16 results about "Linguistic rule" patented technology

The invention provides a low-code platform model generation method and device, equipment, a medium and a product, and relates to the technical field of artificial intelligence and low-code platform development. The method comprises the following steps: taking an interface type keyword; obtaining an association matching result of the first demand description and the keyword, and obtaining a second demand description according to the matching result; key information is taken from the second demand description, and a third demand description is obtained from the information; describing a first query language according to the third demand; splitting the first query language to obtain each minimum grammar unit, and screening to obtain each effective grammar unit; establishing a syntax tree, and when the syntax tree conforms to a pre-stored syntax rule, performing basic semantic check on the syntax tree to obtain a semantic check result; obtaining a second query language according to the check result; verifying the second query language rule according to a preset rule constraint condition to obtain a third query language; and obtaining a visual data model through the third query language and the low-code platform interface. The seamless connection from the natural language requirement to the visual application is realized, and the technical threshold is reduced.

The application discloses a robot control language generation method, system and device and a storage medium, and comprises the following steps: constructing target prompt information according to a user task description and system information, inputting the target prompt information into a large language model to obtain robot skill language; performing lexical analysis, syntax analysis and semantic checking on the robot skill language through a robot skill language compiler to determine whether the robot skill language conforms to robot skill language rules; when the robot skill language does not conform to the robot skill language rules, generating error feedback information through a robot skill language debugger, updating the target prompt information according to the error feedback information, and returning to the step of inputting the target prompt information into the large language model; and when the robot skill language conforms to the robot skill language rules, compiling the robot skill language through the robot skill language compiler to generate robot control language. The application improves the accuracy and executability of the robot control language and can be applied to the technical field of robot control.

Disclosed in the present application are a robot control language generation method, system and apparatus, and a storage medium. The method comprises: on the basis of a user task description and system information, constructing target prompt information, and inputting the target prompt information into a large language model, so as to obtain a robot skill language; performing lexical analysis, syntax analysis and a semantic check on the robot skill language by means of a robot skill language compiler, so as to determine whether a robot skill language rule is met; when the robot skill language rule is not met, generating error feedback information by means of a robot skill language debugger, updating the target prompt information on the basis of the error feedback information, and returning to the step of inputting the target prompt information into a large language model; and when the robot skill language rule is met, compiling the robot skill language by means of the robot skill language compiler, so as to generate a robot control language. The present application improves the accuracy and executability of a robot control language, and can be applied to the technical field of robot control.

The invention discloses a deep learning-based harmonic generation method and terminal, and the method comprises the steps: obtaining a source language text of a to-be-converted language, and extracting a phoneme stream of the source language text; segmenting the phoneme stream according to a language rule of a target language to obtain a plurality of first syllables; obtaining a pronunciation library corresponding to the target language, matching a second syllable corresponding to each first syllable in the pronunciation library, and obtaining a font corresponding to the second syllable; establishing an association relationship between the source language text and all fonts, and training the initial model through the association relationship to obtain a target model; and when a to-be-converted text corresponding to the to-be-converted language is received, obtaining a homophonic font corresponding to the to-be-converted text through the target model. According to the method, the nonlinear mapping rule between the phoneme combination of the source language text and the target language pronunciation is obtained based on deep learning, so that when facing uncommon vocabularies and new words never appearing in a training set, the system still can generate accurate and reasonable homophonic phonetic notation through reasoning according to the learned general pronunciation logic.

The application discloses a fuzzy path selection method of a routing protocol in a power internet of things, and comprises the following steps: calculating the covering time between system input nodes and the system utilization, and respectively obtaining the node covering time and the system utilization language state membership function; formulating a language rule according to the step, and obtaining the relationship between the input parameter node covering time, the system utilization language state and the output fuzzy probability language state; the language rule of all input and output functions is defined as a language fuzzy limit language; calculating the system output fuzzy probability, and obtaining the fuzzy probability language state membership function; calculating the sum of the cost between each node of each route according to the fuzzy probability; comparing the route cost of multiple routes, and selecting the route with the minimum cost. Compared with the prior art, the application has the beneficial effects that the technology can improve the routing selection efficiency of the power internet of things terminal device, and reduce the service data transmission delay.

