89 results about "Lower dimensional space" patented technology

The invention relates to a trajectory prediction model robustness enhancement method based on dynamic subspace projection decomposition. Comprising the following steps: firstly, extracting hidden layer semantic features containing historical tracks and map topology through a multi-modal feature encoder; secondly, constructing a dynamic routing mechanism based on scene self-adaption, and calculating projection weights of input features on a plurality of expert subspaces; then, executing truncation projection operation based on orthogonal decomposition, retaining core semantics located in a low-dimensional space, and filtering out adversarial disturbance located in an orthogonal complementary space; and finally, introducing a feature consistency constraint training mechanism, taking the reconstructed features of the clean sample as anchor points, and compulsively aligning the purified features of the confrontation sample with the anchor points. Compared with the prior art, the method has the advantages that the robustness of the model in white box gradient attack, black box query attack and physical semantic deception scenes is remarkably improved through feature purification of a physical level and structured consistency constraint, and the prediction reliability of the automatic driving system is ensured.

A load feature optimization clustering method based on a one-dimensional convolution auto-encoder comprises the following steps: acquiring load types including daily load curve data as a data set, and performing preprocessing; the method comprises the following steps: constructing a one-dimensional convolution auto-encoder model which comprises an encoder and a decoder, pre-training load data by taking minimization of reconstruction loss as a target, extracting load features, and storing model parameters obtained by pre-training at the same time; the method comprises the following steps: firstly, decoding a network part, removing a decoding network part, retaining an encoding part of feature extraction, constructing a PCA-Kmeans space conversion model, transmitting training data in an encoder, and converting a potential space into a low-dimensional space by using a PCA algorithm to carry out K-means clustering analysis; the encoder is finely adjusted, the encoder is trained by taking minimization of a clustering loss function as a target, and after the encoder is trained for one epoch, K-means clustering is carried out on a newly generated potential space; new clustering distribution is obtained, and power load mode extraction is achieved. Compared with other traditional clustering methods, the method has the advantages that the application complexity is simplified, and the classification efficiency is improved.

The invention discloses a data management system and method based on artificial intelligence, and relates to the technical field of data intelligent processing, and the method comprises the steps: carrying out the frequency spectrum transformation of an encrypted feature vector, calculating the energy density of each frequency spectrum coefficient, constructing a cumulative distribution function, determining an effective frequency spectrum interval based on the energy distribution function, and constructing a projection matrix. Compressing the spectrum vector to a low-dimensional space by using a projection matrix to generate a compressed feature vector; and constructing a neural network model to calculate an abnormal score, setting a detection threshold, performing label detection on the abnormal score, forming a label vector, performing homomorphic decryption on the label vector, and generating a plaintext feature vector. According to the method, a frequency spectrum energy distribution function is introduced on the basis of homomorphic encryption, safe dimensionality reduction and rearrangement of feature vectors are realized by combining periodic mapping and an integer mechanism, the structural identifiability of the feature vectors in a finite field is enhanced, an offset period is optimized through a fluctuation potential function, and the sensitivity to abnormal changes is improved.

Systems and methods are provided for reducing a number of performance metrics generated by network functions to a number of reduced dimension metrics, which can be used to detect anomalous behavior and generate a warning signal of the detected anomalous behavior. The disclosed systems and methods transform raw performance metrics in a high dimensionality space to a reduced number of metrics in a lower dimensionality space through dimensionality reduction techniques. Anomalous behavior in network performance is detected in the high dimensionality space using the reduced dimension metrics. The systems and methods disclosed herein convert the reduced dimension metrics back to the high dimensionality space, such that the performance metrics from network functions can be utilized to understand and address potential problems in the network.

The invention relates to the technical field of wind power plant power control, in particular to a wind power plant dynamic power distribution optimization method based on projection dimensionality reduction. The method comprises the following steps: collecting a high-dimensional original operation data stream of a wind power plant, and preprocessing to output a high-dimensional state vector; extracting a dominant mode matrix through an intrinsic orthogonal decomposition algorithm, and projecting a high-dimensional state vector to a low-dimensional space; future modal coefficient evolution is predicted through an autoregression prediction model, and a low-dimensional rolling optimization problem is constructed and solved through a sequential quadratic programming algorithm; reconstructing the optimal low-dimensional modal coefficient vector into a target power instruction of each fan, and issuing and executing the target power instruction; and updating the dominant mode matrix through an incremental singular value decomposition algorithm and correcting parameters of the autoregressive prediction model. According to the method, projection dimensionality reduction from high-dimensional data to a low-dimensional space is realized through intrinsic orthogonal decomposition, and the problem of low optimization solution efficiency caused by high data dimensionality of a traditional method is solved.

