Large model platform for enhanced university data management based on vector database and retrieval

Abstract

The application relates to the technical field of big data management and retrieval enhancement generation, and particularly discloses a university data management large model platform based on a vector database and retrieval enhancement, which comprises a multi-modal vectorization engine integrated with a multi-modal large language model, realizes deep semantic coding and representation learning of educational data, an intelligent vector retrieval system based on neural information retrieval and a learnable index, realizes high-precision and rapid retrieval, a high-level RAG reasoning engine improves generation quality through an adaptive retrieval strategy, a multi-hop reasoning mechanism and knowledge fusion, and an intelligent education knowledge engine based on dynamic knowledge graph and education ontology modeling provides domain knowledge support and intelligent analysis. The application integrates the latest artificial intelligence technology, constructs an advanced university data management platform, provides strong technical support for education informatization, and has important theoretical significance and practical value.

Description

Technical Field

[0001] This invention relates to the field of big data management and retrieval enhancement technology, and in particular to a large-scale data management model platform for universities based on vector databases and retrieval enhancement. Background Technology

[0002] With the accelerated digital transformation of higher education institutions, educational data is exhibiting multimodal, large-scale, and highly real-time characteristics, and traditional data management technologies have the following limitations: I. Insufficient Intelligence in Traditional Retrieval Technologies: Existing educational data retrieval systems are mainly based on traditional keyword matching and simple vector retrieval, lacking deep semantic understanding capabilities. Faced with complex natural language queries, such as "analyze the learning performance of computer science students who have won national-level mathematical modeling competition awards in artificial intelligence courses and its correlation with innovation ability," traditional systems cannot accurately understand multi-level semantic relationships and reasoning logic.

[0003] Second, the application technology of multimodal large models is lagging behind: the current system lacks deep integration with the latest multimodal large language models and cannot make full use of the multimodal understanding capabilities of advanced models such as GPT-4V, Claude-3, and DeepSeek-VL, resulting in poor processing effect on complex data containing text, images, tables, charts and graphs.

[0004] Third, vector retrieval technology is not advanced enough: existing vector database technologies are mainly based on traditional tools such as FAISS and Annoy, and lack support for the latest vector retrieval algorithms, such as Transformer-based retrieval models, neural information retrieval, and learnable indexes. As a result, retrieval accuracy and efficiency need to be improved.

[0005] Fourth, the lack of intelligent RAG architecture: Traditional retrieval-enhanced generation system architecture is relatively simple and lacks advanced functions such as adaptive retrieval strategies, multi-hop reasoning capabilities, and knowledge fusion mechanisms, which cannot meet the intelligent needs of complex educational scenarios. Summary of the Invention

[0006] The purpose of this invention is to build an intelligent university data management platform based on advanced vector databases and retrieval enhancement generation technology, integrating multimodal large language models to achieve deep semantic understanding, intelligent retrieval, accurate question answering, and knowledge reasoning of educational data.

[0007] To achieve the above objectives, this invention provides a large-scale model platform for university data management based on vector databases and enhanced retrieval, including: a multimodal vectorization engine, an intelligent vector retrieval system, an advanced RAG inference engine, and an intelligent educational knowledge engine; The multimodal vectorization engine integrates a multimodal large language model to achieve deep semantic encoding and representation learning of educational data; The intelligent vector retrieval system is based on neural information retrieval and learnable indexes to achieve high-precision and fast retrieval; The advanced RAG inference engine improves the quality of generation through adaptive retrieval strategies, multi-hop inference mechanisms, and knowledge fusion. The intelligent education knowledge engine provides domain knowledge support and intelligent analysis based on dynamic knowledge graphs and educational ontology modeling.

[0008] Furthermore, the multimodal vectorization engine includes: classifying and streaming multimodal educational data, and generating multimodal vector representations through a text processing module, a visual understanding module, a document intelligent parsing module, and an audio semantic encoding module.

[0009] Furthermore, the text processing module adopts the Transformer-XL architecture, integrates GPT-4-Turbo and Claude-3-Opus models as well as the Chain-of-Thought inference mechanism, supports a context length of up to 128K tokens, and outputs a 4096-dimensional vector. The visual understanding module adopts the Vision-Language Transformer architecture, integrating the GPT-4V and DeepSeek-VL models. GPT-4V is used for understanding and describing complex images, while DeepSeek-VL is used for visual processing of Chinese educational content. The outputs of the two models are fused through ensemble learning. The document intelligent parsing module integrates OCR and document understanding models, specifically: DeepSeek-OCR performs high-precision text recognition, supporting handwritten text and complex layouts; PaddleOCR-PP-Structure performs table and layout analysis; LayoutLM-v3 handles document layout understanding and information extraction; and MathPix OCR handles mathematical formulas and scientific symbols. The audio semantic encoding module adopts the Wav2Vec2.0-Conformer architecture, integrating the Whisper-Large-v3 and SpeechT5 models to achieve semantic understanding and sentiment analysis of multilingual teaching audio.

