977 results about "Graph Node" patented technology

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention provides a multi-modal knowledge extraction method and system based on multi-agent collaborative optimization, and relates to the technical field of knowledge extraction, and the method comprises the steps: carrying out the multi-modal deconstruction of an original document to be extracted; constructing a multi-modal agent, respectively executing feature extraction and preliminary knowledge extraction, and outputting a single-modal multi-component system; based on a cross-modal knowledge graph, mapping information of different modals to a unified semantic node, and establishing cross-modal association and analyzing a logic chain through a graph neural network and a causal reasoning module; dynamically allocating resources according to the importance of map nodes, and screening structured knowledge; and through confidence analysis and node traceability evaluation, an intelligent agent cooperation mechanism is optimized, and increment correction is carried out on a result. According to the method and the device, the technical problem of low knowledge extraction accuracy and efficiency caused by insufficient multi-modal knowledge collaborative mining capability due to knowledge extraction of literatures by adopting a single agent in the prior art can be solved, and the knowledge extraction quality and efficiency are improved.

The invention relates to the technical field of automatic driving, and discloses an automatic driving lane changing trajectory planning method based on deep learning, and the method comprises the steps: carrying out the data collection and preprocessing of a multi-modal sensor; performing spatial feature extraction and time sequence modeling on the preprocessed multi-modal data by adopting a CNN-LSTM hybrid architecture, performing feature fusion through an attention mechanism, and outputting a first feature extraction vector; taking the detected vehicles as graph nodes to construct a traffic graph, learning an interaction relationship between the vehicles through a graph attention network and a message passing mechanism, and calculating a scene urgency score and a safety score; generating a lane changing decision based on the deep Q network and the strategy gradient; and generating a trajectory based on the generative adversarial network. The technical problems that an existing lane changing track planning method cannot adapt to the dynamic traffic environment, lacks the ability of understanding complex multi-vehicle interaction and is difficult to balance safety and urgent conflict requirements are solved, and intelligent, safe and efficient automatic driving lane changing track planning is achieved.

The invention relates to the technical field of industrial Internet of Things, in particular to a machine equipment online state monitoring and fault diagnosis system, which comprises the following steps of: acquiring multi-source heterogeneous sensing data through an edge computing node deployed on an equipment body, performing adaptive noise filtering and feature dimension reduction processing on original data, and acquiring multi-source heterogeneous sensing data; outputting a standardized equipment state vector set; inputting the equipment state vector set into a dynamic knowledge graph engine, constructing a fault evolution network comprising space-time correlation characteristics based on an equipment operation entropy change quantification model, and generating a graph node connection relationship with a weight coefficient; and inputting the fault evolution network into a migration reinforcement learning module, and outputting a diagnosis decision set comprising a fault type, a severity degree and an evolution path through knowledge migration of a cross-device fault mode. According to the method, the problems of edge redundancy and single feature expression in traditional rule-based atlas construction are effectively avoided, and the structuring ability and physical traceability of fault recognition are improved.

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

Apparatuses, systems, and techniques to perform a neural network. In at least one embodiment, an application programming interface schedules two or more graph nodes to be performed by two or more parallel processing pipelines based, at least in part, on an order of layers in the neural network.

Apparatuses, systems, and techniques to perform an application programming interface (API) to cause dependency type information of one or more user-indicated graph nodes of a software graph to be indicated. In at least one embodiment, one or more dependency types from a graph are indicated.

The present application discloses a database query method and apparatus, an electronic device, and a non-volatile storage medium. The method comprises: determining a knowledge graph corresponding to a database to be queried, wherein the knowledge graph is used for representing a logical structure and an association relationship of data in said database; determining similarity scores between user question text and graph nodes in the knowledge graph, and determining a target node from among the graph nodes of the knowledge graph on the basis of the similarity scores, wherein the similarity scores are used for representing the degree of association between the graph nodes and the user question text; and on the basis of the target node, generating database schema information corresponding to said database, and using a large language model to generate, on the basis of the database schema information, a structured query language statement corresponding to the user question text.

