Multi-source data federation governance method and system for vocational education

Abstract

The present application relates to a data processing method and system, in particular to a vocational education multi-source data federal governance method and system, belonging to the field of vocational education informatization and big data technology, which first acquires and preprocesses vocational education multi-source data through network crawler and OCR technology; then constructs a knowledge graph in the form of triplets and maps it to a multi-dimensional tensor space; then evaluates the compatibility of multi-source data through topological feature analysis, and performs feature fusion that maintains topological invariants; finally, based on a quantum probability field model, knowledge reasoning and services are provided, the present application uses tensor representation to improve the expression ability of complex relationships, ensures the quality of heterogeneous data fusion through topological persistence, and introduces quantum probability theory to handle uncertainty in knowledge services, effectively solving the problems of multi-source heterogeneous vocational education data, complex relationships and insufficient service accuracy, and providing a comprehensive data federal governance solution for the vocational education field.

Description

TECHNICAL FIELD

[0001] The present application relates to a data processing method and system, specifically to a vocational education multi-source data federal governance method and system, belonging to the field of vocational education informatization and big data technology. BACKGROUND

[0002] Vocational education data has the characteristics of multi-source, heterogeneity and dynamics, and is distributed among schools, enterprises and platforms, forming many data islands. These data include student information, course materials, teaching plans, internships, employment information and other types, with different formats, making it difficult to effectively integrate and utilize.

[0003] The existing data governance method mainly adopts centralized architecture such as data warehouse or data lake, which requires copying data from the source system to the central storage, increasing the cost of data storage and bringing problems such as data consistency and privacy protection. In addition, the traditional knowledge graph construction method is mainly based on triple structure, with limited expression ability, making it difficult to capture the complex multi-dimensional relationships in vocational education data.

[0004] With the development of artificial intelligence and big data technology, new technologies such as federal learning and knowledge graph have emerged, but the application of these technologies in the vocational education scene still faces many challenges: first, the effective integration of heterogeneous data, second, the expression of complex relationships, and third, personalized knowledge services. Therefore, there is an urgent need for a vocational education data federal governance method that can effectively integrate multi-source heterogeneous data, mine complex relationships and provide accurate services. SUMMARY

[0005] The purpose of the present application is to provide a vocational education multi-source data federal governance method and system, aiming to solve the problems of multi-source heterogeneous data, complex relationships and insufficient service accuracy in vocational education, and to realize the maximum mining and utilization of data value.

[0006] The present application proposes a vocational education multi-source data federal governance method, including:

[0007] Obtain vocational education multi-source heterogeneous data and perform data preprocessing, including:

[0008] Obtain authorized Internet data and vocational education data in paper documents through web crawlers and OCR technology, and perform natural language processing and structured extraction on unstructured documents;

[0009] Clean, denoise, structure and link the obtained data, and generate standard format data;

[0010] Construct a tensor topology knowledge graph, including:

[0011] Based on the standard format data, an initial knowledge graph in the form of triples is constructed, with nodes representing entities and edges representing binary relationships between entities.

[0012] The initial knowledge graph is mapped to a multi-dimensional tensor space to form a tensor network representation, where entities are represented as tensor nodes and relationships are represented as tensor edges.

[0013] Multi-scale topological analysis is performed on the tensor network to extract topological feature descriptors.

[0014] Multi-source data feature fusion is performed, including:

[0015] Based on the topological feature descriptors, the feature compatibility of the multi-source data is evaluated, and the feature fusion strategy is determined.

[0016] According to the feature fusion strategy, a feature fusion operation that preserves the topological invariants is performed to generate a fused feature representation.

[0017] A quantum probability field-driven knowledge service is provided, including:

[0018] Based on the fused feature representation, a quantum probability field model is constructed, and a user query is mapped to a quantum initial state.

[0019] According to the quantum evolution rules, knowledge reasoning is performed to obtain a probability distribution of the reasoning results.

[0020] Based on the reasoning results, personalized knowledge responses are generated to provide search, discovery, and push services for users.

[0021] As an option, the multi-source heterogeneous data of vocational education is acquired and preprocessed, specifically including:

[0022] Configure data source collection components according to data source types, select network crawlers, OCR recognition, database interfaces, or ETL tools;

[0023] Quality assessment of collected data, including integrity, accuracy, and consistency checks;

[0024] Remove incorrect data, outliers, and redundant information through data cleaners;

[0025] Unify field naming and data formats of different data sources through data structure regularizers;

[0026] Filter out interference information through data denoising to improve data quality;

[0027] Identify and link different representations of the same entity through entity linker to establish a unique entity identifier.

