520 results about "Structure generation" patented technology

The invention discloses a report verification batch parallel execution optimization method based on a pluggable rule engine framework, and relates to the technical field of report verification, and the method comprises the following steps: receiving compliance rules, converting the compliance rules into a uniform data structure model, analyzing to generate metadata indexes, and supporting business rule set configuration; loading the matched business rule set based on the meta-information of the to-be-verified report, and registering the matched business rule set to an execution container; s3, receiving a to-be-verified report, analyzing a report structure, and generating a report field index table; performing field dependency analysis based on the report field index and the loaded business rule set, and constructing a rule field dependency graph; graph partition optimization and topological sorting are executed according to the dependency graph, and rule batches capable of being executed in parallel are generated; and uniformly collecting and outputting all rule execution results, hit states, abnormal records and performance indexes, and generating a structured verification report and an audit chain log. The financial statement compliance check execution efficiency can be improved.

The invention relates to the technical field of spherical display screens, and discloses a visual enhancement method and system for an LED creative spherical display screen, and the method comprises the steps: inputting an original display image of the LED spherical display screen into a multi-scale Transform layered CNN network for feature extraction, and obtaining a multi-dimensional feature image set; feature mapping is carried out, pixel points are projected into a spherical coordinate system, display deviation is calculated, and a comprehensive evaluation matrix is generated; calculating a space compensation coefficient through an N-order polynomial function to obtain a space-time compensation matrix; performing gradient calculation on the space-time compensation matrix to obtain a target vector field; inputting the target vector field into a U-Net structure generator for adversarial training to obtain compensation optimization parameters; according to the method, the real-time dynamic optimization of the display image of the spherical display screen is realized, and the display quality under different environment illumination conditions is ensured.

The invention discloses a data management method based on a distributed storage system, relates to the technical field of data management, and is used for solving the problem of storage management behavior strategy mismatch. On the basis of dynamic perception of data access behaviors in a distributed storage system, a behavior feature vector fusing an access mutation rate, periodicity and an access span is constructed, behavior pattern recognition and strategy structure generation are completed, optimal deployment and hierarchical storage of copies are achieved through node resource state perception and construction of a cost function, and the strategy structure generation efficiency is improved. And after compression and consistency configuration are executed and the strategy falls to the ground, the compressibility and the offset trend of the behavior path are analyzed, scheduling management information is extracted, and a stable execution or strategy adjustment signal is generated, so that the problem of strategy execution mismatching caused by behavior perception deficiency in the distributed storage system is reduced, and the strategy execution efficiency is improved. The strategy closed-loop control and the resource scheduling optimization under behavior driving are realized, so that the data management efficiency and the strategy adaptation capability of the distributed storage system are improved.

The invention discloses a multi-modal-based software architecture intelligent design and optimization system, which relates to the field of intelligent design and optimization, and comprises the steps of performing modal perception and preprocessing on architecture design demand information input by a user, converting the architecture design demand information into structured semantic data, and optimizing and enhancing semantic expression by introducing a reinforcement learning strategy, forming a structured user intention vector set; through a semantic mapping and reasoning processing unit, user intention vectors in the user intention vector set are constructed into a semantic-component alignment graph, and graph structure modeling and confrontation generation algorithm combination are adopted to generate a candidate structure graph set; a structure diagram generation step in the structure generation module effectively improves the rationality, diversity and adaptation capability of an automatically generated structure, and provides core support for realizing automatic construction of a target-demand-oriented architecture structure.

The efficient generative task reasoning acceleration method based on the hybrid expert network comprises the steps that context information of related generative tasks is obtained, context data is preprocessed, and it is ensured that the data is suitable for model input; constructing an efficient reasoning model based on the hybrid expert network, and determining a reasoning process of the efficient reasoning model based on the hybrid expert network; training an efficient reasoning model of the hybrid expert network, optimizing model parameters, and storing an optimal model structure; and generating a reasoning result by using the optimal model structure. In a model improved by the method, an expert network is composed of a plurality of independent experts, and each expert is responsible for processing different input characteristics. The gating network dynamically selects which experts participate in the calculation according to input characteristics, and assigns an expert to each input by calculating a probability distribution. In this way, the gating network and the expert network are closely matched, it is ensured that only the most suitable expert is used, and therefore the calculation efficiency and the model performance are improved.

