Context-aware multi-modal information unified representation and content generation system

Abstract

The invention relates to a context-aware multi-modal information unified representation and content generation system, and belongs to the technical field of multi-modal information processing and content generation. According to the system, multi-modal information and associated context information are obtained and preprocessed through a multi-modal perception analysis module, semantic elements are extracted, and a demand implicit intention is mined; a multi-modal unified representation module generates a unified representation vector by adopting a context-guided cross-modal coding strategy; then, a content generation verification module formulates collaborative generation logic based on the vector in combination with context constraint, and semantic and context consistency verification is achieved through dual similarity matching; and finally, the interactive feedback iteration module constructs a feedback-driven optimization mechanism to update system parameters. According to the method, the semantic uniformity and context adaptability of multi-modal content generation are improved, the adaptive optimization capability of the system is enhanced, and the method is suitable for various multi-modal content generation scenes.

Description

Technical Field

[0001] This invention belongs to the field of multimodal information processing and content generation technology, specifically relating to a context-aware multimodal information unified representation and content generation system. Background Technology

[0002] Driven by fields such as intelligent interaction, digital creation, and intelligent services, multimodal content generation technologies, including text, image, and voice, have become a research hotspot in the field of artificial intelligence. The core requirement is to achieve efficient fusion and accurate generation of multimodal information to match diverse application scenarios. However, current mainstream multimodal content generation systems still suffer from many unresolved technical shortcomings, making it difficult to meet the application requirements of high precision and high adaptability.

[0003] Existing systems lack sufficient depth in integrating contextual information and a systematic context-aware mechanism. Most systems simply collect multimodal information from the current input, failing to effectively link it with key contextual information such as user history, current scene environment features, and user preference tags. Even when some systems introduce a small amount of contextual information, they fail to achieve collaborative preprocessing with multimodal information, resulting in subsequent semantic element extraction deviating from actual user needs, inaccurate mining of implicit intent, and generated content that is difficult to adapt to specific application scenarios.

[0004] Current cross-modal coding methods mostly use general coding models, which lack context-guided mechanisms and cannot capture the correlation weights between multimodal information and context. In the feature fusion process, fixed weight fusion strategies are often used, which makes it difficult to dynamically adjust the importance of each modality feature according to the needs and themes. As a result, the generated multimodal unified representation vector cannot fully and accurately reflect the deep correlation between multimodal information and context, thus affecting the semantic consistency of subsequent content generation.

[0005] Existing generation and verification mechanisms are one-sided and lack contextual constraints. Most systems only focus on the semantic matching degree between the generated content and the input requirements, without building a dual verification system for semantic consistency and contextual consistency. At the same time, the lack of a dynamic contextual feedback constraint mechanism during the generation process makes it impossible to correct deviations in real time at key generation nodes, which can easily lead to semantic deviations or poor contextual adaptability in the generated content, making it difficult to guarantee generation quality.

[0006] The current system's feedback processing is mostly a simple application of single user feedback, failing to achieve deep integration of user feedback with contextual information. During parameter updates, key parameters of core modules such as multimodal unified representation and generation verification are not optimized in a targeted manner, nor are semantic element extraction rules optimized simultaneously. This results in weak system adaptability and makes it difficult to continuously improve generation quality through iteration.

[0007] In summary, existing technologies have significant shortcomings in context-aware fusion, accurate and unified representation of multimodal information, dual generation verification, and feedback iterative optimization, which restricts the application and expansion of multimodal content generation technology. Therefore, developing a multimodal content generation system that can fully mine and fuse contextual information, achieve accurate and unified representation of multimodal information, and possess dual verification and efficient feedback iterative capabilities has become an urgent need in the field of multimodal information processing. Summary of the Invention

[0008] To address the aforementioned problems in the existing technology, this invention provides a context-aware, multimodal information unified representation and content generation system. The objective of this invention can be achieved through the following technical solutions: A context-aware multimodal information unified representation and content generation system includes: a multimodal perception parsing module, a multimodal unified representation module, a content generation verification module, and an interactive feedback iteration module; The multimodal perception and parsing module acquires multimodal information and associated context information, performs collaborative preprocessing, and integrates the multimodal information and associated context information to extract semantic elements and uncover the implicit intent of the requirements. The multimodal unified representation module adopts a context-guided cross-modal coding strategy to encode the semantic elements, capture the association weights between the multimodal information and the context, and generate a multimodal unified representation vector. The content generation and verification module formulates collaborative generation logic based on the multimodal unified representation vector and contextual constraints, and introduces contextual feedback constraints during the generation process; it extracts the semantic features of the generated content and performs dual similarity matching with the multimodal unified representation vector and contextual information, and calculates the contextual consistency deviation value and semantic consistency deviation value. The interactive feedback iteration module stores the final generated content, context information, and historical interaction data, constructs a feedback-driven iterative optimization mechanism, and uses user feedback and context information as samples to update system parameters.

