265 results about "Semantic network" patented technology

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention provides an agent context understanding method and system driven by a multi-modal semantic network, and relates to the technical field of data processing, and the method comprises the steps: carrying out semantic label extension and anaphora resolution processing, and marking a time anchor point and an anaphora target of each semantic segment; each context semantic fragment is converted into semantic nodes, and directed connection is generated according to the time relation and semantic association between the semantic nodes; based on a user instruction, matching related semantic nodes, and calculating a comprehensive matching score; comparing the comprehensive matching score with a preset comprehensive matching score threshold value, and screening candidate semantic nodes; dynamically updating the current memory weight of the semantic node according to the comprehensive matching score of the semantic node; extracting a semantic node sequence with the highest current memory weight as a context semantic path, and outputting semantic entities in the path in a structured manner; according to the invention, the autonomy and accuracy of the context understanding of the intelligent agent are improved.

The invention relates to the technical field of building information modeling, in particular to a building drawing geometric feature extraction and visual modeling method and system, and the method comprises the steps: carrying out the self-adaptive noise reduction, contrast enhancement and line refinement processing of an original building drawing image, and carrying out the automatic layer separation based on colors and line types; linear geometric features and specific symbol geometric features in the drawing image are extracted, and a geometric feature set is constructed; component instantiation, attribute assignment and topological relation reasoning are carried out by using a predefined building component semantic rule base, and a building component semantic network is generated; and mapping the semantic network to a parameterized three-dimensional modeling engine, calling an IFC standard three-dimensional template, performing parameter driving and automatic assembly, generating a three-dimensional building model with semantic information and a spatial structure, and performing visual output. According to the method, efficient and standardized conversion from a two-dimensional building drawing to a three-dimensional building model can be realized, and the method has the remarkable advantages of processing complex drawings and high-precision modeling.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

The invention discloses an intelligent archive opening and identifying method based on a large model, particularly relates to the technical field of archive data auditing, and is used for solving the problems of insufficient cross-modal data analysis capability, lagging rule updating and low man-machine cooperation efficiency in the prior art. Fusing cross-modal features of texts, images and metadata through a hybrid expert model to generate multi-modal feature vectors, and dynamically allocating the multi-modal feature vectors to a rule network, a semantic network and a domain network for cooperative processing based on attention weights; the rule network parameters are optimized through gradient projection constraint, and regulation-driven real-time adaptation is achieved; matching sensitive data in combination with a multi-dimensional feature matrix of auditing personnel, and optimizing task allocation accuracy; removing redundant links by utilizing value flow analysis to generate a lightweight process, and recording as a tamper-proof evidence chain through a block chain evidence storage solidification operation; the auditing efficiency and accuracy are improved, and the compliance traceability is guaranteed.

The invention discloses an information extraction task-oriented cue word design and optimization method and system, and the method comprises the steps: carrying out the preprocessing of related texts in a field, obtaining the preprocessing text data, and building an environment system among a front-end interface, a large language model and an ontology database; cue words are designed on the basis of the cognitive linguistics principle, when a query request is input in a front-end interface, the preprocessed text data are sent to a large language model so that domain ontology-based information extraction can be carried out according to the cue words, and matching with an ontology database is carried out to obtain a preliminary information matching result; feedback information is obtained through a multi-round dialogue result of the large language model and a manual proofreading result based on a preliminary information matching result, the design structure of cue words is optimized, and a semantic network constructed based on a domain ontology in an environment system is optimized. According to the method, different levels of linguistics are combined, and the cue words are ensured not only to be correct in language structure, so that a large model is guided to generate more accurate and useful results.

The invention discloses a resume analysis method and system based on a dynamic semantic network, and relates to the technical field of data information processing. The method comprises inputting a resume; according to the file type of the resume, multi-mode resume analysis engine processing is carried out, and information extraction is carried out; constructing a dynamic semantic network according to the extracted information; and outputting structured data based on the stored knowledge graph in combination with an industry self-adaptive mechanism to complete resume analysis. The resume analysis system is used for realizing the resume analysis method. According to the resume analysis method and system, the effects of high resume analysis accuracy, high processing efficiency and high industry adaptability can be achieved.