The invention provides a Beidou intelligent early warning analysis method and system applied to a drainage basin-level reservoir dam group, and the method comprises the steps: obtaining a dam body deformation time sequence record and a corresponding hydrological environment time sequence record which are collected by a monitoring system of the drainage basin-level reservoir dam group through a Beidou satellite positioning terminal; dam body deformation response mode self-encoding processing is carried out on the associated time sequence data set, a deformation response feature element set representing the dam body structure state is generated, the deformation response feature element set is input into a pre-constructed basin reservoir dam group topological relation graph, abnormal state propagation calculation is carried out through the state dependency relation between graph nodes, and the abnormal state propagation calculation is carried out. And obtaining a chain risk transmission path set, performing risk evolution trend deduction on dam nodes in each path based on the chain risk transmission path set, generating a risk evolution index matrix, and generating a threatened risk identification sequence for key dam nodes. According to the method, the structuralization and response operability of the early warning information can be improved, and the scientificity and practicability of early warning analysis of the basin-level reservoir dam group are comprehensively improved.

The invention relates to the technical field of intelligent abstract generation, in particular to an intelligent archive abstract generation system and method based on a natural language processing technology. The system comprises a multi-layer semantic generation core unit which performs semantic hierarchical segmentation analysis on an original file text to construct a multi-layer semantic graph, and constructs an abstract generation model to generate abstract content; the knowledge graph fusion engine unit associates and extracts terms in an original file text, constructs an entity mapping relation, and sets semantic graph node expression weights in an abstract generation model according to semantic association confidence scores; a paging index cache retrieval unit performs fragment division processing on each node in the multi-layer semantic graph, and constructs an abstract content fragment index structure containing a node set; and the version tracing transaction management unit carries out version recording on generation and modification operations of the abstract contents. The invention discloses an intelligent archive abstract generation system which is constructed by fusing a multi-layer semantic graph and is associated with a knowledge graph.

The invention particularly relates to a ship multi-modal image fusion recognition method based on graph neural structure alignment, and the method comprises the following steps: 1, obtaining a ship multi-modal image, and constructing a backbone neural network to extract the features of the multi-modal image; 2, constructing graph nodes of the graph neural network, and generating an adjacent edge relationship; 3, for graph structures constructed in different modes, adopting a two-level graph attention mechanism to complete structure alignment; step 4, utilizing an optimal transmission mechanism to realize structure alignment between the infrared and visible light modal diagrams; step 5, feature re-injection is carried out to fuse space coordinates and global information, and the positioning and expression ability of node features is improved; and step 6, training the constructed ship multi-modal image fusion recognition network by adopting local feature alignment loss, graph-level semantic consistency loss and classification supervision loss. According to the method, the problem of alignment errors caused by inconsistency of infrared and visible light modal images is solved, the structure and semantic information of the infrared and optical images are fully fused, the accuracy and robustness of cross-modal target recognition are effectively improved, and the method is suitable for complex scenes such as multi-modal ship recognition.

The invention discloses a bearing life prediction method and system based on dynamic knowledge embedding, and the method comprises the following steps: S1, encoding bearing field data, expert experience and monitoring data into a structured triple, building a dynamic knowledge graph frame, and designing a sliding window confidence mechanism to achieve the online updating of a graph node relation; s2, extracting knowledge embedding vectors by using a relational graph convolutional network, and extracting vibration signal features by using a hierarchical convolutional network; s3, mapping the knowledge embedding vector and the vibration characteristics to a unified semantic space through a linear projection layer; s4, constructing a Transform encoder based on multi-head self-attention, and establishing a dynamic correlation model between vibration characteristics and knowledge embedding; s5, designing a full-connection network output life prediction result and feeding back the optimized knowledge graph; and S6, adaptively adjusting the size of the sliding window based on the change rate of the working condition, dynamically balancing the contribution weight of new and old knowledge in combination with a gating mechanism, and ensuring the adaptability of the model to the complex working condition.

