66results about How to "Improve build efficiency" patented technology

This invention discloses an online search method for particle spelling variants and properties usable by intelligent agents. The steps are as follows: inputting the baseline spelling of a particle and the constructed particle-specific prompt words into a large language model to generate a core variant corresponding to the particle; integrating the core variant of each particle with the authoritative benchmark information corresponding to the particle to form a full variant set for each particle; each record in the full variant set contains a unique identifier for a particle, a multi-scene benchmark name, a core variant, and extended attributes; dynamically mapping a remotely callable interface set on the server to a local proxy object on the client; after receiving a query request, the local proxy object parses the particle spelling parameters in the query request and performs multi-dimensional spelling matching in the full variant set to realize online search for particle spelling variants and properties. This invention effectively improves the ability of AI intelligent agents to handle domain-specific tasks.

The invention discloses a corpus standardized template construction and file automatic generation method and system, and the system comprises a template construction module, a file generation module, a verification module, a user interface module, a storage and management module, an extension and optimization module, and a quality evaluation module. The standardized management of the corpus is realized, the problem that the structure and format of the corpus lacks unified standards in the traditional method is solved, and the compatibility and sharing of the corpus are improved. Meanwhile, data cleaning, format conversion and multi-modal data integration technologies are combined, corpus files meeting template requirements are automatically generated, the data processing flow is simplified, and the risk of data inconsistency or loss is reduced.

This invention discloses a forward construction method for designing pipe support and hanger models. The method involves constructing a component family, which is a parameterized family of components that make up pipe support and hanger models with different structural forms. A pipe model is constructed, and the centerline of the pipe model is identified using Dynamo software. Based on the centerline, the placement point group of the centerline is analyzed using Dynamo software, and the parameterized component families in the component family are retrieved. Based on the placement point group, various component families are placed sequentially on the pipe to obtain the pipe support and hanger model. The beneficial effect of this invention is that by establishing parameterized families of components that make up pipe support and hanger models with different structural forms, and combining these families with the Dynamo software platform, the method directly calls the component families required by the model to be built for combination, thereby improving the speed and efficiency of pipe support and hanger model construction.

This invention provides a method for optimizing the parameters of a lithography model and related products. The parameter optimization method includes: acquiring a lithography model containing multiple process parameters to be optimized; acquiring measured lithography images obtained from actual lithography results using a metrology device; based on the lithography model, using the minimum deviation between the simulated and measured lithography images as the optimization objective, iterating through multiple rounds of solutions using a global optimization algorithm to solve for the values ​​of each of the multiple process parameters to be optimized, until the generation trend of the deviation obtained from multiple iterations meets the set trend conditions, at which point the iteration stops, resulting in the target parameter set. This method achieves early stopping of iteration based on obtaining good optimization results, avoiding unnecessary waste of computational resources and time, and improving the efficiency of lithography model establishment.

The invention relates to the technical field of knowledge maps, and discloses a knowledge map entity fusion method and system based on RAG, equipment and a medium, according to the method, multi-dimensional vectorization is carried out on an unstructured text, entities are represented more comprehensively and accurately, the accuracy of similarity retrieval is improved, and by combining an RAG architecture and LLM, the similarity retrieval efficiency is improved. According to the method, a mode of firstly performing vector retrieval to provide context and LLM and then performing reasoning decision based on the context is constructed, an intelligent decision closed loop is formed, the dependence on a manual definition rule is thoroughly eliminated, entity fusion is automatically completed by utilizing the reasoning capability of the LLM, the same entity is prevented from being repeatedly created, the maintenance cost of the knowledge graph is reduced, and the reliability of the knowledge graph is improved. The construction efficiency is improved, and high quality and consistency of the knowledge graph data are ensured. Besides, the text described by the natural language can be directly used as input, and the information of the text can be accurately integrated into the existing knowledge graph, so that the maximum utilization of the information value is realized.

This invention belongs to the field of rail vehicle technology. It proposes a method and system for adjusting the alignment of workpieces during the intermediate stage of bogie welding. Based on the design 3D model of the bogie workpiece and the process scaling requirements, key dimensions are selected and adjusted to construct a virtual intermediate model. Feature points are collected from the key feature surfaces of the virtual intermediate model to obtain reference values ​​for each feature point. A 3D scan of the actual bogie workpiece during the intermediate welding stage is performed to acquire point cloud data. Feature points are collected and processed from the corresponding key feature surfaces of the point cloud data to obtain the actual values ​​of each feature point. By comparing the reference values ​​and actual values ​​of each feature point, the deviation of each feature point is obtained. For the key dimensions of the bogie workpiece, a scribing weight mechanism is introduced to assign weight priorities to each key dimension. Based on the deviation of each feature point and the weight priorities, the position of the baseline of the key feature surface is adjusted. This invention achieves a closed-loop process from model adaptation and data comparison to baseline adjustment.

