366results about "Programming languages/paradigms" patented technology

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.

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.

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.

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.

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 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.

The invention discloses a security event research and judgment script generation method and device based on natural language processing, and the method comprises the steps: carrying out the analysis of an obtained user security demand, and obtaining semantic information; based on the semantic information, screening out corresponding asset parameters and mapping the asset parameters to a preset script template to generate an executable script; arranging existing process nodes and gateway nodes according to business process requirements to form a process file; and executing the corresponding security task based on the process file and the executable script. Manual writing time consumption can be greatly shortened, non-technical personnel can participate in natural language, manpower threshold limitation is broken through, and the manpower coverage range is remarkably expanded; in the aspect of process arrangement, dynamic adjustment is achieved, flexible adaptation is achieved for different asset types and threat levels, the matching degree of response strategies in a complex attack scene is greatly improved, the safety operation efficiency and threat coping efficiency are comprehensively improved, and the safety protection requirement in a complex network environment is met.

The invention discloses a CAE script automatic generation method and system driven by a self-improvement RAG technology, and belongs to the technical field of script production, and the method comprises the steps: obtaining and preprocessing an original user instruction, carrying out the intention recognition and key entity information extraction of the preprocessed original user instruction, obtaining the intention and key entity information, and generating a CAE script according to the intention and key entity information. Obtaining at least one RAG query request according to the intention and the key entity information, retrieving a knowledge base to obtain a plurality of related knowledge fragments, and obtaining a context information set based on the plurality of related knowledge fragments; prompt is obtained from the original user instruction, the structured task parameters and the context information set, the Prompt is input into a large language model, CAE script codes are obtained, the CAE script codes are classified, a classification result is obtained, and the knowledge base is updated based on the classification result. Professional knowledge can be deeply fused, the success rate and reliability are improved, the script quality is improved, and the technical threshold of use is reduced.

Example implementations include a method, apparatus, and computer-readable medium configured for generating codelets. An interface between a user and a large language model receives a natural language intent for a codelet to be executed within a network function to output one or more requested pieces of information from the network function. A codelet generation application generates a prompt to the large language model to write a codelet in an imperative procedural language to output the one or more requested pieces of information, the prompt including a hook point of the network function and one or more data fields of the network function associated with the one or more requested pieces of information. The application receives, in response to the prompt, imperative procedural language code for the codelet. The application statically verifies whether the procedural language code for the codelet satisfies constraints for execution within the network function.

The invention provides a front-end memory optimization method and system, electronic equipment and a storage medium. The front-end memory optimization method comprises the steps that in the page initialization stage, multiple pieces of first data needing to be rendered are acquired; determining a label of each piece of rendered first data; the first data of which the label is a first type label is taken as second data, and the first type label is a label for automatically performing event monitoring; and rendering the second data into third data with a second type label according to a preset front-end framework, and displaying the third data on the page, the second type label being a label which does not perform event monitoring. According to the method and the device, the memory performance bottleneck caused by page rendering is solved, such as the problems of large memory usage, page lagging, white screen and long page rendering time.

The invention relates to the technical field of artificial intelligence, in particular to an interface service construction method and device based on multiple Agents, equipment and a medium. The method comprises the following steps: receiving a construction request of an interface service, and calling a demand docking Agent to extract a service demand in the construction request to obtain a demand document; the demand document comprises business logic, a data source table and a field mapping relation; the task planning Agent is called to generate a task flow chart according to the business logic and the field mapping relation, and the task flow chart is sent to the SQL writing Agent and the script writing Agent; calling an SQL compiling Agent to generate an SQL statement corresponding to the interface service according to the data source table and the field mapping relation; constructing a syntax tree corresponding to the SQL statement according to the SQL statement and the task flow chart; and calling a script writing Agent to perform script writing according to the SQL statement, the syntax tree corresponding to the SQL statement and the task flow chart to obtain an interface script corresponding to the interface service.

The present disclosure relates to a computer-implemented method for, in particular automatically, transforming, in particular preparing and / or migrating, a legacy source code associated with at least one legacy system, in particular a legacy software system, comprising: adapting, in particular training, an artificial intelligence (K1) initial model that is trained, in particular pre-trained, on a programming language of the legacy source code using training data associated with the legacy source code to generate a legacy source code transformation model, and processing at least a portion of the legacy source code using the legacy source code transformation model to generate a, in particular at least partially, transformed source code.

