820 results about "Assembly" patented technology

The invention discloses a low-code development automatic generation method based on a large language model, which comprises the following steps: collecting natural language description information input by a user, and preprocessing; inputting the standardized demand corpus set into a large language model, and executing semantic understanding and context modeling; matching the low-code component library based on the structured semantic representation to generate component assembly description information; generating and verifying an engineering skeleton according to the assembly description information, and outputting an executable low-code application initial version; running the executable low-code application initial version, and monitoring and analyzing execution difference to generate an increment adjustment instruction; and inputting the increment adjustment instruction into the large language model, performing reconstruction and adaptive optimization, and outputting an executable low-code application final version. According to the method, large language model semantic understanding and adaptive optimization technologies are fused, automatic generation and continuous optimization of low-code applications are realized, and the method has the advantages of intelligence, high precision and engineering reliability.

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

Provided in the present application are a training / application method for a representation learning model, and a device and a medium. The training method comprises: extracting a plurality of source codes from an open-source repository, and on the basis of the plurality of source codes and a large language model, generating a training sample set, wherein each sample in the training sample set comprises assembly codes and natural language text; on the basis of an assembly code data set, performing pre-training to generate an assembly coder, and on the basis of a natural language data set, performing pre-training to generate a text coder; on the basis of the training sample set and a contrastive learning algorithm, performing alignment training on the assembly coder and the text coder, so as to obtain a code representation learning model and a natural language representation learning model which are semantically aligned; and on the basis of the code representation learning model and the natural language representation learning model, which are semantically aligned, constructing a representation learning model. The method of the present application significantly improves the generalization ability and accuracy of a representation learning model, and greatly reduces dependence of the model on samples and a large volume of tagged data.

The invention relates to the technical field of software development tools, and discloses a micro-application generation system and method based on natural language interaction, and the system comprises four core components: a natural language understanding module, a demand modeling module, a code generation engine and a deployment and integration platform. The natural language understanding module generates structured semantic representation through intention recognition, entity extraction and context analysis; the demand modeling module constructs a data model, a process logic and an interface prototype based on the semantic representation, and optimizes model parameters by utilizing reinforcement learning; the code generation engine generates a cross-platform code of the micro-service architecture through template matching and modularization assembly; and the deployment and integration platform realizes seamless integration of the external system and the Internet of Things equipment through a standardized interface. According to the method, the problems that a traditional low-code platform depends on visual dragging operation, demand analysis is rough and cross-domain adaptation is insufficient can be solved.

The invention relates to the technical field of software assembly, and discloses a contract framework model-based generative software automatic assembly method and system, and the method comprises the steps: obtaining user demand data, and decomposing the user demand data into demand protocols based on a language model; constructing a software database based on historical software data, and generating a contract framework model; retrieving a software database through a language model based on the demand specification, and generating a software similarity candidate list; performing suitability screening on the software similarity candidate list based on an assembly engine, and generating a software similarity recommendation list; assembling the software based on the software similarity recommendation list to generate an assembly PBC; inputting the assembled PBC into a language model to determine a demand matching degree of the assembled PBC; and determining an assembly result based on the demand matching degree. According to the invention, the understanding degree of demands is improved, and the workload of manual coding is reduced.

The invention provides a multi-variety assembly production line reconfigurable switching method and system based on digital twinning. The method comprises the steps that the dynamic mapping relation between the variety of a product to be produced and assembly steps is achieved through a reconfigurable logic tree model; feature parameters of the to-be-produced product are obtained through visual identification or order data analysis; importing the reconfigurable logic tree model into a digital twin system, fusing actual production data to dynamically reconstruct an assembly model, and optimizing an assembly path through simulation rehearsal; automatically generating an executable assembly control program according to the rehearsal result; and issuing the executable assembly control program to a production field to execute actual switching, and feeding back closed-loop adjustment through real-time data. According to the invention, the production line switching efficiency and the flexibility level can be improved, and the human resource consumption is reduced.

The invention discloses an automatic compiling and adapting method for an RISC-V extension instruction, and belongs to the technical field of compilers. According to the method, a dynamic DSL (Digital Subscriber Line) for registering a custom instruction, adding register use constraints and defining a specific code mode is designed. The LLVM plug-in is used for automatically integrating the registered custom instruction in the compiling process. The invention relates to a self-defined instruction rapid adaptation mechanism which is specially provided for a continuously provided RISC-V self-defined instruction expansion scene, and by automatically generating correct assembly codes of registered self-defined instructions, correct registers are automatically distributed and reserved according to register use constraints; the method comprises the following steps of: searching a defined specific code mode and semantics, and automatically generating and inserting a registered custom instruction in a program, so as to realize the rapid adaptation of a compiler to an RISC-V custom extension instruction and the automatic use of a program code to the custom instruction, and the whole process does not need a developer to manually modify the compiler or a program source code.

