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CN-121996804-A - Context-aware multi-mode information unified characterization and content generation system

CN121996804ACN 121996804 ACN121996804 ACN 121996804ACN-121996804-A

Abstract

The invention relates to a system for uniformly characterizing and generating context-aware multi-mode information, belonging to the technical field of multi-mode information processing and content generation. The system acquires and preprocesses multi-mode information and associated context information through a multi-mode perception analysis module, extracts semantic elements and mines the implicit intention of demands, then a multi-mode unified characterization module adopts a context-guided cross-mode coding strategy to generate a unified characterization vector, then a content generation verification module formulates collaborative generation logic based on the vector and context constraint, semantic and context consistency verification is realized through dual similarity matching, and finally an interactive feedback iteration module builds a feedback-driven optimization mechanism to update system parameters. The invention improves the semantic uniformity and the context suitability of the multi-mode content generation, enhances the self-adaptive optimization capability of the system, and is suitable for various multi-mode content generation scenes.

Inventors

  • JIN CHONGYING
  • Heng jing
  • LIU SHUHUA

Assignees

  • 上海数熙科技有限公司

Dates

Publication Date
20260508
Application Date
20260120

Claims (12)

  1. 1. The context-aware multi-mode information unified characterization and content generation system is characterized by comprising a multi-mode perception analysis module, a multi-mode unified characterization module, a content generation verification module and an interactive feedback iteration module; The multi-modal sensing analysis module acquires multi-modal information and associated context information, performs collaborative preprocessing, merges the multi-modal information and the associated context information to extract semantic elements, and mines implicit intention of a demand; The multi-modal unified characterization module adopts a context-guided cross-modal coding strategy to code the semantic elements, captures the association weight of the multi-modal information and the context, and generates a multi-modal unified characterization vector; The content generation verification module establishes collaborative generation logic based on the multi-mode unified characterization vector and in combination with context constraint conditions, and context feedback constraint is introduced in the generation process; extracting semantic features of the generated content, performing double similarity matching on the semantic features, the multi-mode unified characterization vector and the context information, and calculating a context consistency deviation value and a semantic consistency deviation value; And the interactive feedback iteration module stores the finally generated content, the context information and the historical interactive data, constructs a feedback-driven iterative optimization mechanism, fuses the user feedback and the context information as samples, and updates system parameters.
  2. 2. The system of claim 1, wherein the specific process of obtaining the multimodal information and the associated context information comprises adding a timestamp mark to each modality information in the process of obtaining the multimodal information including text information, image information and voice information, extracting a user history interaction record and a user long-term preference tag based on a user identification association call history database, performing format standardization processing on the associated context information, and performing association alignment with the timestamp of the multimodal information to generate a structured information set.
  3. 3. The system of claim 1 wherein the collaborative preprocessing is performed by classifying and screening the preprocessing objects, dividing the structured information set into a text class, an image class, a voice class and a context class, performing word segmentation processing, disabling word rejection processing and redundant information filtering processing for the text class information, extracting core entities in the text, performing noise reduction processing for the image class information, performing size standardization adjustment, positioning key region extraction feature points, performing noise reduction for the voice class information, enhancing effective voice signals, cutting invalid silence segments, performing redundant data cleaning for the context class information, performing time sequence ordering for time stamps, extracting key information fields of the context information, and generating a standardized context data set.
  4. 4. The system according to claim 1, wherein the specific step of extracting the semantic elements comprises the steps of respectively extracting basic semantic units from the preprocessed multimodal information and the standardized context data set to generate a multimodal semantic unit set and a context semantic unit set, constructing a semantic association matrix, carrying out semantic similarity calculation on the multimodal semantic unit set and units in the context semantic unit set in pairs, filling the semantic association matrix based on a calculation result, determining effective association pairs through threshold screening, fusing semantic information corresponding to the effective association pairs, and refining to generate the semantic elements.
  5. 5. The system of claim 1, wherein the mining demand implicit intents specifically comprise generating demand intent candidates based on the semantic elements and combining a preset intent classification system to form a demand intent candidate set, invoking a preset intent knowledge base, performing semantic similarity matching on the candidates in the demand intent candidate set and standard intents in the knowledge base to obtain matching scores of the candidates, screening by combining associated context information, sorting the matching scores of the remaining candidates, and selecting the candidate with the highest score as the demand implicit intent.
  6. 6. The system according to claim 1, wherein the specific steps of the cross-modal coding strategy include performing modal classification on the semantic elements to divide text semantic sub-elements, image semantic sub-elements and voice semantic sub-elements, coding the text semantic sub-elements, identifying time sequence association of the text semantic sub-elements and context semantic units, merging time sequence association features into a coding process to generate text semantic coding features, coding the image semantic sub-elements, extracting environment parameters and key object information in scene context features, supplementing the environment parameters and key object information into the image semantic sub-elements as auxiliary features to generate image semantic coding features, coding the voice semantic sub-elements, calling user voice habit context data to be merged into the coding process, and optimizing to generate voice semantic coding features.
  7. 7. The system of claim 1, wherein the specific step of generating the unified token vector comprises the steps of constructing a dynamic attention fusion network, taking three types of semantic coding features of texts, images and voices and contextual features as network inputs, calculating initial association weights of all the modal coding features and the contextual features through a first layer of semantic interaction layer, optimizing the initial association weights through multi-layer semantic interaction calculation, performing secondary interaction on each layer based on an output result of the previous layer and the contextual features, adjusting weight distribution of all the modal coding features, performing weighted summation on the optimized modal association weights and the corresponding modal coding features to obtain fusion features, and converting the fusion features into the multi-modal unified token vector with fixed dimensions through feature normalization processing.
  8. 8. The system of claim 1, wherein the collaborative generation logic specifically comprises performing feature analysis on the multi-mode unified characterization vector, identifying core demand features corresponding to each mode generation task, analyzing logic dependency relationships among the mode generation tasks based on the core demand features, sequencing each mode generation task according to the logic dependency relationships, determining a generation time sequence, preferentially generating core mode content, and formulating generation constraint rules of subsequent associated mode content based on semantic features of the core mode content to form the collaborative generation logic scheme.
  9. 9. The system of claim 1, wherein the context feedback constraint specifically comprises marking generation key nodes in the collaborative generation logic scheme, including a core modal content generation completion node, each associated modal content generation completion node, and an overall content preliminary fusion node, setting a feedback detection point at each key node, extracting content semantic features of a current generation stage, performing similarity matching detection with the associated context information, and calculating context matching degree of the current stage.
  10. 10. The system of claim 1, wherein the implementation steps of the feedback-driven iterative optimization mechanism comprise performing association labeling on user feedback information, corresponding context information and generated content, supplementing a time stamp and user identification metadata, generating a labeling sample set, performing data cleaning and format standardization on the labeling sample set, screening out effective samples, updating coding parameters of the multi-mode unified characterization module and matching threshold parameters of the content generation verification module, synchronously optimizing semantic element extraction rules of the multi-mode perception analysis module, recording iterative update logs, and storing parameter comparison data before and after updating.
  11. 11. The system according to claim 1, wherein the implementation step of dual similarity matching includes extracting complete semantic features of generated content, performing dual semantic similarity matching operation with the multi-modal unified token vector and associated context information, respectively, calculating the context consistency bias value and the semantic consistency bias value based on a matching result, presetting corresponding qualification thresholds, wherein any bias value exceeds the corresponding qualification threshold, and positioning a generation stage of bias generation in combination with a detection result of context feedback constraint.
  12. 12. The system of claim 1, wherein the updating system parameters comprises filtering feedback content related to the context information based on user feedback information, establishing an association mapping between the feedback content and corresponding feature fields in the context information, fusing the associated feedback-context information with corresponding generated content semantic features, generating labeled sample data, and determining target parameters to be updated.