The invention discloses a multi-Agent cooperation AADL model AGREE verification statement automatic generation and verification method. The method comprises the following steps: S1, constructing a domain knowledge base; s2, the Agent is analyzed by the AADL model to extract key information; s3, the demand analysis Agent splits atomic propositions from the natural language demand; s4, the AGREE generator Agent generates an AGREE attachment code; s5, the model fusion Agent automatically embeds the AGREE specification into the target AADL component; s6, the AADL verification Agent performs iterative verification and automatic repair, and a multi-dimensional verification system is constructed; and S7, executing AGREE-based AADL architecture formalized verification, and performing logic attribute verification on the grammar compliance model. According to the invention, innovative fusion of a formalized verification method and an intelligent technology is realized, and the core problem that formalized verification of the AADL architecture is difficult to implement is effectively solved.

The invention provides a code processing method and device of an integrated circuit, electronic equipment and a storage medium. The method comprises the steps that a source code is obtained, the source code comprises parameter information of a sub-module in the integrated circuit, and the source code is a register transfer level code; constructing a structured encapsulation unit tree based on the source code; packaging the structured packaging unit tree according to a high-level hardware description language to obtain a structured object; instantiating the structured object to obtain a hardware description language module; and in response to the received connection instruction, code generation is performed based on the connection instruction and the hardware description language module to obtain a connection code, the connection instruction comprises a connection relationship between the sub-modules in the integrated circuit, the connection code indicates a signal transmission path between the sub-modules in the integrated circuit, and the connection code is a hardware description language code. According to the invention, the connection efficiency of signal transmission paths between modules in an integrated circuit design can be improved.

The invention relates to the technical field of EDA software application, in particular to a command line completion method, a TCL command line interaction system, equipment, a medium and a product. The command line completion method comprises the following steps: receiving an input to-be-processed command, and obtaining an actual version number corresponding to the to-be-processed command; based on a preset command set corresponding to the actual version number, the to-be-processed command is matched, and matching means that at least part of continuous characters, starting from the first character, of the preset command in the preset command set are consistent with the to-be-processed command; when the to-be-processed command is matched with the preset command in the preset command set, judging whether the matched preset command is unique or not, and if the matched preset command is unique, outputting the unique matched preset command; and if not, outputting a command list containing all the matched preset commands. The TCL command line interaction system, the computer equipment, the computer readable storage medium and the computer program product have the same beneficial effects as a command line completion method.

The invention relates to the technical field of software adaptation, in particular to a Beidou communication software development cross-platform adaptation method and system. The method comprises the following steps: defining an interface function library; constructing a virtual hardware logic module; linking the to-be-tested software, the interface function library and the virtual hardware logic module by utilizing a link rule, and determining an executable file of a single process; in the process of performing adaptation on the to-be-tested software by using the executable file of the single process, setting a simulation state of the virtual hardware logic module in response to calling of the state control interface by the test code, and setting a simulation state of the virtual hardware logic module in response to function calling of the native interface function by the to-be-tested software. And calling the virtual hardware logic module through an interface function library so as to carry out adaptation processing on the to-be-tested software. The invention aims to solve the problem that cross-platform adaptation cannot be realized due to the fact that all behavior characteristics of native hardware interfaces of different operating systems cannot be accurately reproduced in a virtual environment in the prior art.

The invention provides an AI-based code review opinion automatic classification and priority ranking method. The method comprises the steps of collecting code review historical data; data preprocessing: preprocessing the collected data, and constructing a training data set; an AI model is selected and trained; inputting a new review opinion; automatic classification: the model automatically classifies the review suggestions, and classification categories include but are not limited to code errors, code style problems, performance optimization suggestions and potential safety hazard prompts; performing priority ranking: performing priority ranking on the review opinions based on a preset priority rule and the classification result; and generating result output. According to the code review method and device, automatic classification and priority ranking of the code review opinions are achieved through the AI model, a traditional manual processing mode is replaced, the problems that manual classification is low in efficiency and priority ranking is high in subjectivity are effectively solved, and the overall efficiency and result accuracy of code review are improved.

The invention relates to a cross-platform software development method and related equipment. The method is applied to the electronic equipment providing a low-code tool chain, and the low-code tool chain comprises a low-code development platform and a compiler. Based on the configuration of the meta model of the vehicle-mounted resource, the low-code development platform generates an automobile domain specific language VDSL file for describing the vehicle-mounted software function; the compiler obtains the VDSL file, compiles the VDSL file into running program codes running in different controllers and sends the running program codes to a cloud server, and the cloud server is used for sending the running program codes to the different controllers. According to the method and the device, the running program codes corresponding to different controllers are generated through the unified VDSL, so that cross-platform running software programs can be conveniently developed in the same platform, the software development efficiency is improved, and the development cost is reduced.

