41 results about "Syntax error" patented technology

The invention discloses a natural language-to-SQL (Structured Query Language) method and system based on RAG (Random Access Gateway) enhancement, and the method comprises the steps: constructing a query intention graph through dependency syntax analysis, recognizing and complementing semantic missing components, and forming complete semantic representation; vector representation is carried out on the business term segments by adopting vectorization coding, accurate definitions of business terms are obtained from a factory structured knowledge base, and an enhanced context set is formed through expansion retrieval in a low-confidence region; an SQL template mapping network is established based on historical query records, a mapping relation matrix is generated through field candidate matching, mapping conflict positions are identified, and multiple SQL candidate sequences are generated; grammar verification is carried out on the candidate sequence to identify grammar errors, semantic consistency verification is carried out to calculate the intention alignment degree, and an optimal SQL statement is selected through a deviation correction factor; and performing formatting processing and statistical abstract on an execution result, and providing data query capability for scenes such as factory quality management and equipment maintenance.

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.

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

The embodiment of the invention provides a code error repairing method based on a compiler, which comprises the following steps: receiving a target code written by using a target language, and executing a first repairing operation on the target code in the process of compiling the target code by the compiler to obtain a first repairing sequence, the first repairing operation comprises lexical error repairing corresponding to the lexical analysis process and grammatical error repairing corresponding to the grammatical analysis process. Performing a plurality of rounds of semantic repair operation based on an abstract syntax tree, wherein the abstract syntax tree used for the first round of semantic repair operation is generated after the first repair sequence is subjected to syntactic analysis by the compiler; any round of semantic repair operation comprises the steps of cloning a current abstract syntax tree to obtain a cloned syntax tree, and performing semantic attribute labeling based on the cloned syntax tree; and for any error node with the error attribute label in the clone syntax tree, performing semantic repair on a node corresponding to the abstract syntax tree.

The invention relates to the technical field of natural languages, in particular to a method and system for improving SQL generation accuracy of an LLM large model based on the CoT concept. The method comprises the following steps: firstly, preprocessing database metadata, and extracting a table structure and field information; the method comprises the steps that firstly, a complex natural language query task is divided into a plurality of sub-problems with definite semantics on the basis of the Chain-of-Though concept, SQL sub-query statements capable of being independently executed are sequentially generated in a multi-pass SQL mode, and input of each statement is based on an output result or original data summary of the previous statement. A verification mechanism is introduced in the generation process, the grammar and logic validity of the SQL statement is checked, and if a grammar error or no problems of fields and table names are found, the system automatically recognizes and prompts the error, so that the executable performance of the SQL is guaranteed. According to the method, the accuracy and the stability of the large model in the relational database query task are effectively improved, the illusion phenomenon is reduced, and the practicability and the reliability of an intelligent agent system are enhanced.

The invention relates to the technical field of aeronautical text data processing, and discloses an aeronautical text content cleaning and labeling method, system, device and medium wherein the method comprises: noise filtering: identifying and removing noise in an aeronautical text in combination with static cleaning and a general large model; format standardization: converting the aviation text after noise removal into a standardized format text; duplicate removal and error correction: detecting duplicate contents based on a hash algorithm, and correcting spelling errors and grammar errors based on a general large model to obtain an aviation text subjected to duplicate removal and error correction; entity identification: key entities are extracted based on the general large model, and the extracted key entities are labeled; active learning: screening high-value samples in the marked key entities based on an uncertainty query strategy; and dynamic optimization: performing verification and iterative optimization on the marking result in combination with the aviation knowledge base. According to the method, the automation level, the labeling accuracy and the system self-adaptive capability of aviation text processing can be remarkably improved.

The invention provides an automobile software demand document collaborative correction method and system, which are used in the field of software development, and the method comprises the following steps: receiving a demand document input by a user, carrying out grammar detection on the demand document through a semantic analysis engine, and integrating a rule engine and a large language model in the semantic analysis engine; and according to a grammar detection result, marking various grammar errors existing in the demand document through marks of different vision, and synchronously triggering AI automatic correction. Through the scheme, the software requirement document writing and checking efficiency can be improved, and the automobile software development cost can be reduced.

