1151 results about "Syntax" patented technology

The invention discloses a data query method and system based on a natural language text. The method comprises the following steps: dynamically shielding the natural language text to generate a semantic vector sequence; key entities and association relationships thereof in the semantic vector sequence are extracted, and a structured entity relationship set is obtained; matching the structured entity relationship set with knowledge graph nodes to generate a heterogeneous semantic graph; performing heterogeneous fusion on the heterogeneous semantic graph and text syntactic features to generate intermediate query representation; processing the intermediate query representation to generate an executable statement carrying a semantic association identifier; and verifying the semantic consistency of the response content of the executable statement and the original input, and iterating to regenerate the executable statement when the semantic consistency does not pass the verification. And efficient and accurate natural language query conversion and response generation are realized through dynamic shielding enhanced semantic robustness, knowledge graph path extension and weight iterative optimization, heterogeneous feature fusion and a multi-modal verification mechanism.

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

The invention discloses a variation test method and device based on an intelligent automation script. The method comprises the following steps: extracting a statement execution sequence and a branch dependency relationship in an original source code by using a context-aware syntax tree, and generating a control flow diagram and a data flow diagram; performing code semantic analysis on the multi-dimensional code features by using a large language model, identifying potential defect types and variation rules, predicting the test efficiency of variants in combination with a deep Q learning model, and generating to-be-processed variants; performing grammar check and equivalence analysis on the to-be-processed variants to obtain effective variants, and testing the effective variants; variation test indexes are calculated, code weak points are positioned, and test blind areas are identified according to survival variants, so that a visual analysis report is generated; and iteratively generating a new test case according to the current survival variant and the visual analysis report, and testing the current survival variant by using the new test case. According to the invention, the variation test efficiency can be improved.

The invention discloses a structured text code generation and review method, which comprises the following steps of: receiving a control task demand input by a user, supporting natural language, structured form or graphical process input, performing deep semantic analysis by utilizing a demand analysis module, identifying control conditions, control actions and constraint factors, and generating and reviewing a structured text code. The task is converted into a plurality of control units through the SCoT chain; the SCoT chain reader generates a semantic vector according to a task demand, the AST reader generates AST representation through a grammar rule structure and a hierarchical relationship, and the decoder predicts a next grammar rule and generates an ST code in combination with the SCoT chain and AST features; and verifying grammar and logic of the ST code through an interactive automatic review and repair mechanism, if compiling errors occur, analyzing errors by the teacher model and providing repair suggestions, and performing code repair by the student model according to guidance until the code conforms to an expected function and passes compiling.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

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.

Systems, methods, and devices that relate to intelligent query decomposition and parallel routing for specialized model processing are disclosed. In one example aspect, the system receives a query from a user comprising a request relating to a particular domain. The system determines, using a decomposition model, a set of sub-queries based on semantic boundaries, syntactics, tasks, relationships, and rules relating to particular domains. The system inputs the set of sub-queries into a routing model to determine a set of specialized models. For each sub-query, the system routes the sub-query to a respective specialized model, generates an output, and assigns a confidence score. The system detects conflicts among outputs using a conflict detection model configured to identify discrepancies. The system generates an aggregated output by combining outputs according to a weighted aggregation algorithm prioritizing higher confidence scores and conflict resolution rules, then displays the aggregated output.

The invention discloses a Tibetan language multi-dialect real-time semantic conversion method based on a cross-language BERT model, and the method comprises the following steps: S1, collecting original text corpora of each dialect of the Tibetan language, and constructing a standardized training corpus set; s2, performing parameter initialization on the mBERT model, and preliminarily training the mBERT model; s3, constructing a semantic modeling model, and performing fine adjustment on the semantic modeling model; s4, receiving to-be-converted text input, and encoding; s5, obtaining an intermediate semantic representation vector of the text through a semantic coding sub-module; s6, inputting the intermediate semantic representation vector into a semantic generation sub-module, and generating target text output; and S7, executing syntactic consistency correction and language fluency correction. According to the method, mBERT modeling and an adversarial optimization mechanism are fused, real-time semantic consistency conversion of multiple dialects of the Tibetan language is achieved, and the method has the advantages of being high in accuracy, high in robustness and low in response delay.

