229 results about "Atmospheric pollutants" patented technology

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

The invention provides a multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method, and relates to the technical field of environmental information processing, and the method comprises the steps: constructing a hierarchical grid structure of an urban core domain and a peripheral background domain based on a unified data basic set, forming a gridding baseline emission list through unified activity data and emission factors, establishing a multi-source scale coordination mechanism, fusing ground monitoring and satellite observation information, and generating an observation data set; according to the method, an SMOKE sparse matrix processing chain is embedded to construct a joint estimation model, parameter inversion and consistency constraint optimization are implemented, boundary flux coordination is carried out between a core domain and a background domain, and finally an emission list which has cross-atmospheric pollutant and greenhouse gas consistency and can be used for refined emission inversion is formed. According to the method, the problem of unstable data fusion caused by scale mismatching between the multi-source observation data and the emission model in the prior art is solved.

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 provides an atmospheric pollutant traceability analysis method and system. According to the method, a form classification standard is established through particle shape features, and atmospheric particles are divided into naturally raised dust and industrial emission particle swarms. A dual-wavelength laser technology is adopted for natural flying dust, specifically, the first wavelength is matched with the inherent frequency of mineral crystals, amorphous scattering interference is blocked through polarization beam splitting, and a reflection signal sequence is extracted to generate a mineral migration path; and the second wavelength screens heavy metal scattering signals of the industrial particles, and a heavy metal diffusion track is constructed by utilizing spatial intensity gradient. And superposing the two paths and aerosol three-dimensional distribution data to generate a thermal distribution diagram, and when the space density exceeds a threshold value, calculating a migration path deviation value and a trajectory offset, dynamically distributing pollution source contribution weights, and outputting a pollution source analysis result. According to the invention, dynamic and accurate analysis of natural and industrial pollution sources is realized, and the regional pollution traceability and management and control strategy targeting are improved.

The invention provides a rapid source tracing method and system for a pollution source based on dual unmanned aerial vehicle cooperative gradient detection, and belongs to the technical field of environment monitoring and unmanned aerial vehicle application, and the method comprises the steps: initializing system parameters; deploying the first unmanned aerial vehicle to hover at an initial position, and measuring an atmospheric pollutant concentration value at the initial position and recording the atmospheric pollutant concentration value as a first concentration value; the second unmanned aerial vehicle is controlled to fly around, and the pollutant concentration value is measured and recorded as a second concentration value; calculating a pollutant concentration gradient value; based on the concentration gradient value, the direction with the maximum atmospheric pollutant concentration gradient in the whole-cycle range of 0-360 degrees is determined and recorded as the maximum gradient direction; the second unmanned aerial vehicle is controlled to move to the intersection point position of the maximum gradient direction and the fly-around circumference for hovering; the first unmanned aerial vehicle is controlled to fly around, and the concentration gradient measurement and maximum gradient direction determination process is repeated; and alternately executing the operation. Through the working mode that the two unmanned aerial vehicles alternately fly around and hover, rapid and efficient pollution source positioning is achieved.

The invention discloses an atmospheric pollutant traceability method and system based on trajectory clustering. The method comprises the following steps: collecting hourly pollutant concentration data and synchronous meteorological data of a target monitoring station during a pollution event; based on the meteorological data, calculating an air mass backward track arriving at the target monitoring station; carrying out preprocessing and clustering analysis on the backward trajectory of the air mass to obtain a plurality of trajectory clusters; aiming at each track cluster, potential pollution source contribution analysis related to the cluster is respectively carried out, and potential source area space distribution information corresponding to the cluster is identified and generated based on an analysis result; extracting a central track of each track cluster as a typical transmission path, and analyzing vertical transmission characteristics of pollutants of different air mass types; and generating a traceability map for assisting in formulating a differentiated regional joint defense and joint control scheme for different transmission paths and source regions. The mixed pollution source can be effectively deconstructed, and the source area and the conveying channel can be accurately associated.

