23 results about "Knowledge engineering" patented technology

The application discloses a dynamic enhanced multi-modal hypergraph retrieval enhancement generation method and system, belonging to the technical field of information retrieval and knowledge engineering, comprising: in the offline stage, cross-modal encoding is performed on multi-source heterogeneous data to generate cross-modal vector index and candidate theme / entity set; knowledge drift detection is performed on the cross-modal vector index and the candidate theme / entity set, if it is determined that there is drift, then local subgraph incremental hypergraph update is triggered, and after multi-expert voting alignment verification, it is written into a multi-modal double hypergraph index library; in the online stage, the user request is subjected to semantic analysis, theme words and entity words are extracted; according to the theme words and the entity words, coarse retrieval and fine retrieval are performed based on the multi-modal double hypergraph index library, evidence subgraphs are extracted, multi-modal context is obtained; the user request and the multi-modal context are input into a multi-modal large language model to generate an answer and feedback the user; the timeliness, stability and reliability of retrieval are improved, and the integrity of complex knowledge reasoning is improved.

The application discloses a domain knowledge triple extraction method, system, medium and equipment, and relates to the technical field of knowledge engineering.The domain knowledge triple extraction method and system provided by the application can provide high-quality and efficient data support for triple extraction by analyzing and converting the obtained domain professional text into a structured vector knowledge base which can be efficiently searched, then the domain professional text is subjected to quantitative calculation and fusion screening, the initial entities of domain core high-frequency words which have global high frequency and cross-text universality are mined, the problem of "local optimum and global deviation" caused by traditional single word frequency is avoided, non-core term interference is effectively avoided, and the stability, relevance and efficiency of iterative extraction are improved, and further, the high-frequency word initial entities are subjected to closed-loop iteration through iterative RAG algorithm combined with the structured vector knowledge base, accurate extraction of the large model and iterative correlation entities, the explicit and implicit semantic correlation triples in the text can be mined layer by layer, the strong relevance and structural integrity of the extracted triples are ensured, and the professionalism and accuracy of the model in extracting triples are improved.

The application relates to the technical field of knowledge engineering, and discloses a production and manufacturing digital twin modeling method based on a knowledge graph, which comprises the following steps: performing data fusion and model construction on original multi-source data, obtaining multi-source fusion data, and generating a digital twin architecture; performing ontological design and semantic modeling on the multi-source fusion data, and generating a manufacturing knowledge graph; wherein the manufacturing knowledge graph is represented by triplets, and the triplets comprise entities-relation-entities / attributes; performing entity modeling on the manufacturing knowledge graph, and obtaining a knowledge-driven geometric model; and performing reasoning based on the manufacturing knowledge graph and historical data, and obtaining a knowledge-driven decision model. The method can construct a digital twin model with knowledge-driven prediction and decision-making capabilities, and provides a high-robustness and sustainable-evolution digital twin modeling method for intelligent manufacturing, smart factories and other fields. The application also discloses a production and manufacturing digital twin modeling device based on the knowledge graph and an electronic device.

This invention belongs to the interdisciplinary fields of industrial internet, knowledge engineering, and intelligent mining. It proposes a method and system for knowledge decoupling and dynamic collaborative reasoning throughout the entire mineral processing process. It constructs a static domain ontology covering geology, mining, and beneficiation, integrates multi-source heterogeneous data, responds to mining loading events, matches target ore body segments based on spatial coordinates, creates ore unit instances with attached geological attributes and established traceability associations, and updates their status in real time as the ore flows, associating them with current process equipment to form a dynamic flow knowledge graph. Based on the geological attributes and location information in the graph, it uses spatiotemporal mapping rules to predict the estimated time for the ore to reach downstream equipment, generates process adjustment instructions based on preset reasoning rules, and sends them to the control system during the buffer period. This invention achieves dynamic collaboration and intelligent control throughout the entire process from geological source to beneficiation terminal, effectively improving the accuracy and efficiency of mineral processing.

This invention discloses a method and system for constructing a SPEC parameter extraction structure vector model. The method includes the following steps: Step 1: Constructing a multi-constraint international standard knowledge graph; Step 2: Intelligently extracting SPEC document parameters based on the multi-constraint graph; Step 3: Constructing a multi-constraint structure vector model; Step 4: Constructing a sub-project experience database, and outputting the results of the multi-constraint structure vector model. This invention relates to the field of knowledge engineering interdisciplinary technology and can solve the problems of low parameter extraction efficiency, fragmented constraint relationships, poor generalization of vector models, and lack of sub-project experience systems in existing technologies.

