87 results about "Design knowledge" patented technology

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention discloses a boiler design file intelligent identification system and method based on a multi-modal knowledge graph. The method comprises the steps that S1, a data collection module is used for collecting a large number of existing boiler design files as a data set; s2, extracting information on the boiler design drawing in the data set by using a multi-modal feature extraction module to form a text-image multi-modal joint feature vector; s3, constructing a multi-modal boiler design knowledge graph with engineering semantics, graph structure association and reasoning capability; and S4, inputting the extracted key point information on the boiler design drawing to be examined into the knowledge graph model, and judging whether each design point meets the design specification requirements of the boiler drawing or not. According to the method, the identification period is greatly shortened, the working efficiency is improved, and the missed examination rate is remarkably reduced.

The invention provides a ship management system design assisting method based on a knowledge graph, which comprises the following steps of: processing scattered design documents and design criteria of enterprises by utilizing a natural language processing technology, extracting knowledge triple information in the design documents and design criteria, and on the basis of the constructed knowledge graph, further utilizing a retrieval enhancement generation (RAG) technology to generate a knowledge triple; according to the method, subjective query of design information of a graph database is achieved, analysis and proofreading of design information graph-text and document data corresponding to design drawings and models are achieved, and finally design requirement query and automatic check of the design drawings and models in the design process of aided designers are achieved. Integration and sharing of design knowledge are completed, and the design knowledge utilization rate is increased.

The invention relates to the field of knowledge graph data processing, and discloses an indoor space automatic layout and rendering method and system based on a design knowledge graph, and the method comprises the steps: maintaining an entity knowledge graph containing a variation path, and discretizing a continuous geometric space into an environment topological graph; executing node mapping and resource topology handshake verification based on subgraph isomorphism; and responding to the mapping deadlock, triggering recursive topology rewriting along the variation path to reconstruct the query sub-graph, and generating a configuration instruction. According to the method, continuous geometric search is converted into discrete atlas isomorphism and attribute propagation, the high-dimensional space layout calculation complexity is reduced, the deadlock problem under the over-constraint condition is solved by utilizing a recursive variation mechanism, and the configuration efficiency is improved. And the logic leakproofness and engineering compliance of the layout scheme are ensured.

The invention discloses a generative artificial intelligence-based banquet creative design system, which comprises: a demand determination module, which is used for interacting with a user terminal, obtaining a user demand and generating a demand theme; the banquet creativity generation engine is used for determining and operating a proxy artificial intelligence assembly line and generating a banquet creativity design project; comprising a main control unit, a domain knowledge acquisition unit, a creativity generation unit and a integrating degree evaluation unit, each unit is embedded with a multi-level intelligent agent as an execution main body, and prompt information is defined as input of the execution main body; the brand domain knowledge vector library is used for providing domain knowledge; the banquet design domain knowledge vector library is used for providing banquet design knowledge; and the design output module is used for obtaining the banquet creative design project and forming a banquet creative design scheme. According to the technical scheme, multi-modal brand domain knowledge and banquet creative design can be deeply fused, and automatic generation of multi-modal information of a high-end banquet catering art scheme is achieved.

The invention discloses a knowledge graph and RAG-based multi-modal engineering design intelligent generation method and system. The method comprises the following steps of S100, multi-modal data analysis and structured processing; s200, constructing a domain knowledge graph; s210, constructing a standardized module library; s300, performing intelligent retrieval and scheme generation; and S400, compliance verification and feedback learning are carried out. Through deep collaboration of the domain knowledge graph and the standardized module library, spanning of engineering design knowledge from'retrieval 'to'understanding and driving' is achieved, the problems of knowledge fragmentation and low reuse rate are solved, and the design efficiency, quality and automation level are remarkably improved.

