588 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.

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 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.

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 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 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 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 improved moss growth algorithm-based assembly process parameter optimization method. The method comprises the following steps of (1) selecting a plurality of assembly precision indexes; (2) analyzing and evaluating the influence degree of each influence factor on the assembly precision index, selecting the influence factor with relatively large influence, inputting the influence factor into the improved support vector machine precision prediction model, and outputting an assembly precision prediction value; formulating a process optimization objective function by using the assembly precision predicted value; (3) selecting assembly process parameters needing to be optimized, formulating assembly process parameter optimization constraint conditions, and constructing a process parameter optimization model in combination with the process optimization objective function; (4) solving optimal assembly process parameters by the process parameter optimization model by using an improved moss growth algorithm; and (5) outputting the optimal assembly process parameters to form an assembly process adjustment scheme. According to the method, the convergence speed and optimization capacity of an existing algorithm are improved, the assembly precision and quality are improved, and the repair cost caused by trial and error and correction is reduced.

The invention relates to an assembly action sequence anomaly detection method based on multi-stage perception Transform, which designs a multi-stage perception encoder and a double-branch cooperative decoding strategy, and constructs a sudden abnormal behavior detector module and a dynamic boundary optimization algorithm. A set of complete action sequence anomaly detection process from feature extraction, anomaly positioning to result optimization is formed, fragment-level semantic understanding and global time sequence modeling are fused, the difficult problem of complex time sequence feature and sudden abnormal behavior recognition in the assembly process can be effectively solved, abnormal sequences and sudden abnormal behaviors are detected and positioned, and the assembly efficiency is improved. The recognition precision of the action boundary in a complex assembly scene is improved, and the assembly efficiency and the product quality are improved.

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

The invention discloses a templated business process management method, and relates to the technical field of enterprise management.The method comprises the steps that repeatable task units in a business process are represented and packaged into function components capable of being configured in a draggable mode, combined arrangement is conducted in a visual working area template, and process construction is conducted from the development coding level to the development coding level; according to the technical scheme, the method sinks to a configuration level which can be understood by business personnel, in the step of creating the visual workflow template, when the working area templates bearing specific tasks are bound with process nodes, separation and standardized packaging of business logic and execution logic are achieved, and various working area templates are predefined through a graphical interface; the node function assembly can be quickly completed only by quoting the templates in the flow chart, the defect that each node needs to be independently developed or written in a traditional mode is overcome, and the core defects that in the prior art, flow construction is complex, the reuse rate is low, and the method excessively depends on technicians are overcome.

In an example of a game according to an exemplary embodiment, a movement of a movable dynamic object placed in a virtual space is controlled based on physical calculations, and based on an operation input, a player character is caused to perform an object operation action including a first operation of moving a dynamic object specified based on the object operation action and a second operation of forming an assembly object by linking the specified dynamic object to the other dynamic object, and if the assembly object including the dynamic object specified based on the object operation action includes the dynamic object that the player character is mounting, the dynamic object is prevented from moving based on the object operation action.

The invention provides a large language model-assisted coding function bypassing input data automatic generation method, which comprises the following steps of: after compiling a source code of target binary software, extracting assembly fragments, input data parameters and data length parameter information corresponding to a coding and decoding function so as to construct a vector database; disassembling the target binary software, extracting a target function fragment, performing retrieval and recognition on the basis of a large language model and in combination with a vector database, and determining a coding / decoding function and parameter information in the target binary software; in the program execution process, after the related encoding / decoding function is executed, decoding or encoding is carried out according to the recognition result of the step S2, and the input data are compared to obtain the offset of the encoding / decoding data in the input data; when the program is executed to the analysis end point state, the expected data is mapped to the offset position corresponding to the input data based on the offset, and generation of new input data is completed.

