348 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 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 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 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 urban low-altitude dynamic wind field and air quality refined forecasting method and system in the technical field of environmental weather forecasting, and the method comprises the steps: generating a static sub-urban scale low-altitude wind field map under different weather background wind directions, and carrying out the correction, thereby obtaining a static sub-urban scale low-altitude non-uniform wind field map; generating a sub-city scale low-altitude horizontal wind field dynamic map based on the moment-by-moment time sequence data of the background wind speed and direction and the static sub-city scale low-altitude non-uniform wind field map; calculating a horizontal transmission track and a propulsion dynamic state of the target atmospheric pollutants in the urban low altitude in the dynamic wind field to obtain an external background transmission concentration of the target city; calculating the concentration of atmospheric pollutants formed by local emission in the dynamic wind field; and calculating a high-temporal-spatial-resolution forecasting result of the urban low-altitude atmospheric pollutant concentration. The method can be used for diagnosis, prediction and early warning of ventilation galleries and atmospheric pollutant diffusion and transmission channels in the urban low-altitude environment.

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 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 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 discloses a multi-modal air pollutant prediction method and device, equipment, a medium and a product, and relates to the technical field of environmental monitoring, the method comprises the following steps: carrying out preprocessing and standardization processing on to-be-processed multi-modal data to obtain preprocessed multi-modal data; the preprocessed multi-modal data comprises atmospheric pollutant concentration monitoring station data, emission source data and meteorological field data at the current moment in the target area; splicing the atmospheric pollutant concentration monitoring station data at the previous moment and the atmospheric pollutant concentration monitoring station data at the current moment along the time dimension, and extracting the spliced data features to obtain a station position-time-pollutant concentration feature vector; fusing the extracted feature fusion vector with the feature vector of station position-time-pollutant concentration to obtain a multi-modal feature vector; and inputting the multi-modal feature vector into a pre-trained deep learning network model to obtain a prediction result. According to the invention, the pollutant prediction accuracy can be 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 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 provides an air pollution tracing method and system based on an air quality model, and relates to the technical field of air pollution tracing, the air pollution tracing method based on the air quality model adopts CMAQ as the air quality model, and the model is preset based on the obtained data; the presetting comprises grid setting, simulation time range and simulation area range, running a CMAQ model based on preset input data, carrying out transmission and conversion simulation of atmospheric pollutants, simulating the transmission process of the pollutants in the atmosphere, generating a simulation result file, and carrying out analysis based on the simulation result file. The concentration distribution, the spatial distribution and the time change trend of the pollutants are specifically analyzed, so that the technical problems that a conventional method is difficult to comprehensively reflect the spatial distribution and the transmission path of the pollutants and the limitation is relatively large are solved, and the subsequent problem that the transmission and conversion processes of the pollutants are difficult to comprehensively analyze is avoided; and the use problem that the occurrence and propagation process of pollution events are difficult to reflect in time is avoided.

The invention relates to a pollutant monitoring method, device and equipment based on multi-point source superposition diffusion, and belongs to the technical field of atmospheric pollutant diffusion analysis. The latitude and longitude coordinates of the emission source, the evaluation point and the evaluation area are represented by a geographic coordinate system, and the latitude and longitude coordinates are converted into plane coordinates under a projection coordinate system; a two-dimensional rectangular grid is adopted to cover an evaluation area, and a continuous space is discretized into a plurality of grid units; establishing a reference coordinate system and a diffusion coordinate system; the constructed Gaussian plume diffusion model is adopted to calculate pollutant concentration data of each emission source to each grid point, concentration contributions of the multiple emission sources are superposed, and total concentration data of the grid points are obtained; and enabling the pollutant concentration data of the evaluation point to be equivalent to the function output of the total concentration data of one or more grid points in the grid unit where the evaluation point is located, so as to obtain the pollutant concentration data of the target position. The invention provides a refined technical scheme for pollution diffusion simulation of complex multi-point sources.

The invention 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 park atmospheric pollutant total amount accounting method. The method comprises the steps of S1, distinguishing enterprise types; enterprises are divided into A-class enterprises, B-class enterprises and C-class enterprises; s2, determining an accounting method; s3, total amount accounting is carried out; summation is carried out on the pollutant emissions obtained through accounting of the A-type enterprises, the B-type enterprises and the C-type enterprises, and a total amount accounting result is obtained; according to the method, enterprise types are scientifically distinguished, a clear distinguishing mode is set for enterprises with different data bases, and a differentiated accounting method is matched, so that the accounting precision and feasibility are effectively balanced, the problem of underestimation or overestimation caused by uneven enterprise data in the accounting process is solved, and the accounting efficiency is improved. And reliable data support is provided for precise pollution control.