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 discloses a two-stage scheduling model and scheduling method based on a heterogeneous multi-core processor, and relates to the technical field of computer science, the model comprises an associated subtask acquisition module used for analyzing a target task and generating a plurality of associated subtasks; the container distribution analysis module executes first-level scheduling and distributes sub-tasks to K task containers; the static priority configuration module performs second-level scheduling on the sub-tasks according to the dependency relationship to generate a processing priority sequence; the container scheduling structure generation module creates a container scheduling structure; the window distribution execution module forms a cyclic scheduling sub-sequence; and the task cross-container dependency processing module performs cross-container dependency management in the task execution process to ensure the scheduling coherence and the execution of the dependency relationship. The technical problems that in an existing heterogeneous multi-core processor, the complex task scheduling efficiency is low, and task execution is unstable are solved, and the technical effect of improving the task scheduling efficiency and the task execution stability is achieved.

The invention discloses a porous structure generation method and a streamer simulation method thereof, and relates to the technical field of porous structure generation and simulation, and the porous structure generation method comprises the following steps: defining and determining phase parameters of a porous structure; selecting nodes with a preset proportion from all nodes in the set area, setting the nodes as pore seed nodes, and simulating the growth process of pores by using a result of numerical iteration solution processing based on initial values of phase parameters of the set nodes; and constructing a porous structure model. The porous structure streamer simulation method comprises the following steps: removing pores which are not communicated with the surface of a porous structure to obtain a porous solid medium model; setting parameters of gas, and performing transient solution on the discrete electron diffusion model of streamer development; and according to the transient development process of the streamer, analyzing and obtaining the influence of the pore structure on the development characteristics of the streamer. By applying the method provided by the invention, the mesh generation density in porous structure model simulation is reduced, and the efficiency of streamer simulation is improved.

Disclosed herein is a method and system to generate 3D models of small molecules. The methods and systems are disclosed for an equivariant shape-based virtual screening operation that generates small molecule 3D structures that fit into and bind to a provided protein pocket. The model is configured to learn from existing 3D structures of small molecules in a data-driven manner using forward diffusion and backward diffusion processes to learn and represent the major characteristics of molecule 3D structures. Additional disclosure relates to the model that is further configured to learn from the 3D model of the protein pocket.

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 provides an intelligent data analysis and knowledge question and answer method and system based on a multi-modal large model. The method comprises the following steps: receiving a multi-modal query request, performing semantic analysis and intention recognition technologies, determining a question core and a required knowledge field, and generating a personalized question understanding framework; performing manifold embedding processing by applying a manifold learning enhancement algorithm, revealing an internal geometric structure, constructing a shared representation space by adopting a multi-task learning technology, training a plurality of sub-tasks at the same time, and generating cross-modal unified representation; performing recurrent processing by using a recurrent neural network algorithm, recursively updating an internal state, constructing an organized information structure by using a hierarchical clustering method, and generating an ordered information structure chart; a context awareness feedback mechanism is introduced, instant response and environment change are monitored in real time, an answer strategy is dynamically adjusted, instant feedback is recorded, and an intelligent data analysis and knowledge question and answer scheme is generated. According to the technical scheme provided by the invention, the accuracy of intelligent data analysis and knowledge questions and answers and the user experience are improved.