[0009] Specifically, the process of acquiring multimodal information and associated context information includes: the multimodal information includes text information, image information and voice information; during the acquisition process, timestamps are added to each modal information; based on the user identifier, the historical database is called to extract the user's historical interaction records and long-term user preference tags; the associated context information is format-standardized and aligned with the timestamps of the multimodal information to generate a structured information set.

[0010] Specifically, the collaborative preprocessing implementation process includes: classifying and filtering preprocessing objects, dividing the structured information set into text, image, speech, and context categories; performing word segmentation, stop word removal, and redundant information filtering on text information to extract core entities from the text; performing noise reduction on image information, standardizing and adjusting the size, locating key regions, and extracting feature points; performing noise reduction on speech information, enhancing effective speech signals, and pruning invalid silent segments; and performing redundant data cleaning on context information, sorting by timestamp, extracting key information fields from the context information, and generating a standardized context dataset.

[0011] Specifically, the steps for extracting semantic elements include: extracting basic semantic units from the preprocessed multimodal information and the standardized context dataset to generate a multimodal semantic unit set and a context semantic unit set; constructing a semantic association matrix, calculating the pairwise semantic similarity between the units in the multimodal semantic unit set and the context semantic unit set, and filling the semantic association matrix based on the calculation results; determining effective association pairs through threshold filtering, fusing the semantic information corresponding to the effective association pairs, and refining and generating the semantic elements.

[0012] Specifically, the process of mining the implicit intent of a demand includes: generating candidate demand intents based on the semantic elements and a preset intent classification system to form a candidate demand intent set; calling a preset intent knowledge base to perform semantic similarity matching between the candidate demand intents in the candidate demand intent set and the standard intents in the knowledge base to obtain the matching scores of the candidate demand intents; filtering the remaining candidate demand intents based on the associated context information, sorting the matching scores of the remaining candidate demand intents, and selecting the candidate demand intent with the highest score as the implicit intent of the demand.

[0013] Specifically, the cross-modal coding strategy includes the following steps: modally classifying the semantic elements to divide them into text semantic sub-elements, image semantic sub-elements, and speech semantic sub-elements; encoding the text semantic sub-elements by identifying the temporal association between the text semantic sub-elements and the context semantic units, and then incorporating the temporal association features into the encoding process to generate text semantic coding features; encoding the image semantic sub-elements by extracting environmental parameters and key object information from the scene context features, and adding them as auxiliary features to the image semantic sub-elements to generate image semantic coding features; and encoding the speech semantic sub-elements by incorporating user speech habit context data into the encoding process to optimize and generate speech semantic coding features.

[0014] Specifically, the steps for generating the unified representation vector include: constructing a dynamic attention fusion network, using text, image, and speech semantic coding features, as well as context features, as network inputs; calculating the initial association weights between each modality coding feature and the context features through a first semantic interaction layer, optimizing the initial association weights through multiple layers of semantic interaction calculations, with each layer performing secondary interaction with the context features based on the output of the previous layer to adjust the weight allocation of each modality coding feature; weighting and summing the optimized modality association weights with the corresponding modality coding features to obtain the fused features, and converting the fused features into the fixed-dimensional multimodal unified representation vector through feature normalization processing.

[0015] Specifically, the collaborative generation logic includes: performing feature parsing on the multimodal unified representation vector to identify the core requirement features corresponding to each modality generation task; analyzing the logical dependencies between each modality generation task based on the core requirement features, sorting each modality generation task according to the logical dependencies, and determining the generation sequence; prioritizing the generation of core modality content, and formulating generation constraint rules for subsequent related modality content based on the semantic features of the core modality content, thus forming the collaborative generation logic scheme.

[0016] Specifically, the context feedback constraint includes: marking key nodes in the collaborative generation logic scheme, including the core modal content generation completion node, the related modal content generation completion node, and the overall content preliminary fusion node; setting feedback detection points at each key node, extracting the semantic features of the content at the current generation stage, performing similarity matching detection with the related context information, and calculating the context matching degree at the current stage.

[0017] Specifically, the implementation steps of the feedback-driven iterative optimization mechanism include: associating and annotating user feedback information with corresponding context information and generated content, supplementing timestamps and user identifier metadata, and generating an annotated sample set; cleaning and standardizing the format of the annotated sample set, selecting valid samples, and updating the encoding parameters of the multimodal unified representation module and the matching threshold parameters of the content generation verification module; synchronously optimizing the semantic element extraction rules of the multimodal perception and parsing module, recording iterative update logs, and storing parameter comparison data before and after the update.

[0018] Specifically, the dual similarity matching implementation steps include: extracting the complete semantic features of the generated content, performing dual semantic similarity matching operations with the multimodal unified representation vector and associated context information respectively, calculating the context consistency deviation value and the semantic consistency deviation value based on the matching results; preset corresponding qualified thresholds, and if any deviation value exceeds the corresponding qualified threshold, locating the generation stage where the deviation occurs by combining the detection results of context feedback constraints.