The present disclosure provides a document review and approval system comprising a user interface with a source selector, an objective definer, and a corpus selector for receiving source documents, user-defined objectives, and policy documents, respectively. An artificial intelligence engine processes the received inputs. Output interfaces include a syntax auditor for identifying and facilitating correction of syntax errors, a policy auditor for identifying non-compliance issues with policies, and a reporter for generating reports based on findings. The system enables efficient review and approval of documents for both syntax and policy compliance, utilizing artificial intelligence to process natural language inputs and generate outputs, including explanations for identified issues and natural language summaries in generated reports.

The invention belongs to the technical field of software test automation, and discloses an automatic test script repair method and related device.The method comprises the steps that feature data in the test script execution process are obtained; detecting a preset error problem of the test script based on the feature data in the execution process of the test script to obtain a problem detection result of the test script; wherein the preset error problem of the test script comprises a grammar error, a logic error, an interface error and an interface error of the test script; based on the problem detection result of the test script, selecting an optimal repair strategy from a pre-constructed repair strategy library, and generating a repair instruction based on the optimal repair strategy; based on the repair instruction, performing automatic repair operation on the test script; according to the method and the device, automatic repair of the test script is realized, a tester does not need to manually check and modify codes related to the interface elements in the script one by one, time and labor cost are greatly saved, and test efficiency is remarkably improved.

The invention discloses an OCR (Optical Character Recognition) result correction method and device, equipment and a computer storage medium. The correction method comprises the following steps: acquiring a to-be-corrected text after OCR (Optical Character Recognition), wherein the to-be-corrected text comprises one of messy code characters, character confusion errors or grammar errors caused by OCR errors; constructing a semantic correction instruction template, wherein the semantic correction instruction template comprises a task definition, a rule definition and a format constraint target; the to-be-corrected text and the semantic correction instruction template are input into a large language model, a corrected text result is output through the large language model, and the large language model is a pre-training language model. According to the method and the device, messy code characters, character confusion errors or grammar errors caused by OCR recognition errors can be accurately corrected, so that the accuracy and availability of texts are effectively improved, and high-quality basic data are provided for subsequent text processing; the method can adapt to various documents in different formats and multi-language mixed text scenes, the maintenance cost is saved, and meanwhile the correction efficiency is improved.

The invention discloses an intelligent code writing and quality assurance system for multi-language code analysis, which relates to the technical field of intelligent code development and quality control and comprises a multi-language code analysis engine, an intelligent engineering management module, a self-adaptive compiler, an interactive console, a multi-dimensional instance library and a personalized configuration center, each module realizes real-time data interaction through a distributed data bus; the system supports more than 15 mainstream programming languages, new language support can be expanded through plug-ins, and the multi-language code analysis engine integrates static analysis, dynamic tracking and cross-language call detection functions. Static analysis identifies grammar errors and undefined variables through abstract syntax tree analysis. According to the intelligent code writing and quality assurance system for multi-language code analysis, the single-language limitation of a traditional system is broken through, conjoint analysis of programming languages is achieved, cross-language defect recognition accuracy is improved, and potential risks of multi-language projects are remarkably reduced.

The invention provides an IO simulation debugging method for a PLC, and the method comprises the following steps: writing a PLC program and an upper computer picture, downloading the PLC program into a virtual controller, and completing the communication between the virtual controller and the upper computer picture; newly establishing a debugging table, wherein the debugging table is used for debugging personnel to write variables and formulas of a debugging algorithm; linking the table to a dynamic link library by using a development language of the table, writing a calculation result value into a virtual controller by using the dynamic link library, and simulating a real instrument sensor signal on site; after the debugging table is filled, compiling is executed, and the dynamic link library automatically checks grammar errors until the grammar errors are completely correct; and verification of the PLC control logic is completed. According to the method, the PLC program is debugged in the virtual environment by simulating the function of the PLC IO module, so that the debugging efficiency is improved, the debugging cost is reduced, the table and the PLC simulator are deeply integrated for the first time, and low-code IO modeling is realized.

The invention belongs to the technical field of computers, and discloses a script processing method and device, equipment and a readable storage medium, and the method comprises the steps: determining a target code line meeting a preset tracking condition from an original script; inserting an output command for outputting a line number in front of a target code line of the original script to obtain a test script; executing the test script to obtain execution process information, and sequentially extracting line numbers output in the execution process from the execution process information to obtain a line number sequence; and inserting the execution sequence number annotation behind the target code line of the original script by utilizing the line number sequence to obtain a marked script with an execution sequence annotation. According to the method and the device, the marked script with the execution sequence annotation can be obtained, identification, analysis and correction of logic or grammar errors in a program can be assisted, and real and reliable execution sequence information is provided for script debugging.

