10 results about "Semantic technology" patented technology

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a sign language animation generation method, device, equipment and medium based on semantic parse. The sign language animation generation method comprises the steps that voice input is received and recognized as text content, field semantic parse is conducted on the text content to generate a field semantic template, and the field semantic template is used for generating a sign language animation; and converting the domain semantic template into a sign language intermediate representation sequence, generating a three-dimensional sign language action sequence based on the sign language intermediate representation sequence, rendering the three-dimensional sign language action sequence into a virtual image sign language animation, and displaying the virtual image sign language animation. According to the invention, by fusing speech recognition, semantic analysis and three-dimensional action rendering, direct conversion from spoken language content to sign language animation is realized, and a complete visual expression link from speech to sign language is formed, so that a user can intuitively understand the speech content in a sign language form through a virtual image, and the user experience is improved. Therefore, the barrier-free performance of human-computer interaction and the accuracy of information transmission are improved.

The invention relates to the technical field of voice semantics, and discloses a cue word generation method and device based on large model optimization, equipment and a medium, and the method comprises the steps: carrying out the fitness evaluation of an initial cue word, and generating all comprehensive fitness scores; performing semantic crossover processing and semantic variation processing on the parent cue word to generate a child cue word; and according to the comprehensive fitness score, the offspring cue word and the initial cue word population, generating a target cue word population. By means of the mode, the cue word variant positioning optimal solution is explored through multi-target fitness evaluation and a population evolution mechanism, and it is ensured that cue words are professional. A large language model is introduced to perform semantic crossover and semantic adjustment on understanding of semantic content of parent cue words, so that child cue words inherit effective information and keep smooth. The method can be applied to the business fields of financial science and technology, medical health, old-age care and the like, and the accuracy of an intelligent customer service system for understanding complex semantics and generating cue words is improved.

The invention discloses an industrial technology bottleneck identification method based on science and technology service big data driving, and relates to the technical field of science and technology services, and the method comprises the steps: obtaining to-be-analyzed industrial multi-source heterogeneous data through a data collection interface; respectively extracting a text semantic feature vector, a technical association feature vector and a statistical feature vector; fusing the multi-modal features through an attention fusion mechanism; on the basis of the comprehensive feature representation, probability distribution of the industry on various potential technical bottlenecks is calculated through a bottleneck recognition model; and generating an identification result of the industrial technology bottleneck according to the probability distribution. The method has the advantages that by constructing a multi-modal analysis framework fusing scientific and technical literatures, patent networks and industrial statistical data, comprehensive perception of deep semantics, technical association and macroscopic trends is realized, and the recognition precision and comprehensiveness are remarkably improved; and dynamic, accurate and operable intelligent support is provided for industrial technology layout and research and development decision.

A subject-event method and a workflow engine developed based on it are designed for no-code creation and execution of semantic models of business processes and construction of domain ontologies. The operation of the workflow engine is based on original event semantics using the semantic implementation of a dataflow approach to algorithm execution. The use of a unified event data format made it possible to implement: (1) a specification for describing actions and entities, (2) a semantic workflow engine for executing business processes, (3) a semantic temporal data store. The invention provides a unified platform for enterprise content management, process automation, and knowledge base maintenance, addressing the limitations of traditional workflow engines and semantic technologies.

The application provides a medical search recommendation method, system, product and terminal based on a knowledge graph and a semantic vector. The medical search request input by a user is subjected to medical entity recognition and standardized processing to obtain standard medical entity data. On the one hand, based on the standard medical entity data, a pre-constructed medical knowledge graph is used to perform retrieval reasoning to obtain a first retrieval result set. On the other hand, based on the standard medical entity data, a pre-trained semantic vector model and a vector database are used to obtain a second retrieval result set. According to a preset fusion rule, the first retrieval result set and the second retrieval result set are subjected to fusion processing to obtain a recommendation result. The application effectively breaks the limitations of the existing medical retrieval technology, and through double-path collaborative fusion, the defects of the vector semantic technology in lacking explicit medical logic support and the semantic generalization of the knowledge graph being insufficient are compensated for, so that precise, interpretable and safe medical search recommendation is finally achieved.

