4959 results about "Autoencoder" patented technology

The invention provides a home abnormal state signal detection method and system based on multi-modal sensing, and relates to the technical field of detection, and the method comprises the steps: collecting human body motion, acoustics and environment parameters through a millimeter wave radar, an acoustic sensor and an environment sensor, inputting the parameters into a deep fusion network, a spatio-temporal attention mechanism and a multi-scale convolutional neural network are used to extract a spatio-temporal feature sequence, and joint probability features are formed in combination with an acoustic feature spectrum analysis result. And inputting the joint probability features into a time sequence knowledge graph, and obtaining scene adaptive features after processing by a graph attention network and a double-flow auto-encoder network. And finally, the multi-task learning network is combined with the risk propagation neural network and the recursive neural network to carry out anomaly prediction and risk level evaluation, and is combined with historical early warning information to output an early warning strategy. According to the invention, multi-modal data can be effectively fused, the accuracy and reliability of home abnormal state detection are improved, and a more accurate risk assessment and early warning strategy is provided.

The invention provides a thermal power equipment real-time monitoring method and system based on edge computing, and relates to the technical field of thermal power equipment real-time monitoring, and the method comprises the steps: deploying an edge computing node array to collect multi-source heterogeneous data of thermal power equipment, and carrying out the preprocessing through data screening, multi-scale adaptive filtering and wavelet packet decomposition, a conditional variation auto-encoder is used to extract features, a hierarchical attention mechanism and a deep feature fusion network are combined to generate mixed feature representation, refined distribution estimation and abnormal mode recognition are performed on equipment states, cooperative monitoring modeling is performed based on a multi-scale spatial-temporal feature fusion network and a hierarchical depth deterministic policy gradient network, and a multi-scale spatial-temporal feature fusion network is established. The real-time monitoring accuracy and efficiency of the thermal power equipment can be effectively improved, the equipment failure rate is reduced, and safe and stable operation of a thermal power plant is guaranteed.

A method is provided for estimating the number of distinct tokens in a text stream using a modified text-to-text variational autoencoder (T5VQVAE) model. The method includes receiving a continuous input of a text stream; dynamically maintaining a buffer that stores a probabilistic subset of tokens from the text stream; calculating a sampling probability for each token based on a condition related to the current state of the buffer; updating the buffer based on the sampling probability to include or exclude tokens; encoding the buffered tokens into a latent space using the T5VQVAE model; and estimating the number of distinct tokens in the text stream based on the tokens in the buffer and the corresponding sampling probabilities.

The invention discloses an informatization project management system based on big data analysis, which belongs to the field of big data and comprises a data acquisition module, a time sequence modeling module, a task coupling analysis module, a risk clustering identification module, a resource allocation prediction module and the like. The data acquisition module asynchronously and parallelly acquires structured and unstructured data and uniformly encodes the structured and unstructured data; the time sequence modeling module constructs a multi-dimensional time sequence based on an autoregressive residual network; the task coupling analysis module fuses the task trajectory and the dependency relationship to generate a task influence directed graph; the risk clustering identification module identifies risks through variational graph auto-encoder mapping; the resource allocation prediction module constructs a dynamic resource priority based on a graph attention mechanism; a progress deviation traceability module identifies a deviation causal chain; the knowledge graph decision-making module corrects resource priorities and path strategies in a cross-graph manner; and the project global control module dynamically adjusts key paths and resource configuration and performs closed-loop self-correction. The beneficial effect is that the intelligent level and the risk response capability of project management are improved.

