123312results about "Neural learning methods" patented technology

A system and method for implementing a convergent intelligence fabric (CIF) for distributed artificial intelligence operations. The CIF architecture integrates tensor-theoretic foundations, probabilistic cache management, precision-aware memory operations, quantum-resistant security, and neural-based optimization within a unified framework. The system orchestrates asynchronous, multi-hop data flow among computational resources while maintaining data security through per-block encryption and identity-based access control. Key components include a universal multi-model KV cache subsystem, agent-parallel disaggregation pipelines, reinforcement learning-based orchestration, and neuromorphic memory integration. Advanced implementations incorporate graphon-enhanced memory for sparse graph sequences, multi-modal cognitive persistent memory, and quantum-resistant asynchronous multi-domain trust protocols. The system enables efficient cross-agent collaboration, sophisticated knowledge sharing, and secure cross-domain operations while optimizing computational resources and maintaining strict privacy guarantees across distributed AI deployments.

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

Disclosed in the present invention is a salient contour matching-based method for target measurement in a severe imaging environment. The method specifically comprises: (1) acquiring a binocular image of a target; (2) establishing a global-local joint constraint-based background light estimation model, and removing a scattering effect of a medium in an imaging environment to obtain a restored left eye image and a restored right eye image; (3) learning an original image, and on the basis of a residual between a network reconstructed image and the original image, obtaining target localization prediction maps of the left eye image and the right eye image; and (4) respectively extracting contour lines of the target in the left eye image and the right eye image, constructing feature matching descriptors of contour points, performing stereo matching on the two sets of contour lines by minimizing matching cost, and performing three-dimensional reconstruction on the contour lines in light of calibrated intrinsic and extrinsic parameters to complete the measurement of a key size. According to the present invention, the key sizes of different targets in a severe environment can be accurately measured, thereby providing an effective solution for the problem of measuring the sizes of targets in a severe environment.

The invention relates to the technical field of model analysis, in particular to a time-space fusion neural network line topology analysis method for a power distribution network. The method comprises the following steps: obtaining original line topology data corresponding to a power distribution network, and carrying out structured disassembly and preprocessing to construct a space-time double graph structure; constructing a bidirectional dynamic feature interaction mechanism based on the space-time double graph structure, performing multi-scale topological feature extraction, and generating a space-time separated feature vector set; performing deep coupling fusion on the feature vector set subjected to time-space separation to generate corresponding unified topological feature representation containing abnormal topology; and constructing a dynamic topology state prediction model based on the unified topology feature representation to optimize a space-time joint loss function and output a corresponding real-time topology connection relationship and an equipment state change trend, and meanwhile, performing dynamic topology reconstruction to generate a current-moment reliable topological graph corresponding to potential branch disconnection and temporary tripping. The topology analysis accuracy of the power distribution network can be improved.

The invention relates to the technical field of equipment fault diagnosis, and discloses an equipment fault diagnosis and prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-modal data in real time through a plurality of sensors installed on equipment; s2, preprocessing the collected data; s3, constructing a hybrid deep learning model; s4, dynamic weighted fusion is performed on the features of different modal data by using an attention mechanism, and comprehensive feature representation is generated; s5, using the marked fault data and normal data to supervise and train the model; s6, inputting equipment operation data acquired in real time into the trained model, and judging the state of the equipment; and S7, generating a potential fault early warning signal based on a prediction result of the model. A piezoelectric vibration sensor and a thermal infrared imager are arranged on a motor bearing through vibration, temperature and sound sensors, vibration waveforms, thermal imaging slices and time-frequency diagrams are synchronously captured, and composite state characteristics such as mechanical wear and temperature anomaly of equipment are comprehensively reflected.

