952 results about "Graph recognition" patented technology

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention discloses a dynamic intention recognition and GraphRAG fusion method and system for open domain questions and answers, and relates to the technical field of intelligent and natural language processing. The method comprises the following steps: constructing and dynamically updating a heterogeneous knowledge graph; performing dual-stage intention recognition on user query; subtasks are disassembled based on an intention result, and cooperative execution is carried out through multiple agents; service standardization integration is realized by means of a model context protocol; and closed-loop optimization is carried out in combination with user feedback. The system comprises an image knowledge base, an intention recognition module, an agent collaboration module, an MCP service integration module and a feedback optimization module. The system is a novel open domain question-answering system integrating structured knowledge modeling, dynamic intention understanding, intelligent collaborative reasoning and standardized service interaction, and through fusion of structured knowledge modeling and intelligent collaborative reasoning, question-answering accuracy and complex intention understanding ability are improved, system adaptability and efficiency are enhanced, multi-system collaboration is promoted, and the system has a wide application prospect. The method is suitable for scenes such as knowledge services and intelligent assistants.

The invention discloses a large-model real-time voice text intention recognition method and system based on text prediction, and the method comprises the steps: obtaining the real-time voice data of a user, carrying out the real-time voice recognition processing through a streaming voice recognition interface, and obtaining a part of transcriptional text; inputting the partial transcription text into a mask language model for text prediction, and generating a plurality of high-credibility complete sentence candidates; based on the complete sentence candidates, the complete sentence candidates are input into a large language model in parallel for intention recognition, a corresponding intention result is obtained, and a mapping relation between the candidate sentences and the intention recognition result is established; and obtaining a sentence completely expressed by the user, calculating the similarity between the complete actual sentence and a plurality of high-credibility complete sentence candidates through a multi-level text similarity algorithm, selecting the candidate sentence with the highest similarity score, and directly obtaining a corresponding final intention recognition result based on the mapping relationship. The objective of the invention is to solve the technical problem of high response delay of an existing voice intention recognition system.

The invention belongs to the field of natural language processing, particularly relates to a multi-modal document generation method and device based on multi-agent collaboration, and aims to solve the problems that an existing method is low in intention recognition accuracy, limited in retrieval range and not professional enough in content generation. The method comprises the following steps: generating a structured template; analyzing the text input by the user to identify a writing intention, and determining a target template; vectorizing each candidate resource feature to obtain a corresponding sparse vector, a dense vector and a knowledge vector, and performing semantic alignment; extracting context features of the input text, respectively performing multi-path retrieval recall, evaluating and sorting recall results, and screening out target features; and constructing a thinking chain in combination with the knowledge graph, and generating a multi-modal document according to the target template. According to the method, the outline structure can be extracted, the picture / table style can be recognized, the templates adaptive to different document types can be dynamically generated, and full-process automation from user input to document output is achieved.

The invention provides a front-end automatic testing method of a large language model based on LLM, and belongs to the technical field of front-end automatic testing. The method comprises five core steps of test intention recognition and semantic analysis, page structure analysis and target element positioning, automatic generation of a test operation sequence and assertion logic, test script execution and dynamic regression verification, and test feedback analysis and continuous training. The strong semantic understanding and context reasoning capabilities of the LLM are utilized to perform semantic analysis and simulation on a front-end page structure, a user behavior process and business logic, so that automatic generation and maintenance of a test script are realized. According to the method, the test coverage rate and the generation efficiency are greatly improved, the maintenance cost is remarkably reduced, the readability and the intelligence of the test case are enhanced, and the method is suitable for the modern Web front-end application test of rapid iteration.

