399 results about "Concentration prediction" patented technology

The invention discloses an intelligent multi-gas detection module data processing system and method based on NDIR, and relates to the technical field of gas detection.The method comprises the steps that a multi-wavelength NDIR sensor is used for obtaining light intensity changes of a gas sample under different wavelengths, and original spectral signals are generated; performing denoising processing on the spectral signal by adopting wavelet transform, and performing zero calibration and dynamic baseline deduction; based on the Beer-Lambert law in combination with environmental factors, establishing a relation model between gas absorption and spectral signals; updating model parameters by using a recursive least square algorithm, and continuously optimizing gas concentration prediction; separating gas signals by adopting a non-negative matrix factorization algorithm, and predicting the concentration of each gas; and the edge end operates the lightweight model in real time, and regularly uploads the model to the cloud end for global optimization and federated learning. The method can effectively improve the condition that the precision is insufficient when the model is used for a long time in the prior art.

The invention discloses a gas concentration prediction method based on micro-seismic monitoring data, and belongs to the technical field of coal mine safety monitoring. Comprising the steps that micro-seismic sensors and gas sensors which are distributed in a net shape are arranged in a mining area, and data are collected in real time and subjected to denoising and standardization processing; the method comprises the following steps of: constructing a gas concentration prediction model fusing a long-short-term memory network and a convolutional neural network by extracting multi-dimensional characteristic parameters such as a microseismic energy gradient, a seismic source aggregation degree and a gas concentration change rate, and dynamically distributing spatial-temporal characteristic weights by utilizing an attention mechanism; a safety threshold value is dynamically adjusted based on coal seam permeability, mining depth and geological parameters, score calculation of comprehensive danger evaluation indexes is combined, and three-level early warning of underground sound-light alarm, ventilation regulation and control and remote expert cooperation is triggered; and meanwhile, through periodic model calibration and parameter retraining, the prediction precision is improved. According to the invention, space-time collaborative perception and self-adaptive early warning of the gas risk are realized, and the real-time performance and reliability of coal mine safety prevention and control are obviously improved.

The invention discloses a gas concentration identification method and system based on an AI model, and relates to the technical field of data processing. The method comprises the following steps: acquiring an environment infrared image sequence and a gas concentration time sequence signal; dynamically adjusting the length of the sliding window, and intercepting a window image sequence and a window concentration sequence; dTCWT decomposition is adopted to extract multi-direction sub-band energy features of the infrared image, the multi-direction sub-band energy features are compressed into image feature vectors through 1D-CNN, concentration time sequence feature vectors are extracted through an LSTM network, and joint representation is generated through fusion; outputting a concentration predicted value and danger level probability distribution through a mixed deep learning model; a probability thermodynamic diagram is generated by combining Monte Carlo diffusion simulation, and a leakage source coordinate is accurately positioned by fusing a concentration gradient; triggering a grading response instruction according to the highest risk probability, and generating risk map real-time visualization; the problems that a traditional system is delayed in response, inaccurate in positioning and insufficient in utilization of multi-source data are solved, closed-loop management and control of leakage monitoring, early warning, positioning and disposal are achieved, and the positioning precision is improved.

The invention belongs to the technical field of PM10 concentration prediction, and discloses a PM10 concentration prediction method based on a space-time diagram neural network and an expert hybrid model, and the method comprises the following specific steps: S1, time feature extraction (RTAF): the PM10 concentration is influenced by a plurality of time factors, including short-term fluctuation, medium-term trend and long-term trend; a dynamic multi-modal weighted graph is constructed, meteorological factors, geographic positions and historical pollution similarities are coded into features of edges and nodes, a PM10 spatial propagation mechanism is modeled based on an adaptive graph neural network, a residual attention fusion module is introduced into the model in the time dimension, multi-scale time dependence features are effectively extracted, and the time-dependent features are extracted. According to the method, a long-term trend and a short-time fluctuation process are captured, finally, dynamic modeling and expert selection are performed on a complex PM10 propagation mode by using an expert hybrid network, the prediction robustness and generalization ability are improved, the model fully fuses a PM transmission mechanism and a depth space-time modeling ability, and high-precision prediction of the PM10 concentration in the next 24 hours is realized.