A system, method, and computer-program product includes receiving a plurality of vector embeddings having an initial dimensionality and projecting the plurality of vector embeddings into lower-dimensional spaces using at least two different dimension reduction algorithms to generate corresponding sets of projected vector embeddings. Each set of projected embeddings may be quantized and nearest neighbors for the original embeddings and for each quantized set of projected embeddings may be calculated. Additionally, a neighbor preservation metric may be evaluated for each quantized set by comparing its nearest neighbors to those of the original embeddings. Based on the neighbor preservation metrics and a predefined error tolerance, an optimal compression configuration may be selected.

This invention discloses a real-time attitude estimation system for non-cooperative industrial targets based on strong tracking filtering, relating to the fields of industrial machine vision and automation control technology. This system decouples the complete six-degree-of-freedom attitude estimation problem into a linear translational sub-state space and a nonlinear rotational sub-state space. This decomposes the nonlinear calculations that would normally be performed in six dimensions into a three-dimensional linear problem and a three-dimensional nonlinear problem. The translational sub-state only performs a simplified Kalman gain iteration with closed-form solutions, and matrix operations are simplified to low-order linear recursion. Although the rotational sub-state uses strong tracking filtering, it only needs to perform nonlinear updates in the reduced low-dimensional space. The Jacobian matrix sparsification pre-computation and incremental update unit pre-stores the constant part of the observation equation's derivative calculation offline and only incrementally updates the sparsely changing part online. This allows the system to be fully deployed on ARM architecture edge devices and stably meet millisecond-level processing requirements.

The application discloses a dimension reduction processing method for single-cell RNA sequence data sets, and comprises the following steps: searching for k nearest neighbors (KNN) of each cell and counting RNN through the KNN; traversing the cells in descending order of RNN, and if the point is extracted, the KNN of the cell is removed until all cells are traversed, and then the anchor point cells obtained by sampling are output; constructing a KNN graph network of all anchor point cells, calculating the shortest path between all anchor point cells, and calculating their high-dimensional probability distribution; reducing all anchor point cells to a low-dimensional space and constantly updating the low-dimensional coordinates until the iteration condition is met and the iteration is terminated; for each non-anchor point cell that is not sampled, the mapping relationship between the low-dimensional coordinates before and after updating of d+1 anchor point cells is used to calculate the low-dimensional space coordinates of the non-anchor point cell; and integrating the low-dimensional coordinates of all anchor point cells and non-anchor point cells, and outputting the low-dimensional coordinates. The application greatly improves the dimension reduction efficiency and reduces the complexity of calculation.

The application discloses a kind of multidimensional timbre perception space model modeling method based on electroencephalogram characteristics, which comprises the following steps: collecting a variety of musical instrument single timbre samples and pretreating;To timbre sample, extract acoustic time-frequency domain feature, and extract psychological perception feature by behavior psychology experiment;Multiple musical instrument timbre samples are used as auditory stimulus to carry out electroencephalogram experiment, and corresponding event-related potential ERP signal is extracted as electroencephalogram feature;The dissimilarity of different timbre characteristics is represented by the Euclidean distance of different timbre points;The distance between different timbre points is calculated;According to the Euclidean distance matrix between sample timbre points, a low-dimensional space with mutually orthogonal dimensions is fitted, the dissimilarity of timbre feature vector is transformed into low-dimensional space, the timbre feature vector is directly mapped into low-dimensional space, a point set is formed, and the similarity and dissimilarity of each musical instrument timbre are directly displayed.The application represents the mapping relationship between acoustic characteristics, psychological perception characteristics and electroencephalogram characteristics.

The invention discloses a multi-mode body intelligent agent track prediction method, which comprises the following steps of: receiving and processing input data, and standardizing dynamic and static contexts; an encoder-feature fusion device-decoder architecture model is constructed, the encoder maps high-dimensional features to a low-dimensional space and keeps key information, the feature fusion device fuses the features, the decoder extracts a trajectory mode probability, Gaussian noise is injected to enhance variation capture, and a multi-modal three-dimensional trajectory sequence is generated through GRU; and dynamically selecting an optimal path in combination with real-time environment feedback to complete prediction. Through standardization processing, multi-feature fusion and noise injection, prediction accuracy and environmental adaptability are improved, and efficient trajectory prediction is realized.

