54 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 an extensible structured text generation method, device and equipment in the field of PLC and a medium. The method comprises the following steps: constructing a case library and an instruction library; based on semantic similarity of task description, similar task cases are retrieved from a case library through a language model and sorted according to similarity scores; for each task case, common programming instructions used by the task case are retrieved from an instruction library, and an instruction information set is generated according to corresponding instruction information to serve as knowledge of a specific field; constructing a context prompt, inputting a language model, and generating a structured text draft code; taking the structured text draft code as the input of an ANTLR analyzer for grammatical analysis, obtaining grammatical error information and constructing an abstract syntax tree; the abstract syntax tree is traversed through ANTLR Walker for semantic analysis, and semantic error information is obtained; the error information is provided for the language model, a corresponding code repairing scheme is generated, and codes are improved. According to the method, the ST code specific to the supplier can be automatically generated, and the quality of the generated code is ensured.

The invention provides a code dynamic completion method for a large language model. The method comprises the following steps: constructing a mixed training data set; the method comprises the following steps of: initializing a Transform model architecture fused with probability space topological transformation; based on the mixed training data set, executing model training of collaborative optimization of the main target and the auxiliary target; in each step of decoding, the model calculates the probability distribution of the next token according to the current context; the generated probability distribution is restrained in an effective probability subspace through spherical probability mapping, and grammar error candidates are filtered in real time and the probability distribution is adjusted in combination with a tabu table mechanism; and dynamically selecting a sampling strategy according to the current decoding depth, sampling from the adjusted probability distribution to obtain a next code snippet, and finally generating a code completion suggestion conforming to abstract syntax tree rules and semantic constraints. According to the method, the probability distribution constraint and the taboo table mechanism are combined, the model can dynamically adjust the generated probability distribution, the more appropriate candidate token can obtain the higher probability, and therefore the accuracy of code completion is improved.

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 embodiment of the invention provides a project text optimization processing method and device, and the method comprises the steps: deeply analyzing text structure features and semantic features through a knowledge graph and relation extraction, and forming a differentiated text template library; a segmentation processing strategy is adopted, text boundaries are recognized in combination with part-of-speech tagging and syntactic analysis, sequence modeling is conducted on semantic coherence through a recurrent neural network, and semantic breaking points are accurately positioned. A text optimization strategy network is constructed based on a reinforcement learning algorithm, grammar error positions, semantic coherence breaking points and a text template are used as state input, a repair strategy is dynamically generated, and intelligent optimization of a text is achieved. By means of the method, the defects of a traditional technology in the aspects of semantic coherence evaluation, optimization strategy making and the like are effectively overcome, and the professionality and efficiency of project text optimization are remarkably improved.

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.

A novel data processing system that is highly convenient, useful, or reliable is provided. The data processing system is composed of the following three components. The first component receives a document written in a programming language, and extracts and provides a syntax error. The second component performs processing using a large language model, and generates a report sentence in accordance with a prompt. The large language model has learned the programming language. The third component performs processing using a database, searches for whether one line includes a registered character string, and if found, replaces the character string with an alternative character string. Moreover, the third component generates a prompt, and verifies each line and generates a report sentence. The system has functions of receiving a document written in the programming language, extracting an error, and generating a report sentence.

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 invention discloses a verification and operation method for Web front-end codes. The verification and operation method is characterized by comprising the following steps: S100, providing an input interface (hereinafter referred to as codes) of JavaScript codes; s200, obtaining a code input by a user; s300, judging whether the grammar of the input code is correct or not, if yes, executing the step S600, and if not, executing the step 400; s400, feeding back grammar errors of the input codes; s500, correcting the current running code, and executing the step S200; s600, judging whether the running code runs successfully or not, if yes, executing the step S800, and if not, executing the step S700; s700, running errors of the input codes are fed back, and the step S500 is executed; s800, outputting and displaying a running result of the running code; and S900, storing the running code for multiplexing and modifying the current running code.

The invention provides a netlist analysis method, computer equipment, a program product and a storage medium. The netlist analysis method comprises the following steps: splitting a netlist file into a plurality of independent atomic analysis partitions according to grammar and carrying out parallel task scheduling; performing lexical classification on each independent atom analysis partition through a bitmap compression state machine and a dynamic symbol table, and outputting a classification token flow; performing parallel analysis on each independent atom analysis partition according to the token stream to generate a local syntax tree; when a grammar error is detected, storing a current grammar stack state and a current symbol table; determining an error interval and skipping the error interval to reconstruct the grammar stack for subsequent analysis; re-analyzing the error interval after the error is repaired, and recovering the context data based on the current grammar stack state; and according to the dependency relationship of each independent atom analysis partition, merging the local syntax trees. According to the scheme, efficient analysis of a complex and large-scale netlist can be achieved, and the analysis time after error repair can be greatly shortened when grammar errors are triggered in the analysis process.

The present application discloses a data verification method and device based on a K8S container platform. After obtaining a resource configuration file in yaml format, a resource configuration file in json format is generated according to the resource configuration file in yaml format, and the content mapping relationship between the resource configuration file in yaml format and the resource configuration file in json format is obtained. A shell script file is used to verify whether there are syntax problems in the content of the resource configuration file in json format. If there are syntax problems, the content position where the syntax error occurs is located in the resource configuration file in json format, and according to the previously determined content mapping relationship, the content position where the syntax error occurs in the resource configuration file in json format is converted into the content position where the syntax error occurs in the resource configuration file in yaml format. Thus, the problem that the content position where the syntax error occurs in the resource configuration file in yaml format cannot be located in the K8S container platform is solved, and the development efficiency is improved.

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 present invention proposes a coding dynamic completion method for large language models. The method includes: constructing a mixed training dataset; initializing a Transformer model architecture that integrates probability space topological transformation; performing model training with collaborative optimization of the main objective and the auxiliary objective based on the mixed training dataset; at each decoding step, the model calculates the probability distribution of the next token according to the current context; constraining the generated probability distribution within the effective probability subspace through spherical probability mapping, and combining with the taboo table mechanism to filter syntax error candidates in real time and adjust the probability distribution; dynamically selecting a sampling strategy according to the current decoding depth, sampling from the adjusted probability distribution to obtain the next code snippet, and finally generating code completion suggestions that conform to the abstract syntax tree rules and semantic constraints. By combining the probability distribution constraint and the taboo table mechanism, the model of the present invention can dynamically adjust the generated probability distribution, enabling more appropriate candidate tokens to obtain higher probabilities, thereby improving the accuracy of code completion.

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.