The invention discloses a sea surface chlorophyll concentration prediction method and system based on remote sensing and deep learning, and relates to the technical field of marine ecological environment, and the method comprises the steps: inputting the marine chlorophyll a data of a target region into a trained marine chlorophyll concentration prediction model, and obtaining the marine chlorophyll concentration information of the target region; wherein the prediction model is an improved marine chlorophyll a concentration prediction model based on a convolutional long short-term memory network; the periodic feature extraction module is used for extracting historical synchronous related periodic features based on historical synchronous chlorophyll concentration data, and the noise removal and fusion module is used for eliminating noise output by the recent spatial-temporal feature extraction module and the periodic feature extraction module and fusing output results to obtain a marine chlorophyll a concentration data prediction result. According to the invention, the sea surface chlorophyll concentration can be predicted more accurately.

The invention relates to a soil element hyperspectral inversion method based on CARS-IRIV waveband selection, and belongs to the field of soil hyperspectral prediction. The method comprises the following steps: firstly, collecting a soil sample, measuring visible near-infrared hyperspectral data and chromium concentration of the soil sample, removing wavebands with relatively large noise in the hyperspectral data, dividing a data set into a training set and a verification set, preprocessing the training set and the verification set, and then extracting characteristic wavebands by adopting CARS-IRIV; and then establishing a soil chromium concentration prediction model of support vector regression, training the model by using a training set, evaluating prediction precision of the model by using a verification set in combination with evaluation indexes, and finally drawing a soil element chromium concentration spatial distribution diagram in a sampling area. According to the method, the soil elements can be quickly and accurately inverted, and powerful technical support is provided for quantitative inversion of the concentration of the heavy metal chromium in the soil.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

The invention discloses a spectrum aliasing decoupling and concentration inversion method under the cross influence of multi-source environmental factors, belongs to the field of industrial process control and environment monitoring, and constructs an environment-spectrum collaborative fusion decoupling model for concentration prediction. The method specifically comprises the following steps: respectively collecting absorption spectrum signals of specified mixed gas at different temperatures, pressures and known concentrations, meanwhile, collecting environmental parameter data, constructing a multi-source data set, and carrying out denoising, dimension reduction and preprocessing on the multi-source data set; constructing a self-supervised feature extraction network for adaptive modulation of environmental parameters to realize deep fusion of spectrum and environmental information; the feature expression capability and generalization performance of the self-supervised feature extraction network are improved by using a self-supervised learning mechanism; and constructing a BPBO-GRNN self-adaptive concentration inversion optimization model for realizing inversion of mixed gas concentration and self-adaptive optimization of model parameters. According to the invention, high-precision concentration inversion and stable detection of the aliasing gas can be realized.

The invention relates to a mine dust concentration prediction and control method based on GNN and Transform self-supervised learning, and belongs to the field of coal mine disaster early warning. The method comprises the following steps: acquiring multi-modal mine environment parameters and historical dust concentration data, and carrying out standardization and characteristic engineering processing on the data; partially masking the processed data by adopting an MAE strategy, and learning the internal structure of the data by reconstructing the masked part; generating synthetic data similar to an actual mine environment by using the GAN so as to expand a training set; the spatial features extracted by the GNN module and the time sequence features extracted by the Transform module are fused, and comprehensive feature representation is generated; predicting a dust concentration value of each position of the mine based on the comprehensive characteristics, and generating a ventilation control parameter suggestion; and dynamically adjusting the mine ventilation system according to the prediction result to realize dust concentration control.

The invention discloses a method and system for identifying perfluorooctanoic acid pollutants in tap water, and relates to the field of water environment new pollutant detection, and the method comprises the steps of SERS spectral signal collection, signal preprocessing, bimodal learning device construction and training verification, and concentration prediction result generation based on numerical value and image bimodal. The to-be-predicted data is put into the trained bimodal learning device for prediction and identification, and the concentration of the detection sample is output, so that batch rapid detection based on surface enhanced Raman spectroscopy and bimodal deep learning is realized, and high-precision identification and prediction based on small sample data are realized; the accurate detection result of the perfluorooctanoic acid pollutants in the tap water is obtained.

