117 results about "Graph inference" patented technology

The present invention relates to the related technical field of natural language inference. Disclosed is a large language model joint inference method based on a knowledge graph-enhanced chain-of-thought prompt, comprising: constructing a local knowledge subgraph; decomposing an original question text into S sub-question texts and concatenating the original question text and the S sub-question texts; inputting a concatenated question text into a graph inference model to obtain a weighted entity distribution; from the weighted entity distribution, extracting first G answer entities having the highest confidence level, using an inference transition matrix to trace an inference process of each answer entity, and generating an inference path from a question entity to the corresponding answer entity; and using the inference path to assist a large language model in predicting an answer to the original question text. By means of the method, the large language model can quickly and accurately find an answer to a question text.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

The invention discloses a zero sample anomaly detection method and system based on a triple perception learning enhanced visual language model, and relates to the field of computer vision, and the method comprises the steps: extracting global and local visual features from an input image; in the visual coding process, local features in a deep network are corrected through a spatial perception attention enhancement module, and fine-grained attribute text description is generated for abnormal visual features; performing deep semantic alignment on the attribute text description and the general text prompt through an attribute perception guide module; calculating the similarity between the enhanced visual features and the optimized text features, and generating a pixel-level abnormal segmentation map; in the inference stage, the segmented image is converted into a space attention weight through an anomaly perception reconstruction module, the space attention weight is fed back to a visual encoder to generate final global feature representation, and an anomaly score is calculated. According to the method, under the condition that a target domain training sample is not needed, the anomaly detection and positioning accuracy and generalization ability of the model under the scenes of industrial defect detection and the like are remarkably improved.

The invention provides an intelligent agent-based automatic verification and rule matching system for insurance-waiting evaluation reports. The intelligent agent-based automatic verification and rule matching system comprises a report acquisition module which pulls to-be-verified and historical insurance-waiting reports; the auditing processing module calls an attention Bi-LSTM model which is subjected to reinforcement learning fine adjustment and is fused with knowledge graph embedding, and scores the report sentence by sentence; the rule management module is used for storing and analyzing an equal-guarantee 2.0 structured machine rule and carrying out secondary accurate matching on low-confidence sentences; the knowledge graph module is used for maintaining a multi-dimensional association graph of a standard family, a control measure and an evaluation item and providing graph reasoning; the agent decision-making module generates actions of passing, returning, manual rechecking or automatic correction according to the auditing state; the feedback learning module collects artificial recheck differences, an incremental training model, a strategy network and atlas vectors; and the storage module stores reports, models, rules, maps and logs in a multi-modal manner through a relation library, a map library and a vector library, and implements data governance and access control. According to the invention, the audit efficiency, accuracy and standard consistency of the evaluation report can be improved.

The application discloses a kind of based on triple perception learning enhanced visual language model's zero sample exception detection method and system, it is related to computer vision field, method includes: extracting global and local visual features from input image;Visual coding process is corrected local feature in deep network by spatial perception attention enhancement module, and fine-grained attribute text description is generated for abnormal visual feature;Through attribute perception guide module, attribute text description and general text prompt are deeply semantically aligned;The similarity of enhanced visual feature and optimized text feature is calculated, and pixel-level exception segmentation map is generated;Inference stage converts segmentation map into spatial attention weight by exception perception reconstruction module, and feedback is generated to visual encoder final global feature representation and calculates exception score.The method of the application significantly improves the accuracy and generalization ability of model in industrial defect detection and other scenarios without target domain training samples.

The invention relates to a bridge degradation identification and maintenance decision support method and system based on a knowledge graph, belongs to the technical field of traffic infrastructure intelligent operation and maintenance and engineering information processing, and solves the defects of weak data fusion capability, opaque causal modeling and non-traceable decision support in the prior art. The method comprises the following steps: acquiring and preprocessing multi-source heterogeneous data to generate a standardized data set; semantic alignment is carried out, and candidate entities and relations are extracted; constructing a knowledge graph body and loading the knowledge graph body into a knowledge graph; a self-adaptive quantile threshold value is adopted to binarize the factor intensity and the disease severity sequence, and a smooth point mutual information and a Spearman correlation coefficient are fused to learn a causal weight; complementing the influence relation of factors on the bridge through graph reasoning and calculating the weight; and finally, calculating a bridge degradation risk score, and outputting a high-confidence influence path based on a shortest path algorithm. According to the method, multi-source data fusion and degradation causal chain explicit modeling are realized, and a scientific basis is provided for a bridge maintenance decision.

