1676 results about "Similarity matrix" patented technology

The invention provides a cross-modal similarity-based text mining data query method and system, relates to the technical field of data mining, comprises a cross-modal similarity-based text mining data query method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing a multi-level feature extraction network to respectively obtain text, image and time sequence feature vectors; feature fusion is realized by using an adaptive cross-modal contrast learning network, and a cross-modal similarity matrix is constructed; performing hierarchical screening on the target text by adopting a multi-level screening model to obtain a candidate text set; a text relation reasoning module is constructed based on a graph neural network, and text clustering and relation prediction are completed through a multi-task learning framework. According to the method, the accuracy of cross-modal text data query is improved, the adaptivity of feature fusion is enhanced, and efficient text relationship mining is realized.

The invention discloses an APT attack path reconstruction method based on time sequence diagram comparison clustering and a medium. A security event standardized data set is obtained; mapping each security event into a multi-modal node through a heterogeneous time sequence diagram set construction method, and generating a directed edge to construct and complete a heterogeneous time sequence diagram set; a stage embedding time sequence diagram set is obtained through the joint attack stage set; outputting a time sequence diagram similarity matrix by adopting a multi-scale diagram similarity algorithm of time alignment perception; generating an event semantic sparse matrix based on the threat intelligence knowledge graph; obtaining an image clustering result set; uncertain samples in the graph clustering result set are obtained and processed, and an APT attack path reconstruction result is obtained. The problem that an APT attack path reconstruction method mainly depends on rule matching, single-dimensional feature comparison and manual analysis of security logs or alarm streams is solved, the interpretability of APT traceability is greatly enhanced, and subjective errors of manual research and judgment are reduced.

A computer-implemented method that includes receiving a plurality of input images, generating a visual matrix utilizing the plurality of images and an image encoder, wherein the visual matrix includes a list of encoded images, receiving a plurality of text prompts, selecting a text prompt from the plurality of text prompts, send the first one of the text prompts to a language model to generate a candidate list of tokens, selecting tokens, converting the text prompts into updated text prompts via appending the tokens, generating a text matrix utilizing the text prompt and text encoder, and utilizing numerical values assigned at an image-text similarity matrix, determining a score associated with the image-text similarity matrix; and evaluating a criteria and outputting a final token to the updated text prompt in response to identifying a highest score associated with the final token after evaluating each of the plurality of text prompts.

The invention relates to a hydrological flow prediction method and system based on multi-site time-space association. The prediction method comprises the following steps: carrying out dimension reduction and feature reconstruction on original multi-site hydrological data through an auto-encoder; space-time correlation modeling and adjacency matrix dynamic construction are carried out, a multi-dimensional Euclidean distance matrix between stations is calculated based on a multivariable dynamic time warping (MDTW) algorithm, a similarity matrix is generated in combination with dynamic programming, and a dynamic adjacency matrix is constructed by fusing a geographic space adjacency relation; extracting spatial features of a GCN (Graphics Convolutional Network); carrying out adaptive time sequence decomposition and trend-period modeling; and carrying out multi-stage fusion prediction and result output, and generating a final prediction result through a decoder in combination with the decomposed trend item and periodic item. According to the method, accurate extraction and dynamic correlation modeling of spatial-temporal characteristics of multi-site hydrological data are realized, the accuracy and robustness of single-site flow prediction are improved, and the problems that multi-site spatial-temporal correlation modeling is insufficient, non-linear time sequence alignment is difficult, and single-site prediction precision is limited are solved.

The invention provides a large model training data synthesis method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of original military corpus data, generating a semantic vector through a pre-training language model, dividing the semantic vector into a plurality of topic clusters through clustering analysis, extracting the keyword and representative sample of each topic cluster, forming a theme ontology library; selecting associated topic pairs based on the topic similarity matrix, and generating a synthetic sample through a predefined template; controlling the language style of the synthetic sample through the cue word instruction, and generating a stylized sample; dividing difficulty grades of the stylized samples to obtain graded samples; performing multi-dimensional quality evaluation and screening on the graded samples to obtain screened samples; the screened samples are used for training a large language model, and according to the performance of the large language model in each theme task, theme weights are adjusted, a synthesis strategy is updated, and a sample structure is optimized. According to the method, high-quality and diversified training data can be provided for large model training.

