a method based on big data analysis to obtain a forecasting and early warning result of the atmospheric pollutants; and the forecasting and early warning result of the atmospheric pollutants is displayed through an artificial intelligence GIS platform, so that the purposes of accurately monitoring, forecasting and early warning the atmospheric pollutants, improving the atmospheric pollution treatment level, assisting in environmental protection decision making and promoting development of related technologies are achieved.

The invention provides an atmospheric pollution process analysis method, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the following steps: receiving an atmospheric pollution problem sent by a user, and respectively carrying out vector database retrieval and knowledge graph retrieval based on the atmospheric pollution problem to obtain a first retrieval result and a second retrieval result; wherein the vector database retrieval comprises the following steps: calculating cosine similarity between background information vectors in a vector database and atmospheric pollution problem vectors, and determining multiple pieces of background information of which the cosine similarity accords with a preset threshold value as a first retrieval result; the knowledge graph retrieval comprises the following steps: performing entity recognition and standardization on the atmospheric pollution problem, and generating a target knowledge triple as a second retrieval result; and fusing and inputting the atmospheric pollution problem, the first retrieval result and the second retrieval result into a large language model, and outputting an analysis result of the atmospheric pollution problem. Through the method provided by the invention, the illusion problem of a general large language model in the atmosphere field is relieved.

The invention relates to the technical field of air pollution tracing, and particularly discloses an air pollution source accurate tracing method and system, and the system comprises a multi-source data monitoring module, a multi-source data analysis module, a pollution tracing comprehensive analysis module, a pollution diffusion model verification optimization module, and a pollution tracing analysis module. By comprehensively considering various factors, setting a proportionality coefficient to calculate a pollution traceability comprehensive index and comparing the pollution traceability comprehensive index with a threshold value, a pollution traceability area and a potential area can be effectively marked, simulation concentration is calculated according to actual parameters in combination with the verification and optimization process of a pollution diffusion model, a root-mean-square error is reduced by adjusting a source intensity value, and a pollution source diffusion coefficient is increased. According to the method, the model is continuously optimized, so that the model better fits the actual situation, finally, the real pollution source is determined according to the acceptable threshold value, scientific and accurate basis and method are provided for air pollution control, and improvement of the control efficiency and effect and improvement of the atmospheric environment quality are facilitated.

The invention provides a sand and dust scene atmosphere pollution source identification method and system, computer equipment and a medium, and belongs to the field of environmental pollution analys.The method comprises the steps that the types of volatile organic compounds and the types of fine particulate matter PM2.5 in the atmosphere of a sand and dust scene to be identified in a target area are identified, and the concentration of the volatile organic compounds and the concentration of the fine particulate matter PM2.5 are measured; carrying out mineral dust proportion calculation on the types of the fine particulate matters PM2.5 and the concentration of the fine particulate matters PM2.5 to obtain a mineral dust quantification result, and determining a dust source contribution weight according to the active metal increment in the mineral dust quantification result; and performing positive matrix decomposition prediction on the types of the volatile organic compounds, the concentration of the volatile organic compounds, the types of the fine particulate matters PM2.5 and the concentration of the fine particulate matters PM2.5 based on the dust source contribution weight, and determining the pollution source contribution proportion of the atmosphere of the sand and dust scene to be identified. According to the method, the accuracy of pollution source identification is guaranteed, and a basic guarantee is provided for subsequent pollution source treatment.

The invention provides a construction site pollution source analysis method and system, and the method comprises the steps: determining the atmosphere pollution source intensity information of each monitoring region of a construction site based on a preset Gaussian diffusion model, environment monitoring data and meteorological data, and carrying out the detection and recognition of a video stream through a pre-trained target detection model, the comprehensive score of the construction site in a preset time period is determined based on a preset multi-dimensional scoring model, the environment monitoring data, the detection and recognition result and the atmospheric pollution source intensity information, and the key pollution information of the construction site is determined based on the atmospheric pollution source intensity information and the satellite image data and the construction progress information of the construction site; and performing pollution cause analysis based on the environmental monitoring data and historical construction data of the construction site, and determining a pollution source analysis result of the construction site based on the comprehensive score, the key pollution information and the pollution cause analysis result. With the adoption of the method, the high-real-time pollution source analysis requirement caused by quick change of the actual site construction condition of the construction site can be met.

