712results about "Program code adaption" patented technology

The invention discloses a Direct3D rendering model compatible method based on a dynamic template pool, which comprises the following steps: establishing three types of mapping tables between D3D and Vulkan when compiling DXVK, constructing resource metadata, and creating a core, extended and temporary three-level template pool according to the mapping tables after starting; when the D3D application creates resources, metadata is initialized, parameters are verified, physical memories are allocated and grouped, resource groups are pre-verified, a batch binding command is generated, memory binding is completed, and the metadata is updated; when a resource view is created, a template is matched from a template pool, a handle is generated after instantiation, a resource handle is associated, and a descriptor is bound; when a rendering state is set and a rendering instruction is executed, the rendering state and the rendering instruction are respectively converted into a Vulkan related state and a Vulkan related instruction through a mapping table, a handle is bound after a PSO cache is inquired, a command buffer area is submitted to a GPU queue to execute drawing, and compatible operation of the D3D application on a platform supporting a Vulkan operating system is realized under the condition that the GPU does not support VKKHRmaintence5 and VKKHRmaintence6 extension.

The invention relates to the technical field of electric digital data processing, and provides a secondary development code cross-product transplantation method and device, computer equipment and a medium. The method comprises the steps of loading a source project, screening a to-be-transplanted source code file, identifying an original API type, determining a mapping relation between the original API type and a target API type, and generating an adaptation layer code framework according to the mapping relation; copying the to-be-transplanted source code file into the target project; on the premise of keeping the project hierarchical structure of the source project, replacing the calling node of the original API type with the calling node of the adapter type, and replacing the namespace reference with the corresponding namespace in the target project to obtain a migrated source code file; and obtaining a secondary development project of the target project based on the second product API according to the adaptation layer code framework and the migrated source code file. According to the method and the device, the transplantation efficiency and accuracy of the transplantation of the cross-product secondary development codes are improved.

The invention provides a Java-to-ArkTS code conversion method and system based on a large model, and belongs to the field of computers.The Java-to-ArkTS code conversion method comprises the steps that S1, a Java project to be converted is comprehensively analyzed, the dependency relationship and semantic information between files are extracted, and a code knowledge graph is constructed; s2, determining a code conversion sequence based on the code knowledge graph, extracting context information and multi-modal semantic information of a to-be-converted code, and completing conversion from Java to TS codes in combination with a multi-agent cooperation mechanism; and S3, comparing and analyzing the TS code with an official ArkTS coding specification, identifying a violation code, and correcting and verifying the violation code through a plurality of intelligent agents to generate an ArkTS code which accords with the ArkTS grammar specification and is consistent in semantics. According to the method, the automatic migration from the Java project to the ArkTS project can be effectively supported, the project migration efficiency and the code quality are improved, and the manual intervention cost is reduced.

The invention relates to the technical field of software and hardware compatibility testing, in particular to a domestic software and hardware compatibility intelligent evaluation system and method, and the system comprises a compatibility analysis engine, a compatibility detection task execution engine, a software behavior analysis module, a fault positioning module, a knowledge graph module, a performance optimization suggestion module and a visual interface. The software behavior analysis module constructs a high-dimensional feature space of a software running state through deep learning, and extracts abnormal features based on a topological space mapping technology; the fault positioning module constructs a dynamic causal network based on a probability graph model, and identifies a root node through a chaos sensitivity analysis algorithm; the knowledge graph module constructs a software and hardware dependency relationship model and provides priori knowledge for fault location; and the performance optimization suggestion module generates an optimization scheme based on the root positioning result, so that the functions of automatically executing a compatibility test, accurately positioning the root of the compatibility problem and providing targeted optimization suggestions are realized, and the adaptation efficiency of domestic software and hardware is greatly improved.

Techniques for complex code modification via chain of thought prompting are described. A modification service obtains code to be modified according to a modification goal, and can decompose the modification goal, via use of a database or a machine learning model, to identify a set of modification steps. The set of modification steps may be organized according to a chain of thoughts, tree of thoughts, or graph of thoughts. The modification service can execute the set of modification steps via use of a machine learning model to yield modified code, which can be returned to a user.

