268 results about "Modula" patented technology

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 a structured text code generation and review method, which comprises the following steps of: receiving a control task demand input by a user, supporting natural language, structured form or graphical process input, performing deep semantic analysis by utilizing a demand analysis module, identifying control conditions, control actions and constraint factors, and generating and reviewing a structured text code. The task is converted into a plurality of control units through the SCoT chain; the SCoT chain reader generates a semantic vector according to a task demand, the AST reader generates AST representation through a grammar rule structure and a hierarchical relationship, and the decoder predicts a next grammar rule and generates an ST code in combination with the SCoT chain and AST features; and verifying grammar and logic of the ST code through an interactive automatic review and repair mechanism, if compiling errors occur, analyzing errors by the teacher model and providing repair suggestions, and performing code repair by the student model according to guidance until the code conforms to an expected function and passes compiling.

Some implementations of the disclosure provide a computer-implemented method including operations of receiving, by an orchestration agent, user input corresponding to a user question, wherein generating a response to the user question includes one of generating, editing, or refining programming code, and generating, by the orchestration agent, a prompt instructing a first sub-large language model (LLM) to perform a first task. In response to the prompt, generating, by the first sub-LLM, instructions for a second sub-LLM to perform a second task, wherein results of performing the second task by the second sub-LLM are provided to the first sub-LLM and performing, by the first sub-LLM, the first task utilizing the results of the second task generated by the second sub-LLM. An additional operation includes generating, by the orchestration agent, a GUI that displays the response to the user question, wherein the response includes or is based on the programming code.

The invention provides a business code generation method and a business code generation device fusing code specification detection, which adopt a'generation-review-optimization 'closed-loop process to convert traditional post auditing into beforehand quality construction. Based on a pre-training code large model, an initial code is generated under a three-level cue word composed of a user demand, a context abstract and a system instruction, the system instruction is dynamically injected into an enterprise specification, and it is ensured that the generated code is initially compliant. And the auditing module performs semantic and architecture verification through abstract syntax tree analysis and a pre-training auditing model, automatically checks problems, sorts and repairs priorities, and generates an auditing report. And if defects are found, the system drives the model to be iteratively generated according to the priority until codes meeting the standard are output. The whole-process defect data is used for incremental fine adjustment to promote continuous evolution of the model.

Some implementations of the disclosure provide a computer-implemented method including operations of receiving a user question by an orchestration agent, where generating a response to a user question includes generation of programming code, executing, by a sub-large language model (LLM), an instruction to generate the programming code and performing, by the sub-LLM, a validation process including determining whether the programming code generated by the sub-LLM includes a syntax error. When the validation process indicates the programming code does not include the syntax error, the method includes operations of invoking a logic module configured to execute the programming code, wherein the logic module is provided the programming code generated by the sub-LLM and executes the programming code and generating, by the orchestration agent, a graphical user interface that displays the response to the user question that includes or is based on results of execution of the programming code.

Disclosed herein are computer-implemented systems and methods for code value assessment. For example, in one aspect, the system comprises, an input module configured to receive program code submissions from developers, a processing unit equipped with Text-Based Models with Contextual Understanding (TBM-CUs) configured to evaluate the functional meaning, purpose, and value of submitted code segments and distinguish between code contributions from human programmers and machine learning systems, a visualization module configured to present the assessed value of the analyzed code over various time periods and dimensions, offering insights into trends, patterns, and comparative performance, a communication module configured to translate programming code meaning or function into plain language summaries for non-technical stakeholders, and an AI peer review module configured to automatically review, accept or reject code contributions.

A system and method for optimizing instructions for Large Language Models (LLMs). The system and method comprise a text embedding model configured to convert the text prompt into an embedding space. An optimization module is configured to optimize the prompt in the embedding space, and an invertible embedding model is configured to decode the optimized embedding space back into a text prompt that may be input to a blackbox LLM.

The invention discloses an intelligent graphic programming language application system and method based on a large model technology, and the system comprises an input module which is used for obtaining a programming logic document; the large model analysis and generation module is used for performing semantic analysis on the programming logic document and generating corresponding graphical programming language initial code representation; the graphical language generation module is used for converting the initial code representation into a program structure of a graphical programming language; the small model proofreading and optimizing module is used for verifying a program structure according to a preset graphic language grammar rule and automatically modifying and optimizing when a part which does not conform to the grammar rule is detected; and the output module is used for outputting the verified and optimized graphical programming language program. According to the intelligent graphic programming language application system and method based on the large model technology, the advantages of an artificial intelligence large model and an artificial intelligence small model are comprehensively utilized, and automatic conversion and optimization verification from a text requirement to a graphic program are achieved.