[0019] Specifically, the steps for updating system parameters include: filtering feedback content related to context information based on user feedback information, establishing an association mapping between the feedback content and the corresponding feature fields in the context information; fusing the associated feedback-context information with the semantic features of the corresponding generated content to generate labeled sample data, and determining the target parameters that need to be updated.

[0020] The beneficial effects of this invention are as follows: This invention achieves collaborative preprocessing of multimodal information and associated contextual information through a multimodal perception and parsing module. It combines semantic association matrix fusion to extract semantic elements and optimizes the mining of implicit intents based on contextual filtering, solving the problems of insufficient depth in context fusion and inaccurate intent mining in existing technologies. It can capture the adaptation relationship between user needs and scenarios / preferences, making the generated content more closely aligned with actual user needs and specific application scenarios, reducing misunderstandings of requirements.

[0021] This paper adopts a context-guided cross-modal coding strategy, integrating corresponding contextual features into semantic sub-elements of different modalities. A dynamic attention fusion network dynamically adjusts the association weights to generate a unified representation vector, overcoming the shortcomings of existing cross-modal coding techniques such as semantic alignment bias and inflexible fixed-weight fusion. This design ensures that the deep association between multimodal information and context is fully explored, and the generated unified representation vector possesses semantic integrity and accuracy, providing a reliable foundation for subsequent collaborative generation.

[0022] This solution constructs a collaborative generation logic and contextual feedback constraint mechanism through a content generation verification module. It combines dual similarity matching to achieve dual verification of semantic and contextual consistency, and iteratively optimizes parameters when deviation values ​​exceed thresholds. This approach addresses the problems of one-sided generation verification and lack of contextual constraints in existing technologies. It can correct generation deviations in real time, ensuring semantic consistency and high contextual adaptation of generated content across modalities, thereby improving the overall quality of multimodal content generation.

[0023] The interactive feedback iteration module of this invention deeply integrates user feedback with contextual information to form labeled samples, and updates the parameters of core modules such as multimodal unified representation and generation verification in a targeted manner, while simultaneously optimizing semantic element extraction rules. This mechanism solves the shortcomings of existing technologies in terms of limited feedback iteration optimization effects and weak adaptability, enabling the system to continuously iterate to adapt to the personalized needs of different users, gradually improve generation accuracy and scene adaptability, and expand the application value of the technology. Attached Figure Description

[0024] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0025] Figure 1This is a system architecture diagram of a context-aware multimodal information unified representation and content generation system according to the present invention. Figure 2 This is a schematic diagram of the dual similarity matching process in this invention. Detailed Implementation

[0026] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0027] Please see Figures 1-2 A context-aware multimodal information unified representation and content generation system includes: a multimodal perception parsing module, a multimodal unified representation module, a content generation verification module, and an interactive feedback iteration module; The multimodal perception and parsing module acquires multimodal information and associated context information, performs collaborative preprocessing, and integrates the multimodal information and associated context information to extract semantic elements and uncover the implicit intent of the requirements. The multimodal unified representation module adopts a context-guided cross-modal coding strategy to encode the semantic elements, capture the association weights between the multimodal information and the context, and generate a multimodal unified representation vector. The content generation and verification module formulates collaborative generation logic based on the multimodal unified representation vector and contextual constraints, and introduces contextual feedback constraints during the generation process; it extracts the semantic features of the generated content and performs dual similarity matching with the multimodal unified representation vector and contextual information, and calculates the contextual consistency deviation value and semantic consistency deviation value. The interactive feedback iteration module stores the final generated content, context information, and historical interaction data, constructs a feedback-driven iterative optimization mechanism, and uses user feedback and context information as samples to update system parameters.

[0028] Specifically, the process of acquiring multimodal information and associated context information includes: synchronously collecting multimodal information input by the user through a multi-source data interface, wherein the multimodal information includes at least two of the following: text information, image information, and voice information; adding timestamps to each modal information during the collection process; invoking a historical database based on the user identifier to extract the user's historical interaction records and long-term user preference tags; reading the configuration file of the current task to obtain the task scenario configuration information; standardizing the format of all collected and invoked associated context information; associating and aligning it with the timestamps of the multimodal information; and generating a structured information set.

[0029] Specifically, the collaborative preprocessing process includes: classifying and filtering preprocessing objects, dividing the structured information set into four categories: text, image, speech, and context; performing word segmentation, stop word removal, and redundant information filtering on text information in sequence, and finally extracting the core entities in the text using entity recognition algorithms; performing noise reduction on image information, followed by size standardization adjustment, locating key regions using region detection algorithms, and finally extracting feature points within the key regions; performing noise reduction on speech information, followed by strengthening the effective speech signal using signal enhancement algorithms, and finally pruning invalid silence segments; and performing redundant data cleaning on context information, sorting by timestamp, extracting key information fields from each context message, and generating a standardized context dataset.