Disclosed in the embodiments of the present application is a cloud service-based design code generation method, the method being applied to a cloud management platform. The method can not only acquire from a configuration interface an explicit design intention input by a user, but also query a knowledge graph for target design knowledge associated with the current explicit design intention of the user so as to capture an implicit design intention of a target design object. Thus, the explicit design intention and the target design knowledge from the knowledge graph can be combined to guide a large language model in efficiently and accurately generating target design code that meets a design requirement of the user, thereby intelligently generating the target design code so as to accurately and efficiently implement a design task on the basis of the target design code to obtain the target design object.

The invention discloses a home decoration design description specialization optimization method fusing a semi-structure RAG technology, and belongs to the technical field of artificial intelligence and home decoration design crossing. The method comprises the following steps: constructing a semi-structured home decoration design knowledge base, cooperatively processing an original corpus through a script and AI, generating a structured JSON knowledge unit, and performing vectorization storage; receiving natural language query of a user, performing intention recognition and vectorization, and recalling related knowledge units by adopting a mixed retrieval strategy in combination with semantic retrieval and metadata filtering; and dynamically constructing an enhanced cue word through an RAG technology, and inputting the enhanced cue word into the large language model to generate a professional and detailed home decoration design description text. According to the method, the problems that the content generated by the existing AI design tool is lack of specialty and the user demand conversion is inaccurate are effectively solved, the quality and the practicability of the design text are improved, and the use threshold of professional design is reduced.

The invention discloses a personalized design knowledge recommendation method based on knowledge graph and dynamic demand modeling, which comprises the following steps: firstly, constructing a design knowledge presentation layer which comprises a knowledge ontology and a knowledge graph, and then constructing a knowledge recommendation layer which comprises a knowledge ontology and a knowledge graph; the knowledge recommendation layer comprises a task stage knowledge demand model and a packet receiving party knowledge demand model, a dynamic knowledge demand vector and a task stage knowledge demand vector of a packet receiving party are matched with design knowledge nodes in the knowledge graph, and knowledge nodes most relevant to the demand of the packet receiving party are screened out; and according to a knowledge graph matching result, screening out a knowledge node most related to the demand of the packet receiving party, and generating a final recommendation result through an optimization algorithm. The method has the advantages that dynamic changes of knowledge requirements of a crowdsourcing party can be effectively solved, more personalized and accurate design knowledge recommendation can be provided, and the execution efficiency and design quality of crowdsourcing design tasks can be improved.

The invention discloses a numerical control machining method for a wood substitute transition mold based on equal-residue dynamic machining. The numerical control machining method comprises the following steps that S1, a machining path is optimized; s11, designing a knowledge enhanced path planning model; s12, generating an equal-residual-height cutter contact track; s2, cutting parameter collaborative optimization; s21, designing a multi-target parameter optimization model; s22, designing a dynamic parameter adjustment strategy; s3, numerical control machine tool parameter joint optimization design; s31, servo shaft parameter matching is carried out; and S32, cooling system cooperative control is carried out. According to the method, multi-dimensional collaborative optimization is adopted, and path planning, parameter optimization, machine tool debugging and cooling control are fused to form a closed-loop optimization system; according to the method, intelligent decision making is adopted, a knowledge graph and reinforcement learning are combined, and self-adaptive adjustment of a processing strategy is achieved; and high-precision guarantee is achieved, and the machining precision is improved to + / -0.02 mm through servo parameter matching and cooling control.

We provide an invention creation system and program that can systematically analyze technical and market information, accurately identify unexplored areas, and efficiently create and evaluate novel solutions for those areas. The invention creation system creates new inventions by analyzing a database containing technical literature, patent information, and market information. This invention creation system is characterized by comprising: a technology space modeling unit that analyzes the database, classifies technical elements into functional features and structural features, and maps the relationships between them onto a multidimensional technology space; a white space identification unit that identifies, as technological blank areas, areas in the multidimensional technology space where the combination density of existing technologies is low or areas where no structural features corresponding to specific functional features exist; a general-purpose design knowledge base that systematizes the mapping relationship between the functional space and the structural space and technological principles; and an automatic invention creation unit that sets required functions in the technological blank areas, searches for structural combinations that realize the required functions based on the general-purpose design knowledge base, and generates design solutions.