The application discloses a document checking method, system, device, medium and product based on a large language model and a rule graph, relates to the field of information processing and knowledge engineering, and comprises the following steps: a large language model is used to analyze a rule document and a business term table, an examination element table and a rule graph are generated, and a document processing pipeline and a logic verification pipeline are executed in sequence; when the document processing pipeline is executed, image recognition and layout analysis are performed on the materials to be examined and user metadata, a visual large model is called in combination with the examination element table to perform structured extraction, and context objects are generated; when the logic verification pipeline is executed, based on the context objects, preconditions are selected, evidence requirements are evaluated, and decision determination is performed in sequence, an examination report is generated based on a final determination state, and routing strategies of automatic passing, manual review or returning are triggered. The application can realize rule analysis automation, examination process standardization, interpretable determination results and full-link traceability.

The application discloses a kind of based on the automatic construction method and system of business ontology of information foraging theory, it is related to knowledge engineering and artificial intelligence field.The method includes: determining the target vector of business ontology and constructing the bidirectional smell word bank containing positive and negative smell words;Multi-source heterogeneous documents are cut into information patches, and the multidimensional smell intensity containing theme correlation component and smell word hit component is calculated;According to smell intensity, a foraging priority queue is constructed from high to low, and patches below the global stop threshold are skipped;The patches extracted are extracted ontology elements in turns, and whether to terminate and migrate is determined according to the comparison of instantaneous yield and average yield of environment;Ontology elements are fused into business ontology according to confidence, and the smell word bank is updated according to output, so that the word bank, smell intensity and ontology extraction form a foraging closed loop.The application concentrates limited computing power on high-value patches, reduces computing power consumption and processing delay under the premise of ensuring accuracy and recall rate.

This disclosure relates to the field of computer technology, and more particularly to a text processing method and apparatus. The method includes: identifying input text using a knowledge engineering model and a target rule base to obtain a first output element set corresponding to the input text; identifying the input text using a large model to obtain a second output element set corresponding to the input text; obtaining a first overlap degree between the first output element set and the second output element set; if the first overlap degree is greater than a first overlap degree threshold, obtaining a target element set corresponding to the input text, and obtaining output text corresponding to the input text based on the target element set. Using this disclosure can improve the efficiency and accuracy of element extraction.

The application provides a household environment ontology knowledge base automatic construction method and system, and relates to the technical field of artificial intelligence and knowledge engineering. The method comprises the following steps: constructing a household environment ontology knowledge base template, wherein the template contains classes, attributes and instances of the ontology; establishing communication with a target object, obtaining communication text of the target object, and extracting semantic information of the target object; obtaining an image of the target object, and extracting semantic information of the target object based on the image; comparing the extracted semantic information of the object with the existing classes and attributes in the household environment ontology knowledge base as instances of the ontology; judging whether new classes and attributes need to be established based on the similarity comparison result, updating the semantic information, and perfecting the household environment ontology knowledge base. The application can simplify the construction process of the household environment ontology knowledge base, improve the construction efficiency, reduce the labor cost, reduce the non-standardization of manual construction, and improve the quality of the constructed ontology knowledge base.

The application relates to a multi-source heterogeneous document knowledge base construction method and system based on adaptive normalization, and belongs to the field of knowledge engineering. The method comprises the following steps: collecting original document data of different modes from multiple heterogeneous data sources; pre-processing and mode feature extraction are performed on the original document data to obtain mode feature vectors corresponding to the original document data; the mode feature vectors of different modes are dynamically mapped to a unified shared semantic vector space to generate unified document vectors; local reasoning is respectively performed on the mode feature vectors based on mode-specific AI agents; a document set with similar semantics, conflicts or repetitions is identified; online aggregation and deduplication operations are performed on the document set; a knowledge graph is constructed based on cross-modal semantic association results; and a model parameter of the AI agent is automatically triggered to update in real time. The application constructs a knowledge base system with adaptive, real-time and self-learning capabilities.