[0030] Specifically, the steps for extracting semantic elements include: using a semantic unit extraction algorithm to extract basic semantic units from preprocessed multimodal information and standardized context datasets, generating a multimodal semantic unit set and a context semantic unit set; constructing a semantic association matrix, calculating the pairwise semantic similarity between each unit in the multimodal semantic unit set and the context semantic unit set, and filling the semantic association matrix based on the calculation results; determining effective association pairs through threshold filtering, retaining semantic unit pairs in the semantic association matrix with similarity greater than a set threshold; integrating the semantic information corresponding to the effective association pairs, refining core semantic elements containing demand themes, content attributes, style constraints, and generation dimensions, and outputting semantic element association descriptions.

[0031] Specifically, the process of mining implicit intent includes: generating multiple candidate intents based on extracted core semantic elements and a pre-defined intent classification system, forming a candidate intent set; calling a pre-defined intent knowledge base to perform semantic similarity matching between each candidate intent in the candidate intent set and the standard intents in the knowledge base, obtaining a matching score for each candidate intent; filtering the matching results based on contextual information, eliminating candidates that conflict with current scene environment features or user preference tags; sorting the matching scores of the remaining candidates, selecting the candidate with the highest score as the final implicit intent, and further verifying and determining the candidate intent based on historical interaction records if there are candidates with the same score.

[0032] Specifically, the cross-modal coding strategy includes the following steps: modal classification of core semantic elements, dividing them into text semantic sub-elements, image semantic sub-elements, and speech semantic sub-elements; encoding text semantic sub-elements by introducing a context dependency modeling algorithm to first identify the temporal association between text semantic sub-elements and context semantic units, and then integrating the temporal association features into the encoding process to generate text semantic coding features; encoding image semantic sub-elements by extracting environmental parameters and key object information from scene context features, using them as auxiliary features to supplement image semantic sub-elements, and generating image semantic coding features through an enhanced coding algorithm; and encoding speech semantic sub-elements by calling user speech habit context data, integrating user speech intonation, common expression styles, and other features into the encoding process, and optimizing the generation of personalized speech semantic coding features.

[0033] Specifically, the steps for generating the unified representation vector include: constructing a dynamic attention fusion network, using text, image, and speech semantic coding features, as well as context features, as network inputs; the dynamic attention fusion network calculates the initial association weights between each modality coding feature and the context features through a first semantic interaction layer; optimizing the initial association weights through multi-layer semantic interaction calculations, with each layer performing a secondary interaction with the context features based on the output of the previous layer to adjust the weight allocation of each modality coding feature; weighting and summing the optimized modality association weights with the corresponding modality coding features to obtain the fused features; and converting the fused features into a fixed-dimensional multimodal unified representation vector through feature normalization.

[0034] Specifically, the collaborative generation logic includes: performing feature parsing on the multimodal unified representation vector to identify the core requirement features corresponding to each modality generation task; analyzing the logical dependencies between each modality generation task based on the core requirement features to determine whether there is a dependency relationship between "prior modality" and "subsequent modality"; sorting each modality generation task according to the logical dependencies to determine the generation sequence, setting modality generation tasks without dependencies to be executed in parallel, and sorting modality generation tasks with dependencies according to the principle of "prior modality priority"; prioritizing the generation of core modality content, and formulating generation constraint rules for subsequent related modality content based on the semantic features of the core modality content to form a complete collaborative generation logic scheme.

[0035] Specifically, the context feedback constraint includes: marking key nodes in the collaborative generation logic scheme, including the core modal content generation completion node, the related modal content generation completion node, and the overall content preliminary fusion node; setting feedback detection points at each key node to extract the semantic features of the content at the current generation stage in real time; performing similarity matching detection between the extracted semantic features and the associated context information to calculate the context matching degree at the current stage; if the context matching degree is greater than or equal to a preset threshold, then continuing to execute subsequent generation steps; if the matching degree is less than the preset threshold, then generating adjustment instructions based on the detection results to dynamically correct the subsequent generation direction.

[0036] Specifically, the implementation steps of the feedback-driven iterative optimization mechanism include: obtaining user feedback information, which includes content satisfaction scores, context matching correction suggestions, and semantic deviation explanations; associating and annotating the user feedback information with the corresponding context information and generated content, supplementing metadata such as timestamps and user identifiers to form an annotated sample set; cleaning and standardizing the format of the annotated sample set to select effective samples; using incremental learning based on the effective samples to update the encoding parameters of the multimodal unified representation module and the matching threshold parameters of the content generation verification module; synchronously optimizing the semantic element extraction rules of the multimodal perception and parsing module to complete one iterative update; and recording the iterative update log and storing the parameter comparison data before and after the update.