The invention relates to the technical field of intelligent agriculture, and particularly discloses an orchard intelligent water and fertilizer integration-based edge end fruit tree fertilizer demand prediction method, which comprises the following steps of: constructing each fruit tree as a graph node, and fusing canopy spatial characteristics extracted by unmanned aerial vehicle multispectral remote sensing and nutrient time sequence characteristics monitored by a soil sensor; space influence weights among nodes are calculated according to the terrain, the soil texture and the space distance, and a fruit tree individual relation graph is established; then, through a space-time diagram convolutional network, aggregating space neighborhood features and capturing time dynamic changes, and outputting target state features subjected to multi-scale enhancement; when the canopy conflicts with the soil characteristic indication, combined diagnosis is carried out based on a neighborhood consistency index and an adjacent node state, systematic nutrient stress and local non-nutrient factors are distinguished, and final fertilizer demand prediction is generated after the conflict is eliminated; a digital fertilization prescription map is generated through spatial interpolation, soil characteristics and slope correction, and the water and fertilizer integrated equipment is driven to execute precise operation.

The invention discloses an agricultural product supply chain abnormal event tracing method and system based on a knowledge graph, and relates to the technical field of agricultural product supply chain management, and the method comprises the steps: constructing a supply chain knowledge graph, and enabling graph nodes to cover agricultural product batches, production subjects, processing equipment, transport vehicles, storage warehouses and other entities; when an anomaly is detected, performing reverse traversal from an abnormal node based on a graph neural network to determine a candidate propagation path; calculating an abnormal source probability score by integrating the historical abnormal record, the time window goodness of fit and the association strength, and outputting a responsibility subject sequence; the method supports hypothetical reasoning to predict the downstream influence range, and solves the problems that a traditional traceability scheme is difficult to efficiently associate and analyze cross-link data and lacks abnormal path intelligent reasoning ability.

The invention discloses a teaching course recommendation method and system based on English learning data, particularly relates to the field of semantic processing, is used for solving the problem of poor pertinence of a traditional English learning course, and comprises the following steps: aiming at systematic difference of native language and English expression on a syntactic structure, constructing a language structure difference map and extracting a structure offset label; the method comprises the following steps: performing dependency syntactic analysis on semantic consistent sentence pairs of multi-language aligned corpora to generate a structural difference feature set; on the basis, a semantic-grammar dimension mapping graph is constructed in a classified mode, and a general structure expression vector is established for graph nodes. A teaching course is divided into knowledge point units in combination with a context label, and a mapping relation between a structure label and the course is established. The system performs structure analysis and semantic matching on sentences input by the learner, identifies structure migration type expression errors, and recommends accurate teaching content according to context and structure labels.

The invention discloses an electromagnetic field simulation grid adaptive generation method based on a neural network. The method comprises the following steps: performing mesh generation on a semiconductor device simulation model to obtain original generation data, and constructing node features; an undirected graph is constructed, graph nodes of the undirected graph adopt node features of grid points, and connecting edges adopt Euclidean distances between the grid points and adjacent grid points; inputting the undirected graph into a double-branch neural network, and outputting a predicted node position; and correcting each node of the original subdivision data by using the predicted node position obtained by the double-branch neural network to form new subdivision data for electromagnetic field simulation. According to the method, physical field information can be fused to efficiently adjust the node density, and the geometric boundary and topological structure characteristics of the device can be strictly kept.

The invention provides an elevator multi-mode crowd feature perception and intelligent advertisement putting method and system, and relates to the technical field of intelligent advertisement putting, and the method comprises the steps: collecting multi-mode perception data in an elevator through an edge computing terminal; performing feature decoupling on the data, performing cross-modal semantic alignment, establishing a directed association relationship between modals, and constructing a scene feature map; calculating the topology importance degree of map nodes, screening feature nodes, and extracting context information for semantic coding; mapping the scene semantic code and the advertisement audience semantic code to a two-dimensional coordinate system to construct a semantic matching graph, and extracting an optimal matching path to form a candidate set; predicting a scene evolution trend based on the scene characteristic spectrum evolution trajectory, and calculating an advertisement adaptive score to generate a playing sequence; putting and collecting user interaction data feedback according to the sequence to update the graph structure. According to the invention, accurate crowd feature recognition and advertisement dynamic matching are realized, and the advertisement putting efficiency and the user experience are improved.

