618 results about "Data dimensionality reduction" patented technology

The invention relates to the technical field of data processing, and discloses a multi-dimensional financial pressure testing and financial early warning method and system. The method comprises the following steps: forming a financial situation awareness data set through multi-dimensional data acquisition integration and standardization processing; adaptive weight distribution and data dimension reduction are carried out on the five dimension indexes, and a risk measurement standard spectrum is output; a third-level pressure scene is established according to the standard spectrum, a risk conduction path is analyzed, and a financial pressure fluctuation spectrum is generated; a deep learning early warning model is constructed based on the fluctuation pedigree, an early warning threshold value is determined, and a risk pulsation graph is formed; constructing a multi-level early warning system based on the pulsating diagram, and establishing a financial early warning intelligent chain; and matching historical cases based on the early warning intelligent chain, designing a prevention and control strategy, and constructing a risk solution scheme library. According to the invention, by constructing a multi-level risk assessment system, accurate identification, dynamic monitoring and intelligent disposal of financial risks are realized, and the accuracy and timeliness of risk early warning are improved.

The invention relates to the technical field of medicine quality management, in particular to a medicine quality management method and system based on big data and artificial intelligence, and the method comprises the steps: obtaining the quality influence parameters of a medicine product in the production, transportation and storage processes through a multi-modal data collection device; noise is removed through data cleaning, data dimension reduction is carried out through principal component analysis and an auto-encoder, the analysis efficiency is improved, and the efficiency problem of mass data processing of a traditional method is solved. Time sequence analysis is carried out through the long-short-term memory network, abnormal nodes are recognized in time, manual sampling inspection is replaced, and accurate early warning is achieved. The graph neural network constructs a path tracking model, analyzes the relevance between nodes, traces the quality problem, and reckons the batch of affected drugs. Based on a traceability result, an XGBoost model generates quality risk scores of all links by analyzing historical and real-time data and sets a threshold value, and quality risks are early warned in real time.

The invention provides a tunnel safety-oriented safety step pitch monitoring method and system, and the method comprises the steps: deploying an edge calculation module at a sensor node, carrying out the preprocessing and compression of collected monitoring data, reducing the data transmission amount through data dimension reduction and feature extraction, relieving the data transmission pressure, and improving the data transmission efficiency and real-time performance. An incremental learning algorithm is adopted to carry out online analysis on monitoring data, an anomaly detection model is dynamically updated according to a newly collected data sample, an early warning threshold value is adaptively adjusted, the accuracy and the real-time performance of anomaly recognition are improved, and misinformation and missing report are reduced; a multi-sensor data fusion technology is comprehensively applied, correlation analysis is carried out on sensor data of different types and different positions, the reliability and accuracy of monitoring data are improved through data redundancy and complementation, and comprehensive evaluation of the tunnel construction safety state is achieved.

The present invention provides an integrated hyperspectral water quality analysis method, which belongs to the field of hyperspectral water quality analysis. First, data preprocessing is conducted by water quality data collection and water quality image collection in early stage; second, three dimensionality reduction methods are adopted to conduct dimensionality reduction processing, and fused dimensionality reduction is conducted by parameter trade-off selection; third, machine learning algorithms are adopted to train and test hyperspectral water quality inversion models on spectral data after dimensionality reduction; finally, the hyperspectral water quality inversion models are selected and optimized. The present invention adopts an innovative fusion strategy in the aspect of data dimensionality reduction processing, which can achieve a better data dimensionality reduction effect, effectively remove noise and redundant information, and provide a more accurate and reliable data basis.

The present invention belongs to the field of new-energy power prediction of electric power systems. Disclosed is a short-term wind-photovoltaic power prediction method based on high-dimensional multivariate meteorological data fusion. In the present invention, an acquired meteorological data set ERA5 is fully utilized, a massive number of meteorological factors are greatly expanded in terms of physical causality, spatial scales, and temporal scales, and characteristic relationships between the meteorological factors and new-energy power are fully explored, thereby improving the prediction accuracy. Power-related meteorological factors are greatly expanded by using a physical causality method and a spatial longitude and latitude envelope method, so as to form a high-dimensional multivariate meteorological factor set. Dimensionality reduction is performed on ultra-high-dimensional meteorological data for comprehensive correlation analysis and fusion to prevent correlations and information overlaps between multi-dimensional meteorological data from affecting a power prediction model; and then, a sliding time window method is used to form a power prediction sample set. The prediction model is fitted according to meteorological data and actual power station operation data, and is applied to actual engineering projects in Dali, Yunnan for verification. The prediction accuracy of the present invention far exceeds that of conventional prediction methods, thus showing relatively strong robustness and universality.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention discloses an electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