[0037] Specifically, the dual similarity matching implementation steps include: extracting the complete semantic features of the generated content, and performing dual semantic similarity matching operations with the multimodal unified representation vector and associated context information respectively; calculating the context consistency deviation value and semantic consistency deviation value based on the matching results, where the deviation value is the inverse quantization result of similarity, and the lower the similarity, the larger the deviation value; preset the qualified threshold corresponding to the two deviation values, and simultaneously determine whether both deviation values ​​are within the qualified threshold range; if either deviation value exceeds the corresponding qualified threshold, locate the generation stage where the deviation occurs based on the detection results of the context feedback constraint mechanism; for the generation stage where the deviation occurs, adjust the corresponding collaborative generation logic parameters, if it is a context consistency deviation, focus on optimizing the context constraint association parameters, if it is a semantic consistency deviation, focus on calibrating the matching parameters with the multimodal unified representation vector; re-execute the generation stage and subsequent generation process based on the adjusted parameters, recalculate the dual deviation value, and repeat the steps until both deviation values ​​meet the qualified threshold requirements.

[0038] Specifically, the steps for updating system parameters include: based on user feedback information, firstly filtering out feedback content directly related to context information, and establishing an association mapping between the feedback content and the corresponding feature fields in the context information; fusing the associated feedback-context information with the semantic features of the corresponding generated content to form labeled sample data, the labeling information including the deviation type and correction direction corresponding to the feedback; inputting the sample data into the system parameter update engine to determine the target parameters to be updated, the target parameters including the association weight calculation parameters of the multimodal unified representation module and the deviation value qualification threshold parameters of the content generation verification module; fine-tuning and updating the target parameters based on the sample data, retaining the parameter adjustment trajectory during the update process; applying the updated parameters to the current system operation process, and storing the fused sample data in the historical interaction database as a reference for subsequent parameter updates; verifying the semantic consistency and context matching degree of the generated content after parameter update to ensure that the update effect meets expectations.

[0039] This embodiment takes the generation of multimodal teaching content in an intelligent teaching scenario as an example. The user is a junior high school math teacher, and the requirement is to generate multimodal teaching content related to "application of linear functions," including explanatory text, example images, and audio explanations. The system needs to combine contextual information such as the teacher's historical teaching preferences (emphasizing concrete examples) and the current teaching scenario (classroom lecture) to complete the generation. The process is as follows: I. Implementation Process of Multimodal Sensing and Analysis Module 1. Acquisition of Multimodal Information and Related Contextual Information: Multimodal requirement information input by teachers is collected through multi-source data interfaces, including textual requirements such as "generate teaching content on the application of linear functions" and audio requirements such as "audio explanation should be easy to understand and at a moderate pace." At the same time, based on teacher identification, historical databases are accessed to extract historical interaction records (teaching content with real-life examples is frequently generated) and long-term preference tags such as "concrete examples are preferred." The current scene environment characteristics such as "classroom teaching scenario, audience is junior high school students" are collected through scene perception components, and "teaching content difficulty is at the second year of junior high school level" is obtained by reading the task configuration file. The above contextual information is formatted and standardized, and timestamps t1 (text requirement) and t2 (audio requirement) are added to each modal information and aligned with the timestamps of the context information to form a structured information set S.

[0040] 2. Collaborative Preprocessing: The structured information set S is divided into text, image (in this embodiment, there are no initial input images, so this step only preprocesses other categories), speech, and context. For text information (text requirements), word segmentation is performed to obtain the segmentation results {generation, linear function, application, teaching content}. After removing stop words, the core words are retained, and the core entities "linear function, teaching content" are extracted using entity recognition algorithms. For speech information (speech requirements), Gaussian filtering is used for noise reduction, and effective speech signals are enhanced using the short-time energy method, while removing silent segments at the beginning and end. For context information, redundant data is cleaned up, sorted by timestamp, and key information fields {historical preference: concrete cases preferred, scenario: classroom teaching, audience: junior high school students, difficulty: eighth grade} are extracted to form a standardized context dataset C.

[0041] 3. Semantic Element Extraction: A semantic unit extraction algorithm is used to extract basic semantic units from the preprocessed multimodal information and the standardized context dataset C, respectively, resulting in a multimodal semantic unit set M = {linear function, application, teaching content, voice explanation, easy to understand, moderate speaking speed}, and a context semantic unit set Cᵤ = {concrete case, classroom teaching, junior high school students, eighth grade level}; a semantic association matrix A is constructed. m × n (m is the number of units in M, n is the number of units in Cᵤ). The cosine similarity algorithm is used to calculate the semantic similarity between pairs of units. The similarity calculation formula is: sim(a,b)=cosθ=(a·b) / (|a|×|b|), where a and b are the vector representations of the two semantic units, a·b is the vector dot product, and |a| and |b| are the vector moduli. Based on the calculation results, the semantic association matrix A is filled. A similarity threshold T1 is set, and valid association pairs with similarity greater than T1 are retained, such as {linear function - concrete case}, {teaching content - classroom lecture}, and {teaching content - junior high school students}. The semantic information of the valid association pairs is integrated to extract and generate the core semantic element F={requirement theme: linear function application teaching, content attribute: teaching content for classroom lecture, style constraint: concrete case, easy to understand, generation dimension: text explanation + example image + voice explanation}.