The invention discloses an AI (artificial intelligence) agent construction system based on hybrid retrieval and father-child segmentation, which comprises the following steps of: dividing a subclass knowledge base according to domain knowledge, performing father-child segmentation processing, and constructing a hierarchical semantic network; vectorization embedding and deep semantic reconstruction are carried out on the user question text; retrieving the reconstructed problem by adopting a mixed retrieval algorithm combining sparse retrieval and dense retrieval, and forming a high-score sub-segment set according to a comprehensive score obtained by dynamic weight distribution; mapping the sub-segments to the parent segment through a hierarchical backtracking algorithm, aggregating brother nodes to form an extended candidate set, and generating an associated sub-segment set after duplicate removal and re-retrieval; and finally inputting a large language model to generate a complete answer. According to the method, the problems of context segmentation, low retrieval accuracy and complicated knowledge base maintenance of traditional document segments are solved, the answer coverage and accuracy of an intelligent question-answering system are remarkably improved, and the method is suitable for knowledge question-answering scenes in the complicated technical fields such as intelligent network connection automobiles and the like.

The invention relates to the technical field of information pushing, in particular to a platform user interest recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring user action data and environment perception data, analyzing the user action data and the environment perception data, and constructing a unified space-time semantic network; according to the unified space-time semantic network, analyzing scene features of the user, and determining a multi-dimensional dynamic scene feature set; and analyzing the multi-dimensional dynamic scene feature set, determining composite scene demand information of the current user, and retrieving and pushing real-time demand information of the user according to the composite scene demand information. According to the method and the device, the perception capability of the pushed information to the user demand scene is improved, the multi-dimensional dynamic adaptation of the pushed information to the platform user is realized, the information pushing accuracy for the platform user is improved, and the composite information demand of the user in a complex scene is met.

The invention discloses a cue word logic configuration method based on context semantic enhancement, and belongs to the technical field of artificial intelligence and natural language processing. The method comprises the steps of data collection, data preprocessing, dynamic semantic anchor point generation, semantic field strong quantitative modeling and cue word logic configuration. Dynamically generating a semantic anchor point set capable of representing core semantics based on a semantic network structure contribution degree calculation and aging attenuation mechanism; further, a three-dimensional semantic field model is constructed, the timeliness intensity, the emotional intensity and the credibility intensity of each semantic anchor point are quantified, and comprehensive semantic field intensity data are obtained in combination with a multi-dimensional attenuation weighting and credibility correction method; according to the method, the context adaptability, the logic consistency and the dynamic adjustability of prompt word configuration can be remarkably improved, and the method has good application value and popularization prospects.

The invention provides a control method and system of a double-arm clothes folding robot, and relates to the technical field of robot control, and the control method constructs a perceived control closed loop by fusing visual, force and tactile multi-modal data. The touch sense and the force sense make up the blind area of the vision in the self-shielding scene, and grabbing, sliding and pleating perception is improved. Based on clothing joint characterization of a visual semantic network, geometric morphology, semantic components and confidence coefficient are structured and coded, a robust state fusing prior and real-time observation is provided for planning, and decision errors caused by deformation and noise are reduced. Long-time-history folding is decomposed into sub-target sequences through a hierarchical strategy, action closed-loop correction is conducted through multi-modal data, and cooperative control over grabbing, folding and pleating is achieved. And finally, a torque instruction is generated based on a control algorithm, interaction compliance and safety are ensured, and the folding success rate and operation stability of the double-arm clothes folding robot under different materials, sizes and initial forms are integrally improved.

The invention discloses a method for constructing a cultural relic knowledge organization, expression and characterization model for a Song charm theme. The method comprises the following steps: S1, constructing a knowledge classification system according to Song charm Shaoxing cultural relic types; s2, modeling knowledge entities and association thereof by using a semantic network; and S3, performing semantic representation of entities and relationships by using the multi-modal data. According to the method, systematic classification and semantic association of cultural relic knowledge are realized, and the structured level of cultural relic data is improved; the accuracy of knowledge reasoning and recommendation is improved, and intelligent service capability is provided for related fields of cultural relics; cross-medium and multi-dimensional knowledge integration and expression are supported, and technical support is provided for research and propagation of cultural heritage; meanwhile, the method is high in universality and can be popularized to other cultural heritage protection scenes, and wider value application is achieved.

