3373results about "Web data indexing" patented technology

A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.

A coherent, intelligent, packet-switched memory fabric enables predictive, cache-coherent access across distributed compute, accelerator, and memory resources using a Memory-Fabric Transaction Layer Protocol (MF-TLP). MF-TLP defines routable packet formats for read, write, vectorized, atomic, reduction, collective, and predictive-prefetch transactions executed by memory-centric network interface controllers (MC-NICs). Each MC-NIC performs packet parsing, address translation, coherence management, and near-memory arithmetic or tensor operations while coordinating with MF-TLP-aware switches providing hierarchical directory control, multi-path routing, and in-network aggregation. Vectorized and multimodal packets encode multiple addresses or tensor offsets to reduce scatter / gather overhead, and programmable caching and quality-of-service modules manage tiered memory and tenant fairness. MF-TLP supports extension headers for predictive prefetch, collective coordination, and tenant governance, operating across hierarchical leaf-spine topologies using Ultra-Ethernet Transport, InfiniBand, or CXL fabrics. The system delivers scalable, low-latency, memory-centric orchestration for large-language-model training, multimodal AI, and data-intensive analytics.

The invention discloses a multi-source heterogeneous data fusion system based on an RAG. According to the method, through deep knowledge fusion and a dynamic cognitive evolution mechanism, the decision-making intelligence level in a complex data environment is remarkably improved, and equipment operation parameters, environment indexes and a domain knowledge base are deeply associated to form a panoramic data view with space-time continuity. A generative enhancement mechanism endows original data with a self-evolution characteristic, industry empirical rules and real-time situation awareness are injected while the fidelity of the original characteristic is maintained, so that a decision model can capture micro data fluctuation and follow macroscopic business logic, accurate balance between risk early warning and resource scheduling is realized, and the risk early warning efficiency is improved. The dynamic adaptation characteristic enables the system to autonomously update a knowledge system and optimize a decision path in a complex and changeable industrial environment, and a post response mode of a traditional static analysis model is converted into an intelligent center with prospective pre-judgment and real-time regulation and control capabilities.

The invention relates to a network public opinion intelligent classification and emergency decision-making system based on multi-modal fusion and dynamic evolution, and belongs to the field of network public opinion monitoring and big data analysis and artificial intelligence. The system comprises a multi-source data acquisition and preprocessing module used for crawling multi-modal data, constructing a propagation path map after preprocessing, and identifying key propagation nodes; the multi-dimensional classification engine module is used for carrying out conflict intensity quantification on public opinion events and dynamically updating a rule word bank to keep the adaptability of a conflict intensity quantification model; the event graph construction and anomaly detection module is used for constructing a public opinion propagation path and public opinion event generality logic chain mode, monitoring public opinion propagation speed and giving an alarm; the stakeholder dynamic risk assessment module is used for finely classifying network public opinion participants, providing a basis for differential propagation intervention and simulating public opinion evolution to carry out risk simulation; and the intelligent decision-making and emergency response module executes different levels of emergency measures based on the risk index according to the hierarchical response strategy.

The invention discloses a microblog emotion analysis method based on standard dictionaries and semantic rules. The microblog emotion analysis method comprises the following steps: collecting microblog data and manually labeling and marking the emotion value of each microblog; proposing corresponding standard micrblog emotion dictionaries, and establishing an emotion dictionary database; based on the standard emotion dictionaries, adding the semantic rules for assistance, and performing parameter adjustment and optimization on parameters of the semantic rules; based on a real dataset experiment, acquiring the final classification accuracy and precision. The technical scheme provided by the invention is adopted to well analyze the emotion tendency of each microblog user by introducing the standard emotion dictionaries, microblog expression dictionaries and the semantic rules, therefore, higher classification accuracy and precision are achieved.