[0028] As an option, the tensor topological knowledge graph is constructed, specifically including:

[0029] Identify core entities in the education sector from standard format data, including institutions, teachers, students, courses, assignments, and practical training information;

[0030] Extract the explicit and implicit relationships between entities to form a relationship set;

[0031] Construct an initial knowledge graph with triples as the basic unit, where the triples are in the form of <entity, relation, entity>.

[0032] Define a multidimensional feature space, including the basic attribute dimension, relation feature dimension, spatiotemporal context dimension, and knowledge semantic dimension;

[0033] Map entities in the knowledge graph to multidimensional tensor nodes in the feature space;

[0034] Mapping relationships between entities to tensor transformation operators in feature space;

[0035] Integrate spatiotemporal context information to form a complete tensor network representation.

[0036] Preferably, the multi-scale topology analysis of the tensor network specifically includes:

[0037] Based on semantic similarity in the education field, a distance metric function for tensor networks is constructed.

[0038] Generate multi-scale filter sequences to capture topological features at different granularities;

[0039] Simple complexes are constructed at each scale to form a filtered complex sequence;

[0040] Calculate the persistent homology groups for each dimension to obtain topological invariants;

[0041] Generate persistent barcodes and visualize the lifecycle of topological features;

[0042] Extract connected components, cycle structures, and hole features to form a topological feature descriptor;

[0043] Calculate the stability index of topological features to evaluate feature reliability.

[0044] Preferably, the multi-source data feature fusion process specifically includes:

[0045] Calculate the similarity matrix of topological features of multi-source data;

[0046] Analysis of structural compatibility of multi-source data based on topological invariants;

[0047] Based on feature similarity and structural compatibility, features are divided into a high compatibility group and a low compatibility group.

[0048] For high compatibility feature groups, perform connection-type fusion, preserving common topologies;

[0049] For low compatibility feature groups, perform selection-type fusion, preserving dominant topological features;

[0050] Quality assessment of fusion results, verifying the preservation of topological invariants;

[0051] Adjust fusion parameters, iteratively optimize fusion results.

[0052] As a preferred, the constructed quantum probability field model specifically includes:

[0053] Define a Hilbert space representing the knowledge state;

[0054] Map the fusion feature representation to a quantum state vector;

[0055] Map the knowledge relationship to a quantum evolution operator;

[0056] Construct a quantum superposition state representing the possibility of multi-path reasoning;

[0057] Design quantum state evolution rules to simulate the knowledge reasoning process;

[0058] Determine the quantum measurement strategy for obtaining the reasoning result;

[0059] Establish the mapping relationship between the quantum measurement result and the knowledge representation.

[0060] As a preferred, the quantum probability field driven knowledge service specifically includes:

[0061] Receive and parse user queries, extract query intent and key elements;

[0062] Map the query to the initial state in the quantum probability field;

[0063] Adjust the quantum state evolution parameters based on user historical behavior and interest preferences;

[0064] Perform quantum evolution to generate reasoning results containing multi-path possibilities;

[0065] Generate a set of knowledge response candidates according to the reasoning result probability distribution;

[0066] Optimize the content and form of knowledge response based on user context and acceptance ability;

[0067] Provide comprehensive knowledge services including search services, knowledge discovery services and personalized push services.

[0068] As a preferred, the search service specifically includes:

[0069] Based on the keyword query corresponding knowledge graph node data;

[0070] Get the neighbor node information of the entity, and show the associated knowledge network;

[0071] Calculate the similarity between the nodes in the query result set and the query node;

[0072] Based on the similarity ranking, generate a search result recommendation list;

[0073] According to the user feedback, dynamically adjust the similarity calculation parameters, and optimize the search results.

[0074] As preferred, the provision of knowledge discovery services and knowledge push services, specifically includes:

[0075] Through cluster analysis, identify the implicit association patterns in the knowledge graph;

[0076] Based on the node centrality and edge weight, identify the key knowledge nodes and key relationships;

[0077] Analyze the knowledge evolution trend and predict the potential knowledge development direction;

[0078] Build a user interest portrait, including knowledge preference, learning style and ability level;

[0079] Based on the user interest portrait and knowledge association network, generate a personalized recommendation list;

[0080] According to the timeliness and relevance, optimize the content and timing of knowledge push;

[0081] Collect user interaction feedback and continuously optimize the push strategy and content.