The invention relates to an anesthetic gas output control system for an anesthesia machine and a method thereof, and relates to the technical field of anesthesia machines, the system comprises a data acquisition module used for collecting breathing parameters and gas state parameters of a patient and constructing a breathing dynamics model containing nonlinear correction; the concentration prediction module is used for constructing a gas mixing concentration prediction model with pressure dynamic compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamic model; the parameter control module is used for constructing a control objective function based on the concentration deviation, the flow change rate and the pressure deviation of the anesthetic gas; the self-adaptive compensation module is used for determining a self-adaptive compensation factor based on the concentration deviation and the pressure change rate of the anesthetic gas; the flow correction module is used for calculating flow correction values of various anesthetic gases; and the gas output module is used for outputting the anesthetic gas according to the flow correction values of various anesthetic gases. The system can improve the accuracy of anesthetic gas output control.

The invention discloses an air quality index prediction method based on CNN-GRU, and belongs to the technical field of environment monitoring and air quality prediction. The method comprises the following steps: (1) acquiring historical air pollutant concentration data and meteorological data to form a data set; (2) preprocessing the data set, and dividing the preprocessed data set into a training set and a test set; (3) constructing a parallel CNN-GRU double-branch architecture; (4) performing model training and optimization on the parallel CNN-GRU double-branch architecture based on the training set; (5) operating the optimal parallel CNN-GRU double-branch architecture based on the test set, and outputting a pollutant concentration prediction basic value; and (6) carrying out reverse normalization on the predicted basic value of the pollutant concentration, reducing the predicted basic value of the pollutant concentration into an actual concentration unit to obtain the pollutant concentration, calculating a subindex IAQI of the pollutant concentration, and taking a maximum value as a final AQI. According to the method, the parallel double-branch architecture is constructed, so that the parameter quantity is reduced, the prediction speed is increased, and the prediction result accuracy is improved.

The invention belongs to the technical field of traffic pollution prediction, and particularly relates to a LightGBM-based multi-source data fusion pollutant concentration prediction method, which comprises the following steps of: 1, integrating and processing multi-source data, and constructing a plurality of feature sets; and 2, carrying out feature analysis on meteorological, pollutant and traffic index variables through correlation analysis and time sequence analysis, and providing support for modeling and model interpretation. And step 3, dividing a training test set according to a time sequence, and training and optimizing the LightGBM pollution prediction model through parameter tuning by taking minimization of RMSE as a target. And 4, verifying the performance of the model from prediction precision, spatial distribution and wind direction influence. And 5, analyzing and displaying the key driving factor and quantifying the contribution of the key driving factor. According to the invention, through feature engineering processing and model optimization of heterogeneous data such as weather and traffic, high-precision prediction and influence factor analysis of lane-level pollutant concentration are realized.

The embodiment of the invention provides a gas concentration prediction method based on multi-source information fusion. The method is applied to the technical field of coal mining. Performing time sequence decomposition processing on the historical gas concentration data, performing correlation analysis on the historical gas concentration data and the coal mining machine operation data, and determining influence factor data related to gas concentration change; forming a multi-dimensional data sequence by the historical gas concentration data subjected to the time sequence decomposition processing and the multiple pieces of influence factor data; and inputting the multi-dimensional data sequence into a gas concentration prediction model, and analyzing and processing the multi-dimensional data sequence through the gas concentration prediction model to obtain a gas concentration prediction result, thereby improving the accuracy and reliability of the gas concentration prediction result and gas over-limit prediction.

The invention relates to the technical field of industrial flue gas purification, and discloses a desulfurization control method based on blast furnace gas sulfur concentration prediction, which comprises the following steps: step 1, acquiring real-time sulfur concentration, flow velocity, temperature, pressure and current desulfurizer feeding rate of blast furnace gas, denoising and normalizing acquired data, and storing the data in a database; step 2, based on the blast furnace gas sulfur concentration convection-diffusion model and the reaction rate equation, constructing a physical constraint prediction model in combination with a long-short term memory network, and outputting future predicted sulfur concentration; and 3, predicting a result based on the sulfur concentration. According to the method, the physical constraint prediction model based on the long and short term memory network is adopted, the technical effect of accurately predicting the sulfur concentration of the blast furnace gas is achieved, and compared with a prediction method purely based on experience or a traditional time sequence model in the prior art, the defects of low prediction precision and poor generalization ability under complex working conditions are overcome.