The invention discloses an equipment state sensing system based on peak shifting and danger situation sensing, which relates to the technical field of electric digital data processing and comprises a data acquisition unit, a model training and optimizing unit, a situation sensing unit, a peak shifting regulation and control unit and a system interface and user interaction unit. According to the method, the device state data feature similarity is dynamically calculated by using the Gaussian kernel function, the similarity matrix is adjusted in combination with the gradient update item, dynamic weighting is performed in feature extraction, and redundant features are reduced by adopting the adaptive discarding probability, so that the feature extraction efficiency of the model in a high-dimensional feature space is improved; the problem that the dimension reduction effect is affected by too many redundant features of the equipment state data is solved. Equipment state features are extracted layer by layer through a deep forest algorithm of a multi-layer random forest structure, a decision tree is optimized in combination with a dynamic adjustment weight mechanism and a reinforcement learning strategy, and the classification capability of the model for equipment state changes and abnormal states is enhanced.

The invention belongs to the technical field of computer vision, and discloses a sparse point cloud completion method based on multi-view fusion, which comprises the following steps: (1) inputting point cloud data, extracting point cloud features, and obtaining an overlapping region of the point cloud data; (2) calculating a similarity matrix of inter-frame point clouds in the overlapping region, and solving inter-frame rigid transformation by using a weighted singular value decomposition algorithm; (3) realizing accurate registration of multi-view point clouds through an accumulative registration strategy; (4) performing coarse-grained completion on the point cloud by using a 3D convolutional mesh residual network module, and further fusing 3D voxel features by using a multi-layer perceptron and completing fine-grained accurate point cloud completion; and (5) optimizing the weight of the model by calculating a loss function, and improving the overall effect and precision of completion. According to the invention, the accuracy and robustness of vehicle detection are improved by fusing a multi-view registration technology and a coarse-to-fine modular point cloud completion method.

The embodiment of the invention provides a cross-chip platform instruction conversion method, which comprises the following steps: collecting instruction set information of a source chip platform and a target chip platform, establishing an instruction feature analysis model, and calculating a semantic similarity matrix between instructions in a feature vector space by adopting a kernel function; training a multilayer graph attention neural network based on the semantic similarity matrix, mapping feature vectors of a source platform instruction and a target platform instruction to a shared embedding space, and performing iterative optimization on a mapping relation graph by applying a reinforcement learning method to obtain an instruction equivalent conversion rule; the instruction equivalent conversion rule is loaded in the instruction conversion engine, the input source platform instruction is decoded and analyzed, and a converted target platform instruction sequence is output to an instruction execution unit of a target chip platform; the resource overhead in the conversion process can be optimized, and the overall efficiency of the system is improved on the basis of ensuring the conversion correctness.

The invention relates to an experimental data processing method and device, an AI analysis module and computer equipment, and belongs to the field of data processing.The method comprises the steps that multi-dimensional original data are partitioned according to types, and formats are unified; generating a similarity matrix based on time and space neighborhood information, and marking abnormal fluctuation points; effective signals are separated through a time-frequency feature matching noise library; extracting multi-layer features of basic statistics, time sequence correlation and trend change; and dynamically screening core features to update the tracking type experimental model. The matched AI analysis module integrates hardware circuits of data partitioning, similarity calculation, anomaly marking, noise matching, feature extraction and model updating, and whole-process acceleration is achieved. According to the method, through multi-dimensional data compatibility processing, accurate anomaly detection, multilayer feature fusion and model adaptive optimization, the experimental data processing efficiency and conclusion reliability are remarkably improved, and the method is suitable for real-time analysis of multiple scenes such as scientific research and industry.

The invention discloses a multi-modal knowledge question and answer retrieval method and system for a specific professional field, and belongs to the technical field of data processing. According to the method, the cosine similarity between the feature vectors is calculated in each mode through large model preprocessing to form the similarity matrix, and direct vector calculation of the problem and the knowledge base text in the professional field is realized by adopting a vectorization processing mode which is the same as that of the knowledge base text. And according to an input problem vector, through vectorization index retrieval in the professional domain knowledge base, selecting a plurality of text vectors closest to the problem vector from the professional domain knowledge base to obtain a plurality of pieces of knowledge related to the problem and the source of each piece of knowledge. According to the method, the knowledge question and answer reply accuracy of the target field is effectively improved, the knowledge question and answer reply integrity of the target field is guaranteed, basis support is realized, local deployment is supported, the information security problem is solved, and an intelligent application scene of knowledge question and answer retrieval is realized.

