591results about "Programming languages/paradigms" patented technology

Computer-implemented systems and methods use a Large Language Model (LLM) for converting a legacy computer program in a first language to a human-language description of the legacy computer program, which description can be validated as being an accurate description of the legacy computer program. Once validated, the human-language description can be converted, again using an LLM, to a computer program in a target programming language. An LLM can also be used to generate test scripts for the new target-language program to test the performance of the target-language program in a production environment. An LLM can also be used to reconcile outputs from the legacy program to the new target program, such as on a function-by-function basis. If the differences between the outputs (if any) are sufficiently negligible, the legacy computer program can be decommissioned, and the new, target language program can be used in production.

The invention discloses a path planning heuristic function generation platform and method based on collaborative optimization of a large-scale language model and evolutionary computation. According to the technology, the large-scale language model (LLM) and evolutionary computation (EC) work cooperatively. The platform generates or mutates a heuristic function expressed as an executable code through LLM based on a structured prompt containing an environment context and performance feedback; and an EC framework (such as genetic programming) is combined with performance evaluation feedback to perform selection and iterative optimization on a heuristic code population, and population diversity is maintained. The method aims at overcoming the limitation that a traditional heuristic design is difficult and poor in adaptability, a high-quality heuristic function adapting to a complex and dynamic environment is automatically generated, and therefore the efficiency of a path planning algorithm and path quality are remarkably improved.

The invention discloses a code generation method and device based on LLM multi-agent cooperation and computer equipment. The method comprises the following steps: acquiring a user programming demand; analyzing the user programming demand based on LLM and decomposing a programming task to generate a task tree; selecting a to-be-allocated sub-task from the task tree, and evaluating the matching degree of an intelligent agent in an intelligent agent pool and the to-be-allocated sub-task based on LLM to generate a task allocation instruction; sending the task allocation instruction to a corresponding target agent, so that the target agent executes the to-be-allocated sub-task, and feeding back an execution result; and outputting the execution result. By implementing the method provided by the invention, the programming efficiency and quality can be improved, the resource allocation can be optimized, and the efficient development of complex requirements and large projects can be supported.

The invention relates to the technical field of semantic recognition, can be applied to business scenes such as financial science and technology and medical health, and discloses an interactive code generation method, device and equipment and a medium. And a visual preview interface is generated in combination with the context, multi-round modification through interactive adjustment or text editing is supported, and finally user interface element codes are generated. According to the method, natural language processing, context sensing and visual interactive correction are combined, so that the closed-loop process of interface codes from generation to dynamic adjustment is realized, the customization degree and the service adaptability of a generated result are improved, and the problems that an existing tool template is rigid, semantic understanding is deviated, and a multi-round correction mechanism is lacked are solved.

Methods and systems for generating digital threads that connect models and software tools for an interconnected digital engineering and certification ecosystem are provided. In one embodiment, a process for generating a software-code-defined digital thread includes receiving a first model representation of a first engineering model, a second model representation of a second engineering model, and an intent input. Determining, using a machine learning (ML) model, one or more model endpoints in the first and the second model representations. Generating, using a script-generating ML model, a platform orchestration script that accomplishes the intent input by connecting the first model representation and the second model representation, using the one or more model endpoints. Finally, storing the platform orchestration script as the software-code-defined digital thread. The digital threading of digital engineering models, with and without AI assistance, leads to exponential increases in efficiency of designing, testing, certifying, manufacturing, and operating physical products, allowing physical products to be made at digital speeds.

The invention provides a Cypher-stack type alignment generation method based on a large language model, and belongs to the field of knowledge graph questions and answers. The method comprises the steps that user query and knowledge graph mode information are input into the large language model to generate an initial Cypher query statement; based on a thinking chain prompt technology, retrieving from the knowledge graph to obtain a retrieval result related to user query, and correcting the initial Cypher query statement by the large language model based on the retrieval result to obtain a corrected Cypher query statement; and executing the corrected Cypher query statement. The invention further provides a Cypher-stack type alignment generation device based on the large language model. Through the semantic analysis capability of a large language model and a thinking chain guiding technology, a Cypher query statement is automatically generated and corrected in two stages, and the problem that the query intention of a user and knowledge graph data are difficult to accurately align when the Cypher query is generated due to the diversity of query requirements of the user and a semantic gap between the knowledge graphs is solved.

