561 results about "Code development" patented technology

The invention discloses an intelligent low-code development method and system based on deep learning model optimization, and relates to the technical field of deep learning. A multi-dimensional domain knowledge graph is constructed, a three-dimensional space-time fusion training sample set is constructed based on the domain knowledge graph, and cross-modal feature alignment is performed on the training sample set, so that the multi-dimensional domain knowledge graph is constructed; the method comprises the following steps: generating an executable logic flow template, encoding the executable logic flow template into a Markov decision process, and performing joint strategy optimization on an optimization target of logic flow by integrating a feature importance index generated by a gradient back propagation path and a multi-target reinforcement learning framework of a Pareto leading edge analysis module. Extracting a strategy parameterization sequence after joint strategy optimization, injecting the strategy parameterization sequence into a dynamic verification sandbox environment, and performing abnormal mode detection and feedback type parameter distillation iteration on an execution track through an online variational auto-encoder to complete dynamic adjustment of the strategy; and the elasticity, the stability and the expandability of the low-code platform are improved.

The invention discloses a low-code development automatic generation method based on a large language model, which comprises the following steps: collecting natural language description information input by a user, and preprocessing; inputting the standardized demand corpus set into a large language model, and executing semantic understanding and context modeling; matching the low-code component library based on the structured semantic representation to generate component assembly description information; generating and verifying an engineering skeleton according to the assembly description information, and outputting an executable low-code application initial version; running the executable low-code application initial version, and monitoring and analyzing execution difference to generate an increment adjustment instruction; and inputting the increment adjustment instruction into the large language model, performing reconstruction and adaptive optimization, and outputting an executable low-code application final version. According to the method, large language model semantic understanding and adaptive optimization technologies are fused, automatic generation and continuous optimization of low-code applications are realized, and the method has the advantages of intelligence, high precision and engineering reliability.

The invention provides an interactive integration method and system for an AI large model and a low-code platform, and relates to the technical field of interaction of the AI large model and the low-code platform, and the method comprises the steps: obtaining a task demand input by a user, analyzing the task demand into a structured task instruction, automatically matching an optimal AI model, and dynamically generating an adaptive API call parameter, according to the method, the AI large model and the low-code platform can be deeply integrated, the technical problems of integration complexity, interaction mode solidification, updating lag and the like are solved, and the method has the advantages that the method is simple and convenient to operate, and the method is suitable for popularization and application. According to the method, a user can call a complex model without AI professional knowledge, the low-code development efficiency is improved by more than 50%, and the security in a data processing process is enhanced by introducing a security verification mechanism, so that the integration efficiency of a low-code platform and an AI large model is greatly improved.

The invention provides a modular construction method of a low-code visual interaction system, and relates to the technical field of low-code development. The method comprises the following steps: constructing an edge cognitive topology network, packaging each node into a reconfigurable meta-module, analyzing a user operation track by using an intention perception interface to generate a meta-intention vector, and carrying out context correlation analysis through a semantic behavior chain to generate a potential behavior sequence. And establishing an asymmetric coupling relationship based on an implicit context mapper, forming a dynamic semantic resonance field, triggering distributed intention resonance and activating a matching element module, and generating a collaborative logic cluster under the constraint of edge computing node resources. And dynamically adjusting an interaction rule and a resource allocation strategy of the logic unit. According to the method, automatic construction and high-precision intention adaptation of complex business logic are realized, the elastic expansion capability and the real-time response efficiency of the system in an edge computing scene are improved, and the risk of logic fault and resource scrambling caused by node isomerism is reduced.