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention relates to a multi-mode intention recognition method and system, and the method comprises the steps: carrying out the time domain and frequency domain enhancement of the features of text, video and audio modes, carrying out the splicing to obtain non-language mode fusion features, combining the features of an original text, modeling the time synchronization relation of audio-text and video-text, and carrying out the recognition of a multi-mode intention. Standardized audio features, video features and text features are obtained through context alignment processing; fusing the standardized features of the three modalities to obtain a fused feature vector, and mapping the fused feature vector back to the text modal space to be connected with the weighted residual error of the original text feature to obtain a fused semantic vector; extracting global semantic anchor points and mask positions from the fused semantic vector, and splicing the global semantic anchor points and the mask positions with the original text features and the fused semantic vector to obtain input features; and obtaining probability distribution of multiple intention categories by using the input features. Three types of heterogeneous modal input can be supported, and the accuracy and robustness of intention recognition are improved through fine-grained semantic supervision and enhancement strategies.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a business process visualization early warning and strategy response method, which comprises the following steps: setting a physical interrupt node to collect interrupt operation track data, generating multilevel logic interrupt type structured data based on the operation track data, the method comprises the following steps: acquiring a preset business strategy, mapping the preset business strategy into a visual topological graph, identifying a preset business strategy verification failure type, counting interruption frequency, triggering a grading early warning instruction, and generating a page element weight adjustment parameter and a fault-tolerant threshold set. According to the method, the visual topological graph is constructed and the high-frequency interruption behavior is identified, so that real-time identification and graded early warning of business strategy verification failure are realized, and adaptive adjustment of page elements and strategy thresholds is further driven, so that the user insurance conversion rate is effectively improved and the front-end operation loss risk is reduced.

The invention relates to the field of computer vision, and particularly provides a table recognition reconstruction method and system, a terminal and a medium, and the method comprises the steps: firstly decomposing a large-size table image into a plurality of overlapped sub-images, and carrying out the table structure detection and OCR character recognition of each sub-image through parallel recognition; then, sub-graph recognition results are integrated through a coordinate mapping and confidence coefficient weighted fusion algorithm, and boundary errors are eliminated; then, automatically distinguishing common cells based on an area clustering algorithm, merging the cells and a header region, and reconstructing a complete table logic structure; further understanding header semantics through a natural language model and repairing identification errors; and finally, realizing intelligent splicing and standardized output of the cross-page table. According to the method, the memory limitation of the traditional OCR technology is broken through, an oversized table can be processed, the recognition accuracy of a complex structure is improved, and the digitization efficiency of professional documents such as financial statements and engineering drawings is improved.

The invention relates to the technical field of large model intention understanding, and discloses an APP dialogue type service reaching method and system based on large model intention understanding. The method comprises the steps of receiving a natural language text input by a user on an APP dialogue interface and performing large model semantic analysis to obtain service semantic data; inputting the business semantic data into an intention recognition model for business intention understanding to obtain business intention data; performing multi-agent cooperative process planning to obtain execution process data; carrying out interactive confirmation on the service parameters to obtain service execution parameters; transmitting the service execution parameters to corresponding service tool interfaces for calling execution to obtain a service tool calling result, and performing intelligent analysis and card rendering on the service tool calling result to obtain display card data. According to the method, the real business intention of the user is accurately understood, and the problems of execution efficiency and stability of a traditional single interface calling mode in a complex business scene are solved.

The invention relates to a power distribution network distributed energy bearing capacity assessment method and device. The method comprises the following steps: carrying out topology analysis on a network structure of a power distribution network to obtain an initial network topology model; obtaining a node dynamic feature data set based on the initial network topology model and the distributed energy access point data of the power distribution network; wherein the node dynamic characteristic data set comprises operation parameters of each node of the power distribution network in different load scenes; generating a parameter incidence matrix according to the node dynamic characteristic data set, and obtaining a bearing capacity reference model of the power distribution network according to the parameter incidence matrix and real-time data of the power distribution network in an operation state; wherein the parameter incidence matrix is used for quantifying the coupling degree between the operation parameters; and obtaining a risk distribution mapping graph according to the bearing capacity reference model, and identifying a potential overload area of the power distribution network based on the risk distribution mapping graph. According to the invention, power distribution network operation risk assessment can be accurately realized.