[0010] Furthermore, the four modules are optimized through vector representation learning, including model pre-training, vector dimension optimization, vector normalization, and cross-modal alignment. The model pre-training adopts a contrastive learning framework and is pre-trained on a corpus of hundreds of billions of educational words. Positive sample pairs include different modal representations of the same content, synonym pairs, and related concept pairs. Negative samples are generated through hard negative sample mining technology, which is based on the hierarchical relationship filtering of knowledge graphs. The vector dimension optimization is achieved by determining the optimal dimension through principal component analysis and information theory methods, and the output vector representation is defined as a 4096-dimensional vector. The vector normalization adopts a combination of L2 norm and temperature scaling, and the normalization parameters are optimized by the retrieval performance on the validation set; The cross-modal alignment adopts a CLIP-like architecture, specifically optimized for educational scenarios, and incorporates hard negative sample mining and online hard sample generation into the comparison loss function; the alignment loss function is: ; in, To align the loss function values, It is a logarithmic function. It is an exponential function. For similarity function, For visual sample vectors, For text sample vectors, For visual negative sample vectors, This is the negative sample vector of the text. To control the hyperparameters of similarity distribution, Summing for negative samples.

[0011] Furthermore, the intelligent vector retrieval system includes a neural retrieval module and a learnable index module; The neural retrieval module specifically comprises: a query encoder and a document encoder using a RoBERTa-Large architecture trained on large-scale educational question-and-answer data; a hybrid retrieval strategy for initial retrieval: dense retrieval using SimCSE and E5-Large models, sparse retrieval based on SPLADE technology, and graph neural network retrieval enhancing semantic association through knowledge graphs, with the three weight parameters adaptively adjusted through Bayesian optimization; and a hybrid architecture of Dense Passage Retrieval and ColBERT for further retrieval, with Dense Passage Retrieval being an improved version incorporating Cross-Encoder reordering and Hard Negative Mining techniques. The learnable index module adopts a Transformer-based sequence modeling method, and the index structure and index path are trained and optimized end-to-end. The index structure adopts a hierarchical design, with coarse-grained clustering at the top layer and fine-grained indexing at the bottom layer. Each layer uses an independent Transformer model for learning. Dynamic index updates adopt incremental learning technology. New data only updates the relevant index nodes. The update strategy is based on data importance and access frequency, prioritizing the updating of high-value index parts.

[0012] Furthermore, the intelligent vector retrieval system employs retrieval optimization technology, specifically: Caching strategy: A three-level cache design is adopted, with L1 caching hot query results, L2 caching vector representations, and L3 caching the original document; Query understanding module: It adopts T5-based query rewriting technology, and the rewriting strategy includes synonym replacement, concept expansion and relationship supplementation; Vector compression and acceleration: By combining Product Quantization and ScaNN technologies, storage overhead is reduced to 1 / 16 of the original, and retrieval acceleration is achieved through GPU parallel computing and batch processing optimization; Results re-ranking: The initial search results are finely ranked using a BERT-based Cross-Encoder model; the ranking model considers deep semantic matching between the query and the document, authority, and timeliness.

[0013] Furthermore, in the advanced RAG inference engine, the adaptive retrieval strategy is as follows: A reinforcement learning framework is employed, using the Multi-Armed Bandit algorithm to treat different combinations of retrieval parameters as different arms, and leveraging the upper confidence bound algorithm to balance exploration and utilization efficiency; the retrieval agent is based on the Q-learning algorithm and a deep Q-network architecture, using query features, historical feedback, and user profiles as the state space, and discretized retrieval parameter combinations including retrieval depth, candidate quantity, and reordering strategy as the action space to learn the optimal retrieval strategy; a meta-learning framework based on the Model-Agnostic Meta-Learning algorithm is introduced to pre-train general initialization parameters on multiple retrieval tasks; The multi-hop reasoning mechanism employs a reasoning framework combining thought chains and thought trees, achieving multi-step problem-solving through recursive retrieval and reasoning. The thought chain reasoning uses Chain-of-Thought Prompting technology to guide the model in generating intermediate reasoning steps, and incorporates customized reasoning templates for educational scenarios. The thought tree reasoning expands a single reasoning chain into a tree structure, employing breadth-first search and heuristic pruning algorithms. A multi-dimensional reasoning verification mechanism is established to promptly identify and correct reasoning errors through logical consistency checks, fact verification, common-sense reasoning, and intermediate result evaluation. The knowledge fusion employs a knowledge integration network based on a multi-head attention mechanism. Self-Attention captures the internal correlation of knowledge fragments, while Cross-Attention mines the complementarity between different knowledge fragments. Based on a semantic similarity clustering algorithm, duplicate or highly similar knowledge fragments are merged into a representative fragment. The deduplication threshold is optimized and determined by grid search on the validation set. A confidence score is assigned to each knowledge fragment, and low-confidence fragments are downweighted during the generation process.