This invention provides a method and system for constructing a multi-agent cooperative interception decision model in the field of agent control. The method includes determining the actions that multiple agents independently execute at the current decision moment; updating the relative motion state based on the actions to obtain the relative motion state at the next decision moment and determining the individual immediate reward; determining the total reward of the agent after executing the action based on the individual immediate reward of the agent and the individual immediate rewards of neighboring agents; generating an experience tuple for the agent at the current decision moment by combining the state, action, total reward, and state at the next moment; calculating a loss function based on the experience tuple and optimizing the parameters of the agent's deep Q-network to obtain an optimized deep Q-network; and determining the multi-agent cooperative interception decision model based on the optimized deep Q-network of each agent. This method improves the efficiency of constructing the cooperative interception decision model.

The application discloses a kind of collision detection method and system between soft tissue and instrument in virtual surgery, including constructing soft tissue grid model with triangular facet and tetrahedral particle and constructing the proxy model of instrument;Respectively construct the double level bounding box structure of soft tissue grid model and proxy model, and using ray tracing algorithm, obtain the binary hierarchical tree of soft tissue grid model;The shortest distance between the soft tissue grid model and the proxy model is obtained, and the collision detection of the soft tissue grid model and the proxy model is carried out based on the shortest distance and the set dead zone relationship.In the collision detection between soft tissue grid model and proxy model, the binary hierarchical tree of soft tissue grid model is obtained based on ray tracing algorithm, so the time for constructing the binary hierarchical tree of soft tissue grid model can be reduced, the system construction efficiency is improved, and different collision detection methods are used according to specific situation, the detection efficiency and accuracy are improved.

The application discloses a power distribution network topology fast adaptive inference method and device based on meta learning, and belongs to the technical field of smart grids. The method comprises the following steps: acquiring a first data set; the first data set comprises a plurality of groups of first topology data and first measurement data of the power distribution network; the first topology data represents the connection relationship between nodes in the power distribution network, and the first measurement data represents the operating state of each node; the first topology data is taken as the label of the first measurement data, and a meta initial model is trained; second measurement data and topology label data of a target power distribution network are acquired; the topology label data represents the connection relationship between part of nodes in the target power distribution network; the topology label data is taken as the label of the second measurement data, the meta initial model is trained, and a target topology inference model is obtained; real-time measurement data of the target power distribution network is acquired, the real-time measurement data is input into the target topology inference model, and target topology data is obtained. The application can improve the efficiency of power distribution network topology inference.

The application provides an operator knowledge transfer database establishment method and device and electronic equipment, applied to the field of artificial intelligence technology, using the powerful code understanding ability of the large language model, combining the general operator library source code and the specialized operator library source code of the completed chip, analyzing the core logic and code difference of each pair of operators to be analyzed, which can represent the differences existing in the source code of the same operator pair from the core logic and code difference, thereby further using the large language model to obtain knowledge points representing operator knowledge transfer, and structurally processing the knowledge points to obtain an operator knowledge transfer database convenient for querying. The operator knowledge transfer database can be automatically generated, and compared with the manual analysis in the prior art, the automation degree and construction efficiency of the operator knowledge transfer database construction are significantly improved.

The application provides a battery system construction method, device, electronic equipment and readable storage medium. Performance requirements of a target battery pack are obtained, risk reasoning is performed based on a battery safety knowledge graph, a safety margin coefficient is obtained, missing structure parameters are completed, and completed structure parameters are obtained. Based on the safety margin coefficient, the swelling amount of the battery cell under a preset working condition is predicted, and the safety gap of the battery cell arrangement is determined. Based on the target voltage value, the target energy value and the hardware discrete constraint of the battery management system, a topology search is performed, and a series-parallel topology combination is obtained. The arrangement mode of the power distribution unit is used as a decision variable for multi-objective optimization, and a collaborative arrangement scheme of the electrical and mechanical spaces is obtained. Based on the completed structure parameters, the safety gap, the series-parallel topology combination and the collaborative arrangement scheme, the battery system is constructed. In this way, the multi-physical field constraint and the multi-objective collaborative optimization are fused under the performance requirements for automatic construction, and the construction efficiency of the battery system is improved.