The invention relates to the technical field of data analysis, in particular to an intelligent talent data matching system and method based on big data, and the system comprises an insight analysis module, a portrait construction and management module, a matching and recommendation module, a strategy support module and an intelligent assistant module. Compared with the prior art, the method depends on a static database and keyword matching, so that skill ecology which evolves rapidly is difficult to capture, and talent evaluation and post requirements are disjointed; according to the scheme, a dynamically updated skill knowledge graph is constructed, a talent portrait and post model is continuously optimized in combination with a real-time data flow, and deep matching calculation is performed by applying a multi-strategy intelligent engine; especially in the rapid iteration field of high and new technology and the like, the system can automatically identify emerging skill association, and it is ensured that the recommendation result always reflects the real market demand.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention discloses a safety control method based on a knowledge graph, and belongs to the field of safety control, and the method comprises the following steps: crawling various types of data according to keywords through a crawler program to construct a fireproof database; carrying out semantic analysis on data in the fireproof database by using a natural language processing technology, and carrying out data processing on the fireproof database after semantic analysis through word vector modeling and entity relationship extraction; constructing an internal large-scale language model by using the processed fireproof database, and performing hierarchical clustering on the internal large-scale language model by adopting a Leiden technology; establishing a multi-dimensional mapping model based on historic building spatial features, cultural relic features and disaster-inducing factors in a hierarchical clustering result, and constructing a multi-modal dynamic association fireproof knowledge graph by using a knowledge graph technology based on the multi-dimensional mapping model; and dynamically selecting a search mode based on a user query keyword, and generating a security control result according to the query keyword based on the search mode.

The invention relates to the technical field of data security, in particular to a data asset classification and grading and authority dynamic management system, which comprises an asset parameter acquisition module, a classification feature judgment module, a grading weight calculation module and an authority dynamic adaptation module. According to the method, metadata and path parameters are automatically collected through a distributed crawler in combination with a regular expression, naming parameters and content feature parameters are separated to generate a structured parameter set, field naming similarity is quantized through a Levenshtein distance, content character distribution features are verified through chi-square verification, and a naming similarity and distribution feature dual verification mechanism is constructed. According to the method, sensitivity, access frequency and data volume weight are dynamically distributed through an entropy weight method, grading parameters are generated through linear superposition, an RBAC model is combined with a Dijkstra algorithm to verify access path topology legality, unauthorized access is blocked, and the heterogeneous data classification grading and authority control dynamic adaptive capacity is improved.

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

The invention discloses a purchase demand prediction method and system based on big data analysis, and relates to the technical field of supply chain management and intelligent prediction, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the data fusion and preprocessing; based on a causal inference technology, key influence factors of purchase demand changes are analyzed, and a purchase demand prediction model is constructed; dynamically adjusting the prediction model by using real-time data, and generating a real-time or periodic purchase demand prediction result; carrying out data simulation and model optimization aiming at an extreme scene of a purchase demand; and optimizing a supply and demand matching strategy based on a prediction result, and carrying out iterative improvement on the prediction model through a feedback mechanism. Through multi-source heterogeneous data fusion, causal inference, dynamic adjustment and feedback optimization mechanisms, high-precision purchase demand prediction is realized, a supply and demand matching strategy is optimized, the adaptability of the model to market fluctuation and extreme scenes is enhanced, the purchase cost is effectively reduced, and the supply chain management efficiency and stability are improved.

The invention discloses a public opinion risk assessment method and system based on multiple agents and a large language model, and belongs to the technical field of network information security. The method comprises the steps of obtaining multi-source public opinion data; reasoning in combination with a language model to obtain a multi-dimensional semantic vector, and clustering to form a plurality of topic clusters; the emotion opposition level, the credibility and the text quantity increment score of each topic cluster are generated based on an intelligent agent, a comprehensive risk value is obtained through weighted fusion, and high-risk topics are screened through a double-threshold retention mechanism; and generating a knowledge graph according to the high-risk topic, determining an associated entity, a propagation link and an intervention node, implementing an intervention strategy, and generating a public opinion intervention report. According to the method, the monitoring problem of multi-source heterogeneous public opinion data can be effectively solved, the accuracy and timeliness of risk assessment are improved, full-link automation from risk identification to accurate intervention is realized, and efficient support is provided for public opinion management and control of governments, enterprises and other mechanisms.