The invention discloses a pollution reduction and carbon reduction cooperative benefit evaluation method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the calculation of life cycle emission data through a carbon pollution cooperative calculation model according to each life cycle link, and obtaining the carbon pollution emission data before and after the application of a preset pollution reduction and carbon reduction strategy; life cycle emission change data is obtained through calculation according to the life cycle emission data, standardization processing is carried out, preset emission reduction effect indexes are obtained through calculation according to the obtained emission change standard value, and the preset emission reduction effect indexes comprise the atmospheric pollutant emission reduction effect index and the carbon dioxide emission reduction effect index; and performing emission reduction benefit evaluation analysis through a preset quadrant analysis method according to the atmospheric pollutant emission reduction effect index and the carbon dioxide emission reduction effect index to obtain a pollution reduction and carbon reduction cooperative benefit evaluation result. According to the embodiment of the invention, the accuracy of pollution-reducing and carbon-reducing performance evaluation can be improved, and pollution-reducing and carbon-reducing cooperative benefit evaluation before and after the pollution-reducing and carbon-reducing strategy is applied is realized. The method can be widely applied to the technical field of energy conservation and environmental protection.

The invention discloses an atmospheric pollutant transmission diffusion model optimization method and a prediction system. According to the method, the data quality basis is improved through dynamic layered preprocessing. Random noise in environment monitoring data can be effectively filtered by adopting a self-adaptive filtering technology, missing data filling better fits an actual monitoring rule in combination with an interpolation method of an attention mechanism, an abnormal value judgment standard is automatically adjusted when an early warning level changes, the problem of wrong filtering or retention caused by a fixed threshold value is avoided, and the accuracy of early warning is improved. The preprocessed environment data is closer to a real state, reliable input is provided for subsequent feature extraction, and cross-modal feature enhancement extraction enhances the adaptability of the model to a complex environment. The self-supervised learning framework can mine deep correlation features from multi-source data, after physical features and deep learning features are fused, the model is helped to better capture change features of pollutants under different time-space conditions, and more comprehensive feature support is provided for subsequent anomaly detection and early warning.

The invention provides an atmospheric pollutant and carbon dioxide cooperative control method and device, and relates to the field of atmospheric environmental science and urban low-carbon development decision support, and the method comprises the steps: constructing an atmospheric pollutant and carbon dioxide emission reduction technology library, and introducing a carbon pollution coupling emission accounting method to construct a carbon pollution emission accounting framework; performing technical emission reduction calculation by using a carbon emission accounting framework, and evaluating environmental benefits and health benefits of emission reduction measures according to the technical emission reduction to obtain benefit evaluation data; constructing an index system, combining the index system with the benefit evaluation data, and performing multi-dimensional quantitative evaluation on the emission reduction measures to obtain an evaluation result of the emission reduction measures; according to the overall scheme, an emission reduction path is designed and optimized on the basis of an emission reduction measure evaluation result, the emission reduction effect is predicted based on the optimized emission reduction path, and the emission reduction effect is obtained. According to the invention, through full-chain quantitative evaluation, scientific formulation and adjustment of a collaborative governance strategy are effectively supported, and environmental benefits and economic returns are improved.

The invention belongs to the technical field of artificial intelligence and environment monitoring, and particularly relates to an air pollutant concentration prediction method based on an ICLU-CWGAN model. The method comprises the following steps: acquiring and preprocessing data such as atmospheric pollutant concentration and weather, fusing an Inception multi-scale convolution thought with a ConvLSTM network, and constructing a multi-scale ConvLSTM network; the method comprises the following steps: under a Wasserstein generative adversarial network framework with a gradient penalty condition, taking a multi-scale ConvLSTM as a generator, taking an improved U-Net as a discriminator, and constructing an ICLU-CWGAN model; and completing model training by using the preprocessed training grid data, and predicting the atmospheric pollutant concentration. According to the method, the problems of weak long-time-sequence complex space-time correlation modeling capability, spatial distortion and unstable training in the prior art can be solved, so that the precision and the stability of atmospheric pollutant concentration space-time distribution prediction are improved.

The invention relates to the field of atmospheric pollution diffusion simulation, and particularly discloses a multi-scale coupled atmospheric pollution diffusion simulation system, which comprises a data source module used for collecting and integrating various data sources including meteorological data, topographic data, pollution source data and atmospheric pollutant concentration data, and providing basic information for simulation; the multi-scale modeling module further comprises a micro-scale model for simulating a detailed diffusion process of pollutants in a local area by calculating a fluid dynamics equation based on a fluid dynamics principle; by integrating various data sources such as meteorological data, topographic data, pollution source data and atmospheric pollutant concentration data, the system can provide more comprehensive and accurate basic information, so that the simulation precision is improved; the combination of microscopic, mesoscopic and macroscopic scale models can respectively capture pollutant diffusion characteristics under different scales, so that the simulation result is more detailed and real.