The invention provides a matrix asphalt consistency rapid identification method based on a data dimension reduction algorithm, and the method comprises the steps: carrying out the factor analysis dimension reduction processing of a Fourier transform attenuated total reflection infrared spectrum of an asphalt sample based on a data dimension reduction analysis method; preprocessing such as baseline correction, standardization, normalization and abnormal data deduction is carried out on the spectral data, and then a data dimension reduction processing technology is combined with a classification algorithm based on data dimension reduction. According to the method, tedious and time-consuming experimental operation is not needed, and the asphalt brand can be quickly identified on site and the asphalt supply consistency can be monitored in real time, so that the asphalt quality is managed and controlled. According to the method, reliable and rapid asphalt consistency identification and quality control can be realized, and the method also has considerable expandability and can be used for detecting and evaluating the aging condition of matrix asphalt.

The invention provides a cluster network flow prediction method based on multi-scale time feature fusion, and belongs to the technical field of computer network flow prediction. The method comprises the following steps: determining a multi-index prediction sequence based on traffic load characteristics of cluster IP instances, and constructing a high-quality time sequence data set; fourier transform and discrete wavelet transform are used for time-frequency feature analysis, and noise filtering and data dimension reduction are completed; projecting sequences of different time granularities to a unified model dimension, performing one-dimensional channel convolution merging, inputting the merged sequences into a time encoder and a cross-channel encoder, and capturing cross-scale long-term time dependence and a coupling relationship between variables; in the loss function design, time domain and frequency domain loss are fused, double-domain error calculation is carried out on a prediction result and a label through Fourier transform, and the robustness of a model to non-stationary fluctuation is enhanced; and through linear layer decoding and reverse normalization processing, the abstract feature is converted into an actual flow prediction value. According to the invention, the precision and reliability of cluster network flow prediction are significantly improved.

The invention provides a method for carrying out fault judgment based on a computer hard disk state index, and relates to the technical field of computers.The method comprises the steps that a periodic data collection mechanism is set, and multi-dimensional feature data including disk rotation instability, the data transmission rate, the read-write error rate and the like are obtained; carrying out normalization and de-noising processing on the data; then, data dimension reduction is achieved through an auto-encoder, key feature vectors are extracted, and then fault probability calculation is conducted through a support vector machine; on the basis, a threshold fine tuning model is constructed, and the fault probability threshold can be dynamically adjusted according to the relevance between the physical state and the operation state collected in real time and the change trend of the physical state and the operation state; the accuracy and timeliness of fault judgment are improved, the probability of false alarm is reduced through a dynamic adjustment mechanism, and the safety of data storage and the normal operation of the system are ensured.

The invention relates to the field of medical treatment, and discloses a cardiac surgery risk assessment system based on artificial intelligence. The system fuses electrocardiogram, image data and physiological data of wearable equipment, and data quality is ensured through a data preprocessing module; carrying out data dimensionality reduction and nonlinear mapping by using manifold learning and the method, and capturing a relationship between complex physiological signals; the sensor weight is optimized by adopting the maximum entropy principle, and the accuracy of the evaluation result is improved; performing space-time alignment of the multi-modal data based on the Lie group and Lie algebra theories; modeling the fused data by using a machine learning algorithm to generate a risk assessment result; the real-time feedback module dynamically adjusts the evaluation model and system parameters, and the output module displays an evaluation result through a visual interface to assist doctors in accurate decision making. The system improves the accuracy and real-time performance of assessment, and provides scientific and comprehensive risk assessment support for cardiac surgery.