The invention discloses a land investigation quality monitoring system for remote sensing image intelligent segmentation and ground feature recognition, and relates to the technical field of remote sensing, and the system comprises an image recognition module, a label comparison module and a monitoring module, and specifically carries out the remote sensing image acquisition of a to-be-monitored land through a remote sensing sensor, and carries out the structural complexity analysis and image region division. The method comprises the steps of forming a non-uniform region set, analyzing and acquiring category probability vectors of pixel positions of all regions in the formed non-uniform region set to construct a tag map of a whole remote sensing image, comparing pixel-by-pixel categories on a current tag map and a reference tag map to form a change mask map, identifying a structural change region according to the change mask map, and identifying a structural change region through sequential analysis. And determining whether the corresponding candidate tag inconsistent region under different time phase conditions is still a structural change region, so as to obtain a judgment result, and updating the electronic map based on the judgment result.

The invention discloses a consultation method and system based on natural language processing and a legal knowledge graph, and relates to the field of data processing, and the method comprises the steps: receiving a multi-format legal consultation demand of a user, converting the multi-format legal consultation demand into a text, inputting the text into a BERT law NLP model, and analyzing key information through word segmentation, intention recognition and entity extraction; based on a pre-constructed multi-level legal knowledge graph, carrying out accurate and fuzzy retrieval and domain filtering in combination with an analysis result, and obtaining an associated law article, a case and a legal relationship; screening conflict law articles and similar cases, and inputting the conflict law articles and the similar cases into a graph neural network reasoning model to generate a preliminary conclusion; the conclusion is converted into a spoken consultation report through a natural language generation module, and output is customized according to a user scene; and if the user feedback satisfaction degree is less than the threshold value, iteratively optimizing the storage data to the historical library. The method has the advantages that accurate retrieval is realized based on the BERT model and the multi-level knowledge graph in the legal field, the oral personalized conclusion combined with the user scene is generated through GNN reasoning, and iterative optimization is performed through user feedback.

The invention relates to a multi-mode intention recognition method and system based on consistency and difference decoupling, and the method comprises the steps: carrying out the consistency modeling and difference modeling of the features of text, video and audio modes, and enabling the consistency modeling to be used for extracting common information expressing similar meanings in each mode; the difference modeling is used for retaining information which is unique in each mode but possibly has important supplementary value; by constructing a shared-private feature space, carrying out explicit modeling on common information in the consistency features and specific information in the difference features, and fusing the consistency features and the difference features; and based on the fused feature representation, judging the real intention of the user through a classifier, and outputting a result. Through consistency and difference collaborative modeling, in combination with a shared-private feature space, semantic consistency and difference among all modalities are explicitly modeled, and the accuracy and robustness of intention recognition are fundamentally improved.

The invention discloses a micro-grid dynamic collaborative scheduling method based on multi-modal reinforcement learning, and relates to the technical field of micro-grid control, and the method comprises the steps: constructing a multi-modal state space of a micro-grid, designing a layered reward function, and dynamically adjusting the reward weight of each layer of the layered reward function through employing a self-adaptive weight algorithm; dynamically identifying a node connection relationship by using a graph neural network, constructing a microgrid node connection graph to identify an operation mode of the microgrid, updating the node connection relationship in real time, and optimizing a distributed power supply output distribution strategy of the microgrid based on an attention mechanism of the graph neural network; and updating the overall model of the micro-grid by using a cloud edge coordination mechanism. According to the invention, the capability of coping with uncertainty of the micro-grid can be enhanced, and the intelligent operation level of the micro-grid is improved.

The invention relates to a business travel data processing system and method based on a large model and an intelligent agent and a medium, and the system comprises a natural language interaction and structured data conversion module which is used for receiving unstructured business travel text information inputted by a user, and carrying out semantic analysis, entity extraction and context association through a multi-modal intention recognition engine, converting the data into structured travel data; the agent-driven dynamic process adaptation module is used for dynamically adapting the approval process and the system operation logic according to the request input by the user and the business process state; the multi-dimensional intelligent compliance verification module is used for verifying the structured travel data; the cross-system collaboration and data communication module is used for realizing automatic synchronization and data consistency guarantee of the travel data and the heterogeneous system; and the system architecture and key technology integration module is used for providing layered architecture design, large-model lightweight deployment and security and privacy protection of the system. The method has the advantages of being high in efficiency, high in adaptability, low in compliance risk, good in collaboration and the like.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a call service quality auditing method, device, equipment and medium, and the method comprises the steps: collecting call audio data, and translating the call audio data into call text data; identifying a client intention in the call text data by using an intention identification model, and generating a client intention result; using an emotion recognition model to recognize customer emotion, and generating an emotion recognition result; using the service response analysis model to identify service behavior performance, and generating a service response analysis result; and generating an audit report containing service quality analysis content based on the client intention result, the emotion recognition result and the service response analysis result. According to the method, the client intention, the emotional state and the service response behavior in the call content are processed in a unified manner, the unstructured call information is converted into the structured evaluation data, automatic, multi-dimensional and deep auditing analysis of the service quality is realized, and the auditing efficiency and accuracy are improved.