The invention discloses a data updating method and device for a water conservancy knowledge graph and a medium, and relates to the field of water conservancy data management, and the method comprises the steps: constructing an initial water conservancy knowledge graph based on a plurality of data sources of water conservancy data; monitoring the plurality of data sources, and judging whether a data change event is triggered or not; when the data change event is triggered, generating a knowledge graph updating task through a preset synchronization rule; when the number of the knowledge graph updating tasks is greater than 1, determining the processing priority of the knowledge graph updating tasks according to the importance of updating graph nodes in the water conservancy knowledge graph, and generating an updating task queue; and executing the update task queue, updating the water conservancy knowledge graph, and recording an update version snapshot. Through unified monitoring of a structured data source, an unstructured data source and an interface data source, the island effect of water conservancy data is broken, and unified linkage updating of cross-modal data such as reservoir attributes, monitoring reports and real-time water levels is achieved.

The invention discloses a commodity traceability method and system based on a block chain. The method comprises the following steps: acquiring basic information data of a commodity to generate a unique identifier of the commodity, and constructing a distributed account book in combination with a transaction hash value and an operation timestamp; constructing a commodity propagation ecological map by using a graph database technology based on a distributed account book; aggregating the full-node circulation data, and performing standardization processing to form a cross-platform integrated data set to obtain commodity propagation path data; analyzing to obtain transaction data of the commodity propagation path data, and dynamically adjusting graph node weights; and finally, performing anomaly detection and block chain verification on the updated commodity propagation ecological map to generate a commodity traceability report. The problem that in a complex propagation network, it is difficult to ensure the credibility of a commodity data source and achieve cross-system efficient integration is solved, and the effects of improving the transparency and authenticity of supply chain data, optimizing the commodity traceability efficiency and guaranteeing the transaction credibility are achieved.

The invention discloses a new energy multi-mode fault diagnosis method based on dynamic graph convolution and transfer learning. According to the method, multiple types of sensors are deployed to collect operation, environment and historical data of new energy equipment, and a multi-modal data set is generated through preprocessing; the method comprises the following steps: defining an equipment component as a graph node, constructing a directed graph containing a time-varying edge weight by utilizing dynamic time warping and an attention mechanism, and updating a graph structure on line; a dual-branch dynamic graph convolutional network is adopted to extract multi-modal data features, an adversarial transfer learning algorithm and a meta-learning algorithm are combined, distribution of a source domain and a target domain is aligned, and a special diagnosis model is generated by using small samples. And fault classification is carried out, a fault component is positioned in combination with a graph node attention weight, and a fault evolution index is constructed by fusing a fault prediction probability and an attention weight change rate. The method effectively fuses multi-modal data, adapts to complex working conditions, improves small sample diagnosis precision, realizes fault accurate positioning and evolution prediction, and has important significance for guaranteeing stable operation of new energy equipment.

The invention discloses a network security data analysis system and method based on artificial intelligence, and relates to the technical field of network security, and the method comprises the steps: constructing a structured triple, mapping the structured triple into graph nodes and edges, and storing the graph nodes and edges in a graph database; extracting graph data from the graph database, and generating a node feature matrix, an adjacent matrix and an edge feature matrix; utilizing a graph attention mechanism to train a node representation vector, and constructing a semantic propagation matrix; obtaining a predicted attack path model; generating a candidate attack path set; constructing a credible scoring model, training and optimizing, and screening high-credibility paths with scores exceeding a threshold value; constructing a multi-layer perceptron model to obtain an attack source prediction model; according to the method, real-time atlas data is obtained, the nodes exceeding the threshold value are marked as risk nodes, the risk nodes are uploaded to a protection system to trigger alarm and check, and the automation and real-time performance of attack source recognition are improved.