The invention provides a Gaussian modeling method combined with laser scanning point cloud data processing, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: carrying out the registration of a plurality of point cloud data sets through employing a point cloud fusion algorithm, and obtaining a target point cloud data set; performing data precision, position deviation and noise influence analysis on the plurality of point cloud data sets, determining point cloud data precision, a position deviation coefficient and a noise influence coefficient, and comprehensively evaluating and determining point cloud acquisition precision; setting a data dimension reduction proportion according to the point cloud acquisition precision, and performing covariance matrix dimension reduction on the target point cloud data set; and selecting a predetermined kernel function, performing data fitting and Gaussian process regression modeling on the dimension reduction point cloud data set, and outputting a surface fitting model. According to the method, the technical problems that the requirements for storage and computing resources are too high and efficient three-dimensional modeling cannot be realized due to the fact that a large number of covariance matrixes need to be calculated in a traditional Gaussian modeling method can be solved; the dimensionality of data can be effectively reduced, and the efficiency of three-dimensional modeling is remarkably improved.

The invention relates to the technical field of aluminum plate processing, and discloses an aluminum plate processing cleaning intelligent monitoring system, which comprises a three-dimensional deformation sensing module, a surface residue detection module, a thermal stress distribution analysis module, a vibration spectrum identification module, a processing track optimization module, an environmental parameter fusion module, an intelligent cleaning regulation module and the like. The three-dimensional deformation sensing module realizes aluminum plate surface three-dimensional reconstruction and deformation characteristic spectrum generation through an industrial camera and an algorithm; the surface residue detection module identifies residue distribution and classification by using a laser speckle interferometer and the like; the thermal stress distribution analysis module inverts thermal stress by means of an infrared focal plane array; the vibration spectrum identification module analyzes the equipment vibration signal; the processing track optimization module generates an optimized track based on an ant colony algorithm and the like; the environmental parameter fusion module generates a fusion decision index through data dimension reduction and correlation analysis; and the intelligent cleaning regulation and control module combines feedback and learning to optimize cleaning parameters. The system achieves multi-dimensional monitoring and intelligent cleaning regulation and control, and the machining precision and efficiency are improved.

The invention discloses a circuit breaker fault detection optimization method and system based on SVD-XGBoost. The method comprises the steps of obtaining an original vibration signal generated when a circuit breaker is in an operation state; performing data enhancement on the original vibration signal based on an NLMS algorithm so as to adaptively filter noise, and obtaining a time domain signal after data enhancement; constructing an SVD-XGBoost model based on combination of SVD (singular value decomposition) and an XGBoost algorithm, converting an enhanced time domain signal into a frequency domain signal through fast Fourier transform, performing data dimension reduction on the frequency domain signal by adopting singular value decomposition, and extracting target features; according to the method, the extracted target features are input to the trained SVD-XGBoost model, the optimized network is adopted to carry out fault classification on the circuit breaker vibration signals, a circuit breaker fault identification result is obtained, SVD dimension reduction is introduced to convert high-dimensional features into low-dimensional features, the number of the features is reduced, the calculation efficiency is improved, and the fault classification precision and effect are improved.

The invention discloses an integrated hyperspectral water quality analysis method and belongs to the field of hyperspectral water quality analysis. The method comprises the following steps: firstly, acquiring early-stage water quality data and water quality images, and preprocessing the data; secondly, three dimension reduction methods are adopted to carry out dimension reduction processing, and fusion dimension reduction is carried out through parameter tradeoff selection; thirdly, performing hyperspectral water quality inversion model training and testing on the dimension-reduced spectral data by adopting a machine learning algorithm; and finally, selecting and optimizing a hyperspectral water quality inversion model. According to the method, an innovative fusion strategy is adopted in the aspect of data dimension reduction processing, a better data dimension reduction effect can be achieved, noise and redundant information are effectively removed, and a more accurate and reliable data basis is provided; in the aspect of model training and testing, multiple machine learning algorithms are used to train and test the dimension-reduced spectral data, and the most suitable method is selected according to various indexes to train and predict the model, so that the advantages of different algorithms can be fully utilized, and the accuracy and reliability of water quality analysis are improved.

The present application relates to the technical field of financial risk management, and in particular to a big data-based financial risk assessment and control system. By means of acquiring and preprocessing multi-source financial data of an enterprise, comprising data cleaning, data fusion, and data dimensionality reduction, the system generates standardized financial data. On the basis of the standardized financial data, constructing a multi-dimensional risk assessment model, comprising a market risk assessment model, a credit risk assessment model, an operation risk assessment model and a compliance risk assessment model. Using the multi-dimensional risk assessment model to perform risk assessment, generating a risk assessment report, and according to the report, generating a financial risk management suggestion, thereby implementing real-time alert and dynamic adjustment. The present invention improves the comprehensiveness and accuracy of financial risk assessment, achieves real-time monitoring and dynamic adjustment of financial risks, and improves the integration and reliability of the system.