The invention discloses a large and small model collaborative robot autonomous assembly method and system, and belongs to the field of advanced manufacturing technology intellectualization. The problems that traditional robot assembly depends on manual programming, adaptability is poor, and an algorithm cannot be optimized autonomously are solved. The method comprises the following steps: receiving an assembly project request submitted by a user in a natural language; the request is decoupled into a structured action primitive through an arrangement agent, and tasks are allocated; an algorithm design agent retrieves and adapts an algorithm from an open source library based on the action primitive, and completes training in a sandbox environment; the verification agent is responsible for automatic data acquisition and labeling, and carrying out overall performance test and iterative optimization on an integrated algorithm in a simulation environment; and finally generating an executable field control script. According to the method, the problems of algorithm autonomous design, simulation verification and closed-loop optimization when the robot faces a complex assembly task are solved, the development threshold and the deployment risk are reduced, and the intelligent level and the flexible adaptive capacity of an assembly system are remarkably improved.

Apparatuses, systems, and techniques of using one or more circuits (e.g., of a network interface) to obtain assembly code for one or more machine code segments loaded and / or injected into a process, and determine whether the assembly code is likely to perform at least one unauthorized task.

The invention discloses a system and a method for automatically assembling a data analysis process by a large language model based on Faiss, and belongs to the technical field of data analysis and artificial intelligence. The system comprises a client, a server and a data analysis platform. The method comprises: receiving a natural language analysis demand of a user; identifying a user intention as process assembly or suggestion through a large language model; encoding the demand into a query vector, and performing similarity retrieval by using a Faiiss vector library pre-constructed based on the analysis tool description information to obtain a first candidate tool set; inputting the set and original requirements into a large language model for fine arrangement and logic assembly to generate a second candidate tool set; and finally converting into a structured process file which can be directly identified and executed by a data analysis platform. According to the method, the problem that a structured data analysis process cannot be automatically constructed in the prior art is solved, end-to-end intelligent generation from natural language description to an executable analysis scheme is realized, the use threshold is remarkably reduced, and the analysis efficiency is improved.

The invention provides a binary code decompiling system based on large model multi-stage fine tuning. The method comprises two stages: a first stage: a data set acquisition and construction stage: acquiring a source code data set comprising source codes and binary file pairs from an open source project and code data set, and obtaining a high-quality decompilation data set through a data preprocessing process; the second stage is a decompilation stage based on large model fine adjustment and static analysis, decompilation is carried out after a decompilation data set is compiled, an assembly code obtained after decompilation is aligned with a source code in the decompilation data set to be used for fine adjustment of the open source large model, a function call graph analysis technology is cooperated, a program context is obtained, and the open source large model is subjected to static analysis; and obtaining a final decompilation large model. Therefore, the purpose of providing a high-quality decompilation data set and an efficient decompilation algorithm is achieved.

One variation of a method includes: receiving a descriptor of a design problem; selecting a set of output characteristics based on a set of language signals extracted from the descriptor; selecting a set of functions relating a set of input parameters to the set of output characteristics; accessing a virtual model representing a design solution and defining a set of model variables; linking a subset of input parameters to a subset of model variables analogous to the subset of input parameters; in response to the set of model variables omitting a model variable analogous to a first input parameter in the set of input parameters, prompting a user to update the virtual model to include a first model variable analogous to the first input parameter; and compiling the set of functions, the set of output characteristics, and the set of input parameters into a design specification.

The invention relates to an assembly type bridge modeling method based on a large model and an RAG technology. The method comprises the steps that a bridge standard component library is established based on an assembly type bridge standard image set; calling a large language model, and generating standardized knowledge statements based on the bridge standard component library; a word embedding method is adopted to convert the normalized knowledge statement into vectors, and the vectors are stored in a vector database; converting an input natural statement query into a vector by adopting a word embedding method, and retrieving in a vector database to obtain a normalized knowledge statement related to the query; performing parameter analysis and optimization on the retrieved knowledge statements based on a large language model by utilizing an RAG technology so as to generate component parameters; and calling a component modeling algorithm based on each component parameter to generate a component three-dimensional model, and assembling the component three-dimensional model to obtain a fabricated bridge model. According to the method, component parameter sorting, knowledge statement intelligent combination, efficient semantic retrieval, parameter optimization, three-dimensional model automatic generation and the like are organically integrated, and the modeling efficiency and quality are improved.