90 results about "Tucker decomposition" patented technology

The invention provides a multi-mode ultrasonic fusion pressure vessel weld defect nondestructive testing method and system, and relates to the technical field of nondestructive testing. According to the method, geometric parameters of a welding seam are obtained through three-dimensional laser scanning, and an optimal scanning parameter set is generated; driving ultrasonic phased array equipment to scan for one time and synchronously acquire shear wave full-matrix capture and longitudinal wave linear scanning data; performing energy flow angular spectrum analysis and envelope analysis on the bimodal data, extracting defect feature parameters and constructing a three-dimensional feature tensor; carrying out multi-dimensional feature fusion by adopting Tucker decomposition, and enhancing a core tensor through physical modeling; generating three types of defect indication diagrams including a defect existence possibility diagram, a defect relative scale diagram and a defect space orientation diagram from the enhanced feature tensor; and the three types of indication diagrams are visually presented for comprehensive interpretation of detection personnel. Through multi-modal data fusion and physical modeling enhancement, the defect identification accuracy and detection efficiency are remarkably improved, the false alarm rate is reduced, and reliable technical support is provided for pressure vessel welding seam safety detection.

The invention relates to the field of optical material defect detection, and discloses a PC lens defect classification and identification method and system, and the method comprises the following steps: asynchronously collecting lens multi-modal data through polarized light imaging, thermal imaging and an acoustic emission sensor, and constructing a five-order asymmetric tensor containing polarization angle, spectrum, time, space and defect features; performing dynamic dimension reduction on the tensor by using an improved Tucker decomposition method, and extracting a core tensor and a factor matrix; establishing a polarization angle-time correlation coding model based on inverse problem solution of a light field state equation, deducing defect distribution characteristics and optimizing a core tensor; mapping the optimized tensor to the QUBO Hamiltonian of a quantum annealing machine, and carrying out optimization solution; and finally, fusing the multi-mode confidence coefficients of polarized light, thermal imaging and acoustic emission signals, generating defect classification labels and inverting geometric and mechanical parameters of the defects. The lens flaw detection efficiency and the classification accuracy are remarkably improved, and the method is suitable for industrial-grade high-precision quality control scenes.

The invention discloses a pumped storage power station building foundation dangerous point management and control system based on BIM, and the system comprises a data layer which enables linear engineering monitoring data to be organized into a high-order tensor of pile number * time * sensor type * spatial position through four-dimensional tensor modeling, and carries out the Tucker decomposition, strain rate abrupt change points are dynamically recognized in the compression process by combining sliding window anomaly detection, original data are directly stored, and then a hierarchical storage strategy is implemented on non-abrupt change data through a data value evaluation model driven by deep reinforcement learning; the model layer is used for fusing a physical information neural network, embedding a rock-soil mechanics constitutive equation into an LSTM network to construct a mixed loss function, pre-training a general geologic model through transfer learning, and then carrying out few-sample fine tuning in combination with project data; and the application layer is used for dynamically generating a hazard source list through a BIM model and triggering instant early warning based on the compressed and reconstructed data of the data layer and the output of the model layer, and simulating a disaster situation development path in combination with a digital twinborn technology.

The invention discloses a multi-mode image information fusion method based on deep learning, and relates to the technical field of image processing, and the method comprises the steps: obtaining image data of an optical microscopic mode and an electronic microscopic mode, and carrying out the spatial resolution alignment; a quantum entangled state light field coding method is adopted for the optical microscopic image data to generate quantum enhancement features, and topological persistent coherence analysis is performed on the electronic microscopic image data to extract multi-scale structural features; the features are mapped to a tensor space, cross-modal feature fusion is carried out through a tensor ring decomposition method of dynamic rank adjustment, a joint representation tensor is generated, physical constraint reconstruction is carried out on the joint representation tensor, and a super-resolution fusion image is generated. Through fusion of quantum enhancement features and multi-scale topological features, tensor ring decomposition of dynamic rank adjustment and Tucker decomposition dimension reduction compression, efficient fusion of high-resolution images is achieved, calculation redundancy is reduced, and processing efficiency is remarkably improved.

