1585 results about "Atmospheric pollution" patented technology

The present invention belongs to the technical field of environmental monitoring, in particular to an environmental monitoring device for air pollution, comprising a monitoring frame, a monitoring shell arranged on a top end of the monitoring frame and an environmental monitor fixedly installed inside the monitoring shell through a connecting plate. An air sampling assembly is installed inside the environmental monitor; the air sampling assembly comprises air intake covers, and an electric push rod, a rack I, a gear I, a spur gear, an internal gear, a rotating disk, limiting blocks, limiting rods, a cross limiting track, an L-shaped frame and a strip-shaped plate which are used for driving the air intake covers. A cleaning assembly is installed on the air sampling assembly.

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 discloses an atmospheric particulate source analysis method based on deep learning, and relates to the technical field of atmospheric particulate analysis, and the method comprises the steps: constructing a large-scale preprocessing data set which comprises atmospheric particulate concentration data from different regions, different seasons and meteorological conditions, and collecting corresponding potential original data; original data are integrated to form a multi-dimensional preprocessing data set; according to the atmospheric particulate source analysis method based on deep learning, through an adaptive learning rate optimization algorithm, the model can more quickly find an optimal solution in a complex and dynamic data environment, and is prevented from falling into local optimum, so that the accuracy of atmospheric particulate source analysis and the application range of the model are improved; through accurate analysis of the source of atmospheric particulates, formulation of urban atmospheric pollution control measures is effectively guided, the risk that residents are exposed to high-concentration particulates is reduced, and scientificity and effectiveness of air quality management are improved.

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 relates to the technical field of data processing, in particular to an ecological environment data real-time monitoring method based on big data processing. Determining an abnormal index and an abnormal curve segment of the index data of the sensor at the current position in different time periods; determining the relevance between the index data of the sensor at the current monitoring position in the time period and the index data of other sensors in the corresponding time period of the time period; determining an atmospheric pollution index of the current monitoring position at the moment; the atmospheric pollution index is dynamically regulated and controlled through a plurality of types of single sensors at a certain monitoring position at a certain moment, adjacent sensors and the pollution diffusion direction; and outputting early warning information. According to the method, the contribution degree of index data of various sensors at different monitoring positions in the to-be-detected area to the atmospheric pollution condition can be analyzed, the atmospheric pollution index of a certain monitoring position is dynamically adjusted in combination with the pollution diffusion direction, the influence of environmental factors on sensor detection is reduced, and the judgment accuracy of the atmospheric pollution condition is improved.

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 discloses a PM2.5 and ozone combined pollution intelligent prediction method and system and a medium. The method comprises the following steps: collecting historical air quality data, environmental meteorological data and atmospheric pollution source emission data of a to-be-predicted area; collecting cross characteristic data formed by combining different environmental meteorological conditions and different associated pollution sources; enabling the collected data to form a training data set, and carrying out the data matching; training a multi-task neural network model; selecting an optimal input feature parameter according to the predicted increment contribution degree of each input feature to the trained multi-task neural network model, and constructing and forming a final combined pollution identification model; and acquiring real-time air quality data, environmental meteorological data and atmospheric pollution source emission data of a to-be-predicted area, and inputting the data into the combined pollution identification model to predict PM2.5 and ozone concentration data. The method has the advantages of being simple in implementation method, low in cost, high in prediction precision and efficiency, high in robustness and the like.

The invention relates to a denitration catalyst capable of resisting alkali metal, water and sulfur composite poisoning and a preparation method and application thereof, and belongs to the field of environment-friendly catalytic materials and air pollution control. An organic solvent is used as a template agent, cerium oxide, manganese oxide and tungsten oxide are used as active components, and a cerium-manganese-tungsten composite oxide active component with a porous structure and strong acidity is prepared by a coprecipitation-soft template method. The catalyst has a wide denitration temperature window, high denitration efficiency and excellent composite poisoning resistance stability. The catalyst component is environment-friendly, low in cost and simple in preparation method, and is particularly suitable for stable and efficient purification of NOx in complex flue gas in the cement industry.

