9 results about "Knowledge generation" patented technology

The application belongs to the technical field of industrial digitization and artificial intelligence, and particularly relates to a kind of automobile parts enterprise knowledge generation and intelligent question answering system based on large model technology, through integrating multiple function platforms, the knowledge retrieval and generation path of "plug-in-knowledge base-large model" is constructed, and the large model technology is combined with the actual knowledge data of enterprise deeply, in the form of multi-modal of voice / text input / output + digital person audio picture synchronous dynamic interaction, the full-link intelligent service of "intention recognition-knowledge retrieval-business execution" is realized, the goals of efficiency improvement and digital operation agile iteration and continuous upgrading of manufacturing, research and development, operation and other businesses are achieved, the technical problems of poor adaptability of general model industry knowledge, split of multi-modal interaction and business system, low utilization rate of enterprise massive heterogeneous data and low efficiency of internal knowledge acquisition are solved.

The application discloses a kind of food additive knowledge generation method and system based on multi-modal, it is related to food additive knowledge generation technical field, by regulation rule slot disassembly module and product scene slot construction module, regulation side and scene side are respectively transcribed into slot chain, so that regulation constraint object, food category, use condition, label requirement and other information and ingredient table, label identification, process specification, detection specification and question and answer target form the structure basis that can be aligned item by item, avoid only stay in the level of article original display or keyword hit.Multiple modal semantic module sorts out regulation text, ingredient table text, label image identification text, detection item specification, process specification text and question and answer request, so that subsequent processing is carried out under the same semantic fragment system, reduce the alignment fracture caused by the difference of expression from different sources, so that scene input has sustainable acceptance ability.

This application discloses a method for constructing a vehicle diagnosis and repair knowledge base, an electronic device, and a storage medium, relating to the field of vehicle diagnosis and repair. The method includes: Step S1, knowledge base creation involves uploading knowledge documents to the knowledge base, then sequentially performing standardized processing including document reading, cleaning, segmentation, knowledge generation, and knowledge storage, before saving the knowledge to a database. The process of saving knowledge to the database uses a template method to define a unified document parsing and processing flow, and a bridging pattern to decouple each processing stage. The specific processing at each stage uses a strategy pattern to load different implementation methods. The cleaning stage uses a chain of responsibility pattern to flexibly configure the rule execution order. Step S6, diagnosis and repair knowledge query involves standardizing the text of a user's question, retrieving it from the knowledge base, and generating a corresponding question based on a large language model. The method utilizes plug-in technology to enable configurability of knowledge document parsing rules.

This invention provides a federated learning method and system supporting heterogeneous models and data, relating to the field of artificial intelligence, and aims to address the problem of decreased discriminative power of global knowledge in heterogeneous federated learning. The method includes: a server generating and distributing a global prototype using a trainable generator; a client training its local model based on the global prototype, performing intra-class clustering for each data feature after training to generate multiple local clustering prototypes and uploading them; and the server collecting the local clustering prototypes and training its generator using a composite loss such as focus contrastive loss and orthogonal regularization to proactively generate more discriminative global knowledge. This invention effectively improves classification accuracy and robustness, enhances the discriminative power of global knowledge, and ensures communication efficiency and privacy security through a two-way co-evolutionary mechanism of refined knowledge extraction by the client and proactive knowledge generation by the server.

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.

A computer-implemented digital knowledge management system for institutional learning and decision support, comprising the following: A user interaction interface configured to receive structured institutional information from a variety of users connected to a university. This structured institutional information includes strategic issues, institutional goals, performance indicators, academic activity data, and decision-relevant input. an input processing unit that is operationally connected to the user interaction interface and is configured to capture, classify, and convert the received structured institutional information into machine-readable data sets; a collaborative culture assessment unit consisting of at least one processor and a memory in which executable instructions are stored, wherein the collaborative culture assessment unit is configured to collect and process organizational behavior data representing loyalty, engagement, cohesion, well-being and sense of community among members of the institutional leadership; a knowledge generation unit that is operationally linked to the collaborative culture assessment unit, wherein the knowledge generation unit is configured to analyze received knowledge records and generate new institutional knowledge by exploring alternative ideas, identifying new insights, and combining individual and collective institutional contributions; a knowledge exchange unit that is communicatively linked to the knowledge generation unit, wherein the knowledge exchange unit is configured to distribute the generated institutional knowledge to authorized users via controlled communication channels and structured knowledge dissemination procedures; a knowledge application unit designed to transform distributed institutional knowledge into decision support tools, including proposed strategic actions, operational recommendations, and measurable performance targets; an institutional learning repository consisting of a permanent digital storage system configured to store knowledge records, decision outcomes, institutional experiences, and insights gained from implemented decisions; a decision quality assessment processor that is operationally linked to the institutional learning repository and the knowledge application unit and is configured to evaluate generated decisions against predefined institutional criteria, including problem-solving effectiveness, strategic value contribution, achievement of institutional goals, and contribution to organizational learning; and a coordination processor configured to coordinate the operational interaction between the collaborative culture assessment unit, the knowledge generation unit, the knowledge sharing unit, the knowledge application unit, the institutional learning repository, and the decision quality assessment processor, such that the collaborative organizational conditions influence knowledge generation, knowledge distribution, knowledge utilization, and the subsequent evaluation of strategic decisions.

A machine-learning system and method for engineering and optimizing meetings. The method includes defining a meeting agenda comprising agenda items, generating prompts to solicit structured participant inputs, and enforcing contribution thresholds to ensure sufficient input. Participant inputs are integrated with internal organizational information and external information sources and analyzed using machine-learning techniques to generate results for each agenda item, including predictions, recommendations, and other optimized meeting products. Each completed meeting is stored as a structured dataset, and machine learning is applied across multiple completed meeting datasets over time to generate emergent knowledge and build an organization foundation model. The organization foundation model supports evaluation of participant and information source contributions, informs subsequent meetings, and enables time-based analysis. The system enables asynchronous, data-driven meetings that improve efficiency, accountability, and knowledge generation within and across organizations.

This invention provides a multimodal knowledge generation and annotation method, robot control method, system, and medium, comprising: parameterizing different structures of an object using digital genes to obtain a digital gene object representation; and transferring the knowledge annotations defined in the digital genes to the object. This invention, by using a digital gene-based object representation method, can significantly improve the knowledge annotation efficiency of existing 3D objects, increasing it by more than 60%. Furthermore, by using a digital gene-based object representation method, this invention can rapidly synthesize an unlimited number of new objects with knowledge annotations based on existing 3D objects, correspondingly reducing the cost of acquiring digital assets by approximately infinitely much. Using the newly generated objects to train neural networks can improve the effectiveness of knowledge annotation in fields such as computer vision and robot manipulation.

The application provides a microwave filter optimization debugging method and device based on knowledge transfer and a storage device, and comprises the following steps: for the initial microwave filter debugging task, establishing an initial knowledge generation model, determining an initial optimization range and optimizing and debugging the initial microwave filter, and based on the established initial knowledge generation model and the determined initial optimization range, optimizing and debugging the initial microwave filter; for the debugging task of other microwave filters, evaluating the difference degree, determining the transfer strength, establishing the knowledge generation model, determining the optimization range and optimizing and debugging, based on the difference degree and the transfer strength obtained in the difference degree evaluation stage, fine-tuning the initial knowledge generation model to obtain the knowledge generation model; and based on the knowledge generation model and the determined optimization range, optimizing and debugging the other initial microwave filter. The application has the beneficial effect of realizing efficient debugging of microwave filters between individual differences, transferring knowledge between different tasks and improving the overall debugging efficiency.