The invention discloses a park digital twinning modeling method based on a generative AI technology, and relates to the technical field of digital twinning modeling, and the method comprises the steps: carrying out the alignment of point cloud, image and state data, completing the preprocessing through topological adaptive filtering and multi-resolution voxelization, frequency domain harmonic fusion and hypersurface texture excitation and time delay coupling fuzzy clustering, and obtaining a digital twinning modeling result; constructing a cross-modal spine network driven by a holographic entropy film, generating a hierarchical token through graph attention, and inputting a surge tuned diffusion converter model for iterative denoising and focus decoding to obtain a three-dimensional fragment; and fusing the fragments in a voxel space by using a cross attention kernel, and adaptively updating parameters through an entropy pulse closed loop until errors converge, so as to generate a high-precision digital twinborn model. A cross-modal semantic network is constructed by constructing spectral mapping and a holographic entropy film, a three-dimensional fragment is efficiently generated in a self-adaptive surge tuned diffusion converter framework through focus fusion, and the cross-modal semantic coupling efficiency, the generative reasoning convergence speed and the multi-fragment voxel fusion continuity are improved.

The invention discloses an intelligent matching method and system for an intelligent supply chain platform, and the method comprises the steps: receiving user demand data, carrying out the multi-modal semantic analysis, and generating a semantic network containing a technical field label, a technical problem vector, and a technical means association graph; generating a first matching list and a second matching list through cooperative operation of the knowledge graph matching model and the natural language processing model; and executing self-adaptive cross validation, and generating candidate sets: carrying out unified sorting on the candidate sets according to a sorting rule, and outputting a matching result. Through a semantic network of a technical field label, a problem vector and a technical association graph, the limitation of traditional keyword matching is broken through, and multi-dimensional accurate mapping of demands and supplies is realized; the knowledge graph model and the natural language processing model cooperate with each other, so that the problem of semantic deviation of a traditional single model is solved; a self-adaptive cross validation mechanism dynamically switches a fusion strategy through a deviation rate, and the robustness in a complex scene is effectively improved.

The invention relates to the technical field of electric power system digitization, in particular to an automatic efficient modeling method and system for an electricity utilization inspection scene. The automatic efficient modeling method for the electricity utilization inspection scene comprises the four steps of multi-source data collection, dynamic semantic modeling, intelligent modeling decision making and augmented reality rendering, the electricity utilization inspection scene modeling system comprises a data collection module, an intelligent modeling engine, an augmented reality terminal and edge computing equipment, and data interaction is achieved among the modules through a gRPC protocol. According to the method, an L4-level multi-physical field model is generated through unmanned aerial vehicle cluster collaborative collection and multi-source data millisecond-level fusion in combination with a dynamic semantic network and reinforcement learning resource allocation; edge calculation and dynamic LOD rendering are adopted to realize equipment internal perspective and multi-user collaborative labeling, quality closed-loop verification is matched, the bottlenecks of low modeling efficiency, multi-physical field data missing, high interaction delay and the like in the prior art are overcome, and the overall efficiency is improved by 80%.

The invention provides a complex scene-oriented end-to-end semantic extraction system, belongs to the technical field of artificial intelligence and natural language processing, and realizes cross-modal information association through a multi-source heterogeneous data fusion module to construct a dynamic semantic network model. A hierarchical attention mechanism is adopted to carry out context-aware coding on unstructured input, and unsupervised pre-training and a weak supervised fine tuning strategy are combined to optimize a feature representation space. And designing an adaptive inference engine, automatically switching semantic analysis paths based on scene complexity, and generating a structured output result. According to the method, the dependency on specific knowledge in the field is reduced, the semantic understanding generalization ability in a complex scene is remarkably improved, high-precision analysis performance can still be kept in a low-resource environment, meanwhile, calculation resource consumption is reduced, and the method is suitable for practical application scenes with multi-language mixing, serious noise interference and high real-time performance requirements.