[0042] 4. Implicit Intent Mining: Based on the core semantic element F and combined with the pre-set intent classification system (teaching content generation, knowledge Q&A, exercise push, etc.), a candidate set of intents I_candidate is generated, which is {generating linear function application classroom teaching content, generating linear function application exercises, generating linear function application Q&A content}. A pre-set intent knowledge base is invoked, and each candidate in the candidate set is semantically matched with the standard intents in the knowledge base. The cosine similarity algorithm is used to calculate the matching scores s1, s2, and s3. Combined with contextual information filtering, "generating linear function application exercises" and "generating linear function application Q&A content" that conflict with the "classroom teaching scenario" are eliminated. The score s1 of the remaining candidate "generating linear function application classroom teaching content" is confirmed, and it is determined to be the final implicit intent I.

[0043] II. Implementation Process of the Multimodal Unified Characterization Module 1. Context-guided cross-modal coding: Modal classification of core semantic elements F to obtain textual semantic sub-elements F t ={Application of linear functions for teaching, classroom instruction, concrete examples, easy to understand}, Image semantic sub-element Fᵢ={Application examples of linear functions, concrete presentation}, Voice semantic sub-element Fᵥ={Voice explanation, moderate speaking speed, easy to understand}; For text semantic sub-element F t During encoding, a context dependency modeling algorithm is introduced. Temporal associations are identified using the temporal association calculation formula t_rel(a,b)=∑(a_i×b_i×w_t) (where a_i is the text semantic unit vector, b_i is the context semantic unit vector, and w_t is the temporal weight). These temporal association features are then integrated into the encoding process to generate the text semantic encoding feature V. t When encoding the image semantic sub-element Fᵢ, "classroom teaching" and "junior high school students" are extracted from the scene context features as auxiliary features and added to the image semantic sub-element through feature concatenation. An enhanced coding algorithm is used to generate the image semantic coding feature Vᵢ. When encoding the speech semantic sub-element Fᵥ, the user's speech habit context data (the intonation features of the teacher's past speech explanations) is called, and the intonation features are integrated into the coding process to generate the speech semantic coding feature Vᵥ.

[0044] 2. Association Weight Calculation and Unified Representation Vector Generation: Constructing a dynamic attention fusion network to generate V t Vᵢ, Vᵥ, and the context feature vector V_c are used as network inputs; the initial association weights are calculated through the first semantic interaction layer, and the weight calculation formula is w_init=softmax((V m ×W_q)×(V_c×W_k)^T / √d_k), where V m Encode features for each modality (V) t( / Vᵢ / Vᵥ), where W_q and W_k are query and key-value mapping matrices, and d_k is the dimension of the key-value vector; the initial weights are optimized through multi-layer semantic interaction. In each layer, the weights are updated as w_update = w_prev × sim(V_prev, V_c) (where w_prev is the weight of the previous layer and V_prev is the output feature of the previous layer); the optimized cross-modal association weights w t , wᵢ, and wᵥ are used to perform weighted summation with the corresponding encoded features to obtain the fused feature V_fusion = w t ×V t + wᵢ×Vᵢ + wᵥ×Vᵥ; the fused feature is converted into a multi-modal unified representation vector V_unified with a fixed dimension through feature normalization as V_norm = V_fusion / ||V_fusion||2 (||·||2 is the L2 norm).

[0045] III. Implementation Process of the Content Generation Verification Module 1. Collaborative Generation Logic Formulation: Parse the features of the multi-modal unified representation vector V_unified to identify the core requirement features of each modal generation task; analyze the logical dependency relationships to determine that "text explanation" is the core modality (prior modality), and "example image" and "voice explanation" are subsequent associated modalities; sort the generation time sequence according to the principle of "prior modality first": first generate the text explanation, then generate the example image based on the text explanation, and finally generate the voice explanation based on the text explanation; preferentially generate the content of the core modality (the text explanation of the application of linear functions, including cases of travel problems in life), and based on its semantic feature V_text, formulate the generation constraint rules for the subsequent modalities (the example image needs to match the travel problem case, and the voice explanation needs to conform to the text explanation logic and have a moderate speaking speed), forming a collaborative generation logic scheme L.

