391 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 discloses a data analysis method and system and a storage medium, and is applied to the field of natural language processing. The method comprises the steps of obtaining a natural language input by a user; constructing a semantic data model for converting the natural language into a semantic query statement; combining the semantic data model with a user-defined rule to construct a semantic parser; inputting the natural language into the semantic parser for parsing to obtain a semantic query statement; inputting the semantic query statement into a semantic corrector for correction to obtain a target semantic query statement; converting the target semantic query statement into a structured query statement through a semantic translator; and inputting the structured query statement into the database for query to obtain a query result.

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.

Embodiments described herein provide an automated testing pipeline for providing a testing dataset for testing a trained neural network model trained using a first training dataset. A first testing dataset for the trained neural network including a first plurality of user queries is received. A dependency parser is used to filter the first plurality of user queries based on one or more action verbs. A pretrained language model is used to rank the remaining user queries based on respective relationships with queries in the first training dataset. Further, user queries that are classified as keyword matches with the queries in the first training dataset using a bag of words classifier are removed. A second testing dataset is generated using the ranked remaining user queries. Testing outputs are generated, by the trained neural network model, using the second testing dataset.

The invention belongs to the field of data query, and particularly discloses a data query method and system based on a large language model, a terminal and a medium. Analyzing the natural language query statement by using a natural language processing technology to obtain query parameters including a query entity, a statement dependency relationship and a user intention; utilizing a pre-trained large language model to generate a logic SQL query statement according to the query parameters; converting the logic SQL query statement into a physical SQL query statement by utilizing a semantic layer technology; the method comprises the following steps of: converting a physical SQL (Structured Query Language) query statement into a domain specific language for Elasticsearch query by utilizing an SQL parser; and executing the Elasticsearch query on the Elasticsearch cluster by using a domain specific language to obtain a query result. A natural language processing technology, a large language model and a semantic layer technology are utilized to convert a natural language query statement into an Elasticsearch query DSL, the data query process is simplified, the data query efficiency and precision of a user are improved, the cost is reduced, and the query requirement is met.

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.

A system and method for a protocol parser that has native support for parsing TLVs and flag-fields and allow users to code in a language convenient to them and leverage standard tool chains and tools. The same parser source can be compiled to arbitrary software and hardware targets without code change and provide the highest performance possible given its capabilities.

The invention discloses an interface document automatic generation method and related equipment thereof, and belongs to the technical field of artificial intelligence. The method comprises the following steps: receiving an interface document generation instruction, obtaining an interface source code and a code annotation, and obtaining an interface document template; analyzing the interface source code and the code annotation by using a code analyzer to obtain code metadata; constructing a code analysis tree for the interface source code, and performing static code analysis on the code analysis tree according to the source code grammar detection rule to obtain context information; selecting an adaptive controller class and a class processing method to package the code metadata and the context information, and generating response data; and importing the response data into the interface document template to generate an interface document corresponding to the interface source code. The invention further relates to the technical field of block chains, and source codes are stored on a block chain network. According to the method and the device, the problem of document errors or omission caused by lack of metadata or context information in a traditional interface document automatic generation process is solved.

The invention discloses a software project development method based on a low-code platform, which belongs to the technical field of software development, and specifically comprises the following steps: receiving original demand description through an embedded natural language parser, and establishing a semantic incidence matrix to extract demand elements; encoding the demand elements into three-dimensional structured demand vectors; mapping the demand vector to a three-dimensional orthogonal feature space, calculating the coordinates of a functional module, and generating an initial topological structure based on a Delou inner triangulation algorithm; rendering an interactive three-dimensional network map on a visual interface; monitoring user operation, and recalculating the adjacency relation of the affected module when the displacement of the module exceeds a threshold value or the connection edge is reconstructed; compared with a historical version, when a key service path is interrupted or a data flow ring is broken, a compensatory connection bridge is activated; a deployable architecture with a version traceability identifier is output, and a traceability code is built in the module to reversely map an original demand vector.