The invention relates to the technical field of computer vision, computer aided design and artificial intelligence, and discloses a geometric feature recognition method based on a large language model, which comprises the steps of three-dimensional model natural linguistization, large language model feature reasoning, incremental learning and optimization, and feature verification and output. According to the geometric feature recognition method based on the large language model, three-dimensional geometric data are converted into structured text description, and a joint representation space of'geometric parameter-topological relation 'is constructed, so that the large language model can better understand the meaning of geometric features and the structure of parts; the method comprises the following steps: analyzing topological association among geometric features by utilizing long context understanding ability of a large language model, capturing dependency among the features through an attention mechanism, realizing hierarchical recognition of the features in a complex assembly, dynamically updating a geometric feature knowledge base of the large language model by continuously learning new geometric cases and engineering specifications, and realizing hierarchical recognition of the features in the complex assembly. Adaptive recognition of novel features is supported, and the model does not need to be retrained.

The invention discloses a high-precision multi-modal twin model assembly method based on a large language model and a multiple docking optimization mechanism. The whole system framework is composed of three modules: a database, an interaction system and human factors. Wherein the database module integrates a model library module, a user interaction data recording module and a temporary data recording module, and is used for storing and managing various data information required for constructing a virtual scene; the interactive system module is used as a core part, integrates a model dynamic loading module, a model accurate positioning module and a model adaptive assembly module, realizes instruction analysis and model generation by relying on a large language model, and combines a multi-docking optimization mechanism of eye movement tracking, gesture recognition, automatic docking and logic rule binding to realize multi-docking. Efficient and accurate interaction between the user and the database is effectively guaranteed; the method not only provides a solid foundation for virtual experiments and virtual-real symbiosis, but also significantly improves the construction efficiency and precision of digital twin model assembly.

The invention discloses a complex aerospace product man-machine cooperation assembly-oriented body-equipped agent packaging method and device. The method comprises the following steps: a sensing layer realizes multi-modal intention and environment sensing by using a large language model; the reasoning layer analyzes a structured assembly plan sequence through an assembly scene map; and the execution layer converts the target coordinate into a rotation angle to generate an action code. According to the method, the assembly plan sequence is generated according to the sensing result and is automatically converted into the robot execution code through the combination of the body agent packaging and the large language model, so that the self-adaptive capability of the man-machine cooperative assembly process is improved; therefore, the problems of high system integration difficulty, insufficient task adaptability and high-efficiency cooperation difficulty in man-machine cooperation assembly of complex aerospace products are solved.

The invention discloses a game controller dynamic configuration system, and relates to the technical field of computers. The system comprises a controller end and a host end. The controller end adopts a heterogeneous equipment hybrid communication protocol stack to realize bidirectional data synchronization between the host end and the controller end; the controller end comprises a trigger, a selector, an interceptor, a switch assembly, a dynamic resource configuration unit and a firmware upgrading interface; the host end comprises an equipment dynamic identification and registration module, a game semantic analysis module, a dynamic mapping strategy generation model, a user adaptive learning unit, a multi-modal feedback fusion unit and a security verification and exception handling unit. According to the method, the mapping relation library of the controller hardware features and the game scene parameters is established, the physical state data of the controller is collected in real time through the sensor group, and the dynamic matching rule is generated in combination with the role action threshold in the game scene, so that the controller configuration difficulty is reduced, and the controller configuration efficiency is improved.

The invention relates to the technical field of assembly type machine rooms, in particular to an assembly type machine room based on a ventilation and heat dissipation processing mechanism. The computer room comprises a computer room body, ventilation windows are formed in the two sides of the computer room body, an exhaust fan is arranged at the top of the computer room body, and a rainproof assembly is arranged on one side of each ventilation window. As the sponge mat has an adsorption effect on liquid, rainwater can be prevented from entering the machine room body to cause damp damage to equipment, the service life of the equipment is prolonged, as the through holes are formed in the surface of the sponge mat, the ventilation window does not need to be closed, and external air enters the machine room body through the ventilation window after passing through the through holes; heat in the machine room is taken away in the air circulation process, and the heat dissipation effect of the machine room is improved.

