468 results about "Structure extraction" patented technology

The invention provides a self-learning multi-modal emotion recognition method based on multi-scale cavity attention, and solves the problem of low recognition precision caused by different importance of basic action units of a face and different distances between key action units, and the problem of different modality confidence during decision-level fusion. The method comprises the following steps: preprocessing a facial expression image, inputting the facial expression image into a multi-scale cavity attention convolution module, extracting features through a parallel three-branch convolution structure, splicing the features, calibrating through an attention mechanism to obtain an enhanced feature map, and sending the enhanced feature map to a full connection layer to recognize emotion; an original electroencephalogram signal is input into a time-frequency-space three-dimensional feature extraction network, the signal is decomposed, differential entropy features are calculated and processed by a global attention module comprising a frequency spectrum attention module, a space attention module and a time attention module, time-frequency-space multi-dimensional feature representation is output, and emotions are recognized by a full connection layer; and finally, inputting the emotion recognition result of the facial expression and the electroencephalogram signal into a self-learning weight module, and obtaining a final emotion recognition result through dynamic weighted fusion.

The invention discloses a semantic segmentation method for a low-resolution road scene, and aims to solve the problems of difficulty in small target recognition, fuzzy details, texture information loss and the like existing in a low-resolution image in the conventional semantic segmentation technology. The method comprises the following steps: (1) collecting a low-resolution road scene image and a corresponding semantic tag; (2) constructing a semantic segmentation model consisting of an edge guidance module (BGM), a double-domain feature decomposer (DDFD), a domain alignment attention fusion module (DAAFM) and a double-layer attention context aggregation module (HACAM); (3) designing a joint loss function to carry out multi-scale supervision on semantic regions, edges and middle features; (4) carrying out model training by utilizing the road scene image; and (5) outputting a semantic segmentation result map and an edge prediction map. The boundary perception capability is enhanced by introducing learnable pixel difference convolution, the extraction precision of a small target and a global structure is improved by combining frequency domain and spatial domain feature alignment, and context semantic relationship expression is optimized by fusing a channel and a spatial attention mechanism. The method effectively improves the semantic segmentation precision and boundary restoration capability of the model in a low-resolution complex road environment, and is suitable for intelligent analysis tasks of road images in scenes of automatic driving, intelligent traffic, severe weather and the like.

The invention discloses a class case recommendation method based on deep understanding, and the method comprises the following steps: semantic extraction: carrying out the preprocessing of a case text, and carrying out the semantic feature extraction of the preprocessed case text through an encoder; the semantic feature extraction comprises initial crime name prediction and legal entity identification; performing structure extraction, converting nonlinear legal provisions, judicial interpretation and judgment rules into a legal provision map database, performing essential component analysis, and performing entity-essential component matching on a legal entity recognition result and essential components; and performing class case retrieval, performing dynamic fusion on the preliminary crime name prediction result and the entity-essential element matching result to obtain a case feature fusion vector, performing similarity calculation according to the case feature fusion vector, and performing class case recommendation. The technical problems that an existing method is low in recognition accuracy in long legal texts and insufficient in precise semantic boundary recognition of legal terms are solved.

The invention relates to the technical field of pressure detection, in particular to an oil well pressure anomaly detection method and system based on deep learning, and the method comprises the following steps: collecting a control signal and pressure data, calculating a slope difference and fluctuation to mark a sudden change point, extracting a multi-dimensional feature group to generate a training sample, and constructing a deep learning model to recognize pressure difference anomaly. The oil well pressure abnormal time period is predicted in real time. According to the method, accurate attribution of abnormal fluctuations in the oil well operation process is achieved by constructing the time sequence corresponding relation between control signals and pressure difference sudden change, the target pertinence of pressure change recognition is improved, a sliding structure extracts multi-dimensional dynamic characteristics, the capacity of capturing wellhead or underground pressure micro-amplitude abnormity is enhanced, and the training sample is combined with a control behavior label, so that the accuracy of the abnormal fluctuations in the oil well operation process is improved. The distinguishing capability of the model to different abnormal forms is optimized, the adaptability under complex working conditions is improved, a real-time prediction mechanism ensures that oil well pressure state recognition has continuity, rapidness and high confidence, and risk early warning and operation guarantee in the oil extraction process are effectively supported.