[0014] Furthermore, the advanced RAG inference engine also includes a generation control mechanism that integrates Constitutional AI and RLHF technologies; it continuously iterates the model generation capability through user feedback reinforcement learning RLHF and establishes a generation constraint framework through Constitutional AI; and it verifies the generated information from three dimensions: factual verification, logical consistency check, and educational suitability assessment.

[0015] Furthermore, in the intelligent education knowledge engine, the dynamic knowledge graph includes: Entity recognition: Entity extraction is performed using GPT-4-based Few-shot Learning technology in conjunction with Prompt templates. Entity types include courses, knowledge points, skills, evaluation criteria, and learning resources. Relation extraction: It integrates zero-shot and few-shot learning methods and uses prior knowledge to identify semantic relations between entities. Semantic relations include hierarchical relations, dependency relations, and evaluation relations. Knowledge verification mechanism: It adopts a combination of automatic verification and expert review. Automatic verification is based on consistency checks and common sense reasoning, while manual verification is achieved through annotation by domain experts. Dynamic update capability: Based on incremental learning and continuous learning technologies, it supports real-time knowledge updates and version management; The educational ontology modeling includes: Ontology design: Based on OWL and RDF standards, the core concept system covers subject classification, competency framework, and evaluation system, forming a multi-level, standardized educational knowledge framework; Conceptual Relationships and Inference Rules: Complex semantic relationships are expressed using the OWL language, and inference rules are designed based on a descriptive logic framework; Ontology evolution management: Version control and change tracking technologies are used to enable incremental updates of the ontology and query historical versions. Combined with change propagation algorithms, the consistency of related concepts and relationships is ensured when the ontology is updated.

[0016] Furthermore, the intelligent education knowledge engine also includes an intelligent question-answering system, employing knowledge graph-based question-answering technology, including: Problem understanding module: Employs BERT-based intent recognition and slot filling to parse the semantic structure of user queries; Graph query optimization: A cost-based query planning algorithm is adopted to dynamically select the optimal query execution strategy based on query complexity and graph structure characteristics. Query modes support subgraph matching, path query, and aggregation query. Answer generation mechanism: It adopts a hybrid approach of template-based and generation-based methods. Simple queries can quickly generate answers by filling in templates, while complex queries use generative models to output natural language responses. At the same time, it integrates KBQA and generative QA, and achieves accurate answers through a fusion architecture of graph neural networks and sequence generation models.

[0017] The present invention employs the aforementioned large-scale model platform for university data management based on vector databases and enhanced retrieval, and its beneficial effects are as follows: (1) This invention integrates advanced multimodal large language model and neural retrieval technology to improve retrieval accuracy and precision, support complex semantic queries and reasoning queries, and provide high-quality information support for educational decision-making; (2) Based on the multimodal understanding capabilities of advanced models such as GPT-4V and DeepSeek-VL, this invention can simultaneously process multiple data types such as text, images, audio, and video, effectively improving the processing effect of multimodal data; it integrates Chain-of-Thought reasoning and multi-hop retrieval technology, possesses powerful logical reasoning and knowledge association capabilities, significantly improves the accuracy of answering complex questions, and enhances the intelligence level of the system; (3) The present invention adopts learnable index and GPU acceleration technology to support a large number of real-time searches; the knowledge graph and reasoning engine designed specifically for the education field significantly improve the accuracy of answering professional education questions and support a variety of educational application scenarios such as personalized learning analysis, education quality assessment, and intelligent decision support. Attached Figure Description