The invention relates to the field of network link quality evaluation, and discloses a link quality evaluation method based on test data fusion statistics, which comprises the following steps: S1, collecting time delay, flow and topological parameters of a network link, and carrying out sliding window standardization; s2, constructing a four-dimensional non-negative tensor comprising a link number, a parameter dimension, a network slice and time; s3, features are extracted through non-negative Tucker decomposition with sparse constraints; s4, solving a dynamic weight based on a three-party game model of time delay sensitivity, bandwidth competition and reliability guarantee; s5, calculating a link quality score in combination with the weight and the tensor core; and S6, generating a thermodynamic diagram. Through the technical scheme of sliding window standardization and four-dimensional non-negative tensor construction, the technical effects of eliminating parameter dimension differences and adapting to topology dynamic changes are achieved by utilizing window dynamic calculation of mean values and standard deviations and sparse constraint modeling, and the input data quality of a subsequent game model and weight calculation is improved.

A hyperspectral image change detection method based on spectral grouping tensor decomposition reconstruction comprises the following steps: splicing a pair of preprocessed dual-time hyperspectral images in a spatial dimension, extracting histogram features on all spectral channels, determining the number of groups, and grouping the spectral channels according to the histogram features; performing Tucker decomposition on each group of spectral channels of each hyperspectral image to obtain a core tensor and a factor matrix, performing principal component truncation on the decomposed core tensor and factor matrix according to an accumulated singular value ratio, and reconstructing the image; and after the reconstructed image is sent to a change detector to obtain a distance map, a threshold method or a clustering method is adopted to process the distance map, and a final change map is generated and output. According to the method, spectral correlation priori knowledge is fully utilized, low-rank information can be better extracted from grouped data through Tucker decomposition and reconstruction, a more accurate and more robust change detection result can be obtained, and the influence of factors such as image registration errors and noise on the detection performance is reduced.

The invention provides a tobacco industry-oriented multi-modal data fusion and analysis method, and belongs to the technical field of crossing of data mining and industrial digitization. A tensor multi-modal data representation method is applied to the tobacco industry, and a five-dimensional space-time business tensor is defined; a cross-modal attention mechanism is dynamically combined with tensor Tucker decomposition, and interactive modeling of cross-modal heterogeneous data is realized through optimization of attention weights; the rank of each mode is dynamically adjusted according to the information entropy, and the model capacity and the calculation efficiency are balanced.

The invention discloses an energy consumption prediction monitoring system based on BIM, and relates to the technical field of intelligent building energy management, which comprises the steps of defining an optimization objective function based on modal weight, performing iterative updating by using an OIALM algorithm, obtaining a low-rank matrix, outputting initial prediction energy consumption through a prediction model, and optimizing model parameters by using a golden vehicle optimization algorithm. Generating final predicted energy consumption; through a three-mode alignment and sparse suppression tensor modeling method based on BIM and Internet of Things data, building physical semantics and dynamic environment features are effectively fused, the accuracy and robustness of energy consumption feature expression are improved, a prediction framework fusing key time node rearrangement, attention enhancement Tucker decomposition and OIALM low-rank optimization is constructed, and the prediction efficiency is improved. And the modeling capability and the energy consumption prediction precision of the unsteady-state thermal process are improved.

The invention relates to the technical field of operation index analysis, in particular to an operation index multi-dimensional analysis optimization method based on AI large model dynamic weight adjustment, which comprises the following steps: acquiring a market fluctuation index, an operation efficiency index, a financial health index and a supply chain elasticity index of an enterprise; four-dimensional index data of enterprise operation are input into a hypergraph tensor decomposer, the hypergraph tensor decomposer maps four-dimensional indexes into mutually orthogonal hypergraph nodes through orthogonally constrained Tucker decomposition, a decoupling hypergraph is generated, the decoupling hypergraph is injected into a dynamic weight modeling model, and an optimization strategy chain is generated based on a real-time event stream; and the orthogonal constraint condition of the decoupling hypergraph is corrected through tensor gradient back propagation after execution of the optimization strategy, and closed-loop optimization is formed. According to the method, the optimal response action with business semantics and timeliness can be generated according to unexpected situations such as market abnormal fluctuation, supply chain breakage or financial pressure.

The invention relates to the technical field of insulation switch detection, and provides an extra-high voltage GIS graphene-copper-based composite diversion assembly fault diagnosis method and equipment. Dynamic calibration and anti-interference data acquisition are performed on a target diversion component to obtain a multi-source calibration data set, time-space-frequency domain fusion is performed in combination with a Tucker decomposition mode to obtain a cross-modal coupling feature set, and time-frequency-space domain joint feature extraction and coding are performed on the cross-modal coupling feature set to obtain a joint vector. Fault classification is carried out in combination with an attribute graph attention network model to obtain fault category labels, multi-physics field coupling simulation is carried out according to the fault category labels and a multi-source calibration data set to obtain a fault area coordinate set, and finally residual life prediction is carried out through a dynamic Bayesian network model to obtain residual life probability distribution and a maintenance instruction. According to the invention, through multi-modal data fusion, depth feature extraction, intelligent fault classification, simulation auxiliary diagnosis, life prediction and intelligent operation and maintenance decision, the accuracy of fault detection is improved.