[0046] 2. Introduction of Context Feedback Constraints: Mark the key nodes in the collaborative generation logic scheme L: the completion node of text explanation generation, the completion node of example image generation, and the completion node of voice explanation generation; set feedback detection points at each node, extract the semantic features of the currently generated content, and perform similarity matching detection with the associated context information. Use the cosine similarity algorithm to calculate the context matching degree m_c; set the matching degree threshold T2. If m_c ≥ T2, continue with the subsequent generation. If m_c < T2, generate an adjustment instruction. For example, when detecting insufficient matching degree during the generation of the example image, the adjustment instruction is "Replace it with a schematic diagram of travel problems that is more suitable for junior high school students' cognition."

[0047] 3. Dual Similarity Matching and Deviation Value Processing: Extract the complete semantic features V_gen of the generated multimodal content (text explanation T, example image I, and audio explanation V); perform dual similarity matching with the multimodal unified representation vector V_unified and the context feature vector V_c respectively, and use the cosine similarity algorithm to calculate the similarity sim1 (with V_unified) and sim2 (with V_c); calculate the deviation value, with the formulas e1=1-sim1 (semantic consistency deviation value) and e2=1-sim2 (context consistency deviation value). Set acceptable thresholds T3 and T4. If e1>T3 or e2>T4, locate the deviation stage based on the context feedback constraint detection results. For example, if e2>T4 (contextual consistency deviation), it is located that the speech speed is too fast and deviates from the context constraint of "moderate speech speed". Adjust the corresponding stage parameters (speech speed parameters for speech generation), re-execute this stage and subsequent generation process, recalculate the deviation value, and repeat the iteration until e1≤T3 and e2≤T4 to obtain the final generated content (text explanation that fits the requirements of the concrete case, example image that matches the travel problem, and speech explanation with a moderate speech speed).

[0048] IV. Implementation Process of the Interactive Feedback Iteration Module 1. Feedback Acquisition and Sample Construction: Receive teacher feedback information through a visual interactive interface: "The text explanation is clear, the example images fit the case, and the speech rate is slightly fast." Associate and annotate the feedback information with the corresponding context information and the final generated content, and supplement metadata such as timestamp t3 and teacher ID to form an annotated sample set D. Perform data cleaning on D, remove invalid and redundant data, and select valid samples D_valid (including feedback "slightly fast speech rate," the corresponding context "moderate speech rate," and the speech features of the generated content).

[0049] 2. System Parameter Update: Based on the valid sample D_valid, an incremental learning approach is used to update the encoding parameters of the multimodal unified representation module (speech rate feature weights in speech semantic encoding) and the matching threshold parameters of the content generation verification module (context matching threshold T2 for speech explanation). The semantic element extraction rules of the multimodal perception and parsing module are optimized simultaneously to strengthen the extraction weights of semantic units related to "speech rate". Iterative update logs are recorded to store parameter comparison data before and after the update, such as speech rate weight w_v1 before the update and w_v2 after the update.

[0050] Through the above implementation process, the multimodal teaching content generated by this system accurately matches the needs of teachers and teaching scenarios. After feedback and iteration, its adaptability is further improved, effectively supporting the multimodal content generation needs of intelligent teaching scenarios.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A context-aware, multimodal information unified representation and content generation system, characterized in that, include: Multimodal perception and parsing module, multimodal unified representation module, content generation and verification module, and interactive feedback and iteration module; The multimodal perception and parsing module acquires multimodal information and associated context information, performs collaborative preprocessing, and integrates the multimodal information and associated context information to extract semantic elements and uncover the implicit intent of the requirements. The multimodal unified representation module adopts a context-guided cross-modal coding strategy to encode the semantic elements, capture the association weights between the multimodal information and the context, and generate a multimodal unified representation vector. The content generation and verification module formulates collaborative generation logic based on the multimodal unified representation vector and contextual constraints, and introduces contextual feedback constraints during the generation process; it extracts the semantic features of the generated content and performs dual similarity matching with the multimodal unified representation vector and contextual information, and calculates the contextual consistency deviation value and semantic consistency deviation value. The interactive feedback iteration module stores the final generated content, context information, and historical interaction data, constructs a feedback-driven iterative optimization mechanism, and uses user feedback and context information as samples to update system parameters.

2. The system according to claim 1, characterized in that, The specific process of acquiring multimodal information and associated context information includes: the multimodal information includes text information, image information and voice information, and a timestamp is added to each modal information during the acquisition process; based on the user identifier, the historical database is called to extract the user's historical interaction records and long-term user preference tags, the associated context information is format-standardized, and it is associated and aligned with the timestamp of the multimodal information to generate a structured information set.

3. The system according to claim 1, characterized in that, The collaborative preprocessing process includes: classifying and filtering preprocessing objects, dividing the structured information set into text, image, speech, and context categories; performing word segmentation, stop word removal, and redundant information filtering on text information to extract core entities; performing noise reduction on image information, standardizing and adjusting the size, locating key regions, and extracting feature points; performing noise reduction on speech information, enhancing effective speech signals, and pruning invalid silent segments; and performing redundant data cleaning on context information, sorting by timestamp, extracting key information fields from the context information, and generating a standardized context dataset.