The invention relates to the technical field of calibration tests, in particular to an intelligent metering calibration method and system, and the method comprises the following steps: calculating the similarity between data points, constructing a similarity matrix, further calculating the fitting degree, carrying out the spectral decomposition, extracting a feature vector, carrying out the dimension reduction, mapping to a low-dimensional space, carrying out the data point clustering based on a feature vector matrix, and carrying out the calibration of the data points. An error cluster is identified and an error pattern is determined. According to the method, by calculating the similarity between the data points and mapping the similarity to the low-dimensional space, the error modes can be efficiently recognized and classified, the error correction path can be dynamically adjusted, error correction is more flexible and real-time, the error analysis depth is improved through spectral decomposition and feature vector extraction, and the error analysis accuracy is improved. Therefore, the error trends of the equipment under different conditions are accurately identified, the measurement precision and stability are remarkably improved, the error correction path parameters are updated in real time, the adaptability of the system to the operation change of the equipment is enhanced, and the calibration time is effectively shortened.

The invention provides a conference activity execution task decomposition, arrangement and management method based on an AI technology, and relates to the technical field of conference management, and the method comprises the steps: obtaining conference demand information, converting the conference demand information into digital representation data, employing a bidirectional recursive decomposition strategy to carry out task decomposition, and generating subtask data; calculating a task similarity matrix and a conflict matrix to construct a task execution directed graph to generate initial arrangement data; collecting an execution state to generate feedback data, calculating a risk assessment score, and adjusting a task execution scheme when the risk assessment score exceeds a threshold value. According to the invention, the execution efficiency of conference activities can be improved, the resource conflict risk is reduced, and dynamic optimization adjustment is realized.

A system including a machine learning network that includes a controller configured to, utilizing numerical values assigned at an image-text similarity matrix, output at the machine learning network including a text encoder and an image encoder, update parameters of a untrained layer of the machine learning network utilizing sparse logistic regression to generate a sparse logistic regression layer, wherein the image-text similarity matrix is associated with a plurality of input images received at the controller, freeze one or more entries of the sparse logistic regression layer that include zero values, run a plurality of input images at both (1) the image encoder and (2) one or more unfrozen entries at the sparse logistic regression layer, and update, in response to the running of the plurality of input images, parameters of the image encoder and parameters associated with one or more unfrozen entries, and output a tuned machine learning model until a threshold is met.

The invention relates to the technical field of medical data analysis, in particular to a lung cancer dynamic evaluation method and system based on immune monitoring, and the method comprises the following steps: collecting immune index data obtained in the immune monitoring process of a patient, analyzing the mean value change between adjacent segments of an immune index data time sequence, marking an abnormal fluctuation region, and calculating the immune index data; and generating abnormal fluctuation section data. According to the method, the immune monitoring data of the patient are divided into the time sequences, the abnormal fluctuation sections and the abnormal change points are accurately extracted through the mean value change analysis and sliding window method, the remarkable fluctuation of the immune system can be captured more rapidly and accurately, and therefore the timeliness of dynamic monitoring is improved. And further in combination with statistical feature analysis and similarity matrix calculation, differentiated grouping of immune indexes among patients is refined, and personalized evaluation for patient groups with different immune states is facilitated.

The invention relates to the technical field of glass detection, and discloses a method, a device and equipment for detecting glass after light sweeping. The method comprises the following steps: collecting multi-source original sensing data of glass to be detected, carrying out signal denoising, synchronous transformation and multi-scale glass feature extraction on the multi-source original sensing data, and carrying out feature combination, feature dimension reduction compression, region segmentation and region contour reconstruction on the multi-scale glass features to obtain an initial defect region set; performing spatial-temporal feature combination of multi-source features on the initial defect region set to obtain an enhanced defect feature set; and constructing a region similarity matrix corresponding to the enhanced defect feature set, performing defect category judgment and multi-dimensional glass quality scoring on the region similarity matrix to obtain glass quality evaluation data, performing process association on the glass quality evaluation data, and generating a final detection report of the to-be-detected glass. According to the method, the result accuracy of quality detection on the glass subjected to light sweeping is improved.

A method discloses receiving a plurality of input images, receiving text prompts, generating a visual matrix utilizing the images and an image encoder, generating a text matrix utilizing a text encoder, multiplying the text matrix and the visual matrix to generate an image-text similarity matrix that assigns a numerical value indicating similarities between each of encoded visual descriptors and each of the encoded images, wherein similarities are indicated by entries of the image-text similarity matrix having numerical values that determine a loss function associated with the image-text similarity matrix, identify a gradient of the loss function with respect to parameters associated with the image encoder and parameters associated with the text encoder, utilizing the gradient, update parameters associated with the image encoder or the text encoder, and outputting final updated parameters associated with either the text encoder or image encoder of the machine learning network.