The invention discloses an AI low-code development component automatic generation method based on a knowledge graph, and relates to the technical field of AI low-code development, and the method comprises the following specific steps: constructing an AI development knowledge graph: constructing a knowledge graph containing a five-layer structure, collecting and preprocessing multi-source data, and extracting various entities and association relationships, so as to obtain an AI development knowledge graph; constructing an ontology structure, importing the ontology structure into a database, and forming a final knowledge graph through manual auditing and machine reasoning optimization; according to the method, the multi-dimensional AI development knowledge graph is constructed, user requirements are analyzed in combination with the optimized natural language processing model, the component configuration scheme is generated by introducing the multi-level reasoning mechanism, and finally the standardized low-code component is automatically generated, so that full-automatic conversion from the user requirements to the low-code component is realized, and the user experience is improved. The defect that an existing low-code platform depends on manual component configuration is overcome, the development period is greatly shortened, and the development cost and the technical threshold are reduced.

Provided in the present application is a code generation method, comprising: a code development platform receives input information by a user in a first code file, extracts context information of the input information according to a data warehouse to which the first code file belongs, and retrieves a business knowledge base corresponding to the data warehouse according to the input information so as to obtain target business knowledge; then the code development platform combines the input information, the context information and the target business knowledge according to a prompt template, so as to obtain prompt information; and then the code development platform inputs the prompt information into a large language model (LLM) for reasoning, and presents to the user a code segment reasoned by the LLM. Thus, using the prompt information from the same data source in different processes for prompting the LLM can achieve complete sharing and complete alignment of the prompt information, thereby improving the accuracy of generating code in single time. Furthermore, using a unified prompt template to combine the context information and the business knowledge can make a code segment generated by the LLM more accurate, thereby improving the code acceptance rate in professional domains.

Systems and methods described herein enable adaptive, threshold-based modification of node maps representing ontologies, knowledge graphs, or code development pipelines using generative artificial intelligence. The disclosed platform can retrieve a node map and generate one or more candidate perturbations that modify nodes or relationships within the node map. The disclosed platform can evaluate the effect of the perturbations by comparing respective outputs against ground-truth data. Perturbations can be automatically determined based on changes in external datasets, compliance policies, or operational requirements. The perturbations can be implemented when a computed perturbation quality value satisfies a threshold quality criterion. As such, the system enables efficient, policy-compliant evolution of relational system architectures in dynamic environments.

The invention discloses a software development method and device based on artificial intelligence, equipment and a medium, and the method comprises the steps: receiving a development demand of a user through a natural language interaction interface, and capturing context state information of a current development environment; and a historical solution of the development demand is retrieved based on the dynamic knowledge graph through the product agent. And when the historical solution based on the development demand is retrieved, generating an executable code by encoding according to the hierarchical code generation strategy based on the historical solution through the architect agent. And performing grammar verification, operation verification and demand conformity detection on the executable code by combining the context state information based on a hierarchical verification mechanism through a test agent to obtain a verification result. And when the test agent judges that the verification result meets the development requirement, summarizing the executable code into a final code implementation scheme through the summarizing agent, and pushing the final code implementation scheme to a user. And the reuse rate and the quality control capability of code development are improved.

The invention provides an industrial PLC code generation system and method based on artificial intelligence, and the system comprises an input module, a model optimization module, a code generation module and a deployment module, the input module is used for receiving and analyzing a natural language, a flow chart and an I / O configuration table, and generating a structured logic constraint; the model optimization module is used for supporting low-precision reasoning and dynamic expert network load balancing by adopting an artificial intelligence model of industrial control field data fine tuning; the code generation module is used for outputting PLC codes meeting industrial standards and supporting simulation verification and error correction; and the deployment module is used for downloading the generated code to target PLC equipment or integrating the generated code to engineering software. According to the industrial PLC code generation system and method based on the artificial intelligence, a tool for generating the PLC code based on the natural language of the artificial intelligence is provided, the code development efficiency is improved, and the error rate is reduced.