Description

Context-aware multi-mode information unified characterization and content generation system Technical Field The invention belongs to the technical field of multi-mode information processing and content generation, and particularly relates to a system for uniformly characterizing and generating context-aware multi-mode information. Background Under the driving of fields such as intelligent interaction, digital creation, intelligent service and the like, multi-modal content generation technologies such as texts, images and voices become research hotspots in the field of artificial intelligence, and the core requirement is to realize efficient fusion and accurate generation of multi-modal information so as to match diversified application scenes. However, currently, the mainstream multi-mode content generating system still has a plurality of unresolved technical defects, and it is difficult to meet the application requirements of high precision and high adaptability. The existing system has insufficient fusion depth of context information and lacks a systematic context sensing mechanism. Most systems simply collect currently input multi-mode information, key context information such as user history interaction records, current scene environment characteristics, user preference labels and the like cannot be effectively associated, and even if a small amount of context information is introduced into part of systems, collaborative preprocessing with the multi-mode information is not realized, so that subsequent semantic element extraction deviates from the actual requirement of a user, the requirement implicit intention mining is inaccurate, and the generated content is difficult to adapt to a specific application scene. In the feature fusion process, a fixed weight fusion strategy is adopted, and the importance of each mode feature is difficult to dynamically adjust according to a demand theme, so that the generated multi-mode unified characterization vector cannot comprehensively and accurately reflect deep association of the multi-mode information and the context, and further the semantic uniformity of subsequent content generation is affected. The existing generation checking mechanism has one-sided performance and the context constraint is not lost. Most systems only pay attention to the semantic matching degree of generated content and input requirements, a dual verification system of semantic consistency and context consistency is not constructed, and meanwhile, a dynamic context feedback constraint mechanism is absent in the generation process, so that deviation cannot be corrected in real time at a key node, the problems of semantic deviation or poor context suitability of the generated content are easily caused, and the generation quality is difficult to guarantee. The feedback processing of the current system is mostly simple application of single user feedback, the deep fusion of the user feedback and the context information is not realized, in the parameter updating process, the key parameters of the core modules such as multi-mode unified characterization, generation verification and the like are not subjected to directional optimization, and semantic element extraction rules are not synchronously optimized, so that the self-adaption capability of the system is weak, and the generation quality is difficult to continuously improve through iteration. In conclusion, the prior art has obvious defects in the aspects of context sensing fusion, multi-mode precise and unified characterization, dual generation verification, feedback iterative optimization and the like, and restricts the application expansion of the multi-mode content generation technology. Therefore, research and development of a multi-mode content generation system capable of fully mining and fusing context information, realizing precise and unified characterization of multi-mode information and having double verification and efficient feedback iteration capability becomes an urgent need in the current multi-mode information processing field. Disclosure of Invention In order to solve the problems in the prior art, the invention provides a system for uniformly characterizing and generating context-aware multi-modal information, The aim of the invention can be achieved by the following technical scheme: the system for uniformly characterizing and generating the context-aware multi-mode information comprises a multi-mode perception analysis module, a multi-mode uniform characterization module, a content generation verification module and an interactive feedback iteration module; The multi-modal sensing analysis module acquires multi-modal information and associated context information, performs collaborative preprocessing, merges the multi-modal information and the associated context information to extract semantic elements, and mines implicit intention of a demand; The multi-modal unified characterization module adopts a context-guided cross-