[0082] The vocational education multi-source data federation governance system implementing the method, characterized by comprising:

[0083] Data acquisition module, for acquiring vocational education multi-source heterogeneous data through network crawler, OCR technology and database interface;

[0084] Data preprocessing module, for cleaning, denoising, structure regularization and entity linking of the acquired data;

[0085] Knowledge graph construction module, for constructing an initial knowledge graph in the form of triples;

[0086] Tensor representation module, for mapping the knowledge graph to a multi-dimensional tensor space to form a tensor network representation;

[0087] Topology analysis module, for multi-scale topology analysis of the tensor network to extract topological feature descriptors;

[0088] A feature fusion module is configured to perform multi-source data feature fusion that maintains topological invariants.

[0089] A quantum reasoning module is configured to construct a quantum probability field model and perform knowledge reasoning.

[0090] A knowledge service module is configured to provide search, knowledge discovery and push services based on reasoning results.

[0091] An application interface module is configured to receive user requests and return system responses.

[0092] The present application adopts tensor topological knowledge graph fusion technology, unifies multi-source heterogeneous data through a knowledge graph, upgrades it to a tensor network representation, realizes efficient fusion through topological feature analysis, and provides accurate services using quantum probability field driven knowledge reasoning, and has the following beneficial effects:

[0093] 1. Efficient integration of multi-source heterogeneous data is realized. Through various collection methods such as OCR technology and web crawlers, combined with standardized data preprocessing, job education data scattered in different systems is effectively integrated, breaking down data silos.

[0094] 2. The expression ability of complex relationships is improved. Using multi-dimensional tensor space representation, compared with traditional triple knowledge graphs, more complex multi-dimensional relationships can be expressed, especially suitable for the complex knowledge structure of theory and practice in the job education scene.

[0095] 3. The fusion quality of heterogeneous data is enhanced. Based on the topological persistence feature fusion mechanism, the focus is on the essential structural features of data rather than surface characteristics, effectively fusing heterogeneous data while maintaining data topological invariants.

[0096] 4. The accuracy of knowledge services is optimized. Quantum probability field driven knowledge reasoning and service mechanism can handle the uncertainty and multiple path possibilities in the education scene, providing more personalized knowledge services for users.

[0097] 5. Support for data federation governance. Under the premise of protecting data security and privacy, data value sharing is realized through a federal governance mechanism, promoting school-enterprise cooperation and integration of production and education. BRIEF DESCRIPTION OF DRAWINGS

[0098] Figure 1 A flowchart of the job education multi-source data federation governance method of the present application;

[0099] Figure 2 A structural diagram of the data preprocessing module in the present application;

[0100] Figure 3 A schematic diagram of the tensor topological knowledge graph construction in the present application;

[0101] Figure 4 A topological feature extraction and fusion flowchart in the present application;

[0102] Figure 5 A quantum probability field reasoning model schematic diagram in the present application;

[0103] Figure 6 A structural block diagram of the vocational education multi-source data federal governance system of the present application. DETAILED DESCRIPTION

[0104] Please refer to Figure 1 - Figure 6 The present application will be further described in detail below in combination with the drawings and examples.

[0105] As Figure 1 shown, the present application provides a vocational education multi-source data federal governance method, which comprises obtaining vocational education multi-source heterogeneous data and performing data preprocessing, constructing a tensor topological knowledge graph, performing multi-source data feature fusion, and providing quantum probability field driven knowledge service.

[0106] The acquisition and preprocessing of vocational education multi-source heterogeneous data is the basic link of the entire system. In an embodiment of the present application, as Figure 2 shown, first, the original data is acquired through various collection methods, and then a series of preprocessing operations are performed to convert the heterogeneous data into a standard format.

[0107] Specifically, the data acquisition step includes: obtaining authorized vocational education related information on the Internet, such as course introduction, teaching plan, industry standard, etc., through web crawler; recognizing paper teaching plan, student file and transcript, etc. documents through OCR technology, and extracting the text information therein; directly obtaining structured data in school educational administration system and enterprise training system through database interface. Taking the web crawler as an example, the system adopts a distributed crawler architecture, configures the crawling depth to be 3-5 layers, and the crawling frequency to be 1-3 times per second, so as to avoid causing excessive pressure on the target website; natural language processing and structured extraction are performed on unstructured documents.

[0108] The data preprocessing step includes data cleaning, noise reduction, structure regularization and entity linking. Data cleaning mainly solves the problems of error value, missing value and abnormal value in the data. For example, for student performance data, the system will detect and correct the performance value that exceeds the normal range (0-100 points), and estimate and fill in the missing performance data according to the student's performance in similar courses. Noise reduction processing is mainly aimed at the noise in the OCR recognition result, which improves the recognition accuracy from the original 85% to more than 95% through morphological operation and adaptive threshold filtering.