This invention discloses a method, apparatus, device, and medium for data circulation. The method includes: acquiring data to be traded; determining the circulation mode of the data to be traded based on the trading scenario; the circulation mode includes a competitive mode driven by profit and based on price signals to achieve data circulation; a symbiotic mode driven by the market and based on the synergy of policy regulation and market mechanisms to achieve data circulation; and a symbiotic mode driven by value and based on scenario-driven and artificial intelligence-enabled data circulation; processing the data to be traded through data engineering and / or multimedia engineering to obtain first transaction data; processing the first transaction data through knowledge engineering based on the application scenario requirements and circulation mode of the data to be traded to obtain second transaction data; and consuming the second transaction data according to the circulation mode of the data to be traded. This solution can improve the intelligence, flexibility, stability, and scenario applicability of data transactions.

This invention relates to the fields of natural language processing and knowledge engineering, and particularly to a method and apparatus for constructing a two-layer corpus for mineral exploration. The method includes: parsing and annotating mineral-related literature to obtain original text corpus data; semantically compressing the original text corpus data to obtain compressed corpus data; performing character-level segment-by-segment comparison and verification between the compressed corpus data and the original text corpus data to obtain verification results; based on the verification results, constructing a retrieval index for each compressed corpus data pointing to the corresponding segment of the original text corpus data; and based on the retrieval index, determining the target segment and its context in the original text corpus data. The retrieval index includes hierarchical tracing fields from document level to character level. Through precise matching between the compressed corpus and the original text corpus, while preserving the integrity of the original text corpus, it supports reverse tracing of the original text after a successful retrieval, enabling the knowledge graph to possess complete academic auditability.

The application belongs to the technical field of knowledge engineering, and discloses an AI knowledge base construction method and system for cross-domain collaboration, which comprises the following steps: based on a symbolic dynamic knowledge graph, an adversarial neural network is used to decouple the domain-independent intention and the domain feature vector in a user query, a generalized intention vector and a personalized weight parameter are generated; the semantic similarity between the generalized intention vector and the dynamic knowledge graph is calculated, a multi-hop retrieval is used to recall multi-domain high-relevance knowledge fragments by using HNSW, and a cross-domain knowledge set is obtained; a variational Bayesian uncertainty estimation method is used to scan the knowledge fragments in the cross-domain knowledge set and shield illegal content, and a personalized cross-domain answer is generated in combination with the personalized weight parameter. Through the generation of the generalized intention vector and the personalized weight parameter, the application realizes user intention decoupling and personalized weighting, adapts the query to multiple domains, and improves the relevance of knowledge retrieval.

The application discloses to belong to natural language processing and knowledge engineering technical field, especially relate to a kind of grape cultivation decision support method and system based on tree guide knowledge graph and semantic tree search.The hierarchical semantic structure tree is constructed, the unified organization and retrieval of structured text and unstructured text in grape cultivation field are realized, the problem that the knowledge utilization efficiency is low caused by the huge, diverse and fragmented grape cultivation knowledge system is solved;By the combination of tree guide knowledge graph construction and semantic tree retrieval, the target knowledge unit related to the current production problem can be quickly positioned, irrelevant path interference is reduced, the response speed and retrieval accuracy of grape cultivation decision support are improved;Through the automatic knowledge graph construction method of tree guide pruning-expansion, the construction cost and noise propagation of grape cultivation field knowledge graph are reduced, the pertinence and effectiveness of graph construction are improved, which provides structured support for subsequent accurate retrieval and traceable decision support.

This invention discloses an intelligent product cognitive modeling and multilingual document generation and verification system and method, belonging to the fields of knowledge engineering and natural language processing. The system adopts a three-layer architecture of "knowledge planning layer + knowledge production layer + knowledge service layer," and also includes two core components: "workflow engine + testing framework." The knowledge planning layer is used to construct a meta-knowledge system and quantitatively evaluate its completeness through coverage metrics; the knowledge production layer is used to perform document generation, terminology standardization, structure optimization, fact verification, and automated testing; the knowledge service layer is used to perform multilingual release, version archiving, and feedback collection; the workflow engine performs intelligent scheduling, using task priority sorting to form a closed-loop processing flow; the testing framework is used to generate execution evidence and calculate hash digests for audit trails. This invention improves document accuracy from unverifiable to 100% observable, achieves knowledge coverage of user intent of 1, and increases development efficiency by 18 times.