The invention relates to a detection method and system for an early fire behavior, and belongs to the technical field of image processing, and the method comprises the steps: obtaining an image group of a to-be-detected region shot by a camera, and forming a target tensor; obtaining first mapping data and second mapping data; performing Tucker decomposition on the first mapping data to obtain a core tensor, and a first factor matrix and a second factor matrix which are orthogonal; respectively inputting the first mapping data and the second mapping data into a convolutional network, and fusing an output result with the core tensor, the first factor matrix and the second factor matrix to obtain a target fusion feature; and a detection head is adopted to detect the target fusion feature so as to detect and position the fire point. The application provides advanced sensing detection for early fire points, fire misjudgment can be reduced, tiny fire points can be effectively identified, personnel can conveniently and timely deal with the fire points, and accurate positioning for the fire points can be realized through the image and the detection head.

The invention provides a multi-parameter water quality data fusion analysis method and system. The method comprises the steps that water quality parameters are collected to form a three-dimensional data cube; constructing a space-time tensor model by using Tucker decomposition and a graph convolution network, and generating a core tensor matrix; constructing a dynamic constraint library and embedding the generative adversarial network; training a generative adversarial network by using Transform and physical constraint loss, generating synthetic data and verifying the synthetic data; performing space-time fusion by using meta learning weight distribution and Bayesian deep learning to generate a weight matrix and a confidence interval; missing data are restored through physical constraint interpolation and Gaussian process regression, and SHAP and LIME interpretation and path diagrams are generated; and performing real-time analysis by using an edge-cloud collaborative architecture to generate an intelligent report. Through physical constraint modeling, dynamic weight distribution and edge-cloud collaborative architecture, the problems that synthetic data violates physical laws, weight staticization, response lag and insufficient interpretability are solved.

The invention discloses a traffic missing data complementing method based on C-Tucker fourth-order space-time tensor representation, which comprises the following steps: constructing a fourth-order space-time tensor model of four dimensions of time, day, period and space based on space-time relevance of traffic flow data; performing Tucker decomposition on the observation tensor to calculate a core tensor; n-order modal expansion of the observation tensor and the core tensor is calculated, singular value decomposition (SVD) is adopted to extract a feature vector set, and a factor moment Nt and a core matrix are formed based on feature vectors to construct a fusion matrix; and reconstructing the observation tensor through the core tensor and the fusion matrix to realize recovery of traffic data. According to the method, under the condition that discrete data and continuous missing data coexist, efficient and robust data completion is achieved, and the accuracy of data recovery is effectively improved.

The invention provides an SAR three-dimensional enhanced imaging method based on non-local tensor decomposition, and the method comprises the following steps: 1, searching m + 1 SAR images according to a similarity measurement criterion, and constructing a homogeneity data tensor, m being a positive integer; 2, analyzing the low-rank performance of the homogeneous data tensor, and if the low-rank performance requirement is not met, repeating the step 1; if the low-rank requirement is met, the data tensor is a low-rank data tensor; and step 3, for the constructed low-rank data tensor, signal enhancement is carried out by using a nuclear norm optimization method based on tensor Tucker decomposition, and the method can realize high-quality three-dimensional SAR imaging under the condition of limited data resources.

The invention discloses a road network traffic state prediction method based on large language model spatial-temporal feature embedding, and belongs to the technical field of calculation, reckoning or counting. According to the method, semantic embedding of space and time is extracted by pre-training a large language model, and the space-time heterogeneity of a traffic system is quantified; a dynamic adjacent matrix is constructed by using Tucker decomposition, and combined modeling is performed on historical traffic data in combination with a dynamic space-time diagram convolutional network, so that high-precision prediction of a future traffic state is realized. The method does not need manual feature design, has good generalization ability and multi-mode adaptability, can be widely applied to scenes of road traffic, rail traffic demand prediction and the like, and significantly improves prediction accuracy and control response ability in a complex traffic environment.