The invention discloses a multi-modal multi-scale cross attention-based target counting method and system, and the method comprises the steps: constructing a similarity matrix based on a block-level visual embedding vector and a text embedding vector; and further establishing comparison learning loss based on multi-classification cross entropy so as to enhance the text pairing capability of the block-level visual vector under the vision-language model. Meanwhile, text embedding containing type semantic information and quantity semantic information and a visual embedding tensor with the same dimension as the text embedding are constructed, an attention score is output through calculation of a zoom dot product model, and finally feature representation of block-level visual embedding in a text embedding vector space is obtained. A multi-scale visual feature interaction module based on a double-branch Cross-ViT structure is constructed, and access and fusion among visual features of different scales are achieved. According to the method, the feature extraction performance of the model on target details and context information of different scales in a complex scene is enhanced, and good visual coding is provided for a downstream density map regression task of target counting.

The invention discloses a time-space state enhanced XGBoost-LSTM-based vehicle position prediction and abnormity early warning method, system and device, and a medium. The method comprises the steps of obtaining a road section traffic flow sequence, constructing an EM clustering model, classifying traffic flow sequence samples in the EM clustering model, and constructing a time state matrix; determining a physical static adjacency matrix, a dynamic steering ratio adjacency matrix and a road section flow dynamic similarity matrix between the road sections, and constructing a spatial state matrix after weighted fusion; an XGBoost-LSTM model is constructed, and model training is carried out; and inputting the constructed time state matrix and space state matrix into the trained XGBoost-LSTM model, and carrying out real-time vehicle position prediction and abnormity early warning. According to the method, the vehicle position prediction precision can be improved, the data acquisition threshold is reduced, the use scene of the prediction method is expanded, and meanwhile, a new dynamic evaluation standard is provided for abnormal event early warning.

The invention relates to the technical field of medical systems, in particular to an intelligent gastrointestinal postoperative intestinal obstruction diagnosis and intervention decision-making system and method, and the system comprises a data preprocessing module, a multi-modal data fusion module, a gastrointestinal function recovery model construction module, a self-adaptive progressive reinforcement learning optimization module AHRL and a diagnosis and intervention recommendation module. The self-adaptive progressive reinforcement learning optimization module optimizes an intervention strategy layer by layer through a five-layer progressive algorithm from the steps of dynamic adjustment of feature weights, recursive state propagation, layered accumulation optimization, symptom similarity matrix generation, progressive weight reverse feedback and the like, deep fusion of key symptoms by multi-modal data is achieved, and the accuracy of the key symptoms is improved. And the intervention scheme is updated in real time according to the patient state change. In the intervention decision recommendation module, the system generates personalized diagnosis suggestions based on the optimized strategy, carries out real-time risk assessment, and provides continuous and dynamic intervention support for the patient.

The invention provides an active transformer area voltage out-of-limit early warning and photovoltaic bearing capacity evaluation method, which comprises the following steps: acquiring multi-source data of a power distribution transformer area, and generating a standardized multi-source time sequence data set through time alignment processing, data quality evaluation and feature fusion; extracting voltage out-of-limit features based on the time sequence similarity matrix and the node influence matrix; the periodicity, volatility and randomness characteristics of photovoltaic output are analyzed, a coupling relation matrix and a sensitivity vector are established, and an influence evaluation model is constructed; performing out-of-limit early warning based on an early warning index system and a deep learning model; and establishing a multi-dimensional evaluation index system for carrying out bearing capacity evaluation in combination with power flow analysis and the dynamic influence factors. Through multi-source data fusion and multi-dimensional feature analysis, the accuracy of voltage out-of-limit early warning is improved, accurate evaluation of the photovoltaic bearing capacity is realized, and effective technical support is provided for safe operation of the power distribution network.

The invention discloses an intelligent processing method and system for judicial batch filing, and the method specifically comprises the steps: carrying out the cross verification of an original case element through a cross-modal information complementation mechanism, and generating a first case element; constructing a case similarity matrix according to the first case elements, calculating a fact relevance weight between cases by adopting an improved BERT-Siamese network based on the case similarity matrix, synchronously identifying jurisdiction competition and proof conflicts through a legal program conflict detection algorithm, and generating a dynamic grouping scheme; matching a classification document template library according to the dynamic grouping scheme, and calling a Transform-XL enhanced generation model to generate legal documents in batches; and performing full-process intelligent verification on batches of legal instruments through the association map, and detecting material integrity, logic consistency and legal compliance. According to the method, intelligence, high efficiency and precision of judicial batch filing are realized, and the case processing efficiency and the document generation quality are improved.