The application discloses a kind of multi-scale context feature's laser point cloud semantic segmentation method and system, method includes: S1, obtains original laser point cloud, pre-processes point cloud, extracts point cloud initial feature, obtains effective point cloud to be segmented;S2, laser point cloud is transformed into distance image by spherical projection formula;S3, distance image is input into multi-scale context feature fusion module, uses multiple convolution kernels to carry out convolution operation to distance image, obtains 64-dimensional feature map f M ;S4, f M Encoding is carried out by encoder, and decoder is spliced distance image deep feature and edge feature to obtain output result;The probability of each semantic label of output result is calculated, and the maximum probability semantic label is taken as the semantic segmentation result of distance image;S5, result is inversely mapped back to three-dimensional space, and laser point cloud semantic segmentation is completed.The application better retains point cloud feature on low-dimensional space, improves point cloud semantic average intersection-over-union value, and improves the segmentation result of laser point cloud.

The invention discloses a multi-label smell description prediction method, and relates to the field of compound smell prediction, and the method comprises the steps: obtaining compound identification information, molecular structure descriptors and smell label data, and constructing a multi-label smell data set; generating a molecular structure feature vector through a molecular fingerprint coding technology, and extracting a multi-dimensional descriptor reflecting the physicochemical properties of molecules; compressing the molecular fingerprint features to a low-dimensional space through a dimension reduction algorithm; performing unbalanced data processing on the training set, fusing the dimension-reduced molecular fingerprints with the molecular descriptors to form a joint feature matrix, and configuring a class weight balance mechanism and overfitting suppression parameters by adopting a multi-label classification architecture; independently optimizing a probability threshold for each odor label based on the verification set; and outputting a multi-odor label combination prediction result according to the target molecule identification information. According to the scheme, the multi-odor characteristics of the compound can be accurately depicted, and the combined recognition accuracy of the compound odor is remarkably improved.

The application discloses a kind of multi-mode optical fiber wavelength multiplexing wide field imaging quality promotion methods based on dynamic calibration.The method comprises the following steps: collecting the output speckle pattern corresponding to each wavelength after being transmitted by multi-mode optical fiber;The speckle patterns of different wavelengths are spatially registered and spliced to form a high-dimensional composite speckle vector;PCA is used to bidirectionally compress the dimension of input image and composite speckle vector group, and a linear wavelength multiplexing inverse transmission matrix (WITM) is dynamically estimated and updated in low-dimensional space, and real-time pre-reconstruction and continuous monitoring are realized;Set up optical fiber state monitoring module to analyze pre-reconstruction frame in real time, when structural disturbance or optical fiber deformation is detected, automatically trigger in situ dynamic calibration, realize the rapid adaptive update of WITM, without retraining network.The updated WITM will guide the diffusion neural network image optimizer to iteratively denoise and detail repair the pre-reconstruction result, significantly improve the image fidelity and structure restoration ability.

The invention discloses a beam moving gantry crane winch fault diagnosis method, and relates to the technical field of fault diagnosis, and the method comprises the steps: carrying out the wavelet decomposition and reconstruction of a gantry crane winch bearing monitoring signal, and generating denoising data; respectively inputting the de-noised data into a first feature extraction path and a second feature extraction path, generating a symbolized embedded vector by the first feature extraction path through standardization, symbolization and phase-space reconstruction processing, and calculating a relative entropy feature value of mode distribution; the second feature extraction path calculates a yoke divergence matrix and generates a dependency feature value by constructing an original embedding vector and a delay embedding vector; constructing two-dimensional feature vectors, and calculating the Euclidean distance of the feature vectors between the samples to generate a distance matrix; performing multi-dimensional scale transformation on the distance matrix to generate low-dimensional space distribution coordinates of the sample; and determining a fault category based on distance matching between the low-dimensional space distribution coordinates and a known fault category space region. And accurate diagnosis of different faults is realized.