[0109] Structural standardization is a key step in addressing inconsistencies in the formats of multi-source data. In this embodiment, the system defines a unified data model, including entity types (such as students, teachers, courses, etc.) and attribute sets (such as ID, name, time, etc.). For similar data from different sources, the system converts them into a unified model using attribute mapping rules. For example, course information from different schools may use different field names (such as course name / course title / "CourseName"), and the system will uniformly map these fields to the standard field "Course Name".

[0110] Entity linking addresses the issue of inconsistent representations of the same entity across different data sources. The system employs an attribute-similarity-based entity matching algorithm. First, it calculates the entity attribute vectors, then uses cosine similarity to measure the similarity between entities. When the similarity exceeds a preset threshold (preferably 0.85), the entities are identified as the same entity and a link is established. For complex cases, the system also incorporates entity relationship networks for auxiliary judgment, improving link accuracy.

[0111] After preprocessing, the system converts all data into a standardized JSON format, laying the foundation for subsequent knowledge graph construction. JSON format was chosen because of its lightweight, easy-to-read, easy-to-write, and easy-to-parse characteristics, making it suitable as a data exchange format between different modules.

[0112] like Figure 3 As shown, the construction of tensor topological knowledge graph is one of the core innovations of this invention, which includes three key steps: initial knowledge graph construction, tensor network representation, and topological feature extraction.

[0113] First, an initial knowledge graph is constructed based on the preprocessed data in a standard format. The system extracts entities and relationships from the data, forming sets of triples. In the vocational education scenario, typical entities include institutions (schools, enterprises), personnel (students, teachers), and content (courses, knowledge points); relationships include teaching (teacher-course), learning (student-course), and inclusion (course-knowledge point). Through entity relationship mining, the system not only identifies explicit relationships in the data but also discovers implicit relationships through text analysis and statistical inference. For example, by analyzing the similarity of course content, knowledge connections between courses can be discovered; by analyzing student-teacher interaction data, the strength of teacher-student relationships can be inferred.

[0114] Secondly, the initial knowledge graph is mapped to a multidimensional tensor space to form a tensor network representation. This step represents a leap from a planar knowledge graph to a high-dimensional representation, enabling the system to express more complex multidimensional relationships. Specifically, the system first defines a multidimensional feature space, including entity feature dimensions (attributes, types), relationship feature dimensions (types, strengths), time dimensions (creation time, update time), and spatial dimensions (physical location, virtual environment). Then, each entity in the graph is represented as a tensor node in this space, and relationships are represented as tensor edges connecting the nodes.

[0115] For example, the tensor representation of a course entity can contain information in multiple dimensions, such as course attributes (name, credits, difficulty, etc.), time attributes (semester offered, duration), and spatial attributes (teaching location, online platform). Compared to traditional planar graphs, this multidimensional representation can capture the characteristics and environment of the entity more comprehensively.

[0116] Finally, multi-scale topological analysis is performed on the tensor network to extract topological feature descriptors. Topological data analysis focuses on the shape and structural features of the data, enabling the discovery of essential patterns within the data. The system first defines a distance metric function based on the similarity between tensors, then generates a multi-scale filter sequence, constructs simplicial complexes at different scales, and computes persistent homology groups.

[0117] In practice, the system uses the following distance metric function to calculate the similarity between entities:

[0118] ,

[0119] in, Representing entities and entity Distance metric between and These represent two entities in the knowledge graph. Representing entities In the Feature values ​​in each feature dimension Representing entities In the Feature values ​​in each feature dimension Indicates the first The weight coefficients for each feature dimension are used to adjust the importance of different features in distance calculation. This represents the total number of features. The formula is essentially a weighted Euclidean distance, used to quantify the distance between two entities in a multidimensional feature space; the smaller the distance value, the higher the similarity between the entities. Based on this distance function, the system constructs a simplicial complex sequence:

[0120] ,

[0121] in, Indicates the distance threshold The simple complex constructed below, Represent a The simplex, by Composed of vertices, Represents vertices and The distance between them It is a preset distance threshold, which is set if and only if the distance between any two vertices does not exceed the threshold. Only when these vertices form a simplex can this be achieved by adjusting the threshold from small to large. This forms a series of nested simplex complexes for subsequent persistent cohomology analysis. The system typically selects 10-20 uniformly distributed thresholds, covering the range from the minimum effective distance to the maximum effective distance.

[0122] By calculating the homology groups of these simplicial complexes, the system obtains persistent homology information, reflecting the topological characteristics of the data at different scales. These features are encoded as persistent barcodes, visually displaying the birth and death of topological features. The system focuses on bars with long durations, which represent stable structural features in the data.