The invention discloses a forest resource map modeling method and system, and belongs to the technical field of forestry information. The method comprises the following steps: capturing multi-source heterogeneous original observation data through a sensing unit group deployed in a forest region; performing space-time alignment and quality evaluation on the data by a map generation engine to generate an original observation sequence; calling and analyzing auxiliary geographic information in the environment context library to set prior configuration parameters of the atlas reckoning device; and finally, a map reckoning device is driven to perform fusion reckoning on the observation sequence, and a structured forest resource semantic network is output. The system correspondingly comprises a sensing unit group, an environment context library, an atlas generation engine and an atlas reckoning device. According to the method, full-chain intelligent management of forest resources from precise perception and intelligent cognition to prospective planning is realized through space-based collaborative intelligent perception, a depth generation model of historical knowledge injection and operation simulation based on space-time prediction, and the precision, efficiency and decision support capability of forest resource monitoring are greatly improved.

Knowledge-based audio-text modeling via automatic multimodal graph construction is performed. An audio dataset is received, the audio dataset including clips of audio data, wherein each of the clips of the audio data is paired with corresponding metadata descriptive of the audio contents of the respective clip of the audio data. Graph nodes of interest are identified from a sematic network, the graph nodes being descriptive of semantics of the knowledge domain of the contents of the audio dataset. A large language model (LLM) is utilized for categorizing the metadata into the graph nodes and for inferring supplemental data for the graph nodes for which there is no metadata, producing an extracted knowledge graph. The extracted knowledge graph is validated utilizing the LLM to perform relation verification of edges between the graph nodes of the extracted knowledge graph, thereby mitigating hallucination effects in the categorizing and inferring of the supplemental data.

The invention provides a cultural creative design assisting method and system based on element fusion. Belongs to the technical field of creative design. The method comprises the steps that design elements are acquired and classified; on the basis of a deep learning algorithm, a semantic network between elements is constructed, the semantic network is converted into a low-dimensional vector space through a network embedding technology, and user preference, emotional tendency and future trend are analyzed in combination with social media data; and generating a fusion scheme, and carrying out continuous iterative optimization on the design. The semantic relation between the design elements is analyzed, so that the internal relation between the design elements can be deeply understood; through a complex network theory, a language network model between design elements is constructed, the relationship between different elements can be visualized, and high efficiency and systematicness of design decision are facilitated.

The invention provides a data space construction method and system based on dynamic ontology modeling and privacy calculation, and belongs to the technical field of data processing. The method comprises the following steps: respectively constructing a structured semantic network and a vector space of multi-modal data based on the multi-modal data, aligning nodes of the semantic network with the vector space by using an attention mechanism to generate a joint knowledge representation, compressing the joint knowledge representation into a low-dimensional knowledge representation, and constructing a low-dimensional index, modeling by adopting a federated learning improved algorithm combined with differential privacy to obtain an original model; distributing the initial model and the initial model parameters to each participant for federated learning training, and performing model aggregation by using the trained encrypted model parameters to obtain a global model; and finally, distributing model parameters of the global model to each participant for model deployment to form a three-level collaborative data space. According to the invention, the risk of privacy disclosure is reduced, and the storage overhead of the data space is reduced.

The invention discloses an automatic compliance examination method and system based on graph retrieval enhancement. The method comprises the following steps of: obtaining a billing specification and a historical inquiry report as input documents; the input document is subjected to dual-mode document segmentation based on a large language model, logic blocks and physical blocks are generated, and each logic block is a continuous page unit and is attached with a content abstract generated by the model; constructing a precedent graph, extracting key legal elements in the historical inquiry report through multi-stage recursion, generating semantic network nodes carrying traceability identifiers, and establishing cross-document semantic association; and constructing a state graph, automatically identifying chapters and logic and semantic relationships among the chapters based on a document hierarchical structure, and generating a machine-readable structured index and the like. According to the method, the long text semantic understanding depth, the cross-section consistency and the legal reasoning accuracy are remarkably improved, the manual review cost is reduced, and the method is suitable for a listing compliance review scene under a registration system.