The invention discloses a science and technology fast message intelligence perception system based on a large language model, and belongs to the field of intelligence perception, the system comprises an information collection module, a data standardization module and a database, the information collection module obtains multi-mode science and technology fast message data; the data standardization module is used for receiving the multi-modal data output by the information collection module and executing data cleaning, structured processing and semantic standardization operation; after standardization processing, storing the data into a database; the system further comprises an intelligence value screening module, an evaluation system module and a visualization module. And the intelligence value screening module is combined with the evaluation system to identify the intelligence value, and multi-dimensional visual presentation is realized through the visualization module. The method improves the efficiency and accuracy of information recognition and perception, and has significant advantages in the aspects of processing mass information and enhancing the information perception ability.

The invention discloses a territorial space planning dynamic monitoring method based on multi-source data fusion, and belongs to the technical field of territorial space planning intelligent monitoring, and the method comprises the steps: obtaining data, and generating a multi-source heterogeneous data set; performing space-time alignment processing by using a preset regional association rule of a planning knowledge base to generate a space-time unified data set; constructing a dynamic knowledge graph taking planning elements as a core based on the data set, and updating node relation weights in real time; guiding a multi-source data fusion direction through the map relation weight to generate a fusion feature vector; incremental learning monitoring processing is carried out on the feature vectors, parameters are optimized, and a planning implementation state monitoring result is output; and updating the knowledge graph node relation weight in a closed loop manner according to a monitoring result, and synchronously optimizing incremental learning monitoring processing. According to the method, a dynamic knowledge graph is adopted to guide data fusion and an incremental learning closed-loop optimization mechanism in real time, and accurate perception and adaptive decision support of a planning implementation state can be realized.

The invention discloses a course data intelligent multi-dimensional retrieval system based on deep learning, and relates to the technical field of course retrieval. The course data retrieval platform is in communication connection with a knowledge graph construction module, a user intention analysis module, a user portrait construction module, a personalized recommendation engine module and a retrieval display module, and all the modules are in electric signal connection; the knowledge graph construction module is used for collecting course data from multiple sources and constructing a course domain knowledge graph. According to the natural language processing technology based on deep learning, deep semantics queried by a user can be deeply analyzed, fuzzy or implicit requirements can be understood, limitation of traditional keyword matching is broken through, intent features and entity relationships can be extracted by combining a knowledge graph, specific entities and relationships queried by the user can be accurately mapped, and user experience is improved. Therefore, a more accurate retrieval result is provided, and personalized requirements of users are met.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

The invention discloses a legal consultation system construction method based on multi-model collaborative reasoning, and the method is characterized in that firstly, a legal knowledge base capable of being dynamically updated is constructed, and in combination with a retrieval enhancement generation technology, it is ensured that answers generated by the system are always based on latest laws and regulations; the method adopts a two-stage reasoning mechanism: in the first stage, a task analysis large language model deeply analyzes problems based on related laws and articles and an available external tool library, and generates a structured execution plan comprising reasoning steps and a tool calling scheme; and a second stage: performing multi-step reasoning by the content generation large language model according to the execution plan, and finally generating answer content meeting legal professional standards. Compared with the prior art, the method has the advantages that a complex legal problem is decomposed into executable subtask sequences through a multi-model cooperative working mechanism, the limitation of a single model in logical reasoning and knowledge coverage is made up, and the accuracy and credibility of legal dialogue generation content are remarkably enhanced.

The invention relates to the field of computer computing, in particular to an image-text combination-based news recommendation method, which comprises the following steps of: S1, constructing a keyword library; s2, collecting user behavior data; s3, constructing a user interest map; s4, searching news interest matching; s5, image-text correlation degree analysis is carried out; s6, news popularity analysis; according to the news recommendation index analysis method and system, keywords of each piece of news corresponding to each type of news are extracted to form a keyword library of each type of news, the total interest weight of a user in each news type is analyzed to construct a user interest map framework, and the interest weight of each piece of retrieved news is obtained from the user interest map framework; according to the image-text relevancy, the image-text relevancy of each piece of retrieved news of the user is obtained through screening, the news popularity of each piece of retrieved news is obtained according to the popularity parameter of each piece of retrieved news in each time range, then the news recommendation index of each piece of retrieved news of the user is comprehensively analyzed, and the news recommendation index is sorted and pushed to the user. And the utilization rate of news resources is improved.

