447 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 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 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 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 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 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 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 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 rare earth element content quantitative estimation method and device, equipment and a storage medium. The method relates to the technical field of hyperspectrum, machine learning and quantitative estimation. The method comprises the following steps: acquiring hyperspectral data of each sample, eliminating gross error points, performing data dimension reduction and K-means clustering analysis to obtain effective spectral data of each sample, performing denoising processing, averaging to obtain an average spectral curve, and extracting spectral characteristics; analyzing the correlation between the characteristic data of each wave band and the element content by utilizing Pearson correlation, selecting a high-correlation wave band range, carrying out importance test on the high-correlation wave band range by utilizing a random forest, and screening out a characteristic wave band corresponding to each element; and constructing a data set by using the characteristic wave bands corresponding to the elements and the chemical test contents of the elements, and training and evaluating the machine learning model based on the data set to obtain a quantitative estimation model. The rare earth element content can be quickly scanned and evaluated in time.

The invention discloses a sudden drought identification method and system based on a space-time double-branch fusion model. The method comprises the steps that meteorological data are acquired and preprocessed; calculating a composite sudden drought index according to the obtained data; performing data dimension reduction on the obtained data through singular value decomposition (SVD); a deep learning model is adopted to construct a time branch model, a graph attention network GAT is adopted to construct a space branch model, and the time branch model and the space branch model are dynamically fused through a cross attention mechanism to construct a space-time double-branch fusion model; according to the method, data dimensions are compressed and model complexity is reduced by fusing multi-source variables and combining singular value decomposition (SVD), meanwhile, geographic neighborhood weights are dynamically learned by adopting Transforme and based on a graph attention network (GAT), dynamic fusion of spatial-temporal characteristics is finally realized through a cross attention mechanism, a sudden drought recognition result is generated, and the method has the advantages of being high in robustness, high in accuracy and high in reliability. The limitation of a traditional method on nonlinear feature capture, space-time modeling splitting and generalization ability is broken through.

The invention relates to the field of computer big data, and discloses a self-adaptive parallel computing high-dimensional data dimension reduction and classification optimization system which comprises a quantum tensor decomposition module, a dynamic routing module, a heterogeneous computing classification module and a feedback regulation and control module. The quantum tensor decomposition module realizes nonlinear feature extraction of high-dimensional data through super-adjacency tensor modeling and entanglement entropy constraint; the dynamic routing module generates an adaptive communication path based on pulse time sequence weight optimization and quantum key verification; the heterogeneous calculation classification module dynamically schedules a quantum processor according to data characteristics, and FPGA and GPU resources execute mixed gradient aggregation; and the feedback regulation and control module guarantees the robustness of the system through multi-dimensional parameter closed-loop correction and a multi-stage fault-tolerant mechanism. All the modules cooperate to form a closed loop of dimension reduction, transmission, classification and regulation, the problems of feature extraction distortion, low resource utilization rate and poor dynamic environment adaptability in the prior art are solved, and the real-time performance and reliability of high-dimensional data processing are remarkably improved.

The invention discloses a wavelet enhancement cooperative activation method for FES multi-target time sequence regulation and control, and relates to the technical field of biomedical engineering. According to the model, an overlapped sliding window is analyzed through a multi-channel surface electromyographic signal, a time-frequency energy significant feature matrix is constructed by combining an energy ratio, and meanwhile, an inter-muscle co-activation matrix is constructed in the multi-channel electromyographic signal sliding window; constructing a multi-channel high-dimension matrix by using each single-channel time-frequency energy significant feature matrix and the multi-channel inter-muscle co-activation matrix, and performing data dimension reduction on the multi-channel high-dimension matrix to obtain a multi-channel multi-dimension time-frequency energy-inter-muscle co-activation matrix; and a muscle activation time sequence vector is screened by using a self-adaptive threshold logic combination rule, and finally an activation time sequence is applied to an FES experiment. According to the method, the movement of the hand on the sagittal plane is accurately predicted by analyzing the multi-channel surface electromyogram signals and the multi-target muscle co-activation characteristics, functional electrical stimulation FES parameters are dynamically adjusted, and accurate control over the muscle activity stage is achieved.

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a hyperspectral lithology intelligent identification method based on fuzzy clustering, which comprises the following steps: acquiring a geological map of a research area, vectorizing the geological map of the research area, and acquiring initial lithology distribution vector data; preprocessing the hyperspectral data; performing data dimension reduction on the preprocessed hyperspectral data by using principal component analysis transformation to obtain dimension-reduced data; clustering the dimension-reduced data by adopting a spatial fuzzy C-means clustering algorithm, and segmenting a clustering result based on a neighborhood similarity criterion to obtain the clustering result; and according to the initial lithology distribution vector data, determining the lithology category with the maximum proportion in different clustered patches from the clustering result, and taking the category as the lithology category corresponding to the clustered patches to obtain an intelligent lithology identification result. According to the method, the influence of human interference factors can be effectively reduced, and the lithology identification precision and the mineralization-related geological element identification capability are improved.

The invention discloses a complex data dimension reduction method and system based on a neural network, and belongs to the technical field of complex data processing. A feature recognition model is established, target data needing to be analyzed by a user is acquired, the target data is analyzed through the feature recognition model, and a multi-dimensional body is generated; identifying a data processing demand of a user, analyzing the multi-dimensional volume according to the data processing demand, and determining a unit demand classification corresponding to the data processing demand; marking unit classifications corresponding to the unknown data features as unit unknown classifications, and evaluating the unit unknown classifications according to the unit demand classifications and the data processing demands to obtain corresponding unknown influence values; adjusting the multi-dimensional body according to the demand classification of each unit to obtain a multi-dimensional demand body; carrying out dimensionality reduction on the target data according to the multi-dimensional demand body to obtain target dimensionality-reduced data; and analyzing the target dimension reduction data according to the data processing demand to obtain an analysis result, and displaying the analysis result to a user.