The invention discloses an intelligent file service system generation method based on a low-code development mode. The method comprises the following steps: constructing a file service core data model, defining file metadata fields, a storage structure and an association relationship, and forming a standardized data architecture. According to the method, the core data model, the visual page component library and the drag-and-drop workflow template are constructed, linkage binding of data-component-process is achieved in combination with a low-code engine, and the efficiency bottleneck of a traditional development mode is broken through; through a three-way real-time synchronization mechanism, real-time hot repair and dynamic branch judgment, page layout and flow rule adjustment can take effect immediately through visual configuration, and the problem of dynamic demand response lag is solved; through the intelligent resource scheduling logic compiled through environment adaptation, a differentiation scheme is generated for different deployment environments, the configuration difference influence under a private cloud and server mixed deployment scene is remarkably reduced, the deployment failure rate is greatly reduced, and the system online stability is improved.

The invention provides a knowledge graph construction method based on a low-code development platform, and belongs to the technical field of knowledge graph construction. The method comprises the following steps: counting and carding metadata and business entity structures in a low-code development platform, and formulating a mapping specification for mapping from a relational database to semantic data; obtaining a target relational database in the configuration file, extracting table data and column data in the target relational database, and converting the table data and the column data into entity class parameters and attribute class parameters in the knowledge graph based on a mapping specification; extracting an unstructured data source related to the target business from the low-code development platform, and converting the unstructured data source into structured data; and associating the structured data with the entity class parameters and the attribute class parameters, and storing the associated data in the knowledge graph. Automatic mapping and storage from the relational database to the knowledge graph are realized, so that the data utilization rate and the business intelligence level are improved.

The invention discloses an AI semantic understanding-based zero-code multi-terminal application automatic construction method, which comprises the following steps of: receiving a UI design draft image and layer metadata uploaded by a user, respectively extracting visual features and structural features through a double-branch feature extractor based on an AI semantic understanding technology, and establishing an AI semantic understanding model; a cross-modal attention module and a cavity space pyramid pooling module are combined to generate an enhanced feature graph, and a high-precision UI component mask and an interaction dependency graph are generated; according to target end equipment parameters, dynamic weights are generated through a multi-layer perceptron, layout constraints of the interaction dependency graph are adjusted, a layout target function is optimized, and component overlapping and visual unbalance are minimized; based on the predefined control library and the mapping rule, the UI component is mapped into the atomic control of the target platform, the interaction logic is converted into the event-action chain, and the target platform code is generated, and the automation degree and the cross-platform consistency of zero code development are improved through dynamic adaptation of the target end equipment and automatic code generation.

The present application discloses a test code repairing method applied to a code development platform. The method comprises: obtaining a test code corresponding to a source code, and then executing the test code to obtain execution information of the test code, wherein the execution information comprises abnormal stack tracking information output during execution of the test code; on the basis of the execution information, performing fault localization on the test code to obtain location information of a faulty code; on the basis of the location information of the faulty code, extracting context information, wherein the context information comprises at least one of a faulty test case, a fault description, or the faulty content; and inputting a prompt constructed on the basis of the context information into a language model and performing inference, so as to obtain a first repaired code. The method is used to perform fault localization in light of the execution information, extract the context information on the basis of the location information of the faulty code, and, on the basis of the context information, generate accurate and complex repaired code via an advanced language model, thereby adapting to various complex fault scenarios and improving repair efficiency and accuracy.

The invention discloses a low-code development platform event arrangement method and device based on native JS, and relates to the technical field of visual programming, and the method comprises the steps: responding to an event arrangement request sent by a user through a visual development interface, calling a visual framework, and presenting an event arrangement canvas in the visual development interface; configuring event nodes and event process connections in the event arrangement canvas; generating event flow configuration data based on the event node and the event flow connection; converting the event flow configuration data into a native JavaScript code, calling a rendering engine, and rendering a page style and page data of the visual development interface according to the native JavaScript code; and in response to an interactive operation of a user on the visual development interface, triggering the corresponding native JavaScript code to execute a corresponding event process. The development efficiency can be effectively improved, and the development period is shortened.

