74 results about "Syntactic parsing" patented technology

The invention discloses an equipment agent processing program conversion method and device based on cooperation of a large model and a small model. An equipment agent is constructed by analyzing control systems, kinematics structures and process constraint information of a source machine tool and a target machine tool, and line-level instructions are abstracted into system-independent unified semantic representation based on a multi-system numerical control corpus. And performing grammar analysis and semantic mapping on the source program, and generating a conversion task in combination with grammar rules, kinematics accessibility and process security constraints of a target machine tool. A small model is finely adjusted through supervised learning to realize basic generation capability, a large model is introduced as a patch evaluator, candidate results are jointly scored from different dimensions, knowledge migration is realized based on improved near-end strategy optimization, and a small model generation strategy is continuously optimized. And finally, high-quality results are screened through confidence degree sorting, cross-system G / M instruction and multi-axis track generation and verification are completed, and high-precision automatic conversion of numerical control programs is achieved.

The invention relates to the technical field of software engineering and information processing, in particular to a semantic verification method and device based on a large model.The method comprises the steps that a source code file and a corresponding software requirement and design document are obtained, code semantic representation is generated based on grammar analysis and static program analysis, and a semantic verification result is obtained; based on text analysis and business flow chart structure identification, generating a design demand semantic representation; respectively inputting the code semantic representation and the design demand semantic representation into a pre-training language large model to generate a code implementation logic description and a design demand logic description; and performing semantic consistency verification on the two types of logic descriptions to obtain a semantic inconsistency result, determining a code position in the source code file based on the semantic inconsistency result, and outputting a scanning report. According to the invention, the automation degree and the rechecking performance of consistency checking of requirements can be improved, and the leak detection and reworking cost can be reduced.

The invention provides a structured query statement generation method and device, equipment, a medium and a program product, and can be applied to the field of artificial intelligence and the field of financial science and technology. The method comprises the steps that in response to a natural language statement input by a user, the natural language statement is analyzed through a natural language processing model, and semantic elements are obtained; performing syntax analysis on the semantic elements to generate an abstract syntax tree; based on the abstract syntax tree, database structure metadata coding is conducted on the natural language statement through a deep learning model, and a first structured query statement is generated; and performing security verification on the first structured query statement to generate a second structured query statement.

The invention relates to a label extraction model training method and device, a vehicle label extraction method and device and electronic equipment, and the method comprises the steps: inputting a current sample vehicle image into a preset multi-modal model, and obtaining a sample description text; performing grammar analysis processing on the sample description text to obtain at least one segmented word, the part-of-speech of the at least one segmented word and the dependency relationship between the at least one segmented word; performing noun merging based on the at least one segmented word, the part of speech of the at least one segmented word and the dependency relationship among the at least one segmented word to obtain a sample description tag; based on a preset filtering word, filtering the sample description label to obtain a real description label; and training a to-be-trained label extraction model based on the current sample vehicle image and the real description label to obtain a label extraction model. According to the embodiment of the invention, the method can meet the multi-dimensional demands of vehicle recognition, is adaptive to a multi-label scene, greatly improves the readability of a vehicle recognition result, and reduces the decision errors.

The invention discloses an agent intention understanding method based on multi-modal information fusion, relates to the technical field of natural language processing, and solves the technical problem that multi-agent intention understanding cannot be realized in a dynamic environment. Voice, text and visual features are mapped to the same hidden space through maximum mean value difference minimization, distribution difference between modes is eliminated, joint features in the hidden space can capture cross-mode complementary information at the same time, and the richness and anti-noise capability of feature expression are enhanced. CRF (Conditional Random Field) is combined with a special dictionary in the scheduling field to carry out candidate set expansion on fuzzy keywords, and specific semantic variants in the field are captured. The optimal grammar of the scheduling instruction is analyzed and decomposed into a feature vector causal relationship pair, and components of an intention and logic dependence of the intention are clarified. A dynamic knowledge graph is constructed based on the causal relationship, probability dependence among parameters is quantized by using a Bayesian network, the causal relationship strength is dynamically adjusted, and environmental changes are adapted.

The invention provides a source database storage process migration method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the grammar analysis of an original storage process in a source database, and generating a corresponding abstract syntax tree; based on a preset rewriting rule set, performing semantic equivalent transformation on the flow control syntax structure in the abstract syntax tree to generate a transformed abstract syntax tree; and generating a target script in a target execution environment from the converted abstract syntax tree, executing the target script in a script running engine, dynamically generating a standard SQL statement corresponding to the business logic defined by the original storage process, and submitting the standard SQL statement to one or more target data platforms for execution. And the business logic of the original storage process is reproduced. By introducing the target script as the intermediate execution layer, the flexibility of the migration scheme is greatly improved, so that the migration scheme is suitable for various big data platforms without the native storage process capability.