The embodiment of the invention discloses a test question output method and device based on multi-model collaboration, electronic equipment and a medium. A specific embodiment of the method comprises the steps of performing multi-source data fusion on a multi-source test question association data set, and then performing knowledge point association analysis to obtain a dynamic test question knowledge point graph; performing multi-hop graph reasoning query on the dynamic test question knowledge point graph to obtain a test question associated knowledge point set; performing model collaborative generation on the test question generation request information and the test question associated knowledge point set to generate an initial test question set; performing quality evaluation on the initial test question set to obtain a test question quality evaluation value set; dynamically adjusting the initial test question set to obtain an adjusted test question set and storing the adjusted test question set; and carrying out expansion adaptive test paper group rendering display on the adjusted test question set to obtain a test question test paper and printing the test question test paper. According to the embodiment, the test question quality can be improved, the test question generation efficiency is improved, the test question generation duration and display duration are shortened, the test question display quality is improved, and waste of display resources is reduced.

The invention relates to the technical field of crop remote sensing samples, and discloses a crop remote sensing image intelligent sample library construction method based on a knowledge graph. The method comprises the steps of collecting multi-source remote sensing image data, and generating an enhanced image data set through fusion and enhancement processing; and constructing a crop field knowledge graph integrating the crop growth model, the soil type and the climate condition. A knowledge graph is used as guidance, crop areas are recognized through semantic analysis, multi-scale features are extracted, and category attributes and associated contexts are deduced through a graph reasoning algorithm in combination with a relation path; and generating a sample label candidate set through deep learning of the graph attention network, screening and correcting the sample label candidate set by using an optimization algorithm constrained by the knowledge graph, and outputting high-quality sample labels. And integrating and storing labels and image data into a graph structure sample library, implementing a dynamic updating mechanism to adapt to new data, running a quality monitoring process driven by a knowledge graph, and adjusting a construction strategy according to result feedback to realize intelligent and efficient construction of the sample library.

The invention discloses a grassland desertification intelligent closed-loop management method and system based on multi-source remote sensing, and belongs to the technical field of ecological restoration and intelligent decision making. According to the method, technical breakthrough is realized by constructing an intelligent closed loop of perception, cognition, decision and optimization: firstly, multi-source data are fused to construct a dynamic ecological knowledge graph, and a desertification cause diagnosis report for quantifying the contribution rate of each driving factor is generated based on graph reasoning; calling a governance measure knowledge base to generate a scheme according to a diagnosis result, and optimizing a decision through digital twinborn rehearsal; finally, based on treatment effect feedback, the knowledge base is dynamically optimized through reinforcement learning; the corresponding system comprises a multi-source perception fusion module, an ecological cognitive diagnosis module, a decision rehearsal execution module and a self-learning optimization module. According to the method, the bottleneck of lack of causal diagnosis and system stiffness of a traditional method is overcome, the crossing from passive monitoring to active cognition and from static decision to dynamic self-learning is realized, and an innovative solution is provided for treatment of a degraded ecological system.

The invention discloses an artificial intelligence model training and reasoning management system based on a knowledge graph, and the system comprises the following modules: a data analysis module which is used for collecting multi-source perception data and forming an entity set and an event set; the graph construction module is used for constructing a context knowledge graph based on the entity set and the event set; the context mapping module is used for establishing a reversible mapping relationship between the context knowledge graph and the original multi-source sensing data; the problem analysis module is used for carrying out semantic analysis on a problem input by a user and cutting the problem to obtain a context sub-graph; the reasoning chain generation module is used for generating a reasoning chain meeting time sequence consistency and causal consistency; the graph reasoning module is used for generating graph reasoning embedding based on the improved T-GNN model and outputting a question and answer result in combination with a question and answer generation model; and the feedback updating module is used for updating the context knowledge graph and the model parameters. According to the method, structured modeling and credibility closed-loop management of the question and answer reasoning process are realized.