The invention relates to the technical field of intelligent environmental protection, in particular to an unmanned aerial vehicle urban atmospheric pollution monitoring and preventing system based on radar and AI cooperation, which comprises a laser radar scanning module, an edge computing gateway, a cloud server, a small unmanned aerial vehicle inspection module, an unmanned aerial vehicle spraying module and a fog gun vehicle module, the laser radar scanning module scans and generates point cloud data in real time, and pollution information is obtained by combining positioning and aerosol inversion; the edge computing gateway uses a DBSCAN clustering algorithm and an improved YOLOv8 model to determine pollution hot spot coordinates, grades and pollution source types; the cloud server dynamically dispatches all the modules in sequence, an improved ant colony algorithm is adopted to plan a route, and real-time obstacle avoidance and accurate spraying are achieved; the small unmanned aerial vehicle inspection module obtains an image of a polluted area, the unmanned aerial vehicle spraying module carries out precise spraying treatment, and the fog gun vehicle module carries out dust suppressant spraying on a dust raising source. The defects of a traditional atmospheric pollution monitoring and treatment means are overcome, and accurate monitoring and efficient treatment are achieved.

The invention discloses an atmospheric pollution trend analysis method and system, and relates to the technical field of atmospheric pollution trend analysis, and the method comprises the steps: collecting multi-source data in a target region, calculating the concentration change rate of all nodes by using the law of conservation of mass, updating the concentration of the nodes by using an Euler method, and inputting the updated concentration of the nodes into a pre-trained GCN model, calculating the optimized concentration and production abnormity list; modulating a propagation weight by using a Hebbian learning rule, modulating the optimized concentration by using the modulated weight, calculating a joint concentration by using a conservation type path flux method, combining the joint concentration by using a weighted linear combination, and calculating the joint concentration; and constructing a long-short-term memory network model to predict the future trend of the pollutants. The space gridding and the graph neural network are introduced, the recognition and control capability of the pollution source is improved, and the prediction precision and reliability of the analysis model are enhanced by combining the diffusion flux model and the convection flux model.

The invention belongs to the technical field of atmospheric environment monitoring, and particularly relates to an atmospheric pollution channel transmission data analysis system which comprises an acquisition and analysis end and a visual display end which are in communication connection. The acquisition and analysis end comprises a multi-source heterogeneous data acquisition and integration unit, a data preprocessing unit, a pollution channel transmission identification unit, a transmission process analysis unit, a pollution source tracking unit and a dynamic prediction unit; according to the invention, a full-chain analysis framework from multi-source data acquisition and processing, pollution channel dynamic identification to source analysis and prediction and early warning is constructed, the identification precision and timeliness of a pollution transmission path are significantly improved, and a key supervision park in a monitoring area is determined through pollution management and control decision analysis. The key supervision parks are continuously monitored, and the atmospheric pollution emission management performance of the corresponding key supervision parks is judged, so that the targeted industrial park management is realized, and the pollution of each industrial park to the atmospheric environment is reduced.

The invention relates to the technical field of atmospheric pollution control, in particular to atmospheric environmental pollution control cleaning equipment which comprises an air inlet pipeline connected with a thermal oxidation furnace and further comprises a middle pipeline, a gas inlet pipe, a gas outlet pipe, a gas inlet pipe, a gas outlet pipe, a gas inlet pipe and a gas outlet pipe, the middle pipeline is communicated with the gas inlet pipeline, and the diameter of the middle pipeline is larger than that of the gas inlet pipeline; the plurality of filter screens are movably arranged in the middle pipeline and are used for filtering gas entering the gas inlet pipeline; according to the atmospheric environment pollution abatement cleaning equipment, when a worker treats waste gas, gas entering the gas inlet pipeline can drive the automatic switching unit to accumulate force, and after a filter screen in the gas inlet pipeline is blocked, the automatic switching unit can automatically switch the waste gas in the gas inlet pipeline. And the automatic switching unit triggers and releases the force to drive the plurality of filter screens to automatically rotate and switch, so that the gas in the gas inlet pipeline can be filtered all the time, the heat accumulating type thermal oxidation furnace does not need to stop and wait, and the continuity of waste gas treatment is ensured.

