191 results about "Concentration prediction" patented technology

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 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 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 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 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 soil and groundwater pollution diffusion prediction method and system based on a time-space diagram neural network, and relates to the crossing field of environmental information modeling and artificial intelligence, and the prediction method comprises the following steps: S1, obtaining multi-source soil and groundwater monitoring data and external driving data; s2, generating a static hydraulic communication matrix and a dynamic convection dominant matrix, and constructing a multi-relation dynamic graph structure; s3, performing parallel processing on graph convolution operation of the static hydraulic connection matrix and the dynamic convection dominant adjacency matrix, capturing time dynamic characteristics, and obtaining a pollutant concentration prediction result; and S4, outputting structured prediction data and a visual product. By constructing a multi-relation dynamic graph structure fusing a hydrogeological physical mechanism and a dynamic flow field response, distortion of a pollution plume leading edge deduction trajectory caused by misjudgment of a spatial dependency relation can be avoided, so that the prediction precision of a non-uniform anisotropic pollution diffusion path is improved.

The invention provides a sediment concentration prediction method and system based on a freeze-thaw coupling physical constraint model, and relates to the technical field of sediment concentration prediction, and the method comprises the steps: obtaining hydrological data, meteorological data and various original data of soil and vegetation, and carrying out the preprocessing; constructing a benchmark sediment concentration physical model based on the multiple kinds of preprocessed original data, and constructing an input feature vector; inputting the input feature vector into a sediment concentration prediction ANN model, and outputting to obtain the sediment concentration; wherein in the training process of the sediment concentration prediction ANN model, reference sediment prediction and sediment mass conservation constraint of the physical model are introduced into an ANN model loss function, the loss function is enhanced by adopting a freeze-thaw weighting strategy, and the sediment concentration prediction ANN model in which the physical model and the ANN model are deeply fused is obtained. According to the invention, the stability and accuracy of prediction during extreme rainfall, runoff surge or frequent freezing and thawing are improved.

The invention relates to the technical field of spectral analysis and biomedical detection, and discloses an SERS (Surface Enhanced Raman Scattering) spectrum quantitative detection method and system based on interpretable stacked ensemble learning. The method comprises the following steps: acquiring SERS spectral data of serum tumor marker standard substances with different concentration gradients; performing baseline correction and normalization preprocessing on the data, performing sparse feature selection by using an LASSO algorithm, and screening out key spectral features to construct a sample data set; constructing an interpretable stacking integration model, wherein the model adopts a base learner layer and a meta learner layer; training the model by using the training set, optimizing model hyper-parameters by using a cross validation strategy, and establishing a mapping relationship between spectral features and tumor marker concentrations; and collecting SERS spectral data of a to-be-detected serum sample, extracting key spectral features, inputting the key spectral features into the trained interpretable stacked integrated model, and outputting a concentration predicted value of the tumor marker in the to-be-detected serum sample. The method has the advantages of high precision, universality and molecular level interpretability.

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 closed-loop control system and method based on deep-sea ore raising pipe ore pulp concentration monitoring, and the system comprises a water surface support platform control center which is used for outputting a control strategy; the hydraulic ore injector is used for receiving the control strategy and carrying out self-adaptive adjustment according to the control strategy; wherein the water surface support platform control center comprises an intelligent control unit and a controller, and the intelligent control unit is used for collecting operation parameter data of the hydraulic ore injector, processing the operation parameter data and predicting in real time to obtain a solid phase volume concentration predicted value; the controller is used for receiving the solid phase volume concentration predicted value and determining a control strategy according to the solid phase volume concentration predicted value; the intelligent control unit comprises a data acquisition module, a data processing module and a prediction module; the concentration of ore pulp in the pipeline can be more accurately controlled through the system, and the system can better adapt to complex working conditions such as submarine topography and ore supply quantity fluctuation.