[0018] Figure 1 This is the overall architecture diagram of the large-scale university data management platform based on vector database and retrieval enhancement of this invention; Figure 2 This is an architecture diagram of the advanced multimodal vectorization engine of the university data management big model platform based on vector database and retrieval enhancement, which is based on the present invention. Figure 3 This is an architecture diagram of the intelligent vector retrieval system based on a vector database and a large-scale model platform for university data management with enhanced retrieval capabilities, as described in this invention. Figure 4 This is an architecture diagram of the advanced RAG inference engine of the university data management big model platform based on vector database and retrieval enhancement, which is based on the present invention. Figure 5 This is an architecture diagram of the intelligent education knowledge engine of the university data management big model platform based on vector database and retrieval enhancement, which is based on the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0020] like Figure 1 As shown, the embodiment of the present invention is based on a vector database and a retrieval-enhanced university data management big model platform, including: a multimodal vectorization engine, an intelligent vector retrieval system, an advanced RAG inference engine, and an intelligent educational knowledge engine; 1. Multimodal Vectorization Engine: Integrates a multimodal large language model to achieve deep semantic encoding and representation learning of educational data; including: classifying and streaming multimodal educational data, and generating multimodal vector representations through text processing, visual understanding, intelligent document parsing, and audio semantic encoding modules. Figure 2 As shown.

[0021] (1) The text processing module adopts the Transformer-XL architecture, integrates GPT-4-Turbo and Claude-3-Opus models (provided through a combination of API calls and local deployment) and Chain-of-Thought inference mechanism, supports context length up to 128K tokens, can process long educational documents and complex queries, and outputs 4096-dimensional vectors; The visual understanding module adopts the Vision-Language Transformer architecture, integrating the GPT-4V and DeepSeek-VL models. GPT-4V is used for understanding and describing complex images, while DeepSeek-VL is used for visual processing of Chinese educational content. The outputs of the two models are fused through ensemble learning to improve the accuracy of visual understanding. It supports deep understanding of various types of visual content such as teaching charts, student works, experimental data, and course screenshots. The document intelligent parsing module integrates OCR and document understanding models: DeepSeek-OCR performs high-precision text recognition and supports handwritten text and complex layouts; PaddleOCR-PP-Structure performs table and layout analysis; LayoutLM-v3 handles document layout understanding and information extraction; MathPix OCR processes mathematical formulas and scientific symbols; The audio semantic encoding module adopts the Wav2Vec2.0-Conformer architecture, integrates the Whisper-Large-v3 and SpeechT5 models, realizes semantic understanding and sentiment analysis of multilingual teaching audio, and combines a context learning mechanism to achieve deep semantic modeling of audio content.

[0022] (2) The above four modules are optimized through vector representation learning, including model pre-training, vector dimension optimization, vector normalization, and cross-modal alignment; Model pre-training: A contrastive learning framework is used to pre-train on a corpus of hundreds of billions of educational words. Positive sample pairs include different modal representations of the same content, synonym pairs, and related concept pairs. Negative samples are generated through hard negative sample mining technology, which is based on the hierarchical relationship filtering of knowledge graphs to improve the model's discriminative ability. Vector Dimension Optimization: The optimal dimension was determined through principal component analysis and information theory methods, and the output vector representation was set to a 4096-dimensional vector. Experiments have verified that the 4096-dimensional vector has the best balance between representation capability and computational efficiency for educational data. Vector normalization: A combination of L2 norm and temperature scaling is used to ensure that the similarity calculation of vectors of different modalities is comparable; the normalization parameters are optimized by the retrieval performance on the validation set; Cross-modal alignment: A CLIP-like architecture is adopted, specifically optimized for educational scenarios. The comparison loss function incorporates hard negative sample mining and online hard sample generation to improve alignment accuracy. The alignment loss function is: ; in, To align the loss function values, It is a logarithmic function. It is an exponential function. For similarity function, For visual sample vectors, For text sample vectors, For visual negative sample vectors, This is the negative sample vector of the text. To control the hyperparameters of similarity distribution, Summing for negative samples.

[0023] 2. Intelligent Vector Retrieval System: Based on neural information retrieval and learnable indexing, it achieves millisecond-level high-precision and fast retrieval of billions of vectors; it includes a neural retrieval module and a learnable indexing module. For example... Figure 3 As shown.