The invention belongs to the technical field of air pollutant traceability. The invention provides an air pollution prediction method and system based on artificial intelligence and a mechanism model, and the method comprises the steps: extracting geographic features and time dependency relationship features, fusing the geographic features and the time dependency relationship features, outputting potential calculation factors, and calculating the weight of each factor; a high weight factor with the absolute value of the weight larger than a set threshold value is fused into the mechanism model, the reference emission rate of the mechanism model serves as basic input data, the selected high weight factor and the weight are combined to calculate the corrected gridding emission rate, and an improved mechanism model with a dynamic emission source list is formed; the pollutant concentration is predicted through the improved mechanism model, and the main pollution source is determined based on pollution source data and potential calculation factors in the database; according to the method, various factors involved in the atmospheric pollution forming process are fully considered, and the prediction precision is improved.

The invention relates to the technical field of photovoltaic system prediction, in particular to a multi-mode prediction method and system for photovoltaic panel generation power, electronic equipment and a medium. According to the method, illumination data, atmospheric pollutant concentration data and meteorological parameters of the surface of the photovoltaic panel are obtained, and a real-time cleanliness index and an atmospheric transparency correction coefficient of the photovoltaic panel are calculated according to the illumination data, the atmospheric pollutant concentration data and the meteorological parameters; thirdly, inputting various data and parameters of the surface of the photovoltaic panel into the multi-modal prediction model, and outputting direct solar irradiance; and finally, calculating to obtain a power generation power prediction value based on the direct solar irradiance. By introducing a dual correction mechanism of a real-time cleanliness index and an atmospheric transparency correction coefficient, the multi-modal prediction model can dynamically adapt to environmental changes of different pollution degrees, and the problems of poor environmental adaptability and low prediction precision existing in a current photovoltaic power generation power prediction technology are solved. And the accuracy and environmental adaptability of photovoltaic power generation power prediction under the polluted weather condition are improved.

The invention relates to an emergency processing system and method for atmospheric pollution source influence based on DeepSeek, and belongs to the technical field of environmental monitoring, and the method comprises the steps: collecting atmospheric pollutant concentration data, meteorological data and basic information data of surrounding buildings; preprocessing the data; carrying out deep analysis on the preprocessed data based on a DeepSeek model, and judging whether to trigger early warning or not and the early warning degree in combination with a pollutant exceeding standard knowledge base; when abnormal data is found, the constructed knowledge base is retrieved; combining the data retrieved by the knowledge base; the current atmospheric pollution and building influence conditions are retrieved and compared with existing plan cases; and a detailed alarm condition is output, and a targeted emergency plan is generated at the same time. According to the method, based on the DeepSeek data analysis and learning ability, the influence of multiple factors is fully considered, the position of the influenced building is accurately identified, the influence degree is evaluated, and a more reliable basis is provided for environment decision making.

The embodiment of the invention provides an atmospheric pollutant early warning method based on multi-source isomerism, which belongs to the technical field of data processing, and specifically comprises the following steps: step 1, multi-source isomerism data encryption access and transmission; step 2, data quality control and missing measurement processing; step 3, performing space-time alignment and scale transformation; 4, feature engineering and variable construction; 5, carrying out fusion modeling and pollutant prediction; step 6, dynamic threshold and risk scoring; 7, carrying out online learning and drift detection; step 8, uncertainty evaluation and interpretability output are carried out; and step 9, early warning release and closed loop iteration. Through the scheme of the invention, the response capability and the early warning accuracy are improved.

The invention belongs to the technical field of environmental protection monitoring, and particularly relates to a high-precision atmospheric pollution prediction method, which comprises the following steps: S1, dividing a monitoring area into a plurality of monitoring units; s2, marking a potential atmospheric pollutant emission source as a fixed monitoring point; s3, determining random monitoring points in each monitoring unit; s4, constructing an atmospheric pollutant diffusion model of the monitoring area; s5, acquiring real-time atmospheric pollutant data of the fixed monitoring points and the random monitoring points, and inputting the real-time atmospheric pollutant data into the atmospheric pollutant diffusion model to obtain an atmospheric pollution condition prediction result of the monitoring area; and S6, determining the prediction deviation of the atmospheric pollutant diffusion model based on the current monitoring data and the historical atmospheric pollution condition prediction result, and correcting the prediction result according to the prediction deviation. The atmospheric pollution prediction method has the advantages of simple and reasonable model structure, high prediction precision, strong timeliness, strong migration ability and wide application range.