The invention provides a complex question and answer method and system based on adaptive task deconstruction and multi-modal evidence aggregation, belongs to the technical field of natural language processing, and designs a dynamic Few-shot prompt construction method based on dependency syntax fingerprints to ensure that a prompt template is matched with a question structure; the invention discloses a dynamic problem deconstruction method based on confidence evaluation and auto-reflection. The method comprises the following steps: recursively decomposing a problem tree by using a large language model; converting the problem tree into a standardized linear task execution sequence by a problem tree context dependence specification and task sequence generation method; obtaining a high-correlation evidence set of each task based on an evidence generation method of two-way recall and cross encoder rearrangement; and the task sequence is reasoned and dynamically optimized by a question answer extraction method based on double-strategy aggregation reasoning. According to the method, the accurate complex question and answer result can be provided on the premise of ensuring the question disassembling quality, restraining error propagation and comprehensively recalling evidences.

Computer-implemented methods and systems for processing geometry replacements are disclosed. The methods include decoding / encoding a syntax element associated with a coding mode from / into a bitstream associated with geometry displacements; and reconstructing / converting, based on a coefficient configuration associated with the coding mode, a plurality of quantized transform coefficients from / to a plurality of zero-run length codes.

The invention relates to a man-machine interaction dialogue method, system and device based on a natural language and a medium. The method comprises the steps that firstly, multi-modal interaction data is acquired and preprocessed, and segmented words and syntax are analyzed through a natural language processing technology to construct intention feature vectors; combining the intention feature vector with a historical dialogue record, and using a pre-trained language model to generate context semantic elements containing a semantic relationship; if the context semantic elements are matched with the preset scene feature library information, predicting a user intention change trend by adopting a reinforcement learning model to obtain an intention prediction result; and finally, evaluating user intention change based on an intention prediction result, extracting associated domain knowledge by utilizing a knowledge graph if significant change occurs, and inputting the associated domain knowledge into a dialogue generation model to obtain a natural language reply sequence. According to the method, the understanding precision of the user intention is improved, the dynamic prediction of the intention change is realized, the relativity and coherence of reply are guaranteed, and a more efficient processing path is provided for natural language man-machine interaction.

The invention provides a field NL2SQL data automatic synthesis method and system based on a large language model, and the method comprises the steps: building an SQL template library through collecting a public data set and real business query data, carrying out the grading according to the SQL complexity, and forming an SQL template set covering different difficulties; specific SQL queries are generated based on an SQL template library and a large language model, verification is carried out through multiple mechanisms such as grammar check, execution verification and result dimension consistency check, and correctness and performability of the SQL queries are ensured. Generating a plurality of candidate natural language questions according to the verified SQL query and the large language model, calculating a semantic consistency score of each candidate question and the SQL query through a cross consistency evaluation mechanism, screening out the question with the most consistent semantics as a final question-answer pair sample, and meanwhile, eliminating low-quality samples by setting a consistency threshold value, so as to obtain a final question-answer pair sample; and the data accuracy and consistency are further improved.

The invention discloses an intelligent SQL (Structured Query Language) generation method and system based on multistage intention recognition. The method comprises the following steps: receiving a natural language query of a user; vector matching: carrying out vector matching based on a knowledge base to obtain Top-K candidate query objects, the knowledge base containing multilayer structure description information; a large language model intention understanding step: determining a final query object in combination with the candidate query object description; loading a branch workflow, analyzing a field and a table structure, and dynamically configuring a database table and field information; sQL generation: generating SQL statements conforming to grammatical rules, including automatic field addition, time condition conversion and field priority matching operation; and executing the SQL statement and returning a result, and if the result fails, recording a log to prompt correction. By combining a natural language processing technology with a database knowledge base, an efficient, accurate and user-friendly database query solution is provided.

The invention provides a large model tool calling method and device based on multiple constraint sets and electronic equipment. The method comprises the steps that a current constraint set comprises a permission constraint set, a state constraint set and a context constraint set; based on the current constraint set and a tool set cue word builder, obtaining a tool set cue word corresponding to the current constraint set, and generating a calling intention based on the task demand instruction and the tool set cue word; and generating a tool call description based on a cue word formed by the large language model and the call intention, and executing tool function call under the condition that grammar and semantic check of the tool call description is passed. On one hand, a current constraint set uniformly manages authority, state and context constraint conditions, and the constraints are flexibly subjected to set operation to dynamically judge the availability of a tool; and on the other hand, the tool set cue words are dynamically screened, redundancy of the tool set cue words is avoided, the calculation amount of a large language model is reduced, and the possibility of calling wrong tools is reduced.