The invention relates to the technical field of business process management, in particular to a data processing method, system and device based on business process management and a medium, and the method comprises the steps: constructing a business process model driven by an abstraction layer, and configuring a multi-protocol standardized interface; according to the business process model driven by the abstraction layer and the multi-protocol standardized interface, a process instance matched with the business scene is generated, and a task is distributed to a target client according to a task demand; managing form state circulation and node jump logic according to a task control engine; an event monitor is deployed through a process key node to monitor the process, and when the process reaches a specified node, external service calling operation is triggered to form an end-to-end automatic closed loop. Therefore, the problems of code redundancy, high maintenance cost, difficulty in quickly adapting to diversified customer requirements and the like due to the fact that business process management generally needs to be subjected to customized development for different subsystems in related technologies are solved.

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 large-model-oriented heterogeneous computing power adaptive compiling method, which comprises the following steps that: a user inputs a trained large model through a system interface, and a system front-end conversion module analyzes a computational graph of the model and converts the computational graph into an intermediate representation based on a unified operator description language (UDL); the system hardware sensing module automatically detects and extracts hardware feature fingerprints of at least one piece of target hardware; based on the unified operator description language UDL intermediate representation and the hardware feature fingerprint, automatically generating an optimization adaptation rule oriented to at least one piece of target hardware; wherein the basis of parameterized filling comprises specific parameters of hardware feature fingerprints and optimized attribute tags carried in an intermediate representation of a unified operator description language (UDL); and generating and deploying multiple back-end codes. The method has the beneficial effects that intelligent compiling based on hardware features can be realized, and the deployment efficiency and the operation performance of a large model in a complex heterogeneous computing power cluster are remarkably improved.

The invention belongs to the technical field of software management, and particularly relates to a cross-platform repair method and system based on a large language model.The method comprises the steps that a cross-platform package manager interface is used for conducting static analysis on a dependency list of a target software project, a cross-platform dependency tree is obtained, and the target software project is repaired based on the cross-platform dependency tree; respectively injecting lightweight monitoring probes into at least two operating system platforms, and collecting dynamic tracking data of each dependency component in the dependency tree; fusing the dependency tree and the dynamic tracking data, and establishing a cross-platform security map comprising a dependency calling edge, a version conflict edge and a platform vulnerability association edge; according to the method, the lightweight monitoring probe is injected into the multi-operating system platform, the loading sequence, the function call sequence and the memory layout difference of the dependent components are dynamically collected, and the cross-platform security map is constructed in combination with the static dependency tree, so that the specific potential safety hazard of the platform can be accurately identified, and the comprehensiveness and the accuracy of cross-platform risk perception are remarkably improved.

The invention provides an automatic cross-project vulnerability verification method and device based on a large language model, relates to the technical field of vulnerability verification, and aims to solve the problem that whether an upstream vulnerability can still be triggered or not is difficult to efficiently confirm in the prior art in a scene that significant differences exist between parameter semantics and input formats of a source project and a target project. According to the method, a multi-agent parallel target sensing parameter migration framework is constructed, and a legal command line parameter combination related to a vulnerability function is automatically generated from a target software manual in combination with an RAG retrieval enhancement technology; a ReAct intelligent agent is further introduced, a source PoC file is embedded into an acceptable input shell of target software in a cross-format mode under the condition that manual intervention is not needed, and an initial cross-format seed is formed. And iteratively optimizing the parameter-seed pair by taking the function-level trajectory similarity as a feedback index, and driving the parameter sensitive grey box fuzzy test to quickly converge to accurate input capable of triggering vulnerabilities.

The invention provides an LLM command line generation tool based on OpenHarmony and a transplantation method, belongs to the technical field of industrial Internet of Things, and aims to solve the problems that a traditional TypeScript / Node. Js scheme has limitation, is insufficient in local reasoning performance, safety performance and reliability and is high in deployment cost. The tool comprises an ETS development environment module, a code migration module, a dependency library adaptation module, a security enhancement module certificate and a deployment and operation module, a zero-dependency transplantation scheme is designed for special environments of unpacked management tools such as OpenHarmony, the problem of strong coupling of a traditional Node.js application to the environment during operation is solved, and the safety of the Node. Js application is improved. And the deployment threshold of the heterogeneous system is further reduced.