The invention discloses a lightweight cross-platform PLC (Programmable Logic Controller) multi-language code generation method, which relates to the field of industrial process control, and firstly provides a unified intermediate data model, and on the basis, a module for converting each PLC language into the unified intermediate data model is realized; on the basis of a standardized JSON data structure, a modularized unified intermediate language conversion chain is further supported, an analyzer and a generator communicate through a JSON interface, a cross-platform conversion path is supported, and unified intermediate language conversion from PLC to JSON to XML to ST to C is completed; the method comprises the following steps of: integrating an open source compiler and runtime, constructing an open and pluggable automatic execution framework, compiling a generated C code into a platform executable file by using an adaptive GCC tool chain aiming at different target platforms, and deploying a program generated by compiling to a soft PLC runtime system supporting multiple platforms. According to the method, a JSON intermediate data structure which is light in weight, unrelated to languages and uniform in structure and a matched conversion framework of the JSON intermediate data structure are constructed.

The invention provides a code analysis method, system and equipment based on a multi-programming language sandbox and a medium, and belongs to the technical field of code analysis and detection.The method specifically comprises the steps that an input code and a corresponding unit test sample are obtained, and a programming language type specified by the code is determined; sending the code and the unit test sample to a sub sandbox environment of a corresponding language for execution; a compiler in the sub sandbox reports a missing library according to a code compiling result and prompts a user to install the missing library; calling an analysis tool of a code analysis module to analyze the code; compiler feedback of the sub sandboxes and various analysis results generated by the code analysis module are integrated into a large language model; inputting the integration result into a large language model through a preset template; and analyzing and evaluating the code case based on the user instruction. Through multi-language sandbox isolation, automatic analysis tool integration and large language model enhancement, the security, efficiency and quality of a code processing flow are improved.

Post-training quantization of weight values and activation values substantially reduces the memory and processing requirements of floating-point (FP) large language models (LLMs). A quantization parameter training process is performed on the FP LLM to determine quantization parameters. Weight-activation scaling may be applied to linear modules of the LLM, including down projection layers, enabling subsequent per-tensor quantization for activation values. The weight and activations values of the FP LLM are quantized from FP to integer values. Different layers may have different integer sizes. For example, weight values may be reduced to 4 bit integers and activation values to 8 bit integers. Layers within the model are modified to operate on the integer values. For example, an integer SiLU module may provide an integer approximation of a sigmoid-weighted linear unit activation function.

The invention discloses a vehicle-mounted system modular framework and a collaboration method thereof, the vehicle-mounted system modular framework comprises an agent module cluster, and the agent module cluster comprises an interaction agent and at least one function execution agent; a large language model is deployed in each agent, and all the large language models are coordinated through a standardized interface; the interaction agent is used for receiving a natural language instruction of a user and analyzing the natural language instruction by using a large language model deployed in the interaction agent to generate a task instruction; and the function execution agent is used for receiving the task instruction, analyzing and extracting key parameters by using a large language model deployed in the function execution agent, executing security rule check, and executing a corresponding specific task if the task instruction passes the security rule check. According to the method, the rule code quantity can be reduced, the interactive agent realizes cross-agent dynamic task distribution based on the big language model reasoning ability, new function access does not need to modify a bottom layer architecture, only peripheral agents need to be increased or decreased, the development period can be effectively shortened, and the development progress can be accelerated.

The invention discloses a spray combustion sub-model integrated development method and equipment based on a large language model, and belongs to the technical field of engines. The method is realized by relying on a retrieval enhancement generation system and a multi-agent collaborative framework, and comprises the following steps: S1, user instruction and thesis extraction: extracting core information from related technical literatures to form a structured file according to a sub-module extension requirement proposed by a user; s2, source code analysis and comparison data generation; s3, function and interface planning: performing adaptive planning of an original model-OpenFOAM platform; s4, code generation and packaging: code conversion, dependency processing, test example generation and compilation packaging; s5, layered testing and verification graph generation; and S6, debugging and repairing: executing a positioning-repairing-regression closed-loop process through a debugging agent. According to the method, the problems that the spray combustion sub-model is difficult to migrate to the OpenFOAM simulation platform under different frameworks and the development period is long can be solved.