The invention discloses an autonomous inspection and homeward voyage control method and system for a blow-off pipeline robot, belongs to the technical field of intelligent pipeline detection and robot control, and aims to solve the technical problems of how to realize a stable, reliable and continuous clogging exploration task in a complex blow-off pipeline environment and realize safe self-rescue in an abnormal state. According to the technical scheme, the method comprises the following steps: data dimension reduction: receiving high-dimensional environment and motion state data from a multi-modal sensor, performing real-time dimension reduction on the high-dimensional environment and motion state data by adopting a dynamic principal component analysis method, extracting key features of state information of a robot and internal state information of a pipeline, and constructing a low-dimensional state vector; performing deep reinforcement learning control based on a liquid neural network: modeling dynamic environment state change by using the liquid neural network, and realizing path planning and control in combination with a deep deterministic strategy gradient algorithm; tracing the track; and a return flight strategy based on a graph attention network and reinforcement learning.

The invention provides a wind power plant dynamic inertia partition evaluation method based on improved clustering, and relates to the technical field of wind power plants. The method comprises the following steps: taking a Canopy clustering algorithm as a preprocessing step through improved Canopy-Kmeans; the mean value point of the collected data set is calculated, and the measuring point closest to the mean value point is selected as the initial clustering center, so that the result is more reasonable and stable. The grouping precision is improved by considering the wake effect and the time delay effect and measuring multi-dimensional wind turbine generator state variables, in order to avoid a large amount of data dimensionality disasters, PCA principal component analysis is adopted for data dimensionality reduction, components with the variance contribution rate being 85% or above are selected as dominant variables, and the complexity of data collection is reduced. According to the method, the improved Canopy algorithm and the k-means algorithm are combined, the optimal classification number k value and the clustering center can be recognized more accurately, and then large-area wind turbine generator partitioning is achieved quickly and accurately.

The invention relates to the technical field of digital economy, in particular to a visual display method based on multi-source economic data mining. The method comprises the following steps: acquiring multi-source e-commerce economic data; performing data cleaning and standardization processing on the multi-source e-commerce economic data, and performing data quality evaluation and screening to obtain standardized e-commerce economic data; performing multi-dimensional data dimension reduction analysis according to the standardized e-commerce economic data, and performing feature importance sorting to obtain key economic index data; and performing time sequence analysis on the standardized e-commerce economic data according to the key economic index data, and performing sales trend and user behavior prediction based on key economic indexes through a plurality of preset deep learning models to obtain trend prediction data. According to the method, the interpretive data generation of the economic index relation network is realized in combination with the economic knowledge graph, and the interpretability of data analysis is enhanced.

The invention discloses a communication equipment fault intelligent diagnosis method and device, equipment and a medium. The method comprises the steps of collecting equipment operation logs in real time, and extracting an abnormal point set; clustering the abnormal points, and matching a hardware fault, a software error and a network congestion type in combination with a preset mode library; dimensionality reduction is carried out on temperature, voltage and current sensor data based on principal component analysis, and a time-synchronized fusion feature vector is constructed; analyzing features and abnormal modes, and outputting potential fault points; associating the classification model to generate a fault priority by extracting the dynamic characteristics of the network flow; and a dependency network is constructed in combination with Bayesian reasoning, so that accurate fault positioning is realized. The system supports incremental learning, updates an abnormal mode library and automatically expands a knowledge base. Through multi-dimensional data fusion and spatio-temporal feature joint modeling, the problems that a traditional method depends on manual rules and is high in false alarm rate are solved, and the method is suitable for complex scenes such as a 5G base station and a data center.

The invention relates to the technical field of identity authentication, and discloses an identity authentication method and system based on a large-model multi-mode venue, and the method comprises the steps: obtaining a fingerprint image, a face image and behavior interaction data of a user; performing fingerprint feature extraction according to the fingerprint image to obtain a fingerprint feature vector; performing facial feature extraction according to the facial image to obtain a facial feature vector; performing feature fusion on the fingerprint feature vector and the face feature vector, and generating a fusion feature vector after data dimension reduction; performing behavior feature extraction according to the behavior interaction data to obtain a behavior feature vector; and after cross-modal mapping is carried out according to the fusion feature vector and the behavior feature vector, matching degree analysis is carried out in a preset unified semantic space, and an identity authentication result is obtained. The method has the following effect that the identification security of identity authentication can be improved.