The invention relates to the technical field of electronic manufacturing automation, in particular to an SMT equipment multi-machine cooperative control system based on the Internet of Things, which comprises a data acquisition and digital twinning module, a multi-agent reinforcement learning scheduling module, a knowledge graph diagnosis module and a central cooperative controller. The data acquisition and digital twinning module constructs a digital mirror image of a production line, a basis is provided for prediction and simulation, and the problem of data islands is solved; the multi-agent reinforcement learning scheduling module realizes self-adaptive scheduling of dynamic events of a production line based on a real-time state and a simulation environment provided by digital twinning, and solves the problem of production scheduling rigidity; the knowledge graph diagnosis module realizes deep root cause positioning of complex defects by utilizing domain knowledge and graph reasoning, and solves the problem of difficult diagnosis; and the central cooperative controller feeds a diagnosis conclusion back to the first two modules to form a complete intelligent closed loop of perception-analysis-decision-execution-learning.

The invention discloses a vehicle information security identification method and device based on a behavior knowledge graph. The method comprises the following steps: determining the behavior knowledge graph; receiving a node exception event association graph from the vehicle end, wherein the node exception event association graph is determined by the vehicle end through an entity associated with the exception index; adding a suspicious edge connection relationship in the abnormal event association graph as a preset low-confidence abnormal relationship into the behavior knowledge graph to obtain an updated behavior knowledge graph; and based on the updated behavior knowledge graph, analyzing the connectivity of the relationship between the nodes by adopting a graph reasoning algorithm and a preset recognition rule, and recognizing a target attack chain matched with or similar to the known attack mode. Therefore, on the basis of preset recognition rule analysis and a graph reasoning algorithm, intelligent reasoning and automatic judgment are carried out on complex attacks matched with or similar to the known attack mode instead of simple threshold comparison, and the complex attacks implemented across different entities or functional subsystems can be effectively recognized.

The invention discloses an unmanned system multi-intention recognition method and system based on inverse reinforcement learning, and belongs to the technical field of artificial intelligence and autonomous decision making. According to the system, a deep multi-intention inverse reinforcement learning framework is provided for solving the problems of multi-modality, fuzziness and dynamics of unmanned system decision-making intentions in a complex dynamic environment. By designing a multi-scale intention encoder based on Transform, state-intention combined dynamic reasoning is realized; constructing an end-to-end intention perception reward network, and adaptively fusing multi-intention features through an attention mechanism; and proposing an expectation maximization optimization strategy to realize collaborative optimization of intention reasoning and strategy learning. The system can automatically separate and identify various potential intentions from expert demonstration of mixed intentions, and learn corresponding reward functions and decision strategies. According to the method, the intention recognition accuracy and the strategy reproduction capability in a multi-intention scene are remarkably improved, and an effective solution is provided for behavior understanding and autonomous decision making of an unmanned system in a complex environment.

The invention relates to the technical field of three-dimensional computer vision and artificial intelligence, and discloses a three-dimensional scene hierarchical analysis and subgraph reasoning method, device, equipment and medium, and the method comprises the steps: obtaining a plurality of sparse RGB view images of a three-dimensional scene, and constructing a hierarchical plane enhancement scene graph based on the sparse RGB view images; obtaining a user query, and extracting a task adaptive sub-graph from the hierarchical plane enhanced scene graph based on semantic correlation between the user query and the hierarchical plane enhanced scene graph; and based on the task adaptive sub-graph and user query, generating an analysis and reasoning result of the three-dimensional scene. According to the method, the dependence on expensive acquisition equipment and annotation data is remarkably reduced, and the method has strong generalization ability; structured scene representation provides a clear chain for reasoning, and a self-adaptive sub-graph mechanism effectively eliminates redundant information interference, so that unification of high efficiency and high accuracy is realized in complex three-dimensional scene understanding.

The application discloses a high-voltage transmission line external damage hidden danger detection method based on graph reasoning. First, a high-voltage transmission line image containing external damage hidden dangers is acquired. Then, a feature fusion detector based on graph reasoning is constructed, which includes three parts of feature extraction, feature fusion and classification regression. The feature extraction uses a CSPDarknet53 network, and the feature fusion uses an improved PANet network. The improved PANet network is obtained by embedding a stripe pooling-based graph reasoning cross fusion module in an original PANet network. Finally, the high-voltage transmission line image containing external damage hidden dangers is used to train the feature fusion detector based on graph reasoning, and the trained feature fusion detector based on graph reasoning is used for high-voltage transmission line external damage hidden danger detection. The stripe pooling-based graph reasoning cross fusion module captures global context information in the vertical direction and the horizontal direction through stripe pooling, further explores the mutual dependence between arbitrary double-channel mappings, and enhances the representation ability of features.