The invention provides a multi-modal knowledge graph generation method and system for hardware design analysis, which break through design, verification and physics, realize information barriers among the three traditional EDA (Electronic Design Automation) fields, perform semantic fusion and association on information from different data fields, create a unified and queriable hardware design knowledge representation, and improve the reliability of hardware design analysis. And a coherent and operable unified knowledge model is formed. A multi-modal design knowledge graph is created by integrating a simulation result (dynamic data) and a physical implementation feature (performance data) into a static structure model, and a comprehensive design model with structure, behavior and performance features is analyzed in a unified framework. An engineer does not need to manually switch between different tools and data formats, but can access all-dimensional information about a static structure, a dynamic behavior and a physical property of a design through a single query interface.

The invention provides a cross-modal multi-scale information fusion method and system supporting design knowledge push, and the method comprises the steps: constructing a patent data set containing patent abstracts and abstract drawings, extracting long text features and short text features through a text encoder, and extracting fine-grained and coarse-grained image features through an image encoder; the text and image features are input into a cross-modal attention fusion module, cross-modal information interaction of the text and the image is realized through a multi-head attention mechanism, and fused text and image features are generated; a loss function based on comparative learning is adopted, and the alignment effect of the image and text features in the semantic space is optimized; and inputting an innovative design concept, extracting feature representation of the innovative design concept, calculating the matching degree of the feature representation and patent data based on cosine similarity, and pushing related patent abstracts and attached drawings to designers according to similarity sorting. According to the method, the limitation in existing image-text matching and cross-modal information fusion can be solved.

This invention relates to a process architecture for optimizing ship hydrodynamic configurations based on artificial intelligence technology. The architecture includes modules such as expert experience, historical sample data of ship hydrodynamic configurations, design input for ship hydrodynamic configurations, a knowledge base for ship hydrodynamic configuration design and evaluation, a parametric model of ship hydrodynamic configurations, knowledge-driven reconstruction of the ship hydrodynamic model, AI-based optimization design of ship hydrodynamic configurations, rapid evaluation of ship hydrodynamic performance, and a comprehensive evaluation index model for ship hydrodynamic performance. This invention fully leverages the design experience contained in historical sample data, extracting the experience of numerous experts into design knowledge and integrating it into the ship hydrodynamic configuration optimization design process. The process architecture drives intelligent reconstruction of the ship hydrodynamic model through design knowledge, and improves upon the shortcomings of traditional ship hydrodynamic performance prediction methods, such as time consumption and low efficiency, by establishing a rapid prediction proxy model for hydrodynamic performance based on machine learning algorithms.

The application discloses a kind of based on key value memory network's complex electromechanical system design knowledge active recommendation method.First, the scene feature semantic information extraction of software platform log file in the design process of complex electromechanical system is carried out, and scene ontology is established based on scene feature semantic information, and system scene knowledge base is formed by scene ontology and original knowledge base;Then, the scene maximum frequent sequence is used to describe the scene sequence feature similarity between designers;Again, the knowledge item interaction sequence of all designers is learned, and the knowledge item sequence preference vector corresponding to all designers is obtained;Further, input to key value memory network, and the initial design knowledge active recommendation sequence is obtained;Finally, after knowledge item selection, the final design knowledge active recommendation sequence of each designer is obtained.The application obtains design knowledge active recommendation sequence more in line with the needs of designers, and then improves the efficiency of complex electromechanical system design.