The invention discloses an MCP generation and calling method and device in a low-code platform, equipment and a medium, and relates to the field of artificial intelligence low-code, and the method comprises the steps: carrying out the semantic analysis and structure standardization of initial metadata generated in the application development process of the low-code platform, and obtaining target metadata; converting the target metadata into an MCP description file, converting a business description corresponding to the MCP service in the initial metadata into a natural language description which can be understood by a large model, and integrating the natural language description into the MCP description file to obtain a target MCP description file; constructing a standardized MCP warehouse, and managing the MCP service corresponding to the target MCP description file by using the MCP warehouse; and acquiring a user request by utilizing the large model, and performing MCP calling on the MCP service by utilizing the large model based on the current running state data in the application running process, so that the large model responds to the user request. According to the method, deep fusion of low codes and large models is promoted.

Systems and methods for generating and customizing remediation scripts. Generative AI models can analyze existing scripts and tailor scripts according to specific incidents and environmental parameters, thereby creating a faster, more consistent, and error-free incident response process compared to existing solutions.

The invention discloses a structured query language generation method and device, equipment and a storage medium, and relates to the technical field of database query, and the method comprises the following steps: obtaining a natural language input by a target user, and analyzing the natural language by using a preset large model to obtain structured data; determining a corresponding mapping relationship based on the structured data and a preset database knowledge graph, determining an initial structured query language based on a preset large model and the mapping relationship, and performing query by using the initial structured query language to obtain a query result; determining a first reward value based on the structured data, determining a second reward value based on the query result, and adjusting a preset large model by using the first reward value, the second reward value and a strategy gradient algorithm to obtain an adjusted large model; and evaluating the adjusted large model by utilizing a preset verification set, and if the evaluation is passed, generating a target structured query language by utilizing the adjusted large model. Therefore, the SQL generation accuracy can be improved.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

The invention provides a semantic-based migration and consistency verification method, system and device and a medium, and relates to the technical field of databases. The method comprises the following steps: performing metadata topology scanning analysis by obtaining metadata, including generating an abstract syntax tree and constructing a semantic graph; according to a metadata topology scanning analysis result, performing data mapping and conversion, including loading a YAML rule and generating corresponding type mapping and constraint conversion; data migration is carried out through primary key fragmentation parallel migration, batch writing optimization and real-time double-writing verification; through structure-data-business three-layer verification, simulation business SQL comparison and automatic difference repair, the integrity of migrated data and business compliance are ensured, the semantic gap problem in heterogeneous database migration is solved, high-precision and high-efficiency database migration is realized, the migration efficiency is improved, and the data migration efficiency is improved. The method is suitable for scenes with strict requirements on data consistency and migration efficiency, such as financial, government affair and enterprise-level data centers.

The invention discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

A generative language model (e.g. large language model) may be used to generate programming language code. However, the generative language model may sometimes generate an output that is not compliant with the grammar of the programming language. In some embodiments herein, a generative language model may be modified to only generate an output that is grammar-compliant. A method may include generating a plurality of values using the generative language model, where each of the values is indicative of a probability of a respective token being a next token of a token sequence generated by the generative language model. A mask may be applied to the plurality of values. The mask may operate on each value that corresponds to a token not compliant with the grammar of the programming language to reduce or zero the probability of that token being the next token.

The invention discloses a code generation model training method and electronic equipment, and relates to the technical field of computers, and the method comprises the following steps: inputting a sample cue word into a code generation model to obtain a sample code; based on code operation information of the sample code, determining a first reward parameter used for representing execution stability, and analyzing the code operation information, the sample code and the sample cue word by using a reference language model to obtain a second reward parameter used for representing a grammar error degree and a third reward parameter used for representing a code style; adjusting model parameters of a code generation model based on the first reward parameter, the second reward parameter and the third reward parameter; and under the condition that the adjusted code generation model reaches a convergence condition, determining a target code generation model according to the adjusted code generation model. The technical problem that the training efficiency of the code generation model is low is solved, and the technical effect of improving the training efficiency of the code generation model is achieved.

The invention discloses a low-code platform and Wasm high-performance computing integration system and method, and relates to the technical field of front-end development. In order to solve the problems that an existing low-code platform is limited in calculation performance and poor in security isolation performance, the scheme adopted by the invention comprises five modules: a description and registration module establishes a metadata structure for a Wasm module; the dynamic loading and asynchronous compiling module realizes loading as required and compiling during operation, and supports caching, version verification and hot replacement; the authority sandbox and resource isolation module constructs a security environment based on authority configuration to prevent illegal operation; the parameter binding and safety bridging module encapsulates a uniform interface to realize standardized interaction; the packaging and life cycle management module packages the module into a visual component, and supports full life cycle management. According to the method, high-performance, high-safety and visual integration of the Wasm module in a low-code platform is realized.

