355 results about "Structure generation" patented technology

The invention discloses a 3D intelligent simulation generation method for clothing styles based on a model library, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the entity relation extraction and semantic mapping of multi-source clothing data, constructing a cross-modal knowledge graph, and extracting a physical constraint rule of the cross-modal knowledge graph; performing association reasoning and cross validation on the physical constraint rule and the version library to obtain a basic version template set; inputting the basic model template set into an intelligent clothing model, performing structured feature decoupling by the parameter analysis layer, dynamically adjusting a topological structure by the geometric generation layer, and generating 3D simulated clothing; and carrying out digital modeling on the basic model template set through a digital twinning algorithm, constructing a digital twinning body, carrying out physical simulation and optimization verification on the 3D simulation clothing by using the digital twinning body, and outputting a clothing style 3D simulation scheme. According to the invention, by constructing the pattern library and the intelligent garment model, the efficiency, accuracy and individuation level of garment 3D simulation generation are improved.

The invention relates to the technical field of three-dimensional model reconstruction, in particular to an existing building three-dimensional model reconstruction system based on point cloud data, and the system comprises a point cloud data processing module which carries out the octree recursive space subdivision of input point cloud data, builds a hierarchical index structure, and generates a multi-view fusion point set. In the method, octree recursive space subdivision is performed on the input point cloud data to establish a hierarchical index structure, so that the key area of the building surface can be positioned more accurately in subsequent data processing, data redundancy is reduced, and the precision and efficiency of three-dimensional model construction are improved; then voxel depth field data is obtained by calculating a truncation symbol distance function value of voxels, the description accuracy of a building surface structure is improved, and an initial surface grid is formed through contour surface extraction; meanwhile, plane slicing is conducted on the multi-view fusion point set in the preset building main axis direction, and contour details of all building structure sections are captured.

The invention relates to the field of computer systems, in particular to an operation stability testing method and system oriented to a heterogeneous computing system, and the method comprises the steps: constructing a system overall operation logic model comprising a resource topological structure, a task scheduling rule and a communication link matrix; identifying a key communication path according to the communication link matrix, analyzing a resource competition hotspot in combination with a task scheduling rule, and establishing an interference model library; calling a specific model instance in the interference model library according to a test target, configuring injection parameters, and dynamically applying interference in a system operation process; key performance indexes in the system operation process are collected in real time; based on the real-time monitoring data, a multi-index weighted scoring algorithm is adopted to calculate an overall stability coefficient, a robustness grade and a self-healing capability index of the system, and a stability evaluation result is generated; and generating a visual report according to the topological structure of the operation logic model, and supporting comparison and analysis of historical versions. Unified centralized management is realized, and test efficiency and evaluation accuracy are improved.

The invention discloses a financial knowledge graph construction method and system based on artificial intelligence, and relates to the field of artificial intelligence data processing. The method comprises the following steps: performing multi-dimensional semantic analysis on a heterogeneous financial data source, and extracting a structured semantic fragment; constructing a financial entity perception unit, identifying a multi-granularity entity and generating a unique code; generating a preliminary relation graph based on the event cascade relation and the attachment structure, and injecting a semantic translation label; normalizing the atlas relationship through semantic separation and a label reconstruction mechanism to form a financial relationship network with consistent semantics; executing evolution increment iteration in combination with the newly added corpus, and dynamically updating nodes and edge sets; and performing semantic consistency and structural integrity evaluation on an iteration result, and outputting a stable financial knowledge graph structural body. By introducing a multi-factor semantic analysis model, a causal relationship modeling mechanism and a graph evolution iteration strategy, systematic improvement of the financial knowledge graph in the aspects of structural expression precision, semantic reasoning ability and dynamic adaptability is achieved.

The invention discloses a standard document writing method and system based on knowledge retrieval enhancement, and the method comprises the steps: carrying out the knowledge retrieval to obtain domain multi-source heterogeneous knowledge, constructing a domain knowledge graph and a content increment model, carrying out the semantic extraction, structure division, content segmentation and text granularity perception, determining first matching information and second matching information, and writing a standard document according to the first matching information and the second matching information. Matching a standard paragraph structure and a standard statement structure to generate a standard template, performing compliance examination to obtain a compliance text and a recommended text, performing exception screening and exception correction to obtain a compliance numerical value, inputting the compliance text, the recommended text and the compliance numerical value into the content increment model, and performing standardized conversion to obtain a standard material, and inputting the standard material of each section of statement into the standard template to obtain a standard document. According to the method, the standard document writing efficiency and accuracy can be improved, meanwhile, the method has good interpretability, and the method can be directly applied to a standard document writing system based on knowledge retrieval enhancement.

