656 results about "Atmospheric pollution" patented technology

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

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

The invention relates to the technical field of intelligent traffic and Internet of Vehicles, in particular to a traffic and air pollution bidirectional coupling flow prediction method based on double-path dynamic fusion. Comprising the following steps: collecting traffic flow and air pollutant concentration data, and carrying out space-time alignment and reversible instance normalization; the data is divided into two branches, the first branch extracts time-dependent features through gated convolution and probability sparse self-attention, and the second branch obtains variable interaction features through dimension remodeling, context extraction and reversible coupling transformation; bidirectional feature interaction is carried out through cross attention, weights are dynamically generated based on channel attention, and residual connection is carried out after weighted fusion; and performing linear mapping and inverse normalization on the fused features to obtain a traffic flow predicted value. According to the method, the prediction precision and robustness in a pollution sensitive scene are remarkably improved.

The invention discloses a satellite remote sensing atmospheric pollution gas concentration inversion method and system based on DOAS, and belongs to the technical field of atmospheric environment remote sensing monitoring. The method comprises the following steps: acquiring solar back scattering spectrum data observed by a satellite load and preprocessing the solar back scattering spectrum data; performing spectrum correction by using a high-precision instrument response function; in a selected fitting window, constructing a fitting model comprising a target gas differential absorption cross section, a Ring effect, Mie scattering and multiple scattering contributions; carrying out spectrum fitting by adopting a nonlinear least square algorithm, and carrying out inversion to obtain a differential batter column concentration (DSCD) of the target gas; in conjunction with an Atmospheric Mass Factor (AMF), the DSCD is converted to a Vertical Column Concentration (VCD). The system comprises a data acquisition and preprocessing module, a spectrum fine correction module, a DOAS spectrum fitting module, a dynamic AMF calculation module and a column concentration inversion and output module, and optionally comprises a data assimilation and traceability analysis module. According to the satellite remote sensing atmospheric pollution gas concentration inversion method and system based on the DOAS, the parameterized multiple scattering correction items are introduced, a dynamic AMF calculation scheme is developed, and the inversion precision and reliability under the complex atmospheric condition are remarkably improved.

The invention relates to a CO2 absorbent with high cycle capacity and high cycle stability and application of the CO2 absorbent, and belongs to the technical field of atmospheric CO2 pollution treatment in the environmental protection industry. The CO2 absorbent is a polyamide-amine dendritic polymer (represented by PAMAM-2 (2 + G) NH2, G is the algebra, and 2 (2 + G) is the number of terminal amino groups), wherein primary amino is used as a terminal group, alkylene diamine is used as a core, and the algebra G is 1-5. The polyamidoamine dendritic polymer provided by the invention has the advantages of high-density terminal amino groups, high CO2 absorption capacity, large circulation capacity and high circulation absorption stability.

The invention relates to the technical field of air pollution source tracing, and discloses an unmanned aerial vehicle system and method for air pollution source tracing. The method comprises the following steps: collecting gas concentration distribution data of different spatial positions of a target area, and synchronously obtaining a meteorological parameter data set; determining a plurality of pollution diffusion stability factors based on the spatial distribution characteristics of the gas concentration distribution data and the meteorological parameter data set, and constructing an atmospheric pollution traceability spatial model; generating a pollution source search strategy of the unmanned aerial vehicle cluster based on the model, and dynamically adjusting the strategy according to gas concentration distribution data collected in real time; each unmanned aerial vehicle independently generates a local traceability decision by combining the environment characteristic data of the unmanned aerial vehicle and the adjacent unmanned aerial vehicle, and a collaborative decision result is obtained after consistency adjustment of a collaborative decision mechanism; the pollution source tracing response amount is determined according to the pollution source search strategy and the collaborative decision result, the pollution source positioning instruction is executed when the response amount exceeds the preset threshold value, and the comprehensiveness, accuracy and efficiency of pollution source tracing can be improved.

This invention relates to the field of special agents for treating air pollution, specifically to a rare earth water-based environmentally friendly urea crystal remover for vehicles and its preparation method. The raw materials for preparing the rare earth water-based environmentally friendly urea crystal remover for vehicles, by weight, include: 0.005-0.2 parts rare earth-based catalyst, 3-10 parts surfactant, 1-3 parts dispersant, 5-10 parts stabilizer, and 80-100 parts deionized water; wherein the rare earth-based catalyst includes at least a cerium-based catalyst. The urea crystal remover provided by this invention has excellent performance, effectively removing urea crystals, and the cerium emission concentration is far below the requirements of the EURO 7 draft (EURO 7 does not require cerium). NOx conversion rate is significantly improved, making it more suitable for vehicles using high-sulfur fuels, and greatly benefiting the control of air pollution from mobile sources. It is also environmentally safe and environmentally friendly.