The invention relates to the technical field of computer programming, and discloses a Go language structural body automatic conversion method and system. The method comprises the following steps: firstly, receiving definitions of a source structure body and a target structure body as input, automatically matching fields of the two structure bodies through a field mapping rule engine according to a preset rule, and generating a field mapping relationship; then, based on the field mapping relation, a code generator is used for automatically generating a conversion code containing field assignment and type conversion logic; finally, the generated conversion codes are compiled into an executable conversion function, and the function can convert data in the source structure instance according to the field mapping relation during program running and fill the data into the target structure instance. According to the method, automation of structural body conversion is achieved, the problem that errors are prone to occurring when conversion codes are written manually is solved, and the maintainability of the codes is improved; the mode of reflection during operation is replaced by code generation, so that the conversion efficiency is remarkably improved; meanwhile, the method supports batch conversion and concurrent processing, and can meet the performance requirements of large-scale data processing.

Using complexity metrics to assess code generated using artificial intelligence includes generating, by an artificial intelligence (AI) language model, output source code based on input source code; identifying respective complexity scores for the input source code and the output source code using one or more complexity metrics; and generating, based on an evaluation of the respective complexity scores, a validation score for the output source code.

The invention relates to a software code generation method based on retrieval enhancement generation and large model fine tuning. The method comprises the following steps: constructing a question and answer pair data set; according to the question and answer pair data set, performing fine tuning on a pre-trained large language model based on a low-rank adaptation technology; designing a retrieval enhancement generation process; and generating a comprehensive cue word according to a retrieval enhancement generation result. The method has the advantages that the retrieval enhancement generation technology and the low-rank adaptation fine tuning technology are combined, the accuracy of large language model code generation is remarkably improved, the accuracy of the generated codes is high, and the cost for a user to iteratively modify the codes for multiple times is reduced.

The invention discloses an intelligent template generation method and device based on static codes and a medium, and relates to the technical field of computers, the method comprises the following steps: obtaining user configuration data, and performing dynamic analysis configuration conversion on the user configuration data to obtain a standardized data model; performing context-aware dynamic template rendering on the standardized data model to determine a preliminary code framework; based on the preliminary code framework, code problem parameters are obtained through incremental code quality detection; according to the preliminary code framework and the code problem parameter, determining a user rule code through code problem repair; and performing template dynamic optimization of federal learning on the user rule code to obtain a template logic adjustment instruction. According to the method, the technical problem that template generation logic cannot be optimized according to user feedback due to the fact that code generation and static analysis cannot be coordinated is solved, and coordination of code generation and static analysis and dynamic optimization of the code template are achieved.

The invention discloses an architecture system applied to embedded system development, and relates to the field of embedded system development, and the architecture system comprises an application layer which is used for executing business logic in an embedded system and managing the configuration of the embedded system, and the application layer is developed by adopting a Lua script; the hardware abstraction layer provides a unified hardware access interface; and the virtualization interface layer is used for providing a uniform interface for the application layer, so that the application layer can access the hardware resources of the hardware abstraction layer and call the system service in the embedded system. According to the method and the device, the problem that cross-platform transplantation is difficult due to the fact that a C language direct programming mode is adopted in an embedded system in the related technology is solved.

Techniques described herein may be implemented in the context of a computing resource service provider. A machine learning (ML) service provides an interface to clients which can be used to create, read, update, and delete ML pipelines. ML pipelines can be converted to a human-readable format, which a server persists. Clients of a machine learning service can start, stop, and resume executions a ML pipeline based on the human-readable format of the ML pipeline.

The invention relates to the technical field of deep learning, in particular to a PyTorch framework-based compatibility expansion system, which comprises a cross-framework model converter, a heterogeneous hardware abstraction layer, a hybrid computational graph execution engine, an intelligent distributed trainer and a self-adaptive optimizer, the cross-frame model converter converts input model formats of different frames into PyTorch executable formats, the heterogeneous hardware abstraction layer supports rear ends of various hardware and automatically selects an optimal calculation path, and the hybrid calculation graph execution engine fuses dynamic graph flexibility and static subgraph optimization and supports dynamic control function execution flow. An intelligent distributed trainer automatically selects a parallel scheme and is compatible with multi-protocol communication, a self-adaptive optimizer performs dynamic optimization based on hardware characteristics and a PyTorch tensor, a cross-frame model converter supports model conversion of multiple deep learning frames, the cost of migration among different frames by a user is reduced, and the universality of the PyTorch frame is improved.