The invention discloses a unified programming model compiling and runtime system oriented to a Shenwei supercomputing platform, which comprises a compiling module used for compiling SYCL programming model codes and detecting kernel functions to generate equipment end intermediate representation; processing the equipment end intermediate representation to obtain a kernel function dynamic link library and an executable code of a host end; and the public runtime module is used for calling the kernel function dynamic link library when the executable code at the host side runs. Based on a compiling module and a public runtime module, a vertical optimization system from a compiler to runtime is constructed, efficient operation of SYCL in a domestic supercomputing system is realized, a semantic gap between a domestic many-core architecture and a universal programming model is overcome, and the development of the system is facilitated. The SYCL programming model is processed by a host end and an equipment end respectively after being extracted by a kernel during compiling, and is dynamically called by a common runtime module, so that the cooperative computing capability of the host end and the equipment end is fully exerted.

The invention discloses a system for realizing bidirectional conversion between a model and a code, and the system comprises a meta-model architecture module which is used for constructing a unified model specification; the meta-model engine module is used for generating, analyzing and processing the meta-model architecture module; and the bidirectional conversion module is used for realizing forward and reverse synchronous conversion between a zero code model and a Java model serving as a program code by adopting the meta-model engine module. By means of the scheme, a unified specification with the meta-model architecture as the core is established, a powerful meta-model engine is used for automatic processing, and the barrier between a zero-code visualization model used by business personnel and bottom-layer Java codes written by developers is seamlessly broken through. The synchronization capability of the model and codes ensures the continuous alignment of business design and technical implementation, and effectively supports the quick iteration of agile development and business.

The invention discloses a reasoning-based multi-language security protection framework, a medium and equipment, and belongs to the technical field of artificial intelligence. According to the framework, cross-language knowledge migration and interpretability enhancement are realized in a mode of combining thinking chain reasoning and constraint alignment optimization; comprises: an SFT-based cold start module configured to perform knowledge distillation on a basic large language model through supervised fine tuning so as to endow the model with a preliminary reasoning ability for a safety protection task; the reasoning training module based on the GRPO is configured to be capable of improving normalization, accuracy and diversity of a model reasoning chain and enhancing interpretability; and the CAO-based cross-language alignment module is configured to realize knowledge migration from a high-resource language to a low-resource language and avoid performance reduction of the high-resource language. The method can solve the problems that an existing method mainly depends on a classifier lacking interpretability, the performance of a low-resource language safety fence is insufficient, and the performance of the low-resource language safety fence is poor.

The invention discloses an automatic code debugging and optimizing system and method based on an AI agent, and relates to the technical field of artificial intelligence and automatic programming, the system comprises a coordinator module, a plan generation module, a code generation module, a test verification module, a deployment operation module, a project summary module, a language model module and a context management module; the coordinator module supports to select and call an Agent for specific task execution according to the current task process, and the plan generation module is configured to automatically generate a development task plan according to the requirements of a user and transmit a task list to the code generation module. Through the exception retry mechanism and the task backtracking mechanism, the system can automatically detect errors in the code generation process and repair the errors, and the tedious process of traditional manual debugging is avoided. The system optimizes the code generation process by backtracking the historical task and adjusting the generation strategy, and the quality of the final code is ensured.

A computer-implemented technique for generating program code includes receiving a natural language description of a hardware module, generating a first plan based on the natural language description, extracting first circuit information from the natural language description, generating a second plan based on the first plan and the first circuit information, and generating first program code in a hardware description language based on the second plan.

The invention relates to a teaching experiment automatic generation and intelligent deployment device and method. An input module is arranged to collect experiment steps of experiment configuration and natural language description; the method comprises the following steps of: converting an experimental step described by a natural language into an operation script of the experimental step and a judgment script for judging whether the experimental step is correctly executed by arranging a judgment engine module, and integrating experimental configuration, the experimental step described by the natural language and the operation script and the judgment script of the experimental step into an experimental configuration document; and mapping the experiment configuration document into an experiment environment which is arranged and constructed by using Kubernetes through setting an environment deployment management module. The lesson preparation efficiency is improved, hardware investment is saved, and the teaching effect is improved.