The invention discloses a data mapping and structured integration method based on heterogeneous threat intelligence. The method comprises the following steps: S1, constructing an initial heterogeneous graph based on threat intelligence types, sources and association strength; s2, constructing a local Transform semantic aggregation sub-graph, and realizing local semantic enhancement; s3, optimizing the fused semantic features by using a flying fox optimization algorithm, and determining fusion parameters; s4, a cross-subgraph Transform network is constructed based on parameter combination, and node and edge feature fusion is realized; s5, mapping data by adopting a dynamic semantic propagation fusion strategy to generate a heterogeneous threat knowledge graph; s6, identifying a high-risk node by using a Transform adaptive threshold mechanism; and S7, dynamically correcting a graph node state to form a structured threat data set. According to the method, the fusion efficiency and response accuracy of threat intelligence are improved, and the real-time monitoring and active defense capabilities of the security situation are enhanced.

The invention discloses a small sample remote sensing image classification method based on hierarchical spatial structure learning. The method comprises the following steps: firstly, extracting multi-scale features of a remote sensing image by using a ViT (Visual Transform) model, and capturing rich semantic information and spatial structure relationships in the image; secondly, constructing a graph structure based on spatial adjacency and attention weight to model a structured relationship between samples, and encoding graph node features through a graph convolutional network (GCN) so as to enhance the discrimination ability of the features in a structural semantic space; thirdly, a residual enhancement mechanism is introduced to fuse global semantic information, and the discrimination capability of graph embedding is improved; then, based on the structural similarity between the support set and the query set, performing classification decision, and realizing accurate classification under a small sample condition; and finally, carrying out joint optimization on the whole model by adopting a training strategy of a small sample meta learning task and a supervision loss function.

The invention relates to the field of compliance management, and particularly discloses a real-time compliance management system dynamic evaluation system and method based on data driving. External law and regulation documents and enterprise internal compliance rule documents are respectively mapped into knowledge maps; semantic association analysis is carried out on cross-graph regulation provisions and compliance rule provisions to identify the relationship type between the two provisions, and when conflicts are found, a correction mechanism is triggered to realize cross-graph node relationship bridging. When the regulation is changed, further positioning the embedding position of the change provision in the map, capturing the diffusion path of the affected compliance rule provision, and performing semantic analysis and relation reasoning on the regulation change provision and the affected compliance rule set to reveal the chain influence mode of the regulation change on the enterprise compliance rule; and generating compliance rule updating suggestions. According to the method, dynamic evaluation and optimization of the compliance management system can be realized, and the efficiency and accuracy of enterprise compliance management are improved.

The invention provides an intelligent data consanguinity tracking and visualization method based on graph calculation, and the method comprises the steps: carrying out the data structure analysis and metadata extraction of original data assets, so as to generate a standardized data asset package; performing graph node attribute definition on the data entities in the standardized data asset package, and performing graph edge attribute definition on the association relationship between the data entities to generate a first data blood relationship model; performing blood relationship path mining on graph nodes and graph edges in the first data blood relationship graph model to obtain a basic blood relationship path set, and performing quantitative calculation and feature labeling on path association strength in the basic blood relationship path set to generate a second data blood relationship graph model; and performing visual rule mapping on graph node attributes and graph edge attributes in the second data blood relationship graph model to construct a standardized visual data set and generate a data blood relationship visual interaction interface according to the standardized visual data set.

The invention relates to the technical field of infrared light machine system thermal deformation prediction, in particular to a multi-material structure thermally induced stress deformation prediction method based on a graph neural network. The method comprises the steps of data set establishment, graph structure establishment, graph neural network model establishment and training and model and parameter optimization. Finite element nodes correspond to graph nodes, finite element edges correspond to graph edges, an encoder-message passing-decoder architecture model is established, and node states are updated through a three-layer physical symmetry message passing mechanism. Physical constraint loss including minimum displacement smoothness constraint and stress continuity constraint is innovatively added into a loss function. Compared with traditional finite element calculation, the method has the advantages that the speed is increased by more than 100 times, high hardware adaptability is achieved, the black box limitation of a data-driven neural network model is broken through, thermally induced stress deformation analysis caused by different material coefficients can be processed, the adaptability to geometric changes is high, and good engineering application value is achieved.