The disclosed concepts relate to contextualization of generative language models. In some implementations, a linked entity database is populated with entity resource identifiers of entities extracted from a search log by an entity linker. A contextualized prompt data structure is generated based on the linked entity database, e.g., by including linked entity context information in the contextualized prompt data structure. A response to the contextualized prompt data structure is received, where the response is conditioned on the linked entity context information.

A process assessment platform for automatic query calibration in sequenced models can be used to generate system actions in management of process deployments. System actions can be generated by using a set of input data from one or more process management systems that define a workflow. The input data can include a set of observed task board information item properties for a particular structured process of the workflow. Using the set of input data, a first AI model can dynamically identify a set of actions from the structured process to be executed. Using the identified set of actions, a second AI model can dynamically classify particular dimensions of the set of task board information items and determine a degree of completion therefor.

The invention belongs to the technical field of webpage data analysis, and particularly relates to a webpage data analysis method based on a large language model, which comprises the following steps: acquiring a target website URL (Uniform Resource Locator) input by a user; carrying out preliminary analysis on the input URL by utilizing a large language model; the DOM structure of each website is analyzed, and HTML elements and the hierarchical relation thereof are extracted; generating a corresponding data crawling script according to an analysis result; executing the data crawling script, and extracting required data from each website; the extracted data is preprocessed; performing multi-source fusion on the preprocessed data; extracting main information from the fused data; and storing the finally processed and extracted information in a database. According to the method, automatic analysis of complex webpage data, intelligent entity extraction and knowledge expansion and efficient data fusion and duplicate removal are realized.

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 an agent-based webpage information processing method, device and equipment and a medium. And distributing the data to a capturing agent to obtain webpage original data. According to the method, model context information is generated by extracting webpage source information and release time information, page structure representation is constructed, key information nodes are positioned, structured data are generated, then multi-scene decision data are generated in combination with the structured data and the model context information in the analysis process, and a decision result is output through a decision reasoning module. By constructing a uniform data processing flow, webpage capture, context generation, structured analysis, analysis processing and decision reasoning are organically linked, efficient acquisition and intelligent processing of webpage information are realized, and timeliness and accuracy of decision are improved.

This disclosure describes utilizing a generative document system to dynamically build and provide generative search result documents. The generative document system utilizes an aggregated framework that leverages one or more large generative models (LGMs). For example, the aggregated framework includes three stages where local processes are applied to generative outputs from LGMs, with each stage building upon the generative inputs from previous stages. The generative document system uses the aggregated framework to create generative search result documents based on search queries and their corresponding search result links. These generative search result documents provide interactive, intuitive, comprehensive, and flexible curation of answers that address the respective search queries.

A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost / benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost / benefit indicators.

The invention discloses a multi-modal harmful model factor detection method based on a thinking chain, and belongs to the field of harmful model factor detection, and the method comprises the steps: firstly obtaining an image-text model factor, and automatically generating the thinking chain containing a problem summary, an image subtitle, and harmful reasoning and conclusion; constructing an annotation data set according to the annotation data set; then, the multi-modal model is finely adjusted in two stages, in the first stage, the visual encoder and the language model are fully finely adjusted to improve image-text understanding, and in the second stage, the visual encoder is frozen, and the language model is finely adjusted through LoRA to strengthen reasoning; introducing reinforcement learning rewards to optimize implicit harmful discrimination; then extracting an entity relationship to construct a knowledge graph and a causal graph, and embedding into the model; and finally, dynamically updating the atlas through an RAG mechanism, so that a detection result adapts to an emerging model cause in real time.