The invention relates to the technical field of visualization of an air pollution information distribution state, in particular to a visualization method based on the air pollution information distribution state. The method comprises the following steps: acquiring atmospheric pollution information through a remote sensing technology to obtain atmospheric pollution data, and constructing an atmospheric pollution geographic position topological graph in combination with atmospheric pollution geographic feature analysis; through diffusion analysis and distribution state analysis, a pollutant hot spot area is further identified, the acid rain forming probability and air quality attenuation data are calculated, and therefore the pollution degree of a high-concentration area is evaluated; atmospheric pollution risk assessment is carried out through analysis of atmospheric pollution information abnormal data, and an assessment result is uploaded to a cloud platform for real-time early warning; according to the method, the air pollution distribution is identified and optimized, so that the air pollution information is identified more accurately.

The invention discloses a multi-scale atmospheric pollutant and greenhouse gas emission data assimilation and fusion modeling method, and belongs to the field of atmospheric pollution monitoring. According to the method, the accuracy and physical interpretability of emission estimation are improved through adaptive fusion and high-precision complementation of multi-source observation data in combination with collaborative assimilation of a physical model and a deep intelligent model; and by adopting multi-scale feature dynamic fusion and a hierarchical graph neural network, high-resolution and refined emission spatial-temporal distribution modeling is realized. And meanwhile, Bayesian optimization and transfer learning are adopted to realize continuous adaptive optimization and knowledge generalization of model parameters. The method effectively solves the problems of heterogeneity, data missing, model migration, insufficient generalization ability and the like of multi-source data, and can be widely applied to the fields of atmospheric environment management, carbon emission monitoring and the like.

The invention discloses a space-time sandstorm event AI forecasting method fusing satellite remote sensing and meteorological data, and relates to the technical field of atmospheric pollution prediction and artificial intelligence. In order to solve the problem that multi-source space-time sand and dust data is difficult to model efficiently in the existing method, the invention designs a sand and dust storm forecasting model DustMama based on a multi-task architecture, and aims to predict the future PM10 concentration and the occurrence condition of the sand and dust storm through the combination of satellite remote sensing and meteorological data. The DustMama is composed of a space-time encoder, a feature aggregation layer and a task specific layer. The space-time encoder is combined with the visual Mamba and the three-dimensional convolutional network, so that the space-time dependent information in the multi-source data can be efficiently extracted. The feature aggregation layer adopts a global attention module to enhance the capability of the model in cross-dimension feature interaction. And the task specific layer is based on an independent two-dimensional convolution predictor so as to realize accurate prediction of different sand and dust tasks.

The invention relates to the technical field of environmental monitoring and atmospheric pollution early warning, in particular to a Yangtze River Delta composite extreme weather ozone pollution early warning model construction method, which comprises the following steps of S1, acquiring high-resolution meteorological data and pollutant concentration data of a Yangtze River Delta region to form an original data set; and S2, carrying out missing value interpolation and abnormal value elimination on the meteorological data and the pollutant data, and carrying out grid alignment according to time and space to generate a unified spatial-temporal characteristic matrix. According to the method, by collecting Yangtze Delta high-resolution weather and pollutant data, performing data cleaning, bimodal feature coding and joint representation modeling, predicting the ozone concentration and generating regional early warning through multi-layer Transform self-adaptive attention, the problems that traditional ozone early warning mostly depends on a single-modal prediction model, and the reliability of the ozone early warning is greatly improved are solved. And due to the lack of multi-modal space-time dependent capture, the problem of early warning information lag is caused.

The invention relates to the field of satellite remote sensing image data processing and modeling, in particular to a geostationary satellite remote sensing AOD image data missing filling method and a geostationary satellite remote sensing AOD image data missing filling system. According to the method, a time interpolation technology is adopted to process reanalysis data, and data of a plurality of geostationary satellites such as Himawari-8 / 9, GOCI-II, GEMS and FY-4B are fused, so that the data of the geostationary satellites are obtained. According to the method, the generation of the full-coverage AOD product per hour day and night is realized, the space-time coverage and resolution of the AOD product are remarkably improved, and a finer data basis is provided for atmospheric pollution monitoring and climate research. By constructing the fusion model based on the error weight and the sample size weight, the precision of fusion data is remarkably improved, and the problem that inversion of a single satellite product is inaccurate under complex conditions is solved.