The application discloses a kind of based on knowledge graph's simulation clinical experiment environment interaction method, specifically related to medical artificial intelligence and clinical simulation technical field;Initial disease entity node and its corresponding symptom feature set are acquired, and initial pathological state graph with time factor is constructed;Based on user interaction behavior extraction operation feature vector, utilize graph neural inference model to carry out multi-hop semantic path inference, generate post-interaction state graph;Further calculate the semantic shift degree caused by user operation, dynamically adjust graph inference weight;Extract relevant etiological entity and generate pathological evolution chain, calculate response difference degree in combination with user subsequent behavior, dynamically adjust virtual patient feedback, trigger path rollback operation if necessary;The application can realize the precise modeling and response control based on knowledge graph in clinical simulation environment, improve the interaction intelligence, explainability and stability of system, applicable to medical teaching, virtual diagnosis training and the like scene.

The invention discloses a graph reasoning-based judgment document event joint extraction method. According to the method, a lexical graph inference mechanism and a trigger word centralization decoding architecture are fused, the lexical graph inference mechanism strengthens the relevance between trigger words and argument roles and solves the problem of semantic segmentation of entities and events, and the trigger word centralization decoding architecture avoids multi-stage cascade errors and improves the complex logic chain processing capacity. The method comprises the following steps: constructing a joint event extraction model: performing corpus labeling and BIO labeling on a data set, and constructing an event type-role label set; processing the text subjected to BIO labeling into numeric vector representation capable of being input into a model, and constructing a lexical element pair label matrix; the hidden lexical elements output by the text embedding layer are classified through a classification layer, a softmax function is adopted to predict relation marks between lexical element pairs, label distribution corresponding to the lexical elements is generated, and a basis is provided for subsequent event extraction; and establishing an event center graph according to lexical tag distribution, performing graph structure decoding to obtain an event prediction result, and training a model.

The application discloses a cross-agency financial data federated learning modeling system and a privacy compliance verification method, relates to the technical field of financial data processing, and comprises the following: a federated aggregation module, which is used for aligning financial time sequence characteristics of each agency by a timestamp hashing alignment strategy on a server side, and aggregating the aligned financial time sequence characteristics by using a federated time convolution network to generate time sequence model parameters for capturing cross-agency global time sequence dependence; a federated reasoning module, which is used for generating a minimum spanning tree representation of global enterprise correlation relationship based on a local subgraph of enterprise correlation relationship constructed by each agency client on the server side based on a secure multi-party computing strategy; the server side aggregates graph embedding vectors by using a federated graph attention network and performs cross-agency graph reasoning in combination with the minimum spanning tree representation to construct graph model parameters for identifying enterprise correlation risks; and a model joint training module, which is used for fusing the time sequence model parameters and the graph model parameters on the server side to determine global model parameters.

The invention provides a sofa control method based on a mobile terminal and an intelligent sofa. The method belongs to the technical field of smart home, and comprises the steps: carrying out the multi-dimensional data collection of the physiology, environment and equipment state of a user, and generating the physiological data, environment data and equipment data of the user; constructing a data fusion model according to the multi-dimensional data; constructing an intention inference engine by using a graph neural network based on the data fusion model; and inputting the user physiological data, the environment data and the equipment data into an intention inference engine, performing deep analysis and learning, inferring the current demand intention of the user, and generating user demand intention data. A graph neural network intention reasoning engine is constructed by fusing physiological, environment and equipment multi-dimensional data, the current state of a user can be accurately perceived, potential demands can be reasoned, the situation that immersive experience is interrupted due to explicit operation of the sofa by the user is reduced, and inconvenience caused by the fact that traditional sofa control depends on active operation of the user is avoided.

The application provides a substation equipment leakage oil segmentation method based on double graph reasoning, which comprises the following steps: constructing a data set, taking manually collected substation inspection pictures as a data source, selecting substation inspection images containing leakage oil defects to construct a data set, labeling the data set by using a labeling software, dividing a training set and a test set, building a leakage oil segmentation model, completing iterative training of the model based on the training set, and calculating the visualization and evaluation indexes of the segmentation effect of the model in the test set. The substation equipment leakage oil segmentation method based on double graph reasoning provided by the application utilizes the texture and edge characteristics of the leakage oil, and solves the problems of difficult defect feature extraction, low segmentation precision and poor segmentation effect caused by the strong correlation of the leakage oil components, the variable morphology and the diffusion change characteristics.