Provided is a cyber threat information processing method including receiving a CTI analysis request for assembly code from a client; analyzing the assembly code to obtain analysis information of the CTI for the assembly code; generating a CTI query related to a file based on the analyzed CTI and delivering the CTI query to a natural language model; and providing natural language description information according to the CTI query obtained from the CTI for the assembly code and the natural language model.

The invention discloses a method for automatically capturing semantic information of a binary code block and a related device, and relates to the technical field of computers.The method comprises the following steps that code disassembling and instruction level analysis are conducted on the binary code block, and a binary stream original instruction sequence is generated; performing semantic intermediate representation conversion and vocabulary overflow standardization on the binary stream original instruction sequence to obtain a binary code standardized instruction sequence; obtaining an instruction data dependency relationship and an instruction control dependency relationship corresponding to the binary code block, and constructing a corresponding binary code program dependency graph; semantic code node embedding is carried out on the binary code program dependency graph to generate a binary code representation vector corresponding to context semantics; and performing automatic semantic capture reasoning on the binary code representation vector corresponding to the context semantics through a pre-trained semantic understanding model to obtain semantic information of the binary code block. According to the method, the semantic information of the binary code block can be efficiently and accurately captured.

The invention relates to the technical field of software, in particular to a low-code-based industrial simulation software development and application method and system.The method comprises the steps that firstly, component packaging is conducted on multiple subjects, and an industrial simulation component library is generated; then, based on a user dragging instruction, assembling a target component in an industrial simulation component library to determine a function node corresponding to the industrial simulation software; generating and operating industrial simulation software on the basis of the assembly result and configuration parameters which are input or preset by a user and correspond to the components; training an AI agent model based on historical simulation data of a plurality of industrial simulation software; and receiving and analyzing the natural language instruction of the user, and generating result data corresponding to the natural language instruction of the user based on an analysis result and the AI proxy model. Therefore, the industrial simulation software can be simply and efficiently generated, the development difficulty and cost of the industrial simulation software are reduced, and the computing power cost of actual simulation calculation is greatly reduced through the use of the AI proxy model.

The invention relates to the technical field of computers, and discloses a method for generating a test program for testing a Solidiity smart contract compiler, which comprises the following steps of: defect directional triggering: inserting a Solidiity variable through an inline assembly block to expose an optimizer defect, and limiting a dynamic array / character string to be expanded to at most one element at a time; semantic consistency control: eliminating intermediate representation behavior differences by adopting a placeholder counter, and preventing border crossing through an array length check instruction; and resource conflict arbitration: managing EVM stack variables by using a block scope, and nesting hierarchies based on a depth threshold truncation function. Through triple innovative design of defect directional triggering, semantic consistency control and resource conflict arbitration, the fundamental contradiction among semantic ambiguity, low defect detection rate and resource constraint in the Solidity compiler test is systematically solved.

The invention provides a component-level collaborative editing control method, which is applied to a multi-user collaborative editing control system, and the system comprises a plurality of front-end engines and a back-end server. A front-end engine obtains user editing operation data and a rear-end real-time version snapshot, component structure difference comparison is carried out, and a component operation list is generated; and performing component conflict judgment based on the operation list, merging and editing operation data according to a conflict judgment result and a preset conflict resolution strategy, generating processed version snapshots, sending the processed version snapshots to a rear end, storing the processed version snapshots by a rear-end server, and sending the processed version snapshots to each front-end engine. Compared with the prior art, the method has the advantages that the data transmission quantity and the load of the rear-end server are reduced by a lightweight architecture through the front-end component level difference comparison and the preset conflict resolution strategy, the rear-end server does not need to be frequently requested for conflict resolution and data merging, the response speed and the collaborative editing efficiency are remarkably improved, and the user experience is improved. And better user experience is brought to the user. The system has the same beneficial effects.

The invention relates to the technical field of digital assembly processes, in particular to an adjacent matrix-based assembly hole structure characteristic visual analysis method, system and equipment and a medium. According to the method, firstly, an assembly attribute information table is obtained, a contact-based adjacent matrix is constructed, a description model of assembly hole structure characteristics is established, secondly, the number and spatial positions of assembly holes are obtained through the attribute information table, and then the assembly relation between zero standard parts in an assembly is analyzed. And finally, displaying the structural characteristics of the assembly holes in different positions and different assembly relations in the assembly body in a three-dimensional environment, and constructing a three-dimensional visual analysis environment for the structural characteristics of the assembly holes. The method gets rid of the traditional mode which seriously depends on visual recognition, achieves the automatic analysis and visual display of the structural characteristics of the assembly holes, improves the process preparation efficiency of process personnel, and reduces the probability of misrecognition.