The invention discloses an overhead layer climbing frame wall-attached support steel truss stand column safety monitoring system fused with big data analysis, relates to the technical field of construction monitoring, and is used for solving the problem of recognition misjudgment caused by construction disturbance. According to construction behavior control signals, behavior labels are extracted, a semantic mapping relation between construction behaviors and structure responses is constructed, a behavior-driven data set is generated, a disturbance change trend is extracted through node response tensor and time difference analysis, the behavior labels are fused to construct a structure state vector, and a structure behavior distribution model is established. Analyzing the deviation degree between the current state and the model response, identifying the propagation path of the anomaly in space by combining a structure layout diagram, extracting a high-risk abnormal block mass region by adopting a diagram clustering method, analyzing the structural state change and construction behavior of the abnormal region, triggering different monitoring response strategies, and obtaining a high-risk abnormal block mass region; the structural risk identification and control in the construction process are realized, and the structural safety monitoring efficiency in the floor construction stage is improved.

The invention belongs to the technical field of artificial intelligence, and relates to a super-long audio and video understanding method, system and equipment based on a visual language model, and the method comprises the steps: 1) carrying out the multi-granularity intention recognition of a user question through a fine-adjusted large language model, so as to determine an inquiry mode of the user question, the inquiry mode comprises a single-picture inquiry mode, an audio content inquiry mode and a video content inquiry mode; 2) identifying pictures, audios and videos input by a user based on the inquiry mode and the user question to obtain identification content; 3) performing multi-modal information fusion on the identified content by using a large language model based on a space-time prompt mechanism and a hierarchical generation mechanism; and 4) inputting the user question and the multi-modal information fusion result into the visual language model, and generating a corresponding answer to the user question. According to the method, the computing resource requirement can be reduced, the system architecture is simplified, the time sequence information dependency is improved, and the generalization ability is enhanced, so that the technical problem of super-long audio and video understanding is effectively solved.

The invention provides a pseudo-tag-based intention recognition model training method, an intention recognition method and an intention recognition device. The method comprises the following steps: inputting a sample text into a language model to extract a feature vector; clustering the sample text based on the feature vector, taking a clustering result as a pseudo tag, and calculating normalized mutual information of the real tag and the pseudo tag of the labeled sample text; determining a confidence score corresponding to each sample; the confidence score is used for quantifying noise in the pseudo tag, screening a high-confidence sample and taking the corresponding pseudo tag as a self-supervision signal, and iteratively optimizing the language model until convergence; after iteration, clustering is initialized again, a clustering result is updated, and mutual information and the number of iterations are normalized; when the number of iterations reaches an upper limit or the normalized mutual information amplification is smaller than a threshold value, training is terminated, and the language model is determined as an intention recognition model; the problem that the new intention recognition capability of the model is reduced due to continuous propagation and accumulation of noise pseudo labels can be solved; and the new intention recognition capability of the model is improved.

The invention discloses an intelligent metering box operation anomaly detection method, and particularly relates to the technical field of electric power informationization, a behavior context information group is constructed based on metering box operation data, and a historical behavior evolution template is matched to judge whether an anomaly intention evaluation process is activated or not; if so, generating a behavior chain sequence and calculating an intention rationality credibility index; extracting electrical characteristics and environment variables to calculate a context offset aggregation index; inputting the two indexes into a pre-training model, outputting an intention deviation grade score, and executing response strategies such as behavior feature freezing, risk label annotation, access permission limitation or abnormal early warning according to a risk interval to which the score belongs; according to the method, abnormal intention recognition and risk grade scoring of the operation behaviors of the intelligent metering box are realized by constructing the behavior context information group, establishing the behavior chain sequence and performing context state modeling and combining with the pre-training intention recognition model, so that the response strategy is executed in a graded manner, and the accuracy, intelligence and response efficiency of abnormal recognition are improved.