Methods and systems for improving modernization of legacy software using an intelligent discovery platform are described herein. A client-based agent may generate metadata regarding the received legacy software. The code metadata may be analyzed by a code classifier module, which computes a plurality of score factors from the metrics from the legacy software metadata using a knowledge base from a modernization platform. The classified code metadata may be used by a project-specific model to derive a plurality of sub-scores based on the plurality of score factors associated with the legacy software. An analytics engine may then identify a code module from the legacy software having a greatest derived vulnerability score factor. A graphical interface including reconstructed code, corresponding modern code, and an explanation of vulnerabilities may then be generated by the analytics and reporting component for the identified code module.

The invention relates to a cross-architecture code automatic migration method, system and device based on a large language model agent and a medium. The method comprises the following steps: acquiring a source code, a construction script and an architecture related header file of a target project; based on a pre-constructed transplantation target system architecture knowledge base, analyzing system architecture differences in combination with construction scripts and positioning architecture related positions needing to be modified in source codes to obtain a preliminary modification scheme; constructing scripts through static analysis and carrying out abstract syntax tree analysis on source codes to obtain a global dependency directed graph; based on a transplantation target system architecture knowledge base, performing migration code semantic matching on the source code according to the preliminary modification scheme and the global dependency directed graph, and generating a semantic equivalent replacement code to obtain a patch list; and sequentially executing incremental compiling, function consistency testing and performance regression verification on the migration project copy after the patch list is applied to generate verification information and a verification result. By adopting the method, cross-architecture code automatic migration can be realized.

Methods and systems for improving modernization of legacy software using an intelligent discovery platform are described herein. A client-based agent may generate metadata regarding the received legacy software. The code metadata may be analyzed by a code classifier module, which computes a plurality of score factors from the metrics from the legacy software metadata using a knowledge base from a modernization platform. The classified code metadata may be used by a project-specific model to derive a plurality of sub-scores based on the plurality of score factors associated with the legacy software. An analytics engine may then identify a code module from the legacy software having a greatest derived vulnerability score factor. A graphical interface including reconstructed code, corresponding modern code, and an explanation of vulnerabilities may then be generated by the analytics and reporting component for the identified code module.

The invention discloses a data processing device, a data processing method, a chip and electronic equipment, and relates to the technical field of data processing. The data processing apparatus includes: a storage module configured to store data; the calculation module is coupled to the storage module and is configured to access an original data vector stored in the storage module, and the original data vector comprises m data elements; splitting the original data vector based on the target k value to obtain a plurality of first data vectors; performing odd-even merging sorting on the data elements in the first data vectors to obtain a plurality of first ordered data vectors; and merging and sorting the data elements in the plurality of first ordered data vectors to obtain top-k data elements in the m data elements. According to the data processing device, the data processing method, the chip and the electronic equipment provided by the invention, the top-k data elements can be obtained without a special hardware sorting network.

The invention discloses a code conversion method and device, a page display method and device, a medium and equipment. The method comprises the steps that code conversion demand information is acquired, and cue words are constructed based on the code conversion demand information; inputting the cue word into a code generation model to enable the code generation model to generate a source code file of the initial conversion tool; performing transcoding processing on the test case through the initial conversion tool, and deploying a converted code to the environment of the target platform to run to obtain a code running result; constructing a repair prompt word based on the code operation result, and inputting the repair prompt word into a code generation model, so that the code generation model corrects a source code file of the initial conversion tool; a code conversion tool is obtained through testing and correction of multiple iterations; and transcoding the project source code file of the source code platform through the code conversion tool to obtain a code file of the target platform. According to the method, the cross-platform code conversion efficiency can be improved.

A computer-implemented method and system standardize code patterns within enterprise software environments. A code modernization system utilizes a Large Language Model (LLM) and a specialized prompt library. The library includes pattern recognition prompts to guide the LLM in identifying specific code patterns within selected software code, potentially using enterprise-specific context. It also includes standardized solution prompts to guide the LLM in generating replacement code conforming to predefined enterprise standards for the identified patterns. The system orchestrates communication, transmitting code and relevant prompts to the LLM and receiving identified patterns and subsequently the generated standardized replacement code. This automated approach facilitates improved code quality, consistency, maintainability, and can support code translation efforts within the enterprise.