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention discloses a base station facility abnormity intelligent monitoring method and system, and the method comprises the steps: employing a multi-scale wavelet fusion pulse neural network to construct a dynamic signal perception topology according to the real-time data of a base station physical layer, and generating a space-time aligned base station full-dimension feature tensor; inputting the full-dimensional feature tensor of the base station into a fault mode confrontation distillation module, reconstructing a normal working condition manifold of the base station based on a physical constraint variational auto-encoder, and outputting an abnormal feature vector with a fault fingerprint identifier; fault propagation graph modeling is carried out on the abnormal feature vectors, and a base station fault root cause topological graph is generated in combination with back propagation credibility verification; and inputting the base station fault root cause topological graph into a toughness self-healing strategy generator, and finally outputting a base station facility self-healing strategy set meeting real-time constraint. By utilizing the embodiment of the invention, the accuracy and the real-time performance of abnormity monitoring can be improved, and the operation reliability and the communication service quality of the base station are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent anomaly recognition and intervention processing method, device, equipment and medium. The method comprises the following steps: carrying out feature fusion by using a gating fusion network and generating a preliminary abnormal score, determining a reconstruction error through an auto-encoder and triggering abnormal early warning, calculating a causal effect value of key features in combination with a causal graph model and anti-factual reasoning, and calibrating the abnormal score to generate a final abnormal score and an intervention instruction. And executing an intervention action and recording a result. According to the method, the multi-dimensional feature information and the causal reasoning mechanism are fused, the self-encoder reconstruction error is combined to carry out anomaly judgment, the intervention instruction is generated and executed, closed-loop control of anomaly detection, reasoning analysis and intervention execution is achieved, and the recognition accuracy of complex events and the system response capacity are improved.

The invention discloses a multi-modal-based power equipment health state monitoring method, and relates to the technical field of power equipment detection.The power equipment monitoring method is based on multi-modal data and knowledge graph fusion, cross check, expert rule cleaning and label correction are implemented by collecting sensing data such as chromatography, temperature, current and vibration in oil, and the detection result is obtained. Outputting high-credibility data; a graph model is constructed based on equipment topology by adopting self-encoder dimension reduction fusion, early anomaly detection is realized by utilizing a graph neural network, and an anomaly alarm is generated; mechanism matching and consistency evaluation are carried out based on the fault mechanism knowledge graph, and interpretable diagnosis is output; and when the diagnosis result is significantly deviated from the actual operation and maintenance conclusion, triggering an online increment and transfer learning updating model and expanding the knowledge graph to form a closed-loop self-learning mechanism. According to the method, the fault detection accuracy is remarkably improved, false alarms and missing alarms are reduced, the operation and maintenance decision-making efficiency is improved, and meanwhile operation and maintenance intelligence and real-time alarm are enhanced.

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

The invention relates to an underground equipment fault early warning and diagnosis method based on big data analysis. The method is suitable for equipment operation state monitoring and intelligent diagnosis in underground operation scenes such as mines. The method comprises the following steps: collecting multi-source data such as an equipment running state, environment parameters and operation behaviors and preprocessing the multi-source data; multiple signal features are extracted and fused to construct a unified feature vector; performing health modeling by using the residual self-encoder model to generate a health index; an early warning threshold value is dynamically set through clustering analysis and Bayesian reasoning, and anomaly recognition is achieved; after early warning is triggered, fault type identification is carried out by adopting the fusion discrimination model; performing causal reasoning and maintenance suggestion generation based on the equipment fault knowledge graph; and continuously optimizing the model in combination with operation and maintenance feedback information, and constructing a closed-loop diagnosis mechanism. The method has the characteristics of high recognition precision, high response speed, explainable result and sustainable optimization of the model.

The invention relates to a multi-modal AI data fusion processing method, device and equipment and a medium, and the method comprises the steps: firstly extracting visual, auditory and text modal features through a pre-training encoder, executing dimension alignment, and generating a standard data feature set with unified dimensions; a cross-modal semantic graph is constructed based on a cosine similarity algorithm, and the problem of semantic mismatch of heterogeneous data is solved; residual enhancement is carried out on the map nodes, and noise interference is eliminated; fusing the optimized features and the semantic topology in combination with a graph convolutional network to generate aggregation graph representation; the fusion features are mapped to a low-dimensional semantic space through a variational auto-encoder, and cross-modal correlation essence is captured; the key dimension contribution degree is quantified, a visual report is generated, and semantic association rules among modals are disclosed, so that the dimension isomerism limitation of a traditional fusion technology is broken through, quantifiable cross-modal semantic mapping is established, the whole process traceability from feature fusion to decision interpretation is realized, and the method is suitable for popularization and application. And the multi-modal decision black box problem in the fields of medical diagnosis, automatic driving and the like is effectively solved.