The invention discloses a three-dimensional physical field real-time prediction method and system based on geometric deep learning and physical constraint, and relates to the technical field of computer-aided engineering and artificial intelligence. The method comprises the following steps: directly extracting native boundary representation data (B-Rep) of a three-dimensional model from a computer aided design system; constructing a heterogeneous dual graph taking a parameterized curved surface as a graph node, skipping finite element grid division, and aggregating local and global topological features by using a graph neural network; in combination with a physical information driving mechanism, a partial differential equation (PDE) residual error is introduced as a loss function for constraint training, and generalization prediction of a novel geometric structure is realized; and finally, the physical field state quantity is predicted through direct regression and is rendered in real time. An incremental reasoning mechanism based on a local topology subgraph is adopted, millisecond-level physical field real-time feedback under design modification is achieved, and the method is suitable for scheme rapid screening and trend prediction in the initial stage of design.

The invention discloses a multi-terminal collaborative modular power operation and maintenance method and system based on digital twinning, and the method comprises the following steps: constructing a global power topological graph of a power energy site, mapping power equipment, sensors and connecting lines into graph nodes and graph edges, and binding the static attributes of the equipment with the topological structure; and acquiring real-time operation data of the power equipment, and distributing a unified time label for the multi-source heterogeneous data. According to the method, a unified twinborn model of cross-voltage class, cross-region and cross-equipment type is established in a digital twinborn platform, and operation data of different equipment such as photovoltaic equipment, battery energy storage equipment, an inverter, a protection device, an electric energy meter and temperature control equipment are expressed in a unified data semantic mode; according to the invention, the system can still automatically establish a consistent device portrait under the conditions of a large number of multi-terminal devices and diversified communication protocols, and the multi-terminal cooperative computing capability is significantly improved.

The invention provides a drainage basin water quality abnormity tracing method and system based on time sequence fluctuation characteristics, and the method comprises the steps: taking each monitoring point in a drainage basin as a graph node, constructing a directed edge connected with the graph node according to the spatial relation between the monitoring points and the water flow direction, and forming a drainage basin graph network; acquiring historical monitoring data, extracting amplitude variation features and fluctuation features from the historical monitoring data, and calculating a time sequence fluctuation feature vector; traversing graph nodes with directed edge connection, constructing index pairs corresponding to the graph nodes, analyzing the correlation of the index pairs under different lag dimensions, and calculating response lag dimension coefficients; according to the time sequence fluctuation feature vector and the response lag dimension coefficient, calculating an abnormal correlation coefficient between graph nodes, and constructing an abnormal transmission path; and according to the abnormal transmission path, dividing a pollution troubleshooting area for a worker to carry out troubleshooting treatment. The water quality abnormal fluctuation excitation source position can be accurately positioned, and the water quality abnormal analysis efficiency and the abnormal source positioning precision are improved.

The invention relates to the technical field of user behavior analysis marketing, in particular to an intelligent marketing system and method based on customer behavior real-time calculation, and the system comprises an edge behavior collection unit, a cleaning and desensitization unit, a spatial-temporal feature coding unit, a real-time prediction unit and a collaborative decision-making unit. According to the method, an edge behavior acquisition unit captures native data such as page staying duration and contact tracks, a cleaning and desensitization unit generates compliance time sequence fragments, and a spatial-temporal feature coding unit converts the data into spatial-temporal feature vectors containing time decay weights, topological graph nodes and behavior integrity indexes; the real-time prediction unit fuses multi-dimensional vectors through a graph neural network and outputs real-time preference probability distribution of commodity categories, and the collaborative decision-making unit dynamically allocates computing power and generates an optimal marketing action set, so that the problems of large user real-time intention capture deviation and lagging computing power allocation of traditional marketing are solved, the marketing accuracy and timeliness are improved, and the marketing efficiency is improved. And resource waste is reduced.