The invention discloses an electric energy metering box fault prediction method and system based on big data analysis, relates to the technical field of smart power grids, and solves the problems of progressive aging missing detection and instantaneous interference misjudgment caused by dependence on single parameter threshold alarm and fault positioning misalignment caused by multi-source data isolated analysis in the prior art. According to the scheme, electrical, environment and equipment state parameters are collected in real time through a multi-dimensional sensing network; the error drift of the mutual inductor is dynamically predicted based on an LSTM-Kalman filtering model, and core breakdown early warning is realized in combination with wavelet transform; outputting a corrected resistance value and a fault mark by using a BP neural network; predicting the life of the piezoresistor by adopting a gradient boosting decision tree and fusing lightning overvoltage characteristics; the transient interference is suppressed through the combination of a Transform self-attention mechanism and dynamic time warping; according to the method, the aging detection precision and the complex environment adaptability are remarkably improved, the misjudgment rate is reduced, and the multi-fault associated positioning and active defense capability is realized.

The invention belongs to the technical field of equipment management, and discloses an industrial equipment fault prediction and health management method based on multi-sensor fusion, and the method comprises the steps: obtaining multi-source sensing data of industrial equipment, carrying out the signal decoupling analysis, and obtaining a decoupling characteristic spectrum; performing frequency domain conversion and modulation analysis to form a multi-dimensional characteristic spectrum system; analyzing the modal correlation of the multi-dimensional feature pedigree to obtain a fault feature mapping network; a mixed time sequence prediction model is constructed, residual life prediction and degradation trend evaluation are carried out, and an equipment health trend graph is obtained; establishing a health state evaluation index system, and performing reliability evaluation to obtain an equipment health state report; and generating a maintenance decision suggestion, and realizing real-time anomaly detection and maintenance suggestion pushing through edge calculation. Through multi-sensor data fusion and advanced analysis technologies, early warning and accurate prediction of industrial equipment faults are realized, and the operation reliability and production efficiency of the industrial equipment are remarkably improved.

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

The invention discloses a visible light and infrared image fusion method based on cross-modal dynamic collaboration. The method comprises the following steps: respectively extracting texture detail features of a visible light image and thermal radiation features of an infrared image through a visible light encoder and an infrared encoder; spatial alignment and channel complementarity optimization of cross-modal features are realized by using a heterogeneous attention collaboration module; and performing layered fusion on deep semantics and shallow detail features through a dynamic gating multi-scale decoder to generate a high-resolution fusion image. According to the method, the problems of feature dislocation, detail loss and unreasonable fusion weight distribution caused by modal difference in the prior art are solved, the detail fidelity, the thermal target saliency and the complex scene adaptability of the fusion image can be remarkably improved, and a high-robustness fusion result is provided for low-illumination environment perception and multi-modal target recognition.

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention discloses a traffic supervision system applied to an intelligent street lamp and an intelligent supervision method thereof, relates to the technical field of intelligent traffic, and solves the problems that an existing intelligent street lamp system lacks a physical-digital mapping relation, edge computing resource allocation is low in efficiency and cloud computing delay is high. According to the scheme, on the basis of multi-sensor data fusion, space-time reference unification is carried out by adopting an atomic clock and a GNSS, and a dynamic causal graph is constructed through a graph neural network, so that abnormal event detection is optimized; an improved Jaccard space-time similarity algorithm is adopted to optimize calculation task allocation, an edge calculation cluster is constructed based on 5G-V2X, and high-risk region identification and traffic flow prediction are carried out; a LiFi or 5G-UWB communication medium is adaptively selected through a multi-modal fusion reinforcement learning algorithm, and efficient early warning information synchronization is realized; according to the method, the multi-source data fusion value and the early warning precision are remarkably improved, the computing power resource utilization rate is optimized, and the instruction real-time performance and the system self-adaptive capability in a complex environment are enhanced.