The invention provides a supporting cable production monitoring method and system for 3D visual detection, and relates to the technical field of image processing and intelligent detection, and the method carries out multi-source fusion processing based on collected cable three-dimensional space image data, and comprises coordinate alignment, point cloud reconstruction and multi-scale feature extraction, and construction of a unified space geometric model. On the basis, the center line of the cable is extracted, coordinates of starting and ending points are calculated, and then geometric evaluation parameters such as the axial deviation rate and the straightness are obtained. The method has the advantages of high precision, high robustness and high universality, and is suitable for real-time detection and quality control of slender components such as cables and hoses in the industrial production process. According to the invention, the technical problems of inaccurate three-dimensional structure extraction, insufficient defect identification, difficult defect cause tracing and untimely early warning response in the prior art can be solved.

The invention relates to the technical field of financial bill management, and discloses a financial bill automatic identification generation and decision-making method and system, and the method comprises the steps: firstly receiving an original bill image, carrying out the preprocessing of the original bill image, carrying out the format analysis and classification of the enhanced image through a computer vision technology, and distinguishing a known standard format from an unknown format. For a known format, accurately extracting a key region text block by applying an OCR module based on template positioning; and for an unknown format, a general document understanding model is adopted to obtain a preliminary key information key value pair. And then, the extracted information is converted into structured data through a key information structured extraction module, and the structured data is verified by a data verification and business rule engine. If the verification is passed, structured bill information is output; otherwise, sending to a manual intervention queue for further auditing. According to the method, the efficiency and accuracy of financial bill automatic processing are effectively improved, and meanwhile, the flexibility of the system for adapting to different bill formats is enhanced.

The invention discloses a multi-mode fusion product document and source code association retrieval method based on a knowledge graph, and relates to the technical field of software engineering and artificial intelligence. The method comprises the steps that source codes are preprocessed, and code structure information and business semantics are mapped in combination with a predefined business term dictionary; performing controlled induction on a code file and a product document by utilizing a large model, extracting business terms, logic intentions and a subject relationship, fusing with original codes, and establishing a vector retrieval index system; further analyzing a code structure by using an abstract syntax tree, and extracting an entity and a calling relationship; semantic enhancement and relation normalization are performed in combination with the large model, entities and relations are stored in a graph database, and a knowledge graph is formed; and performing parallel processing on user query based on a full-text retrieval index, a vector retrieval index system and a knowledge graph, and finally generating a product concept. According to the method, the retrieval speed, the semantic depth and the logical reasoning ability can be considered at the same time, and the retrieval accuracy is improved.

The invention discloses a multi-modal index knowledge base and a construction method thereof. The construction method comprises the following steps: processing a heterogeneous document to obtain a semi-structured document; identifying a title hierarchical relationship of the document to construct a document logic structure; carrying out minimum chapter blocking on the text of the semi-structured document to obtain logic blocks; performing semantic segmentation on each logic block to obtain text blocks; the method comprises the following steps: constructing text block nodes by meta-information of text blocks, constructing non-text block nodes by meta-information of non-text elements, extracting document nodes, chapter nodes and chapter-chapter inclusion relationships according to a document logic structure, respectively extracting semantic information from the text blocks and the non-text elements, and storing the semantic information in a database; recording the corresponding relationship between the text block nodes and the semantic information and between the non-text block nodes and the semantic information; constructing a knowledge graph based on each node and relationship and storing the knowledge graph into a graph database; and constructing semantic knowledge based on the semantic information and storing the semantic knowledge into a vector database. According to the scheme, lossless retention of multi-modal information and structured organization of document logic are realized, and efficient indexing and accurate recall are facilitated.