The invention discloses a management service platform system based on big data. The management service platform system comprises a dynamic data lake construction module, a semantic map generation module, a self-adaptive label engine, a reinforcement learning push module and a closed-loop optimization center. The dynamic data lake module realizes hierarchical storage of multi-source heterogeneous data by calculating a data value coefficient; the semantic graph module constructs a multi-dimensional semantic network based on a knowledge graph and a BERT model, and excavates implicit topic association; the self-adaptive label engine dynamically generates and updates a user label by using a graph neural network and a time decay factor; the reinforcement learning pushing module is combined with a multi-arm machine model and an A / B test optimization pushing strategy; and the closed-loop optimization center realizes global model updating and system dynamic expansion through federated learning and a micro-service architecture. According to the method, the problems of data layering and stiffness, label generation staticization, strategy optimization lag and the like of a traditional central station system are solved, and a closed-loop system of data intelligent management, accurate service pushing and system self-adaptive optimization is realized.

The invention belongs to the technical field of computer aided process design (CAPP), and particularly relates to a body-based shaft part fixture scheme automatic generation method. The method specifically comprises the following steps: (1) constructing a shaft part clamp scheme generation body; (2) establishing a shaft part clamp scheme to generate an SWRL inference rule; (3) extracting relevant feature constraint information of the part; (4) constructing and generating an instantiated ontology model; and (5) combining a Drools inference engine with an SWRL rule, and carrying out inference selection and adjustment on a shaft part fixture scheme. Constructing an ontology reasoning knowledge framework by utilizing ontology pair shaft part clamp scheme domain knowledge, and reasoning implicit knowledge according to dominant domain knowledge; and reasoning an optimal clamp scheme according to the feature constraint condition of the geometric product in combination with a semantic network rule language rule base. According to the method, a consistent knowledge description framework generated by the shaft part clamp scheme can be provided, so that a computer can automatically select a proper clamp scheme and the like according to various constraints of geometric product parts, and a quick and effective method is provided for intelligent selection of the shaft part clamp scheme.

The invention relates to the technical field of land resource management, in particular to a land stock index management method based on data analysis. The method comprises the following steps: establishing a cross-data-source data sharing mechanism through a block chain technology, and obtaining multi-source land data; performing natural resource, planning and building information fusion on the multi-source land data based on a knowledge graph to obtain a geographic space semantic network; performing multi-scale space-time clustering on the geographic space semantic network through a self-organizing mapping algorithm, and performing key feature capture of land utilization change based on an attention mechanism to obtain land utilization change feature data; and performing land utilization mode identification according to the land utilization change characteristic data to obtain a land utilization dynamic characteristic spectrum. Through a multi-agent system and a hierarchical reinforcement learning algorithm, a complex land utilization decision process can be simulated, and an optimal decision scheme is realized.

The invention relates to the technical field of data management, in particular to a data weaving semantic integration method based on a semantic network and a knowledge graph, which comprises the following steps: acquiring multi-source heterogeneous data and preprocessing to obtain standardized data, the multi-source heterogeneous data comprises structured data, semi-structured data and unstructured data from different business scenes; performing rule injection on the standardized data to generate semantic web ontology data; extracting entities and attribute values thereof in the standardized data to generate structured knowledge graph data of the instance layer; and establishing a semantic association mapping network of the semantic network ontology data and the structured knowledge graph data, and generating a semantic integration result of data weaving. According to the method, the basic differences of the multi-source data in the aspects of formats, codes and the like are eliminated, a unified semantic standard is provided, and conflicts caused by non-unified semantics are reduced.

The invention discloses a natural language processing technology-based cultural and creative element data analysis method, which comprises the following steps of: based on AI intelligent plush doll interaction data, generating a cultural and creative structure embedding degree index through named entity recognition and semantic network topology analysis; an impulse response function is constructed based on the cultural creative structure embedding degree, and a cultural creative memory cycle index is calculated in combination with the path length and information backflow; fusing the multi-modal features and the ideal innovation region, and outputting a perception novelty score; screening high-potential-energy elements to construct a semantic sub-graph, simulating a thermodynamic process, and identifying a text-creative fusion hotspot chain; and decoding the hotspot chain into a design semantic tag, matching a material database to generate an appearance scheme, and converting the appearance scheme into an interaction template, thereby realizing a customized design closed loop from a user dialogue to a doll form and behavior. Through data driving and cross-modal analysis, implicit culture preferences in user dialogues are dominated, and creativity of the appearance of the AI intelligent plush doll and humanization of interaction are improved.