The application discloses a kind of multi-modal sentiment classification methods based on progressive neural network, which includes three modules of perception scanning, fine reading, heterogeneous graph reasoning;Perception scanning module is used to the feature representation of visual picture and corresponding text description, and the content information of picture and text is roughly perceived using VL-BERT;Fine reading module uses memory attention network to focus on important features in picture and text, models fine-grained complementary information between them;Heterogeneous graph reasoning module constructs multi-modal heterogeneous graph using the social relationship network between pictures, fuses the scan embedding and fine reading embedding generated in the previous two stages into graph node embedding, and uses graph convolutional neural network to perform multi-modal sentiment polarity reasoning.The application comprehensively utilizes feature representation, cross-modal attention mechanism, graph neural network and other technologies to infer the feature correlation and complementary information between multi-modal, which has a significant effect on improving the accuracy of multi-modal sentiment classification.

The invention provides a contract auditing method and device based on graph constraint reasoning, equipment and a medium, and the method comprises the steps: taking a legal reasoning path structure as a constraint to carry out graph reasoning, generating a candidate reasoning path set, and guaranteeing that a reasoning path sequence accords with the constraint of an auditing rule graph based on pre-generation of the legal reasoning path structure. And then the path generation process is constrained through a graph constraint reasoning mechanism, a contract auditing result is generated, constraint reasoning is performed in combination with structured legal knowledge, generation of false terms is reduced, and the accuracy of the contract auditing result is improved. The method can be applied to credit contract auditing in the financial field or medicine purchasing contract auditing in the medical field, so that the contract auditing result accuracy in the financial field or the medical field is improved.

The application relates to a knowledge graph-based automatic attack and defense strategy generation method, device and equipment. The method comprises the following steps: constructing a multi-layer knowledge graph fusing network security knowledge; based on a preset reasoning rule set and the multi-layer knowledge graph, deducing an attack path through a graph reasoning engine to generate a candidate defense strategy; constructing a double-layer game model of an attacker and a defender, and applying a multi-objective optimization algorithm to search and optimize the candidate defense strategy to generate a defense strategy plan containing operation instructions; verifying the effectiveness of the defense strategy plan through a simulation verification environment, and deploying the defense strategy plan that passes the verification to a production environment; collecting execution feedback data of the defense strategy plan in the production environment, and dynamically adjusting the multi-layer knowledge graph and the reasoning rule set. By using the method, attack semantic understanding, intelligent path deduction, dynamic strategy generation and continuous evolution can be realized.

The invention relates to the technical field of databases and artificial intelligence, and discloses a data lake table association relationship automatic discovery method and system, and the method comprises the steps: generating a table description and a column description of a new table through automatic reasoning based on a knowledge base and a language model; calculating a semantic similarity and a value overlapping distance between the query column and a target column of any table in the data lake; screening out a candidate column associated with the query column based on the semantic similarity and the value overlapping distance, and considering that an association relationship exists between the candidate column and the query column; and constructing a metadata graph based on the table description, the column description, the affiliation relationship between the table and the column and the discovered association relationship between the columns, and performing multi-hop reasoning by calculating a pass closure to derive the potential association relationship between the columns. According to the method, through knowledge-enhanced metadata construction, fusion of semantic features and value overlapping features, and a mapping metadata organization and graph reasoning technology, automatic inference of the connection relation between tables in a data lake is achieved.

The application provides an equal protection evaluation report automatic checking and rule matching system based on an intelligent agent, comprising a report acquisition module for pulling a to-be-reviewed and historical equal protection report; an audit processing module for calling a Bi-LSTM model with attention embedded by reinforcement learning fine-tuning and knowledge graph fusion to score the report sentence by sentence; a rule management module for storing and analyzing equal protection 2.0 structured machine rules to perform secondary accurate matching on low-confidence sentences; a knowledge graph module for maintaining a multi-dimensional association graph of standard families, control measures and evaluation items and providing graph reasoning; an intelligent agent decision module for generating pass, return, manual review or automatic correction actions according to the audit state; a feedback learning module for collecting manual review differences, incrementally training the model, strategy network and graph vector; and a storage module for saving the report, model, rule, graph and log in a multi-modal manner in a relational database, a graph database and a vector database to implement data governance and access control. The application can improve the audit efficiency, accuracy and standard consistency of the evaluation report.

