279 results about "Unparser" patented technology

The invention relates to the technical field of civil engineering informatization and automation design, in particular to a parameterized construction drawing generation method based on a constraint solution algorithm, and the method comprises the steps: defining a construction drawing foundation structure and variable parameters through an engineering design model embedded with a parameterized template; a parameter constraint relation set is generated in combination with geometric constraints, size constraints and logic constraints input by a user, constraint rules are converted into intermediate expressions through a formula analyzer, a syntax tree is constructed, and an executable parameter association rule set is generated through semantic verification. The auditing module verifies the drawing compliance based on the engineering specification database and generates a correction suggestion; and realizing cross-system data linkage between the multi-terminal collaborative interface and external BIM model parameter mapping. According to the method, the problems of detail loss and specification deviation in three-dimensional model projection are effectively avoided, and the drawing generation efficiency and the multi-specialty collaborative robustness are improved.

A natural language to database query converter (converter) receives a natural language query from a user (i.e., a user utterance) and identifies a cybersecurity domain related to intent of the natural language query. The converter then generates a database query for a query language of the cybersecurity domain corresponding to the natural language query with a large language model (LLM). An initial prompt to the LLM generated by the converter specifies a grammar of the query language and instructs the LLM to generate an initial database query that functions like the natural language query and satisfies the grammar. If a lint program determines that the initial database query is not valid for the query language, the converter generates a follow-up prompt to the LLM that indicates valid database queries from which to generate a follow-up database query. A query parser retrieves data that satisfy the initial or follow-up database query and a visualization / summarization module generates graph visualizations and summaries of the retrieved data.

The invention relates to a long-range task-oriented VLA model method for a humanoid robot, which comprises the following steps of: S01, analyzing a natural language instruction through a space-time semantic analyzer to generate an atomic operation sequence with a space-time dependency relationship; s02, maintaining a task state machine by using a dynamic memory network, and tracking the task execution progress in real time; s03, integrating vision, language and sensor data through a multi-modal perception fusion engine; s04, calling a predefined action primitive based on an adaptive execution system and optimizing a motion track; and S05, performing online updating and optimization on the model through a continuous learning mechanism. According to the method, a natural language instruction is analyzed into a structured task sequence with space-time dependence through a space-time semantic analyzer, an execution sequence and preconditions are defined, the semantic understanding ability and the structuring degree of task planning are improved, and task decomposition and replanning in a dynamic environment are supported; according to the method, the LSTM and the knowledge graph are combined, and the task state is maintained in real time.

The invention discloses a streaming knowledge injection and adversarial self-optimization large language model training method and system, and the method comprises the steps: collecting the newest knowledge of an authoritative information source in real time, converting the newest knowledge into a structured constraint rule through a semantic analyzer, and dynamically updating a knowledge base; based on the updated knowledge base, adopting a PPO algorithm to optimize a generator, actively constructing a high-risk adversarial sample, and forcing the model to expose security vulnerabilities; after user input and adversarial samples are input into the model, total loss is calculated through constraint detection, gradient updating is blocked if the total loss exceeds a threshold value, and otherwise, a multi-level safety verification stage is started; and finally, fusing a security verification result and the adversarial loss, updating a total loss function and cooperatively adjusting model parameters to form a continuous self-optimization training cycle. According to the method, compliance is guaranteed through streaming knowledge injection, vulnerabilities are actively mined in combination with adversarial training, verification precision is improved by means of multi-level detection and dynamic threshold adjustment, and safety and reliability of a large language model in a complex scene are enhanced.