The invention discloses a power grid abnormal flow detection method based on multi-modal data fusion, and the method comprises the steps: collecting the multi-source heterogeneous data of a power grid through an edge calculation node, including the current waveform of an intelligent electric meter, the state variable of an SCADA system, network protocol metadata and an equipment log event; performing space-time alignment preprocessing on the original data, converting an unstructured log into a time sequence by adopting a sliding window mechanism, and eliminating sensor noise through an LSTM auto-encoder; constructing a multi-dimensional feature space which comprises a frequency domain feature, a spatial-temporal feature and a protocol feature, and obtaining a feature vector; the feature vectors are input into a hybrid detection model, the model is composed of an isolated forest algorithm, an improved CNN-LSTM classifier and an information entropy-based rule engine which are connected in parallel, and a dynamic weight fusion strategy is adopted to output an abnormal probability; and when the abnormal probability exceeds a dynamic threshold, triggering a multi-level response mechanism: sending a traffic shaping instruction to the edge device, generating a device fingerprint portrait on the cloud platform, and storing abnormal event features through a block chain.

A system and methods for processing diverse financial data types using a jointly trained Vector-quantized Variational Autoencoder (VQ-VAE) and neural upsampler. This system efficiently handles time-series, textual, sentiment, and structured tabular data through specialized encoding modules. A novel fusion module integrates these encodings, capturing cross-modal relationships via attention mechanisms and gated fusion units. The fused representation is compressed into a discrete latent space by the VQ-VAE encoder, then reconstructed and enhanced by the VQ-VAE decoder and neural upsampler, respectively. Joint training optimizes all components simultaneously, using a comprehensive loss function that balances reconstruction quality across modalities with upsampling performance. This approach enables superior data compression, reconstruction, and analysis, leveraging inter-modal correlations to improve financial forecasting, risk assessment, and decision-making. The system's ability to explore the latent space facilitates generation of new, synthetic financial scenarios for robust model testing and strategy development.

A system and methods for implementing adversarial-robust compression and reconstruction using a vector quantized variational autoencoder (VQ-VAE) with secure latent space management. The system provides comprehensive protection against adversarial attacks through multi-channel threat detection, adaptive defensive parameters, and coordinated response mechanisms. Input data is continuously monitored for potential threats, and defensive parameters are dynamically adjusted based on detected threat levels. The system implements bounded constraints and hierarchical projections to maintain latent space security while preserving compression efficiency. Multi-stage reconstruction with progressive validation ensures reliable data recovery even under adversarial conditions. The system coordinates defensive responses across all compression and reconstruction processes, implementing various recovery mechanisms when security violations are detected. This approach enables robust compression and reconstruction of time-series data while maintaining protection against various forms of adversarial manipulation.

The present disclosure relates to the technical field of videos. Disclosed are a video anomaly event detection method, apparatus and device, and a storage medium. The method comprises: acquiring the current video frame of an industrial site, and reconstructing, by means of an anomaly detection model, a current reconstructed frame corresponding to the current video frame, wherein the anomaly detection model is a model constructed on the basis of a two-stream autoencoder network, the two-stream autoencoder network being used for extracting a spatial feature and a temporal feature; calculating the current dynamic threshold of the current video frame, wherein the current dynamic threshold is used for performing anomaly detection on the current reconstructed frame; and on the basis of the current dynamic threshold and the current reconstructed frame, detecting abnormal events in the industrial site. In the present disclosure, whether abnormal events occur is detected by means of an anomaly detection model and a dynamic threshold value; therefore, the reconstruction capability of the model on the abnormal events can be suppressed, and an auto-encoder is encouraged to produce a higher reconstruction error for the abnormal events, thus reducing the problems of false detection or missed detection, and improving the accuracy of video anomaly event detection.