The present disclosure relates to a map construction method, a storage medium, an electronic device and a vehicle, and aims to solve the technical problem of low map construction efficiency in the prior art. The method comprises the following steps: determining target position information of a region to be constructed; sending the target position information to a target vehicle, so that the target vehicle executes a map collection operation to obtain initial map information of the region to be constructed under the condition that the target vehicle meets a preset triggering condition according to the target position information, and sends the initial map information to the server; and constructing a high-definition map of the region to be constructed according to the initial map information.

The specification discloses a method and device for generating a training set. According to actual requirements, a reference training sample is selected first, and then a reference feature representation corresponding to the reference training sample is determined, so that a training sample matched with the reference training sample is screened from each candidate sample according to the reference feature representation, and a training set for training a ranking model is generated. This greatly improves the construction efficiency of the training set, thereby reducing the cost of training the ranking model, improving the efficiency of screening the training sample, and improving the accuracy of the screened training sample.

The invention provides an infrared temperature measurement curve model construction method, a medium and equipment, and relates to the technical field of infrared temperature measurement, and the method comprises the steps: obtaining the focal plane temperature of an infrared thermal imaging equipment detector and AD values of a plurality of radiation calibration sources collected by the equipment; taking the focal plane temperature of the detector and the AD values of the radiation calibration sources at the lowest temperature point, the middle temperature point and the highest temperature point as input, taking the AD values of the radiation calibration sources at the other temperature points in the plurality of radiation calibration sources as output, and constructing a multiple linear regression model; acquiring the focal plane temperature of the infrared thermal imaging equipment detector to be constructed and the acquired AD values of the radiation calibration source at the lowest temperature point, the intermediate temperature point and the highest temperature point, inputting the multiple linear regression model to obtain the predicted AD values of the radiation calibration source at the other temperature points, and performing curve fitting based on the acquired AD values and the predicted AD values to obtain the radiation calibration source of the infrared thermal imaging equipment. And obtaining an infrared temperature measurement curve model. According to the method, the construction efficiency of the infrared temperature measurement curve model can be improved.

The application relates to the technical field of electronic design automation, in particular to an integrated high-performance software construction system for EDA simulation model compilation acceleration, which is characterized in that a scheduling control module, an integrated compilation thread pool and a shared memory area are placed in the same process space to save the creation and destruction time of a large number of processes in a single-process multi-thread architecture, thereby improving the construction efficiency, and the memory object pool is used to store pre-compiled module data to realize the cross-compilation task reuse of the intermediate representation of the header file. When multiple compilation tasks contain the same header file, only the first task performs parsing, and subsequent tasks directly obtain the pre-compiled result through a memory pointer, thereby avoiding repeated reading and parsing, greatly reducing the CPU computing amount and I / O overhead, and significantly improving the construction efficiency of the simulation model compilation.

The application provides a kind of thermal protection interlock logic knowledge base construction method and system, comprising: based on graph database construction knowledge graph;Combining the initial knowledge item generated by preset hazard dimension, and retrieve the citation basis related to initial knowledge item from multi-source thermal data, the citation basis is associated with initial knowledge item, form the knowledge item after association;The associated knowledge item is verified in multiple rounds, and the verification includes calling the existing knowledge in the knowledge graph for logical self-checking, cross verification with historical knowledge items and backstepping verification based on accident cases, and output the knowledge item that passes the verification;Continuously monitor the thermal data source, generate knowledge update proposal based on the knowledge item that passes the verification, and update the knowledge graph and knowledge item after compatibility verification and artificial review of the update proposal.The application reduces the risk of knowledge deviation caused by human error through automated process and multiple verification mechanisms.

This invention discloses a data and knowledge fusion-driven method for generating wafer image defect detection models. First, a process-defect-module adaptation knowledge graph is constructed, and defect feature condition vectors and module priority vectors are generated based on directed knowledge reasoning along the defect-process-module adaptation chain. Second, a set of functional module agents is constructed, and a multi-agent reinforcement learning model is established to generate the optimal detection model structure under module priority and order constraints. Subsequently, the optimal detection model structure is encoded, a conditional generative adversarial network is constructed, and convolutional neural network parameters are generated based on the structure encoding vector and defect feature condition vectors. The optimal model parameters are then obtained through adversarial optimization. Finally, the optimal model parameters are loaded into the optimal detection model structure to generate the wafer image defect detection model. This invention achieves the collaborative generation of the detection model structure and parameters, improving the model's adaptability under different process conditions and multi-form mixed defect scenarios.

The application relates to the technical field of computers, in particular to a page construction method and device, electronic equipment and a storage medium, to improve the page construction efficiency. The method comprises the following steps: obtaining an original text set in a to-be-constructed page; in response to a language setting operation on the original text set, a language setting interface is presented, and each candidate language control is displayed in the language setting interface; in response to a selection operation on a target language control in each candidate language control, a target text set obtained by translating the original text set is acquired; and an application page corresponding to the target language is constructed based on the target text set and each target function component. In the application, after a target object selects a target language control, the original text set is translated into a target text set, and then an application page corresponding to a language other than the first language is obtained, so that the efficiency of constructing application pages in different languages can be effectively improved.