The invention relates to the technical field of medical image processing, and discloses a method for constructing nerve disease positioning, qualification and evaluation by applying multi-modal magnetic resonance imaging (MRI) image data, which comprises the following steps of: standardizing the multi-modal MRI image data to enable each modal image to have the same spatial resolution; unifying the MRI images of all the modes into a high-order tensor, inputting the high-order tensor into a Tucker decomposition model, outputting a low-dimensional factor matrix and a core tensor, and extracting interaction features between the modes; the extracted interaction features are used for constructing an energy function, a difference term and a regular term are input, the energy function is minimized through a variational method, and an optimized spatial transformation function is output; the feature points are mapped to a unified space through a transformation function, a feature graph structure is constructed, the similarity between nodes is input, optimized graph structure information is output, and image fusion is assisted; by adopting the technical scheme of multi-modal image standardization and tensor decomposition, interaction information between modals is effectively extracted, and the problems of information loss and local feature neglect are solved.

The invention discloses a multi-modal denoising method for a hyperspectral image, which is characterized by comprising the following steps of: S1, respectively performing low-rank tensor representation on the hyperspectral image and a registered multispectral image by utilizing Tucker decomposition, and extracting core tensors of the hyperspectral image and the registered multispectral image; s2, establishing a correlation model between the hyperspectral core tensor and the multispectral core tensor through model-driven linear mapping or data-driven multi-layer perceptron network; s3, iteratively solving a core tensor, a factor matrix and correlation model parameters by adopting an alternating direction multiplier method; and S4, reconstructing a denoised hyperspectral image by using the optimized hyperspectral core tensor and factor matrix. Compared with the prior art, the method has the advantages that the spectral details of the hyperspectral image and the high-signal-to-noise-ratio spatial information of the multispectral image are fully mined and utilized, the restoration precision in the mixed noise scene is effectively improved through the double-Tucker decomposition framework and the core tensor association strategy, and the high-quality hyperspectral image is obtained.

The present invention discloses a hyperspectral super-resolution method and system based on a cross-attention deep tensor network, the method comprising: using the concept of Tucker decomposition to regard an image as a combination of a core tensor and a factor matrix, and realizing image reconstruction through a constructed coupling factor matrix; establishing a feature tensor fusion network based on a cross-attention mechanism, and using the introduced cross-attention mechanism and a U-shaped structure feature tensor fusion network to fuse the original hyperspectral and multispectral images into a shared core tensor; establishing a simulated degradation structure of a hyperspectral image to simulate the degradation of spatial and spectral dimensions to assist model training and parameter iteration. The present invention is applicable to unsupervised hyperspectral and multispectral image fusion coupled tensor networks, and provides an innovative solution for hyperspectral image super-resolution processing.

The application relates to an abnormality detection method based on a hyperspectral image. The main body is based on a space-spectrum combined feature extraction method of filtering and low-rank decomposition to perform abnormality detection on the hyperspectral image. The specific method comprises the following steps: firstly, in the spatial dimension, a reduced dimension image is obtained through a data dimension reduction and eigenvalue weighted fusion method, and then an improved spatial filtering method is used to extract the spatial features of the image to obtain an initial spatial feature image. In the spectral dimension, a background reconstruction image of the approximate background is obtained by using a Tucker decomposition method on the original hyperspectral image, and a background dictionary of the image is obtained by using an improved k-means clustering method, then the background dictionary is input into a low-rank decomposition model to obtain a sparse matrix, and an initial spectral feature image is obtained, finally, the initial spectral feature image is fused with the spatial feature image to realize abnormality detection.

The invention discloses a production line equipment comprehensive efficiency monitoring method and system based on machine learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: carrying out the space-time alignment and heterogeneous feature fusion of a production equipment coupling data set, and obtaining a synchronous data cube; extracting a low-rank nuclear tensor of the synchronous data cube by using a Tucker decomposition algorithm, and performing multi-modal projection to form an equipment fusion feature matrix; and inputting the equipment fusion feature matrix into a causal inference model, performing potential semantic projection and subspace division by the feature decoupling layer, performing contribution degree quantification and root cause influence analysis by the causal inference layer, and outputting equipment root cause association features. Through the causal inference model and the Pareto optimization algorithm, the accuracy and interpretability of the production line efficiency anomaly diagnosis are improved, and the relevance between the scheduling decision and the underlying causal mechanism is enhanced.