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.

The invention relates to the technical field of SQL (Structured Query Language) grammar conversion, and provides a general SQL grammar conversion method, device and equipment and a medium, the method comprises the following steps: performing lexical analysis on a source end SQL statement to obtain a lexical unit set; based on the type features and semantic attributes of the lexical unit set, determining parser combiners, and constructing a specific parser by infinitely combining the parser combiners; the specific resolver is used for conducting grammar analysis on the lexical unit set, a general abstract syntax tree is constructed through a mapping assembly, and nodes of the general abstract syntax tree correspond to the specific resolver one to one; and traversing the general abstract syntax tree, and generating a target end SQL statement according to an SQL syntax rule of a target end database. Through the technical scheme, any complex source end SQL can be accurately converted into the target end SQL.

A dialogue-based artificial intelligence system for interaction management in digital commerce, consisting of: a housing that encloses an arrangement of interconnected printed circuit boards; a conversation processing unit arranged on a first circuit board, comprising a syntactic parsing processor for generating hierarchical parse structures from user input texts, a semantic embedding processor for generating high-dimensional semantic vectors from the parsed structures, and a dialogue state calculation circuit for managing a multi-level conversation memory using clocked recurrent memory buffers; a retrieval augmentation unit arranged on a second circuit board, comprising a vector indexing processor for storing and retrieving domain-specific embedding vectors, a similarity calculation processor for calculating cosine-based semantic distances between the semantic vectors and vectors stored in a knowledge store, and a document reconstruction structure for merging retrieved text segments into a unified context representation; a generative response unit arranged on a third circuit board, comprising a transformer execution processor for executing a quantized transformer architecture for natural language generation, a context fusion circuit for merging the unified context representation with the semantic vectors to generate response conditioning data, and a hallucination suppression circuit for comparing the generated text with retrieved text segments and attenuating content divergence beyond a predetermined semantic threshold; a domain integrity check unit that is communicatively connected to the generative response unit, wherein the domain integrity check unit includes a regulatory restriction store that stores domain-driven rules, and a conformance assessment processor configured to check generated responses for conformance with the domain-driven rules, blocking non-conforming segments or replacing them using verified text content from the retrieval extension unit; a knowledge storage array comprising at least one static domain repository for storing commercial specifications, regulatory guidelines, product descriptions, and domain documents, a dynamic interaction buffer for storing anonymized conversation state data, and a vector storage partition for storing embedding vectors that can be retrieved by the vector indexing processor; and a multi-interaction device consisting of a touchscreen panel, an array microphone, a speaker structure and a contactless communication interface, each configured to receive multimodal user inputs or generate multimodal system outputs.

The invention belongs to the technical field of industrial dynamic formula execution, and discloses an analysis-free high-availability and high-performance formula execution method. The method comprises the following steps: S1, formula extraction and function body construction: receiving a calculation formula input by a user, and taking the calculation formula as a function body core calculation logic; s2, function packaging: adding a function head and a return value statement for a function body, and constructing a complete function definition character string; wherein the function head comprises a function name and a parameter list; and S3, function persistence and dynamic import execution: writing a function character string into an independent Python module file, and calling and executing through a dynamic import mechanism. According to the method, function packaging is adopted, the repeated lexical analysis and grammar analysis process which must be carried out when a traditional event () method is executed each time is eliminated, and the problems that a formula is forcibly required to only comprise functions of input parameters, calculation expressions and return values, and the bottleneck of industrial real-time application and the fragility of a whole framework are solved.

This invention discloses an automated rule engine execution system and method for communication events, belonging to the field of communication technology. The system includes: collecting multi-source heterogeneous communication events; obtaining standard communication event objects through a dedicated rule meta-model for each communication event; loading a rule set; generating an abstract syntax tree through syntax parsing and pre-compilation; constructing a rule dependency graph and performing intersection and mutual exclusion detection to form a scheduling strategy containing priority, mutual exclusion whitelists, and execution order; locating a subset of candidate rules based on system context; determining the target rule through condition matching; encapsulating automated operations into atomic action units; and executing them concurrently or sequentially according to orchestration logic; simultaneously generating a unique execution trace ID to record the entire link log; monitoring anomalies in real time; triggering a circuit breaker if the anomaly rate exceeds the limit or execution times out; taking down abnormal rules and demoting their execution; evaluating the rule effectiveness and providing optimization suggestions after the session ends; this invention improves the efficiency of communication event processing and the stability of rule execution, ensuring reliable system operation.