The invention relates to the technical field of code pre-configuration, in particular to a demand-oriented code pre-configuration method, system and equipment and a storage medium. Matching a plurality of performance function number sequences from the code function library according to the function requirements of the target scene; performing function demand sample frequent mining on the target scene to obtain a function demand input parameter and a function demand output identifier set; inputting the function demand input parameters into a plurality of function number sequences to obtain a plurality of function demand prediction outputs; traversing the plurality of function demand prediction outputs for outlier analysis to obtain a plurality of output prediction outlier factors; traversing the plurality of performance function number sequences to carry out calculation power demand prediction to obtain a plurality of expected calculation power; and based on the plurality of expected computing power and the plurality of output prediction outlier factors, sorting the plurality of performance function number sequences to obtain a target performance function number sequence, and executing code pre-configuration. The code development efficiency is improved, and the code practicability is enhanced.

The invention provides a sandbox isolation style debugging method and equipment based on a generative AI technology, and belongs to the technical field of low-code development, and the method specifically comprises the following steps: creating an isolated sandbox rendering environment, loading a user-defined style, and establishing an accurate corresponding relation between a preprocessed code and a final CSS; the custom style is injected into the last position of a head area of a sandbox environment through a created stack label to provide a real-time preview interface, so that when a user modifies the style in a style editor, a page in a sandbox updates and displays a modification effect; receiving a modification operation of a user on the style through the style editor or the style editor, and injecting a sandbox environment to trigger interface updating; and storing the final custom style file. By creating the isolated sandbox rendering environment, the style debugging process is limited in an independent space, the influence of style conflicts on the whole page is effectively avoided, and the debugging safety and stability are improved.

The embodiment of the invention discloses a contract generation method and device based on a low-code platform and a medium, and relates to the technical field of low-code development, and the method comprises the steps: obtaining a contract creation demand form inputted by a user through a form interface corresponding to the low-code platform, the contract creation demand form comprises contract dynamic data and contract attribute information; based on the contract attribute information, a corresponding contract template file is loaded on a front-end page through a preset plug-in, contract configuration information corresponding to the contract template file is determined, and the contract configuration information comprises form fields and business logic rules; and according to the contract configuration information, performing front-end and back-end verification on the contract dynamic data, and when the cooperative verification is passed, performing dynamic matching on the contract dynamic data and the contract template file to generate a target contract file.

The invention discloses a solution for intelligent low-code construction and automatic deployment of a multi-terminal application full stack, belongs to the technical field of software development, and solves the problems that an existing low-code development platform is high in use threshold, does not support customized development and is disjunction in application construction and application use. The invention provides an intelligent low-code construction platform. The intelligent low-code construction platform comprises a low-code construction platform, an application rendering unit, an application deployment unit and a display interaction unit, the low-code construction platform is used for constructing a plurality of application pages according to the developer personalized configuration information, and associating the plurality of application pages according to set association logic information among the plurality of application pages to obtain an application program; generating application data according to the application program, and storing the application data; the page cross-end rendering unit is used for performing Web end and App end rendering on the application program according to a defined page rendering protocol to obtain a Web end application and an App end application; and the display interaction unit is used for displaying the interaction function and the application logic of the Web end application and the App end application.

Disclosed in the present application is a code generation method, which is applied to a code development platform. The code development platform comprises a main plug-in and a plurality of extension plug-ins, wherein the main plug-in is used for providing an actual value of a basic variable of a prompt, and the plurality of extension plug-ins are used for extending the prompt in terms of a programming language, scenario or development framework. The method comprises: a main plug-in receiving a code generation instruction, and then determining, in response to the code generation instruction, at least one extension plug-in corresponding to the code generation instruction from among a plurality of extension plug-ins; the main plug-in receiving a prompt returned by the at least one extension plug-in, wherein the prompt is obtained by means of the at least one extension plug-in extracting an actual value of an extension variable on the basis of extraction logic and replacing an extension variable in a prompt template with the actual value of the extension variable; the main plug-in performing inference by means of a language model and on the basis of the prompt, so as to obtain a second code segment. In the method, a prompt of a specific code generation task and the implementation of prompt engineering is entrusted to corresponding extension plug-ins, thereby realizing flexible extension of prompts.