[0123] Finally, the system extracts topological feature descriptors, including connected components (reflecting independent substructures), cyclic structures (reflecting cyclic dependencies), and hole features (reflecting missing information), laying the foundation for subsequent feature fusion.

[0124] like Figure 4 As shown, feature fusion of multi-source data is a key step in solving the data silo problem. This invention innovatively adopts a feature fusion mechanism based on topological persistence to ensure that the essential structural features of the data are preserved during the fusion process.

[0125] First, the system calculates a similarity matrix for the topological features of multi-source data. For topological feature descriptors extracted from different data sources, the system calculates the similarity between them. The similarity calculation uses Wasserstein distance (also known as bulldozer distance), a metric suitable for comparing persistent barcodes.

[0126] .

[0127] in, For barcodes and Wasserstein distance between them; for and The set of all possible joint distributions; Indicates the infimum (minimum value); Point and The Euclidean distance between them; It is a power of the distance (usually 1 or 2); Indicates in joint distribution Lower point Infinite element; Integral Indicates in Spatial integral. This distance measures the minimum amount of work required to transform one barcode into another.

[0128] Secondly, based on topological invariant analysis of the structural compatibility of multi-source data, the system divides features into high-compatibility and low-compatibility groups. The high-compatibility group refers to feature groups with similar topological structures that can be directly fused; the low-compatibility group consists of feature groups with significant structural differences that require special processing. Preferably, the system uses a similarity threshold of 0.7 as the division criterion: features with a similarity greater than 0.7 are classified into the high-compatibility group, and those with a similarity less than 0.7 are classified into the low-compatibility group. This threshold is determined based on a large amount of experimental data, achieving a balance between maintaining data structural integrity and allowing for appropriate fusion.

[0129] Then, the system employs different fusion strategies for different compatibility groups. For highly compatible feature groups, connectivity-based fusion is performed to preserve common topological structures. Specifically, the system identifies common structures among different features and fuses them based on these structures to ensure the preservation of key topological features. For low-compatibility feature groups, selective fusion is performed, retaining dominant topological features according to their importance to avoid structural distortion caused by forced fusion.

[0130] For example, when integrating course evaluation data from schools and enterprises, if the evaluation dimensions and standards of the two are similar (high compatibility), the system will retain the common evaluation dimensions and weight relationships; if the evaluation systems are significantly different (low compatibility), the system will select the more representative evaluation system as the dominant one based on factors such as the completeness, timeliness, and applicability of the evaluation data.

[0131] Finally, the system performs a quality assessment of the fusion results to verify the preservation of topological invariants. Assessment metrics include information retention rate, structural consistency, and anomaly detection rate.

[0132] The formula for calculating information retention rate is:

[0133]

[0134] in, Indicates information retention rate. Indicates the first A feature set of source data Represents the fused feature set Represents the feature set The amount of information contained (calculated via feature entropy) This indicates the number of data sources. This metric measures the degree to which original information is preserved during satellite fusion; a higher value indicates less information loss.

[0135] The formula for calculating structural consistency is:

[0136] ,

[0137] in, Indicates structural consistency. Indicates the first The topology of the source data (represented by persistent barcodes), This represents the merged topology. The similarity between two topologies is expressed by a normalized Wasserstein distance: ,in yes and Wasserstein distance, (the largest Wasserstein distance observed in the dataset) This indicates the number of data sources. This metric measures the degree of consistency between the fused result and the original data in terms of topology; a higher value indicates better preservation of the structure.

[0138] The formula for calculating the anomaly detection rate is:

[0139]

[0140] in, Indicates the anomaly detection rate. This represents the set of abnormal features detected in the fusion result. This represents the complete set of features after fusion. Represents a set The number of elements. Anomaly detection is achieved through outliers in the statistical feature distribution. The Local Anomaly Factor (LOF) algorithm is used to calculate the anomaly score for each feature point. Anomalies are identified when the score exceeds a preset threshold (usually 2.0). This metric measures the proportion of anomalies in the fusion result; a lower value indicates higher fusion quality.

[0141] The system requires an information retention rate of no less than 90%, a structural consistency of no less than 85%, and an anomaly detection rate of no more than 5%. If the evaluation results are unsatisfactory, the system will adjust the fusion parameters and iteratively optimize until the preset quality standards are met.

[0142] like Figure 5As shown, quantum probability field-driven knowledge service is another innovation of this invention. By introducing quantum probability theory, the system is able to handle the uncertainty in knowledge reasoning and provide more intelligent and personalized services.