This invention relates to the field of air pollution control technology, and in particular to a spray tower for effectively purifying air pollution. The spray tower includes an air inlet fixed to the bottom of one side, a suction hood fixed to the upper surface of the spray tower, and an exhaust pipe fixed to the top of the suction hood. An anti-clogging recovery component and a spraying mechanism are installed inside the spray tower, and a collection box is fixed to one side of the spray tower corresponding to the position of the anti-clogging recovery component. This invention utilizes a spray pipe mechanism to enhance the rate of the neutralization reaction through the synchronous rotation of a rotating disk and a rotating block, and enhances the spatial separation of the two solutions through centrifugal force, reducing cross-mixing. This avoids ineffective neutralization of acid and alkali solutions and achieves continuous and efficient treatment. Combined with the anti-clogging recovery component, the activated carbon balls inside the fixed cylinder are promptly recovered and cleaned, ensuring that efficiency is not reduced due to clogging during air pollution purification, and preventing ineffective adsorption of activated carbon balls due to prolonged use.

The invention discloses an urban atmospheric pollution real-time monitoring and tracing method based on reinforcement learning, and relates to the technical field of atmospheric environment monitoring, and the method comprises the following steps: S1, collecting a pollution source data set; s2, constructing a Gaussian plume fidelity simulation model; s3, constructing a CFD fidelity simulation model; s4, constructing a multi-fidelity simulation model based on a Gaussian process proxy model; s5, constructing a DPPO reinforcement learning model; s6, using an improved multi-fidelity Bayesian optimization algorithm to continuously train and iterate the DPPO reinforcement learning model; and S7, generating a standardized pollution source real-time traceability analysis report. According to the method, the limitations of much manual intervention, low efficiency and poor real-time performance in a traditional urban pollution monitoring and tracing method are overcome, and an efficient and accurate solution is provided for intelligent real-time monitoring of urban environmental pollution and accurate and rapid tracing of pollution sources.

The invention discloses a fire slash area-based straw open-air incineration pollutant emission amount estimation method, and relates to the technical field of air pollution prevention and control. According to the scheme, the method comprises the steps that a fire slash in satellite image data is extracted through a fire slash recognition model, the incineration area of a target crop is calculated through spatial intersection analysis, the pollutant emission amount of open-air incineration of straw of the target crop is obtained through calculation, and the fire slash is accurately recognized through the fire slash recognition model. And the accuracy of the estimated value of the pollutant emission is improved.

The invention relates to the technical field of air pollution prevention and control, and discloses a tail gas drying device based on sorbic acid production process, which comprises two groups of drying tanks which alternately operate, the two groups of drying tanks are respectively used as an adsorption tank and a desorption tank, a drying inner cylinder penetrates through the tanks, and an interlayer cavity is enclosed by a first separation assembly and a second separation assembly in the cylinder; the interior is divided into three groups of adsorbent bed layers; the lifting assembly drives the second separation assembly to ascend and descend, and the bed space, the cavity volume and the air outlet size can be dynamically adjusted according to the swelling and shrinking state of an adsorbent in the early and later periods of desorption, so that the problems that an existing drying tank is large in flow resistance due to direct filling of the adsorbent and poor in desorption efficiency are solved; layered design and cavity blocking reduce airflow resistance, secondary adsorption of the lower layer is avoided, sufficient desorption is guaranteed through dynamic adjustment, the desorption efficiency can be improved, the period is shortened, the service life of an adsorbent is prolonged, and continuous and stable tail gas drying is guaranteed.

The invention relates to the technical field of pollution traceability, in particular to a precise traceability system and method for an atmospheric pollution source. The method comprises the following steps: collecting the concentration value, the wind speed and the wind direction of pollutants; carrying out statistics on common pollutants of different monitoring points, and determining a change characteristic value and concentration variability based on a concentration change condition so as to obtain a pollution approximation degree between the monitoring points; determining a pollutant propagation factor based on the concentration change direction and the wind direction of the monitoring point and the wind speed; therefore, optimization factors are determined to optimize the pollution approximation degree; clustering the optimized pollution approximation degree, and determining a pollution characteristic value of the characteristic point in a cluster based on the pollution approximation degree between the monitoring points and the distance at the wind direction convergence position; and completing source tracing of the pollution source based on the pollution characteristic values of the characteristic points. According to the invention, the traceability precision and efficiency in a multi-pollution source complex scene are improved.