The invention belongs to the technical field of atmospheric pollution monitoring, and discloses an atmospheric pollution traceability method based on regional distribution identification and screening, which comprises the following steps of: performing interpolation processing on atmospheric pollution concentration data by using a diffusion interpolation method containing obstacles, performing overall aggregation analysis on the data after interpolation processing, and determining the concentration of atmospheric pollution. And obtaining a result that the concentration of the target pollutants in the to-be-detected area is high-value aggregation or low-value aggregation in space, taking the area corresponding to the high-value aggregation result as a hot spot area, and tracing the hot spot area. Compared with the process of directly tracing the source of the target pollutants in the to-be-detected area, the method has the advantages that the time for tracing the source of the area with low concentration value or low concentration degree is saved, the tracing efficiency is further improved, and the hot spot area is divided according to the pollutant concentration and the concentration degree, so that the accuracy of tracing the source is improved. Therefore, the accuracy of the obtained traceability result can be ensured in the traceability process based on the division result.

The invention discloses an atmospheric pollution gridding prediction method and system based on meteorological parameter optimization, and the method comprises the steps: constructing a parameter combination-meteorological result mapping database, carrying out the matching of a recommendation parameter combination corresponding to regional meteorological forecast data in the mapping database, and outputting the gridding meteorological forecast data corresponding to the recommendation parameter combination; taking the historical pollutant concentration monitoring data and the historical meteorological data as an observation instance set to screen a preliminary candidate variable set; performing causal verification on the preliminary candidate quantity set to obtain a single variable set, and performing functional coupling to obtain an associated variable set; inputting the single variable set and the associated variable set into the pollutant concentration prediction model, and outputting a pollutant concentration prediction result; according to gridding weather forecast data, a pollutant concentration prediction result is interpolated into each grid to obtain grid distribution of pollutant concentration, and high-precision modeling of weather prediction and pollution distribution is realized based on mapping database matching, multi-dimensional pollutant feature screening and dynamic mapping fusion methods.

The invention discloses an atmospheric pollutant and greenhouse gas collaborative emission reduction effect evaluation method, and relates to the technical field of atmospheric pollutant emission reduction, and the method comprises the following steps: at the initial stage of the whole process, setting an initial reference flow rate for flue gas according to a historical average empirical value, and introducing the flue gas into a flue; when flue gas enters a flue at an initial reference flow rate, multiple types of sensors are arranged at key positions of the flue, and real-time monitoring and high-frequency sampling are carried out on the flue gas flow state; and carrying out comprehensive analysis on the collected flue gas flow state data by utilizing a pre-trained deep learning model, and identifying a potential turbulent flow risk in the flue gas. The turbulent flow risk is intelligently identified and regulated through the deep learning model, the flue gas flow rate is dynamically optimized, the SCR reaction efficiency and the flow field uniformity are effectively improved, the problems of NOx exceeding and ammonia escape are solved, and cooperative emission reduction of pollutants and greenhouse gas and multi-target optimization of system operation are achieved.

The invention discloses a tracing method and system for atmospheric pollution in a chemical industry park, and relates to the technical field of tracing of atmospheric pollution. The method comprises the following steps: determining at least one first pollution source according to an enterprise-pollutant-raw material incidence matrix and at least one pollutant in the chemical industry park; and according to the real-time pollutant concentration data and a Gaussian diffusion model simulation result, screening out an enterprise with a relatively high fitting degree with the real-time pollutant concentration data, and according to a screening result, determining at least one second pollution source. And determining at least one third pollution source according to the position of the at least one pollution source. And determining at least one fourth pollution source in the high-concentration change area. And determining a pollution traceability result. According to the invention, multi-source data are fused to trace the air pollution, the comprehensiveness and timeliness of the data are ensured, and the accuracy of pollution traceability can be improved.