The invention discloses a rainfall sampling device and method for meteorological monitoring based on the Internet of Things, and relates to the technical field of rainfall sampling, the rainfall sampling device comprises a supporting outer frame, and the inner wall of the supporting outer frame is provided with a metering tipping bucket mechanism used for qualitatively and quantitatively detecting rainfall. By arranging the collecting mechanism, suspended particles and atmospheric pollutants in rainwater can be intercepted and sampled in real time in the initial rainfall stage, high-concentration pollutants carried in first rainfall are effectively captured, the collecting mechanism automatically switches sampling channels according to a preset time sequence sampling strategy, and the sampling efficiency is improved. According to the rainwater pollutant monitoring system and method, spatial partition and independent collection of pollutants in different time periods are achieved, cross contamination between samples is fundamentally eradicated, the time resolution and the sample purity of rainwater pollutant monitoring are improved, and high-fidelity data are provided for atmospheric sedimentation process analysis and non-point source pollution traceability.

The invention provides an atmospheric pollutant prediction and tracing method and device, electronic equipment and a storage medium, and belongs to the field of environmental monitoring, and the method comprises the steps: obtaining multi-source heterogeneous data of a target research region in each preset time period; generating a plurality of dynamic graphs based on the multi-source heterogeneous data in each preset time period, wherein one preset time period corresponds to one dynamic graph; and training the space-time adaptive graph neural network model based on the plurality of dynamic graphs to obtain a trained space-time adaptive graph neural network model, the trained space-time adaptive graph neural network model being used for predicting and tracing the ozone concentration of the target research area. According to the invention, the prediction accuracy of the ozone concentration can be improved, and rapid traceability of pollutants can be realized.

This invention discloses an outdoor air pollutant treatment device, belonging to the field of air pollutant treatment technology. It includes a mounting frame, on the upper side of which multiple motor protective shells are fixedly mounted. A power motor is fixedly connected inside each motor protective shell, and multiple axial flow fans are fixedly connected to the outer side of the output shaft of each power motor. A fixed frame is located on the outer side of the motor protective shells, and a filter element is fixedly mounted within the fixed frame. A cleaning assembly for the filter element is also installed within the fixed frame, and the cleaning assembly includes a positioning plate fixedly mounted on the outer side of the filter element. During the air pollutant treatment process, the cleaning box and cleaning plate can clean the pollutants accumulated on the outer side of the filter element, improving the filter element's service life. Furthermore, during this process, the cleaning plate moves up and down relative to the cleaning box, allowing for better cleaning of the pollutants accumulated on the outer side of the filter element.

The invention relates to the technical field of atmosphere quality monitoring, and discloses an underway atmosphere quality monitoring method and system, and the method comprises the steps: constructing a vehicle-mounted underway atmosphere quality monitoring system based on a front-end sensor assembly, a data collection module, a wireless transmission unit, a positioning system and a power supply system, and generating an underway monitoring execution scheme; driving the vehicle to collect atmospheric pollutant concentration and meteorological parameter data; performing timestamp matching and fusion processing with the original GPS position data to obtain atmospheric pollutant spatial distribution data; transmitting to an environment monitoring cloud platform in real time, and generating an atmospheric pollutant concentration distribution thermodynamic diagram; and performing air quality grading evaluation and pollution source identification on the monitored area, and generating an area air quality risk grade map and pollution early warning information, thereby realizing stable acquisition of environmental parameters in the mobile monitoring process, effectively eliminating interference of vehicle vibration and environmental change on monitoring data, and improving the monitoring accuracy. And the accuracy of atmosphere quality monitoring is improved.

This invention discloses an integrated air pollutant monitoring station suitable for cross-border areas, belonging to the field of environmental monitoring technology. It includes a mounting plate, with a monitoring instrument fixed to its top side. A protective component for the monitoring instrument is located on the top of the mounting plate. A support cylinder is located below the mounting plate, and a pushing component for pushing an internal insertion rod is located on the support cylinder. A support rod b is hinged between support rod a and the support cylinder. A stabilizing component is located at the bottom of support rod a. This invention enables the monitoring instrument to operate continuously in rainy weather. The protective component, while shielding from rain, ensures that water droplets are blocked without affecting necessary airflow exchange, thus protecting the sampling and analysis process from precipitation interference and obtaining a continuous data sequence that more accurately reflects the true pollution situation. The stabilizing component, inserted into the ground, stabilizes the overall placement, effectively improving the stability of the overall placement and use, ensuring the smoothness of the monitoring process, and thereby improving the accuracy of the monitoring data.