The invention relates to a localized intelligent adaptation system and method based on multi-agent interaction, the system is realized based on a large language model and multi-agent cooperation, and the system comprises an offline knowledge extraction module, a diagnosis prompt generation module, a multi-agent cooperation diagnosis module and a man-machine cooperation module. Wherein the offline knowledge extraction module is used for extracting knowledge blocks and dynamically registering an API (Application Program Interface) of an operation and maintenance tool into the system; the diagnosis prompt generation module is used for screening out most relevant knowledge blocks and operation and maintenance tools; the multi-agent cooperative diagnosis module is used for cooperatively generating a diagnosis report among the agents based on asynchronous communication and a cross review mechanism; and the man-machine cooperation module realizes cooperation of manual auditing and automatic repairing through a natural language dialogue interface and a work order system. The invention aims to improve the intelligent level of fault diagnosis and repair through the following innovation breakthrough, and better meet the requirements of localization adaptation.

The invention discloses a cross-platform software component lightweight migration method, which comprises the following steps of: establishing a standardized storage system, and uniformly storing software assets to be migrated, including components, pages generated by the components, modules formed by the pages and applications formed by the modules, as XML (Extensible Markup Language) files; the cross-platform migration adaptation engine comprises a platform information analysis module, an XML (Extensible Markup Language) file analysis module, a difference analysis module, an automatic adaptation conversion module and a manual intervention module; executing a software asset migration process by utilizing a cross-platform migration adaptation engine; and finally, uniformly storing the successfully migrated software asset target XML file and the source platform XML file into a software asset library for management. By the adoption of the cross-platform software component lightweight migration method, cross-platform rapid and efficient migration of software assets is achieved, the asset reuse rate is increased, customization and standardization requirements are balanced, flexible deployment and secondary development are supported, and the problem of existing low-code platform migration is solved.

The invention relates to a storage test vector generation method and device, equipment and a storage medium, and is applied to the field of semiconductor testing, and the method comprises the steps: obtaining a chip manual and a chip test plan of a to-be-tested chip, and generating a chip description file according to the chip manual and the chip test plan; carrying out abstract processing according to the chip description file, and generating a chip abstract model; converting test logic in a test algorithm into a test sequence according to the chip abstract model; and obtaining a target file format requirement of a target ATE equipment platform, and converting the test sequence according to the target file format requirement to generate a storage test vector. The method has the technical effects that the reusability of cross-platform use of the test vector is improved, the test development efficiency is improved, and the time is saved.

The invention discloses a construction method for replacing an iframe embedded page, which comprises the following steps of: replacing an original iframe component with a common component under a front-end framework, and mounting the common component on a DOM (Document Object Model) tree of a main page; obtaining an src link character string which is originally transmitted to the iframe component; analyzing the src link character string, extracting a query character string, and converting the query character string into a parameter object; transmitting the parameter object as an attribute to a common component; and acquiring data in the parameter object by monitoring the change of the attribute in the common component, and driving business logic execution by using the data. Under the condition that routing configuration is not changed, complex control logic is reduced, and the page rendering speed is greatly increased.

The invention provides a code migration method and system based on holographic environment perception and dependent coevolution, and relates to the technical field of code migration. Comprising the following steps: analyzing a Java source code and a configuration condition of the Java source code; determining a target JDK version, combining a JDK compatibility rule and a knowledge base, identifying a part needing to be modified in the Java source code based on a large language model, and generating a modified code; modifying the configuration condition of the Java source code to obtain a modified configuration condition; the modified codes and configuration conditions are applied and verified; generating a migration report based on the verification result; and obtaining feedback of the developer to the migration report, and circulating until the modified code and the configuration condition are confirmed by the developer to obtain the target Java item after code migration. According to the method, the JDK upgrading efficiency is improved, the upgrading success rate and quality are improved, and upgrading failures caused by configuration omission or dependency problems are reduced.

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 mock server is configured to mimic the operation of one or more microservices used in enterprise software. When a new microservice is completed for use in the enterprise software, the new microservice and the mock server can be deployed in a pre-production environment for testing. One or more test cases are created and then executed in the pre-production environment, causing the new microservice to access various other microservices represented by the mock server with one or more predetermined requests. In this manner, the new microservice may be tested in a simulated production environment before other microservices in the enterprise software design have been fully implemented.

