12 results about "Association mining" patented technology

This invention belongs to the field of text retrieval technology, specifically referring to an automatic retrieval system for digital economy texts. The system includes a basic task instance identification module, a key instance association mining module, a tag local score estimation module, a global tag configuration module, a noise tag configuration generation module, and a precise digital economy text retrieval application module. This solution uses two dedicated multilayer perceptrons to calculate semantic and syntactic dependency weights and fuse them to obtain the final association strength, achieving precise capture of digital economy instances and their associations. It combines global tag configuration to quantify the tightness of instance tag associations, and uses the noise tag configuration generation module to transform the global optimal tag configuration problem into a binary classification problem. By constructing a contrastive loss function and optimizing it through backpropagation, it efficiently solves the problem of computational explosion, achieving concept-based precise retrieval and intelligent decision support, providing users with an efficient digital economy information acquisition experience.

This invention discloses a highly robust web crawler system, belonging to the fields of big data acquisition and artificial intelligence technology. The system is a closed-loop intelligent system with multiple modules working collaboratively. It includes a multi-data source database construction and pre-training module, a real-time monitoring module for crawler operation status, a data semantic analysis and association mining module, a data request-matching evaluation and parameter optimization module, and a dataset generation and delivery module. Through tight coupling between modules and effective transmission of data flow and control flow, the system achieves full-process automation and intelligence from data discovery, intelligent crawling, dynamic adaptation to high-quality delivery. The system features high robustness and anti-crawling capabilities, strong data semantic understanding and association accuracy, and low manual costs.

The application discloses a cross-domain user portrait construction and content matching method based on federal learning, relates to the technical field of federal learning, and realizes high-precision learning of user dynamic representation by modeling full-dimension interaction context of users, items, domains and time windows through a four-order time sequence hypergraph and realizing user dynamic representation based on a time sequence hypergraph neural network THGNN; a double-path federal security aggregation mechanism is designed, cross-domain security exchange and association mining based on a user interest track evolution operator are carried out while generating user cross-domain global fusion representation through security aggregation, and accurate capture of user cross-domain behavior lag influence is realized; by constructing a cross-domain evolution mode knowledge base, group-level general evolution modes and user individual-level personalized evolution rules are deposited, cross-domain cold start problems are effectively alleviated, and model generalization performance is greatly improved.

The application provides an insurance anti-fraud association mining method and system combining a graph neural network and a transaction sequence, relates to the technical field of insurance anti-fraud, and first acquires insurance transaction data and subject association data in an insurance business scenario; an insurance anti-fraud dynamic transaction association graph is constructed based on the insurance transaction data and the subject association data, and contains transaction nodes, subject nodes and dynamic weight association edges; a bidirectional interaction channel is established by calling a graph neural network and a transaction sequence mining module, interaction optimization of node features and time sequence features is realized; a multi-level association feature set is generated based on the insurance node features and the insurance time sequence features after interaction optimization; anti-fraud association mining is performed on the multi-level association feature set, and a result containing a suspected fraud association path and a basis is generated. The application improves the accuracy and efficiency of insurance anti-fraud.

This invention relates to the field of database construction technology, and discloses a knowledge base construction method and system based on AI text parsing and hybrid retrieval. By integrating semantic relevance and document layout features through adaptive block segmentation, it achieves accurate identification of core semantic units in the text and maintains logical integrity in the division, fundamentally avoiding information fragmentation and laying the foundation for high-quality knowledge organization. Secondly, through a hybrid retrieval mechanism combining keyword, semantic vector retrieval, and multi-dimensional re-ranking, it effectively balances retrieval response speed with the depth and accuracy of results, meeting users' multi-level query needs from rapid location to in-depth correlation mining. Finally, through dynamic updates and automatic association mapping functions, it achieves real-time synchronization and intelligent association of newly added knowledge, not only ensuring the timeliness of the knowledge system but also proactively building a cross-document knowledge network, thereby breaking down information silos and enhancing the overall utilization value and discovery capability of knowledge.

This invention relates to a personalized learning recommendation method for e-commerce, belonging to the field of information recommendation technology. The method integrates user basic attributes and historical behavior data, deeply integrating multi-dimensional user profiles through adaptive weighting; it utilizes the association mining between products and knowledge points, establishing a product knowledge graph based on graph neural networks to achieve product topic clustering and multi-level knowledge point summarization; based on user profiles and product knowledge associations, it leverages deep reinforcement learning to dynamically plan personalized learning paths, intelligently recommending optimal resource sequences according to the user's current interests and knowledge level. The system collects user feedback information in real time and completes real-time adaptive adjustment of the recommendation strategy through local model updates and federated learning, achieving highly accurate and privacy-preserving personalized learning resource recommendations under interest shifts and behavioral changes.