The invention discloses a multi-feature fusion binary code similarity detection method based on a large language model, and belongs to the technical field of information security. Compared with a conventional detection method which only depends on a single feature, the method has the advantages that fine grammar and global structure information of a function can be bidirectionally captured by utilizing a large language model which is finely adjusted by prompt word engineering and a GraphCNN graph convolutional neural network, so that the comprehensiveness and accuracy of feature expression are greatly improved; meanwhile, a hierarchical Navigation Small World Graphs (HNSW) algorithm is applied to efficiently retrieve candidate similar functions in an offline vector library, so that the method is suitable for large-scale database application; and finally, through fine comparison of the Siamese network, similarity detection with higher accuracy is realized under the condition of low cost. According to the method, through multi-dimensional fusion of semantics and structure information, the accuracy and efficiency of similarity detection are remarkably improved, and a more accurate and robust solution on the basis of low cost is provided for binary code similarity detection.

Tagging deterministic code in artificial intelligence-generated code, including: receiving code generated by a generative artificial intelligence (AI) model; identifying at least one portion of code by identifying at least one of: one or more portions of deterministic code or one or more portions of non-deterministic code; and tagging the identified at least one portion of code.

A system for generating non-fungible token-based test suites from design diagrams comprises a processor associated with a server. The processor is configured to obtain image objects representing a workflow associated with a design diagram and a user profile of a user. The processor is configured to generate a design non-fungible token (NFT) associated with the design diagram to verify a design diagram ownership. The processor is configured to convert the image objects of the design diagram into corresponding text objects and to process the corresponding text objects of the design diagram to generate a plurality of text datasets corresponding to the workflow of the design diagram, analyze the plurality of the text datasets and generate a test suite which comprises a set of test scripts corresponding to the design diagram, and generate a test case NFT associated with the test suite to verify a test suite ownership.

The invention relates to the technical field of data detection, and particularly discloses a detection method and system for preventing DDL duplication in data synchronization, and the method comprises the steps: obtaining a data change record in a data synchronization process, the data change record comprising data definition language DDL sample set information, an operation timestamp and operation content; acquiring multiple pieces of statement sample information according to the DDL sample set information, and acquiring semantic similarity of each piece of statement sample information; and obtaining an operation identifier corresponding to each piece of statement sample information according to the operation content. According to the method and the device, the problem of insufficient operation analysis in the prior art is comprehensively and deeply solved by acquiring the operation identifier, calculating the operation similarity value, the operation data difference index and the like. Operation key feature information can be accurately extracted, the actual significance and association of data operation are clarified in combination with context identification, and misjudgment caused by inaccurate information extraction and lack of context consideration is avoided.

Various systems and methods are presented regarding using generative artificial intelligence / machine language (AI / ML) to resolve a merge conflict between two or more versions of a program code. The program code can be industrial automation software utilized to control one or more PLCs in an industrial environment. Interaction with the program code can be via a programming tool that presents the program code in a human readable format, while the AI / ML can be applied to the underlying source code. Hence, while the source code is applied to the PLC, the programming tool enables interaction various human readable summaries of the program code and respective options available to resolve the merge conflict. Accordingly, a process engineer, or suchlike, does not have to be familiar with the source code format to readily understand a summary of the merge conflict or one or more options available to correct the conflict.

The invention provides a script language-oriented seismic batch processing job editing method and device, and the method comprises the steps: obtaining a to-be-processed seismic job demand, and determining a seismic job project and database information; initializing each module object in a pre-established seismic batch processing operation framework, wherein the framework is a scripting language framework comprising a preset core library, a module library and a tool chain; determining a standard type parameter corresponding to each module object; initializing an operation topological structure of the seismic batch processing operation framework; selecting a target module object and adding the target module object to the target operation based on the earthquake operation project and the database information; and outputting the topological graph of the module according to the debugging requirement, and generating a target operation corresponding to the seismic operation requirement by calling the XML format file. According to the method, seamless connection of job editing and code editing is realized through a script language-oriented seismic batch processing job framework, so that the method is more flexible and efficient; and diversified requirements of a user for job editing by using a script programming language are met.

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.

The invention relates to a simulation modeling method and system based on a natural language and computer equipment. Comprising the steps that natural language modeling simulation is achieved according to the technology of a large model, an intelligent entity and a protocol module, and the intelligent entity serves as a communication bridge between the protocol module and the large model. After a user question based on a natural language is obtained through an intelligent entity, a response of a large model to the natural language and communication interaction between the large model and a protocol module are triggered; the related target tool interface provided by the protocol module is called by the intelligent entity, so that the tool calling specification is ensured to be accurate, and the protocol module is enabled to determine and execute the target tool after the request is responded for multiple times. Therefore, through interactive use of the natural language of the large model, the use threshold of the user is reduced, the accessibility of the model is improved, codes do not need to be written or complex graphs do not need to be drawn, and the simulation modeling efficiency is greatly improved.