The invention provides a complex curved surface forming deviation evaluation method and system based on equidistant mapping, and the method comprises the steps: extracting the boundaries of a measurement point cloud and a standard point cloud, and carrying out the rough registration of the point clouds with the respective boundaries as reference; constructing an adjacent graph for each of the original high-dimensional measurement point cloud and the standard point cloud; calculating a geodesic distance based on the adjacent graph, and obtaining a shortest path matrix; mapping the shortest path matrix to a low-dimensional space at equal intervals, and expanding curved surfaces of the measurement point cloud and the standard point cloud to obtain a plane point cloud; fine registration is carried out by using the plane point clouds expanded by the measurement point cloud and the target point cloud; and comparing the measurement point cloud subjected to fine registration with the target point cloud to obtain a deviation cloud picture. According to the point cloud registration based on equidistant mapping, the deviation between the measurement point cloud and the standard digital model can be efficiently and accurately evaluated, and reliable data support is provided for secondary processing technology planning. And the method is particularly suitable for the integral wall plate with weak-texture variable-curvature molded surface characteristics.

The embodiment of the invention provides a high-name-duplication user identification and retrieval method and device based on multi-feature fusion, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring a doctor-seeing information record of a to-be-tested person; obtaining N tuple data of the to-be-tested person based on the treatment information record; obtaining low-dimensional space expression data of the to-be-tested person according to the N-tuple data of the to-be-tested person; calculating the similarity between the low-dimensional space expression data of the to-be-tested personnel and the low-dimensional space expression data of the pre-processed personnel relation knowledge graph data; and extracting N tuples of which the similarity ranks at the top M in the personnel relation knowledge graph data as candidate results. The knowledge graph is constructed by utilizing the historical information of the hospital, so that the rapid matching of the information of the patient is realized.

The invention relates to the technical field of intelligent generative model and diffusion model sampling, and discloses a rapid robust sampling method based on a hierarchical scheduling optimizer and a diffusion model, comprising the following steps: constructing a hierarchical optimizer, and performing global search on the upper layer in a low-dimensional space to output an optimal initialization strategy; the lower layer receives the initial scheduling generated by the strategy and carries out local optimization in a high-dimensional space; according to the local optimization, midpoint error proxy is taken as a target, an interval penalty fitness function is adopted for evaluation, the fitness function is combined with midpoint error proxy and a penalty over-short-step-length robustness item, and an evaluation result is fed back to an upper layer to form closed-loop iterative optimization. The high-dimensional scheduling problem is decomposed into double-layer optimization, the dimension disaster is effectively avoided, the search efficiency is remarkably improved, and the innovative error proxy target and the fitness function ensure that the finally generated sampling scheduling has low theoretical error and high actual robustness.

The invention relates to the technical field of electric digital data processing and pattern recognition, and discloses a wind turbine generator-oriented multi-dimensional time series data abnormal feature recognition method, which comprises the following steps of: acquiring a multi-source time series data stream of a controlled object state, and constructing a feature stream track of a low-dimensional space based on a manifold learning algorithm; resolving a topological acceleration component of the trajectory relative to the logic time axis; verifying track evolution legality by using physical inertia characteristics; according to the method, the logic smooth correction of the pulse interference is realized by utilizing the physical motion continuity constraint, and the early warning oscillation is inhibited; a recessive degradation trend is captured by extracting an orthogonal residual feature flow, early warning trigger is established before curvature fluctuation, and logic decoupling of ontology anomaly and environmental disturbance is realized in combination with spatial correlation.

The invention discloses a sound signal periodic feature extraction method, a network model training method, a storage medium and equipment, and belongs to the technical field of sound event detection. The objective of the invention is to solve the problems of high sensing difficulty and poor decoupling effect of overlapped acoustic events in the current acoustic detection process. The method comprises the following steps: for a sound signal i, mapping the sound signal i to a low-dimensional space through two different linear layers to obtain p and g, and respectively carrying out expansion convolution operation on p and g to obtain pconv and gconv; for p and g, feature coding is carried out based on a Fourier basis function and a gating mechanism to obtain Fourier features, for pconv and gconv, Fourier features are obtained in the same mode, and Hadamard product is carried out on the pconv and the gconv to obtain representation of periodic features. And in the training process of the corresponding model, performing reconstruction error on the sum and the original signal i, respectively calculating two norms of the sum, and adding the two obtained two norms to obtain a Fourier series regular term for training the model.