[0024] (1) The neural retrieval module is specifically as follows: ① The query encoder and document encoder adopt the RoBERTa-Large architecture trained on large-scale educational question-answering data; ② A hybrid retrieval strategy is adopted for preliminary retrieval: dense retrieval (weight 0.6, suitable for semantic similarity queries) uses the SimCSE and E5-Large models, sparse retrieval (weight 0.3, suitable for exact match queries) is based on SPLADE technology, and graph neural network retrieval (weight 0.1, suitable for relational reasoning queries) enhances semantic association through knowledge graphs. The three weight parameters are adaptively adjusted through Bayesian optimization (on the validation set). ③ Further retrieval is carried out using a hybrid architecture of Dense Passage Retrieval and ColBERT. Dense Passage Retrieval is an improved version that incorporates Cross-Encoder reordering and Hard Negative Mining techniques. (2) Learnable index module: The sequence modeling method based on Transformer is adopted to transform the vector indexing problem into a sequence generation task. The index structure and index path are optimized through end-to-end training. The model learns the mapping relationship from the query vector to the candidate document ID sequence. The index structure adopts a hierarchical design, with coarse-grained clustering at the top level and fine-grained indexing at the bottom level. Each layer uses an independent Transformer model for learning, and information is passed between layers through an attention mechanism. Dynamic index updates employ incremental learning techniques. New data only updates the relevant index nodes. The update strategy is based on data importance and access frequency, prioritizing the updating of high-value index portions.

[0025] (3) Search optimization techniques, specifically: ① Caching strategy: A three-level caching design is adopted, with L1 caching hot query results (1GB capacity, hit rate >90%), L2 caching vector representations (10GB capacity, hit rate >70%), and L3 caching the original documents (100GB capacity, hit rate >50%). ② Query understanding module: It adopts T5-based query rewriting technology to automatically identify and expand the query intent; the rewriting strategy includes a variety of methods such as synonym replacement, concept expansion, and relation supplementation; ③ Vector compression and acceleration: Product Quantization and ScaNN technologies are combined; while maintaining retrieval accuracy, storage overhead is compressed to 1 / 16 of the original. Retrieval acceleration is achieved through GPU parallel computing and batch processing optimization, realizing sub-millisecond response. ④ Result re-ranking: The preliminary search results are finely ranked using a BERT-based Cross-Encoder model; the ranking model considers multiple factors such as deep semantic matching between the query and the document, authority, and timeliness.

[0026] 3. The advanced RAG inference engine improves generation quality through adaptive retrieval strategies, multi-hop inference mechanisms, and knowledge fusion; such as... Figure 4 As shown.

[0027] (1) Adaptive retrieval strategy: The reinforcement learning framework is adopted, and the Multi-Armed Bandit algorithm is used to treat different combinations of retrieval parameters as different arms. The upper confidence bound algorithm is used to balance the exploration and utilization efficiency. The reward function is based on multiple indicators such as retrieval quality, response time, and user satisfaction.

[0028] The retrieval agent is based on the Q-learning algorithm and deep Q-network architecture. It uses query features, historical feedback and user profile as the state space and discretized retrieval parameter combinations including retrieval depth, number of candidates and re-ranking strategy as the action space to learn the optimal retrieval strategy. We introduce a meta-learning framework that uses the Model-Agnostic Meta-Learning algorithm to pre-train general initialization parameters on multi-class retrieval tasks, and then quickly adapt to new tasks with a small number of samples.

[0029] (2) Multi-hop reasoning mechanism: The reasoning framework combining thought chain and thought tree is adopted to achieve multi-step problem solving through recursive retrieval and reasoning; The Chain-of-Thought Prompting technology guides the model to generate intermediate reasoning steps, and combines educational scenarios with customized reasoning templates. The reasoning templates include steps such as problem decomposition, information retrieval, logical deduction, and conclusion synthesis.

[0030] Mind Tree Reasoning extends a single reasoning chain into a tree structure, supports parallel multi-path reasoning, and employs breadth-first search and heuristic pruning algorithms to achieve a balance between reasoning quality and computational efficiency. Establish a multi-dimensional reasoning verification mechanism to monitor the reasoning process and promptly identify and correct reasoning errors through logical consistency checks, fact verification, common sense reasoning, and intermediate result evaluation.

[0031] (3) Knowledge fusion network: A knowledge integration network based on multi-head attention mechanism is adopted. Self-Attention captures the internal correlation of knowledge fragments, and Cross-Attention explores the complementarity between different knowledge fragments. Knowledge deduplication: Based on semantic similarity clustering algorithm, duplicate or highly similar knowledge fragments are merged into a representative fragment. The deduplication threshold is optimized and determined by grid search on the validation set. Confidence assessment: Each knowledge fragment is assigned a confidence score, calculated based on factors such as consistency verification, source reliability, and domain expert ratings. Fragments with low confidence are downweighted during the generation process.

[0032] (4) Generation control mechanism: It integrates Constitutional AI and RLHF technologies; it uses user feedback to reinforce learning RLHF to continuously iterate model generation capabilities and establishes a generation constraint framework through Constitutional AI; it verifies the generated information from three dimensions: factual verification, logical consistency check, and educational suitability assessment.