The invention discloses a physical constraint-based multi-mode indoor carbon dioxide concentration prediction method, which comprises the following steps of: acquiring time sequence environment data of indoor carbon dioxide concentration, temperature and humidity, and obtaining indoor panoramic image data; performing time sequence feature extraction on the time sequence environment data, and extracting image high-level visual features through a pre-trained image model; in the multi-modal fusion process, a conflict decoupling gating fusion network is introduced to detect and suppress multi-modal time conflicts between static visual information and dynamic environment parameter changes, and contribution weights of an environment modal and a visual modal are dynamically adjusted through a self-adaptive gating mechanism; a time-delay perception cross-modal attention network is introduced to predict and model a physical delay characteristic between a visual event and an environmental parameter response, and cross-modal causal alignment is realized through lag distribution prediction and a weighting mechanism; and inputting the multi-modal features after conflict decoupling and time-lag alignment into a prediction network, and outputting a future indoor carbon dioxide concentration prediction result.

The invention discloses a sodium aluminate solution concentration prediction method and system based on dynamic weight, a processor and a storage medium, and belongs to the field of alumina production, S1, industrial process parameters are input into an improved Transform-based data-driven sodium aluminate solution concentration prediction sub-model, and a preliminary concentration prediction value is output; s2, outputting an error compensation value by using the sequence error prediction sub-model, and taking the error compensation value as a correction value of the initial concentration prediction value to obtain a corrected concentration prediction value; s3, utilizing the mechanism prediction sub-model to output a concentration prediction value under the physical constraint; and S4, carrying out dynamic weight fusion on the corrected concentration predicted value and the concentration predicted value under the physical constraint to obtain a final concentration predicted value. By the adoption of the sodium aluminate solution concentration prediction method and system based on the dynamic weight, the processor and the storage medium, high precision, high robustness and working condition adaptability of sodium aluminate solution concentration prediction are achieved.

A water pollutant detection method based on absorption-electrochemical characteristic fusion belongs to the technical field of environmental monitoring, and comprises the following steps: respectively acquiring absorption spectrum data and current-potential data through a spectrometer and an electrochemical workstation, highlighting key peak position characteristics by using an attention mechanism, and inhibiting redundancy and noise interference; extracting local and global features of the two types of signals by using a convolutional neural network, and splicing the local and global features to obtain joint representation; and cross-modal feature fusion is realized through a dynamic routing mechanism of the capsule network, and a spatial relationship and a hierarchical structure between features are captured. And finally, constructing a classification and regression double-branch output structure to realize pollutant type identification and concentration prediction. Compared with an existing single detection technology, the detection sensitivity of the low-concentration pollutants can be remarkably improved, the distinguishing problem of peak value overlapping is effectively solved, the selectivity and robustness of the system are enhanced, and therefore high-precision integrated detection of the water pollutants is achieved.

The invention discloses an odor concentration prediction method and system based on graph neural network and Monte Carlo coupling, and the method comprises the steps: obtaining the original data of the historical wind direction and wind speed of a meteorological station, carrying out the data preprocessing through a self-adaptive Monte Carlo algorithm based on Sobol global sensitivity, and obtaining a working condition directory data set; performing wind direction grouping on the working condition directory data set to obtain standardized meteorological data; based on the standardized meteorological data, constructing a graph data basic structure, obtaining two-dimensional node-level features, and determining species concentration; constructing an odor concentration prediction neural network model; and based on the odor concentration prediction neural network model, predicting the concentration of the species, and outputting an odor concentration prediction result. By considering the physical conservation of the vertical flux and the concentration, high-precision and real-time prediction of peculiar smell diffusion is realized. The peculiar smell concentration prediction method and system based on the graph neural network and Monte Carlo coupling can be widely applied to the technical field of peculiar smell concentration prediction.

The invention discloses a PM2.5 depth prediction method and system fused with a physical mechanism, and belongs to the technical field of PM2.5 concentration prediction.The method comprises the steps that spatial dynamic characteristics of expected changes of pollutants in space are extracted through a differential equation network based on a GCN; time dynamic features are extracted through a bidirectional time perception Mama model; according to the extracted spatial dynamic features and the time dynamic features, a feature fusion module is used for fusion to obtain a spatial-temporal feature fusion model; training a spatio-temporal feature fusion model by using the training set of the PM2.5 historical concentration; and performing prediction by adopting the trained spatial-temporal feature fusion model to obtain the PM2.5 concentration in the target area. According to the invention, the PM2.5 prediction precision can be improved.