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 embodiment of the invention provides a small game general graph rendering method and device based on a layered architecture, decoupling of an operation system and a game engine is realized through a window abstraction layer, and core operation is packaged into a unified game canvas interface. And standard interfaces such as scene initialization, object drawing and animation updating are provided in the rendering implementation layer, so that a real-time rendering function is realized. A language binding layer is innovatively designed, efficient conversion between a script engine and a native code is achieved through a state object and a parameter analyzer, and automatic recovery of resources is ensured by adopting a weak reference mechanism and end callback. According to the method, the platform limitation of a traditional rendering system is broken through, and a lightweight and cross-platform general rendering solution is provided for small game development.

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 embodiment of the invention provides a large model interaction page rendering method and system, electronic equipment and a storage medium, and belongs to the technical field of front-end development. The method comprises the following steps: acquiring a front-end page processing rule based on a domain-specific language input by a user, and registering the front-end page processing rule into a domain-specific language analyzer; and processing the response message through a front-end page processing rule in the domain-specific language parser to obtain a target code, and executing the target code to render the response message on the front-end interaction page. According to the application, a front-end page processing rule custom function is provided for a user based on a domain-specific language, a custom rule of the user is registered in a domain-specific language parser, and when a response message of a large model is received, a target code for rendering the response message is determined through the domain-specific language parser; custom rendering of the response message is achieved, and the large-model front-end interactive display requirement of a business party is met.

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 discloses a method and device for detecting code quality, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: acquiring a code file of which the quality is to be detected, and matching an analyzer according to attribute information of the code file; processing the code file by using a parser to obtain an abstract syntax tree; traversing the abstract syntax tree, and determining the circle complexity of the code file through a strategy of counting and judging the number of nodes; and counting the code line number of the code file, determining a quality analysis result of the code file according to one or more of the circle complexity and the code line number, and triggering an alarm in response to the condition that the quality analysis result is greater than or equal to a preset threshold value. According to the implementation mode, the abstract syntax tree aiming at the code characteristics can be generated, the code quality is systematically evaluated from multiple dimensions, and developers are helped to quickly identify complexity and potential problems in the codes, so that subsequent continuous improvement and optimization of the codes are facilitated.

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.

Implementations of semantic parsing using pre-trained language models are provided. One aspect includes a computing system for semantic parsing of natural language. The computing system comprises processing circuitry and memory containing instructions that, when executed, cause the processing circuitry to receive a request comprising a natural language utterance and generate a formal meaning representation using the natural language utterance and a language model comprising a semantic parser that has been prompted with training data generated by providing a dataset comprising a set of unlabeled programmatic scripts and a seed programmatic script, generating a set of parsed natural language descriptions by inputting the set of unlabeled programmatic scripts into an inverse semantic parser, generating a set of re-parsed programmatic scripts by inputting the set of parsed natural language descriptions into the semantic parser, and determining a set of labeled programmatic scripts by validating the set of re-parsed programmatic scripts.

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 embodiment of the invention discloses a message parsing system and a message parsing method.The message parsing system comprises a fixed parser and a programmable parser, the fixed parser is configured to receive and parse an original message, and in response to the fact that the message is not parsed completely, the programmable parser is configured to parse the original message; message analysis information including the analyzed result and the message header is sent to a programmable analyzer, and the programmable analyzer is configured to continue to perform message analysis according to the received analyzed result and the received message header to obtain a message analysis result. Therefore, according to the embodiment of the invention, the fixed parser and the programmable parser can be combined, the message protocol which can be parsed by the fixed parser is parsed by the fixed parser, and the message protocol which cannot be parsed by the fixed parser is parsed by the programmable parser, so that the message parsing system can meet the low-delay requirement and the flexibility requirement; and meanwhile, the system resource demand is also reduced.

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 discloses an intelligent document reviewing and traceability positioning method based on LLM natural language processing. The method comprises the steps that 1, a reviewing document enters an OCR analyzer to be split into text blocks and positioning information; 2, vectorizing the text blocks and storing positioning information corresponding to each text block into a vector database; step 3, vectorizing the review rule; step 4, performing enhanced hybrid retrieval based on the vectorized review rule; step 5, rearranging the most relevant K text blocks with the positioning information retrieved in the step 4; step 6, the large language model extracts key information from the rearranged text blocks according to an examination rule based on a cue word project; 7, the big language model returns a judgment result and positioning information based on the cue word project according to the review rule and the key information; and 8, displaying a judgment result and positioning traceability information. According to the invention, the efficiency and accuracy of examination can be improved, and accurate traceability and positioning of error contents can be realized.

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.