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 belongs to the technical field of environment-friendly treatment, and provides a treatment process for natural gas combustion waste gas generated by zinc chloride production based on zinc ash and zinc slag and acid mist waste gas generated by regenerated acid decoction, which comprises the following steps: S1, introducing the natural gas combustion waste gas and the acid mist waste gas into a spray tower, carrying out dynamic alkali neutralization treatment and cooling treatment on the basis of an intelligent pH value regulation and control method to obtain primary purified gas; s2, the primary purified gas enters primary plasma dedusting and demisting equipment through a pipeline to be treated, and secondary purified gas is obtained; and S3, the secondary purified gas enters secondary plasma dedusting and demisting equipment to be treated, and final treated tail gas is obtained. According to the invention, the chemical neutralization based on the spray tower and the two-stage plasma deep cracking are combined, the treated waste gas meets the emission limit value in the secondary standard in the Integrated Emission Standard of Air Pollutants, the treatment efficiency is high, the medicament consumption is low, the energy consumption is low, and the problems in the prior art are effectively solved.

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 honeycomb activated carbon capable of highly adsorbing atmospheric pollutants and a preparation process thereof, and belongs to the technical field of activated carbon preparation. The preparation process of the honeycomb activated carbon with high adsorption of the atmospheric pollutants specifically comprises the following steps: mixing coconut shell powder, basalt fibers and ammonium bicarbonate, then adding a silica sol solution, carrying out ultrasonic treatment, pressing the obtained mixture by using a honeycomb mold, carrying out freeze molding to obtain a honeycomb solid, carrying out gradient heat treatment, carrying out acid pickling, then washing with water to be neutral, and drying to obtain the honeycomb activated carbon with high adsorption of the atmospheric pollutants. Drying to obtain the honeycomb activated carbon. The silica sol solution is uniformly dispersed in the raw materials through ultrasonic treatment, and freezing forming and gradient heat treatment are performed, so that the adsorption performance and the mechanical strength of the honeycomb activated carbon 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 relates to the technical field of data processing, and discloses an atmospheric pollutant emission data processing method, which comprises the following steps: SS1, data collection and distribution diagram generation: collecting multi-source meteorological real-time data, multi-source meteorological prediction data, multi-source geographic feature data, human activity data and historical pollutant data of a pollutant atmospheric pollution monitoring area in a classified manner, and generating distribution diagrams according to the multi-source meteorological real-time data, the multi-source meteorological prediction data, the multi-source geographic feature data, the human activity data and the historical pollutant data; the collection frequency is set according to the characteristics of different data sources, preprocessing and data fusion are carried out on the collected multi-class data, and finally a real-time distribution diagram of pollutants is generated. According to the method, multi-source data such as meteorological data, geographic data and human activity data are comprehensively collected, deep preprocessing and fusion are carried out, an accurate pollutant real-time distribution diagram is generated, the pollutant type and concentration of each monitoring point position can be visually presented, meanwhile, the weight is constructed by comprehensively considering multiple factors, and the real-time distribution diagram of the pollutants is obtained. The diffusion path and concentration of the pollutants are predicted by combining the diffusion model based on the physical equation and the prediction model based on the neural network, and the prediction precision is high.

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 discloses a system and a method for measuring and calculating the emission amount of atmospheric pollutants of underground coal fire, and relates to a system for measuring and calculating the emission amount of atmospheric pollutants of underground coal fire. Comprising a power supply module, a methane emission monitoring module, a carbon oxide monitoring module, a nitrogen oxide monitoring module, a sulfur dioxide monitoring module, a black carbon emission monitoring module and an atmospheric pollutant emission flux calculation module. The environmental factor acquisition module, the methane emission monitoring module, the oxycarbide monitoring module, the nitrogen oxide monitoring module, the sulfur dioxide monitoring module, the black carbon emission monitoring module and the atmospheric pollutant emission flux calculation module are all connected with the power supply module; the measuring and calculating method of the underground coal fire atmospheric pollutant emission amount measuring and calculating system comprises the following steps: S1, placing the instrument at a proper position, and adjusting a proper sampling frequency; the problem that the atmospheric pollutant emission flux is difficult to measure and calculate due to intermittency and dispersity of underground coal fire combustion is solved.

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 invention relates to the technical field of atmospheric pollutant removal, in particular to an atmospheric pollutant removal device which comprises a removal tank and an impurity removal plate arranged on the inner side of the removal tank, one side of the removal tank is provided with an air inlet pipe, one end of the air inlet pipe is provided with a pressurizing pipe, the pressurizing pipe is connected with the removal tank, and the impurity removal plate is arranged on the inner side of the removal tank. And a preliminary filtering plate is arranged on the inner side of the air inlet pipe. Through the arrangement of the preliminary filter plate, preliminary impurity filtration can be performed on atmosphere conveyed into the removal tank, so that the replacement time of the impurity removal plate can be prolonged, the use cost of the device is reduced, the disassembly and assembly steps between the preliminary filter plate and the removal tank are simplified through the arrangement of the connecting assembly, the replacement efficiency of the preliminary filter plate is improved, and the use cost of the device is reduced. By arranging the filter plate cleaning assembly, dust impurities filtered on the preliminary filter plate can be cleaned, so that the replacement time of the preliminary filter plate can be prolonged, and the removal cost of atmospheric pollutants is reduced.

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.