The invention provides an underground pipe network defect intelligent identification and early warning method and system based on deep learning, and relates to the technical field of pipe network detection, and the method comprises the steps: employing a pipeline detection robot to obtain multi-source data, and carrying out the preprocessing; mapping the features to a manifold space, constructing a similarity matrix, and carrying out geometric difference weighted fusion; constructing an incidence matrix to calculate a spatial distance and mechanical strength, and carrying out iterative propagation to generate a defect characteristic spectrum; and acquiring an evolution sequence by adopting a self-adaptive sliding window, determining influence factors based on anti-fact intervention, and calculating a state transition probability to determine an optimal maintenance scheme. According to the method, the pipe network defect identification accuracy is improved, and pipeline state prediction and maintenance decision intelligence are realized.

The invention relates to an article sequence recommendation method and system based on collaborative filtering and an LLM perspective, and the method comprises the steps: obtaining a user historical data set and an article data set, and carrying out the preprocessing, and obtaining an article title similarity matrix, a user behavior sequence, and historical collaborative filtering interaction information; historical collaborative filtering interaction information is enhanced through a large language model; constructing a sequence recommendation model, inputting the user behavior sequence into the sequence recommendation model for training, and correcting the deviation of the user behavior sequence through comparative learning according to the item title similarity matrix and the enhanced historical collaborative filtering interaction information to obtain a trained sequence recommendation model; and inputting the user behavior sequence of the user into the trained sequence recommendation model for prediction, calculating prediction scores, and generating a recommendation list according to score sorting, thereby completing article sequence recommendation. According to the method, the performance of the sequence recommendation system is greatly improved by solving the cold start problem.

The invention discloses a multi-stage spatial-temporal clustering method and system based on a fused mahalanobis distance. The method comprises the following steps: acquiring a spatio-temporal data set, and determining a spatio-temporal neighbor relation of samples in the data set; calculating a space communication distance and a time decay distance of the sample; fusing the space communication distance and the time decay distance by using a mahalanobis distance to obtain a relative distance of the sample; selecting a class cluster center from the data set according to the local density of the sample and the relative distance; adopting a multi-stage distribution strategy to distribute non-class-cluster center samples to corresponding class clusters; wherein the multi-stage allocation strategy comprises an inevitable allocation stage based on space-time shared neighbor and a similarity allocation stage based on a weighted similarity matrix. According to the method, the key problems that an existing space-time clustering algorithm is insufficient in space-time attribute coupling processing and sensitive to distribution errors are solved, and the clustering accuracy, robustness and practicability in the fields of intelligent traffic analysis, seismic sequence recognition and the like are remarkably improved.

The invention provides an open set cross-domain hyperspectral classification method and system based on multi-mode and optimal transmission, and relates to the technical field of hyperspectral image classification. The method specifically comprises the following steps: acquiring and utilizing an original hyperspectral image to construct a labeled source domain data set and an unlabeled target domain data set; utilizing a spatial-spectral feature extractor to respectively obtain spatial-spectral features of the source domain data and spatial-spectral features of the target domain data; and determining a source domain prototype, calculating a similarity matrix between the target domain data and the source domain prototype, solving an optimal transmission matching matrix, distributing a pseudo tag for each target domain data, and performing text correction on all the source domain data and all the target domain data by using a contrast learning loss function to obtain the target domain data. And the source domain prototype and the spatial spectrum feature extractor are optimized by constructing the total loss of minimizing hyperspectral image classification, so that classification prediction can be carried out on the hyperspectral image to be classified, and target domain data can be classified more accurately.

The invention discloses an intelligent storage system supporting multi-source heterogeneous data fusion management, which relates to the technical field of multi-source heterogeneous data fusion, and comprises a data source adaptation module, an intelligent data source adapter is configured in a DataWorks data integration module, multi-source heterogeneous data is accessed through the intelligent data source adapter, and the data source adaptation module is connected with the DataWorks data integration module; extracting a semantic feature vector and a technical feature set; the neural symbol hybrid inference module inputs the semantic feature vector and the technical feature set into a neural symbol hybrid inference engine, calculates a similarity matrix between multi-source heterogeneous data fields through a BERT-based neural network, and imports the technical feature set into a field knowledge graph constructed by Neo4j for symbol logic verification to generate a unified metadata model; according to the method, a semantic similarity matrix calculated by a BERT-based neural network is combined with symbol logic verification of a Neo4j knowledge graph through a neural symbol hybrid inference engine, so that automatic semantic alignment and logic consistency verification of multi-source heterogeneous data are realized.