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

The invention discloses an underground engineering geological safety dynamic risk assessment method based on multi-source data fusion, which relates to the technical field of risk assessment, and comprises the following steps: collecting multi-source heterogeneous data related to underground engineering, extracting implicit information, modeling underground engineering geological safety risk factors into a risk network, and establishing a risk network model; calculating the comprehensive importance of the nodes based on a Stacking integration algorithm, and identifying key risk factors; acquiring characteristic parameters of key risk factors by using spatio-temporal characteristics of implicit information, introducing a random walk mechanism to acquire a dynamic accident prediction chain, and performing learning representation by using a graph attention network to acquire probability distribution of an accident evolution path; and assessing the vulnerability of the connection edge in the risk network, establishing a dynamic risk assessment model based on the node importance and the edge vulnerability, and obtaining a dynamic risk value corresponding to the accident according to the accident occurrence probability and the risk mitigation factor. According to the invention, intelligent identification, dynamic evaluation and accurate early warning of risk factors are realized, and the accuracy and real-time performance of risk identification and evaluation are improved.

In example embodiments, an industrial technology stack for an industrial environment includes a set of computational resources and a set of layers executed by the set of computational resources, the set of layers including a governance layer, an enterprise layer, an offering layer, a transaction layer, an operations layer, a network layer, a data layer, and a resource layer. In example embodiments, the industrial technology stack may include one or more artificial intelligence models for implementing one or more components of one or more layers of the set of layers.

Disclosed in the present invention are an aviation equipment reliability evaluation method and system based on a knowledge graph and model inference. The method comprises: acquiring data of human factors, equipment systems, and a working environment of aviation equipment; carrying out preprocessing and text labeling on the acquired data; inputting the labeled text information into a constructed entity relationship joint extraction model to form a high-quality structured triple of the knowledge graph; constructing an elastic knowledge graph for the aviation equipment, wherein the elastic knowledge graph comprises an online knowledge graph and an offline knowledge graph which has aviation equipment reliability; and extracting semantic features, and analyzing the similarity between the extracted features to realize indirect inference of the aviation equipment reliability. The present invention fully fuses expert experience and knowledge data, and exerts respective advantages of a human brain and machine intelligence, so as to achieve accurate analysis and prediction of aviation equipment reliability, thereby providing intelligent risk analysis, early warning and optimization suggestions for command and control personnel, and reducing a fault occurrence rate.

The invention discloses a Transform-based cross-modal fusion multi-modal emotion recognition method and device, which are used for solving the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in a multi-modal emotion recognition task, and the method takes the accuracy and robustness of emotion recognition as performance evaluation indexes. Firstly, feature information of three modes of vision, voice and text is obtained, feature extraction is performed on each mode through a deep learning model, then features of different modes are fused by using a cross-mode Transform module, and a complex dependency relationship between the modes is dynamically modeled through a multi-head self-attention mechanism, so that more accurate emotion recognition is realized, and the emotion recognition efficiency is improved. And finally, performing emotion prediction on the fused features based on time sequence modeling and an emotion classification module. According to the method, the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in multi-modal emotion recognition can be effectively solved.

The invention discloses a big language model reasoning optimization method and system based on dynamic video memory compression and memory isomerism, and intelligent management of video memory resources is realized by integrating a dynamic compression strategy of KV Cache and a memory parallel architecture. The method comprises the following steps: 1) analyzing the spatial-temporal characteristics of the KV Cache in real time, adaptively selecting a quantization compression algorithm, a rarefaction algorithm or a low-rank decomposition algorithm, performing hierarchical storage based on attention head importance scores, keeping high precision of a core head, and implementing low-bit quantization on a secondary head; (2) the compressed inactive data are divided into a plurality of data blocks to be stored in a system memory, a parallel data channel group is established according to the number of physical channels, the compressed blocks are concurrently read through multiple channels during loading, and parallel decompression of a sparse matrix is accelerated through a GPU tensor core; and 3) constructing a KV Cache multiplexing mechanism and a parallel channel, and parallelizing a compression / decompression process and model calculation by adopting a hardware acceleration compression and asynchronous pipeline mechanism.