The invention discloses a hyperspectral inversion method for the concentration of sodium sulfate in a mural with consideration of temperature and humidity, and belongs to the technical field of cultural relic protection and spectral analysis. According to the method, simulated mural samples with the sodium sulfate concentration of 0%-1% and the temperature and humidity ranges of-14 DEG C to 38 DEG C and 15% RH to 100% RH are manufactured, spectral reflectivity data of 350 nm to 2500 nm are collected through a ground feature spectrometer, and environmental parameters are recorded; after the data are preprocessed, characteristic wave bands are screened based on Pearson correlation analysis, a multivariable data set containing reflectivity, temperature and humidity of the characteristic wave bands is constructed, a training set and a test set are divided, and a prediction model is established by adopting a regression algorithm; and finally, inputting the characteristic wave band reflectivity and the real-time temperature and humidity of the mural to be detected, and outputting a sodium sulfate concentration predicted value. The method can accurately invert the concentration of sodium sulfate in the mural, and provides technical support for salt damage monitoring and protection of the mural.

The invention belongs to the technical field of water environment monitoring and early warning, and discloses a lake and reservoir chlorophyll a concentration prediction method based on an SO-KNN model. According to the invention, multi-time scale meteorological cumulative effect features are introduced to enrich information representation, and an SO-KNN intelligent prediction model is constructed. According to the method, under the conditions of data scarcity and non-equilibrium, the chlorophyll a concentration, especially the high-precision and strong-generalization prediction capability of the water bloom risk critical point, is remarkably improved. The model is simple in structure and efficient in calculation, the common defects of overfitting, insufficient generalization ability and the like of a complex machine learning model in the scene are effectively overcome, and a reliable and practical innovative technical solution is provided for early water bloom warning of northern reservoirs and water areas with similar data conditions.

The application discloses a tunnel gas concentration intelligent prediction system and method based on deep learning, belongs to the technical field of tunnel gas concentration prediction, can effectively capture the dramatic abnormal fluctuation of gas concentration caused by geological structure mutation, and improves the prediction accuracy; the method comprises the following steps: collecting original gas concentration sequences of each monitoring point in a tunnel and geological structure mutation parameters; dynamically adjusting the length of a time window according to the geological structure mutation parameters, adaptively fragmenting the original sequences, and generating variable-length concentration segmented sequences; merging the variable-length concentration segmented sequences into a multi-monitoring-point concentration segmented sequence matrix; constructing a dynamic weight gas concentration spatial relationship fusion graph based on the spatial positions of the monitoring points and the distribution of the geological structure mutation zones; inputting a trained graph neural network to extract dynamic spatial relationship representation vectors of the monitoring points; inputting a trained recurrent neural network encoder again to generate space-time features containing the influence of geological mutations; and finally, outputting the gas concentration in the prediction period through variable-length prediction decoding.

The application discloses a continuous acquisition and data model construction system and method of an intelligent sensor. In order to overcome the problems that the training data acquisition of the chemical sensor in the prior art is inconsistent with the actual application, the data acquisition efficiency is low, and effective data is insufficient, in the training process, the sensor unit is placed in the test unit, and the concentration control unit is used to adjust the concentration of different samples, so that the data is continuously acquired. In the prediction process, the sensor unit is placed in the sample input unit, is input into the concentration prediction model after correction by the state calibration unit, and the sample concentration output is obtained. The continuous data sampling method is used, the recovery process is not performed, and the balance state is reached in a relatively short time, so that the training time is greatly reduced; the response and recovery stage data are continuously acquired, so that the training data is consistent with the signal and concentration change process in the actual application; in the prediction process, the state of the sensor is dynamically detected and different models are called, so that the model accuracy is improved.

The invention discloses a chlorogenic acid component detection method fused with deep learning, and relates to the technical field of chlorogenic acid. The method comprises the following steps: acquiring a chlorogenic acid hyperspectral image; clustering the chlorogenic acid hyperspectral image through a fuzzy C-means clustering algorithm based on a plurality of subclass centers to obtain a chlorogenic acid hyperspectral cluster, and calculating a chlorogenic acid spectral cluster feature vector; the method comprises the following steps: calculating mutual information of chlorogenic acid hyperspectral cluster feature vectors to obtain hyperspectral cluster feature weights; obtaining a chlorogenic acid hyperspectral cluster weighted vector by combining the chlorogenic acid hyperspectral cluster feature vector and the hyperspectral cluster feature weight; inputting the chlorogenic acid hyperspectral cluster weighted vector into a support vector machine regression model based on high-dimensional multiple scales, and outputting to obtain a chlorogenic acid concentration predicted value; and constructing a chlorogenic acid concentration diagram according to the chlorogenic acid concentration predicted value, calculating a concentration abnormal value through a sliding window method, and performing early warning if the concentration abnormal value is greater than a preset threshold value.