The application provides a Chinese oral pronunciation comparison method and system based on voice recognition, relates to the field of voice recognition, obtains text information to be pronounced and performs linguistic rule analysis, simulates the continuous motion track of the pronunciation organ and the acoustic adjustment law, thereby generating a standard reference voice containing voice continuity and rhythm patterns, compares the acoustic characteristics of the learner's pronunciation with the acoustic characteristics of the standard reference voice, and determines whether the difference is a pronunciation defect according to the natural voice changes reflected in the standard reference voice; the application can effectively distinguish the subtle differences in the learner's pronunciation that belong to natural voice changes from the real pronunciation defects, solve the technical problem that the existing system misjudges the natural coarticulation effect as a pronunciation error, improve the authenticity and authenticity of oral expression, overcome the deficiency that the system feedback is disconnected with the actual perception in the prior art, and significantly improve the efficiency and effect of Chinese oral learning.

The present application relates to the technical field of language recognition, in particular to a multi-dialect speech text conversion system and processing method, comprising: an audio acquisition module: responsible for collecting the speech input of a user, supporting PCM, MP3, WAV format and 16kHz, 44.1kHz sampling rate; a dialect detection module: using a pre-trained language classifier to identify the dialect type of the speech input; a speech feature extraction module: converting the speech signal into feature representation, such as mel spectrum, MFCC; a multi-dialect speech recognition module: based on a multi-task learning speech recognition model, mapping the features into a text sequence; a text post-processing module: optimizing the recognition result according to language rules and context, and outputting the final standardized text. Multi-dialect support: covering main dialect languages, and can be quickly expanded to new dialects through transfer learning.

The application discloses an intelligent real-time language translation system and method and a terminal, and belongs to the technical field of language translation, and comprises a multi-modal acquisition module, a voiceprint feature extraction module, an emotional and semantic fusion module, a dynamic context module, a cross-language translation engine, a self-defined language rule engine, a real-time voiceprint cloning engine, an emotional migration module, a culture adaptation module and a multi-modal output module. The system supports multi-person dialogue scenes and can be continuously iteratively improved through an adaptive optimization module. The system effectively solves the problems of loss of emotional information, single tone, lack of personalized customization and cultural inadaptation in the prior art, and realizes natural, accurate and personalized intelligent cross-language communication.

To provide an information processing device, an information processing method, and a program that can correct text according to user attributes.SOLUTION: The information processing device of one embodiment comprises: first acquisition means for acquiring text data and user attributes of a user utilizing the text data; read means for reading language rules corresponding to the user attributes acquired by the acquisition means from reference data that defines language rules for the languages used according to the type of user attributes; generation means for generating query information containing an instruction sentence that instructs the correction of the text contained in the text data based on the language rules read by the read means; second acquisition means for inputting the text data and the query information generated by the generation means to a generation AI that is configured to perform corrections to a sentence according to the contents of the instruction in the query information and generate the corrected sentence; and output means for outputting the corrected sentence acquired by the second acquisition means.SELECTED DRAWING: Figure 7

The invention discloses a streaming interaction method for dynamic generation and real-time rendering, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the multi-modal session stream data of a target user in real time, extracting the features of the multi-modal session stream data, and building the dynamic context perception vector representation of the target user; driving an LLM large language model, generating a potential interaction purpose intention probability distribution matrix of the target user, and dynamically matching an initial UI component tag of the target user; historical session data authorized by a target user is acquired, a target user session long-term memory vector library is established in combination with a known relational database of the target user, dynamic context awareness vector representation of the target user is coupled, a hierarchical memory architecture is activated, and context awareness pre-filling of an initial UI component tag of the target user is realized; and dynamic adaptation of a target user business scene is realized by combining an LLM large language rule generator, and streaming interaction from dynamic generation to real-time rendering is realized. The development efficiency is improved.

Methods and systems for predicting an upcoming data point are disclosed. Method performed by a server system includes accessing a plurality of encoded features associated with each data point of a plurality of data points corresponding to an entity. Method includes generating a data point word representation for the entity based on the plurality of encoded features associated with each data point. The data point word representation includes one or more words. Method includes generating a data point sentence representation based on the one or more words associated with each data point. Method includes generating, by a Large Language Model (LLM), an upcoming data point representation in the data point sentence representation based on the data point sentence representation. Method includes decoding the upcoming data point representation to obtain one or more features associated with an upcoming data point based on the set of predefined language rules.