The invention discloses a bacteria Raman spectrum chemical component cross-domain analysis method based on deep transfer learning, and belongs to the field of Raman spectrum intelligent analysis, the method comprises the following steps: collecting multi-source Raman spectrum data, and pre-processing the multi-source Raman spectrum data to obtain pre-processed spectrum data; constructing a deep transfer learning model, extracting spectral features of the preprocessed spectral data by the deep transfer learning model through a deep full-connection neural network and a multi-head self-attention mechanism, and eliminating distribution difference between devices by using adversarial transfer learning to obtain a Raman spectrum feature vector; performing nonlinear dimension reduction on the Raman spectrum feature vector to obtain a dimension-reduced low-dimensional feature matrix; based on the dimensionality-reduced low-dimensional feature matrix, a dynamic field adaptation and meta-learning fine tuning strategy is adopted to realize small sample fine tuning, and an optimized model is obtained; and outputting chemical component category judgment and concentration prediction results based on the optimized model.

The invention relates to the technical field of soil pollution detection, and discloses a soil pollution assessment method and system based on artificial intelligence. The artificial intelligence-based soil pollution assessment method comprises the following steps: step S101, determining a sensitive wave band and a contribution degree value of a pollutant; s102, judging whether the pollutants are included in a pollutant list or not according to the second characteristic value; step S103, constructing an incidence matrix; step S104, correcting the hyperspectral data of the pollutants in the pollutant list; step S105, obtaining the concentration of the pollutants through the pollutant concentration prediction model; and S106, judging whether the soil is polluted or not according to the comprehensive pollution index. According to the method, the enhanced absorption effect between the pollutants is converted into incidence matrix representation by utilizing the characteristics of the sensitive wave bands of the pollutants in the hyperspectral data, and the precision of pollutant concentration measurement is improved by utilizing the nonlinear learning capability of the pollutant concentration prediction model.

The invention provides an intelligent isotope gas concentration regulation and control method based on concentration prediction, which comprises the following steps: collecting multi-dimensional data in a closed box subjected to emptying treatment to construct a standardized control-response data sequence; predicting the isotope gas concentration through a neural network model to obtain the predicted isotope gas concentration and a long-term concentration drift trend estimation item; inputting the current standardized control-response data sequence into Transform to obtain current state representation, and generating a control instruction of valve opening and closing time and sucking pump power on the basis of the current state representation in combination with predicted concentration and target concentration; executing the control instruction, comparing the actual concentration with the predicted concentration, and coding the deviation into a deviation risk code through a multi-layer perceptron in combination with the control instruction; receiving the current state representation and the deviation risk code to establish a state-deviation mapping model, fitting deviation distribution parameters of the control deviation, and accumulating control experience to optimize a long-term strategy.

The invention discloses an SNCR outlet NOx concentration prediction method and system based on a random forest, and the method comprises the steps: S1, obtaining the operation data of an SNCR system, selecting a plurality of input variables affecting the outlet NOx concentration, and carrying out the normalization processing of the input variables; s2, carrying out maximum information coefficient analysis on the normalized data set, determining the delay time of each input variable relative to the NOx concentration of the outlet, and reconstructing the data set according to the delay time; s3, dividing the reconstructed data set into a training set and a test set, constructing a random forest model, and initializing model parameters; s4, optimizing parameters of the random forest model to obtain an optimal parameter combination; and S5, predicting by using the random forest model of the optimal parameter combination to obtain a predicted value of the NOx concentration at the outlet of the SNCR system. The method has the advantages of high prediction precision and the like.

The invention relates to the technical field of pollutant monitoring, in particular to an industrial solid waste blending combustion pollutant intelligent monitoring system which comprises a data fusion module, a diffusion judgment module, a concentration prediction module, a risk monitoring module and an early warning distribution module, and the trend of particulate matter and gas monitoring data is analyzed based on a blending combustion reaction furnace outlet and a smoke exhaust pipeline section. According to the invention, through combined processing of multi-point time sequence monitoring data, various pollutants and environmental parameters are collected in real time, precise spatial partitioning of a monitoring object, coupling analysis based on meteorological factors and spatial diffusion characteristics, dynamic identification of response differences of pollutants in each area of a factory, and through extraction of a concentration change interval and a risk trend signal, a real-time monitoring result is obtained. According to the method, emission risk types are subdivided, graded intervention information is generated, the response speed and the judgment capability of abnormal states of pollutants under complex environment disturbance are improved, dynamic adaptation and detailed management and control of an intelligent monitoring system are promoted, and high-reliability decision making of emission control in various scenes of a plant area is further supported.