The invention discloses an intelligent monitoring system for a municipal drainage pipe network, and particularly relates to the technical field of drainage pipe network monitoring. The node operation mode identification module carries out real-time classification and confidence evaluation on the operation state of the pipe network, constructs a multi-attribute pipe network weighted graph based on pipe diameter difference, gradient and confluence density, and extracts multi-scale features through graph Fourier transform. A hybrid anomaly detection link is constructed in combination with an LSTM self-encoder, an isolated forest model and chemical oxygen demand and turbidity water quality verification, and the problems that traditional single-index monitoring is prone to false alarm and missing alarm and inaccurate in positioning are solved; sensor data compensation is realized through cooperation with digital twinning, a rapid detection mode is started during rainstorm early warning, key nodes are processed preferentially, and drainage scheduling is controlled in a closed-loop mode; and target nodes which are easy to accumulate grease are screened based on pipe network topology connectivity, accumulation risks are predicted by fusing multi-sensor data, and a preventive clearing instruction is triggered.

A method is provided for mapping the latent space of a Vector Quantized Variational AutoEncoder (VQ-VAE) to polynomial basis vectors. The method includes training a VQ-VAE model on a dataset to obtain a set of codebook vectors representing the latent space; defining a polynomial basis for the latent space, the polynomial basis containing terms up to a predetermined order; mapping each codebook vector to the polynomial basis by determining polynomial coefficients that represent each codebook vector in terms of the polynomial basis; and using the polynomial coefficients to reconstruct and manipulate latent space representations.

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

A method for structuring the latent space of an autoencoder is provided. The method includes analyzing natural language descriptions related to input data; creating language-guided libraries that categorize and abstract data features based on the analyzed descriptions; mapping input data into the categorized and abstracted features within the latent space of the autoencoder; and training the autoencoder to minimize reconstruction loss while adhering to the structure imposed by the language-guided libraries.

The invention discloses a high-resolution radar echo extrapolation prediction method fused with satellite data, and the method specifically comprises the following steps: firstly, inputting historical radar echo sequence preprocessing at a previous T moment, including denoising, normalization processing and data set segmentation, and obtaining cleaned data; then, through a deterministic modeling method (SimVP), a fuzzy prediction sequence of a future T duration is obtained, then a variational auto-encoder (VAE) maps an original radar echo image and the fuzzy prediction sequence to a low-dimensional potential space, and two-stage diffusion modeling is carried out on the basis; in the first stage, a space-time converter (ST-Translator) is used to extract space-time evolution characteristics of radar echoes; in the second stage, satellite data at the corresponding time of the previous T moment is input, preprocessing including normalization processing, feature selection and data set segmentation is carried out, cleaned data is obtained, and the influence of the satellite data is dynamically adjusted in the diffusion process by adopting a multi-source fusion denoising network Fsrform so as to make full use of satellite information; and finally, inversely transforming output results of the two stages into a pixel space to obtain a high-resolution radar echo extrapolation prediction result of the future T duration. According to the invention, computing resource consumption can be effectively reduced, and the precision and detail fidelity of short temporary rainfall prediction are improved.

The invention discloses an electric power system abnormal remote signaling detection method based on a graph auto-encoder model, and the method comprises the steps: collecting measurement data of an electric power system, carrying out the preprocessing, obtaining a graph data set with an abnormal label, and dividing the graph data set into a training set and a test set; inputting the graph data in the training set into the graph auto-encoder model for training; after training is completed, abnormal score distribution is counted based on normal edge samples in a training set, a threshold value is set to serve as a follow-up judgment basis, and threshold value selection takes the accuracy rate and the recall rate on a test set as an adjustment and optimization target; in a test stage, image data in a test set are input to carry out edge feature reconstruction and anomaly scoring, anomaly judgment is carried out on edges in combination with a set threshold value, and a preliminary abnormal edge detection result is output; and the output abnormal edge detection result is input into the graph restoration module, the restored edge structure and edge features are output, the damaged remote signaling state in the power grid is restored, and the integrity of the graph structure and the operation credibility of the power system are improved.