The invention relates to the technical field of data processing, and discloses a multi-modal relation extraction method based on large model generation and knowledge graph integration, which comprises the following steps of: firstly, generating description for an image by using a large language model, constructing an image graph, analyzing a text, constructing a text graph, linking a knowledge graph based on a tagged entity, and extracting a multi-modal relation between the tagged entity and the knowledge graph; and constructing a knowledge graph sub-graph. And then, taking the labeled entity as an anchor point, splicing the three through similarity calculation to form a unified combined graph, and extracting and fusing multi-modal features by utilizing a graph information bottleneck technology and a GCN optimization graph structure, so that the accuracy and the processing efficiency of relation extraction are improved. And through multi-modal data fusion, the accuracy and the processing speed of relation extraction are remarkably improved. A graph structure is optimized by using a graph information bottleneck technology and a graph convolutional network, and redundant information is eliminated, so that semantic features in multi-modal data are effectively extracted and fused.

The invention relates to a satellite network traffic spatio-temporal characteristic prediction method based on multiple convolutional neural networks, comprising the following steps: step 1, preprocessing input data including satellite network traffic data and satellite network topology information; 2, extracting spatial features, and processing a spatial dependency relationship of satellite network traffic through a GCN module; 3, time features are extracted, global features and local features of the satellite network traffic in the time dimension are extracted through a double-branch Transform encoder structure, the global branch processes long-term dependence through causal convolution and a global attention mechanism, and the local branch processes short-time mutation through dynamic convolution and a local attention mechanism; and 4, fusing the spatio-temporal features and output prediction, fusing the spatial features and the time features, and outputting a prediction result of the satellite network traffic through a linear layer. The accuracy and robustness of satellite network traffic prediction can be improved.

The invention provides a water conservancy industry electronic dark bidding document enterprise internal examination method and system based on artificial intelligence, and belongs to the field of water conservancy industry bidding. Performing automatic pre-auditing on the bidding file: performing compliance inspection and integrity inspection, identifying problems existing in the bidding file, and providing improvement suggestions to ensure that the problems conform to the basic requirements of electronic dark label review; carrying out data standardization and cleaning on the bidding file: carrying out semantic extension and ambiguity elimination, identifying images, tables and handwritten contents in the bidding file, identifying a main body structure of the water conservancy design drawing, extracting engineering quantity list data, and carrying out compliance verification; identifying an abnormal behavior in the bidding file through a machine learning model; and an internal examination decision tree is constructed according to preset key indexes and score weights of the water conservancy project, an interpretable artificial intelligence algorithm is utilized to carry out internal examination decision making on the bidding document, an internal examination score result is fed back, and specific score deduction reasons are explained. And the standardization and the accuracy of internal examination of the bidding document are improved.

The invention discloses a highly dense broken ice image segmentation method based on an iteration MGAC and an SAM model, and the method comprises the steps: carrying out the sea ice instance segmentation of a preprocessing image based on the SAM model, and obtaining a sea ice mask image corresponding to the preprocessing image; according to the binary image and the sea ice mask image, obtaining an initial sea ice residual region image which is not identified by the SAM model; obtaining initial sea ice residual region images under different gray threshold values to obtain an initial seed mask graph; taking the initial sea ice residual region image and the initial seed mask image as inputs of a preset MGAC contour model, and obtaining an MGAC sea ice recognition result based on a multi-round iteration partitioning mechanism; and performing union operation on the MGAC sea ice identification result and the sea ice mask image to obtain a crushed ice segmentation mask result. The method solves the problem that the existing method is insufficient in structure extraction precision and boundary integrity of the dense broken ice area.