The invention relates to the technical field of automation and artificial intelligence crossing, in particular to an industrial monitoring and early warning method and system based on a multi-modal large model, and the method comprises the steps: obtaining multi-modal industrial data, carrying out the initial feature processing of the obtained multi-modal industrial data, and carrying out the initial feature processing of the obtained multi-modal industrial data; and performing deep feature extraction fusion based on the extracted preliminary features, taking a multi-modal fusion feature matrix as input to construct a multi-modal large model, performing model training on the constructed multi-modal large model, and performing real-time monitoring and abnormity early warning by using the trained multi-modal large model. According to the method, space-time alignment and feature association are realized through a defect feature matrix of a visual mode, a trajectory similarity matrix of a motion mode and a time sequence reconstruction matrix of a sensing mode and through dynamic time warping and cross-modal parameter sharing; and associated abnormalities of product quality, equipment actions and running states can be captured at the same time, so that the problem that the collaborative analysis capability of a traditional method on multi-source heterogeneous data is insufficient is solved.

The invention provides a generative adversarial-driven intelligent security defense method and system, and solves the problem of dynamic network security defense through three-layer architecture innovation: 1, data fusion layer reconstruction: employing a multi-modal feature extraction engine driven by an MoE architecture, dynamically allocating computing power resources to a plurality of expert models, and improving the heterogeneous data distillation efficiency; an LLM for fine adjustment in the security field is introduced, a cross-modal semantic similarity matrix is constructed, and the accuracy of unstructured threat intelligence analysis is improved; a second dynamic attack and defense layer is constructed, a GPT-4 architecture attack generator is deployed, and generation of a multi-stage APT attack chain is simulated; a double-agent reinforcement learning framework is designed, and the confrontation training efficiency is improved; upgrading a three-cognitive decision-making layer, constructing a dynamic threat map based on a time sequence diagram neural network, and updating an adjacent matrix in real time; a plurality of agent clusters are deployed, the capabilities of encrypted traffic analysis and attack blocking are improved, and the problems of data layer defects, attack and defense confrontation limitation and decision-making layer bottleneck in the prior art are solved.

The invention discloses a supply chain production scheduling optimization method and system based on multi-source data. According to the method, supplier purchase data is extracted through a data crawler, raw material sharing is analyzed in combination with a knowledge graph, and a raw material sharing list is generated; on the basis of delivery records and product attributes, a quality stable value is predicted through linear regression, process similarity is calculated through a text similarity algorithm, and a process similarity matrix is constructed; in combination with historical sales data, a market demand prediction model is constructed by applying multiple linear regression and time sequence analysis. A complex relation among raw material sharing, process similarity and market demands is described through a graph neural network, a product association network graph is generated, an optimal balance point is found between delivery time and a resource utilization rate through Pareto optimization, and a production scheduling scheme is formed; and finally, integrating the performance score, the quality stable value, the raw material sharing list and the production scheduling scheme through an integer programming algorithm, and optimizing a supplier combination. According to the invention, the production scheduling efficiency and precision are improved, and complex business requirements are met.

The invention discloses a tensor depth semi-supervised learning method for high-dimensional small sample data classification. The tensor depth semi-supervised learning method comprises the steps of preprocessing original high-dimensional small sample data; constructing a deep neural network comprising a feature extraction module and a classifier module; constructing a second-order similarity matrix and a third-order similarity tensor based on the low-dimensional embedding representation obtained by pre-training; combining the second-order similarity matrix and the third-order similarity tensor to construct an objective function containing multi-order smooth constraints; then, taking the low-dimensional embedded representation obtained by pre-training as input, performing iterative optimization on the target function by adopting a gradient descent algorithm through a label propagation network formed by a full connection layer, and generating a pseudo label; and inputting the original high-dimensional small sample data, the low-dimensional embedded features and the pseudo labels into the deep neural network, iteratively updating the network in a semi-supervised mode until convergence, and outputting a final prediction result. According to the method, more accurate label propagation is realized, and the semi-supervised classification precision is improved.