Disclosed in the present application is a code generation method. The method comprises: a code development platform receiving input information from a user for a first code file in a project; acquiring in-file context and first cross-file context on the basis of a static structure of the project; acquiring second cross-file context on the basis of behavior characteristics of the user when developing the project; acquiring third cross-file context on the basis of the degree of evolution coupling between at least one second code file in a code warehouse of the project and the first code file; generating prompt information on the basis of the input information, the in-file context, the first cross-file context, the second cross-file context and the third cross-file context; inputting the prompt information into a code generation model for reasoning, so as to obtain at least one group of generated codes; and presenting to the user the at least one group of generated codes in parallel. The method takes multi-dimensional project-level context and a user input as inputs of a code generation model, thereby improving the capability of the code generation model using cross-file context during code generation, and improving the code generation performance.

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for efficiently generating code development summaries. In embodiments, commit message data for a commit message associated with code is obtained. In embodiments, the commit message includes a description associated with a corresponding modification in the code. Thereafter, a model prompt to be input into a large language model is generated. The model prompt includes the commit message data. As output from the large language model, a code development summary is obtained that summarizes the commit message data for the commit message associated with the code.

The invention belongs to the technical field of low-code development platforms, and particularly relates to a low-code platform model construction and dynamic execution method and system based on a hybrid DSL (Digital Subscriber Line), and the method comprises the steps: describing static model information in an application by using a structure DSL in a declarative grammar; defining a behavior DSL in a mounting mode under a corresponding element of the structure DSL; analyzing the structure DSL into a meta-model object tree in the platform, and converting the behavior DSL into an executable expression tree / intermediate representation; jointly driving the meta-model object tree and the expression tree / intermediate representation by utilizing a template engine, and automatically generating at least one application code in a front-end page, a back-end interface and a database script; a common structure DSL and a behavior DSL are combined and packaged into a scene template, and the scene template is stored in a template library so as to be reused by different service scenes, and modularization and templating of service modeling are realized. The complexity of the system is reduced, and the development efficiency and quality are improved.

The invention provides a code generation method, which comprises the following steps that: a code development platform receives input information of a user in a first code file, extracts context information of the input information according to a data warehouse to which the first code file belongs, and retrieves a business knowledge base corresponding to the data warehouse according to the input information to obtain target business knowledge, and then the code development platform splices the input information, the context information and the target business knowledge according to a prompt template to obtain prompt information, then the code development platform inputs the prompt information into a large language model (LLM) for reasoning, and a code snippet generated by LLM reasoning is presented to a user. The LLM is prompted by using the prompt information of the same data source in different processes, complete sharing and complete alignment of the prompt information are realized, the accuracy of single code generation is improved, and context information and business knowledge are assembled through a unified prompt template, so that code snippets generated by the LLM are more accurate. Therefore, the code acceptance rate in the professional field is improved.

The invention is applicable to the technical field of code development, and provides a code modification method based on a CURD scene, which comprises the following steps: acquiring a business demand of code modification, and acquiring a data model required by code modification according to the business demand; based on the data model, obtaining a code file related to a CURD scene during code modification; analyzing a calling relationship among functions related to the CURD scene in the code file, and constructing a function calling graph; on the basis of the service requirements and the data model, service code snippets are generated, the service code snippets are combined and modified to the code file, code modification of the service requirements can be processed, and development efficiency and code quality are further improved.

The invention provides a configurable processing method, system and device for multi-form process event linkage and a medium, and belongs to the technical field of low-code development platforms. A basic database containing associated fields is constructed, a process database is constructed, and a data mapping relation is established between the process database and the basic database through the associated fields; according to the current node identifier of the process database and the service state of the basic database, determining a relationship between nodes; cross-form data query or editing is realized by binding the process model and the associated field; configuring an operable button according to the business rule and the operation authority; the method comprises the following steps: configuring a custom script, creating a process model, configuring a process event, defining a process approval node and a process rule, ensuring process jump after state change, and forming end-to-end state synchronization. According to the invention, the business logic realization process can be simplified, and all related business rules and verifications can be ensured to be executed at correct opportunities, so that the business processing efficiency is effectively improved.