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 relates to the technical field of air pollution prevention and control, in particular to a sewage deodorization system and method based on a biological filter, and the system comprises a data acquisition module, a system preprocessing module, a system deodorization effect evaluation module, a system operation performance evaluation module, a system efficiency comprehensive analysis and processing module and a system database. According to the sewage deodorization system and method based on the biological filter, the system performance and the deodorization efficiency are comprehensively analyzed, and comprehensive system energy efficiency evaluation is provided. By analyzing the two indexes, the strong and weak points of the system can be identified, so that the resource configuration is optimized, and the overall efficiency of the system is improved. The deodorization effect and the operation performance are comprehensively considered, and more comprehensive and more accurate decisions can be made. By improving the deodorization effect, adverse effects on the surrounding environment and residents can be reduced. Meanwhile, the good operation performance can help to reduce the maintenance and operation cost.

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 relates to the technical field of air pollution control, in particular to a risk-oriented industrial park VOCs comprehensive tracing method and system, and the method comprises the steps: setting monitoring distribution points and monitoring elements, and carrying out the monitoring of the monitoring elements through a preset monitoring method; setting risk factors, and evaluating the risk factors according to the monitored monitoring factors; performing risk source quantitative traceability to generate a risk source quantitative traceability result; wherein the risk source quantitative traceability comprises PMF source analysis, receptor and pollution source component spectrum matching, risk source comprehensive contribution evaluation and risk source space matching; performing risk assessment uncertainty and sensitivity analysis on a risk source quantitative traceability result; according to a risk assessment uncertainty and sensitivity analysis result and a risk source quantitative tracing result, adopting IMTs to formulate a comprehensive emission reduction method for VOCs in the industrial park; according to the scheme, VOCs in the industrial park can be precisely traced, the uncertainty is reduced, and the fine control capability of pollutants in the industrial park is improved.

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 relates to the technical field of environmental protection detection, and discloses an industrial park atmospheric pollution prediction method comprising the following steps: S1, collecting meteorological data, emission data, historical air quality data and geographic information data in an industrial park; s2, preprocessing the collected data, including data cleaning, normalization and feature extraction; s3, based on a time sequence analysis method and a machine learning algorithm, constructing a multi-level pollution source prediction model, and identifying and predicting emission characteristics of different pollution sources; s4, through a multi-scale modeling method, modeling is carried out on the emission behavior of a single pollution source on the microscopic scale; and S5, coupling and integration of the models are implemented, and cross-scale pollution prediction is realized. By combining a micro-scale long-short-term memory network model and a macro-scale Gaussian diffusion model, complex emission behaviors of a single pollution source and diffusion paths of pollutants in a regional range can be captured at the same time.

The invention belongs to the field of industrial waste gas treatment, and particularly relates to an industrial waste gas treatment device for preventing atmospheric pollution and an operation method thereof.The industrial waste gas treatment device comprises a shell, the top of the shell is fixedly connected with an air pump, the output end of the air pump is connected with an air exhaust pipeline, and one end of the air exhaust pipeline extends into the shell; according to the industrial waste gas treatment device for preventing atmospheric pollution and the operation method of the industrial waste gas treatment device, a closed loop is formed through the four nozzles to spray limestone slurry to gas flowing upwards, the gas enters the shell along the gas inlet groove and then flows downwards along the arc-shaped surface at the bottom of the guide ring, and the gas impacts on the limestone slurry in the first liquid accumulation cavity; sulfur dioxide in gas reacts with limestone slurry, gypsum and the limestone slurry are separated by controlling rotation of a containing plate, the containing plate drives a plurality of first spoilers to rotate while rotating, part of the gas is pushed to impact downwards, and the gas makes contact with the limestone slurry circulating along a flow blocking ring; therefore, the reaction time of the gas and the limestone slurry is prolonged.

The invention relates to the technical field of environmental monitoring, provides an intelligent monitoring and early warning system and method for atmospheric pollution, solves the problems of insufficient distribution density and coverage range of monitoring equipment, and realizes comprehensive real-time monitoring and early warning by combining a digital twin model, a dynamic monitoring unit and an efficient data processing technology. The air quality sensor is used for collecting data, dynamic mobile terminal equipment (such as an unmanned aerial vehicle and a mobile inspection robot) is combined to fill the monitoring blank, and the data accuracy is improved. The dynamic monitoring unit optimizes sensor operation parameters through a deep learning model and improves data reliability, the air quality early warning unit comprehensively compares air quality data and generates early warning information in time, and the system further has knowledge base optimization and result visualization functions and provides powerful technical support for handling atmospheric pollution.

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.