The invention discloses a large-scale heterogeneous computing power cluster training and pushing acceleration method, which comprises the following steps: S1, collecting operation characteristics of each node of a heterogeneous cluster, and constructing an equipment state parameter set; s2, loading a training and reasoning model, constructing an intermediate representation graph structure, and generating graph structure information; s3, inputting the graph structure information and the equipment state parameter set into a scheduling strategy model constructed based on a neural network ordinary differential equation, and generating an optimization decision result; s4, reconstructing a model graph according to an optimization decision result, and dividing the model graph into a training sub-graph and a reasoning sub-graph; s5, mapping the sub-graphs to heterogeneous nodes, performing resource matching and task distribution according to the equipment state parameter set, and generating a scheduling result; s6, collecting performance feedback information; and S7, inputting feedback information into the scheduling model to update parameters, and adjusting evolution function strategy parameters. According to the method, the scheduling acceleration of the training and pushing task of the deep model in the heterogeneous cluster is realized.

The invention discloses an HTML (Hypertext Markup Language) information extraction method and device based on a multi-LoRA cascade strategy, equipment and a medium, and relates to the technical field of HTML information extraction. The information extraction method comprises the following steps: acquiring a document and inputting a large language model; and the large language model judges whether a table is included. And if the table is included, calling a table processing LoRA adapter to extract table content and convert the table content into pseudo natural language description, calling a text processing logic module to extract adjacent text contexts of the table, and then performing semantic integration to obtain first text information. And if the table is not included, calling a text processing module of the table processing LoRA adapter to extract the text content, and obtaining the first text information. And calling a key information extraction LoRA adapter, and extracting the structured key value pair from the first text information to generate JSON data. And calling a nested structured generation module to convert the JSON data into a target sequence in a multi-layer nested JSON format.

The invention provides a motion route generation method and device, computing equipment and a computer readable storage medium. The motion route generation method comprises the steps that a target graph drawn by a user at the front end is acquired; segmenting at least one graphic instance from the target graph based on the graphic features of the target graph by using a graphic instance segmentation model; key points are extracted from the at least one graph instance, and a graph path of the target graph is generated based on a graph structure with the key points as vertexes and connection between the key points as edges; and performing matching based on the graph path of the target graph and the road set of the target map, and generating a motion route of the target graph on the target map. The exercise route is generated in an interaction mode of front-end drawing, the exercise route is generated visually and has high interestingness, the spatial structure of a graph is accurately restored at the instance level and the route level, it is ensured that the generated exercise route conforms to the drawing intention of a user, and the accuracy of exercise route generation is improved.

According to the self-adaptive supporting structure generation method for pull-up type photocuring 3D printing, external force serves as a constraint condition to be included in the supporting generation process, and therefore the number and the positions of supports are optimized; layers with similar features are gathered together to significantly reduce the time cost of generating the support and reduce the number of simulations. According to the method, multiple factors such as model geometrical characteristics, material attributes, the printing direction and external force distribution are comprehensively considered to generate the optimal supporting structure, the use of the supporting structure can be minimized while successful printing is ensured, and therefore the efficiency and quality of 3D printing are improved.

The invention provides a multi-stage structure topological optimization method based on a potential diffusion model, and relates to the technical field of engineering design and structure design optimization. Creating a structure topology optimization image data set by using a traditional SIMP method and performing preprocessing; mapping the structure topological optimization image to a low-dimensional potential space for representation so as to reduce the data dimension and improve the calculation efficiency; a forward diffusion process and a reverse diffusion process are carried out on the initial potential variable in the low-dimensional potential pair, a denoising network is added in the reverse diffusion process, and a cross attention mechanism and a condition mechanism are introduced, so that a topological structure is adjusted and designed according to physical and functional requirements, and the structural fidelity is ensured; after training is completed, potential variables of a low-dimensional space are mapped back to a high-dimensional space, a topological structure is generated, few-step SIMP direct optimization is carried out on the topological structure, the structure is rapidly refined, physical information is directly injected into the design so as to adapt to given boundary conditions, and generation of the optimal topological structure can be completed through few-step iteration.