The invention discloses a distribution network multi-source design data integration system based on AI association fusion, and the system comprises a data collection module which is used for collecting and standardizing CAD, GIS, equipment ledger and operation monitoring data; the feature extraction module is used for extracting text semantics, numerical values and spatial features to generate feature vectors; the semantic embedding modeling module is used for constructing a semantic embedding space for the TransE model; the self-adversarial optimization module is used for introducing a self-adversarial negative sampling mechanism to dynamically optimize model parameters; the strategy training module is used for outputting strategy parameters according to the A3C algorithm; the joint optimization module is used for updating the embedding model according to the strategy and the state value; and the knowledge graph construction module is used for fusing the multi-source data to generate a power distribution network design knowledge graph. According to the method, efficient fusion and intelligent optimization of the distribution network multi-source data are realized, and the design precision and the processing efficiency are improved.

The invention relates to the technical field of knowledge skeleton construction, and provides a cross-domain knowledge skeleton construction method for multi-process equipment, and the method comprises the steps: dividing knowledge domains for design knowledge, the knowledge domains comprising a demand domain, a structural domain, a behavior domain, a control domain and a performance domain; extracting knowledge elements corresponding to the knowledge domains from the equipment and the components thereof; knowledge elements are divided into interface knowledge elements, external knowledge elements and internal knowledge elements, the interface knowledge elements are used for transmitting information between equipment and components, the external knowledge elements comprise performance domain knowledge elements and behavior domain knowledge elements which are used for establishing association between every two of the equipment and the components, and the internal knowledge elements are included in the equipment or the components. Knowledge elements not associated with knowledge elements in another equipment or component; and according to a division result of the knowledge elements, establishing association among the internal knowledge elements of the equipment or the components by using an adjacent matrix, and associating external knowledge elements among different equipment or components to obtain a cross-domain knowledge skeleton.

The invention belongs to the technical field of software engineering, and particularly relates to a code digital twinning construction method based on a large model. According to the method, domain concept extraction, software functional characteristic knowledge extraction, high-level design decision analysis, code units and structural relations thereof are comprehensively utilized to construct a code digital twinborn model which is co-constructed, shared and synchronously evolved with codes and is used for conceptualized representation of software domain knowledge; the method comprises the following specific steps: knowledge representation design: designing different representation modes for various knowledge; product-oriented skeleton knowledge is extracted; explanatory knowledge extraction with the design principle as the center; product-knowledge mapping construction is carried out; and joint evolution and incremental updating are carried out, a construction process and software product evolution are in butt joint, and synchronous evolution of the knowledge base and the software product is ensured. According to the method, developers can be helped to understand field background knowledge, functional characteristics and design knowledge contained in complex software codes, so that software development and maintenance tasks are better completed, and intelligent development and maintenance support is provided.

The invention discloses a multimedia recommendation method based on cross-modal feature decoupling and knowledge distillation. The method comprises the following steps: 1, constructing a user-article interaction graph; 2, graph learning; 3, carrying out multi-modal feature processing; 4, a cross-modal difference feature learning module; 5, a cross-modal common feature learning module; 6, score calculation and loss function construction; and 7, model training and recommendation result generation. According to the method, an explicit decoupling strategy is provided for a recommendation task of multi-modal data, the multi-modal preference of a user is divided into independent learning processes of cross-modal difference features and common features, meanwhile, efficient learning of the cross-modal features is promoted by innovatively designing a knowledge distillation mechanism, and the user experience is improved. Therefore, the problems of confusion and entanglement of user preferences in the multi-modal content can be effectively relieved, and the understanding depth and prediction precision of a recommendation system for complex multi-modal data are remarkably enhanced.

This invention discloses a temporally enhanced knowledge graph-driven dialogue agent and its implementation method, belonging to the fields of artificial intelligence and natural language processing technology. The dialogue agent includes a dialogue interface, natural language understanding, a temporal knowledge graph, a temporal reasoning engine, dynamic update and failure management, and a natural language generation module. It includes designing the basic representation unit of the knowledge graph as a temporal quintuple; designing a temporal reasoning engine and a dynamic update and failure management method; designing a method for matching, verifying, and performing multi-hop reasoning of temporal information in natural language problems with temporal facts in the knowledge graph; achieving accurate parsing of temporal constraints in dialogue, real-time adaptation of dynamic knowledge, rigorous temporal logic reasoning, and continuous maintenance of contextual temporal consistency; and outputting a temporally accurate, knowledge-reliable, and logically coherent intelligent response, capable of meeting the needs of complex scenarios such as vertical industry decision support and high-reliability intelligent interaction.