Embodiments of the present disclosure disclose a method and device for querying building data and synthetic data. The specific implementation of the method comprises: extracting a graph embedding vector from input building-related graph data, and storing the graph embedding vector into a vector database and storing the graph data into a graph database; converting a question related to the graph data into a question embedding vector, and searching for a target vector similar to the question embedding vector in the vector database; generating a query instruction in a predetermined language based on the target vector through a first large language model; executing the query instruction in the graph database to determine a query result; and outputting a natural language answer to the question based on the query result through a second large language model. The implementation realizes efficient querying of complex building data and reduces the user's dependence on professional knowledge of semantic technology.

The invention relates to the technical field of speech semantics, and discloses a speech synthesis method, device and equipment for hierarchical emotion condition control and a medium, and the method comprises the steps: extracting three layers of acoustic feature vectors through a feature extraction model, and determining three layers of emotion distribution; generating to-be-synthesized text information through the original text information; and synthesizing the three-layer emotion distribution and the to-be-synthesized text information to generate target synthesized voice information. By means of the mode, the hierarchical emotion distribution is constructed by generating the three-level timestamps and extracting the acoustic feature vectors. The quality problem caused by alignment error accumulation is eliminated through a speech synthesis model, three-layer emotion distribution and to-be-synthesized text information are fused through a cross attention mechanism, and intensity values of phonemes, words and sentences are adjusted. The method can be applied to the business fields of financial science and technology, medical health, old-age care and the like, and the voice synthesis accuracy of a voice synthesis system in combination with user emotion is improved.

The invention relates to the technical field of voice semantics, and discloses an emotion perception and cross-language dialogue information generation method and device, and the method comprises the steps: extracting a current discrete voice unit sequence and a current emotion feature vector; multi-modal emotion joint representation is generated through an extended vocabulary library and an attention fusion mechanism; and according to the multi-modal emotion joint representation, generating a target voice signal, and based on the context cache pool and the target voice signal, generating interactive dialogue information. By means of the mode, the multi-mode emotion joint representation is generated by extracting the discrete unit sequence and the emotion feature vector, and the emotion state of the user is captured while the semantic content is understood. And multiple rounds of dialogues fusing semantics and emotions are generated by means of an attention mechanism. The method can be applied to the business fields of financial science and technology, medical health, old-age care and the like, and the accuracy of dialogue information generation by an intelligent interaction system in combination with user emotion and cross-language cross-mode is improved.

The invention relates to the technical field of speech semantics, and discloses a speech generation method and device based on a self-guiding diffusion model, equipment and a medium, and the method comprises the steps: generating a semantic mark sequence; generating a Mel spectrogram according to the coarse-grained diffusion model, the fine-grained diffusion model and the semantic marking sequence; and generating target voice information through a feature conversion technology and the Mel spectrogram. By means of the mode, the weakening semantic guiding model and the main semantic prediction model work cooperatively, self-guiding enhancement is carried out in the semantic mark generation process, and the generation probability of wrong semantic marks is reduced. Through self-guiding optimization of coarse-grained and fine-grained two-stage diffusion models, hierarchical refined generation of speech features is realized, fine noise easily generated by a traditional single diffusion model is avoided, and the method can be applied to the business fields of financial science and technology, medical treatment, health, old-age care and the like, and has a wide application prospect. And the reliability of converting the text information into the voice information by the intelligent customer service system is improved.

The application discloses a kind of multimodal depth semantic fusion integrated machine system and method, system uses integrated design, including hardware base, fusion engine, enable platform and scene application relevant module, hardware base provides heterogeneous computing, high-speed storage and interconnection resources, fusion engine realizes multi-channel semantic understanding, depth fusion decision and resource elasticity scheduling, enable platform provides visual configuration and model management tool, scene application module provides industry application template;Method generates intermediate semantic result by parallel processing multiple source heterogeneous input, generates structured instruction by multidimensional confidence evaluation and tree fusion decision, realizes system closed-loop optimization in combination with interactive feedback;The application realizes depth collaboration of software and hardware and private deployment, solves the problems of complex deployment, insufficient adaptation, multi-source information fusion difficulty and data security risk etc. of traditional semantic technology.