The invention discloses an intelligent low-code development method and system based on deep learning model optimization, and relates to the technical field of deep learning. A multi-dimensional domain knowledge graph is constructed, a three-dimensional space-time fusion training sample set is constructed based on the domain knowledge graph, and cross-modal feature alignment is performed on the training sample set, so that the multi-dimensional domain knowledge graph is constructed; the method comprises the following steps: generating an executable logic flow template, encoding the executable logic flow template into a Markov decision process, and performing joint strategy optimization on an optimization target of logic flow by integrating a feature importance index generated by a gradient back propagation path and a multi-target reinforcement learning framework of a Pareto leading edge analysis module. Extracting a strategy parameterization sequence after joint strategy optimization, injecting the strategy parameterization sequence into a dynamic verification sandbox environment, and performing abnormal mode detection and feedback type parameter distillation iteration on an execution track through an online variational auto-encoder to complete dynamic adjustment of the strategy; and the elasticity, the stability and the expandability of the low-code platform are improved.

The invention relates to the field of power equipment, and discloses a power equipment fault early warning system, which comprises a data acquisition module, a data fusion module, a state evaluation module, a trend prediction module, an early warning judgment module and an information interaction module. Key operation parameters are cooperatively acquired through multiple types of sensors, time series data are uniformly calibrated by adopting a timestamp mechanism, the problems of fragmentation of operation state information of power equipment and superposition of acquisition errors are effectively solved, and then feature fusion and dimension reduction compression are performed on high-dimensional heterogeneous data by introducing a principal component analysis and auto-encoder neural network, so that the operation state information of the power equipment is acquired. According to the method, redundant information is eliminated, meanwhile, key discrimination features are reserved, the sensing dimension of the system for the equipment operation state is more comprehensive, the representation capacity is higher, the Bayesian network and the support vector machine are adopted to jointly evaluate the equipment state health level, higher state recognition accuracy is achieved in a dynamic scene, and the method is suitable for popularization and application. And the model generalization ability is enhanced through historical samples, so that the equipment state can be judged more stably.

A system and method for temporal acceleration encoding in Lorentzian latent space enables real-time event forecasting within navigable spatiotemporal media. The system encodes media data into compact Lorentzian latent patches using variational autoencoders and organizes them within a multi-dimensional hyperspace spanning spatial, temporal, orientation, scale, and spectral coordinates. Temporal acceleration encoding computes velocity and acceleration vectors along geodesic trajectories, extracting event signatures through multi-scale aggregation over sliding windows. An acceleration-indexed memory stores dynamic descriptors with composite keys comprising hyperspace coordinates and motion characteristics. Event forecasting retrieves similar historical patterns and conditions a forecast head to produce event probabilities and time-to-event estimates with uncertainty calibration. The system streams forecast metadata to edge devices for real-time prediction and adaptive navigation, supporting applications in surveillance, autonomous systems, predictive media exploration, and anomaly detection where both temporal forecasting and multidimensional navigation capabilities are essential.

The invention relates to the technical field of knowledge maps, in particular to a knowledge map method and system for multi-source heterogeneous data fusion. The method comprises the following steps: cleaning multi-source data through an auto-encoder, dynamically weighting and standardizing after low-rank decomposition and PCA denoising, aligning GNN entities and authenticating standard approval serial numbers; combining CNN / GNN to extract texts, images and sensor multi-modal features, fusing time sequence information with BiLSTM, and performing weighted aggregation; a BERT-BiLSTM-SelfAttention-CRF is adopted to identify an entity, a GCN inference relationship is adopted, a TransE is embedded into an entity relationship to a low-dimensional space, and the entity relationship is stored to Neo4j to support real-time query; dynamically updating the atlas by incremental learning; and visually displaying the constructed knowledge graph. According to the method, a complete closed loop from data cleaning, multi-modal fusion and graph construction to dynamic optimization is formed, and comprehensiveness, accuracy and expandability of the knowledge graph in a multi-source heterogeneous scene are ensured.

A system and method for processing and generating multimodal data using a combination of Vector Quantized Variational Autoencoder (VQ-VAE) and Latent Transformer architectures. The system efficiently handles diverse data types including time-series, textual, sentiment, and structured tabular data through specialized encoding modules. A novel fusion module integrates these encodings, capturing cross-modal relationships. The fused representation is compressed into a discrete latent space, processed by a latent transformer, and then reconstructed and enhanced. This approach enables superior data compression, reconstruction, analysis, and generation of synthetic scenarios.