The invention provides a mechanical design chart hybrid intelligent analysis method and system based on interactive learning, and provides an efficient and systematized solution for intelligent processing of mechanical design chart knowledge by adopting a multi-modal data fusion technology and combining deep learning, an OCR (Optical Character Recognition) technology and natural language processing. Through an accurate chart and text recognition method, the analysis precision and the practical efficiency of design knowledge are remarkably improved, and the precision requirement in standardized mechanical design is met; the system adopts a convolutional neural network and an attention mechanism, solves the problems of insufficient precision and inaccurate structured data output when a complex chart is processed in a traditional technology, and optimizes data integration and processing flows by seamlessly connecting an analysis result with an agent knowledge base and a database.

The invention provides an architectural space information model strong generalization generation method and system integrating a feature matrix. The system comprises a data extraction module, a design task translation module, a visual reconstruction module and a question and answer interaction module. According to the method and the system, a representation normal form of building space parameters can be constructed in combination with an architect perspective, building geometric parameters are translated into a mathematical matrix, automatic and rapid coding of the parameters is realized, and the understanding ability of a large model for building space is improved; and as a data level front-end technology, the method supports the application of a large model in the aspects of building simulation model automatic construction, building design knowledge questions and answers, building effect picture rendering and the like.

The invention discloses a knuckle test fixture intelligent design system based on a knowledge rule base and a design method thereof, and relates to the field of automobile part tests. The system comprises a data input module, a central processing engine, a knowledge rule base module, a parameterized CAD integration module, a CAE integration module and a result output module. Design knowledge, experience and rules are subjected to software processing and algorithm processing, full-process automation and intelligentization of knuckle test fixture design are achieved, a traditional design mode which depends on manual experience and is low in efficiency and uneven in quality is thoroughly changed, the design period is greatly shortened, and the design quality and consistency are improved.

The invention discloses a sheet metal part manufacturability analysis method and system, and belongs to the field of sheet metal part digital manufacturing. The method comprises the following steps: establishing a structured rule base containing unit feature rules and inter-feature position relation rules; analyzing the three-dimensional model of the sheet metal part by using a rule-based feature recognition algorithm, recognizing manufacturing features such as bending, hole, sinking and the like, extracting parameters and topological information, and dividing a structured surface set; feature information and a design knowledge model are constructed based on ontology modeling, and structural mapping of feature parameters and topological relations and association of part ontologies and feature ontologies are achieved; and finally, through automatic matching of a design knowledge model and a rule base, combining an inference engine to complete compliance verification, and outputting a part-level manufacturability evaluation conclusion and a violation optimization suggestion. According to the method, through combination of the structured rule and the knowledge model, automation and accuracy of sheet metal part manufacturability evaluation are improved, and efficient technical support is provided for design and manufacturing process adaptation.

The invention discloses a professional content intelligent generation method based on AI writing, and relates to the technical field of professional content generation, and the method comprises the steps: designing the structure of a knowledge base, and collecting text data from a target data source for preprocessing; semantic representation of the text data is generated based on the BERT model, and the knowledge base is updated; and according to the preprocessed text data, a semantic generation formula is constructed in combination with a BERT model, and a semantic correlation score is obtained. Synchronization of generated content and latest domain knowledge is guaranteed through dynamic updating and semantic compression of a knowledge base, capture of deep semantics of a text is enhanced through a BERT model, high relevance and logic consistency of the generated content are guaranteed through semantic relevance scoring, the logic sequence and semantic density of the generated content are further optimized through a feedback mechanism, and the reliability of the generated content is improved. And it is ensured that the final output content not only meets user requirements, but also has high semantic density and coherence.