The invention provides an engineering drawing intelligent processing system based on a ComfyUI, and the system comprises an algorithm service module which integrates a plurality of drawing processing algorithm assemblies, such as drawing layout analysis, OCR recognition, drawing direction detection, semantic understanding, drawing frame disassembly, and axis net recognition, and provides services for the outside through a standardized interface; the workflow arrangement module is used for packaging the algorithm components into parameterized drawing processing nodes based on a visual node configuration framework of a ComfyUI, and supporting flexible combination, real-time preview and batch execution of drawing processing flows; and the workflow management module is used for importing, analyzing and publishing the workflow configuration file in the ComfyUI format, and realizing metadata management, publishing verification, API document generation, interface registration and call authority control of the workflow. The method supports multi-type input of PDF and CAD drawings, is suitable for engineering drawing processing tasks such as drawing label extraction, sub-drawing recognition, drawing frame segmentation and drawing filing, and is high in intelligent degree, flexible in process configuration, easy to integrate and high in expansibility.

The invention discloses an intelligent financial risk early warning method based on multi-modal data fusion, particularly relates to the field of financial risk management and control, and is used for solving the problem of existing enterprise transaction authenticity risk prevention and control. According to the method, a cross-modal transaction behavior chain of supervision enterprises is constructed through merging of transaction data in continuous account periods and modal information aggregation; generating an initial transaction relation graph based on the behavior chain, identifying business associated enterprises, and screening pseudo independent enterprises with control path crossing; performing modal semantic consistency check on the transaction path of the pseudo independent enterprise, identifying a semantic mismatch node, tracing a fund return path by taking the semantic mismatch node as a starting point, identifying a transaction closed-loop structure, and constructing a pseudo compliance risk chain; finally, the control relation and modal data are integrated, a structured evidence chain is generated, risk early warning output of involved enterprises is rapidly completed in a high-risk marking mode, and the automation degree, interpretability and accuracy of abnormal financial risk discrimination are improved.

The invention discloses a behavior-driven twinborn prediction method, and relates to the technical field of intelligent information processing and prediction modeling, and the method comprises the following steps: building a unified event time baseline, collecting nanosecond clock offset information of each data source, building a cross time sequence suspicion chart, recognizing time synchronization abnormal nodes, and forming a credible time anchor point set; and based on the trusted time anchor point set, executing anti-fact playback, reconstructing a historical evolution process of behavior data, generating a time offset vector set, and constructing a corresponding time sequence offset spectrum. According to the method, through construction of a unified time baseline, trusted anchor points, anti-fact replay, causal topology and time reversal control, time sequence dislocation identification, calibration and false trajectory elimination of multi-source behavior data are realized, a dynamic self-healing closed-loop prediction mechanism is established, and the twin system prediction accuracy and stability are improved.

The invention discloses a multi-mode intelligent linkage 3D visual data center operation and maintenance system and method, and relates to the technical field of data center operation and maintenance. The system comprises a multi-modal data fusion module used for mapping physical sensor data and video monitoring and operation and maintenance logs to a unified three-dimensional coordinate system and constructing a multi-modal fusion feature tensor; the three-dimensional particle modeling module is used for constructing a particle space structure based on a Voronoi diagram and Delaunay triangulation and adaptively adjusting the particle resolution; the multi-modal score analysis module is used for calculating a particle multi-dimensional score and generating a semantic heat map to recognize an abnormal region; the structured noise prediction module is used for simulating future responses under different instructions based on a diffusion model; the instruction generation and regulation module is used for realizing causal analysis and instruction optimization in combination with a knowledge graph; and the three-dimensional visual interaction module supports real-time rendering and interaction operation at a client. According to the method, the intelligence, the real-time performance and the visualization level of data center operation and maintenance can be improved.

