16 results about "Process knowledge" patented technology

A blockchain-based knowledge-sharing intelligent customer service AI response accuracy optimization system, belonging to the fields of blockchain and artificial intelligence technology, is disclosed. The system includes a multi-source knowledge acquisition unit, a blockchain knowledge sharing unit, a knowledge quality assessment unit, an AI response optimization unit, and an interactive feedback and iteration unit. The multi-source knowledge acquisition unit collects customer service knowledge data from different departments, business lines, and application scenarios. The blockchain knowledge sharing unit receives pre-processed knowledge data and constructs a decentralized knowledge-sharing network based on distributed ledger technology. The knowledge quality assessment unit performs multi-dimensional quality assessments of the knowledge stored in the blockchain. The AI ​​response optimization unit trains and optimizes the intelligent customer service AI model based on high-quality knowledge samples. The interactive feedback and iteration unit collects user feedback from the intelligent customer service system. All units operate collaboratively to form a closed-loop mechanism.

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, system, and device for pushing product assembly process knowledge in the field of semantic recognition and knowledge graph construction. The method includes: constructing a knowledge graph based on acquired product assembly process information using a trained Bert-BiLSTM-CRF model and an LLM-KE model to obtain a first initial knowledge graph and a second initial knowledge graph; filtering the first and second initial knowledge graphs using a structural rationality judgment module (SRDM); evaluating the filtered first and second initial knowledge graphs using a trained LLM-KGE model; constructing a product assembly process knowledge graph (APKG-CP) based on the evaluation results; and pushing assembly process knowledge based on Bayesian network intelligent reasoning. This invention can complete knowledge pushing under different process design requirements, improving the efficiency of human-machine collaborative work.

The application discloses an intelligent project special consultation management system and method based on multi-source data fusion, and belongs to the technical field of engineering project management. The method comprises the following steps: accessing multi-source heterogeneous data through a unified data platform, and performing deep fusion based on a semantic alignment algorithm to generate semantic enhanced structured data packets; dynamically scheduling special work flows and tasks based on reinforcement learning; realizing multi-special model coupling simulation through a model federation interface, and performing policy analysis and risk prediction by using AI; constructing a knowledge graph based on a graph neural network to realize intelligent causal reasoning and detection of cross-special conflicts; finally, generating a visual report in a multi-person collaborative three-dimensional immersive environment, and archiving the whole-process knowledge to a graph database to support system self-evolution. The application realizes the whole-process intelligentization from data fusion, intelligent analysis, collaborative management to knowledge sedimentation, and significantly improves the efficiency, quality and collaboration level of special consultation.

The application provides a kind of spent fuel reprocessing process knowledge object processing method, device and equipment, wherein the method comprises: obtaining at least two knowledge objects in spent fuel reprocessing process;The knowledge object includes: at least one spent fuel reprocessing process, at least one equipment corresponding to the spent fuel reprocessing process, at least one process parameter corresponding to at least one equipment of the spent fuel reprocessing process;According to at least two knowledge objects, obtain the knowledge graph chromosome population of spent fuel reprocessing process;According to the preset algorithm, obtain the target chromosome individual vector of the knowledge graph chromosome population;According to the target chromosome individual vector, adjust the preset knowledge base of spent fuel reprocessing process, and obtain the target knowledge base.The application scheme improves the optimization ability of reasoning and problem solving of knowledge object in spent fuel reprocessing process, and improves the efficiency of spent fuel reprocessing process.

The application discloses a vehicle assembly space checking method and related equipment, and relates to the technical field of vehicle assembly. The method comprises the following steps: obtaining bill of materials data and to-be-detected assembly data of a target vehicle model; based on a preset multi-modal process knowledge base, corresponding target tool models are matched for to-be-detected parts in the bill of materials data, wherein the preset multi-modal process knowledge base is a semantic association database constructed based on a part geometric feature vector, a tool envelope parameter vector and a process constraint rule vector; based on the to-be-detected assembly data and the target tool models, batch space path planning and interference checking are performed on each to-be-detected part, and checking results for the to-be-detected parts are generated. According to the application, semantic matching of parts and tools is realized based on structured assembly data and a multi-modal vectorized process knowledge base, batch path planning and space interference checking are performed on multiple to-be-detected parts, and the automation degree, matching accuracy and processing efficiency of assembly space checking can be improved.