The invention provides a private intelligent code collaborative optimization method and system based on graph retrieval, and relates to the technical field of software engineering intelligent auxiliary tools, and the method comprises the steps: obtaining a source code file and operation log data of a private project; the method comprises the following steps: performing syntax tree analysis on a source code file through a Tree-siter analyzer to generate a corresponding abstract syntax tree; constructing a code knowledge graph by analyzing a function call relationship, an inheritance relationship and a module dependency relationship; monitoring the change of a project file in the code knowledge graph through a code change event processor, and updating the code knowledge graph in real time when the file is changed; establishing an association relationship between the running log data and a corresponding function node in the code knowledge graph; the method comprises the following steps: analyzing a natural language problem of a developer into a Cypher query statement through an intelligent Agent; obtaining a query result from the graph database; and formatting the query result to generate a final result.

A system and method for scalable generations of synthetic <logical form, utterance> pairs for training a semantic parser is disclosed. A semantic parser is trained on pairs of <logical form, utterance>. An ontology graph is constructed and derived from a plurality of enterprise documents and provides relationships among the concepts or classes of an organization. One or more paths are traversed among a plurality of source and destination node pairs, facilitating comprehensive semantic representation. Attributed query subgraphs are generated of source nodes, destination nodes, and hidden nodes. Each path is recognized among a variety of possible paths between source and destination nodes. Each path in the ontology query subgraph is validated by considering a plurality of predicates and a knowledge graph generates a natural language utterance. The utterances are refined and rephrased using a large language model, enhancing their coherence and linguistic quality.

A method for artificial intelligence-based automated parser creation includes obtaining a first event log of one or more first event logs of first telemetry data. The first event log includes one or more event log key-value pairs. The method includes generating, using a first artificial intelligence (AI) model, a portion of parser code to map a first event log key of an event log key-value pair of the one or more event log key-value pairs to a predefined field. The method includes generating an event log parser that includes the portion of the parser code. The method includes causing the event log parser to be executed on a second event log of one or more second event logs of second telemetry log data.

The invention provides a code quality adaptive evaluation and optimization method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: performing redundant information cleaning and standardization processing on an original code; selecting an adaptive parser to convert the processed code into an abstract syntax tree, and optimizing the abstract syntax tree; performing embedding processing on each node of the abstract syntax tree by using a pre-trained deep learning model to generate a node semantic vector, and performing aggregation, feature extraction and vector normalization operation to obtain a semantic vector representing code semantic features; extracting a dependency relationship from the abstract syntax tree to construct a program dependency graph, extracting node features by using a neural network model, and generating a dependency relationship analysis result; based on the semantic vector and the dependency analysis result, identifying a code potential problem by using an optimization algorithm and generating an optimization suggestion; codes are automatically modified according to optimization suggestions, the optimization effect is re-evaluated, and related models and algorithms are iteratively optimized according to feedback information.

The invention relates to the field of wisdom education, and particularly discloses a teaching strategy optimization model and grammar error early warning method based on data mining, and the method comprises the following steps: S1, obtaining original code data in a student code library in real time, and preprocessing the original code data to obtain a structured data matrix; s2, for the structured data matrix, generating a grammar parse tree through a grammar parser, positioning error nodes and extracting context features by using a node traversal algorithm, and constructing a single-error multi-dimensional feature vector; meanwhile, an error association rule base is constructed based on historical error data, a causal relationship across error types is identified, and dynamic knowledge graph data is formed. According to the technical scheme, association analysis and deep mining can be carried out on the multi-source learning data, dynamic solution suggestions for student individual errors or group generality errors are formed, and teachers are assisted to quickly adapt to dynamically changing learning requirements of the students.

In some examples, a code file in a development system is selected and the selected code file is segmented, using a language-specific parser, to create multiple parsed blocks. A block of the multiple parsed blocks is selected and a cryptographic hash function used to create a block signature that uniquely identifies the selected block. The block signature is compared to multiple signatures of blocks in third-party libraries. The multiple signatures are stored in an index. When the block signature matches one of the multiple signatures of blocks in third-party libraries, the index is used to determine a source file from which the selected block was copied. Vulnerabilities included in the source file and fixes to address the vulnerabilities are identified to enable a software developer to apply the fixes to the identified vulnerabilities.