The invention discloses a road occupation construction identification method and device based on deep learning, electronic equipment and a program product. According to the recognition method, an AW-MSSPP module is introduced into a backbone network, multi-level information in a first input feature is extracted by using a multi-scale fusion structure, feature weights of different positions are adjusted through an adaptive space attention mechanism, accurate alignment of each scale feature in space is ensured, and the recognition accuracy is improved. Therefore, the recognition effect of the model on road occupation construction of different scales is improved. The neck network integrates an MSFSA module, performs multi-scale feature extraction and fusion in combination with different convolution kernel sizes, focuses a key region through space attention, inhibits interference information, enhances feature expression ability, and improves the perception effect on complex targets with different scales. Besides, an MSE-C2f module is arranged in the backbone network, and multi-scale convolution and an improved CSP bottleneck structure are fused, so that the modeling capability of the model for a complex mode is remarkably enhanced, and the overall recognition performance is effectively improved.

The invention relates to the technical field of network security, and discloses a computing power network abnormal node identification method and device, equipment and a storage medium. The computing power network abnormal node identification method comprises the following steps: constructing a network topological graph based on node information of each node and link information of each link in a current computing power network; based on the graph structure of the network topological graph, graph structure features and communication behavior features of the to-be-tested node are extracted; obtaining a normal node feature distribution model, and calculating the deviation degree of the to-be-detected node based on the normal node feature distribution model, the graph structure features and the communication behavior features; and identifying the to-be-detected nodes of which the deviation degrees are greater than a preset deviation degree threshold value as abnormal nodes. Through graph structure modeling and multi-dimensional feature fusion, the abnormal nodes of the computing power network can be effectively identified, the accuracy and real-time performance of abnormal identification are improved, an accurate decision basis is provided for abnormal node positioning and subsequent repair, and safe and stable operation of the computing power network is guaranteed.

The invention relates to a new media AI marketing content creation method and device, equipment and a medium. The method comprises the steps of obtaining user demand configuration, and obtaining a creation intention vector through natural language analysis; based on the platform characteristic knowledge base, extracting a corresponding structure specification and a propagation mechanism according to the target platform, and encoding to obtain a platform characteristic vector; obtaining historical content interaction data corresponding to the audience group, and generating a user-content interaction vector by adopting collaborative filtering and a label similarity algorithm in combination with the content keyword; and calling an artificial intelligence large model, and performing content generation according to the creation intention vector, the platform feature vector and the user-content interaction vector to obtain content creation data. By adopting the method, the goal of automatic, high-quality and personalized new media marketing content creation in a multi-platform environment can be realized by means of natural language analysis, knowledge structure extraction, large model generation constraint and the like.

The invention provides a method and system based on NLP file analysis, and relates to the technical field of natural language processing. According to the method, time and identifier unification and format and character set standardization are carried out on the multi-source file, layout segmentation, table structure extraction, reference analysis, term standardization and anaphora resolution are combined, semantic representation is constructed, a hierarchical index and a unique traceability identifier are generated, intention recognition, retrieval sorting, incremental updating and consistency verification are supported, and the method is suitable for large-scale popularization and application. Unification, semantization and traceability of the file analysis process are achieved, and the processing efficiency and accuracy are improved.

The invention discloses a PCB defect real-time detection method based on multi-scale feature fusion, and relates to the technical field of PCB defect real-time detection methods, and the method comprises the steps: obtaining a to-be-detected PCB image, carrying out the size normalization and pixel value standardization processing of the image, and obtaining a standardized image meeting the input requirements of a model; inputting the standardized image into a backbone network of a teacher detection model, and extracting a multi-scale primary feature map containing texture information in different directions through a grouping convolution structure; transmitting the multi-scale primary feature map to a neck network of a teacher detection model, and performing weighted fusion on feature maps of different scales by using a learnable weight to generate a multi-scale fusion feature map; and in an up-sampling path of the neck network, generating channel description information after global pooling is performed on the deep fusion feature map, generating a channel attention weight through nonlinear transformation, acting the weight on a primary feature map of a corresponding level, and outputting an enhanced feature map.