The invention discloses a narrow space mechanical arm path planning method based on a large language model. According to the method, narrow space path planning is carried out by adopting reinforcement learning, a path multi-scale attribute weighting function is constructed and generated as a reward function of reinforcement learning, and a reward function expression form capable of being directly operated and a reinforcement learning training algorithm matched with the reward function expression form are generated under the guidance of a large language model (LLM). The reinforcement learning training indexes are combined to guide the large language model to optimize the generated reward function, the defects that in traditional reinforcement learning, the reward function is difficult to formulate, and a large amount of experience needs to be provided are overcome, a feasible path can be quickly found, and the generated path is high in quality and directly available; balance of path length, safety and execution precision can be achieved through weight dynamic adjustment, rapid migration from narrow space obstacle avoidance to precise assembly and other different scenes is supported, the path quality and the exploration speed can be considered, and the method is suitable for mechanical arm path planning in the complex and narrow environment.

The invention discloses a method and a system for asynchronous execution of JavaScript at a server side, and relates to the technical field of computer software, the method comprises the following steps: constructing a multi-language runtime context pool on the server side based on GraalVM, and pre-compiling a script and a core class; when the script is loaded, asynchronous execution strategy parameters are injected into the corresponding isolation context; calculating the fragmentation number of the overall task submitted by the script, and carrying out overall task fragmentation; mapping the fragments to the corresponding isolation assembly lines according to the fragment execution categories, constructing feature vectors according to the fragments in the isolation assembly lines, calculating fragment priorities, and executing in sequence; after fragment execution is completed, execution time delay is collected, historical time delay prediction is updated, and when conditions are met, isolation retry is carried out, and compensation fragments are generated. Through pre-compilation, asynchronous execution strategy, adaptive fragmentation and priority scheduling, and compensation fragmentation generation, fault self-recovery and service continuity guarantee with low time delay response, high scheduling flexibility and controllability and automation are realized.

The invention discloses a formal verification method based on a hierarchical verification framework, which comprises the following steps of: firstly, dividing a target program into a C code and an assembly code, modeling and verifying the assembly code, then modeling and verifying the C code, and in the modeling process of the C code, carrying out verification on the C code; and defining a specific state and an abstract state of each data structure, establishing mapping through a refinement relationship, defining a function model based on the abstract state, constructing a function API specification, and finally defining system properties for a target program according to the function model and verifying the system properties. According to the method, a hierarchical verification framework and a formalization tool are combined, formalization verification is carried out on the target program from specific implementation to abstract division into three layers, comprehensive verification from bottom-layer codes to high-layer system properties is realized, the risk of security vulnerabilities is reduced, and the reliability of the target program is improved.

The invention provides a performance analysis method and device for a cross-architecture semantic equivalence instruction stream and a storage medium, and the method comprises the steps: taking a high-level language program as input, aiming at a plurality of target architectures, generating a multi-dimensional semantic equivalence instruction stream set from a high-level language form to an intermediate representation and then to an assembly code through a compiler; based on semantic equivalence instruction streams of code units with different granularities and compiler debugging information, establishing a mapping relationship between a target region in a source code and an intermediate representation and assembly codes, and forming a cross-hierarchy equivalence region fully-connected graph; and based on the semantic equivalence instruction stream set and the cross-level equivalence region fully-connected graph, automatically embedding a performance monitoring code at the boundary of the target region, and carrying out performance test on the elastic region. According to the method, the accurate performance test of the cross-architecture semantic equivalence instruction stream can be realized.

The invention discloses an assembly type machine room sub-module design system based on a BIM model and a three-constant system, and high-precision modular design of a machine room is realized through BIM parametric modeling, three-constant system performance analysis, graph theory module division, prefabricated member design, assembly process planning and CFD simulation verification. The system integrates the block chain and the knowledge graph technology, supports the prefabrication rate being greater than or equal to 85%, reduces the energy consumption by 25%, solves the problems of low construction efficiency and poor precision of a traditional machine room, and provides technical support for a smart building.

The invention relates to the technical field of software engineering, and discloses an assembly line flow adaptive configuration method and system. The method comprises the following steps: obtaining source code data of a software project and performing static analysis and feature extraction to generate structured language description data; querying and matching in a preset template library based on the structured language description data to generate a task template set; performing task dependency relationship analysis and process topology assembly on the task template set to generate initial pipeline data; and performing dynamic strategy adjustment on the execution process of the initial pipeline data by adopting the real-time context data corresponding to the software project to generate target pipeline data. Through automatic language recognition and intelligent process generation, automatic configuration of the continuous integration process is realized, and the technical problem of low automation level caused by lack of automatic recognition and adaptation capability for a project development language environment in a project continuous integration and continuous deployment technology is solved.