The application provides a robot process skill generation system and method based on a double helix coupling architecture, applied to the technical field of intelligent manufacturing, robot autonomous decision and adaptive control, by designing "knowledge generation" and "performance evaluation" as two spiral chains that can evolve independently but are tightly coupled, and realizing real-time bidirectional feedback through a bidirectional coupling and evolution control center, forming a real closed-loop evolution system. The knowledge spiral layer is responsible for storing and dynamically mutating the process knowledge graph to generate candidate skills, and the performance spiral layer executes and quantitatively evaluates in a simulation or real environment, and the evaluation results directly drive the knowledge spiral layer to update the graph in a targeted manner, fundamentally breaking the barrier between knowledge and execution, forming a closed-loop co-evolution mechanism of "knowledge generation-performance verification-feedback update", so that the robot process skill generation system has the ability of continuous self-evolution, high sample efficiency, explainability and safety controllability.

The application discloses a rivet die combination scheme recommendation method and system, relates to the technical fields of riveting process, artificial intelligence and the like, and the rivet die combination scheme recommendation method comprises the following steps: acquiring scene demand data of riveting production, wherein the scene demand data at least comprises material parameters to be riveted; performing reasoning processing on the scene demand data based on a pre-constructed riveting process knowledge graph and a riveting process correlation data table to obtain an initial rivet die combination scheme recommendation result, wherein the riveting process knowledge graph is constructed based on riveting process field knowledge documents, the riveting process correlation data table is constructed based on riveting historical case data, riveting basic process data and riveting production parameters, and the riveting process knowledge graph and the riveting process correlation data table at least represent the relationship between a rivet die process and the material to be riveted.

This invention relates to a knowledge mutation system for embodied intelligent mobile robots based on semantic guidance and multi-strategy fusion, comprising: a semantic encoder, which encodes the topological structure and node and / or edge semantic information of a process knowledge graph into a unified structural semantic joint embedding vector; a mutation operator, which performs multi-level constraint verification through symbolic reasoning and lightweight physical simulation to generate a candidate knowledge graph set; a fitness evaluator, which performs multi-objective performance pre-evaluation on the candidate knowledge graph set generated by mutation and calculates the comprehensive fitness; and a selection mechanism, which dynamically guides the direction of knowledge evolution to form a closed-loop knowledge evolution process. This application achieves dynamic automatic evolution and closed-loop optimization of the process knowledge graph through collaborative work, enabling dynamic automatic evolution of the process knowledge graph, improving exploration efficiency, ensuring safe verification, and forming a closed-loop knowledge evolution process, thereby enhancing the autonomous adaptability of multi-armed mobile robots in dynamic industrial scenarios.

The application discloses a kind of dynamic knowledge calculation and reasoning system and method for digital twin, it is related to digital twin technical field, including: knowledge point is defined as six tuple, knowledge logic diagram is defined as directed graph;Establish multi-modal knowledge label system, carry out label similarity calculation;Process modeling is carried out using modeling language, and business logic is visualized as knowledge calculation flow chart;Based on knowledge label and knowledge calculation process, execute reasoning mode on knowledge logic diagram;Optimization algorithm is used to filter and recommend the reasoning result, and the online optimization knowledge calculation process is carried out using the feedback data of digital twin platform.The application is combined by knowledge logic diagram and business process modeling, solves the problem that traditional knowledge graph cannot effectively process process knowledge, utilizes multi-modal knowledge label system, realizes flexible, semantic rich knowledge retrieval and association, utilizes machine learning model to continuously optimize the effect of knowledge calculation, so that the system has growth.

This invention relates to a method and system for intelligent generation of commercial aircraft processes based on graph retrieval enhancement. The method includes: collecting and processing historical process information; structuring unstructured text descriptions to construct a commercial aircraft process knowledge graph; establishing relationships between entities by inferring the hidden entities through masking process entities in the knowledge graph, repeating this inference until all possible relationships are established; clustering the inferred entities, summarizing the process compilation, and distributing the summary statements as attributes to entity relationships; when a new process compilation task is received, organizing the task information into a subgraph and matching it with the existing knowledge graph to obtain the most similar subgraph; extracting chain-like influence relationships from the most similar subgraph, constructing prompt words for enhanced generation, thereby generating the most suitable process; and directly generating missing entities in the process using similar subgraphs. This invention effectively improves the accuracy and efficiency of commercial aircraft process generation.