The invention discloses a high-pressure cylinder flow field analysis method based on computational fluid dynamics, and particularly relates to the field of high-pressure cylinder flow field analysis, and the method comprises the steps: constructing a flow field model through a cross-manufacturer design knowledge base, recognizing a sensitive area according to a flow loss criterion, carrying out the meshing, carrying out the partitioning parallel solving of a fluid equation set, and dynamically terminating the iteration based on the entropy yield change. And finally, packaging the analysis result. According to the high-pressure cylinder flow field analysis method based on computational fluid dynamics, by automatically recognizing a secondary flow loss sensitive area and combining gridding processing, intelligence and light weight of grid generation are achieved, the manual intervention time is shortened, and the grid generation efficiency is improved; a computational domain is divided into a mainstream region, a boundary layer region and a vortex core region, and a corresponding fluid control equation set is matched, so that the utilization rate of computational resources is improved.

The invention relates to the technical field of auxiliary generation decision making, in particular to an art design scheme auxiliary generation decision making system based on big data. The system comprises the following steps: analyzing an artistic design demand instruction to obtain an instruction entity label corresponding to the artistic design demand instruction; performing retrieval, reasoning and combination in the design knowledge graph based on the instruction entity tags, and generating a plurality of preliminary art design schemes by using a diffusion model; respectively calculating a demand tendency matching value between the art design demand instruction and each preliminary art design scheme; determining a priority selection value of each preliminary art design scheme relative to the art design demand instruction by using an analytic hierarchy process; and the final art design scheme is determined based on the demand tendency matching value and the priority selection value between each preliminary art design scheme and the art design demand instruction, and the system can improve the accuracy of understanding the user demand, ensures that the scheme is highly matched with the user demand, and provides accurate and reliable auxiliary support for design decision making.

The invention discloses a river-sea direct ship design optimization method and system based on CBR-RBR hybrid reasoning, and relates to the field of ship design, and the method comprises the steps: obtaining a design target parameter input by a user and an initial weight of an evaluation index; according to the design target parameters, case retrieval is carried out in a river-sea direct wide, flat and narrow ship type design knowledge base, and an initial design scheme is obtained; determining a search space of an optimization variable according to a main scale and a ship form coefficient in the initial design scheme; performing Pareto optimization on the evaluation indexes in the search space of the optimization variables by using a multi-objective evolutionary algorithm to obtain a non-dominated solution set; determining a score sequence according to the non-dominated solution set and the initial weight of the evaluation index; and selecting the design scheme of which the score sequence is within a set range as a user preference scheme. According to the method, the preliminary design scheme of the river-sea direct ship can be efficiently and intelligently generated and optimized.

The invention discloses an intelligent optimization design method for a barrel welding flanging structure, and the method comprises the steps: constructing a parameterized finite element model to output a mechanical response index through determining an optimization target and input comprising a crimping radius, a geometric material parameter, a load parameter and a design criterion; further constructing an optimization objective function integrating static strength evaluation, fatigue life evaluation and constraint penalty terms, calling an intelligent optimization algorithm to perform iterative optimization, finally obtaining an optimal hemming radius, and further training an explicit empirical formula capable of directly outputting a recommended radius through machine learning regression by using a plurality of groups of optimization samples. Therefore, a complex simulation optimization process is converted into efficient and reusable design knowledge, normal form upgrading from empirical design to simulation driving and global automatic optimization is achieved, and design efficiency and economical efficiency are remarkably improved while structural safety and service life are guaranteed.

The invention provides a material generation method, electronic equipment and a computer program product. The material generation method comprises the following steps: in response to a received material generation instruction input by a user, obtaining material design knowledge and material design style characteristics related to the material generation instruction; performing cross-modal semantic alignment on the material generation indication, the material design knowledge and the material design style features to obtain material alignment features; and generating a material corresponding to the material generation indication based on the material alignment feature.