The invention provides a business processing method and device for an online transaction scene and electronic equipment. Comprising the following steps: acquiring business data, defining a logic arrangement keyword based on an online transaction execution language, arranging the business data according to the logic arrangement keyword to obtain DSL arrangement data, constructing a grammar verification analyzer, and performing grammar verification processing on the DSL arrangement data by adopting the grammar verification analyzer to obtain the DSL arrangement data. An abstract syntax tree is constructed based on the verified DSL arrangement data, the abstract syntax tree comprises root nodes and leaf nodes, the root nodes represent arrangement rules and scheduling logic, and the leaf nodes represent actually called atomic transactions in the service data; and traversing each node in the abstract syntax tree by adopting an execution engine, and calling a node processor corresponding to each node to process the service data. The problem that an existing rule arrangement engine is poor in business reliability when being applied to banking businesses is solved.

The invention discloses an aspect emotion triple extraction method for a large language model annotation data set, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the initial triple labeling of a standardized text data set, calculating a cognitive bias index based on a labeling reference set, and generating a data set with a bias label; a dynamic attenuation suppression method is adopted to generate a bias mask matrix, exponential mask enhancement is performed on samples exceeding a preset bias threshold value, and bias mask enhancement representation is obtained; inputting the bias mask enhanced representation into a differentiable grammar parser, calculating a grammar dependency matrix by using an attention mechanism, optimizing a topological structure of the grammar dependency matrix in combination with structural entropy loss, and generating a boundary judgment matrix; and extracting candidate triple embedding based on a boundary judgment matrix, calculating a contradiction coefficient by using a quantum emotion entangled state, and generating a correction triple set. According to the method, the accuracy and availability of emotion recognition results in the fields of medical evaluation and the like are enhanced.

The invention relates to a reinforcement learning verification reward-based big language model log anomaly automatic detection method and device, and the method comprises the steps: 1, training a big language model-based log anomaly automatic detection model, dividing a log sequence through a sliding window, and carrying out the log analysis through a log analyzer, inputting a large language model based on a Transform encoder to perform semantic extraction, using a multi-layer perceptron and a large language model based on a Transform decoder to perform dimension mapping, and inputting a large language model based on supervised fine tuning and reinforcement learning fine tuning to perform reasoning analysis to obtain anomaly detection information; and 2, performing reasoning detection by using the automatic anomaly detection model based on the large language model obtained in the step 1. According to the method, the excellent semantic extraction capability of the semantic vector extraction module and the text generation capability of the reinforcement learning optimization module are fully utilized, redundant information is further removed, the memory requirement of the reinforcement learning optimization module is reduced, and the calculation efficiency is improved.

The multi-source data automatic collection method based on the large language model comprises the steps that feature distribution of historically collected data is analyzed, and an optimal collection strategy is dynamically generated in combination with the state of a current data source; scheduling the acquisition task in real time to obtain a dynamically scheduled data stream; performing real-time anomaly detection and self-adaptive compensation on the dynamically scheduled data stream to obtain a self-fault-tolerant data stream; analyzing entity association and service logic of the self-fault-tolerant data stream, and predicting potential data demand points; automatically expanding a data acquisition range to obtain knowledge-enhanced data assets; identifying protocol features of the data transmission stream of the data assets, and analyzing the data by an analyzer based on protocol feature mapping to obtain protocol-independent standardized data; and performing semantic noise filtering and cross-modal cleaning to obtain semantic pure data. According to the invention, the defect that protocol adaptation and adaptive strategy optimization cannot be carried out in the existing multi-source data acquisition technology is overcome.