The invention belongs to the technical field of aviation airborne system design, and provides a helicopter airborne system-oriented novel software architecture and a construction method thereof, and the architecture comprises a hardware abstraction layer, a framework resource layer, an intelligent platform service layer and an application software layer from bottom to top. And the hardware abstraction layer realizes decoupling of hardware and basic software of an operating system. The framework resource layer supports operation of a hybrid key operating system, supports and isolates tasks and application programs with different key levels, software bases and computing power requirements, and provides basic computing communication resources and AI computing power resources. The intelligent platform service layer comprises middleware, a servitization function unit and a servitization management unit, and the servitization function unit comprises atomic services and enhanced services. And the application layer interacts with the intelligent platform service layer and is decoupled from a bottom software and hardware environment. The framework is suitable for unmanned / manned helicopters, and generalization of platform support and maximum reuse of software assets are achieved.

Various embodiments of the present disclosure include systems, methods, and / or apparatuses for executing an agentic-AI-based software modernization system. An example system may be configured to receive one or more portions of monolithic legacy code of a legacy software program product, the monolithic legacy code comprising a plurality of code segments, group the monolithic legacy code by functionally related code segments of the plurality of code segments to define a plurality of groups, generate an API for each identified group, generate modern microservice code for each identified group to define a modernized software program product, and validate the modernized software program product.

The invention relates to a programming language migration operation method and device, an M language function can be recognized from M language script codes and converted into a Python function, in addition, for other codes with complex M language grammar forms, the other codes can be uniformly mapped and converted into the Python function, and through the functions of context and the like in the Python function, the programming language migration operation can be achieved. Different functions are implemented or different processing logics are executed for different types. The realization difficulty is greatly reduced, complex functions can be quickly realized, and the M language can be effectively operated without depending on a core library of Matlab.

The invention discloses a code processing method and device and a storage medium. The method relates to the field of artificial intelligence and comprises the steps of receiving a to-be-processed code file; analyzing the code file by using a syntactic analyzer to extract label information, style information and program logic information of the code file; and generating a target project file corresponding to the code file according to the label information, the style information and the program logic information. Through the method and the device, the problem of relatively low efficiency of migrating the JSP code to the Vue project in related technologies is solved.

The invention relates to the technical field of graphical user interface generation and rendering, in particular to a multi-terminal-oriented industrial configuration picture self-adaptive presentation system and method, and the system comprises a picture semantic analysis unit, a terminal context acquisition unit, a self-adaptive layout calculation unit and a picture reconstruction and rendering unit. The method comprises the following steps: analyzing an original industrial configuration picture, and generating a picture semantic structure tree containing graphic element attributes and association relationships; collecting target terminal screen parameters, types and interaction modes to form terminal context information; in combination with a preset adaptive rule, calculating an optimal display attribute of the element to generate a layout scheme; and reconstructing the graphic elements according to the scheme and performing layered rendering display. According to the method, multi-terminal precise adaptation of one set of original images is realized, core elements are highlighted, interaction and display effects are optimized, the cost is reduced, the industrial operation efficiency and safety are guaranteed, and the method is suitable for industrial monitoring and control scenes.

The invention discloses a Direct3D12 sampler addressing mode compatible method based on double buffer preprocessing, which comprises the following steps of: establishing a preprocessing parameter cache pool, a first association table, a second association table and a resource management linked list in a system initialization and starting stage, and registering a Vulkan expansion structure body for expanding a texture coordinate range and marking MSAA sampling point offset; when the textures are created, the VKD3D judges whether the textures are MIRROONCE related textures or not, allocates resource marks to the related textures, sets dependency marks, allocates double buffers and expands a coordinate range, and executes standard conversion if the textures are not related; when a sampler is created, marking an analog sampler, constructing and configuring a Vulkan sampler structural body, and updating an association table and a resource management linked list; when a pipeline is created, the D3D12 shader is converted into SPIR-V to update the second association table; and when the recording command list is applied, generating a command buffer, and submitting the command buffer to complete rendering.