The invention relates to an aerogel-derived ultralow-temperature water-sulfur poisoning-resistant flue gas denitration catalyst and a preparation method thereof, and belongs to the technical field of air pollution control and environment catalytic material frontier. CeMnNiOx composite oxide is used as an active component of the catalyst, a catalyst precursor gel with a three-dimensional network structure is formed through a sol-gel method, and then the hydrophobic aerogel derivative denitration catalyst with a high specific surface area, low density and a three-dimensional porous structure is obtained through aging, freeze drying and high-temperature heat treatment. The denitration efficiency of the catalyst can reach 90% or above at the temperature of 80-270 DEG C in a complex atmosphere containing 5-10% of water vapor and 50-100 ppm of SO2. The method is suitable for deep purification of NOx in ultralow-temperature and high-humidity SO2-containing complex flue gas after industrial desulfurization and dust removal.

The invention relates to the technical field of particulate matter source analysis, and discloses a refined source analysis method and system.The method comprises the steps that a plurality of pollution sources and dust falling particulate matter sampling points are determined according to soil pollution distribution of a target area and an atmospheric pollution source survey result, by collecting single particle mass spectrum data of pollution sources, sampling points and receptors, constructing a mass spectrum library and combining an ART-2a algorithm and a deep learning model, the heavy metal pollution contribution proportion is accurately analyzed and traced to specific discharge ports of enterprises, and the problem that specific pollution enterprises and working sections cannot be positioned in the prior art is solved. According to the method, a pollution source and an environment receptor are sampled and analyzed by utilizing a single-particle mass spectrometry technology aiming at atmospheric dust fall particles in soil, and the heavy metal in the atmospheric dust fall particles in the soil is finely traced to specific enterprises and working sections by analyzing the heavy metal, so that a fine technical support is provided for soil heavy metal pollution management and control.

The invention relates to a PM2.5 concentration prediction method based on RF-RFE and TCN-BiLSTM-Attention, and belongs to the technical field of atmospheric environment monitoring. According to the method, firstly, PM2.5 concentration monitoring data and data of related influence factors such as weather, other pollutants and time characteristics are preprocessed, recursive feature elimination (RFE) is combined with random forest (RF) for feature screening, and a time sequence input sequence is constructed; extracting local time sequence features of the sequence through a time convolutional network (TCN), and capturing bidirectional long-range time sequence dependence features of the sequence by using a bidirectional long-short-term memory network (BiLSTM); an attention mechanism is introduced to endow a BiLSTM output feature vector sequence with a differentiation weight, and key time sequence feature contribution is highlighted; and finally, outputting a PM2.5 concentration predicted value through a full connection layer, and completing model training and optimization based on the training set and the verification set. According to the method, the advantages of TCN, BiLSTM and Attention mechanisms are combined, the precision and stability of PM2.5 prediction are improved, and the method can be widely applied to atmospheric pollution early warning and environmental governance decision making.

The invention discloses a regional atmospheric pollution and carbon emission reduction cooperative benefit evaluation method, system and device, and relates to the technical field of atmospheric pollution and carbon emission reduction, and the method comprises the steps: carrying out the evaluation of the regional policy scene parameters of a target region based on a greenhouse gas-atmospheric pollution cooperative effect model according to the regional policy scene parameters of the target region; determining a pollutant emission list and PM2.5 space distribution concentration prediction data; based on a machine learning algorithm, determining O3 spatial distribution concentration prediction data according to the pollutant emission list and the PM2.5 spatial distribution concentration prediction data; calculating health benefits by adopting an environmental benefit evaluation model according to the PM2.5 spatial distribution concentration prediction data and the O3 spatial distribution concentration prediction data; and based on a life value method, calculating economic benefits according to health benefits. The method is high in evaluation efficiency, more comprehensive in evaluation, high in precision and good in practicability.

The invention discloses an atmospheric pollution case system, a case analysis method and a medium, and the system comprises a basic information layer which is used for storing the whole-process feature data and treatment measure data of a pollution event; the logic analysis layer is used for carrying out quality control on the data, carrying out source analysis and cause analysis on the whole-process feature data of the pollution event after the quality control, and carrying out effect evaluation on the treatment measures after the quality control; and the comprehensive decision-making layer is used for comparing historical cases according to the retrieval conditions and matching similar historical cases, and finding out treatment measures corresponding to the similar historical cases according to the similar historical cases, or formulating targeted new treatment measures according to the corresponding treatment measures. The invention provides a'basic information-logical analysis-comprehensive decision 'three-layer progressive architecture case library, so that other modules can be conveniently expanded on the architecture of the case library subsequently; and multi-source data are systematically integrated, so that the problem of fragmentation of existing case data is solved.