The invention relates to the technical field of air pollution treatment, and discloses an air pollutant sampling device which comprises a base, the top of the base is rotationally connected with a dust removal cylinder, the top of the dust removal cylinder is fixedly connected with a wind indicator, a dust removal sampling assembly is arranged in the dust removal cylinder, and the dust removal sampling assembly comprises a gear shaft, a gear shaft and a gear sleeve, the fan is rotationally connected to the inner side wall of the dust removal cylinder in a manner of being perpendicular to the axis of the dust removal cylinder and is parallel to the axis of the wind indicator; and the filtering driving part comprises an inner cylinder fixedly connected to the gear shaft, the outer surface of the inner cylinder is fixedly connected with a plurality of guide vanes and a filtering ring, and the outer ends of the guide vanes and the filtering ring are fixedly connected with an outer cylinder. The invention has the following advantages and effects: the wind indicator is utilized to automatically face the wind to ensure normal air intake; the gear shaft is driven to rotate through the filtering driving piece, dust on the filtering ring is automatically and continuously cleaned, the filtering effect on an atmospheric sample is guaranteed, and the judgment accuracy of atmospheric pollution is improved.

The invention discloses an atmospheric pollutant prevention, control, monitoring, early warning and traceability system, and belongs to the technical field of environmental science. The system comprises the following steps that S1, a sample collection module collects all pollution source samples in a target area, a source sample processing and analyzing module analyzes source sample feature vectors after preprocessing all the pollution source samples in the area, and a traceability matching module constructs an atmospheric pollutant matching model according to the source sample feature vectors of all the pollution source samples; and S2, when the source of the atmospheric pollution source needs to be traced, a sample collection module collects an environmental gas sample in the target area, and pollutant feature vector analysis is carried out on the collected environmental gas sample. A target area pollution source sample is collected through a sample collection module according to a standard, after pretreatment, source sample feature vectors such as chemical components, isotopes and physical features are comprehensively analyzed, a multi-dimensional and standardized data source is provided for subsequent traceability, and the accuracy and reliability of a matching model are ensured.

This invention relates to the field of air pollutant detection technology and discloses a method for detecting excessive emissions of air pollutants. The method includes: acquiring pollutant concentration data from multiple monitoring points in a target area to form a raw dataset; performing outlier detection and correction, removing sensor malfunction outliers to obtain a preprocessed dataset; extracting concentration change trend characteristics within a preset time window for each monitoring point to construct a spatiotemporal feature matrix; inputting the matrix into a pollutant diffusion model to calculate the transmission paths and intensities between monitoring points, generating a regional transmission network; identifying potential source areas of abnormal pollutant concentration fluctuations based on the network and marking them as candidate areas for verification; performing multi-scale concentration gradient analysis on the candidate areas to determine key monitoring areas; deploying mobile equipment in the key monitoring areas to collect high-precision component data; comparing this data with a standard emission source feature library, matching emission source types with similarity exceeding a threshold, determining whether emissions exceed standards, and generating a detection report.

The application discloses a response surface model construction method and device for atmospheric pollutant concentration analysis, wherein the method comprises the following steps: performing precursor emission simulation on a research area based on a Lagrangian diffusion model to generate precursor emission concentration data; using a statistical method to establish a response relationship of precursor concentration to precursor emission change based on the precursor emission concentration data; using a polynomial response surface method to establish a response relationship of atmospheric pollutant concentration to precursor concentration change based on multi-scenario simulation of a three-dimensional air quality model; and coupling the response relationship of precursor concentration to precursor emission change with the response relationship of atmospheric pollutant concentration to precursor concentration change to obtain an atmospheric pollutant concentration response surface model to precursor emission change. The application can reduce the cost and time of response surface model modeling, and quickly quantifies the influence of small-scale regional emission source change on atmospheric pollutant concentration.

The application discloses a kind of regional atmospheric pollutant distribution prediction method and system, comprising: establishing calculation domain to be predicted region, and carrying out grid division;With the data of pollutant measuring point and meteorological measuring point and pollutant emission source intensity as known condition, non-steady-state numerical simulation is carried out, and the data set of global variable on time sequence is calculated;According to the data set of global variable on time sequence, neural network group for pollutant distribution prediction is established and trained;According to the data collected by pollutant measuring point and meteorological measuring point, the pollutant distribution prediction in the region to be predicted is carried out by neural network group.The application carries out a large number of calculations through the numerical simulation of first step, and the grid scale can be much smaller than the grid scale of other commonly used numerical calculations, to provide sufficient and accurate data set for the machine learning prediction of second step, and the data amount and spatial resolution are significantly higher than the existing discrete measurement point method.