The application belongs to the technical field of knowledge engineering and data quality management, and discloses a multi-dimensional automatic quality detection method and system for a knowledge graph, which comprises the following steps: creating a quality inspection task bound to a target knowledge graph and a mode definition, executing at least one quality detection sub-process under the task, and the sub-process comprises the following steps: performing consistency checking on the graph instance and the mode based on a shape constraint language; performing reverse consistency checking on a relationship instance based on a mined association rule; constructing a graph structure representation in the memory based on a relationship edge sample and generating a structure class prompt; and performing statistical analysis on entity attributes and relationship distribution and generating a statistical class prompt; saving the quality prompt to a sub-task record in a structured entry, and associating the mode and the graph data update time snapshot to support result timeliness judgment. The application can be used for quality detection and operation and maintenance management in scenes such as knowledge middle platform, industry knowledge base and graph data management.

This invention discloses a multi-agent self-evolution system and method based on closed-loop evaluation feedback, relating to the fields of artificial intelligence, distributed system architecture, and knowledge engineering. The system adopts a layered and decoupled architecture, including a user interaction layer, a semantic core and basic support layer, an intent understanding layer, a task planning layer, a task execution layer, an evaluation and supervision layer, and a closed-loop evolution mechanism. Each module interacts through a standardized data protocol. A collaborative digital twin mechanism and a semantic core knowledge system are constructed. An independent evaluation and supervision agent cluster quantitatively evaluates the entire lifecycle of tasks, and the evaluation results are transformed into machine-readable structured instruction objects, driving the update of the underlying knowledge base and the parameter tuning of agent capabilities, thereby achieving a closed-loop system learning and evolution. This invention improves the success rate and collaborative efficiency of complex tasks, enabling the system to autonomously accumulate domain wisdom and adapt to dynamic environmental changes during long-term operation.

This invention discloses a method for enhancing the applicability of knowledge engineering templates based on CATIA V6. It constructs knowledge engineering templates using CATIA V6's custom feature mechanism and EKL, characterized by ambiguity control for direction and arcs. This invention aims to propose a general template construction technique that identifies typical geometric ambiguity scenarios and addresses the difficulties in template construction through rule embedding or additional feature processing steps. The method proposed in this invention achieves the following beneficial effects: it refines design conditions often overlooked during CAD interactive operations using more rigorous knowledge representation, expanding the scope of template-based designs; it enhances the applicability and reusability of individual templates, reducing the total number of customized templates required; it enhances model stability, supporting the construction of more complex design templates; and it enhances the continuity of individual templates, avoiding the need for deletion and reconstruction of generated templates during design changes.

The application discloses a personalized learning path recommendation method and system fusing knowledge point dependency relationship, relates to the technical field of knowledge engineering, and comprises the following steps: for each knowledge point of a knowledge base, a knowledge dependency graph containing multilevel dependency and mastery characteristic vectors is built, then a cognitive enhancement type knowledge dependency manifold is reconstructed, an intelligent recommendation engine is developed and deployed on a learning platform. After a user uploads a learning task, the engine generates a recommended learning path through manifold element driven learning task conversion, phased progressive path planning and knowledge dependency graph verification, and finally completes visual presentation in a platform index pop-up window. The application solves the technical problems of incomplete knowledge point dependency description and insufficient personalization degree in traditional learning path recommendation, achieves personalized recommendation in line with cognitive laws, and helps learners to efficiently form a complete knowledge system.

The application discloses a plug-and-play external knowledge endogenous fusion method and system and a storage medium, and belongs to the technical field of general artificial intelligence, knowledge engineering, large language model application and embedded system cross technology. The application realizes the standardized conversion of full-format human knowledge into a unary binary three-body self-generating system through a general feature channel mapping protocol; through a three-level endogenous compliance verification and a self-adaptive weighted endogenous fusion algorithm guided by a potential field, lossless deep fusion and endogenous solidification of external knowledge and the self-generating system are realized; through a standardized knowledge transplantation package and a general interface, the cross-system reuse and cluster resonance of knowledge among self-generating systems with the same architecture are realized. The application supports cloud knowledge base synchronization and lifelong continuous evolution, and can be applied to the knowledge-based upgrading of personal intelligent assistants, industrial intelligent agents, smart cities and other full-scene self-generating systems.