The invention relates to the technical field of artificial intelligence, in particular to a sheet part performance rapid prediction method based on deep learning, which comprises the following steps: collecting multi-working condition simulation data to generate a training sample, constructing and coding a grid topological structure to extract multi-dimensional features, and inputting a perceptron to predict stress and evaluate errors after feature fusion and self-attention mechanism processing. According to the method, a structured training sample set is constructed by introducing simulation information, a geometric structure feature set is formed by combining node space coordinates, boundary constraints and a connection relation, so that mutual positions and constraint conditions among nodes are completely expressed in a graph structure, and through node-level feature extraction and feature fusion processing, a graph structure is obtained. According to the method, deep embedding of node geometric layout and boundary interrelation is realized, learnable expression of a stress evolution path in a space structure is established through local subgraph and context analysis, a multi-layer feature aggregation and attention mechanism is introduced in a node graph embedding process, and feature response expression of a key area is enhanced.

The invention provides an intelligent conference memo generation method based on a robot, and the method comprises the steps: collecting a multi-channel voice signal through a microphone array, and enhancing the voice of a target speaker through a beam forming technology; background noise is separated by adopting a self-adaptive filtering algorithm, and the voice signal quality is improved; speech features are extracted, language model parameters are adjusted, and a transliteration text is generated; analyzing the text structure, and extracting conference themes, participants and decision contents to form structured information; calculating semantic similarity among the knowledge graph nodes, and if the semantic similarity is higher than a preset threshold value, associating historical records to generate extension information; constructing a structured memorandum based on the extended information, organizing a conference theme, participants, decision contents and associated historical records, and generating an initial memorandum; and monitoring a memorandum editing operation, updating a knowledge graph node relationship, and generating a final memorandum document. According to the method, the accuracy, integrity and availability of conference records are effectively improved, and the conference efficiency is remarkably improved.

The invention relates to the technical field of hydrological prediction, in particular to a drainage basin distributed runoff prediction method and system based on a graph neural network, and the method comprises the following steps: obtaining a drainage basin multi-source runoff data set to construct a multi-relation dynamic graph structure, and extracting node feature vectors; based on the node feature vector, obtaining a watershed evolution trend forward feature by establishing a watershed diffusion fitting architecture; constructing a distributed runoff probability prediction model, and taking the watershed trend forward features as model input to obtain runoff initial condition probability distribution of each sub-watershed in multiple periods in the future; establishing a mixed loss function, and performing physical constraint optimization on the runoff initial condition probability distribution to obtain distributed runoff optimization probability distribution; and performing uncertainty quantification on the distributed runoff optimization probability distribution to obtain a drainage basin distributed runoff prediction result. According to the method, the hydrological process simulation capability of the complex watershed is improved, and accurate prediction of the distributed runoff volume is realized.

A protein complex model interface quality evaluation method based on a multi-scale isovariant graph neural network comprises the following steps: firstly, screening out a co-crystallized natural protein complex structure from a non-redundant protein interaction database PRISM, and generating a bait structure by using a HDock docking algorithm; the method comprises the following steps: firstly, extracting molecular surface interaction fingerprints, atomic-level features and residue-level features on the basis of each compound bait structure, obtaining graph representation of the compound bait structures, then fully capturing and fusing multi-scale information through a depth isotropic graph neural network, and finally obtaining an interface mass fraction through prototype comparison prediction. According to the method, the interface quality evaluation of the protein compound model can be accurately carried out, and the problems of low precision and poor generalization of the interface quality evaluation of the protein compound model are effectively solved.

The invention relates to the technical field of topographic change analysis, in particular to an unmanned aerial vehicle-based topographic change detection system, which comprises a slope direction sensing track control module, a texture structure extraction module, a crack evolution track construction module, a direction trend comparison module and a patrol recheck positioning module. According to the method, a continuous elevation point column of an unmanned aerial vehicle scanning area is extracted, laser reflection point coordinates are fused, a space relation of transition point distribution is constructed, dynamic adjustment of a ground-imitated flight path is achieved, and a texture structure area with continuous directivity is recognized in combination with a high-angle image boundary communication relation; texture boundary evolution is compared at different time nodes to form a crack path, the stability of the path and the slope direction is judged through an included angle sequence, recognition and sorting of areas with the consistent direction are completed, a space comparison result is registered in a three-dimensional coordinate system, terrain change areas are accurately marked, and rapid positioning and continuous tracking of high-risk areas are achieved.