The invention belongs to the technical field of composite material design, and discloses a polymer composite material design method based on LLM knowledge database construction and reasoning optimization, and the method comprises the following steps: S1, target performance setting; s2, constructing a knowledge database; s3, constructing a reverse design model; s4, constructing a performance verification module; and S5, performing multi-round test-verification design loop optimization. According to the method, an intelligent design method integrating target setting, semantic knowledge database construction, a design assistant, a performance verifier, a prompt engineering module and a language model material knowledge system is provided, closed-loop reasoning and feedback of structure-mechanism-performance are achieved through multiple rounds of DA-VM collaborative optimization, and the design efficiency is improved. The method has the advantages of high design efficiency, strong structure generation rationality, accurate performance prediction, knowledge evolvable and the like, and is suitable for intelligent rapid development of the high thermal conductivity-wave absorbing-shielding-mechanical integrated functional polymer composite material.

The invention provides an unbiased missing modal learning method based on a multi-stage double diffusion network, and belongs to the field of computer vision, natural language processing and multi-modal information fusion. The method comprises the following steps: a multi-modal feature extraction module maps text modal data, image modal data and audio modal data into a unified potential representation space; the potential space representation of the three modals is used as the original feature of the missing modality and the original feature of the available modality; based on the missing modal original features and the available modal original features, training a multi-stage double-diffusion module through forward diffusion and reverse diffusion to obtain a trained multi-stage double-diffusion module, and based on the trained multi-stage double-diffusion module, performing global structure generation and modal conversion through available modal data to obtain a multi-stage double-diffusion model; and performing local detail optimization reasoning to generate missing modal data. According to the method, the problem of modal generation deviation in the existing multi-modal learning is solved, and the effectiveness of the multi-modal learning effect in the missing scene is enhanced.

The invention relates to a training method for providing an underwater vehicle main body structure design model and an underwater vehicle main body structure generation method. The training method of the underwater vehicle main body structure design model comprises the following steps: firstly, generating a noise adding main body structure based on a randomly initialized ordinal number stamp k, a sample main body structure and randomly initialized Gaussian noise generation data; secondly, determining predicted Gaussian noise data based on the ordinal number stamp k, the performance parameters of the sample main body structure and the noise adding main body structure by adopting a main body structure design model; and finally, determining a training loss function based on the generated Gaussian noise data and the predicted Gaussian noise data, and training the main structure design model based on the training loss function. Under the condition that only few sample main body structures exist, enough training sample data used for main body structure design model training can be generated, and model training under a small amount of sample main body structure data is achieved.

The invention relates to the technical field of data sampling, in particular to a neural network training data set generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining the shape data, material data and target load position space of a part virtual model; determining boundary constraint conditions of the part virtual model according to the shape data and the material data; carrying out load point stratified sampling in the target load position space to obtain a plurality of sampling points and sampling parameters; according to the boundary constraint condition and the sampling parameters of the plurality of sampling points, constructing a simulation working condition, performing topological structure simulation under the simulation working condition, and determining a part topological structure corresponding to the simulation working condition according to a simulation result; and generating a training data set based on the simulated working condition and the corresponding model topological structure, training a neural network model by using the training data set, and generating a part topological structure under the target real working condition by using the trained neural network model. Therefore, the problem that the quality of the training data set is poor in the related technology is solved.

The invention relates to the technical field of gas pipeline construction, and discloses a polyethylene buried gas pipeline tracer line construction quality informatization management system. The system comprises a trace line feature extraction module, a topological graph construction module, a resource conflict analysis module, a quality node judgment module and a quality structure generation module. Wherein the feature extraction module obtains a detection signal and constructs an original feature sequence; the topological graph construction module establishes a feature point connection path based on the feature sequence and generates a feature topological graph; the resource conflict analysis module extracts resource allocation paths and screens conflict path combinations; the quality node judgment module collects quality evaluation labels and generates a construction quality label set; and the quality structure generation module counts the quality nodes associated with the labels, establishes an affiliation mapping structure and outputs a classification structure table. According to the system, informatization management of the construction quality of the tracer line is realized, and systematic control of the construction quality is assisted.