The invention discloses an infrastructure contract intelligent management and control method, system and device based on artificial intelligence and a medium, and belongs to the technical field of natural language processing, and the method comprises the steps: automatically converting the core obligation terms of an infrastructure contract into a structured contract WBS knowledge graph through the natural language processing technology, and forming a contract obligation map through binding performance conditions; in the project execution process, the system identifies the event entity in the performance data in real time and automatically associates the event entity to the corresponding node of the knowledge graph, and finally, automatic performance monitoring of contract obligations based on event triggering is achieved. According to the method, the dynamic contract WBS knowledge graph is constructed, semantic analysis and graph reasoning technologies are combined, intelligent management of contract obligations and accurate association of performance events are achieved, and the intelligent level of engineering management and control is improved by means of an automatic review and risk early warning mechanism.

The invention relates to a graph data reasoning method and device, equipment and a program product. Comprising the following steps: in a trusted execution environment, first graph data and a homomorphic encryption key pair sent by a client are received, the homomorphic encryption key pair comprises a private key and a public key, and the first graph data comprises graph structure information and first graph node features after homomorphic encryption processing based on the public key; performing feature extraction processing on the first graph node features to determine second graph node features, determining second graph data based on the second graph node features and the graph structure information, and performing homomorphic encryption processing on the second graph data by using a public key to obtain encrypted second graph data; the data size of the second graph data is smaller than that of the first graph data; and determining a first graph reasoning result based on the encrypted second graph data, and sending the first graph reasoning result to the client, so that the client decrypts the first graph reasoning result based on the private key to obtain a second graph reasoning result. By adopting the method, the reasoning efficiency of the graph data can be improved.

This invention discloses a reliable question-answering method and system for archival knowledge graphs, relating to the field of knowledge graph processing technology. After receiving an archival query, the method parses the subject entities, relational clues, and constraint sets, constructs an initial evidence subgraph in the archival knowledge graph, and writes it into a graph evidence cache. Based on the initial evidence subgraph, candidate answers are inferred, and an uncertainty score is calculated by combining confidence, multi-sample consistency, support paths, relation coverage, constraint coverage, and candidate competition. When the uncertainty exceeds a threshold, a structured diagnosis of the failure cause is performed, identifying it as an entity gap, relation gap, constraint gap, or support gap. Graph evidence is incrementally supplemented according to the failure type, and the incremental evidence is merged into the graph evidence cache. After further inference and support verification, a reliable answer with evidence sources is output. This scheme can reduce the noise introduced by blindly expanding the scope of the query, significantly improving the reliability, accuracy, and traceability of archival question answering.

The invention provides an AI engine user demand analysis method for multi-source data input, relates to the field of artificial intelligence, and solves the technical problems that in the prior art, relevance and dynamic insight of user demands are insufficient, and user motivation is difficult to mine. The method comprises the following steps: acquiring multi-modal data from different data sources, and carrying out preprocessing and semantic fusion to generate a multi-modal data stream with unified representation; wherein the data source comprises a public network data source and an Internet of Things data source; based on the multi-modal data flow, through a graph neural network, constructing a dynamic demand graph with demand entities as nodes and association relationships as edges; based on the dynamic demand graph, identifying user demands and user motivations by using graph reasoning and causal discovery algorithms, and generating a structured demand analysis report; and based on real-time monitoring data, updating the dynamic demand map through an incremental learning algorithm, and carrying out continuous tracking of demand evolution.

The application discloses an OTA traffic dynamic expansion method and system, relates to the field of communication, and realizes active monitoring of states such as remaining amount of a package, terminal activity and network switching by deploying an Applet module in a user SIM card, and generates quantitative state data. When a preset trigger condition is met, an expansion request is initiated to an operator cloud platform. The platform combines user historical behaviors, location information and network load, performs traffic peak value prediction and expansion level determination based on a multi-task graph reasoning model. After evaluating expansion benefits through a cause-effect reasoning module, OTA instructions are generated and delivered. The SIM card temporarily authorizes and records feedback accordingly, and realizes accurate, moderate and intelligent expansion control.