The invention provides a large language model JSON output repairing method and system based on ANTLR grammar analysis, and belongs to the technical field of model repairing. The method comprises the following steps: preprocessing an original output text of a model to obtain a to-be-repaired JSON text; constructing a fault-tolerant grammar rule, and generating a JSON grammar parser according to the fault-tolerant grammar rule; a JSON text to be repaired is input into an analyzer, grammar analysis is carried out in stages by adopting a progressive grammar matching strategy, and error node position coordinates and error types of grammar analysis anomalies are positioned by analyzing a context tracking mechanism; according to the position coordinates of the error nodes and the error types, performing context-aware repair on the text by adopting a hierarchical repair strategy to obtain a repaired JSON (JavaScript Object Notation) text; and performing multi-layer verification on the repaired JSON text by using a multi-layer verification mechanism, and outputting the repaired JSON text after the multi-layer verification is passed. According to the method, the error classification accuracy and the error positioning precision in the JSON output repairing process are improved, and then the repairing success rate of large language model JSON output is improved.

The invention discloses an ANTLR4-based multi-manufacturer network equipment configuration analysis method and system, and the method comprises the steps: carrying out the preprocessing of a configuration file; based on a machine learning algorithm of multi-dimensional feature analysis, automatic identification of configuration file manufacturer types is realized; based on the identification result of the manufacturer type, loading an ANTLR4 grammar parser corresponding to the manufacturer type; a lexical analyzer in ANTLR4 realizes lexical analysis on the configuration file through lexical rule definition, a Token classification system and an optimized analysis algorithm; a syntactic analyzer in ANTLR4 constructs an abstract syntax tree according to a lexical analysis result and a syntactic rule defined based on a context-independent grammar; traversing the abstract syntax tree, and extracting key configuration information; and converting the specific configuration format of each manufacturer into a unified standard model. According to the method and the system, network equipment configuration files of different manufacturers can be automatically identified and analyzed, and key configuration information is extracted and converted into a unified model.

The invention relates to the technical field of vulnerability detection, in particular to a code-level security vulnerability detection method, electronic equipment and a storage medium. The method comprises the steps of obtaining a logic graph corresponding to a target code, wherein the logic graph comprises a role node, a service entity node, an operation edge and an access constraint edge; obtaining an application scene label of the target code according to the logic graph; inputting the target code into an AST resolver to obtain a syntax tree corresponding to the target code; obtaining a sensitive function library B and a suppression function library C matched with the target code according to the application scene label of the target code; and judging whether the target code has security vulnerabilities or not according to the syntax trees corresponding to the B, the C and the target code. According to the method, the unauthorized risk vulnerabilities in the program codes can be effectively identified.

The invention discloses a lightweight cross-platform PLC (Programmable Logic Controller) multi-language code generation method, which relates to the field of industrial process control, and firstly provides a unified intermediate data model, and on the basis, a module for converting each PLC language into the unified intermediate data model is realized; on the basis of a standardized JSON data structure, a modularized unified intermediate language conversion chain is further supported, an analyzer and a generator communicate through a JSON interface, a cross-platform conversion path is supported, and unified intermediate language conversion from PLC to JSON to XML to ST to C is completed; the method comprises the following steps of: integrating an open source compiler and runtime, constructing an open and pluggable automatic execution framework, compiling a generated C code into a platform executable file by using an adaptive GCC tool chain aiming at different target platforms, and deploying a program generated by compiling to a soft PLC runtime system supporting multiple platforms. According to the method, a JSON intermediate data structure which is light in weight, unrelated to languages and uniform in structure and a matched conversion framework of the JSON intermediate data structure are constructed.

System, method and interface for generating data visualizations are provided. The system receives a user input to specify a natural language command directed to a data source. The system also generates a prompt for generating a data visualization based on relevant data fields and data values, rules that characterize the data visualization, and a context free grammar. The system also prompts a trained large language model using the prompt to generate a structured document following a domain-specific schema based on a shorthand notation. The system also uses a parser that uses the context free grammar to map the structured document to a visual specification. The visual specification specifies the data source, visual variables, and data fields from the data source. The system also generates and displaying a data visualization based on the visual specification, including displaying visual marks representing data, retrieved from the data source, for the data fields.