The invention provides a training method and system of a network community dynamic graph generation model and a graph generation method, and is applied to the technical field of data mining. The method comprises the following steps: acquiring a historical dynamic time sequence diagram of a target network, and carrying out step-by-step feature extraction on the historical dynamic time sequence diagram by utilizing a feature extraction module according to a time sequence to sequentially obtain a diagram embedding representation, a hidden variable and a hidden state; inputting the graph embedded representation into a community detection module to obtain a community detection result; inputting the hidden state into a community prediction module to obtain a community prediction result of the prediction time step; inputting the hidden variable and the hidden state of the previous time step into a topological structure generation module to obtain a generated network diagram; determining training loss based on the community detection result, the community prediction result of the prediction time step and the generated network diagram; and updating model parameters based on the training loss. According to the method, the problems that application scene requirements sensitive to dynamic changes cannot be met and the quality of generated dynamic graph data is not high in the prior art are solved.

The application discloses a standard document writing method and system based on knowledge retrieval enhancement, comprising the following steps: acquiring field multi-source heterogeneous knowledge by knowledge retrieval and constructing a field knowledge graph and a content increment model; performing semantic extraction, structure division, content segmentation and text granularity perception; determining first matching information and second matching information; matching standard paragraph structures and standard sentence structures to generate a standard template; performing compliance review to obtain a compliant text and recommended text; performing abnormality screening and abnormality correction to obtain a compliance value; inputting the compliant text, the recommended text and the compliance value into the content increment model to perform normalized conversion to obtain standard materials; and inputting the standard materials of each sentence into the standard template to obtain a standard document. The method can improve the efficiency and accuracy of standard document writing, has good interpretability, and can be directly applied to a standard document writing system based on knowledge retrieval enhancement.

The invention discloses a flexible business process automatic configuration method and system based on semantic intention recognition, and relates to the technical field of semantic recognition, and the method comprises the steps of semantic fragment extraction, process fragment atlas construction, process structure generation, process configuration visualization and flexible business process configuration. The method comprises the following steps: carrying out word segmentation, named entity recognition, semantic role labeling and relation extraction on natural language input by adopting a hierarchical guide type semantic decomposition combined extraction model, establishing a business semantic labeling system, and realizing modeling and extraction of semantic elements such as actions, objects, roles and conditions; on the basis, a process fragment graph improved by structural semantic fusion is constructed, and through node-level splitting, semantic tag binding and logic connection abstraction, process fragment graph data with tag attributes are generated, so that semantic understanding, process matching and cross-scene multiplexing capabilities are improved, and efficient, flexible and intelligent configuration of a business process is realized.

The invention discloses an automatic design and optimization system for a hydraulic speed control loop, which takes a large language model intelligent body as a core, realizes automatic design and optimization of the hydraulic speed control loop through semantic comprehension, knowledge retrieval and parameter optimization, and specifically comprises an LLM intelligent body, a data processing module and a data processing module, the analysis module is used for analyzing natural language input containing design requirements and performance targets of a user, generating a loop structure in combination with a local knowledge base and calling an optimization algorithm; the local knowledge base comprises a basic hydraulic loop, a key empirical relationship and a typical structure template which are artificially extracted; and the simulation and optimization module is used for executing a swarm intelligence optimization algorithm to optimize and adjust parameters of the loop structure. The invention constructs a hydraulic speed control loop automatic design and optimization system with semantic understanding, automatic generation and adaptive optimization capabilities, and aims to realize full-process intelligent design from user demand input to loop structure generation and performance optimization.