[0143] First, the system constructs a quantum probability field model. A Hilbert space representing knowledge states is defined, mapping fused feature representations to quantum state vectors and knowledge relations to quantum evolution operators. In this embodiment, the system employs a finite-dimensional Hilbert space, the dimension of which depends on the type and number of knowledge entities. Typically, a medium-sized vocational education knowledge base (containing approximately 1000 entities and 5000 relations) corresponds to a Hilbert space dimension of approximately 100-200, which achieves good representational performance within the limits of computational resources.

[0144] The quantum state vector is represented as follows:

[0145] ,

[0146] in, Let be a quantum state vector, representing the state of the system; These basic knowledge states (corresponding to individual entities or relations) form an orthogonal basis of the Hilbert space. The corresponding complex amplitude represents the state. The weights; The total number of basic states; summation. Indicates all A linear combination of fundamental states. Complex amplitudes must satisfy a normalization condition. ,in This represents the probability of obtaining the corresponding state through measurement.

[0147] Knowledge relationships are represented as quantum operators that act on quantum state vectors:

[0148] ,

[0149] in, For relationship The corresponding quantum operator; From state Transition to state The complex magnitude; From state to state Projection operator; summation Represents all state pairs Summation of matrices. It must satisfy the property of the element ... ,in express The conjugate transpose of . This represents the identity matrix. This constraint ensures that the normalization properties of quantum states remain unchanged during evolution.

[0150] Secondly, the system receives and parses user queries, extracts query intent and key elements, and maps the query to an initial state in a quantum probability field. For example, when a user searches for the match between CNC technology courses and corporate job requirements, the system identifies CNC technology courses and corporate job requirements as key entities, the match degree as the relationship type, and constructs the corresponding initial quantum state.

[0151] Then, the system adjusts the quantum state evolution parameters based on the user's historical behavior and interests. The system analyzes the user's past queries and interaction records to identify the user's key areas of interest and preferred information. For example, if it finds that the user is more interested in practical skills than theoretical knowledge, the system will increase the weight of states related to practical training content. This adjustment is achieved by modifying the transition amplitude in the quantum evolution operator:

[0152]

[0153] in, For the adjusted user-specific quantum operator; For standard relational operators; For adjustments based on user preferences, the preferred adjustment range is within ±0.2 to maintain the rationality of the overall evolution.

[0154] Next, the system performs quantum evolution, generating inference results that include multiple path possibilities:

[0155] ,

[0156] in, This represents the final quantum state after evolution. A user-specific quantum evolution operator; This is the initial quantum state. The final quantum state contains multiple possible inference outcomes and their probabilities.

[0157] In the process of quantum evolution, the system simultaneously considers the possibility of multiple reasoning paths, which is impossible for traditional deterministic reasoning. For example, when analyzing the matching degree between courses and positions, the system will simultaneously consider multiple paths such as direct correlation (direct correspondence between course content and job requirements) and indirect correlation (correspondence established through intermediate knowledge points or ability elements).

[0158] Finally, based on the probability distribution of the inference results, the system generates a candidate set of knowledge responses and optimizes the content and form of the knowledge responses according to the user's context and comprehension ability. The system provides three types of knowledge services: search service (based on keyword query), knowledge discovery service (exploring implicit connections), and knowledge push service (personalized recommendation).

[0159] In the search service, the system queries corresponding knowledge graph node data based on keywords, while simultaneously obtaining information about the entity's neighboring nodes to display the associated knowledge network. Query results are sorted by similarity to form a recommendation list. The system also collects user feedback on search results, dynamically adjusting similarity calculation parameters to optimize the future search experience.

[0160] In knowledge discovery services, the system identifies implicit association patterns in knowledge graphs through cluster analysis. Preferably, the system employs a spectrum-based clustering algorithm, first constructing a similarity matrix, then calculating its eigenvectors, and dividing nodes into different clusters based on these eigenvectors. By analyzing the relationships within and between clusters, the system can discover potential knowledge patterns and trends in the data. For example, the system might discover significant overlap in the curriculum systems of CNC technology and industrial robotics, providing data support for curriculum integration.

[0161] In the knowledge recommendation service, the system constructs user interest profiles, including knowledge preferences, learning styles, and ability levels, and generates personalized recommendation lists based on these profiles. The system employs a hybrid recommendation strategy, combining content recommendation (based on content similarity) and collaborative filtering (based on user behavior similarity) to improve the accuracy and diversity of recommendations. Preferably, the system performs a weighted calculation based on the timeliness and relevance of the recommended content.

[0162] ,

[0163] in, For content For users Recommended score; For content With users Relevance of interest (normalized value in the range of 0-1); For content The timeliness (usually a normalized value calculated based on the publication time, with higher scores for more recent publications); For content With the already recommended list The diversity (usually calculated by the average distance to already recommended content); , , The weighting parameters control the importance of the three factors to satisfy... The weight parameters are typically set to... , , It can be dynamically adjusted according to the specific scenario.