The invention discloses a network card data local preprocessing system fused with edge computing, and relates to the technical field of edge computing and artificial intelligence collaborative optimization. Comprising an edge computing unit, a hierarchical collaborative architecture, a model hot switching and generative fragmentation module, an intention recognition and adaptive scheduling module, a delay energy consumption optimization scheduling module, a CXL zero-copy sharing module, an edge computing unit integrated processor, an FPGA or ASIC and a neuromorphic computing unit. According to the method, an FPGA, an ASIC and a neuromorphic computing unit are integrated in an intelligent network card, microsecond-level dynamic connection reconfiguration and adaptive generative model fragmentation execution are realized through a reconfigurable Mesh interconnection matrix, an attention layer and a feed-forward layer of a Transform class model are fragmented and allocated to different computing units for parallel execution, and cross-card streamlined processing is realized in cooperation with a zero-copy shared memory. And the intention recognition module is deeply coupled with the model hot switching module, so that dynamic model switching and fragmentation strategy optimization based on service priorities and system loads are realized.

The invention provides a context-based intelligent voice semantic understanding analysis method and system, and relates to the technical field of voice interaction, and the method comprises the steps: obtaining an input voice signal, extracting an acoustic feature, and decoding the acoustic feature to obtain a candidate text; constructing context semantic representation to carry out disambiguation processing; establishing a semantic dependency graph and carrying out multi-level correlation analysis; spreading context constraint information to perform multi-hop reasoning; and finally generating intention recognition and slot filling results and updating the session state. The semantic comprehension accuracy and the intelligent degree of the voice interaction system are improved by introducing the context information and the multi-level semantic dependency relationship.

The invention discloses a natural language intention recognition system based on rule and large model dynamic collaboration, and relates to the technical field of artificial intelligence, and the method comprises the following steps: an input preprocessing layer is used for converting a user dialogue voice stream into text information; the complexity judgment module is used for judging whether the text information is complex or not, and the matching module is used for performing rule matching on high-priority rule base rules under a multi-level rule tree architecture; the rule engine module is used for performing intention recognition on the text information based on the rule matching method selected by the matching module under the condition that the text information is not complex so as to obtain an intention recognition result, and the large model processing module is used for performing intention recognition on the text information under the condition that the text information is complex so as to obtain an intention recognition result; and the output layer is used for outputting an intention recognition result. The intention recognition method is beneficial for solving the problems of relatively low intention recognition accuracy, relatively slow response speed and relatively poor processing capability for services in a specific field in the prior art.

A method for implementing data loss prevention (DLP) includes: generating an asset lineage map from file system metadata; identifying, based on the asset lineage map, an input feature linked to the asset, a type of the asset, and a plurality of activities linked to the asset; obtaining a sensitivity score for the asset based on the input feature and the type of the asset; obtaining, based on the plurality of activities, a malicious score and a data loss score for the asset; determining a user level of a user; and initiating implementation of a first DLP policy for the user based on the user level, the malicious score, the data loss score, and the sensitivity score.

The invention belongs to the field of traffic control, and relates to a high-risk scene-oriented risk, dynamics and intention collaborative trajectory prediction method, which comprehensively utilizes an outward risk clue, vehicle dynamics characteristics and driving intention information (historical trajectory characteristics), and constructs a priori enhanced risk attention mechanism and a three-body interaction micrograph module to predict the risk, dynamics and intention collaborative trajectory of a high-risk scene. Early perception of potential risks is realized; meanwhile, a multi-scale dynamic time sequence encoder is adopted to capture the dynamic change of the vehicle from short-term disturbance to long-term behavior trend; an explicit intention recognition auxiliary task is introduced, and semantic constraint and optimization are carried out on the multi-modal trajectory; through collaborative modeling of the modules, the trajectory prediction precision and robustness of the automatic driving system under complex high-risk situations such as sudden cut-in and sudden brake can be remarkably improved, more reliable prior support is provided for a subsequent decision and control module, and therefore the driving safety of a vehicle in a complex traffic environment is effectively improved.