The application discloses a logistics service evaluation method and system based on data mining, and relates to the technical field of logistics.The method overcomes the defects of the existing method, such as relying on manual subjectivity, single index, data fragmentation, static evaluation, etc., through a complete technical closed loop of multi-source data fusion, index system construction, weight calculation, comprehensive scoring, dynamic updating and correlation mining, and optimization verification.In S1, the multi-source data is associated with the transportation task ID and timestamp as the key to avoid manual collection bias;in S2, a multi-dimensional evaluation index system is constructed by grouping according to dimensions to achieve comprehensive coverage;in S3, the eigenvalue method is used to calculate the index weight vector;in S4, the evaluation grade and comprehensive score are determined by combining the triangular membership function and the fuzzy synthetic operator;in S5, the Apriori algorithm is used to mine the association rules and generate optimization strategy instructions to realize the conversion from evaluation to action;in S6, the optimization effect is verified and the weight is iteratively adjusted to form a closed loop mechanism of evaluation, optimization and improvement.

This invention discloses a product service entity association mining method based on spectrum decoupling, belonging to the field of product service modeling and relationship mining. The method includes: First, collecting multiple product services and constructing a historical call matrix and a product service set; second, encoding the functional text and categories in the product service metadata, calculating the similarity between product services, and forming product service pairs based on the similarity scores to construct a modal feature isomorphism graph; next, further calculating a collaborative feature isomorphism graph by combining the adjusted historical call matrix, and extracting low-frequency and mid-frequency representations of the "ugly skin" (a term referring to a specific product or service) using low-pass and mid-pass frequency domain filters, further fusing features using a pairwise attention mechanism to obtain a low-dimensional representation; finally, updating parameters through a loss function and generating a product service feature retrieval table. This invention can effectively improve the accuracy and conversion rate of product service recommendations.

The application discloses a multi-source data fusion enterprise credit report intelligent generation method and product. The method comprises the following steps: constructing and registering a heterogeneous data tool library based on an MCP protocol; an LLM analyzes a natural language instruction input by a user; a multi-source API is dynamically routed and scheduled through an MCP server; unified structured data is obtained by performing entity alignment on the obtained multi-source heterogeneous data, and then performing conflict resolution based on a time decay factor credibility weighting mechanism; feature calculation and association mining are performed based on fusion true values, and then a dynamic Prompt is constructed by combining analysis features and user intentions; a large language model generates a credit report and adds a data source anchor point. The application fundamentally alleviates the calculation deviation caused by the cross-modal semantic gap, effectively suppresses the "data illusion", and generates a report that guarantees the flexibility of natural language interaction while achieving commercial compliance levels in terms of accuracy and auditability.

The invention discloses an automatic discovery method for database mode association for penetrating audit. The table field portrait data and the topological compatibility matrix are used for performing dual verification on the proposal association pair set, so that the illusion problem generated when a large language model processes a numerical type can be avoided, the proposal association pair set can be summarized to generate a global context, the conflict between transmission redundancy and a close-to-parent structure is eliminated, and the accuracy of the whole context is improved. Then, the logic rationality of a graph structure is guaranteed through global context auditing, so that key auditing clues are not missed, complex semantics of auditing data can be coped with, the accuracy and recall rate of association discovery are improved, meanwhile, the association mining problem caused by foreign key missing is avoided, and the accuracy and recall rate of association discovery are improved. Therefore, the accuracy of the restored auditing evidence chain is greatly improved, the bottom layer quality of data governance and knowledge graph construction is ensured, the auditing evidence obtaining result is stable and controllable, and high efficiency and leakproofness are achieved.

The application provides a network security analysis method and device, a storage medium and an electronic device. The method comprises the following steps: collecting multi-source heterogeneous network data, extracting network entity interaction relationship and time sequence attribute, and constructing a dynamic heterogeneous graph structure network security knowledge graph; performing external communication monitoring on the network entity, and taking the periodic external communication feature as a target entity when the periodic external communication feature is detected; inputting the target entity and its neighborhood subgraph into a pre-trained graph neural network model, and outputting an intermediate state representing suspicious confidence by the model through graph embedding and association mining; matching the intermediate state with an attack mode library, predicting a future attack event of the target entity, and performing dynamic defense. The application can accurately identify hidden malicious communication, predict attacks in advance, and improve the pertinence and initiative of network security protection.

The application discloses a kind of financial risk association mining model construction method and system based on knowledge graph, it is related to financial risk data mining and knowledge graph construction technical field, including acquisition enterprise finance and associated multi-source data, through standardization API interface docking;Clean up and preprocess structured and unstructured data, fill in missing values, delete duplicate data and convert unstructured data;Build the financial risk knowledge graph containing entity, attribute, relationship, store with graph database;Extract three kinds of financial risk characteristics;With graph neural network as foundation to build model, cross-validation divides dataset training;Correlation analysis risk result, trace source and transmission path and generate report;Periodically iterated update model and graph, set cycle and trigger condition.The application improves data reliability, multi-source integration and fourfold check filtering problem data;Enhance risk mining accuracy, entity alignment and attention mechanism optimize model, adapt to different industries.