The invention discloses an SLAM dynamic point semantic filtering method based on DDMA-SAM, and belongs to the technical field of synchronous positioning and mapping. A decoupling distillation mechanism is introduced, an image encoder in an original SAM model is subjected to lightweight optimization, a DDMA-SAM semantic network integrating a multi-scale aggregation detection module and an efficient mask decoding module is constructed, and the SLAM dynamic point semantic filtering method based on DDMA-SAM is obtained. And the segmentation performance is improved while the model parameters are greatly compressed. Based on the semantic network, providing a semantic prior and geometric consistency combined-driven double filtering strategy; based on a semantic mask and a confidence threshold, carrying out preliminary dynamic point identification; in combination with the epipolar geometric constraint and the triangulation reprojection error of random sampling consistency estimation, fine elimination of dynamic feature points is realized, and only static points are reserved to participate in camera pose estimation. According to the method, the real-time performance of the system is kept, and meanwhile, the mapping quality and the track stability in a dynamic scene are effectively improved.

The invention relates to the technical field of clothing color matching, in particular to an intelligent recommendation method for clothing color matching, which comprises the following steps: S1, constructing a multi-modal user data intelligent acquisition feature project; s2, deep learning of clothing color semantic feature analysis; and S3, intelligently constructing and optimizing the personalized recommendation model. Multi-source data of a user is acquired in real time through a multi-modal data acquisition engine, feature extraction and engineering construction are performed by using a deep learning technology, a complete user portrait is formed, garment color features are analyzed by using an advanced image processing technology and a deep learning model, a color semantic network is constructed, a fashion trend is captured, and a user experience is improved. A mixed recommendation model based on a graph neural network and Transform is designed, distributed training is achieved through federated learning, a personalized recommendation result is generated in combination with a multi-objective optimization algorithm, a recommendation strategy is continuously optimized through reinforcement learning and an online feedback mechanism, and system security and user privacy protection are ensured by adopting a zero-trust architecture.

The invention provides a multi-application scene-oriented window layout cross-screen collaborative nonlinear restoration system, which belongs to the technical field of computers and comprises a system data acquisition module, an application scene generation module, an intelligent layout strategy module and a scene layout restoration module. The system data acquisition module is responsible for acquiring application-related data; the application scene generation module is in communication connection with the system data acquisition module and the intelligent layout strategy module to generate a high-relevance application combination scene; the intelligent layout strategy module is in communication connection with the system data acquisition module, the application scene generation module and the scene layout restoration module to generate multiple sets of window layout snapshots; and the scene layout restoration module is in communication connection with the system data acquisition module and the intelligent layout strategy module and is responsible for scene layout restoration. According to the method, a software combination semantic network is established, and multi-screen application automatic layout based on application data flow and user interaction is realized.

The invention discloses a social engineering self-adaptive dynamic protection method based on post risk assessment, relates to the technical field of network security, and solves the problem that in the prior art, the aspects of employee security awareness modeling, multi-modal data fusion and personalized security policy recommendation still have obvious deficiencies. The method comprises the following steps: firstly, constructing an electric power post knowledge graph, and carrying out structured modeling on four entities of posts, permissions, assets and vulnerabilities and association relationships thereof to form a global semantic network; secondly, a post risk baseline model is established, a normal behavior mode is defined by quantifying inherent risks of posts and analyzing historical operation logs, and a static and dynamic combined risk reference is formed; and finally, designing a post exclusive feature extractor, fusing personal behavior data of the employees with semantic constraints and risk baselines of the knowledge graph, and generating highly personalized feature vectors, thereby laying a foundation for subsequent real-time monitoring of abnormal behaviors of the employees, quantification of safety consciousness and providing of differentiated protection.