A bridge RC pile reinforcement design method and system based on a two-stage diffusion model of mask information constraint, comprising: obtaining the RC pile structure reinforcement design requirements to be processed; extracting key information from the design requirements and performing matrix processing to generate a pile foundation design mask matrix; sampling from random Gaussian noise to obtain an initial noise tensor, inputting the pile foundation design mask matrix and the initial noise tensor into a pre-trained RC pile reinforcement diffusion network model constrained by mask information, and repeatedly executing the step of the pre-trained diffusion network model until the iteration time step reaches a preset value to obtain an RC pile reinforcement design latent feature tensor; inputting the RC pile reinforcement design latent feature tensor in the low-dimensional space into a pre-trained variational autoencoder to obtain an RC pile steel reinforcement arrangement design drawing in the pixel space. The present application realizes efficient and reliable intelligent pile structure reinforcement design and belongs to the field of civil structure engineering and computer deep learning application technology.

The invention relates to the technical field of natural language processing, in particular to a model prefix parameter and hyper-parameter joint optimization method and system. Comprising the following steps: S1, acquiring a high-dimensional prefix parameter and hyper-parameter joint optimization instruction; s2, generating a random projection matrix, and mapping the high-dimensional prefix parameters into low-dimensional prefix parameters through the projection matrix; s3, searching an optimal low-dimensional prefix parameter in a low-dimensional space through a covariance matrix adaptive evolution strategy, reconstructing the low-dimensional prefix parameter into a dynamic high-dimensional MLP parameter, generating a key-value prefix required by each layer of the model, and embedding and injecting the key-value prefix into the large model; s4, training hyper-parameters are dynamically adjusted based on a particle swarm algorithm and a cosine annealing strategy, and the hyper-parameters are transited step by step in the training process; s5, training the model based on the optimal prefix parameter and the hyper-parameter, and outputting the trained model; and S6, applying the trained model to the medical question and answer task. According to the method, the precision and stability of prefix tuning in the medical question and answer task can be improved.

The invention relates to the technical field of metallogenic prediction, in particular to a metallogenic prospective area prediction system and method based on embedded knowledge reasoning, and the system comprises a collection module which is used for collecting exploration data and geological map data; the data preparation module is used for cutting the exploration data into fishing net units; the knowledge graph construction and embedding module is used for constructing a knowledge graph database by utilizing the geological map data and embedding entities and relationships into a low-dimensional space; the knowledge-data fusion module is used for generating a knowledge-data fusion matrix of the corresponding fishing net unit for each fishing net unit; the model training and optimizing module is used for inputting the knowledge-data fusion matrix of the labeled fishing net unit into a deep learning network to construct a metallogenic prediction model; and the metallogenic prediction and result interpretation module is used for calculating the metallogenic probabilities of all the fishing net units in the research area by using the metallogenic prediction model. According to the method, knowledge graph embedding and exploration data are subjected to structured fusion in an input layer, so that the model not only learns a data mode, but also can understand a geological background.

The invention relates to a petrochemical engineering emergency early warning method and device based on large model data distillation. The method comprises the steps of obtaining original data, preprocessing and standardizing the original data, and constructing a data set. The method comprises the following steps: mapping a data set from a high-dimensional space to a low-dimensional space through linear transformation, and performing eigenvalue decomposition on a covariance matrix of data in the data set to obtain eigenvalues and eigenvectors; and sorting the feature vectors according to the feature values from large to small, selecting the feature vectors of which the feature value ranking is not lower than a first threshold value to form a new feature space, and extracting important features from the feature space through LASSO regression. And calling a CNN deep learning model to extract key features from the important features, distilling representative features in the original data, and outputting a feature set after distillation. And performing anomaly detection on the petrochemical engineering system through a clustering algorithm according to the feature set after distillation, and triggering early warning when a detection result does not meet a preset condition.

A computer system and associated processes for grouping similar real estate properties into contiguous neighborhoods and generating neighborhood-specific models capable of estimating property values within their neighborhoods. An artificial intelligence system directed to using a graph neural network framework to identify relationships between different parcel groups based on similar property features and embed the parcel groups into low dimensional space vectors. The method can include generating a graph and features relevant to the parcel groups that can train an embedding function that generate an embedding vector for each parcel group in a geographic unit grouping, such as a census tract. Embedding vectors of two or more parcel groups can then be compared to each other to determine whether the parcel groups are similar or to determine a housing valuation of a parcel group.