The invention provides a water chlorophyll a concentration inversion method and system based on multi-modal data and a lightweight model, and relates to the technical field of water environment remote sensing evaluation. The method comprises the following steps: firstly, acquiring a Gaofeng No.5 satellite remote sensing image, a sentinel No.3 satellite image and ground actual measurement data, and completing image preprocessing and water body pixel extraction; constructing a hyperspectral index and an aquatic vegetation index, and fusing the hyperspectral index and the aquatic vegetation index with the water body temperature, the pH environmental factors and the spectral reflectivity to form a multi-dimensional feature sample set; a core feature subset is obtained through random forest and XGBoost coupling feature selection, and a lightweight student model is trained based on knowledge distillation; and constructing a to-be-predicted feature sample for the to-be-predicted time phase image and the environment factor, inputting the to-be-predicted feature sample into the lightweight student model to obtain a chlorophyll a concentration predicted value, and generating a spatial distribution map and a quality control map layer. According to the invention, high-precision, low-redundancy and efficient deployment chlorophyll a concentration inversion is realized.

The invention discloses a hydrogen leakage concentration prediction method and system, and relates to the technical field of hydrogen energy safety, and the method comprises the following steps: collecting hydrogen leakage concentration sequences and environmental parameters of different monitoring points at the current time step; splicing the space coordinates of the monitoring points, the environmental parameters, the hydrogen leakage concentration sequence of the current time step and the corresponding time into an input vector; performing feature extraction on the input vectors to obtain spatial correlation features; and mapping the spatial correlation characteristics to obtain a hydrogen leakage concentration sequence of the next time step. According to the method, high prediction precision is maintained, the real-time performance of prediction is remarkably improved, and the method is particularly suitable for a safety monitoring scene of a high-pressure hydrogen storage facility.

The invention discloses a greenhouse gas concentration time sequence prediction method based on a sudden change perception attention mechanism. The method comprises the steps of data preprocessing, sudden change intensity sequence construction with boundary processing, time sequence feature coding, sudden change perception attention weight calculation, context vector generation and concentration prediction, model training and optimization and model prediction. The method aims to solve the problem that a standard deep learning model is slow in sensing and lagged in prediction for a sudden change event in a concentration sequence, and finally realizes high-precision prediction for future concentration change, especially a sudden concentration peak value by endowing the model with the capability of actively identifying and reinforcing the learning of a historical sudden change mode. The urgent demand for early warning of abnormal emission in practical application is met. The method is particularly suitable for processing foundation observation data with small resolution and even higher resolution, has the core value of improving the prediction capability of concentration dramatic change driven by sudden emission events, and can be widely applied to key scenes such as accurate carbon emission monitoring, environmental pollution early warning and climate model simulation.

The invention provides a water chlorophyll a concentration inversion method based on a hyperspectral remote sensing image, which relates to the technical field of remote sensing inversion and comprises the steps of image preprocessing, water pixel extraction, actually measured sample space registration, spectral feature construction, model score optimization, concentration prediction and the like. According to the method, an optimal scheme is screened in a feature-model combination through cross validation and a unified scoring function, the optimal scheme is applied to whole image calculation, and a chlorophyll a concentration spatial distribution map and a matched quality control map layer are output. The method has the characteristics of high precision, self-adaption and high engineering practicability, and is suitable for a water quality remote sensing inversion scene driven by multi-source hyperspectral data.

The invention discloses an LIBS rare earth multi-element quantitative detection method based on a deep learning model, and the method comprises the steps: constructing a multi-scale feature fusion deep learning architecture MSFF-Net, carrying out the weighted fusion of local features extracted by a 1D-CNN and long-range dependence features of BiLSTM modeling through a channel attention mechanism, and enhancing weak signal features in combination with a first-order derivative spectrum. Meanwhile, a synthetic data enhancement strategy based on a physical model is adopted to simulate a matrix effect and a spectral line overlapping scene, and spectral feature migration under different matrixes is achieved in cooperation with a matrix self-adaptive migration learning strategy. And after data acquisition and preprocessing, a trace element concentration prediction value is synchronously output through a multi-task learning framework. According to the method, the detection precision and the anti-interference capability of the LIBS technology on trace elements are effectively improved, and the method has remarkable application value in the industrial fields of mineral resource exploration, strategic metal recovery and the like.