[0164] like Figure 6As shown, the present invention also provides a vocational education multi-source data federated governance system for implementing the above method, including a data acquisition module 1, a data preprocessing module 2, a knowledge graph construction module 3, a tensor representation module 4, a topology analysis module 5, a feature fusion module 6, a quantum reasoning module 7, a knowledge service module 8, and an application interface module 9.

[0165] Data acquisition module 1 is used to acquire multi-source heterogeneous vocational education data through various methods. This module includes a web crawler component 11, an OCR recognition component 12, a database interface component 13, and an ETL tool component 14.

[0166] The web crawler component 11 is responsible for crawling vocational education-related information from the internet. In this embodiment, the component adopts a distributed architecture and supports customized configurations for different websites, including crawling depth, frequency, and content filtering rules. For example, for authoritative information sources such as official websites of education departments, the system will set a higher crawling priority and more comprehensive content coverage; for unstructured information sources such as industry forums, it will focus on crawling specific sections and topics.

[0167] The OCR recognition component 12 is used to process paper documents, including lesson plans, exam papers, and student records. This component integrates image preprocessing, text recognition, and layout analysis functions, and can adapt to document images of different qualities and formats. Preferably, the system uses a deep learning model for text recognition, with special optimizations for professional terminology and symbols in the vocational education field, achieving a recognition accuracy rate of over 95%.

[0168] Database interface component 13 provides connectivity to various relational databases, supporting SQL queries and data export. This component comes pre-installed with interface adapters for common academic affairs systems and practical training management systems, enabling rapid connection establishment and extraction of structured data.

[0169] ETL tool component 14 is responsible for data extraction, transformation, and loading, handling complex data migration tasks. This component supports scheduled task settings, which can automatically synchronize data updates according to a pre-defined plan.

[0170] The data preprocessing module 2 is used to clean, reduce noise, normalize the structure, and link entities in the acquired data to generate standard format data. This module includes a data cleaner 21, a data structure normalizer 22, a data noise reducer 23, and an entity linker 24.

[0171] Data cleaner 21 is responsible for handling erroneous, missing, and outlier values ​​in the data. Different cleaning strategies are employed for different data types. For example, for numerical data (such as grades and scores), the system detects outliers and corrects them based on statistical distribution; for text data, the system performs spell checking and format standardization. For missing values, the system uses methods such as mean / median imputation, similar record imputation, or predictive model imputation, depending on the data type.

[0172] The data structure regularizer 22 is responsible for standardizing field naming and data formats across different data sources. This component maintains a field mapping table containing different representations of common fields and their standard mappings. For example, a field representing a student's name might be named "student_name," "name," "stu_name," etc., in different systems. The system will uniformly map these different representations to the standard field "student_name."

[0173] The data denoising unit 23 primarily filters noise from unstructured data (such as text and images). For text data, the system removes meaningless punctuation, special characters, and stop words; for OCR recognition results, the system removes recognition noise through morphological operations and adaptive threshold filtering. In a typical vocational education document processing case, the text information extraction accuracy improved from the initial 78% to 96% through denoising.

[0174] Entity linker 24 is used to identify and link different representations referring to the same entity. This component first extracts key attributes of the entities, constructs feature vectors, and then calculates the similarity between entities. In this embodiment, the system uses a weighted Jaccard coefficient to calculate the similarity of text attributes:

[0175] ,

[0176] Where A and B represent the attribute sets of two entities, This represents the weight of attribute x. For different types of attributes, the system selects an appropriate similarity calculation method and weights multiple similarity scores to synthesize the final similarity. When the similarity exceeds a preset threshold (usually set to 0.85), the system determines that they are the same entity and establishes a link.

[0177] The knowledge graph construction module 3 is used to construct an initial knowledge graph in the form of triples. This module includes an entity extraction component 31, a relation recognition component 32, and a triple construction component 33.

[0178] The entity extraction component 31 identifies core entities from the preprocessed data. In vocational education scenarios, typical entity types include institutions (such as schools and enterprises), personnel (such as students and teachers), educational resources (such as courses and textbooks), knowledge points, and competency elements. The system employs a combination of domain dictionary and named entity recognition technology to accurately identify entities and their types in the text. Preferably, for structured data, the system directly extracts entity information from the data pattern; for unstructured data, it extracts entities using natural language processing technology.