[0033] 4. Intelligent Education Knowledge Engine: Based on dynamic knowledge graphs and educational ontology modeling for the education field, it provides domain-specific knowledge support and intelligent analysis capabilities. For example... Figure 5 As shown.

[0034] (1) Dynamic knowledge graph: Entity recognition: Employing GPT-4-based Few-shot Learning technology, combined with Prompt templates, entities are extracted from educational texts. Entity types include multiple categories such as courses, knowledge points, skills, evaluation criteria, and learning resources. Relation extraction: It integrates zero-shot and few-shot learning methods and uses the prior knowledge of large language models to identify semantic relations between entities. Semantic relations include hierarchical relations, dependency relations, evaluation relations, etc. Knowledge verification mechanism: It adopts a combination of automatic verification and expert review. Automatic verification is based on consistency checks and common sense reasoning, while manual verification is carried out through domain expert annotation for quality control. Dynamic update capability: Based on incremental learning and continuous learning technologies, it supports real-time knowledge updates and version management.

[0035] (2) Education ontology modeling: Ontology Design: Based on the international education standard OWL and the domestic education standard RDF, a multi-level conceptual system is established. The core conceptual system covers subject classification (according to the Ministry of Education's subject catalog), competency framework (according to Bloom's Taxonomy), evaluation system (according to the OBE concept), etc., forming a multi-level, standardized educational knowledge framework that supports complex semantic reasoning and querying. Conceptual Relationships and Inference Rules: Complex semantic relationships are expressed using the OWL language, supporting logical properties such as transitivity, symmetry, and antisymmetry; inference rules are designed based on a descriptive logic framework to ensure the correctness and completeness of the reasoning. Ontology evolution management: Version control and change tracking technologies are used to enable incremental updates of the ontology and query historical versions. Combined with change propagation algorithms, the consistency of related concepts and relationships is ensured when the ontology is updated.

[0036] (3) Intelligent question answering system, which adopts knowledge graph-based question answering technology and supports complex multi-entity and multi-relationship queries, including: Problem understanding module: It adopts BERT-based intent recognition and slot filling to accurately parse the semantic structure of user queries, supports complex compound queries and multi-constraint queries, and achieves a parsing accuracy of over 95%. Graph query optimization: It adopts a cost-based query planning algorithm to dynamically select the optimal query execution strategy based on query complexity and graph structure characteristics. The query modes support subgraph matching, path query, aggregation query, etc. Answer generation mechanism: It adopts a hybrid approach of template-based and generation-based methods. Simple queries can quickly generate answers by filling in templates, while complex queries use generative models to output natural language responses. At the same time, it integrates KBQA and generative QA, and achieves accurate answers through a fusion architecture of graph neural networks and sequence generation models.

[0037] Therefore, this invention adopts the aforementioned large-scale model platform for university data management based on vector databases and enhanced retrieval. This invention integrates advanced multimodal large language models and neural retrieval technology to improve retrieval accuracy and precision, supporting complex semantic and inferential queries, and providing high-quality information support for educational decision-making. Based on the multimodal understanding capabilities of advanced models such as GPT-4V and DeepSeek-VL, this invention can simultaneously process multiple data types such as text, images, audio, and video, effectively improving the processing effect of multimodal queries. Integrating Chain-of-Thought reasoning and multi-hop retrieval technology, it possesses powerful logical reasoning and knowledge association capabilities, significantly improving the accuracy of answers to complex questions and enhancing the system's intelligence level. This invention employs learnable indexing and GPU acceleration technology to support a large number of real-time retrievals. The knowledge graph and inference engine specifically designed for the education field significantly improve the accuracy of answers to professional education questions, supporting various educational application scenarios such as personalized learning analysis, education quality assessment, and intelligent decision support.