An information processing apparatus includes a graph structure generation unit and a path planning unit. The graph structure generation unit generates a graph where a door on a passage has been replaced by a shunting line of a virtual robot. The path planning unit plans paths of a mobile body and the virtual mobile body such that the mobile body does not conflict with the virtual mobile body at an intersection between the passage and the shunting line when passing through the passage.

The invention relates to the fields of industrial design, constructional engineering, energy and the like, discloses a topological graph structure information processing system based on image recognition, and overcomes the problems of insufficient topological relation restoration, low recognition accuracy, lack of optimization and correction mechanisms, difficulty in editing operation, poor data reusability and the like in the prior art. An efficient, accurate and easy-to-edit solution is provided, powerful support is provided for digital processing, informatization management and data accumulation of drawings, and the system comprises an image recognition result acquisition module, a preliminary topological information analysis module, a line segment detection and duplicate removal module, a graph structure information construction module, an element fitting module and a bending fitting module. The point merging module is used for merging two identified non-coaxial points into one point; and the final graph structure generation module is used for integrating the connection relation between the points formed by the element fitting module and the bending fitting module into a complete graph structure.

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 provides a transaction customer social network construction and expansion method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining an original data set of a target customer; pre-processing the unstructured data in the original data set; according to the feature vector sets of the structured data and the unstructured data, respectively calculating association indexes between the target customer and other customers; according to the association indexes, constructing a multi-dimensional association matrix of the target customer and other customers; element values in the multi-dimensional incidence matrix are converted into edge weights, according to a preset edge weight threshold value, the relation that the edge weights are lower than the threshold value is eliminated, a graph structure containing nodes and edges is constructed, and a customer social network graph is generated; calculating a centrality index of each client node according to the client social network graph; and sorting according to the centrality indexes, and selecting client nodes with the top N ranks as high-value potential clients. According to the invention, the autonomy and accuracy of social network management are improved.

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 provides a model code generation method and system based on a computational graph, and relates to the technical field of model code generation. The model code generation method comprises the following steps: in response to a user creation instruction, creating a target calculation graph based on a visual interface; analyzing calculation graph information of the target calculation graph; mapping a graph structure of the target calculation graph to a target structure of a target model based on the calculation graph information; and generating a corresponding model code based on the target structure of the target model. According to the method and the device, interaction with the user can be realized based on the visual interface, so that visual editing of the computational graph can be realized. The model code of the target model can be accurately generated according to the computational graph created by the user. According to the method, a close relation can be established through visual editing and code generation, so that the efficiency and accuracy of model development can be improved, the development cost can be reduced, and the flexibility of code generation and the adaptability to a complex model structure can be enhanced.

The invention discloses a big data comprehensive supervision platform based on intelligent decision, and the platform comprises a heterogeneous graph construction module which is used for constructing a supervision graph structure; the node feature generation module is used for generating node embedding representation; the structure aggregation module is used for generating a pooling graph on the basis of a DiffPool structure controlled by Transform; the graph enhancement module is used for generating a graph enhancement sample pair; the comparison training module is used for optimizing embedded representation; the anomaly detection module is used for identifying an abnormal node set; the risk map generation module is used for constructing a risk map structure; and the response scheduling module is used for generating and executing a supervision response instruction. According to the invention, the intelligent level and the structure expression ability of the supervision task are improved.

The invention discloses an environmental chemistry research automation system and method based on a large language model, and relates to the technical field of environmental chemistry automatic research. The environmental chemistry research automation system based on the large language model comprises a key information extraction module, a structure generation and optimization module, an experiment scheme generation module, an experiment management module and a visual editing module. According to the method, full-automatic workflow from experimental design to data analysis is achieved, the research efficiency and accuracy are remarkably improved, through the method, complex scientific problems can be converted into understandable input of a language model, complex analysis and reasoning processes are completed through the model, and a physical and chemical calculation tool is combined, so that the research efficiency is improved. According to the method and the system, comprehensive simulation and calculation of a research object are realized, the degree of human participation is effectively reduced, subjective deviation caused by human decision is reduced, and scientific research becomes more systematized and accurate.