The invention provides a hierarchical exploration thinking-driven large-model complex graph question and answer processing method and device, relates to the technical field of artificial intelligence, and aims to solve the technical problem that an existing complex graph question and answer method is insufficient in the aspects of reasoning depth, path search strategies and cross-layer information fusion ability. The method comprises the steps of obtaining a to-be-processed complex graph question and answer task; inputting the complex graph question and answer task into a pre-constructed large model, adding an induction mark for a node set by using a first-layer structure, and generating an induction graph; extracting a core entity feature and a trunk logic structure feature in the problem description by using a second-layer structure, and generating a context prompt; according to the induction graph and context prompts, reasoning and exploring are conducted through the third-layer structure, a preliminary question and answer result is generated, and the semantic relation and the logic relation of the preliminary question and answer result are verified; and in response to verification failure, iteratively updating the induction graph and regenerating a question and answer result until verification is passed, and outputting a target question and answer result.

The invention discloses an environmental protection system data processing method and system based on big data, and relates to the technical field of data processing. Environmental protection exception reports submitted by the public are received in real time to construct a three-dimensional spatio-temporal index structure, visual feature vectors and text feature vectors of geographic positions to which the environmental protection exception reports belong are extracted to generate credibility scores; marking an abnormal event to be verified; for the to-be-verified abnormal event, calling the virtual patrol prediction data to carry out cross verification; for an abnormal event passing cross validation, scheduling real-time data of surrounding monitoring nodes, analyzing the relevance between pollutant diffusion and biological response through an attention mechanism causal model, and determining the position and the type of a pollution source; and starting an adaptive monitoring protocol to maintain and optimize the data of the environmental protection system for an environmental protection abnormity report area without public submission within a preset period. The problems that in traditional environmental protection data processing, a single information source is low in reliability, pollution tracing depends on experience, and monitoring resource distribution is rigid are solved.

The invention discloses a multi-level detail automatic simplification method for an oblique photography live-action three-dimensional model, and relates to the technical field of three-dimensional model simplification and computer graphics, and the method comprises the steps: obtaining oblique photography original data and three-dimensional model basic information; preprocessing the model, performing adaptive Gaussian filtering denoising, improving RANSAC to remove outer points, compressing textures in a blocking manner, correcting mapping coordinates, and repairing a topological structure; extracting multi-scale features; constructing a simplified decision model, and determining a simplification rate and a priority by combining an observation distance, scene precision and hardware performance; performing hierarchical simplification, vertex hierarchical improved edge folding, patch hierarchical adaptive deletion and regional hierarchical grid reconstruction; performing multi-dimensional quality evaluation, and if the requirements are not met, performing backtracking adjustment; and outputting a simplified model stored according to the LOD hierarchy, wherein the simplified model comprises transition information and a simplified log. According to the method, the data quality is improved through refined preprocessing, the simplification pertinence is enhanced through multi-dimensional feature extraction and intelligent decision, and the application value of the model is improved.

The invention discloses an original script-oriented AI autonomous plot structure adaptive generation system, and relates to the technical field of creation assistance, and the system specifically comprises the following modules: a structure extraction analysis module, an emotional role analysis module, a plot inference module, a scene generation optimization module, a conservation target generation module, an adaptive control module, and a constraint punishment module. According to the method, a multi-level narrative structure and a causal relationship graph are constructed to form an emotion vector and a trajectory curve, an optimal causal path is generated based on a graph neural network, a graph convolution / attention mechanism and a graph generation algorithm, and emotion toning and conservation target driven text generation are performed on scene and dialogue levels. Structural consistency and emotional arcs are optimized in real time in combination with self-adaptive control, plot path weighting and rewriting triggering are performed by utilizing a multi-dimensional emotional space and a neural oscillator network, intelligent structured management of a script is realized, and script creation efficiency and quality are improved.