The invention discloses a server power supply system, a server, a topological structure generation method and a storage medium, and relates to the technical field of power electronics. According to the invention, a high-voltage centralized conversion-medium and low-voltage distributed distribution two-stage architecture and a physical isolation design of the first direct-current bus and the second direct-current bus are adopted, and an adaptive mounting platform is provided for load nodes such as a super-capacitor node and an energy storage node depending on the second direct-current bus; the mounting number of the load nodes can be determined based on the power required by the load nodes and the voltage and through-flow level of the second direct current bus, and high-voltage power supply conversion and power supply of the load nodes are separated, so that physical isolation of high-voltage direct current of the first direct current bus and low-voltage direct current of the second direct current bus is realized. The high-voltage direct current input is ensured to be safely, efficiently and stably distributed to each load node in the server after being converted. The problems of potential safety hazard and reliability risk caused by direct access of the high-voltage direct current to the load node, poor mounting adaptability of the load node and the like in the prior art are solved.

The invention discloses a modular robot automatic design method and system based on a large language model, and aims to realize full-process automation from natural language input to modular robot structure generation. The method comprises the following steps: constructing a module library comprising basic components and composite components; receiving a natural language description input by a user; performing semantic analysis by using a large language model, and automatically generating a component planning list; calculating spatial positions and connection relations of the components based on recursive assembly reasoning; and generating a three-dimensional assembly code and outputting the structure model. The system comprises an input module, a planner module, an assembler module, a module library and an output module, and all the modules cooperate to realize an automatic design process from a text to a structure model. According to the method, the use threshold of non-professional users is lowered, and the method has good adaptability and expansibility and is suitable for various robot structure design tasks.

The invention discloses a data warehouse table structure generation method and device, a medium and a product, and belongs to the technical field of data warehouses. According to the method, the Excel template configured with multiple pieces of source database table structure information is obtained, table field definition is automatically analyzed, the source data type is intelligently mapped and converted into the target data type according to the source database type, meanwhile, partition key fields are automatically recognized based on the preset strategy, and finally a complete and accurate data definition language (DDL) statement is generated. According to the method, remarkable technical breakthrough and value are achieved in practical application, the table building efficiency can be improved by 90% or above, batch processing of hundreds of tables is supported, and manual errors are reduced by 95% or above; mainstream database type conversion is comprehensively supported, and various data models such as Dores are adapted; the method has the capabilities of automatic type detection and conversion, intelligent key field selection and partitioning and bucket dividing strategy optimization.

The invention relates to the technical field of machine learning, and provides an emotion analysis method and device, computer equipment and a readable storage medium, and the method comprises the steps: carrying out the data preprocessing of a to-be-analyzed input text, and obtaining a word vector sequence; obtaining a metaphor feature library; according to the metaphor feature library, processing the word vector sequence by utilizing a long-short-term memory network based on an attention mechanism to obtain a metaphor recognition result; if the metaphor identification result shows that the metaphor exists, fusing the implicit meaning corresponding to the metaphor identification result into the semantic representation of the input text; and converting the processed input text into a target sequence according to the aspect term set so as to finish sentiment analysis on the input text according to the target sequence. Aiming at complex text analysis pain points in the fields of finance, medical health, old-age care and the like, an efficient and accurate solution is provided for deep understanding fed back by cross-industry customers through an innovative architecture of'metaphor recognition-semantic fusion-structured generation '.

The invention discloses a semantic analysis method and system based on history teaching, and relates to the technical field of artificial intelligence. Comprising the steps of obtaining text data, and generating a standardized text through character-level cleaning and grammar rule matching; performing semantic extraction on the standardized text, identifying a historical event entity, a character relationship and a timeline grammar structure, and generating a structured semantic unit; constructing a dynamic knowledge graph based on the structured semantic unit, and defining event-character-time three-dimensional node attributes and a causal-time sequence-space edge relationship; converting the knowledge graph into multi-modal visual results such as a time axial flow type display result, a relational network topological graph and an event scene recovery graph; and collecting user interaction feedback data, optimizing knowledge graph node weights through a semantic similarity algorithm, and iteratively updating the semantic analysis model. The problems of complex text processing and weak knowledge association in history teaching are solved, and the accuracy and interactivity of semantic analysis are improved.