Provided are a method and device for parsing a database query statement, and a storage medium. A query statement for a database table to be queried is obtained, and an empty database table corresponding to the database table to be queried is created; the query statement is executed in the empty database table, and a parser installed in a database is called to parse the query statement to obtain a syntax tree, and an execution plan is generated based on the syntax tree for execution in the empty database table; and the syntax tree is obtained from the parser in an execution process of the query statement, and a syntax tree serialization result is generated and output as a parsing result of the query statement.

System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive an aspect term-sentence pair; embed the aspect term-sentence pair; parse the sentence using multiple parsers to obtain dependency trees, and perform edge union to obtain a merged graph; combine the embedding and the merged graph to obtain a relation graph; perform a relation graph neural network on the relation graph; extract hidden representation of the aspect term from updated relation neural network; and classify the aspect term based on the extracted representation to obtain a predicted classification label of the aspect term. During training, the computer executable code is further configured to calculate a loss function based on the predicted label and the ground truth label, and adjust parameters of models.

The invention provides a bidding and tendering field information extraction method and device, relates to the field of information processing, and solves the technical problem of insufficient bidding and tendering multi-modal data processing precision and association analysis in the prior art. The method comprises the steps of obtaining bidding and tendering data information including bidding and tendering text data, bidding and tendering table data and bidding and tendering picture data; extracting text semantic features of the bidding and tendering text data through a Transform model; analyzing the bidding and tendering table data through a row and column structure analyzer, and encoding an analysis result into a structured feature reflecting semantic dependency of a cell; performing convolutional coding on the bidding and tendering picture data through a ResNet model, and extracting visual features; performing cross-modal alignment on the semantic features, the visual features and the structured features of the text based on an attention mechanism, and fusing the features in a feature space to obtain fused features; and inputting the fusion features into a Qwen model, and outputting target field information. The method and the device are used in the bidding field information extraction process.

A system provides a language model that utilizes an optimized parser and operates at the byte- level in a context free grammar (CFG) and a tokenizer vocabulary. The CFG is constrained and automatically transformed to a byte-level grammar. A prediction mechanism is used to predict the allowed sequences of bytes that can appear after a given prefix. In some instances, only the next token is constrained so that candidates for the next token can be intersected with tokens in the tokenizer vocabulary. To achieve this, lattice parsing is performed using a finite state automaton (FSA) that is generated and then minimized.

The invention relates to the technical field of knowledge graph generation, in particular to a dynamic and static dual-state knowledge graph generation system, which is characterized in that an object is used for replacing an entity as a node, a traditional triple is upgraded into a tetrad containing a timestamp, and a state tetrad is newly added; quadruple elements (including timestamp mining) are extracted through a semantic parser, a parameter space is maintained, a construction engine constructs a quadruple, stores the quadruple into a graph database and executes four-item rationality detection, a vacancy filling engine calls a fine-tuned large language model, and an intelligent agent screens high-credibility answers to complement knowledge. Traditional static representation limitation is broken through, object dynamic description is realized, accurate and complete atlas is guaranteed, and an important logic basis is provided for next-generation artificial intelligence interpretable reasoning.

The invention discloses a rich text intelligent processing method, a rich text intelligent processing device, a network attached storage device and a network attached storage system, which are implemented to support the analysis of rich texts in different formats through a text analyzer and a structure conversion operation, so that the compatibility of text processing in a Gawan system is improved; and based on a structure traversal and component generation mechanism, the rich text nodes can be mapped into the Nwan original UI component, the rendering path is optimized, and the rendering overhead of the complex text scene is reduced; moreover, through rule analysis and interface style adaptation operation, the design specification of the swan-mong system can be automatically matched, and the rich text analysis efficiency and the overall rendering accuracy are further improved; besides, a user interaction event processing module is integrated, flexible text editing, style adjustment and event monitoring of the user can be supported, and the response accuracy of the interaction function is improved while the user interaction experience is enhanced.