The invention provides an interactive real-time loop feedback programming teaching system based on a large language model, and the system comprises a task driving module which is used for generating a multi-language exercise library based on the large language model and dynamically recommending tasks in combination with the programming capability of a user and an interest label; the code writing and debugging module is used for writing multi-language codes by a user, receiving the multi-language codes submitted by the user, executing multi-language compilation and generating compilation data, and the compilation data comprises execution logs, performance data and error information; the programming capability portrait module is used for analyzing the compiling data by utilizing a large language model to obtain programming behavior data, and processing the programming behavior data to obtain a user programming capability portrait; the intelligent interaction module is used for analyzing the programming ability portrait by adopting a large language model to generate targeted teaching feedback, and calling AI assistant teaching to execute five-step guide teaching based on the targeted teaching feedback; the reflection internalization module is used for generating a reflection log based on the user ability portrait generated by the programming ability portrait module, the reflection log comprises an error mode, a learning habit and a cognitive blind spot, and an internalization strategy is recommended according to the content of the reflection log. The internalization strategy refers to providing personalized learning methods, learning plans, learning guide, learning guidance and recommendation tasks of updating the task driving module for the user according to the current error type.

The invention discloses an FPGA test case automatic generation system and method based on a large language model. The system comprises a demand input module, a demand structuring module, a large language model case generation module, a case confirmation module, a verification execution module and a closed-loop optimization module. The large language model can automatically analyze the unstructured demand document and extract key information such as function points, input and output, boundary conditions and the like, and manual one-by-one interpretation is not needed. A big language model is guided through Prompt Engineering to dismantle complex requirements into test key points which cannot be subdivided, and it is ensured that all logic branches are covered by tests. The large language model can dynamically generate diversified test cases based on test key points, including a normal process, an abnormal process and an edge scene. And in combination with FPGA design specifications, historical error cases and other plug-in knowledge bases, the large language model can generate test cases closer to actual hardware behaviors.

The invention provides an operation and maintenance system based on a large language model, and relates to the technical field of operation and maintenance, in the operation and maintenance system, a dialogue module determines an associated entity type name according to an intermediate question text; establishing an association relationship between each preset operation and maintenance tool corresponding to the association entity type name and the large language model; sending the intermediate problem text to a large language model to enable the large language model to work with an entity operation and maintenance agent identity corresponding to the associated entity type name, and obtaining target data according to the intermediate problem text and a preset operation and maintenance tool having an association relationship with the large language model; obtaining an initial feedback text corresponding to the intermediate problem text according to the target data; a corresponding entity operation and maintenance agent identity is given to the large language model, so that the large language model can dynamically associate and call a preset operation and maintenance tool corresponding to the entity, target data are automatically collected, and an initial feedback text is generated; and the response efficiency and the accuracy of the obtained initial feedback text are improved.

The invention discloses a game function development system and method based on a natural language instruction, and relates to the technical field of game development, the system comprises a project code library analysis module, a development instruction receiving and analysis module, a function deconstruction and code generation module, an automatic integration and debugging module and a development report generation module; the project code library analysis module constructs a code dependency graph and recognizes an API interface to provide a precise basis for subsequent development, the development instruction receiving and analysis module converts natural language requirements into structured information, efficient understanding of the requirements is achieved, and the development efficiency is improved. The function deconstruction and code generation module disassembles requirements and calls a specific engine programming large model to generate standard codes, manual coding and automatic integration are reduced, the problems of automatic code insertion and test development of the debugging module according to a graph and rapid recognition and repair are solved, dependence on professional programmers is reduced, the function development period is shortened, and efficiency is improved. And compatibility errors caused by manual operation are reduced.

The invention discloses a software outsourcing-oriented heterogeneous code warehouse intelligent coupling degree detection system and method, and relates to the technical field of software engineering.The method comprises the steps that an instruction is detected, and code file information, dependency information and development log information are obtained through a distributed crawler module, an abstract syntax tree analysis tool and a natural language processing module; carrying out modular segmentation on the code file by utilizing semantic coupling analysis, inputting the code file into a large language model equipped with a LoRA adapter, identifying a coupling inverse mode, and carrying out preliminary optimization based on a quantum heuristic coupling optimization algorithm; after optimization, a multi-user concurrent scene is simulated through digital twin simulation, and the coupling degree is dynamically detected; meanwhile, a cross-language interface testing tool is used for testing interface compatibility and call chain coupling; the system comprises an information acquisition module, a semantic coupling analysis module, a quantum optimization module and a coupling degree detection module, provides a deep optimization basis for developers, and improves the coupling degree detection and optimization efficiency of the heterogeneous code warehouse.