The invention discloses a tunnel lining structure generation method and device, and relates to the technical field of tunnel engineering.The method comprises the steps that geometric parameters of a four-center circle section are obtained through a data interface; a multi-element polar coordinate system is constructed based on the geometric parameters of the four-center circle section, coordinate calculation is carried out based on the multi-element polar coordinate system, and a structure node coordinate set and a constraint node coordinate set are generated; constructing a finite element model based on the structure node coordinate set and the constraint node coordinate set, and performing internal force analysis on the finite element model to obtain an internal force analysis result; and based on an internal force analysis result, through discretized reinforcement ratio analysis and safety coefficient evaluation, outputting a target reinforcement scheme, and based on the target reinforcement scheme, generating a tunnel lining structure. Model generation is directly driven through parameterized input, the accuracy of load application is improved, and the safety of the tunnel lining structure is guaranteed.

The invention discloses a self-adaptive table structure generation and index optimization method for JSON data driven by a real-time mode, and relates to the field of computer information management application. According to the self-adaptive table structure generation and index optimization method for the JSON data driven by the real-time mode, efficient and automatic persistence from the JSON data to a relational database is achieved through a four-layer technical architecture including dynamic analysis, intelligent mapping, self-healing expansion and index optimization. Comprising a dynamic mode analysis engine which analyzes a JSON nested structure in real time and generates a mode feature tree with weight; the self-adaptive storage engine is used for dynamically generating or expanding a table structure according to the mode feature tree and executing data type mapping; the index optimizer is used for automatically creating / cleaning indexes based on query log analysis; and the uniform interface layer is compatible with multi-database dialects and shields bottom layer differences. Dynamic table structure generation, intelligent type inference and storage optimization, self-healing Schema extension, query-driven index self-optimization and cross-database compatibility are realized.

The invention relates to the technical field of computers, and discloses a topological structure generation method, a communication method, equipment, a chip and a program product. The topological structure parameters comprise a plurality of master port identifiers, a plurality of slave port identifiers, a mapping relationship between a plurality of master ports and a plurality of slave ports and first data bit widths of the plurality of master ports and the plurality of slave ports, and the mapping relationship comprises that at least two master ports in the plurality of master ports correspond to the same slave port in the plurality of slave ports, and / or at least two slave ports in the plurality of slave ports correspond to the same slave port in the plurality of slave ports. At least two slave ports in the plurality of slave ports correspond to the same master port in the plurality of master ports; determining communication parameters based on the topological structure parameters, wherein the communication parameters comprise the number of arbiters and arbitration parameters; and generating a topological structure file based on the communication parameters, the plurality of master ports and the plurality of slave ports, the topological structure file comprising connection lines among the plurality of master ports, the plurality of slave ports and the at least one arbiter. A user does not need to manually configure an arbiter and calculate, and the labor cost is saved.

The invention provides a knowledge graph and deep learning fused research course automatic generation method and system. The method comprises the steps of receiving event stream data from a plurality of knowledge sources; performing source identification and theme clustering processing on the event stream, and generating a standardized update demand unit; writing the update demand unit into a temporary change buffer area outside the atlas; inputting the knowledge fragments in the change buffer area into a cross-source semantic alignment model, and executing entity mapping and relation prediction operation; generating a multi-scale updating plan based on the influence hierarchy of the candidate nodes and the edge set, and executing a graph updating operation; based on the updated knowledge graph, determining a learning target and ability dimension of the research course, a required core knowledge point sequence and an association relationship; inputting the core knowledge point sequence into a course structure generation model, and outputting a preliminary study course outline; and generating a final study course by using the course outline. According to the method, the research course can be automatically generated, and the timeliness and accuracy of the course are ensured.

The invention discloses a part manufacturing-oriented lattice structure mechanical property prediction method. Aiming at the bottlenecks of the traditional method in precision, efficiency and speed, the method is optimized through four modules: a structure generation module generates VTK format data; the feature extraction module analyzes five types of features including point coordinate statistics, unit topology and the like; the deep crossover network prediction module outputs porosity and compression modulus through double-path processing of a crossover network and a deep network; and the optimization module optimizes the printing parameters according to the prediction result. The method can accurately capture the interaction effect of the microstructure and the technological parameters, accelerates the whole process of'design-prediction-optimization ', and is suitable for performance prediction of core components of high-end equipment.