The application discloses a lightweight Verilog code generation method based on dual learning, comprising the following steps: adopting a Verilog code corpus to perform domain self-adapting pre-training on a pre-trained language model to obtain a domain-adapted language model; under a dual learning framework, fine-tuning the domain-adapted language model on a paired annotation data set to establish a mapping relationship from a natural language digital circuit hardware design description to a Verilog code, and obtaining a Verilog code generation model; receiving a natural language digital circuit hardware design description input by a user, generating multiple candidate Verilog codes by adopting a probability sampling strategy based on a temperature parameter; verifying the syntax compliance of the multiple candidate Verilog codes, and eliminating the candidate Verilog codes with syntax errors; calculating the confidence of the candidate Verilog codes with syntax compliance, sorting the candidate Verilog codes with syntax compliance according to the confidence, and outputting the Verilog code with the highest confidence as a generation result.

The invention discloses a database SQL statement correction method and device, equipment and a medium, and the method comprises the steps: obtaining an error SQL statement, and determining whether the error SQL statement has a grammar error or not; when it is determined that the wrong SQL statement has a grammar error, correcting a part of the abstract syntax tree corresponding to the wrong SQL statement based on grammar error information corresponding to the wrong SQL statement to obtain a first updated abstract syntax tree; obtaining a database semantic map corresponding to the wrong SQL statement, and determining whether the first updated abstract syntax tree has a semantic error according to the database semantic map; when it is determined that a semantic error exists in the first updated abstract syntax tree, performing semantic correction on the first updated abstract syntax tree according to a database semantic map to obtain a second updated abstract syntax tree; and according to the second updated abstract syntax tree, generating the wrong SQL statement after the grammar and the semanteme are corrected, so that the efficiency and the accuracy of correcting the wrong SQL statement are improved.

The invention relates to a dangerous key scene generation method and system based on big language model retrieval enhancement, and belongs to the technical field of automatic driving simulation testing, and the method comprises the following steps: constructing a scene knowledge base and a trigger condition base based on real traffic accident data; receiving a natural language function test requirement, extracting matched real accident features by using a retrieval enhancement technology, and generating a structured hierarchical scene description; comparing the scene static elements with a pre-labeled map library by adopting a mixed thinking chain compiling strategy with map matching priority; and according to a comparison result, adaptively selecting and multiplexing an existing map file and only generating a dynamic script, or simultaneously generating a static map file and the dynamic script, and finally outputting an executable simulation code. According to the method, a hybrid compiling framework with dynamic and static separation is provided, grammar errors generated by complex road network codes are effectively avoided, and the compilable rate and simulation fidelity of OpenDRIVE and OpenSCENARIO codes are greatly improved.

The invention provides a code generation method and device, equipment, a storage medium and a program product, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: inputting a pseudo code into a code generation model to generate a first candidate code; the error type of the first candidate code is determined, and the error type comprises a grammar error, a code logic error and a code intention error; according to the error type, adopting a corresponding correction strategy to correct the first candidate code to obtain a second candidate code; the second candidate codes are scored based on code quality attributes, and the code quality attributes comprise the conciseness degree of the second candidate codes and the adaptation degree of the second candidate codes and service scenes corresponding to the pseudo codes; and determining a finally generated code according to the scoring result of the second candidate code.

This application relates to the field of automated migration technology for computer software, specifically a declarative method for automated cross-platform UI translation. The method includes: parsing a source UI file, extracting component types, attributes, modifiers, and child node information, and constructing a structured UI tree; mapping the component types, attributes, and modifiers in the UI tree to the basic structure of the target platform ArkUI based on a predefined cross-language component mapping table, generating an ArkUI code skeleton; constructing prompts, inputting the ArkUI code skeleton and prompts into a large language model, and using the large language model to generate preliminary ArkUI code; correcting syntax errors and incompatibilities in the generated preliminary ArkUI code using deterministic transformation rules, and outputting target code that can be directly compiled and run. This invention significantly improves the engineering implementation efficiency of cross-platform UI translation while maintaining high-fidelity visual effects.