The invention discloses an application automation operation method and system based on a large language model, and belongs to the technical field of network security, and the method comprises the following steps: 1, receiving the content input by a user; 2, performing control adjustment on task circulation; step 3, collecting and processing an application page state, and outputting multi-modal data; 4, multi-modal input is constructed, and an operation instruction is generated; 5, executing an operation sequence according to the operation instruction generated in the step 4, and recording an operation process; 6, converting the operation instruction generated in the step 4 into an underlying equipment command; 7, after task circulation is finished, a judgment intelligent processing unit module is constructed, global analysis is conducted based on the complete execution history and the user instruction in the process of the step 5, and the completion condition of automatic operation in the step 5 is obtained; the technical problems that in the prior art, the workload is high, and ambiguity is prone to occurring in recognition are solved.

A method, apparatus, a product comprising: obtaining a propagator module of a quantum function of a quantum program, the propagator module is programmed using a classical programming language, the propagator module configured to obtain as input a first domain of values for a first circuit parameter and a second domain of values for a second circuit parameter, and to output first and second sub-domains of the first and second domains of values, respectively; obtaining constraints of the quantum function; obtaining an optimization scheme that is defined over the first and second circuit parameters; generating a constraint problem based on the propagator module, the constraints, and the optimization scheme; resolving the constraint problem based on a constraint solver, a resolution comprising at least first and second values for the first and second circuit parameter; and synthesizing the quantum function according to the resolution.

The invention provides a code generation method and device, equipment and a computer program product, and the method comprises the steps: carrying out the semantic analysis of an original code snippet, and predicting a target system to which the original code snippet belongs; generating a development demand document of the target system, wherein development demand information of the target system is recorded in the development demand document; generating a system architecture of the target system based on the development demand information of the target system; based on the system architecture of the target system, iteratively generating a module code of each functional module, and obtaining a system code of the current round of the target system; and iteratively executing the code generation process to realize code generation of the next round until a preset iteration completion condition is met, thereby obtaining a final system code of the target system. According to the technical scheme, the code of the target system can be iteratively generated on the basis of the original code snippets based on the working mode of the waterfall model, and the code with high complexity and complex dependency relationship can be generated.

Systems and methods are provided for mitigating third-party code vulnerabilities in AI code generation services. A large language model generates an inference based on a user's code generation request. A middleware layer intercepts the inference prior to its availability within an AI pair programming client and parses the inference to identify third-party packages. A validation module checks the identified third-party packages against one or more registries of certified packages. If an uncertified third-party package is detected, the middleware layer either redacts the inference or modifies it by identifying a certified third-party package alternative. The system enhances security in AI-assisted code generation by ensuring the use of certified third-party packages.

The invention provides a controller program generation method based on semantic planning and quality optimization, and belongs to the technical field of automation. A multi-stage PLC program generation and optimization method oriented to natural language task description is adopted, and semantic retrieval enhanced task planning, structured code synthesis and a quality closed-loop optimization mechanism based on feedback are fused. A natural language task is automatically converted into a verifiable plan with logic constraints by constructing a semantic index, reasoning scheduling and an intermediate representation structure; through module clustering and multi-round prompt guiding type logic reconstruction, generation of a high-quality PLC program meeting the IEC 61131-3 standard is realized. And static analysis and test feedback are combined, and the code quality is evaluated and enhanced by adopting iterative optimization and a scoring mechanism, so that the problems of intelligence, structuring and verification of the whole process from natural language to PLC program are systematically solved, and the development efficiency, the program robustness and the feasibility of industrial deployment are remarkably improved.

The invention discloses a software code defect automatic detection and repair system based on deep learning, and relates to the technical field of deep learning technology.A data acquisition module acquires multiple source codes and metadata and adapts to a mainstream programming language; the preprocessing module cleans codes, extracts features and divides data sets; the detection model is used for identifying 12 types of defects by using a Bi-LSTM (Bidirectional Long Short Term Memory) and Transform mixed architecture; the repair model generates a repair scheme based on CodeBERT; defect verification is integrated with static / dynamic verification; the system management realizes configuration, user, task and log management, and supports RBAC authority control. According to the method, the code defect detection accuracy and repair efficiency are improved, and multiple defect types and programming languages are covered; the repairing scheme adapts to the scene and is explained, so that the labor cost is reduced; model dynamic updating and cross-language adaptation are supported, a development process can be integrated, and the comprehensive effects of code quality improvement, efficiency improvement and maintenance cost reduction are achieved.