The embodiment of the invention provides a form generation method and related equipment, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining form item configuration from a form item knowledge base; acquiring a natural language input by a user; generating a current question word based on form item configuration and a natural language; inputting the current question word into the large language model to obtain a form configuration JSON (JavaScript Object Notation) content; analyzing the form configuration JSON content through a JSON analyzer to obtain a current front-end page; in response to the first instruction, a current front-end page is displayed in the designer. According to the embodiment of the invention, the visual form configuration can be generated and previewed through the natural language, the user operation is reduced, the development threshold is lowered, and the development efficiency and the user experience are improved.

The invention discloses a warehouse level code translation method and device based on a large model, and is used for solving the technical problem that the existing warehouse level code translation method only relates to a function translation pair, so that a code translation task in a warehouse level context is poor in performance. The method comprises the steps that a tree-shaped analyzer is utilized, a large model is preset, and a target language code sample self-evolution knowledge base, a dependent use example self-evolution knowledge base and a successful translation function pair self-evolution knowledge base are constructed according to an obtained open source project, a to-be-translated warehouse and a plurality of historical successful translation function pairs; and outputting the target triple translation knowledge corresponding to each to-be-translated function, and outputting a warehouse level code translation result in combination with the obtained implementation code corresponding to each to-be-translated function and the generated warehouse architecture.

The invention discloses a data analysis method and device based on a grammar analysis generator, and relates to the technical field of data processing. Obtaining byte stream data to be analyzed in the task scene; creating a CSG grammar file according to a grammar specification of the grammar analysis generator, and defining a context-related grammar rule of a protocol; processing the CSG grammar file by using a grammar analysis generator, generating abstract syntax tree information related to the CSG grammar file, and generating a Java parser based on the abstract syntax tree information; on the basis of the generated Java parser, a CsgVisor class is self-defined, a CSG grammar file is parsed, and a Protocol protocol object is constructed; and creating a CSGPVisor accessor and a CSGPParser parser, and analyzing the byte stream data in combination with a Protocol protocol object and a preset analysis rule.

The invention provides a domestic database compatibility comparison evaluation and migration method and system and a medium, and belongs to the technical field of database migration. The method comprises the following steps: acquiring a creation statement of each database object in a pre-connected target source database; and based on a preset grammar parser and a preset grammar feature library, analyzing each creation statement, generating an abstract grammar tree and traversing the abstract grammar tree to determine grammar features corresponding to each database object, and generating a mapping list between each database object and each grammar feature. And comparing the mapping list with a plurality of preset grammar feature templates of the domestic databases, generating compatibility evaluation results corresponding to the domestic databases according to comparison results, and determining corresponding migration working hours and an automatic rewriting grammar set according to the compatibility evaluation results. And based on each compatibility evaluation result, each migration man-hour and the automatic rewriting grammar set, generating a multi-dimensional comparison view corresponding to each domestic database, and sending the multi-dimensional comparison view to an evaluation migration user terminal.

The invention relates to an NL2SQL semantic parsing method and device, and the method comprises the steps: inputting a natural language query statement into a business data model, and obtaining a context vector; fusing the context vector with the natural language query statement to obtain a joint semantic vector; decoding the joint semantic vector and the structured feature based on a constraint decoding strategy to obtain an initial SQL statement, and verifying the initial SQL statement through a grammar parser to obtain a target SQL statement; the context vector of the natural language query statement can be more comprehensively captured through the business data model, and the optimal joint semantic modeling is realized through the context vector and the natural language query statement, so that the problems of information bias and redundancy caused by fixed weight or simple splicing are effectively solved; the context features, the structured features of the database and the constraint decoding strategy are dynamically fused, so that complex service logic and anaphora and omission phenomena in the long dialogue context can be better understood.