The invention discloses a fermentation strain yield prediction method based on artificial intelligence, particularly relates to the field of biological fermentation production, and is used for solving the problems that in the prior art, due to the fact that the yield is difficult to directly map in concentration prediction, monitoring indexes are normal, the final yield deviates from expectation, and reliable strain comparison and production management decision cannot be supported. A historical batch process observation sequence and a material account sequence are aligned to generate a conversion efficiency characterization sequence to form a yield learning sample training yield prediction model, and the conversion efficiency characterization sequence is generated on batches and input into the model to obtain a yield prediction value. And calculating an accounting volume trajectory consistency parameter and a conversion contribution steady-state proportion parameter, inputting the parameters into a coefficient analysis model to obtain a decision credible coefficient, generating a yield risk judgment result for a yield prediction value based on the decision credible coefficient, and realizing a new-batch online prediction record.

The invention discloses a river total phosphorus concentration prediction method based on an XGBOOST model. The method comprises the following steps: obtaining river basin water quality data provided by an online water quality monitoring station; preprocessing the water quality data of the river basin; constructing a data set; performing total phosphorus concentration prediction by using an XGBoost regression model; basic parameters of the prediction model are initialized and trained, and SHAP analysis is introduced; and inputting real-time data to be measured into the trained prediction model, and outputting a prediction result. The system comprises a data set construction module, a model construction module, a model training module and a model application module. By using the method, high-precision prediction of the total phosphorus concentration of the river basin is realized. The method can be widely applied to the field of water quality monitoring.

The invention provides a thermal power plant SCR denitration system outlet NOx concentration prediction method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the outlet NOx concentration prediction of a thermal power plant SCR denitration system based on a pre-established TTAO-CNN-LSTM model; the step of pre-establishing the TTAO-CNN-LSTM model comprises the steps that the historical NOx concentration and a plurality of historical SCR inlet factors are preprocessed; performing model input dimension reduction processing on the plurality of historical SCR inlet factors after preprocessing; a CNN-LSTM prediction model is constructed based on the multiple preprocessed historical SCR inlet factors subjected to dimension reduction processing and the preprocessed historical NOx concentration after model input; and setting an optimization range of a neural network hyper-parameter, carrying out optimization by adopting a triangular topology aggregation algorithm, and automatically updating an optimal value of the hyper-parameter to the CNN-LSTM prediction model to obtain a TTAO-CNN-LSTM model. The accuracy of predicting the NOx concentration at the outlet of the SCR denitration system of the thermal power plant is improved to a great extent, and automatic optimization of parameters is realized.

The invention discloses a PM2.5 concentration prediction method and device, a medium and a product, and relates to the technical field of atmospheric environment monitoring and deep learning prediction.The method comprises the steps that laser radar vertical profile data containing extinction coefficients and depolarization ratio vertical profiles and a scalar time series data sequence are obtained; converting the laser radar vertical profile data sequence into a vertical sequence containing position information at any moment; adopting a multi-channel spatial feature extraction model, and obtaining a multi-channel spatial feature vector based on the vertical sequence; and after the scalar time sequence data sequence and the multi-channel spatial feature vector are spliced to obtain a fused feature vector, a PM2.5 concentration prediction value is obtained by adopting a time sequence prediction model. According to the method, the technical pain point caused by the fact that aerosol types cannot be distinguished and'high altitude-ground 'cross-layer physical association cannot be captured in the prior art is solved, so that accurate and robust prediction of the PM2.5 concentration on the ground is realized under complex weather conditions such as sand and dust transportation and boundary layer evolution.

The invention discloses a microfluidic nitration reaction platform based on in-situ Raman and a reaction optimization method. The platform integrates a microreactor, an in-situ Raman detection module, a spectrogram processing and quantifying module, an experimental optimization module and a central control module, and is suitable for monitoring and optimizing complex reactions under a non-mutual solution phase plunger flow system. According to the method, an indirect hard modeling (IHM) method is used for processing an overlapped Raman spectrogram, a standard curve is combined for establishing a multi-component concentration prediction model, and high-precision quantification of reactants and products is realized. According to the platform, a Gaussian process model is combined with dynamics prior, a multi-target Bayesian optimization process based on expected hypervolume improvement (EHVI) is driven, and Pareto optimal conditions in a temperature and residence time space are effectively explored. The system has the functions of automatic focusing sampling, spectrogram real-time decoupling, concentration inversion and experimental condition decision making, and is suitable for intelligent reaction development and optimization in the fields of fine chemical engineering, high-throughput reaction screening and the like.