The invention provides a customer group dynamic division method and system, and relates to the technical field of customer group management.The method comprises the steps that firstly, commodity related data are collected in real time through a multi-channel data collection module, commodity behavior analysis is conducted in combination with service requirements, features are extracted according to analysis results, and commodities are divided into multiple groups; and then, dynamically dividing customers by combining service requirements, generating initial customer group information, and optimizing and adjusting the customer group information according to the updated user portrait information. And finally, performing preference calculation according to the optimized customer group information, and generating a personalized push strategy. According to the method, through dynamic division and optimization of customer group information, unique requirements and behavior characteristics of each customer can be better met, and personalized services are provided.

The invention discloses a quick retrieval method for e-commerce information, and relates to the technical field of e-commerce information.The method comprises the steps that features are extracted, multi-modal feature learning is conducted through image-text semantic matching and high-order semantic fusion, and commodity retrieval efficiency and accuracy are optimized through hash coding; analyzing and structuring user query through natural language processing and a deep semantic reasoning technology, and extracting a user intention in combination with numerical reasoning and logic processing; commodity matching is accelerated through Hash coding and approximate nearest neighbor retrieval, a retrieval result is optimized in combination with image-text semantic fusion and multi-factor sorting, and recommendation accuracy and personalization are continuously improved through a user feedback mechanism; the interpretability of a result is enhanced through reasoning chain echoing, and commodity recommendation is dynamically optimized by utilizing user feedback and reinforcement learning. According to the technical method, a more efficient, accurate and personalized commodity retrieval scheme is provided for the e-commerce platform, and the technical method has a wide application prospect.

The invention discloses an intelligent crawler generation method and system based on a large language model and an MCP protocol, belongs to the technical field of network data collection, and solves the problem that the capability of LLM in dynamic webpage analysis and anti-crawling strategy generation links cannot be fully exerted due to the fact that LLM and browser interaction protocols cannot be effectively integrated in the prior art. The method comprises the steps of analyzing an acquisition demand based on a large language model and generating a standardized demand description document, realizing interaction between the large language model and a browser based on an MCP protocol, analyzing a page complete DOM tree structure through a crawler script generation system, and performing quality verification and intelligent repair on a generated crawler script. According to the method, the complete DOM tree and the dynamic data rendered by the browser are obtained through the MCP, and the large language model can be called to automatically analyze the element positioning strategy, so that the collection script is adaptively generated, and high efficiency, intelligence and automation of webpage data collection are ensured.

The invention discloses a method and a system for intelligently acquiring and analyzing webpage merchant information. Automatic acquisition of the merchant information is realized through five main steps of intelligent webpage capture, intelligent field identification, accurate address information analysis, intelligent merchant grouping and automatic page discovery. According to the method, a traditional rule-based extraction method is abandoned, information such as merchant names, phone numbers and addresses in webpages is automatically recognized by using context semantic analysis and mode recognition technologies, and accurate association grouping of fields is realized through a dynamic weight multi-dimensional similarity calculation method; and the merchant information can be continuously discovered and collected by using an automatic page discovery mechanism without manually specifying a collection path. The technical bottlenecks that specific rules need to be written for different websites, webpage structure changes are difficult to deal with, multi-merchant information grouping cannot be processed and the like in a traditional method are broken through, the method adapts to various webpage structure changes, and the automation degree, accuracy and efficiency of merchant information collection are greatly improved.

A method in a contact center for generating insights from conversation data derived from interactions and storing the insights in an index. The method may include: determining an insight type; based on the insight type, determining inputs including a question prompt, answer prefix, and relevant portion of the conversation data; inputting the inputs into a LLM configured to receive the inputs and generate output text answering a question contained in the question prompt pursuant to an answer form suggested by the answer prefix given content contained in the relevant portion of the conversation data; generating the output text via operation of the LLM; transforming the output text of the first insight via a sentence transformer into vector embedding representative of a semantic meaning of the output text; and storing the computed vector embedding of the first insight in the index.