The invention relates to the technical field of edge computing and intelligent voice interaction crossing, in particular to an embedded software real-time interaction method and system based on a large model, which comprises an embedded software module used for providing hardware control and business logic functions of a bottom layer and constructing a local model context protocol tool set, and a large model module used for establishing a local model context protocol tool set. And the large model module is deployed on embedded equipment, is used for carrying out natural language understanding and task planning, and comprises a small large language model subjected to supervision fine tuning, quantification and reasoning acceleration, an intelligent agent module and a man-machine interaction module. By means of the strong natural language understanding and generating capacity of the large language model, a user can use natural and spoken instructions to interact with the system, the constraint of a fixed command mode is thoroughly got rid of, the large language model has deep context understanding and logical reasoning capacity, multi-step and multi-condition complex instructions can be processed, and the user experience is improved. And an execution path is automatically planned.

The invention discloses a compiling method for compiling a C language source code into an RISC-V assembly code. The method comprises the following steps: acquiring a C language source code; performing lexical analysis on the C language source code to generate a mark flow; performing syntactic analysis on the mark flow, and constructing an abstract syntax tree; performing semantic analysis on the abstract syntax tree to generate a target abstract syntax tree; constructing a runtime environment of the RISC-V assembly code; generating an intermediate code and a control flow diagram corresponding to the intermediate code according to a rule in the runtime environment and the target abstract syntax tree; generating a target code by using the intermediate code and the control flow diagram corresponding to the intermediate code; according to the technical scheme, the intermediate representation more adaptive to RISC-V custom instruction mapping can be generated, so that the execution efficiency of assembly codes is improved; and meanwhile, by designing a lightweight runtime environment, the performance overhead is further reduced.

Various embodiments of the teachings herein include a workflow creation method. Some examples include: receiving an operation performed by a user on a GUI constructing a functional block model; receiving an operation performed by the user including nesting a behavior tree into a second functional block, wherein the behavior tree defines an operation for a first workcell, and the functional block model defines an operation for a second workcell; in response to a first functional block in the functional block model triggering an input event of the second functional block, then the second functional block triggers an execution signal to a root node in the behavior tree at a preset frequency, so the behavior tree starts execution from the root node and outputs an execution result; the second functional block triggers a corresponding output event according to the execution result; and generating an instance of the functional block model.

A method and a system for using a large language model (LLM) to automatically translate textual instructions into executable software code via skill distillation and composition are provided. The method includes: receiving a request for performing a task and a prompt; providing, as an input to an LLM, the first request and a response to the prompt; receiving, from the LLM, a set of code that implements a function that corresponds to a skill that is usable for performing the task; generating a test that relates to the task; performing the test by executing the set of code and checking whether the task has been successfully completed; and when the task has been successfully completed, storing the set of code in a skills library. Sets of code stored in the skills library may then be accessed and combined in order to perform larger tasks.

The invention discloses a cross-platform page code generation method based on a large model, and relates to the technical field of front-end development, and the method comprises the following steps: cooperatively analyzing a UI design drawing through a plurality of special large models, and carrying out real-time cross validation and dynamic compensation on an analysis result by adopting a multi-model mutual verification mechanism; based on a predefined standard specification, dynamic adjustment is carried out through a platform adaptation rule base; generating a page object description tree embedded with the input / output processing function; cross-platform page codes are generated through a code generation engine, and sandbox testing and automatic correction are executed; outputting a target platform code by utilizing a unified compiling engine; an adaptive optimization loop is constructed based on test feedback. According to the method, through integration of multi-model collaborative analysis, platform rule dynamic adaptation, sandbox verification and closed-loop optimization, the problems of large analysis deviation, poor cross-platform compatibility, uncontrollable code quality and the like are solved, and end-to-end high-quality automatic generation from a design drawing to multi-platform codes is realized.

The invention discloses a workflow configuration method and device, a medium, equipment and a computer program product, and relates to the technical field of large language models.The method comprises the steps that in response to configuration of a target node in a workflow, candidate reference nodes corresponding to the target node are determined according to layout information of the workflow, the layout information comprises a plurality of nodes in the workflow and association relationships among the nodes; determining an input text of the agent based on the layout information, the description information of the target node, the cue word of the agent and the node information of the candidate reference node; and configuring the target node based on the input text and the intelligent agent to obtain configuration information of the target node. Therefore, the accuracy of the determined configuration information of the target node can be improved, and the problem that parameters referenced by node configuration are wrong due to the deviation of action scope understanding of the intelligent agent on the variables in the layout information is avoided.