The invention particularly relates to a code generation method based on multi-modal fusion and adaptive adjustment. According to the code generation method based on multi-modal fusion and adaptive adjustment, multi-modal data are collected, a deep learning model is adopted to fuse preprocessed data, and unified multi-modal feature representation is generated; presetting a code generation strategy, constructing a strategy library, and dynamically adjusting a strategy weight according to an evaluation result; and selecting a code template to generate a preliminary code in combination with the multi-modal fusion data, optimizing the preliminarily generated code by using a deep learning model, detecting grammar errors, generating a targeted optimization suggestion according to an error detection result, and presenting the optimization suggestion to a developer. According to the code generation method based on multi-modal fusion and adaptive adjustment, a man-machine cooperation mechanism is optimized, the development cost is reduced, the code reliability is enhanced, the limitation of a single modal is broken through, the problem of code intention misjudgment in a complex scene can be solved, and cross-domain project code adaptation is rapidly achieved.

An error handling method and apparatus for a video bitstream for determining an error type and a frame type to select a recovery technique. The method receives a video bitstream, detects errors during decoding, and classifies the errors as syntax parsing errors, reference picture data missing or corruption, and decoding errors. The frame type is divided into a reference frame, a non-reference frame or a key frame. Based on these classifications, the method applies appropriate error handling: for partial corruption, generating replacement reference data; for complete reference loss, discarding the frame or the frame group; performing row-level hiding on the reference frame to limit error propagation; performing stripe-level hiding on the non-reference frame to improve visual quality; analyzing key frame errors to ignore slight edge errors; for serious damage, discarding the frame group; for unrecoverable syntax errors, frames are dropped.

A method and an apparatus of error handling for video bitstream decoding determines error types and frame types to select recovery techniques. The method receives a video bitstream, detects errors during decoding, and categorizes errors as syntax parsing errors, missing or corrupted reference picture data, or decoding errors. Frame types are classified as reference frames, non-reference frames, or keyframes. Based on these classifications, the method applies appropriate error handling: generating replacement reference data for partial corruption, dropping frames or frame groups for complete reference loss, performing row-level concealment for reference frames to limit error propagation, performing slice-level concealment for non-reference frames for visual quality, analyzing keyframe errors to ignore minor edge errors or drop frame groups for significant corruption, and dropping frames for unrecoverable syntax errors.

Implementations described herein relate to methods, systems, and computer programs that combine a Large Language Model (LLM) with an adversarial feedback loop for automated software development. The process involves prompting an LLM to generate code, iteratively refining it through self-prompts or external prompts, and employing adversarial agents to check for syntax errors, logical inconsistencies, and functional compliance with the original requirements. This method ensures the production of robust, error-free software. Some implementations may include a hierarchy of LLM instances where a subset focuses on coding while another subset supervises the process, akin to a managerial role.

The invention provides a text-to-structured query language generation method and device based on a self-evolution rule base, and the method comprises the steps: obtaining a mixed analysis strategy which comprises a dynamic mapping relation between an intention phrase in a natural language and a structured query language, and carrying out the analysis of a user query based on the analysis strategy, when the initial analysis result is incomplete, the preset language model is called for complementation, grammar errors and field mapping misalignment caused by direct dependence on the model are remarkably reduced through a controllable dynamic mapping relation, and the analysis capability of complex query is ensured; the completion query statement is placed in the isolated security verification environment to be executed, feedback is obtained, and possible error or unauthorized operation is effectively intercepted; and when the execution is successful and the initial analysis result is incomplete, the effective fragment obtained by complementing is automatically converted into a new mapping relationship and added to the dynamic mapping relationship, and repeated manual intervention or model retraining is not needed, so that the maintainability of the text-to-structured query language generation is improved.

The present invention provides a netlist parsing method, computer device, program product, and storage medium, including: splitting a netlist file into several independent atomic parsing partitions according to syntax and scheduling them in parallel; lexically grading each independent atomic parsing partition using a bitmap compression state machine and a dynamic symbol table, outputting a classified token stream; parsing each independent atomic parsing partition in parallel based on the token stream to generate a local syntax tree; saving the current syntax stack state and the current symbol table when a syntax error is detected; determining the error interval, skipping the error interval, and rebuilding the syntax stack for subsequent parsing; re-parsing the error interval after the error is fixed, and restoring context data based on the current syntax stack state; and merging local syntax trees based on the dependency relationships of each independent atomic parsing partition. This solution can achieve efficient parsing of complex and large-scale netlists and can significantly reduce parsing time after error repair when syntax errors are triggered during parsing.