The invention discloses a secondary PM2.5 prediction result calculation and visualization method based on an AERMOD model simulation area in the technical field of atmospheric pollutant observation. The method comprises the following steps: acquiring pollution data of pollutants in a pollution source and corresponding meteorological information; according to the method, pollution data and corresponding meteorological information are subjected to simulation calculation through an AIRMOD model to obtain primary PM2.5 concentration prediction data, text vector data obtained through an Aermod model are calculated through a mode of being combined with Arcgis to obtain secondary PM2.5 concentration prediction data, prediction result data of the mass concentration of PM2.5 pollutants are obtained, and the prediction result data of the mass concentration of the PM2.5 pollutants is obtained. The method is used for realizing visualization of regional secondary PM2.5 concentration results corresponding to different time spans, and can quickly obtain regional secondary PM2.5 concentration and visualization results. The defects that an air quality model is large in basic data demand quantity, the model structure is very complex, high-performance calculation is needed, and an AERMOD model can only obtain one-time pollutant concentration result visualization are overcome.

The invention discloses a near-surface atmospheric pollutant deep learning prediction method based on hyper-spectral satellite remote sensing and ground observation data. The near-surface atmospheric pollutant deep learning prediction method comprises the following steps: preprocessing collected multi-source data; constructing an assimilation network adopting a convolutional neural process, ViT and ResNet combined architecture, training the assimilation network by using the preprocessed multi-source data, and generating an initial assimilation state at the current moment; and constructing a prediction network with the same architecture as the assimilation network, taking the initial assimilation state at the current moment as input, carrying out time extrapolation in combination with multi-source data at future moments, respectively constructing independent prediction networks at different future moments, carrying out independent training, and outputting pollutant concentration probability prediction results at different time periods in the future. Therefore, the probabilistic pollutant concentration prediction with high spatial resolution of 0.1 degree * 0.1 degree and 72-hour time efficiency is realized without depending on a chemical transmission model.

Embodiments of the present application provide a method and device for determining atmospheric pollution hotspots, computer equipment and a medium, relating to the technical field of atmospheric pollution monitoring, which comprises: dividing atmospheric pollution concentration data into grid data of a preset resolution; in all grids in the grid data, determining a grid with an atmospheric pollution concentration value higher than a first dynamic concentration threshold as a first potential hotspot grid; determining a second potential hotspot grid in the first potential hotspot grid based on a time-dimension anomaly identification algorithm; determining a third potential hotspot grid in the first potential hotspot grid based on the dynamic change of a time-space dynamic anomaly index in a time-space coupling dimension; determining the intersection of the second potential hotspot grid and the third potential hotspot grid as a candidate hotspot grid, verifying the environmental rationality of each candidate hotspot grid using multi-source data, and determining a trusted hotspot grid. The scheme can improve the accuracy of hotspot identification, has strong dynamic adaptability, and reduces false positives of hotspots.

The invention discloses a VOCs waste gas continuous treatment device and method based on secondary activated carbon adsorption in the field of air pollution treatment, and aims to solve the technical problems that an existing VOCs waste gas treatment process depends on a boiler, treatment is discontinuous, the investment cost is high, and adaptability is poor. According to the device, an original gas collection pipeline and a fan of an enterprise are reformed, and a first-stage granular activated carbon adsorption unit and a second-stage fine-particle-size activated carbon adsorption unit are connected in series to form a closed-loop treatment system of collection, graded adsorption, pressure-boosting discharge and maintenance and update; according to the treatment method, continuous deep purification of the VOCs waste gas with medium and low concentration is realized by controlling the retention time of the waste gas in the two-stage adsorption unit and the particle size gradient of the activated carbon. The investment is less than or equal to 10 thousand yuan, the treatment gas amount is 8000m / h, the NMHC emission concentration is less than or equal to 50mg / m, the secondary standard of the table 2 of Integrated Emission Standard of Air Pollutants (GB16297-1996) is met, the device can continuously and stably run for more than or equal to 6 months in 24 hours, and the device is adaptive to the waste gas treatment requirements of chemical devices such as acetic acid, methanol and the like.