The invention discloses an industrial internet data model construction method, which comprises the following steps of: data intercommunication and acquisition: realizing comprehensive acquisition and intercommunication of network system data, platform system data and application layer data in an industrial internet system by utilizing an industrial internet system platform; data preprocessing: preprocessing the collected industrial internet data, and eliminating noise and redundant information in the data; data optimization: performing optimization processing on the preprocessed data by adopting an algorithm to improve the construction efficiency and accuracy of a data model; constructing a data model based on the optimized industrial internet data, and considering the characteristics and application scenarios of the data in the construction process; the industrial internet data model building method has the advantages that the problems existing in an existing data model building method are effectively solved, the building efficiency and accuracy of the industrial internet data model are improved, and powerful support is provided for intelligent transformation and industrial upgrading of the industrial internet.

The application provides an electronic medical record construction method and device, electronic equipment, storage medium and product, and relates to the technical field of intelligent medical treatment. The method comprises the following steps: acquiring a disease description text; inputting the disease description text into a multi-task large language model, and identifying a task type corresponding to the disease description text, wherein the task type comprises an information extraction task and an information quality control task; calling an expert module corresponding to the task type in the multi-task large language model to process the disease description text, and generating medical record data; and constructing an electronic medical record based on the medical record data. The application can effectively improve the construction efficiency and data quality of the electronic medical record.

The application provides a household environment ontology knowledge base automatic construction method and system, and relates to the technical field of artificial intelligence and knowledge engineering. The method comprises the following steps: constructing a household environment ontology knowledge base template, wherein the template contains classes, attributes and instances of the ontology; establishing communication with a target object, obtaining communication text of the target object, and extracting semantic information of the target object; obtaining an image of the target object, and extracting semantic information of the target object based on the image; comparing the extracted semantic information of the object with the existing classes and attributes in the household environment ontology knowledge base as instances of the ontology; judging whether new classes and attributes need to be established based on the similarity comparison result, updating the semantic information, and perfecting the household environment ontology knowledge base. The application can simplify the construction process of the household environment ontology knowledge base, improve the construction efficiency, reduce the labor cost, reduce the non-standardization of manual construction, and improve the quality of the constructed ontology knowledge base.

The application provides a construction method and device of a patch combination model and electronic equipment. A target model and a preset number of initial patch models are acquired. A patch model corresponding to the target model is generated based on the target model and the initial patch models. The position parameters and / or size parameters of the patch model match the target model. Rendering parameters of the patch model are determined based on rendering parameters of the target model. A patch combination model corresponding to the target model is constructed based on the patch model and the rendering parameters of the patch model. This method simplifies the construction process of the patch combination model and improves the construction efficiency.

This invention relates to an automatic construction method and system for indoor 3D scene data oriented towards interactive tasks, belonging to the field of computer vision and virtual simulation. Addressing the problems of limited scale, insufficient layout realism, missing interactive attributes, and reliance on manual construction in existing indoor 3D scene datasets, this invention proposes an automatic generation method based on multi-source indoor structural information. This method obtains room topology and functional zoning by parsing floor plans, 3D structural data, or scanned models; it generates a furniture candidate set by combining room type and statistical layout priors, and uses multi-constraint optimization to achieve reasonable placement, ensuring functionality, accessibility, and geometric consistency. Simultaneously, it evaluates and optimizes through collision detection, reachability analysis, and path connectivity verification. This method can efficiently construct large-scale, semantically complete, layout-realistic indoor 3D scene data that supports complex interactive tasks, and is suitable for scenarios such as embodied agent training.

The application belongs to the technical field of data processing, and particularly relates to a cross-platform knowledge graph construction method and a construction system. The cross-platform knowledge graph construction method comprises the following steps: S1, after source data is obtained from a data source platform, the source data is subjected to data cleaning to obtain standard data; S2, the standard data is segmented into data segments; S3, standard entity pairs and standard entity relationship pairs in the data segments are obtained, and meanwhile, data verification and error tracing processing are performed according to the proportion of the standard entity relationship pairs relative to the standard entity pairs; and S4, the standard entity relationship pairs are sent into a graph database, a knowledge graph is constructed and stored. The application can efficiently and accurately and reliably process cross-platform data to construct a corresponding knowledge graph.