The invention provides a syntactic structure tree-based code change influence intelligent analysis method and system, and the method comprises the steps: extracting a control flow, a data flow and a call flow according to an AST generated through the analysis of an original code and a changed code, and constructing a static dependency graph SDG containing a multi-dimensional dependency relationship; the method comprises the steps that a base layer is used for comparing AST differences before and after code change and generating grammatical structure change feature vectors; the service layer matches a corresponding service label for the changed AST node by using the service domain template; the change labeling layer constructs a cross-version dependency chain for the change AST nodes and generates historical feature vectors; and calculating a risk value of changing the AST node by utilizing a feature calculation result and combining with a multi-dimensional influence diffusion algorithm, and automatically generating a decision suggestion. According to the method, multiple dimensions of a control flow, a data flow and a call flow are deepened, risk prediction is optimized by utilizing natural language processing and historical change data, and the influence brought by code change is assisted to be better understood and managed.

The invention provides a method and equipment for constructing a text entity recognition model based on large model data enhancement. The method comprises the following steps: firstly, constructing an initial model comprising a preprocessing unit, a syntax dependency analysis unit, a data correction enhancement unit, a context coding unit, a syntax enhancement unit, an expression fusion unit and a sequence decoding processing unit; preprocessing the training sample to obtain a preprocessed text, and establishing a preliminary dependency graph through syntactic analysis; correcting and enhancing the text and the dependency graph by the large language model to obtain a text sequence and a dependency graph for subsequent use; encoding the text sequence to obtain an initial representation vector, and enhancing the vector in combination with the dependency graph; after fusion, decoding and outputting a prediction label containing a lexical entity label; and calculating loss by using a function containing conditional random field structure loss, judging convergence, and if not, updating parameters and continuing training until a target model is obtained. According to the method, data are optimized and expanded by means of a large language model, syntactic dependency enhancement features are combined, and the recognition capability of the complex text named entities is improved.

The invention discloses a large model driven automobile model modeling method based on a Modelica language, and belongs to the technical field of intelligent modeling and automobile simulation. The method comprises the following steps: firstly, accurately analyzing a natural language demand into a structured triple by adopting a BERT-CRF (domain knowledge enhanced) multi-task model; matching an optimal component combination through a multi-objective optimization algorithm driven by a graph neural network, and cooperatively predicting an interdisciplinary parameter feasible region in combination with symbolic mathematical derivation and machine learning; a topological connection matrix is innovatively optimized by using a graph attention network, and intelligent generation and dynamic verification of simulation codes are realized by fusing a template engine and syntax tree analysis; and finally, constructing a multi-target reward function optimization control strategy through reinforcement learning, and establishing a closed-loop knowledge iteration mechanism. Compared with a traditional modeling method, through deep combination of the large model and Modelica, the technical difficulty of automobile system modeling is remarkably reduced while the preciseness of physical modeling is kept, and the method is particularly suitable for complex scenes such as new energy vehicle model development and intelligent driving system integration.

Provided are systems and methods for enhancing the output accuracy and consistency of sequence processing models, particularly when generating text that must conform to a specific formal language. Systems and methods can integrate finite-state or push-down automata with the decoding process to ensure that the generated sequences adhere to the desired syntax or grammar rules. A system can generate or otherwise leverage a finite-state or push-down automaton that encodes constraints associated with a particular formal language or syntax. At each decoding iteration of a sequence processing model, the decoding system can use the finite-state or push-down automaton to evaluate the token validity for each of a number of possible output tokens. The output of the sequence processing model can then be limited to or otherwise guided towards tokens that are indicated as valid by the finite-state or push-down automaton.

Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM) through novel processes for performing retrieval augmented generation (RAG). In some examples, such a method includes operations of receiving a natural language user-provided prompt, performing a first RAG process including retrieving first RAG data including natural language, programming syntax pairings from a vector database and performing a second RAG process including retrieving second RAG data including additional programming syntax examples from a vector database that have a second level of similarity with programming syntax of the natural language, programming syntax pairings retrieved during the first RAG process. The method may also include generating an auto-generated prompt requesting generation of programming code by a large language model (LLM) and generating a graphical user interface that displays the response to the auto-generated prompt from the LLM.

The invention provides an official document auditing method, device and equipment based on a large model and a medium, and the method comprises the following steps: preprocessing an official document to realize format and language standardization; using a retrieval enhancement generation (RAG) technology to complete vectorization conversion and fragmentation processing; then grammar and semantic analysis is carried out through a text analysis module fused with the large model, and matching detection is carried out according to a preset rule base in combination with an automatic rule engine module; then, deep auditing is conducted by means of a machine learning auditing model, content problems are recognized, manual rechecking is triggered in case of high uncertainty or key content, and an auxiliary tool is provided; receiving an audit opinion optimization model and a rule base; and finally outputting a result, and feeding back the problem by generating a report without annotation. The document auditing efficiency and accuracy can be improved, continuous optimization is supported, and the method is suitable for multiple scenes.

The invention provides a multi-database SQL blood relationship analysis method and system based on a G4 file, and relates to the technical field of database management.The method comprises the steps that SQL statements are analyzed based on a G4 grammar file, a front-back dependency relationship is constructed through a statement dependency directed graph, grammar rules are optimized through a dialect feature matrix and a Markov decision process, and a multi-database SQL blood relationship analysis result is obtained. According to the method, the SQL data extraction operation and the circulation sequence are sequentially analyzed, the field blood relationship link diagram is generated, and the structured result is output, so that multi-database SQL grammar features can be accurately recognized, the complex SQL analysis problem is solved, the blood relationship analysis accuracy and efficiency are effectively improved, and cross-database unified blood relationship management is supported.

The invention discloses an accurate formula calculation method and device based on a large language model, and the method comprises the steps: receiving a mathematical formula image inputted by a user and a calculation request described by a natural language, carrying out the recognition of the mathematical formula image, generating a standard LaTeX expression, carrying out the grammar and semantic verification, employing the large language model to analyze the calculation request, and carrying out the analysis of the calculation request. The method comprises the steps of identifying a calculation type, a target variable and a parameter, outputting a structured operation instruction, converting a standard LaTeX expression into a mathematical object compatible with a symbol calculation library according to the structured operation instruction, dynamically declaring a symbol variable according to the operation instruction, and executing symbol operation based on a parameter extraction result to obtain a symbol calculation result. A symbol calculation result is converted into a readable format to be output, LaTeX mathematical expressions and natural language description are supported, a large language model and a multi-modal model are used for accurately recognizing a formula and analyzing a user natural language problem, complex symbol calculation and equation solving are automatically completed, tasks can be automatically completed through natural language interaction, and the use threshold is lowered.

In an embodiment, a method includes receiving a prompt provided to a large language model (LLM), generating a semantic fingerprint for the prompt based on semantic and syntactic features associated with the prompt, generating a vector representation incorporating the semantic fingerprint for the prompt, calculating semantic distances between the vector representation and multiple vector representations associated with multiple malicious intents, determining an intent associated with the prompt based on the semantic distances, and determining a defense action on the prompt based on the intent and multiple policies to mitigate a security risk associated with the prompt.

The invention provides a test script generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining test input information containing demand information and test data, carrying out the structural processing of the test input information into a parameter dictionary, and determining a target feature based on the test input information to match a target test template constructed based on a specific grammar template engine, and finally, binding the parameter dictionary with the target test template, and dynamically rendering to generate a script file conforming to a test rule. According to the method, the test input information is subjected to structured processing to form the parameter dictionary, the target template is accurately matched and dynamically rendered, and the script generation efficiency and quality are greatly improved.