The invention belongs to the technical field of heating, ventilation and air conditioning operation and maintenance, and provides a model feature generation method of a heating, ventilation and air conditioning system, and the method comprises the steps: obtaining to-be-processed data of the heating, ventilation and air conditioning system, the to-be-processed data comprising multi-source heterogeneous data generated in the operation process of the heating, ventilation and air conditioning system; a feature field special language rule in a feature field special language rule base is called through a feature field special language interpreter, the to-be-processed data is processed into target feature data, and the target feature data is standardized feature data which has a fixed field set and a field sequence and meets the input requirement of a time sequence prediction model; generating a feature fingerprint according to the mode hash value of the target feature data and the rule hash value of the special language rule in the feature field; and the target feature data and the feature fingerprints are stored in an associated mode so as to be called by a time sequence prediction model of the heating ventilation and air conditioning system.

The application provides an interactive hospital infection risk early warning method and system based on a large language model, relates to the technical field of intelligent medical treatment, and constructs a factor function library and a rule expression library by acquiring hospital infection diagnosis standard data, acquires user natural language rule description, performs semantic analysis by using a large language model in combination with the factor function library, and generates intermediate rule codes based on formal grammar constraint decoding, so that after the intermediate rule codes are compiled into executable rule instructions, real-time monitoring of patient medical data is performed to trigger early warning. The application realizes that non-technical personnel can directly dynamically configure infection early warning rules by using natural language, significantly shortens a rule response cycle, and improves hospital infection management response efficiency.

The invention discloses a linguistic prior and progressive cross-modal contrast disambiguation network-based lip language recognition method, which is suitable for the field of computer vision speech recognition and comprises the following steps of: 1, on the basis of a linguistic vision-phoneme mapping rule, constructing a fuzzy text and realizing replacement by replacing similar phonemes in a pronunciation text; and 2, constructing a progressive comparison disambiguation network which is composed of a vision-phoneme disambiguation sub-module and a text semantic disambiguation sub-module, mapping in coding through vision-phoneme feature alignment and text semantic alignment in a mode, and realizing end-to-end ambiguity analysis in decoding. According to the method, through fusion of linguistics rules and a deep learning technology, the whole process optimization of fuzzy sample construction-progressive disambiguation-targeted evaluation is realized, so that the decoupling capability of the model for visual morpheme-phoneme asymmetric ambiguity can be improved, and the lip language recognition precision and accuracy of the fuzzy sample are improved.

The invention discloses a natural language to scripting language conversion method and device, equipment and a storage medium, and the method comprises the steps: generating natural language rule data based on business demand information input by a user, and converting the natural language rule data into an intermediate programming language code through a preset large model; performing grammatical structure analysis on the intermediate programming language code to obtain a grammatical analysis result; and converting the intermediate programming language code into a target scripting language code based on a syntactic analysis result. According to the method and the device, the natural language rule data is converted into the intermediate programming language code by utilizing the preset large model, and the intermediate programming language code is converted into the target script language code based on the grammatical analysis result of the intermediate programming language code. According to the method, the intermediate programming language is introduced as transition between the natural language and the target script language, so that the flexibility of the conversion process is improved, and the complexity and the application cost of the conversion process from the natural language to the script language are reduced.

The invention discloses a guide type writing feedback method and system based on a large model, and the method comprises the steps: carrying out the preprocessing and deep diagnosis of a user text, generating an internal report containing error details, only outputting a weak feedback for prompting an error position to a user at the initial stage, so as to induce the user to carry out the autonomous correction, and on this basis, carrying out the deep diagnosis of the user text. The system evaluates a user correction fragment in real time, dynamically upgrades a feedback level based on a current bias error record only when correction is incorrect, and gradually provides medium feedback containing meta-language interpretation or strong feedback of a direct correction scheme. Through the layered teaching support, the active reflection and self-monitoring ability of a learner are effectively stimulated, the cognitive requirements of different levels of users are accurately met while excessive correction of the model is avoided, and the internalization efficiency of language rules and the pertinence of writing teaching are remarkably improved.