The invention discloses a transformer hotspot inversion method based on a time convolutional neural network, and electronic equipment. The method comprises the steps that a first training data set is acquired, and the first training data set is constructed based on an orthogonal design mode and a finite element simulation mode; performing data dimension reduction processing on the first training data set to obtain a target training data set; training the initial hotspot inversion model by using the target training data set, and determining a target hotspot inversion model; and using the target hot spot inversion model to predict hot spot data of the target transformer to obtain a prediction result, the prediction result including a target hot spot temperature value and a target hot spot position. According to the invention, the technical problems of low accuracy and high cost of the determination method of the hot spot temperature value and the hot spot position when the three phases of the transformer are unbalanced in the prior art are solved.

The invention discloses a new energy power generation equipment intelligent regulation and control method and system based on big data. The method comprises the steps of obtaining operation data of new energy power generation equipment, performing data dimension reduction processing to obtain structured data, and performing feature extraction and clustering to obtain an influence factor candidate set; according to the influence factor candidate set, performing spatial-temporal feature fusion analysis to obtain a multi-dimensional feature vector, and performing continuous sampling and quantization to obtain a quantization sequence; on the basis of the quantization sequence, potential risks are predicted through a long-short term memory network, risk marking is carried out, risk distribution map dynamic adjustment control parameters are obtained, and an optimization strategy set is obtained; based on the optimization strategy set, equipment operation parameters are selected and adjusted through a real-time feedback mechanism, a system stability index is obtained, multi-dimensional evaluation verification is carried out, and if an expected threshold value is not reached, regulation and control parameters are optimized through a genetic algorithm, and a final control instruction is output. According to the invention, real-time prediction of risks and intelligent regulation and control of equipment can be realized.

The invention relates to the technical field of sewage traceability positioning, and discloses a sewage traceability positioning method and device for carbon nitrogen ratio imbalance. The method comprises the following steps: performing carbon-nitrogen ratio standard curve deviation calculation and data dimension reduction processing on carbon-nitrogen space-time attenuation data to obtain carbon-nitrogen imbalance feature vector data; performing pipe network connectivity calculation and transmission path analysis on the coordinate data of the multi-level sewage system to obtain migration trajectory data of carbon and nitrogen pollutants; performing multi-dimensional data fusion on the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of the carbon-nitrogen imbalance pipe section; and carrying out pollution contribution calculation and region boundary identification on the spatial distribution data to obtain position region data of a carbon-nitrogen ratio abnormal source. According to the method, correlation analysis of the carbon and nitrogen space-time attenuation characteristics and the pipe network connectivity is established, and data dimension reduction and space analysis technologies are combined, so that the problem region range can be quickly reduced, and a more accurate tracing positioning result can be provided.

The invention provides a broadband oscillation mode identification method and device, equipment and a storage medium, and the method comprises the steps: firstly collecting new energy power grid oscillation data, segmenting the data, carrying out the dimension reduction processing, and simplifying the data through calculating the average value or characteristic value of each segment; then, classifying the data by using a k-Shape clustering algorithm, and constructing a classification data set; and randomly extracting various samples to train a random forest model to form a pattern recognition model. New data is input into the model after dimensionality reduction to obtain an oscillation mode. According to the method, through data dimension reduction, clustering database building and random forest rapid matching, accurate identification of broadband oscillation data is realized, historical, real-time and simulation data are integrated, and the identification efficiency and accuracy are improved.

The invention relates to the technical field of network communication, and discloses a network load balancing evaluation method based on a deep learning model, which comprises the following steps: S1, data preprocessing: collecting flow data, and carrying out normalization, noise reduction and enhancement processing; s2, manifold modeling: dimension reduction is performed on high-dimensional flow data, and a local structure is reserved; s3, feature extraction: extracting optimized low-dimensional features, and reducing redundancy; s4, performance evaluation: comprehensively evaluating throughput, delay and balance; s5, anomaly detection: detecting abnormal traffic and outputting a result; s6, feedback optimization: dynamically adjusting a load strategy, and improving the performance. Through deep learning, variational information bottleneck and manifold learning, accurate modeling and optimization of dynamic network traffic features are realized, the extracted features accurately reflect the traffic change trend, the adaptive capacity of the model to sudden and abnormal traffic is enhanced, performance reduction caused by feature distortion in a traditional method is effectively avoided, and the method has a good application prospect. And reliable support is provided for a load balancing strategy.