The invention relates to an elastic sheet structure generation method and device, computer equipment and a storage medium. The method comprises the following steps: generating an initial structure control parameter of an elastic sheet based on a preset spatial constraint parameter; fitting is carried out based on the initial structure control parameters, and a three-dimensional elastic piece model is obtained; the elastic sheet three-dimensional model is an elastic sheet three-dimensional model conforming to a preset geometric verification rule; performing performance simulation on the elastic sheet three-dimensional model to obtain a performance simulation result; and based on the performance simulation result, performing iterative optimization on the initial structure control parameter to obtain a target structure control parameter and a target elastic sheet three-dimensional model corresponding to the target structure control parameter. By adopting the method, the elastic sheet design efficiency can be improved, the labor cost is reduced, and the elastic sheet performance is improved.

The invention relates to the technical field of big data analysis and user behavior modeling, and provides a user portrait generation method and system based on big data. The method comprises the following steps: extracting a time sequence and interaction frequency characteristics from user behavior data, constructing a user access mode characteristic set and analyzing data missing distribution; boundary features are extracted based on missing distribution, and if the correlation between the boundary features and complete data is significant, surrounding data are detected to identify behavior rules; extracting a potential association path of the missing area, and deducing a user behavior pattern prototype; spreading the behavior rule to the missing region, and generating a data inference value; fusing the path and the boundary features, determining the internal structure of the missing region, and generating a user activity sequence; and verifying the consistency of the internal structure and the inferred value to form a complete user feature model. The missing data is inferred through the context, and the accuracy and integrity of the user portrait are improved.

A method, computer program product, and computing system for identifying a plurality of headings from a document by processing a hierarchical structure associated with the document including the plurality of headings and a plurality of content portions within the plurality of headings. A plurality of respective chunks are generated using the plurality of headings and a prompt size limitation associated with a prompt of a generative artificial intelligence (AI) model. The plurality of respective chunks are provided for generating a prompt for the generative AI model.

The invention discloses an operating system containerized kernel compatibility method based on mixed binary translation, which comprises the following steps of: constructing a container kernel data structure feature library containing field access frequency through static source code analysis and dynamic runtime tracking, generating a unique identifier consisting of a structure name and a high-frequency field offset sequence for a target structure, and generating a container kernel data structure feature library containing field access frequency; establishing an association graph between the system call and the target field; in a host machine, a static translation service is adopted to decompile a binary code of a container application, instrumentation is performed on a high-frequency site and a low-frequency site respectively, and an intermediate operation code sequence containing an operation code, a field name and double-end offset is pre-generated for the high-frequency site and stored in a cache; during operation, a pre-translation result is preferentially loaded from the cache for a high-frequency site, the pre-translation result is processed by the dynamic translation engine if the pre-translation result is not hit, and the pre-translation result is processed by the dynamic translation engine as required for a low-frequency site, so that efficient conversion combining field-level static preprocessing and on-demand dynamic conversion is realized.

The invention discloses a time sequence knowledge graph alignment method for an information incomplete scene. The method comprises the following steps: constructing a time sequence knowledge graph; modeling local and global structures of the time sequence knowledge graph to generate entity representation; extracting multi-granularity time information by using a multi-granularity time encoder; generating a self-adaptive similarity matrix and a pseudo-aligned entity pair; performing scale adaptive entity projection on the pseudo-aligned entity pair and the similarity matrix; performing multi-scale hypergraph retrieval on the similarity matrix, the pseudo entity pair and the projection hypergraph; performing multi-scale interaction enhancement fusion on the multi-scale hypergraph; and finally generating a fusion result through multi-round iterative fusion and a multi-scale hypergraph retrieval process, namely predicting the congestion area in the peak period. According to the method, in-scale interaction and cross-scale conflict detection are combined, so that the dynamic relationship can be modeled explicitly and the cross-time-scale inconsistency can be solved.

The invention provides a clothing pattern intelligent generation method and system based on multi-modal visual identification, and relates to the technical field of visual identification. The method comprises the following steps: firstly, acquiring clothing image data and text description data, performing unified preprocessing, obtaining a style category label, an appearance attribute label and a local contour key point set through multi-modal visual identification, generating structure semantic elements and geometric anchor points by utilizing a semantic-to-structure mapping knowledge base, and constructing a plate type structure diagram; further analyzing the structural constraint and executing constraint solution to generate a candidate pattern; and in combination with manufacturability verification and constraint correction, closed-loop optimization is formed, a target plate type file meeting the structure and manufacturing requirements is output, and effective connection from semantic recognition to plate type structure generation is realized.