A user specifies a natural language command to a device. Software on the device generates contextual metadata about the user interface of the device, such as data about all visible elements of the user interface, and sends the contextual metadata along with the natural language command to a natural language understanding engine. The natural language understanding engine parses the natural language query using a stored grammar (e.g., a grammar provided by a maker of the device) and as a result of the parsing identifies information about the command (e.g., the user interface elements referenced by the command) and provides that information to the device. The device uses that provided information to respond to the command.

The invention discloses an ancient modern text machine translation method based on deep learning, and belongs to the technical field of natural language processing. The invention provides a multi-task collaborative optimization framework aiming at the problems of domain knowledge deficiency, frequent word activation, corpus scarcity and the like of an existing neural network model in ancient language translation. Precise domain knowledge injection is realized by constructing a closed ancient language parallel corpus retrieval library and combining historical term alignment and logic chain reasoning; a probability weighted part-of-speech embedding mechanism is adopted, and grammar feature distribution of virtual word ambiguity and word type activity is dynamically fused, so that the grammar analysis robustness is improved; and semantic generation, retrieval alignment and part-of-speech constraint tasks are jointly optimized through a fixed weight strategy. According to the method, the semantic accuracy and the logic continuity of ancient language translation are improved, and extended application of complex ancient language styles is supported. Experimental results verify that the performance of the method is improved in terms of BLEU and ROUGE-L indexes, and efficient technical support is provided for ancient book digitization and cross-time explanation.

The invention relates to the field of computing, and more particularly to methods for automated text processing. A method for classifying a text parse includes inputting into a classification model a text parse containing a main entity and, associated with said main entity, an associated end entity. A text processing method consists in performing the following steps of: identifying a text including three segments, and subsequently identifying said segments; selecting one of the segments for subsequent marking, and marking in this first segment only one part to be parsed; parsing marked parts by semantic-syntactic parsing; extracting from the semantically-syntactically parsed part of the first segment a main entity of said first segment and an associated entity related to the main entity of the first segment, wherein one associated entity is an associated end entity, and extracting from each semantically-syntactically parsed part of a selected second segment and / or a selected third segment one assertion; associating said assertion with the main entity of the first segment. The invention provides for more precise automated generation of a text corpus.

The application discloses a device intelligent agent processing program conversion method and device based on large model and small model cooperation. A device intelligent agent is constructed by analyzing the control system, kinematic structure and process constraint information of the source machine tool and the target machine tool, and the line-level instructions are abstracted into system-independent unified semantic representation based on a multi-system numerical control corpus. The source program is parsed and mapped, and the conversion task is generated in combination with the syntax rules, kinematic accessibility and process safety constraints of the target machine tool. The small model is fine-tuned through supervised learning to realize the basic generation capability, the large model is introduced as a patch evaluator, the candidate results are jointly scored from different dimensions, and the knowledge transfer is realized based on the improved proximal policy optimization to continuously optimize the small model generation strategy. Finally, high-quality results are selected through confidence ranking, cross-system G / M instructions and multi-axis trajectory generation and verification are completed, and high-precision automatic conversion of numerical control programs is realized.

This invention discloses an intensity-aware emotion classification method based on a hierarchical knowledge-enhanced graph neural network. Its key features include: preprocessing the Chinese text to be classified by word segmentation, syntactic parsing, and dependency tree construction; extracting word-level contextual semantic vectors and global sentence representations using a pre-trained language model; identifying sentiment words and degree words in the text, calculating the dynamic sentiment intensity of lemmas, and adaptively expanding the sentiment lexicon; extracting a sentiment knowledge subgraph and aggregating knowledge embeddings, followed by seed-driven pruning to obtain a sparse dependency subgraph; fusing semantic and knowledge features to generate initial node representations, and learning these representations using a hierarchical intensity-aware graph neural network; finally, generating graph-level features through dual-path pooling, fusing global semantics, and outputting the emotion classification result through a multi-task framework. This invention solves the problem of ambiguous emotion classification caused by insufficient modeling of the combined intensity of "degree words + sentiment words" in existing technologies.