The invention discloses a hydraulic ring geological disaster intelligent identification method based on deep learning, and the method comprises the following steps: 1, collecting original data of a plurality of sensors, and carrying out the preprocessing of the original data, and generating the original data of a unified structure; 2, projecting the original data of the unified structure to a low-dimensional space through local linear embedding, and generating a spatial feature vector; 3, calculating a Manhattan distance between the spatial feature vectors to construct a cumulative distance matrix, obtaining an optimal alignment path, and generating a time sequence feature vector; 4, analyzing the persistent features of the time sequence and spatial features, and constructing a persistent feature map; 5, generating a multi-dimensional feature vector set through an improved Markov random field; and 6, performing anomaly detection according to the Gaussian mixture model, generating disaster early warning information, and performing visual output. According to the method, multi-sensor data and deep learning are fused, and the accuracy and real-time performance of hydraulic ring geological disaster early warning are improved.

This invention discloses an approximate nearest neighbor connection optimization method for encrypted databases based on PM# trees, belonging to the field of approximate nearest neighbor connection optimization technology for encrypted databases. It solves the problems of low retrieval efficiency and insecure retrieval processes in existing technologies. The method includes: projecting a high-dimensional dataset into a low-dimensional space using hash projection to obtain a low-dimensional dataset; encrypting the high-dimensional dataset to obtain an encrypted high-dimensional dataset; deleting redundant nodes generated during node splitting in the PM# tree to obtain a PM# tree, and using the PM# tree to build an index on the high-dimensional dataset to obtain an index file; encrypting the index file to obtain an encrypted index; and performing an approximate nearest neighbor connection query on the high-dimensional dataset based on the encrypted index, the low-dimensional dataset, and the encrypted high-dimensional dataset to obtain the query results. This achieves encryption of the high-dimensional dataset while reducing computational load, thus accelerating retrieval speed and improving retrieval quality.

The invention discloses an unsupervised dimensionality reduction visualization method for cell image data. The method comprises the following steps: acquiring an unlabeled cell image high-dimensional data set; processing the high-dimensional data set by adopting an unsupervised dimension reduction algorithm to generate low-dimensional embedding representation; generating a first visual chart based on the low-dimensional embedded representation to display the distribution of the data in the low-dimensional space; performing unsupervised clustering analysis on the data points in the low-dimensional embedding representation, and identifying at least one clustering center point; generating a second visual chart based on a clustering analysis result, and marking a clustering center point in an identifiable manner; according to the method, the clustering center point can be accurately identified, and a visual data distribution overview is provided for a user.

The invention belongs to the technical field of data processing, and particularly relates to an online analysis instrument nonlinear error correction method based on manifold learning, and the method comprises the steps: obtaining a high-dimensional feature vector set containing original physical quantity readings and auxiliary environment parameters; and calculating a signal transient response entropy and an environmental coupling stress index of the data of each dimension, and further deducing a manifold tangent space distortion rate representing the bending degree of a data structure. On this basis, distortion weighted distance measurement is constructed to replace a traditional Euclidean distance, high-dimensional features are mapped to a low-dimensional space by using an improved local linear embedding algorithm, and finally an error prediction model is established through a least square support vector machine. According to the method, physical perception measurement is introduced, so that the problems of data manifold curling and Euclidean distance failure caused by sudden change of the environment are reduced, neighborhood selection errors are avoided, and the measurement precision and stability of the instrument in the multi-physics coupling environment are improved.

This invention discloses a method for constructing a digital twin for a water electrolysis hydrogen production system, relating to the field of digital twin technology. The method includes: collecting multi-source operational data from the water electrolysis hydrogen production system, extracting all sampling points, constructing proximity relationships between all sampling points to form a proximity connection graph; traversing the proximity connection graph, organizing the shortest path distances between all pairs of sampling points to form a high-dimensional spatial distance matrix, using multi-dimensional scaling transformation to map the sampling points to a low-dimensional space, establishing a correspondence between the low-dimensional coordinate vectors and the high-dimensional original data, forming a digital twin state representation model; based on the state representation model, constructing a probabilistic graphical model containing three types of nodes: components, sensors, and state variables; setting initial node confidence and information transmission rules, iterating until the confidence stabilizes; performing corrections based on node state deviations; and iterating in real-time according to the correction cycle to achieve real-time synchronization between the digital twin and the water electrolysis hydrogen production system.