The invention discloses a hyperspectral image unsupervised change detection method based on a tensor convolution structure, and belongs to the technical field of remote sensing image intelligent analysis, and the method comprises the following steps: S1, constructing a training tensor for a dual-temporal hyperspectral image; s2, performing Tucker decomposition on the training tensor, and determining a spectrum rank of a spectrum mode factor matrix; s3, determining a layer-by-layer stacking structure according to the spectrum rank of the spectrum mode factor matrix, and outputting a dual-time-phase depth feature map; s4, obtaining an independent spectral difference chart and a local space average chart according to the double-time-phase depth feature map; and S5, obtaining a final change chart according to the obtained independent spectrum difference chart and the local space average chart. The double-view change detector constructed by the invention can effectively suppress noise interference, and has good resistance to illumination change, seasonal fluctuation, system noise and the like.

The invention discloses an analysis scheduling method and system based on multi-label routing. The method comprises the following steps: receiving multi-label hypergraph data, performing tuck decomposition on a hypergraph adjacency tensor, applying sparse constraint, extracting a core multi-label dependency structure, and generating a low-rank factor matrix; extracting hyperedge embedding vectors according to the low-rank factor matrix, compressing the hyperedge embedding vectors into binary hash codes through a locality sensitive hash module, and splicing the binary hash codes with the residual floating point features to form mixed hyperedge embedding vectors; for nodes in the graph neural network, attention weights are calculated through a multi-label compatibility function based on mixed embedding vectors, neighborhood aggregation is carried out, node states are updated through a gating circulation unit, and node embedding representation is obtained; and performing routing scheme generation on the node embedded representation through a path search algorithm, evaluating different schemes through a path scoring function, and outputting an optimal scheduling route according to a scoring result. According to the method, the calculation and message transmission efficiency of the large-scale multi-label graph is improved.

The invention discloses a charging station-oriented intelligent drainage cabinet advertisement effect analysis method and system, and relates to the technical field of digital outdoor advertisement intelligent analysis, and the method comprises the steps: carrying out the subfield division of an advertisement people stream response field, calculating the space center and scale difference of a subfield, constructing an affine mapping matrix, and generating a standard response field through iterative optimization. Constructing a causal graph, calculating an average processing effect, generating a comprehensive response index, and setting an advertisement putting strategy; through fusion of normalized multi-modal data, the accuracy of advertisement crowd response analysis is improved, through Tucker decomposition and residual enhancement, stable extraction of spatio-temporal characteristics of an advertisement effect is realized, and in combination with causal graph construction and return on investment calculation, the decision-making precision of an advertisement putting strategy is improved.

The invention discloses a heat map sequence downsampling reconstruction method based on tensor space constraint, and relates to the field of photo-thermal science and detection and signal processing. The invention aims to solve the problem that an active infrared thermal wave imaging detection technology cannot meet the requirements of high temporal-spatial resolution, low data volume transmission and real-time monitoring in engineering application. According to the method, the core tensor of the infrared image sequence is obtained through Tucker decomposition and is updated until the convergence condition is met, and the infrared image sequence is reconstructed through the updated core tensor; the updating process comprises the following steps of: performing time mode expansion on a core tensor of an infrared image sequence, and obtaining a spatial reconstruction field under each time mode; applying thermal diffusion physical constraint on the spatial reconstruction field by adopting a lattice Boltzmann method to obtain a physical evolution field; and projecting the difference between the physical evolution field and the corresponding space reconstruction field to the corresponding position of the core tensor, and updating the core tensor slice through Adam optimization so as to update the core tensor.

The application discloses a drug synergistic effect prediction method, system, device and medium based on heterogeneous graph tensor decomposition, and the prediction method comprises the following steps: obtaining a SMILES sequence of a drug, extracting a molecular structure feature of the drug, and obtaining a molecular structure feature representation of the drug; constructing a drug pair heterogeneous graph in each cell line, and obtaining a local interaction feature of the drug through a heterogeneous graph conversion network; performing Tucker decomposition on a three-channel heterogeneous graph relationship tensor, splicing the decomposition result with the local interaction feature of the drug, and extracting a global interaction feature vector of the drug; and predicting a synergistic score of a current drug-drug combination in a cell line according to the molecular structure feature representation of the drug and the global interaction feature vector of the drug. The application integrates the SMILES sequence of the drug and gene expression information of the drug pair in the cell line, and also converts the SMILES sequence into a drug molecular structure graph by using a graph convolution network, so as to extract the molecular structure feature of the drug. Such a design enables TensoGraph to more accurately reflect the complexity and diversity of the drug interaction network in the real world, thereby improving the prediction performance.