The application discloses a data processing method and device, and relates to the technical field of computers. A specific implementation manner of the method comprises the following steps: receiving a definition operation of a syntax rule and a syntax label of an index calculation expression; using the syntax rule to perform lexical analysis and syntax analysis on the index calculation expression, and obtaining a syntax analysis tree; creating an accessor to traverse the syntax analysis tree, and replacing the syntax label in a node in the syntax analysis tree with implementation logic code of the corresponding syntax rule by analyzing the node; determining an index identifier in the index calculation expression, obtaining index data corresponding to the index identifier, processing the index data in sequence according to the position of the node where the implementation logic code is located in the syntax analysis tree, obtaining processing result data, and returning the processing result data. In the implementation manner, for an index definer who can give a data expression, the index definer can efficiently define and efficiently execute a new index without writing redundant codes, and the learning cost and operation cost are relatively low.

The application provides an English text context self-adaption method for cross-cultural communication, comprising: performing word segmentation and grammar analysis on the English text to obtain a preliminary language feature vector; performing similarity matching on the preliminary language feature vector and a cultural knowledge base to determine a cultural attribution label of the preliminary language feature vector; if the cultural attribution label corresponds to multiple cultural meanings, obtaining surrounding sentence information through English text context to obtain an extended context segment; performing emotion analysis on the extended context segment in combination with the cultural attribution label to obtain an emotion tendency label, and filling in missing cultural details after fusion processing of the emotion tendency label and the preliminary language feature vector to obtain an enhanced context framework, completing cross-cultural context representation, realizing dynamic analysis of cultural meanings and accurate labeling of emotion tendencies, significantly improving the accuracy and depth of language understanding in cross-cultural communication, and being suitable for intelligent translation and cultural adaptive interaction in a multilingual environment.

A computer-implemented method of generating a hardware description for configuring a digital electronic circuit 2000 to parse input data comprising a sequence of input tokens against a grammar that de

The present specification relates to the technical field of distributed database, and provides a syntax parser generation method, a SQL access control method, a device and equipment, the method comprising: before an application program executes a SQL statement, intercepting the SQL statement based on a filter chain integrated in the application program; calling a filter in the filter chain to perform corresponding filtering processing on the SQL statement; wherein one of the filters is a syntax analyzer configured with a custom access control rule; when the SQL statement fails to pass the audit of the syntax analyzer, the application program is prohibited from executing the SQL statement. The embodiments of the present specification can reduce or avoid the database performance decline caused by the non-standard SQL statement accessing the distributed database.

This invention provides an intelligent design method for building walls based on a semantic-physical dual closed loop, comprising the following steps: acquiring project data and environmental element data of the target project, generating semantic tags and project context, retrieving standard provisions based on semantic tags, and compiling the standard provisions into constraint expressions; constructing an executable design grammar for wall design, establishing a bidirectional mapping relationship between the executable design grammar and the building information model, and generating multiple candidate wall schemes conforming to the executable design grammar under the constraints of the constraint expressions; using a surrogate model to perform multiphysics performance evaluation on the candidate wall schemes, triggering a high-precision solver to verify based on the uncertainty evaluation results, and selecting a set of compliant schemes; iteratively optimizing the set of compliant schemes under the penalty mechanisms of hard and soft constraints, outputting a Pareto optimal solution set, parsing the Pareto optimal solution set based on the executable design grammar, and generating design documents and a compliance audit report.

The invention provides a Kubernetes application deployment security detection and repair method and system based on an RAG mechanism, and the method comprises the steps: S1, obtaining a resource list file of Kubernetes, and obtaining structured configuration data through grammar analysis; s2, key security fields of the structured configuration data are extracted and subjected to semantic characterization processing; and S3, constructing a security knowledge index database based on the key security field, performing real-time dynamic retrieval and obtaining a corresponding retrieval knowledge result, and further enabling the large language statement model to generate a corresponding repair scheme report and deploy repair configuration according to the retrieval knowledge result and the structured configuration data. According to the method, external knowledge dynamic retrieval is realized based on retrieval enhancement, repeated training of the model is avoided, and real-time, semantic and interpretable automatic detection and repair are provided.

The method and device for generating a query statement based on a syntax analysis tree, equipment and medium provided by the application relate to the technical field of computers. The method comprises the following steps: receiving a question query statement sent by a target object; generating an initial syntax analysis tree according to the question query statement; extracting a syntax mapping result of each initial node from a preset database according to each initial node to obtain the syntax mapping result of each initial node; sending at least two candidate query syntax components to the target object so as to obtain a target query syntax component according to component feedback information; assembling the initial nodes according to the target query syntax component to obtain a target syntax analysis tree; and translating the target syntax analysis tree to obtain a target query statement; wherein the target query statement comprises an SQL statement. Through interaction with the target object, the ambiguity problem can be solved, and the query intention of the target object can be mined. In summary, the accuracy of the target query statement can be improved.