The invention discloses a tractor transportation operation working condition construction method based on improved particle swarm optimization and KMeans fusion, and relates to the technical field of agricultural machinery working condition analysis. The method comprises the following steps: acquiring original data of tractor transportation operation through a plurality of data acquisition modes, and carrying out preprocessing and three-stage screening to obtain an effective kinematics fragment; selecting multi-dimensional characteristic parameters to construct a characteristic matrix, and performing data dimension reduction by adopting principal component analysis; optimizing a KMeans clustering initial center by using an improved particle swarm optimization (IPSO) algorithm which introduces a dynamic inertia weight and a Gaussian mutation strategy, and performing clustering analysis on the feature space after dimension reduction; and selecting representative fragments based on feature similarity, and synthesizing a standardized working condition curve by taking the sum of average relative errors of all feature dimensions as a target function. According to the method, the problems that a traditional clustering algorithm is prone to falling into local optimum and the working condition construction accuracy is insufficient are solved, the constructed working condition can truly and comprehensively reflect the actual transportation operation characteristics of the tractor, and a reliable basis is provided for tractor power system optimization, operation efficiency improvement and energy consumption reduction.

The invention discloses a risk early warning method, training method and device of a risk identification model. The method comprises the steps that N kinds of performance index data of an information system in a target time period are collected, and a target index data set is obtained; the target index data set is input into a risk recognition model, a risk recognition result is output, the risk recognition model is obtained after an initial recognition model is trained based on a target training sample, and the target training sample comprises data obtained by conducting dimension raising on historical index data in a historical index data set and data obtained by conducting dimension raising on the historical index data in the historical index data set; the historical index data comprises N kinds of performance index data of the information system in a historical time period; and generating early warning prompt information under the condition that the risk identification result indicates that the information system has the risk. According to the method and the device, the problem that a risk prediction result of an information system is inaccurate due to data dimension reduction when a machine learning algorithm in related technologies processes multi-dimensional data is solved.

The invention relates to the technical field of remote sensing image processing, in particular to a joint sparse representation hyperspectral image classification method based on dual domain constraints, which comprises the following steps: preprocessing: carrying out spectral feature-based wave band grouping on hyperspectral image data, and then carrying out MNF data dimension reduction on the grouped hyperspectral image data; extracting a main component feature map by using morphology; performing superpixel segmentation on the hyperspectral image by using an improved watershed algorithm, and performing FCM clustering on the hyperspectral image; adaptive selection of the neighborhood is carried out through weight calculation under double constraints of the obtained superpixel neighborhood and the clustering field; multi-view angles of the superpixel field, the clustering field and the constraint field are used for joint sparse representation; and a majority voting method is adopted to integrate classification results, and the classification results are adjusted through a correction rule, so that the classification effect of the hyperspectral image is improved, and the classification precision of edge pixels is improved.

The invention relates to the technical field of financial service management, in particular to an intelligent supply chain financial service platform, and the platform comprises a supply chain data analysis module which receives and integrates market dynamics, carries out the format standardization and data cleaning of the market dynamics, and generates a data integration result; and performing dimension reduction on the data integration result by applying principal component analysis, and extracting factors influencing the health degree of the supply chain to obtain a key factor extraction result. According to the invention, standardized integration and data cleaning are carried out on market dynamics, reliability and consistency of supply chain data are enhanced, data dimension reduction is realized in cooperation with principal component analysis, key factors having the most influence on the health state of the supply chain are extracted, and accuracy and sensitivity of supply chain state evaluation are improved; the risk features of the supply chain entity are analyzed by adopting a clustering algorithm, behavior pattern analysis is performed on the basis of risk feature grouping, potential risk points are found in advance, and the risk pre-judgment capability and prevention and control accuracy are enhanced.

The invention discloses a method for monitoring gas leakage based on spectral vector data processing. The method comprises the following steps: collecting spectral vector data, inputting generative adversarial network training, distinguishing true sample data and false sample data to realize data expansion, inputting feature extraction model training, adopting different strategies to adjust the weight and bias of a neural network in each stage, inputting feature dimension reduction model training, representing reconstructed features from low dimensions, and extracting the reconstructed features. According to the technical scheme of inputting a classifier model for training, converting into quantum bits and processing sample data by using the trained feature extraction model, the data dimension reduction model and the classifier model, the technical problems of poor model generalization ability, weight updating depending on global gradient information and overfitting caused by insufficient training samples are solved; the technical effects of improving the quantity and quality of the data generated by the spectral data sample, enhancing the flexibility of the algorithm in different data stages, minimizing the difference between input and reconstruction output, enhancing the performance of the classifier and accurately predicting the leakage states of different types of gases are achieved.

The invention discloses a wastewater biological treatment process effluent prediction method and system based on multi-modal learning. The method comprises the following steps: firstly, collecting multi-modal data of a wastewater biological treatment process; secondly, respectively encoding different modal data based on a feature extraction technology; dividing the feature vector into low-dimension data and high-dimension data, and combining the low-dimension data and the dimension-reduced high-dimension data to respectively establish a reference effluent prediction model; and finally, performing effluent prediction on the wastewater biological treatment process based on a multi-modal decision fusion strategy and the prediction model. The system comprises a multi-modal data collection module, a multi-modal data representation module, a reference model construction module and a multi-modal decision fusion module. According to the method, multiple data modes in the wastewater biological treatment process are combined, the effluent prediction model is constructed based on the multi-mode learning fusion strategy, the accuracy and robustness of the effluent prediction model are remarkably improved, and intelligent development of the wastewater treatment process is facilitated.