The invention provides an underflow concentration self-adaptive tailing thickening device and method based on deep learning, and relates to the technical field of paste filling. The device comprises a material conveying and distributing system, a thickener main body device, a material mixing and homogenizing system and a data integration and control system, the material conveying and distributing system conveys materials to the material mixing and homogenizing system, the materials enter the thickener main body device after being uniformly stirred, and the materials are collected into a material collecting pool after being subjected to the rotary shearing action of a rake frame; discharging the materials which meet the requirements, and returning the materials which do not meet the requirements to the cylinder of the thickener from the circulating tank; and the data integration and control system monitors and dynamically regulates and controls the slurry concentration in real time. According to the method, the stability of the underflow concentration, personalized concentration prediction and accurate regulation and control are realized, the technical bottleneck of continuous thickening of all tailings is overcome, reliable technical support is provided for a paste filling process, and reference is provided for introducing an intelligent solution to an industrial thickener.

The application discloses a SO2 concentration prediction method based on a two-stage attention and a two-flow memory adjusted GRU, belongs to the field of industrial process soft measurement, and comprises the following steps: after a DS-MRGRU network is constructed, feature attention is introduced in a feature extraction stage to capture key features and inhibit irrelevant features and noises; and in a prediction output stage, timing attention is introduced to capture long time sequence dependency relationship, so that an FTA-DS-MRGRU soft measurement model with enhanced key information extraction capability is constructed for predicting the SO2 content of net flue gas. The application extracts historical information and key features of current input by means of the DS-MRGRU to generate complementary information flow, enhances the generalization capability and prediction performance of the model, inhibits redundant information by fusing feature attention and timing attention, improves the model interpretability, mines important time information in history, improves the long time sequence prediction effect, and realizes accurate prediction of the SO2 content of net flue gas.

The application discloses a kind of fault prediction method, device, equipment and computer readable storage medium, applied to fault detection technical field, the method includes: obtaining the first concentration sequence of methane in transformer, the second concentration sequence of ethylene and the third concentration sequence of acetylene;According to the first concentration sequence, the second concentration sequence, concentration sequence, respectively, concentration prediction is carried out, determines the first predicted concentration value of methane, the second predicted concentration value of ethylene and the third predicted concentration value of acetylene at future time point;In the case where at least one of first predicted concentration value, second predicted concentration value and third predicted concentration value exceeds corresponding preset threshold value, using three-ratio method, according to first predicted concentration value, second predicted concentration value and third predicted concentration value, fault diagnosis is carried out on transformer.And current model input multiple parameters for fault diagnosis, resulting in high fault prediction complexity, compared with the input parameter of the present application is single, so it can reduce the prediction complexity.

The invention provides a method for quantitatively detecting arsenic element in ciliate desert-grass leaves, which comprises the following steps: preparing a sample tablet of the ciliate desert-grass leaves, collecting LIBS spectral data of the sample tablet, obtaining the true value of the arsenic element content of the sample tablet, and constructing an original spectral view angle, a first derivative view angle and a second derivative view angle of the LIBS spectral data. The method comprises the following steps: preliminarily establishing a ciliate desert-grass leaf arsenic element quantitative model by adopting a table priori data fitting network model, introducing spectral physical priori constraint for an original prediction result of the table priori data fitting network model at each view angle, establishing a multi-view prediction result fusion model, and generating a final arsenic element concentration prediction result. According to the method, the respective advantages of the three perspectives can be combined, the sensitivity and stability of arsenic content detection are remarkably improved, the method can be applied to rapid detection of arsenic elements in the leaves of the ciliate desert-grass, and an efficient and reliable technical means is provided for ciliate desert-grass arsenic element absorption mechanism research, pollution remediation efficiency evaluation and treatment strategy optimization.