This invention discloses an automated server interface testing system and method, belonging to the field of software automated testing technology. The system includes receiving a test configuration command containing the address of the interface to be tested, expected response, and concurrency scale parameters; obtaining the latest version of the associated business code from the version control repository, calling a code dependency analysis tool for syntax parsing, and generating an interface function call relationship topology diagram; dynamically creating multiple logically isolated test execution containers based on the concurrency scale; automatically constructing independent test request message sequences for each interface and covering different input conditions based on the topology diagram and expected response data using a test case generation engine; and distributing the message sequences to each container, driving it to send requests to the corresponding interface. This method automatically constructs test models and intelligently generates test cases through code parsing, improving the automation level, scenario coverage, and execution efficiency of testing.

This invention discloses a multi-view skeletal reconstruction method and system for digital evaluation of canine training behavior, relating to the fields of computer vision and canine behavior recognition. The method includes: acquiring image signals; detecting two-dimensional keypoints using a neural network integrating a canine attention mechanism; obtaining a three-dimensional skeletal sequence through three-dimensional reconstruction and bundle adjustment optimization; performing anomaly detection and interpolation repair to obtain a standardized skeletal sequence; extracting features using a spatiotemporal graph convolutional network and generating behavioral feature vectors through a cross-attention mechanism; obtaining semantic action segments through frame-level primitive classification and grammatical parsing; and performing spatiotemporal alignment and deviation analysis between the action segments and a standard digital twin model to generate phenotypic vectors and a behavior evaluation report. Through multi-view skeletal reconstruction and fusion, high-precision capture, semantic segmentation, and multi-dimensional performance quantification evaluation of canine training behavior are achieved.

The invention discloses a maneuvering target tracking method and system based on interactive multi-model and intention reasoning, and belongs to the technical field of target tracking. According to the maneuvering target tracking method based on interactive multi-model and intention reasoning, a random context-independent grammar is introduced to model a target behavior intention, the target behavior intention and an interactive multi-model filtering framework are subjected to closed-loop fusion, specifically, a dominant motion mode is quantized into a character sequence, grammar analysis is performed by using SCFG, and the target behavior intention is tracked. Therefore, accurate recognition of the structured behavior intention is achieved, the intention posterior probability obtained through analysis is fed back to the weight of the IMM model for fusion, closed-loop optimization is formed, the target tracking and positioning precision can be improved, and inherent defects caused by discrete processing of motion state estimation and behavior intention reasoning in the prior art are effectively overcome.

The invention provides a question and answer query method, system and device for a data table and a medium, and the method comprises the steps: obtaining a natural language question, a row-level permission bitmap and a permission configuration table, inputting the natural language question into a pre-training model for statement generation, and obtaining a first query statement; according to the row-level permission bitmap and the permission configuration table, performing syntax analysis on the first query statement to obtain a query syntax tree; performing parameterized query conversion on the query syntax tree to obtain a second query statement and a parameter list; according to the second query statement and the parameter list, querying the target data table to obtain a data query result; and inputting the data query result into a pre-training model for answer generation to obtain a natural language answer corresponding to the natural language question. According to the method, the data query success rate in a big data scene can be effectively improved, and the tracing difficulty when query fails is reduced. The invention relates to the technical field of big data processing.

The invention discloses a medical knowledge query language design and semantic mapping method, and belongs to the technical field of information. Aiming at the problems that a general knowledge graph query language is poor in adaptability and high in use threshold in a medical knowledge query scene, the invention designs a domain-specific query language ClinicalDSL, a keyword system of the domain-specific query language clinicalDSL fits clinical terms, a grammar rule adopts an EBNF-like specification, and the use threshold is high. Five high-frequency clinical query types including condition query, statistical query, time sequence query, fuzzy query and set query are supported; a mapping algorithm from ClinicalDSL to a semantic model is put forward, clauses such as query conditions, targets, sorting and grouping are converted into the semantic model formed by element operations such as node selection, node positioning, time sequence operation, set operation, statistical operation and output control by recursively traversing a grammar analysis tree from top to bottom, and decoupling with a bottom layer graph database is achieved. According to the method, the diagnosis and treatment intention can be accurately expressed without mastering a complex query language, and the availability and efficiency of the electronic medical record knowledge graph in query are improved.