The invention relates to a high-voltage equipment defect detection method based on deep learning and multispectral image fusion, and relates to the technical field of electric power high-voltage equipment state detection. The method comprises the following steps: acquiring an ultraviolet image, an infrared image and a visible light image of the surface of the high-voltage equipment; carrying out image pixel feature-based fusion processing on the ultraviolet image, the infrared image and the visible light image through an image fusion method; establishing a high-voltage equipment defect detection model, and training the high-voltage equipment defect detection model by using the fused image data to obtain a high-voltage equipment defect identification model based on the YOLO-STrans multispectral fusion network; and inputting the ultraviolet image, the infrared image and the visible light image of the outer surface of the power high-voltage equipment into a high-voltage equipment defect identification model to obtain a fault identification result of the to-be-detected power high-voltage equipment. The method can improve the recognition precision of the extremely early insulation degradation and temperature anomaly defects of the surface of the high-voltage power equipment.

A method includes obtaining an image, the image associated with a mask corresponding to a portion of the image, generating a plurality of images based on the image and the mask, each image of the plurality of images depicting a different color in the portion of the image corresponding to the mask, executing a machine learning model to generate an image performance score for each of the plurality of images, ranking the plurality of images according to the image performance scores for the plurality of images, and generating a record comprising one or more images of the plurality of images based on the rankings of the plurality of images.

The invention discloses an underground construction decision-making method based on three-dimensional geological modeling and risk hot area identification, and relates to the field of fusion of artificial intelligence and geological engineering. The method comprises the following steps: firstly, acquiring drilling data, geological radar images and seismic reflecting layer information, constructing a three-dimensional geological voxel model with spatial topology constraints, and accurately describing a geological unit structure by adopting an irregular grid mode; and then, extracting a time sequence characteristic index under construction disturbance, forming a continuous time sequence characteristic vector, inputting the continuous time sequence characteristic vector into a convolutional recurrent neural network model with a space attention aggregation mechanism and a deep memory unit, and predicting a risk heat value of each space position. And on the basis, through heat gradient clustering and neighborhood consistency analysis, a dynamic high-risk hot area is identified, and a risk hot area map is constructed. And finally, in combination with the construction stage, the equipment plan and the sensor feedback information, constructing a multi-target auxiliary decision function, and generating a construction decision result including operation path reconstruction, rhythm adjustment and power limit and control suggestions.

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 relates to the technical field of LED display, solves the problem that the existing LED display defect detection and parameter adjustment technology is lack of multi-modal information fusion and intelligent process control capability and is difficult to meet the quality control requirement of a high-precision display product, and provides an LED display defect prediction and process adjustment method and system based on multi-modal fusion. The method comprises the following steps: performing multi-modal data fusion processing on optical image data, electrical test data and thermal infrared imaging data corresponding to a to-be-tested LED display screen to obtain fused data; inputting the fused data into a pre-trained defect recognition model to obtain a defect recognition result; according to a process parameter adjustment strategy corresponding to the defect identification result, adjusting the original process parameter to obtain a target process parameter; and according to the target process parameters, process flow correction processing is carried out, and a qualified LED display screen is produced. According to the method, the defect identification precision is improved, and the quality control requirement of high-precision LED display screen production is met.

The invention discloses a turbofan engine operation monitoring method and system based on digital twinning, belongs to the technical field of turbofan engine monitoring, and aims to solve the problems that weak fault signals such as early cracks and abrasion are difficult to extract and the prediction precision of a multi-source fault propagation path is low under a strong noise background. An original operation signal is collected through a sensing array, and is processed by an adaptive resonance demodulation chain to generate a demodulation signal. The method comprises the following steps: carrying out time-frequency transformation on a demodulation signal, constructing an initial candidate feature set by combining feature frequency prior matching actual measurement and theoretical feature frequency, and generating an independent feature set by fusing multi-scale decoupling network separation features of digital twin constraints; for independent features, effective causal pairs are screened by adopting a physical coupling relationship combining Granger causal analysis and digital twinborn simulation, a dynamic Bayesian network is constructed to simulate fault propagation, a posterior probability is calculated through digital twinborn verification and Monte Carlo simulation, early warning is triggered, and a maintenance decision is generated. And weak signal extraction and accurate fault prediction under strong noise are realized.