The present application belongs to the technical field of conference transcription, and particularly relates to a real-time transcription system device and method based on a multi-language conference system, which comprises a multi-language conference system, a real-time transcription system, an Internet terminal, a self-defined program code and a self-defined extension plug-in, the multi-language conference system is provided with a syntax error compensation function, the multi-language conference system is provided with a paragraph self-definition function, the multi-language conference system is provided with a keyword display function, the multi-language conference system is provided with a multi-language insertion system, one end of the multi-language conference system is connected with the Internet terminal, and one end of the Internet terminal is connected with the real-time transcription device. The real-time transcription system device and method based on the multi-language conference system can complete text editing after transcription, so that the transcribed text is directly usable, and the text has strong watchability during viewing, thereby improving the work efficiency of participants on the transcribed text.

This application discloses a training method and electronic device for a code generation model, relating to the field of computer technology. The method includes: inputting sample prompts into a code generation model to obtain sample code; determining a first reward parameter to characterize execution stability based on the code execution information of the sample code, and parsing the code execution information, sample code, and sample prompts using a reference language model to obtain a second reward parameter to characterize the degree of syntax errors and a third reward parameter to characterize code style; adjusting the model parameters of the code generation model based on the first, second, and third reward parameters; and determining the target code generation model based on the adjusted code generation model when the adjusted model reaches convergence. This solves the technical problem of low training efficiency in code generation models and achieves the technical effect of improving the training efficiency of code generation models.

The invention relates to the technical field of civil engineering and artificial intelligence, in particular to an automatic modeling method and system for a bridge finite element model. Comprising an interaction module, a large model cascade engine, a compiler and a modeling program language grammar error debugging module. The large model cascade engine comprises a natural language pseudo code generation and check module, a modeling program language cue word generation module and a bridge structure parameter generation module; the technical threshold of finite element modeling in the field of civil engineering is greatly reduced, modeling personnel do not need to have a firm computer programming capability, an operable finite element program and a calculation result can be quickly obtained only through input requirements, and the bridge finite element modeling efficiency is remarkably improved.

The invention relates to the technical field of natural language processing, and discloses an enhanced XiYan-SQL (Structured Query Language) framework optimization method based on a GRPO plug-in, which comprises the following steps: optimizing SQL query by adopting a generation-rejection mechanism in a training stage through the GRPO plug-in, dynamically adjusting a strategy and outputting an optimization result; in the reasoning stage, error correction is triggered when query fails; the GRPO plug-in comprises a reward function and a loss function, and values are calculated from the dimensions of accuracy, efficiency, grammar correctness, grammar errors, logic errors, mode mismatching and the like to optimize a strategy; m-Schema information is integrated in the XiYan-SQL framework, and mode mismatching is automatically corrected; the GRPO plug-in is based on a DeepSeek model, a specific training normal form is adopted, the modular design is convenient to use independently, and a user can start the plug-in according to needs. According to the method and the device, the generation quality of the Text-to-SQL task can be improved.

The invention provides an enhanced DSL (Digital Subscriber Line) analysis method. The method comprises the following steps: S1, performing lexical analysis on an input DSL command, and converting the input DSL command into a lexical unit; s2, performing grammatical analysis, namely, detecting and processing grammatical errors in the command after a lexical unit is converted into an abstract syntax tree; s3, performing error processing and repairing operation on grammar errors; and S4, generating an operation instruction according to the abstract syntax tree, executing the operation instruction and recording a result. According to the method, the DSL command is decomposed more efficiently, and an abstract syntax tree AST is generated in a syntax analysis stage to detect and process errors.

The application discloses a three-dimensional static layout scheme generation method based on an LLM supporting iterative correction, and comprises the following steps: setting structured data; constructing an LLM, and generating an operation sequence generator and an executable code generator respectively according to the LLM; inputting the structured data into the operation sequence generator for analysis and outputting an operation sequence draft; evaluating the eligibility of the operation sequence draft, and if eligible, determining that the current operation sequence draft is an eligible operation sequence; inputting the eligible operation sequence into the executable code generator for analysis and outputting an executable code draft; and evaluating the eligibility of the executable code draft, and if eligible, determining that the current executable code draft is eligible executable code. The application solves the problem that, in a traditional three-dimensional static layout scheme construction scheme, when structured data is directly converted into executable code with the help of an LLM, the output uncertainty of the LLM leads to logical defects or syntax errors in the generated code.