The invention relates to the technical field of dynamic knowledge base supporting modular scheme construction, in particular to a dynamic knowledge base driven modular scheme component construction system, which comprises a knowledge labeling module, a dynamic knowledge base supporting module, a dynamic knowledge base module and a dynamic knowledge base module, and generating a standardized label according to two dimensions of the function attribute type and the scene adaptation requirement, and outputting the knowledge data with the standardized label. According to the invention, the knowledge labeling module preprocesses dispersed knowledge in a dynamic knowledge base and generates standardized labels, and then the knowledge splitting module establishes knowledge-component mapping rules according to the labels; and extracting the core function unit and splitting the core function unit into standardized basic components with initial scene adaptation identifiers and basic interfaces by referring to a standard structure to form a component library, so that the problem that dispersed knowledge cannot be automatically converted into standardized modular components in the prior art is solved.

The invention discloses an AI-assisted vehicle evaluation scene library construction method and system, and relates to the technical field of vehicle testing. The method comprises the steps of obtaining user demand data; performing semantic analysis on the user demand data by using a pre-trained language processing model, and extracting a user demand tag; based on the user demand labels, constructing a label knowledge base; generating a vehicle evaluation scene description corresponding to the user demand based on the tag knowledge base; and screening the vehicle evaluation scene description, and constructing a vehicle evaluation scene library based on a screening result. According to the invention, intelligent, efficient and comprehensive construction of the vehicle evaluation scene library can be realized.

This disclosure proposes an automated data warehouse construction method and system based on dimensional modeling. The method includes: constructing a detailed layer logical model, establishing relationships between the detailed layer logical models, and generating detailed layer physical tables; receiving predefined atomic indicators, business constraints, and derived indicators through a visual interface; receiving first configuration metadata through a visual interface and generating a first summary layer logical model based on the first configuration metadata; the first configuration metadata includes first model relationship information and first business indicators; responding to physicalization instructions, automatically generating a first structured query language (SQL) script for creating physical tables and processing data according to the first summary layer logical model; and automatically executing the first SQL script to generate the first summary layer physical tables. This method provides a data warehouse solution that can reduce data modeling complexity, improve development efficiency, and support standardized and automated construction.

The present application belongs to the field of artificial intelligence geoscience application and dense reservoir evaluation technology, and particularly to a dense reservoir structure porosity inversion method based on rock physical prior embedding. The method comprises the following steps: S1: obtaining conventional logging data and calibration data; S2: logging data preprocessing; S3: constructing rock physical prior features; S4: constructing a rock physical prior embedding input matrix; S5: constructing an integrated coupling model; S6: extracting bidirectional sequence features in the well depth direction; S7: performing nonlinear inversion; S8: constructing a rock physical prior constraint loss function; S9: performing joint global optimization; and S10: outputting intelligent inversion results of structure porosity. The continuous curves of free porosity, micro-microporosity and clay porosity output by the present application can directly serve reservoir effectiveness evaluation, movable fluid identification, reserve calculation and development interval optimization, and provide reliable parameter support for oil and gas exploration and development.

The application provides a navigation data construction method and device, equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a first navigation data construction request sent by a main process of an application program, a virtual scene is run in the main process, and the first navigation data construction request indicates that the main process is sent in the case that at least one virtual object in the virtual scene exists update; in response to the first navigation data construction request, updating the position information of the virtual object in the virtual scene through a secondary process of the application program; based on the updated position information, constructing navigation data of the area where the virtual object is located through the secondary process, and the navigation data is used to update the navigation data of the area where the virtual object is located through the main process. Through the application, the efficiency of navigation data construction can be improved.

The invention provides a three-dimensional scene reconstruction method and device, electronic equipment and a storage medium, and relates to the technical field of three-dimensional modeling. The method comprises the following steps: acquiring an inclined image data set; according to the inclined image data set, an initial three-dimensional scene model is constructed, and the initial three-dimensional scene model comprises a vehicle; converting the initial three-dimensional scene model into original three-dimensional point cloud data, and filtering the original three-dimensional point cloud data to obtain a ground point cloud set and non-ground point cloud data; vehicle point cloud data are recognized from the non-ground point cloud data, the vehicle point cloud data are removed from the original three-dimensional point cloud data, the point cloud data after vehicle removal are obtained, three-dimensional reconstruction is carried out based on the point cloud data after vehicle removal, a three-dimensional scene model is generated, the three-dimensional scene model does not contain vehicles, and the three-dimensional scene model does not contain the vehicles. The spatial consistency and visual authenticity of the final three-dimensional scene model are ensured, and the problems of three-dimensional reconstruction distortion, texture disorder, structure distortion and the like caused by the existence of road vehicles in the prior art are solved.