[0038] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A large-scale data management platform for universities based on vector databases and enhanced retrieval, characterized in that: include: Multimodal vectorization engine, intelligent vector retrieval system, advanced RAG inference engine, intelligent educational knowledge engine; The multimodal vectorization engine integrates a multimodal large language model to achieve deep semantic encoding and representation learning of educational data; The intelligent vector retrieval system is based on neural information retrieval and learnable indexes to achieve high-precision and fast retrieval; The advanced RAG inference engine improves the quality of generation through adaptive retrieval strategies, multi-hop inference mechanisms, and knowledge fusion. The intelligent education knowledge engine provides domain knowledge support and intelligent analysis based on dynamic knowledge graphs and educational ontology modeling. In the advanced RAG inference engine, the adaptive retrieval strategy is as follows: A reinforcement learning framework is employed, using the Multi-Armed Bandit algorithm to treat different combinations of retrieval parameters as different arms, and leveraging the upper confidence bound algorithm to balance exploration and utilization efficiency; the retrieval agent is based on the Q-learning algorithm and a deep Q-network architecture, using query features, historical feedback, and user profiles as the state space, and discretized retrieval parameter combinations including retrieval depth, candidate quantity, and reordering strategy as the action space, to learn the optimal retrieval strategy; a meta-learning framework based on the Model-Agnostic Meta-Learning algorithm is introduced to pre-train general initialization parameters on multiple retrieval tasks; The multi-hop reasoning mechanism employs a reasoning framework combining thought chains and thought trees, achieving multi-step problem-solving through recursive retrieval and reasoning. The thought chain reasoning uses Chain-of-Thought Prompting technology to guide the model in generating intermediate reasoning steps, and incorporates customized reasoning templates for educational scenarios. The thought tree reasoning expands a single reasoning chain into a tree structure, employing breadth-first search and heuristic pruning algorithms. A multi-dimensional reasoning verification mechanism is established to promptly identify and correct reasoning errors through logical consistency checks, fact verification, common-sense reasoning, and intermediate result evaluation. The knowledge fusion employs a knowledge integration network based on a multi-head attention mechanism. Self-Attention captures the internal correlations of knowledge fragments, while Cross-Attention uncovers the complementarity between different knowledge fragments. Based on a semantic similarity clustering algorithm, duplicate or highly similar knowledge fragments are merged into a representative fragment. The deduplication threshold is optimized and determined by grid search on the validation set. A confidence score is assigned to each knowledge fragment, and low-confidence fragments are downweighted during the generation process. The advanced RAG inference engine also includes a generation control mechanism that integrates Constitutional AI and RLHF technologies; it continuously iterates the model generation capability through reinforcement learning RLHF based on user feedback, and establishes a generation constraint framework through Constitutional AI; it verifies the generated information from three dimensions: factual verification, logical consistency check, and educational suitability assessment. The intelligent education knowledge engine also includes an intelligent question-answering system, which employs knowledge graph-based question-answering technology, including: Problem understanding module: Employs BERT-based intent recognition and slot filling to parse the semantic structure of user queries; Graph query optimization: A cost-based query planning algorithm is adopted to dynamically select the optimal query execution strategy based on query complexity and graph structure characteristics. Query modes support subgraph matching, path query, and aggregation query. Answer generation mechanism: It adopts a hybrid approach of template-based and generation-based methods. Simple queries can quickly generate answers by filling in templates, while complex queries use generative models to output natural language responses. At the same time, it integrates KBQA and generative QA, and achieves accurate answers through a fusion architecture of graph neural networks and sequence generation models.

2. The large-scale university data management model platform based on vector database and retrieval enhancement as described in claim 1, characterized in that, The multimodal vectorization engine includes: classifying and streaming multimodal educational data, and generating multimodal vector representations through a text processing module, a visual understanding module, a document intelligent parsing module, and an audio semantic encoding module.

3. The large-scale university data management platform based on vector database and retrieval enhancement as described in claim 2, characterized in that, The text processing module adopts the Transformer-XL architecture, integrates GPT-4-Turbo and Claude-3-Opus models as well as the Chain-of-Thought inference mechanism, supports context lengths of up to 128K tokens, and outputs 4096-dimensional vectors. The visual understanding module adopts the Vision-Language Transformer architecture, integrating the GPT-4V and DeepSeek-VL models. GPT-4V is used for understanding and describing complex images, while DeepSeek-VL is used for visual processing of Chinese educational content. The outputs of the two models are fused through ensemble learning. The document intelligent parsing module integrates OCR and document understanding models, specifically: DeepSeek-OCR performs high-precision text recognition, supporting handwritten text and complex layouts; PaddleOCR-PP-Structure performs table and layout analysis; LayoutLM-v3 handles document layout understanding and information extraction; and MathPix OCR handles mathematical formulas and scientific symbols. The audio semantic encoding module adopts the Wav2Vec2.0-Conformer architecture, integrating the Whisper-Large-v3 and SpeechT5 models to achieve semantic understanding and sentiment analysis of multilingual teaching audio.