The invention provides an urban viaduct concrete crack width monitoring and early warning method and equipment, and the method comprises the steps: respectively installing displacement sensors at the concrete cracks of all urban viaducts, carrying out the grouping and collection of the displacement sensors in a Zigbee communication mode, and uploading the monitoring information obtained through sensing to a user side; and based on the received monitoring information, an early warning network model is correspondingly established for each crack at the user side, model parameters of the early warning network models are updated in real time, and large-range cluster type crack width monitoring and early warning are carried out. According to the method, the feature extraction time is shortened and the extraction capability is improved by using data dimension reduction, long and short term memory and a self-attention mechanism network, a corresponding early warning network model is rapidly established for each crack, and the characteristics of the Internet of Things, 5G communication, a one-dimensional convolutional neural network, a recurrent neural network and a self-attention mechanism are fully utilized. The problems of large-range cluster type crack width monitoring and early warning are effectively solved, the crack width monitoring number and early warning precision are improved, and a guarantee is provided for the safety of an urban infrastructure structure.

The invention discloses a rail transit equipment health degree prediction method and system based on multi-source information fusion, and relates to the technical field of rail transit equipment health degree prediction, and the method comprises the steps: collecting the multi-dimensional fault index data of rail transit equipment; carrying out data dimension reduction processing; performing time sequence analysis on the fault feature vector after dimension reduction processing in combination with timestamp data; the time sequence analysis result and the multi-source data are coupled through an integrated algorithm model for health degree prediction; and carrying out evaluation and early warning on the overall operation state and the potential fault risk of the equipment based on the health degree predicted value. According to the method, collaboration of a data level, an algorithm level and an optimization level is realized, fault features output by the PCA model serve as one of inputs of the Prophet model and the XGBoost model, meanwhile, the XGBoost is combined with other multi-source data to carry out comprehensive prediction, the accuracy, the real-time performance and the reliability of prediction are ensured through effective model collaboration, and potential faults and problems can be found in time.

The invention belongs to the technical field of industrial flue gas purification, and discloses an ammonia desulfurization optimization control system based on a machine learning algorithm, which comprises a feature extraction module, a working condition clustering module, a dynamic optimization module and a self-adaptive feedback module, the feature extraction module is used for collecting multi-dimensional operation parameters in a coal burning process, performing data dimension reduction through a PCA algorithm, and extracting key working condition features; the working condition clustering module identifies different operation working condition modes; the dynamic optimization module can construct a multi-modal optimization neural network based on different operation condition modes, and generates optimal control parameters in real time. Through PCA dimension reduction processing of the feature extraction module and in combination with a working condition clustering algorithm, automatic mode recognition and strategy switching under complex working conditions are achieved, fluctuation of desulfurization efficiency is reduced, and meanwhile the problems that the ammonia water adding amount depends on experience setting, raw material waste and secondary pollution are likely to be caused, and the operation and maintenance cost is large are solved.

The invention provides a high-performance material visual screening system based on machine learning and an application method thereof. The system comprises a feature extraction module, a machine learning model training module, a prediction module and a feedback optimization module. The data preprocessing module is used for performing primary processing on the original data, wherein the primary processing comprises removal of invalid data and abnormal values; the feature extraction module is used for converting a time domain signal into a frequency domain signal by adopting Fourier transform, extracting a frequency domain feature, and performing data dimension reduction by adopting principal component analysis (PCA); the machine learning model training module is used for ensuring the generalization ability of the model by adopting a cross validation technology in the training process, and accelerating the convergence process of the model through a gradient descent method; the prediction module is used for predicting the material performance of the high-performance material to obtain a prediction result; and the feedback optimization module is used for automatically optimizing model parameters by adopting Bayesian optimization, analyzing a prediction result of the model and dynamically adjusting the model parameters so as to improve the prediction precision. Through the scheme of the embodiment of the invention, the law can be learned from a large amount of experimental data by a machine learning technology, and the material performance can be quickly and accurately predicted.