The invention provides a PM2.5 concentration prediction method fusing an STL-VMD decomposition mechanism and an IBKA-TCN-ITransform deep learning model, and the PM2.5 concentration prediction method comprises the following steps of: firstly, carrying out deep learning on the STL-VMD decomposition mechanism; firstly, pollutants and meteorological data are acquired and preprocessed, and then features are screened by using Pearson's correlation coefficients. STL-VMD decomposition is carried out on PM2.5 data to obtain a plurality of intrinsic mode components IMF, a K-MEANS algorithm is adopted to obtain four components with different frequencies, and the four components are spliced with other features to construct a sub-data set. According to the method, an independent TCN-ITransform model is adopted for training, and hyper-parameters are optimized through an IBKA algorithm. And finally, fusing a prediction result of the model to obtain a PM2.5 concentration prediction value, and evaluating the model performance by calculating a model performance evaluation index and combining SHAP interpretability analysis. According to the method, the capturing capacity of the time sequence dependence and variable cooperation relation in the PM2.5 data set is enhanced, meanwhile, the focusing capacity of key information is improved, the convergence speed and PM2.5 prediction precision are high, and reliable technical support is provided for air quality early warning.

The invention discloses a pollutant concentration prediction method and a pollutant concentration prediction system. The pollutant concentration prediction method comprises the following steps: acquiring urban pollutant data in a statistical area; determining the pollutant concentration in the statistical area according to the acquired urban pollutant data in the statistical area; determining an annual pollutant concentration predicted value in the region according to the pollutant concentration in the statistical region; according to the annual pollutant concentration predicted value in the region and the pollutant constraint index, determining the annual pollutant concentration residual control value in the current year; and performing graded early warning according to the control values of the remaining time periods. According to the method, the achievement condition of the PM2.5 concentration of a city in a specific area in a future long-time sequence can be scientifically and objectively predicted in a quantified mode, the PM2.5 concentration control reference value in the remaining time period required for ensuring target achievement can be synchronously calculated, and timely and quantified action guidance is provided for dynamic regulation and control measures.

The invention discloses a coal mine gas concentration abnormal fluctuation identification and prediction method based on a wavelet transform frequency diagram and a CNN-LSTM fusion model. The method is specifically implemented according to the following steps: step 1, data acquisition and preprocessing; step 2, constructing a wavelet transform frequency diagram; step 3, constructing and training a CNN-LSTM fusion model; 4, identifying abnormal fluctuation; and 5, concentration prediction and early warning. The invention further discloses a coal mine gas concentration abnormal fluctuation recognition and prediction system based on the wavelet transformation frequency diagram and the CNN-LSTM fusion model. The problems that in gas concentration analysis and early warning in the prior art, abnormal fluctuation capture is poor, feature extraction is single, the model fusion degree is low, and the misjudgment rate is high are solved.

The coal mining face gas concentration prediction method based on a multi-factor generalized linear regression belongs to the technical field of coal mine gas detection, solves the problems that a traditional safety monitoring system can only passively monitor gas concentration, cannot early warning analysis, and previous big data prediction can only analyze single sensor historical data, and data accuracy is poor; the present application is based on coal seam thickness, gas extraction amount, daily output, wind speed, T0 methane sensor gas concentration and the T2 methane sensor gas concentration to be predicted, establishes a multi-factor generalized linear regression model, can process a large amount of historical data, can effectively mine the corresponding linear relationship from each related factor affecting the T2 methane sensor concentration, is beneficial to accurately predicting the methane concentration, so as to achieve the effect of predicting the methane concentration of the coal mining face return airway, early understanding the gas concentration change trend, and taking control measures in advance.

The invention discloses a garbage incinerator HCl concentration prediction method based on a hybrid model, and the method comprises the following steps: obtaining historical operation data containing all operation parameters from a garbage incinerator DCS system, and carrying out the preprocessing of the historical operation data; calculating an MI value between each preprocessed operating parameter and a target value HCl concentration by using a mutual information MI method, selecting a plurality of variables as input feature variables according to the MI value, and standardizing the variables as original input features of a feature extractor; the standardized feature variables are input into a Transform feature extractor, and a feature extraction result and an HCl concentration auxiliary prediction result are output; performing feature fusion on the original input features, the feature extraction result and the HCl concentration auxiliary prediction result; and inputting the fused characteristic variables into an XGBoost predictor to predict the HCl concentration, and outputting a final HCl concentration predicted value after the HCl concentration is subjected to anti-standardization.