In some implementations, a code testing device may receive, from a first device associated with a developer, a first instruction to merge a first code branch. The code testing device may perform a first set of tests, associated with a first environment, in response to the first instruction. The code testing device may merge the first code branch based on the first set of tests resulting in a passing outcome for the first code branch. The code testing device may receive, from a second device associated with a reviewer, a second instruction to deploy the merged first code branch. The code testing device may perform a second set of tests, associated with a second environment, in response to the second instruction. The code testing device may deploy the merged first code branch based on the second set of tests resulting in a passing outcome for the merged first code branch.

Disclosed in the present application is a code generation method. The method comprises: a code development platform receiving a code generation task, and extracting code context on the basis of the code generation task, wherein the code context comprises code snippets in a project which are related to a code to be generated; the code development platform determining, on the basis of the code context and by means of a language model, whether to call an application programming interface (API) of a language service; when determining to call the API of the language service, the code development platform sending an API calling request to the language service, so as to obtain an API calling response from the language service; and on the basis of the code context and the API calling response from the language service, the code development platform performing inference by means of the language model, so as to obtain a generated code. The method endows a language model with the capability of autonomously calling an external tool, e.g., a language service, such that the language model can simulate, during inference, a human developer to use the language service to assist the language model in code generation, thereby alleviating and even eliminating the problem of hallucination caused by the language model during code generation.

The invention relates to the technical field of software development, and discloses a development platform and a development method of a visual low-code system, and the development platform comprises a data management module which supports a plurality of database types, provides data backup, recovery, encryption, compression and caching mechanisms, and can realize the storage, management and security protection of data; the intelligent service module is used for providing code generation, error detection and repair and authority management services and integrating data mining and machine learning algorithms to realize optimization of a business process; the application development module supports multiple development modes and multi-user cooperative development, and provides intelligent form generation, intelligent code association and intelligent algorithm library functions; the dynamic display module provides a visual graphic operation interface, supports multi-terminal access and real-time data display, and can dynamically adjust service logic and respond to changes of service requirements in real time. According to the method, the development threshold is obviously reduced, and efficient, flexible and internationalized low-code development is realized.

The invention discloses a method and a system for automatically generating a business APP of a fusion terminal, which are applied to the technical field of smart power grids, and the method comprises the following steps: accessing an external storage device to a target fusion terminal; the management APP of the target fusion terminal obtains basic information of a service APP needing to be generated, and sends the basic information to the packaging APP and the guarding APP; the packaging APP selects a corresponding adaptive template from a template library according to the adaptive data, and performs parameter configuration on the selected adaptive template to form a service APP needing to be generated; the management APP is locally deployed and operated in the target fusion terminal; and the guarding APP maintains the running service APP according to the configuration data. According to the method, code development is not needed, and the service APP is automatically configured and generated; meanwhile, automatic operation and maintenance of the generated service APPs are achieved, the service APPs which are not needed temporarily are directly deleted from the fusion terminal, and storage resources of the fusion terminal are effectively saved.

Embodiments of the present invention provide a system for performing multi-platform software code merging and migration using hybrid neural networks. The system is configured for identifying source files in a software code development environment, wherein each of the source files comprise software code associated with entity applications that is developed by one or more users in one or more software programming languages, translating the software code in each of the source files that is developed in the one or more software programming languages into a translated code that is in a first software programming language, merging the translated code associated with the software code in each of the source files into a unified code, wherein the unified code is in the first software programming language, automatically performing build and packaging of the unified code for deployment, and automatically deploying the packaged unified code into a software code production environment.