4. The system according to claim 1, characterized in that, The specific steps for extracting semantic elements include: extracting basic semantic units from the preprocessed multimodal information and the standardized context dataset respectively, generating a multimodal semantic unit set and a context semantic unit set; constructing a semantic association matrix, calculating the pairwise semantic similarity between the units in the multimodal semantic unit set and the context semantic unit set, and filling the semantic association matrix based on the calculation results; determining effective association pairs through threshold filtering, fusing the semantic information corresponding to the effective association pairs, and refining and generating the semantic elements.

5. The system according to claim 1, characterized in that, The specific steps of mining the implicit intent of the demand include: generating demand intent candidates based on the semantic elements and in conjunction with a preset intent classification system, forming a demand intent candidate set; calling a preset intent knowledge base, performing semantic similarity matching between the candidate options in the demand intent candidate set and the standard intents in the knowledge base, and obtaining the matching scores of the candidate options; filtering based on the associated context information, sorting the matching scores of the remaining candidate options, and selecting the candidate option with the highest score as the implicit intent of the demand.

6. The system according to claim 1, characterized in that, The specific steps of the cross-modal coding strategy include: classifying the semantic elements into text semantic sub-elements, image semantic sub-elements, and speech semantic sub-elements; encoding the text semantic sub-elements by identifying the temporal correlation between the text semantic sub-elements and the context semantic units, and then integrating the temporal correlation features into the encoding process to generate text semantic coding features; encoding the image semantic sub-elements by extracting environmental parameters and key object information from the scene context features, and using them as auxiliary features to supplement the image semantic sub-elements to generate image semantic coding features; and encoding the speech semantic sub-elements by calling user speech habit context data into the encoding process to optimize and generate speech semantic coding features.

7. The system according to claim 1, characterized in that, The specific steps for generating the unified representation vector include: constructing a dynamic attention fusion network, taking text, image, and speech semantic coding features and context features as network inputs; calculating the initial association weights between each modality coding feature and the context features through the first semantic interaction layer, optimizing the initial association weights through multiple layers of semantic interaction calculation, and performing secondary interaction between each layer and the context features based on the output of the previous layer to adjust the weight allocation of each modality coding feature; weighting and summing the optimized modality association weights with the corresponding modality coding features to obtain the fused features, and converting the fused features into the fixed-dimensional multimodal unified representation vector through feature normalization processing.

8. The system according to claim 1, characterized in that, The collaborative generation logic specifically includes: performing feature parsing on the multimodal unified representation vector to identify the core requirement features corresponding to each modality generation task; analyzing the logical dependencies between each modality generation task based on the core requirement features, sorting each modality generation task according to the logical dependencies, and determining the generation sequence; prioritizing the generation of core modality content, and formulating generation constraint rules for subsequent related modality content based on the semantic features of the core modality content, thus forming the collaborative generation logic scheme.

9. The system according to claim 1, characterized in that, The context feedback constraint specifically includes: marking key nodes in the collaborative generation logic scheme, including the core modal content generation completion node, the related modal content generation completion node, and the overall content preliminary fusion node; setting feedback detection points at each key node, extracting the semantic features of the content at the current generation stage, performing similarity matching detection with the related context information, and calculating the context matching degree at the current stage.

10. The system according to claim 1, characterized in that, The implementation steps of the feedback-driven iterative optimization mechanism include: associating and annotating user feedback information with corresponding context information and generated content, supplementing timestamps and user identifier metadata, and generating an annotated sample set; cleaning and standardizing the format of the annotated sample set, selecting valid samples, and updating the encoding parameters of the multimodal unified representation module and the matching threshold parameters of the content generation verification module; synchronously optimizing the semantic element extraction rules of the multimodal perception and parsing module, recording iterative update logs, and storing parameter comparison data before and after the update.

11. The system according to claim 1, characterized in that, The specific implementation steps of the dual similarity matching include: extracting the complete semantic features of the generated content, performing dual semantic similarity matching operations with the multimodal unified representation vector and associated context information respectively, calculating the context consistency deviation value and the semantic consistency deviation value based on the matching results; setting a corresponding qualified threshold, and if any deviation value exceeds the corresponding qualified threshold, locating the generation stage where the deviation occurs by combining the detection results of context feedback constraints.

12. The system according to claim 1, characterized in that, The steps for updating system parameters include: based on user feedback information, filtering feedback content related to context information, establishing an association mapping between the feedback content and the corresponding feature fields in the context information; fusing the associated feedback-context information with the semantic features of the corresponding generated content to generate labeled sample data, and determining the target parameters that need to be updated.

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