The invention provides a code compiling method and device based on a P4 assembler, the assembler and a medium. The code compiling method comprises the steps that a to-be-processed assembly code file is obtained; splitting and compiling the assembly code file to obtain a plurality of code segment compiling results; according to the auxiliary information, by taking Parser components, Mat components and Deparser components contained in the Ingress unit and the Egress unit as classification types, classifying the code segment compiling results to obtain code segment groups; linking the P4 table item data and the machine instruction code contained in the code segment group, and endowing a link result with a global instruction identifier and code segment header information to obtain a code segment binary code; and generating a firmware file based on the code segment binary code. The compiling process of the network processor can be simplified, so that the development efficiency is improved.

The invention relates to the field of front-end development, and discloses an AI assembly system and method for front-end development, and the system comprises a multi-modal data collection and processing module, an intelligent component library module, a demand understanding and analysis module, an AI assembly module, a code evaluation and test module and a user interaction and feedback module which are in communication connection. The method corresponds to the system. According to the application, data is acquired from a multi-modal data source and preprocessed, component information is stored through an intelligent component library, a preliminary assembly scheme is generated by using natural language processing and machine learning technologies, an AI assembly module can generate and optimize codes according to the scheme, and a code evaluation and test module comprehensively tests and adjusts the codes; the user interaction and feedback module allows users to participate in iterative optimization, an automatic and intelligent front-end development system supporting user interaction is formed, and the problems that in traditional front-end development, components are difficult to integrate manually, the development efficiency is low, and the development process is tedious are solved.

The application discloses a notebook shell lightweight structure injection molding equipment and relates to the technical field of plastic processing and injection molding. The notebook shell lightweight structure injection molding equipment comprises two bottom plates, a front mold frame, a rear mold frame, a front mold base, a rear mold base and a fixed plate. The front mold frame is fixedly arranged on the front mold frame bottom plate and is provided with a positioning guide column fixedly arranged on the front mold frame. The rear mold frame is fixedly arranged on the rear mold frame bottom plate and is slidably connected with the front mold frame through the positioning guide column. The front mold base is fixedly installed on the front mold frame and is internally provided with a cavity one. The rear mold base is fixedly installed on the rear mold frame and is internally provided with a cavity two. The cavity two can be combined with the cavity one to form an injection molding cavity. The fixed plate is slidably arranged on the rear mold frame bottom plate and is provided with a plurality of air-assisted ejector pin assemblies fixedly arranged on the fixed plate. The air-assisted ejector pin assembly comprises a guide rod and a push rod. The notebook shell lightweight structure injection molding equipment can guarantee the consistency of the demolding effect of each position of the mold, improve the product stability and the yield.

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

Provided in the present application are an active learning and augmentation method for a complex skill in a heterogeneous scenario and a sim-to-real transfer method. The active learning and augmentation method comprises: first, collecting state data by means of the interaction of multiple executors and an environment; storing same in a shared experience replay buffer; after sampling, alternately training the executors and discriminators; and finally selecting the optimal executor to be deployed, so as to implement active learning and augmentation of a complex skill. The sim-to-real transfer method comprises: in a 3C assembly digital twin environment, collecting multi-modal data to construct a skill knowledge base; generating a policy sequence; using technologies such as residual reinforcement learning to perform optimization; and, by means of a coding and decoding model, transferring a skill policy from simulation to reality. Without a teacher model or teaching data, the solution provided in the present application improves skill learning efficiency by means of combining reinforcement learning and knowledge distillation techniques, and implements the sim-to-real transfer of operating skills from a simulation environment to a real assembly environment.

The invention discloses an aircraft general assembly information association rule mining method and device based on a fine tuning large language model and a medium, and relates to the field of electronic data digital processing, the method comprises the following steps: S1, collecting multi-source production element data in an aircraft general assembly process file; s2, performing field adaptation by a large language model fine tuning method based on low-rank adaptation; s3, extracting structured element information, and constructing a semantically optimized item set; s4, mining association rules of the production elements and the final assembly links based on an association rule mining algorithm; and S5, optimizing and outputting a high-confidence association rule set. According to the method, the accuracy of information extraction and the depth of production element association analysis are effectively improved by combining fine adjustment of a large language model with association rule mining, introduction of a low-rank matrix and deep combination of a mining algorithm, so that the recognition precision of the association relationship between production elements is greatly improved; and reliable data support and decision basis are provided for optimization of the final assembly process.