Embodiments of the present application provide a parallel computing method based on tensor Tucker decomposition result and related equipment, without reconstructing the original tensor from the Tucker decomposition result for operation, and the parallel computing mode used in operation greatly improves the computing efficiency of the Tucker decomposition result. The method comprises: performing tensor Tucker decomposition on the target tensor data which has been constructed to obtain the core tensor and the factor matrix corresponding to the target tensor data; determining the operation rule corresponding to the target tensor data; determining the parallel computing mode of the target tensor data according to the operation rule, wherein the parallel computing mode comprises inter-core parallel computing mode and intra-core parallel computing mode; and performing parallel computing on the core tensor and the factor matrix based on the parallel computing mode and the operation rule to obtain the computing result.

The present application relates to the technical field of fault diagnosis, in particular to a power grid fault analysis method and system, which comprises constructing a fusion tensor and performing dimension reduction processing on the fusion tensor to generate a fusion feature matrix; constructing a convolutional autoencoder model to detect abnormal fusion feature matrix; classifying power grid faults according to abnormal data of the abnormal feature matrix, and calculating power grid equipment fault area based on the result of power grid fault classification. The present application has the beneficial effect of ensuring the integrity and accuracy of data by using a high-precision low-rank tensor completion algorithm to complete power grid operation data. Key features are extracted from multi-dimensional data collected by multiple source sensors and a fusion feature matrix is constructed using Tucker decomposition and matrix compression technology, effectively improving the efficiency of data processing and the ability of feature expression. The application of the convolutional autoencoder model enhances the detection accuracy of abnormal features, and the improved weighted summation and hierarchical clustering-based method improves the accuracy of fault classification and positioning.

The application discloses a direct-current system grounding fault safety early warning method based on current characteristics and belongs to the technical field of power system fault diagnosis. The method comprises the following steps: firstly, injecting a safe micro-current disturbance signal into a direct-current system and synchronously collecting bus voltage and branch current response; then, solving a branch coupling admittance matrix at multiple frequency points, performing tensor Tucker decomposition and principal component analysis on the branch coupling admittance matrix, and extracting a first principal component direction vector; next, calculating the variance contribution rate of the vector, combining a dynamic reference and a fault probability accumulator model, and generating a system insulation state index; finally, using a dynamic decision threshold and a confidence positioning mechanism to realize accurate determination and positioning of a grounding fault; the method solves the problems of early warning, anti-interference and complex fault positioning of traditional methods, and improves the safety and reliability of direct-current system operation.

The invention provides an irradiation hazardous chemical trace multi-mode joint inspection method and system, and relates to the technical field of intelligent monitoring of hazardous chemical trace detection.The method comprises the steps that three paths of original response signals of electrochemical impedance, surface enhanced Raman and plasma-assisted ionization mass spectrometry arranged in an irradiation environment are obtained, and a multi-mode characteristic matrix is obtained; performing Tucker decomposition to obtain a core tensor and a factor matrix, and performing interpretability screening and vectorization to form a low-dimensional fusion feature vector; constructing an attribute graph by taking the low-dimensional fusion feature vector as mutual information, and outputting a trace hazardous chemical category label and a concentration estimated value through three-layer graph attention network embedding; substituting the category label and the concentration estimated value into the ERPG parameter to generate a digital twinning risk map; and driving ventilation, isolation and spraying actuators to complete closed-loop response through a federal learning consensus risk map. The invention provides an efficient and reliable technical scheme for hazardous chemical trace detection and safety management and control in the irradiation environment.

The invention discloses a method for analyzing an influence value of operating parameters of a coal-fired unit on power supply coal consumption. The method comprises the following steps: acquiring operation parameter data and a power supply coal consumption value from a coal-fired unit DCS (Distributed Control System), and preprocessing and discretizing; a mutual information matrix M belongs to the set is constructed, and conditional mutual information I (X; x [delta] C) quantizes the coupling relationship between the parameters; establishing a three-order tensor T which belongs to a characteristic'parameter-parameter-coal consumption 'nonlinear coupling relation; carrying out Tucker decomposition on the tensor; and calculating an independent influence item and a coupling influence item of the parameter based on a decomposition result to realize quantitative separation of the coupling effect. The method further comprises a dynamic updating mechanism, incremental updating is carried out every 8 hours, and working condition changes are rapidly adapted through combination of CP decomposition and self-adaptive weight adjustment. According to the method, the defect that the parameter coupling effect is ignored in a traditional method is overcome, the coupling effect recognition accuracy is improved by 35-40%, the power supply coal consumption prediction error is reduced to + / -0.8 g / (kW.h), and a reliable decision basis is provided for optimized operation of a unit.