The application relates to a smelting process intelligent optimization method and system based on multi-source data fusion, which comprises the following steps: obtaining target process parameters, real-time sensor data and production process data; dividing thermodynamic and chemical component parameter sets according to characteristics, constructing corresponding sub-atlas and fusing into a process knowledge atlas. Time series are preprocessed and aligned, and a comprehensive smelting state space model is constructed. Key state vectors are extracted to input a hierarchical reinforcement learning framework, the framework coordinates double-network decoupling decisions according to parameter priority and coupling relationship, and generates a collaborative optimization action set. The knowledge atlas is called to solve multi-objective constraints and conflict resolution, and an executable instruction set is generated. The instructions are executed, feedback data is collected, a differential reward value is calculated, and the framework parameters and atlas dynamic weight edges are updated. Through multi-source data fusion and hierarchical reinforcement learning collaborative optimization, dynamic adjustment and precise control of smelting process parameters are realized.

The application discloses an RPA process scheduling method based on large model fine tuning, comprising the following steps: constructing an RPA process knowledge base, fine tuning an intention classification model and an entity recognition model, collecting user instructions, inputting the user instructions into the fine-tuned intention classification model for classification, obtaining corresponding intentions, inputting the corresponding intentions into the fine-tuned entity recognition model and the RPA process knowledge base respectively, extracting relevant entities and matching corresponding processes to obtain entity extraction results and process matching results; and inputting the entity extraction results and the process matching results into the RPA to execute the RPA process. Through the organic combination of the double model, the RPA knowledge base architecture and the RPA process, the application realizes accurate recognition of user instructions and flexible scheduling of the RPA process.

This invention discloses a method and system for modeling discrete manufacturing workshops based on a hierarchical fusion of Petri nets and static knowledge graphs. By constructing a hierarchical structure that integrates time-varying and logical Petri nets, the upper layer models workshop-level resource flow, while the lower layer characterizes equipment-level state transitions, achieving a dynamic formal description of the concurrency, asynchronicity, and dependencies of the manufacturing system. Simultaneously, a specialized workshop knowledge graph is constructed, and a joint extraction model combining a RoBERTa-BiLSTM-CRF model and a focus loss function is employed to automatically extract entities and relationships from unstructured text, effectively improving knowledge construction efficiency and robustness. By defining a dual fusion mechanism of modeling and knowledge, deep coupling and collaborative reasoning between dynamic Petri nets and static knowledge graphs are achieved. This method supports multi-level modeling from system to equipment, simultaneously describing production logic, resource states, and process knowledge. Through continuous learning, the model can continuously accumulate and solidify production knowledge, ensuring long-term collaborative reliability.

The application discloses a user self-filling washing requirement and standard process difference checking method, and particularly relates to the field of life technology. Based on a process knowledge base, a clothes characteristic vector is searched by a standard process searching algorithm to obtain a corresponding standard process parameter vector, a secondary searching index of the knowledge base is constructed, the process knowledge base is iterated, a candidate set satisfying a constraint is obtained, a record with the highest score is screened out, a recommended process and a safety boundary are obtained. The application is realized based on a standard parameter vector, a rule engine and a statistical learning method, can be conveniently integrated into an existing Internet washing platform or a traditional washing management system, and can be flexibly extended according to a standard process library of different enterprises, and is easy to integrate and expand.

The application discloses a kind of cable process knowledge management and recommendation method based on knowledge graph, it is related to cable data management technical field, the application includes the following steps: constructing process knowledge graph, define process chain entity mode, convert process data into knowledge graph and store;Take process chain case, train first transfer model of front to rear quality index and second transfer model of rear to final quality index;Each process quality index is converted into hard constraint interval, define violation value calculation mode;Receive task and retrieve case, extract process parameter to form candidate set;With process parameter, quality predicted value and constraint boundary as node, build heterogeneous graph, calculate constraint violation degree as edge attribute;In graph attention network, constraint violation degree is negatively embedded attention coefficient, after the aggregation updates node feature, output recommendation score;According to score, simulate whole chain quality using transfer model and generate feasibility annotation by comparing constraint, issue collaborative process parameter.