The invention discloses an English text automatic proofreading system and method based on intelligent AI, and relates to the technical field of natural language processing, and the method comprises the steps: inputting an original English text, generating a syntactic graph through dependency syntax analysis, calculating a node syntactic centrality weight, and generating a triple description set after screening key nodes; on the basis of the triple description set, constructing a structured prompt template, executing English-in-English bidirectional conversion verification through a multi-language model, generating an initial image, and outputting an aligned final image through iterative optimization circulation; and searching a syntax compliance candidate word library for the conflict text node set, selecting a candidate word with the highest comprehensive score, and outputting a corrected text, a video stream and an error analysis report. According to the method, the text assertion and the visual assertion are synchronously verified through the differentiable grammar rule matrix, and the problem that context sensitive errors are difficult to detect through a traditional method is solved.

To effectively resolve the issue of inappropriate handovers during communication sessions, methods, systems, and machine-readable mediums which utilize speech features captured by microphones in the original wireless peripheral device and / or the wireless peripheral device to which the communication session is to be handed over to determine if a handover should proceed or should be reversed. Speech features refer to the composite attributes of spoken language that encompass both acoustic and linguistic features. Acoustic features characterize the sound properties of speech and include, but are not limited to, timbre, pitch, intonation, speaking rate, articulation, prosody, melody, spectral features, formant frequencies, and the like. Linguistic features pertain to the actual content conveyed, comprising words, phrases, syntax, and semantics. This includes the analysis of words, phrases, syntax, and semantics to understand the context and continuity of the conversation.

A video decoding method includes obtaining a to-be-entropy-decoded syntax element in a current block by parsing a received bitstream, where the to-be-entropy-decoded syntax element includes a syntax element 1 or a syntax element 2 in the current block, obtaining a context model corresponding to the to-be-entropy-decoded syntax element, where both of a context model corresponding to the syntax element 1 and a context model corresponding to the syntax element 2 are determined from the same preset context model set, entropy decoding the to-be-entropy-decoded syntax element based on the context model corresponding to the to-be-entropy-decoded syntax element, and obtaining a reconstructed image of the current block based on the syntax element obtained by entropy decoding.

The invention relates to the field of computer software, and provides an AI-based low-code visual component dynamic generation method and system. The method comprises the following steps: receiving a component demand description input by a user, and converting the component demand description into structured cue word data to obtain an initial cue word sequence; retrieving matched template data and grammatical rule data from a component knowledge base according to the initial cue word sequence, and fusing the template data, the grammatical rule data and the initial cue word sequence to obtain an enhanced cue word sequence; performing semantic analysis and code generation processing on the enhanced cue word sequence through a large language model to obtain a component source code; and performing real-time rendering processing on the component source code through a component rendering engine, and generating an interactive component instance in a preview interface to obtain reusable component resources. According to the method, the code quality consistency is ensured, and the development efficiency is improved.

The invention discloses a natural language and machine vision-based alignment method, which comprises the following steps of: providing a three-level alignment architecture, and respectively extracting local-global features of a visual scene and grammar-semantic features of a natural language by adopting a double-flow feature extraction network; through a space-time attention enhanced cross-modal alignment module, a dynamic gating mechanism is adopted to complete feature space adaptive projection; a joint optimization strategy is constructed based on comparative learning, and vision and language embedding space consistency is optimized by using a multi-granularity comparative loss function. And meanwhile, semantic topology constraint and visual causal reasoning are introduced, so that the calculation complexity is reduced, and the task robustness is improved. Through experimental test data, the Top-1 accuracy rate of the method in image-text retrieval reaches 92.3%, the visual question and answer F1 value is 83.4%, the model parameter quantity is reduced by 40%, the method can be widely applied to the fields of intelligent interaction systems, automatic driving scene understanding, industrial quality inspection knowledge base construction and the like, and the semantic perception and reasoning ability of a multi-modal system is remarkably improved.

The embodiment of the invention discloses a code conversion method and device based on a domain-specific language and a medium, and relates to the technical field of code conversion, the method comprises the steps that a DSL file and a target conversion language input by a user are received, the DSL file comprises at least one class definition, and the class definition comprises a class name, a field declaration and a method signature declaration; the DSL file is analyzed to extract a plurality of class annotations marked in the class definition, and the class annotations comprise target language identification information, unique class identification information, construction mode identification information and array access mode identification information; and according to the target language identification information in the target conversion language and the class annotations, a matched specified class annotation set is extracted from the class annotations, a code file conforming to the grammar of the target conversion language is generated based on the specified class annotation set, and the specified class annotation set comprises the class annotations, field annotations and method annotations.