The invention belongs to the technical field of environment detection, and particularly relates to atmospheric pollution environment governance detection equipment which comprises a mounting rod, a detection device is mounted at the top end of the mounting rod, a control box is mounted in the middle of the mounting rod, a rotating sleeve sleeves the top end of the mounting rod, and a supporting rod is fixed to one side of the rotating sleeve. A connecting plate is rotationally connected to the side, away from the rotating sleeve, of the bottom end of the supporting rod, a photovoltaic panel is fixed to the bottom end of the connecting plate, rotating plates are rotationally connected to the two sides of the rotating sleeve, and transmission assemblies are arranged below the rotating plates; the wind blows the photovoltaic panel to drive the rotating plate to rotate through the transmission assembly, and the rotating plate drives the protective shell to be closed. The photovoltaic panel is blown by wind power, and the photovoltaic panel drives the protective shell to be closed to cover the detection device, so that the outside of the detection device is protected, and the detection device can be prevented from being damaged due to inaccurate data caused by detection in extreme weather or being smashed by an object in airflow.

The invention relates to the technical field of environment monitoring, and discloses an air pollution intelligent monitoring method and system based on multi-source data fusion, and the method comprises the steps: obtaining original heterogeneous data, carrying out the preprocessing and multi-modal data fusion, and obtaining a fusion data set; according to the fusion data set, spatial feature extraction is carried out, and pollutant spatial feature representation is obtained; according to the spatial feature representation, performing time sequence dynamic analysis by using a recurrent neural network, identifying and marking an abnormal peak value, and obtaining a pollutant time sequence evolution sequence; according to the time sequence evolution sequence, determining the diffusion trend of the pollutants by constructing a graph model, and carrying out conjoint analysis to obtain a pollutant propagation rule; and finally, according to the propagation rule and abnormal peak information in the time sequence evolution sequence, carrying out space mapping processing, and generating a real-time pollution map. According to the invention, accurate and real-time dynamic monitoring of pollutants can be realized, and technical support is provided for formulating a scientific treatment strategy.

The invention belongs to the technical field of atmospheric pollution, and particularly relates to dust removal equipment for a mining machine, the dust removal equipment comprises a base, a cyclone separator and a bag-type dust remover which are communicated with each other are fixedly mounted at the upper end of the base, an air inlet of the cyclone separator is further fixedly communicated with an induced draft pipe, an induced draft fan is mounted on the induced draft pipe, and the bag-type dust remover is fixedly connected with the air inlet of the cyclone separator. The induced draft fan is fixedly installed at the upper end of the base, the front end of the induced draft pipe is further fixedly communicated with a dust hood through an elastic telescopic pipe, and the device further comprises a PLC, a dusty gas state detection mechanism, a pre-spraying water conditioning mechanism, an air inlet speed self-regulation mechanism and a feedback regulation mechanism. The power and the ventilation quantity of the induced draft fan are automatically adjusted based on the dust concentration, water is pre-sprayed based on the dust water content, the inlet air speed of the cyclone separator is adjusted based on the ventilation quantity, and the input number of filter bags of the bag-type dust remover is adjusted. The comprehensive functions of efficiently capturing dust, preventing overflow, avoiding equipment blockage, guaranteeing stable operation of dust removal equipment and saving energy are achieved.

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 provides a carbon monoxide plume identification and quantification method based on a domestic hyperspectral satellite, and the method comprises the steps: carrying out the radiometric calibration and atmospheric correction of the data of a visible short-wave infrared hyperspectral camera AHS I carried by the domestic hyperspectral observation satellite (GF-5B, Gaofen-5 B satellite); generating a carbon monoxide unit absorption spectrum by using a medium-resolution atmospheric transmission model MODTRAN; inverting a carbon monoxide enhancement value by adopting a matched filtering method; generating a binary plume mask through threshold segmentation; and constructing a deep learning model based on a residual neural network ResNet network, and realizing plume pixel-level identification and carbon monoxide column concentration enhancement value prediction. The method can identify the carbon monoxide plume with high precision and high efficiency, and is suitable for atmospheric pollution monitoring and pollution source quantification.

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 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 discloses an urban environment quality monitoring method based on deep learning. The urban environment quality monitoring method is used for predicting an air pollution index API in the future 6 hours. The method comprises the following steps: collecting concentration data of six pollutants, namely PM2.5, PM10, SOO, NOO, Oand CO, and constructing a deep learning model comprising a double-layer time sequence feature extraction network and a pollution index prediction network; in the double-layer time sequence feature extraction network, a time scale layer adopts an LSTM structure to capture short-term change features, and a periodic scale layer adopts an attention-enhanced GRU structure to capture periodic features; after the time scale feature and the periodic scale feature are spliced, calculating a weighted feature through a self-attention mechanism, and inputting the weighted feature into a full-connection layer to generate a predicted value; according to the method, the prediction precision of the atmospheric pollution index is remarkably improved, the model robustness is enhanced, and a scientific basis is provided for environment decision making.

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.