The invention discloses an enterprise intelligent compliance auditing method based on data driving. The method comprises the following steps: S1, automatically collecting auditing data of various heterogeneous data sources in an enterprise in real time through a cross-domain data access interface; s2, performing feature automatic identification and standardization processing on the audit data by adopting a semantic adaptive coding method; s3, a cross-domain collaborative characterization model is constructed by extracting and fusing shared features through a sub-domain multi-expert structure; s4, generating a visual feature heat map by using a hierarchical attention mechanism of the model, and outputting an anomaly detection result; s5, extracting long and short period correlation mode features, inputting the features into a contrast learning framework, and outputting an abnormal risk score; s6, constructing a dynamic enterprise compliance risk knowledge graph; s7, strategy training is carried out, and risk rating is output; and S8, generating an audit report according to the risk rating. According to the invention, efficient and accurate enterprise compliance risk identification and audit decision support are realized.

The invention relates to the technical field of medical image recognition, in particular to a fat content analysis method and system based on a liver pathological section image, and the method comprises the following steps: extracting a liver fat vacuole gravity center sequence, recognizing and arranging mutation nodes, removing abnormal vacuoles, analyzing a boundary structure, extracting a closed boundary, and generating a fat content analysis result. According to the method, through selection of the adjacent pixel group of the structure edge in the fat cavitation region and construction of the gravity center sequence, deep characterization of the fat cavitation arrangement characteristics is realized, and stable reference is provided for subsequent structure identification; abnormal change nodes are identified based on an evolution trend of an included angle of adjacent gravity center vectors in the gravity center sequence, arrangement direction mutation points are effectively positioned, impurity interference is screened out, and misjudgment of fat assessment caused by irregular accumulation is avoided; by means of calculation of a gravity center drift curve and a compactness variable coefficient, a form fluctuation area is recognized, abnormal cavitation bubbles are removed, and the consistency and stability of structure extraction are enhanced.

The invention discloses a VR interactive control management system and method, and relates to the technical field of virtual reality. The method comprises the steps of obtaining multi-source interaction data of a user in a virtual reality environment, performing intra-modal representation conversion and fusion, and constructing a unified multi-modal input tensor; semantic intention representation is extracted based on the cross-modal perception structure; generating a control instruction vector in combination with the historical state information and the current semantic intention; mapping the control instruction vector into an equipment control signal set conforming to various VR terminal interface specifications; after the equipment executes the control instruction, multi-dimensional feedback information is collected, the structure of the multi-dimensional feedback information is reconstructed, feedback representation capable of flowing back to the sensing module is generated, and closed-loop interaction between sensing and control is achieved. Through unifying a multi-source interaction data structure, a dynamically adaptive control instruction vector and a high-precision cross-modal semantic representation mechanism are constructed, and a closed-loop interaction process with consistent sensing and control structures, flexible response and semantic alignment in a virtual reality system is realized.

The invention discloses a power distribution network 30-degree phase angle difference non-perception loop closing dynamic protection constant value cooperation method, and relates to the technical field of power grid automation, and the method comprises the following steps: extracting a protection equipment connection relation based on a power distribution network topology structure, and constructing a topology model containing a hierarchical membership relation; according to the real-time measurement data, generating an interval load distribution model by adopting a periodic entropy weight correction removing algorithm; simulating a load transfer process under a 30-degree phase angle difference based on the topology model and the interval load distribution model, and outputting current change time sequence data of the transfer path protection equipment; according to the hierarchical membership relationship and the current change time sequence data, calculating and checking a protection constant value, and dynamically adjusting the protection constant value by using an impact tolerance coefficient to generate a final protection constant value; and issuing the final protection constant value to the protection equipment, and triggering load transfer operation.