The invention discloses a Mama-based spectrum dynamic fusion and double-attention enhancement medical image segmentation method, which comprises the following steps of: firstly, constructing a Mama integrated spectrum domain and attention pyramid module, fusing spectrum dynamic characteristics and a self-attention pooling mechanism, and performing frequency domain information compensation and local characteristic enhancement to obtain a spectrum dynamic fusion image; the spatial correlation loss caused by image blocking processing is relieved; secondly, designing a layered enhanced U-shaped architecture, deploying an MISAP module in a shallow layer of an encoder to capture multi-scale global context features, introducing a bipolar routing attention mechanism in a deep layer, and dynamically allocating sparse attention weights to focus a key pathological region; according to the method, the segmentation precision of complex edge textures and tiny lesions in medical images can be remarkably improved, and the Dice coefficient in breast tumor, polyp and abdominal organ segmentation tasks is averagely improved by 6.5%.

The invention discloses a multi-source heterogeneous data knowledge graph construction method for railway disaster prevention monitoring, and relates to the technical field of knowledge graph construction, and the method comprises the steps: gathering multi-source heterogeneous data related to railway disaster prevention monitoring, and constructing a domain ontology model used for guiding knowledge extraction and fusion; extracting entities, attributes and relationships among the entities from different modal data after standardization preprocessing by using a targeted extraction algorithm; obtaining fused structured knowledge based on a multi-strategy knowledge fusion process of domain ontology constraint and confidence evaluation; the fused structured knowledge is stored in a graph database, and construction of the knowledge graph in the railway disaster prevention monitoring field is completed; through combination of domain ontology construction, a mixed knowledge extraction engine and a multi-strategy knowledge fusion technology, deep semantic fusion of multi-source heterogeneous data in the railway field is realized. The invention aims to construct a knowledge graph capable of comprehensively and accurately reflecting complex characteristics in the railway disaster prevention field.

The invention relates to the field of battery fault prediction, in particular to a lithium ion battery fault prediction method and system based on a BMS. The method comprises the following steps: extracting multi-dimensional operation monitoring parameters of a battery through a BMS (Battery Management System), carrying out multi-state evolution perception and label mapping processing, and constructing a global multi-state perception map of the battery; short-term abnormal sudden change detection is carried out according to the multi-dimensional operation monitoring parameters of the battery, and normal characteristic deviation trend analysis is carried out, so that an abnormal fluctuation deviation evolution trajectory is constructed; and performing deep topological correlation learning on the global multi-state sensing map of the battery based on the abnormal fluctuation deviation evolution trajectory, performing heterogeneous node global sensing, performing abnormal behavior causal relationship mining on heterogeneous deviation nodes in the battery, and performing multi-causal fission simulation to generate a battery behavior deterioration chain under an abnormal trend. According to the method, accurate and efficient fault prediction is realized, transfer learning is carried out, and the perspectiveness of subsequent BMS fault prediction is improved.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

The invention provides an industrial control network security advanced threat detection system fused with artificial intelligence. The system comprises a multi-source data acquisition module, an intelligent analysis engine, a threat detection module, a dynamic defense module and a self-evolution learning system which perform data interaction in sequence. The industrial control network security advanced threat detection system fused with artificial intelligence realizes collaborative decision-making among the modules through a dynamic knowledge graph. Through multi-source data fusion, dynamic knowledge graph and lightweight model design, the core problems of protocol analysis, threat association, defense collaboration and model adaptability in the industrial control network security field are solved, and a full-stack protection system covering'perception-analysis-decision-response-evolution 'is constructed. The deep analysis capability of an industrial protocol is improved, the dynamic threat association analysis is broken through, the agility of a defense strategy is enhanced, and the feasibility of continuous optimization of a model is improved, so that a systematic solution is provided for advanced threat defense in a complex industrial control environment.