4. The large-scale university data management platform based on vector database and retrieval enhancement as described in claim 3, characterized in that, The text processing module, visual understanding module, document intelligent parsing module, and audio semantic encoding module have been optimized through vector representation learning, including model pre-training, vector dimension optimization, vector normalization, and cross-modal alignment. The model pre-training adopts a contrastive learning framework and is pre-trained on a corpus of hundreds of billions of educational words. Positive sample pairs include different modal representations of the same content, synonym pairs, and related concept pairs. Negative samples are generated through hard negative sample mining technology, which is based on the hierarchical relationship filtering of knowledge graphs. The vector dimension optimization is achieved by determining the optimal dimension through principal component analysis and information theory methods, and the output vector representation is defined as a 4096-dimensional vector. The vector normalization adopts a combination of L2 norm and temperature scaling, and the normalization parameters are optimized by the retrieval performance on the validation set; The cross-modal alignment adopts a CLIP-like architecture, specifically optimized for educational scenarios, and incorporates hard negative sample mining and online hard sample generation into the comparison loss function; the alignment loss function is: ； in, To align the loss function values, It is a logarithmic function. It is an exponential function. For similarity function, For visual sample vectors, For text sample vectors, For visual negative sample vectors, This is the negative sample vector of the text. To control the hyperparameters of similarity distribution, Summing for negative samples.

5. The large-scale university data management model platform based on vector database and retrieval enhancement as described in claim 1, characterized in that, The intelligent vector retrieval system includes a neural retrieval module and a learnable index module; The neural retrieval module specifically comprises: a query encoder and a document encoder using the RoBERTa-Large architecture trained on large-scale educational question-and-answer data; a hybrid retrieval strategy for initial retrieval: dense retrieval using SimCSE and E5-Large models, sparse retrieval based on SPLADE technology, and graph neural network retrieval enhancing semantic association through knowledge graphs, with the three weight parameters adaptively adjusted through Bayesian optimization; and a hybrid architecture of Dense Passage Retrieval and ColBERT for further retrieval, with Dense Passage Retrieval being an improved version incorporating Cross-Encoder reordering and Hard Negative Mining techniques. The learnable index module adopts a Transformer-based sequence modeling method, and the index structure and index path are optimized through end-to-end training. The index structure adopts a hierarchical design, with coarse-grained clustering at the top level and fine-grained indexing at the bottom level. Each layer uses an independent Transformer model for learning. Dynamic index updates adopt incremental learning technology. New data only updates the relevant index nodes. The update strategy is based on data importance and access frequency, prioritizing the updating of high-value index parts.

6. The large-scale university data management model platform based on vector database and retrieval enhancement as described in claim 5, characterized in that, The intelligent vector retrieval system employs retrieval optimization technology, specifically: Caching strategy: A three-level cache design is adopted, with L1 caching hot query results, L2 caching vector representations, and L3 caching the original document; Query understanding module: It adopts T5-based query rewriting technology, and the rewriting strategy includes synonym replacement, concept expansion and relationship supplementation; Vector compression and acceleration: By combining Product Quantization and ScaNN technologies, storage overhead is reduced to 1 / 16 of the original, and retrieval acceleration is achieved through GPU parallel computing and batch processing optimization; Results re-ranking: The initial search results are finely ranked using a BERT-based Cross-Encoder model; the ranking model considers deep semantic matching between the query and the document, authority, and timeliness.

7. The large-scale university data management platform based on vector database and retrieval enhancement as described in claim 1, characterized in that, The intelligent education knowledge engine includes a dynamic knowledge graph comprising: Entity recognition: Entity extraction is performed using GPT-4-based Few-shot Learning technology in conjunction with Prompt templates. Entity types include courses, knowledge points, skills, evaluation criteria, and learning resources. Relation extraction: It integrates zero-shot and few-shot learning methods and uses prior knowledge to identify semantic relations between entities. Semantic relations include hierarchical relations, dependency relations, and evaluation relations. Knowledge verification mechanism: It adopts a combination of automatic verification and expert review. Automatic verification is based on consistency checks and common sense reasoning, while manual verification is achieved through annotation by domain experts. Dynamic update capability: Based on incremental learning and continuous learning technologies, it supports real-time knowledge updates and version management; The educational ontology modeling includes: Ontology design: Based on OWL and RDF standards, the core concept system covers subject classification, competency framework, and evaluation system, forming a multi-level, standardized educational knowledge framework; Conceptual Relationships and Inference Rules: Complex semantic relationships are expressed using the OWL language, and inference rules are designed based on a descriptive logic framework; Ontology evolution management: Version control and change tracking technologies are used to enable incremental updates of the ontology and query historical versions. Combined with change propagation algorithms, the consistency of related concepts and relationships is ensured when the ontology is updated.

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