[0179] The relationship identification component 32 is responsible for identifying various relationships between entities. This invention categorizes relationships into explicit and implicit relationships. Explicit relationships are those clearly marked in the data, such as student course registration and teacher instruction, which can be directly extracted from the data structure. Implicit relationships, on the other hand, require inference through data analysis, such as knowledge dependencies between courses and collaborative relationships between students. The system employs a combination of rule-based reasoning and statistical analysis to uncover implicit relationships. For example, by analyzing the similarity of course content, the system can infer knowledge connections between courses; by analyzing the co-occurrence patterns of student assignments and exams, the system can discover potential collaborative relationships between students.

[0180] The triplet construction component 33 organizes the extracted entities and relations into triples to construct an initial knowledge graph. Each triple is represented in the form of <subject, predicate, object>, such as <student A, elective, course B>, <course C, contains, knowledge point D>, etc. The system also adds temporal, spatial, and other contextual information to each triple to enhance the completeness of knowledge representation. Preferably, the system uses a graph database (such as Neo4j) to store the knowledge graph, supporting efficient graph structure querying and analysis.

[0181] Tensor representation module 4 is used to map the knowledge graph to a multidimensional tensor space to form a tensor network representation. This module includes a feature space definition component 41, an entity tensor quantization component 42, a relation tensor quantization component 43, and a tensor network construction component 44.

[0182] The feature space definition component 41 is responsible for designing the coordinate system of the multi-dimensional feature space. In this embodiment, the system defines a multi-dimensional feature space that includes entity attribute dimensions, relation feature dimensions, time dimensions, and spatial dimensions. For example, for a course entity, its feature space may contain 10 to 20 dimensions, covering various aspects such as basic course information (name, credits, etc.), content features (keywords, difficulty, etc.), and spatiotemporal attributes (semester, location).

[0183] The entity tensor component 42 maps entities in the knowledge graph to tensor nodes in the feature space. Specifically, each entity is represented as a multidimensional tensor, with each dimension of the tensor corresponding to a different coordinate axis in the feature space. For example, a course entity might be represented as a third-order tensor, with the three dimensions corresponding to attribute features, temporal features, and spatial features, respectively. Mathematically, an entity tensor can be represented as:

[0184] ,

[0185] in, Tensor representation of an entity; These are feature values ​​across multiple dimensions, with each index corresponding to a feature dimension.

[0186] The relation tensor component 43 represents relations in a knowledge graph as tensor transformation operators. In traditional knowledge graphs, relations are simply represented as connections between entities, while in tensor representation, relations are modeled as tensor transformations that can capture more complex interaction patterns. For example, a professor relation can be represented as a transformation operator that maps the teacher tensor to the course tensor:

[0187] ,

[0188] in, It is a tensor operator representing the professor-teacher relationship, which reflects the mapping relationship of how teacher characteristics affect course characteristics.

[0189] Tensor network building component 44 integrates tensor entities and relationships into a complete tensor network. This component first connects tensor nodes according to the knowledge graph's topology, then organizes the hierarchy according to the domain ontology, and finally integrates the sub-networks to form the complete tensor network. Preferably, the system also optimizes the network, including removing low-strength connections, merging highly similar nodes, and supplementing implicit transitive relationships to improve the network's quality and efficiency.

[0190] Topology analysis module 5 is used to perform multi-scale topology analysis on tensor networks and extract topological feature descriptors. This module includes a distance metric component 51, a complex construction component 52, a persistent homology computation component 53, and a feature extraction component 54.

[0191] The distance metric component 51 defines the similarity / distance function between tensors. Considering the characteristics of vocational education data, the system uses weighted Euclidean distance as the basic metric, and adjusts it in conjunction with domain knowledge. As mentioned earlier, the distance function is expressed as:

[0192] ,

[0193] in Representing entities and The distance between them and Indicates that they are in the first Values ​​in each feature dimension Indicates the first The weights of each feature, This represents the total number of features. In practical applications, the system typically determines the weights by combining expert experience and data analysis. For example, when analyzing course relevance, the weight of content relevance features is usually set to 0.5-0.7, while the weight of external features such as time and location is set to 0.2-0.3.

[0194] Complex construction component 52 is responsible for constructing simple complexes at multiple scales. The system selects a series of incremental distance thresholds. Construct nested simple complex sequences:

[0195] ,

[0196] in, Indicates at the threshold The simple complex constructed below contains all vertices with a distance not exceeding [a certain value]. The simplex. The threshold sequence typically covers the range from the minimum effective distance to the maximum effective distance, uniformly distributed on a logarithmic scale. In a typical vocational education data analysis case, the system selects 10–15 threshold points, covering a distance range from 0.1 to 1.0.

[0197] The persistent homology computation component 53 calculates the persistent homology groups of simplicial complex sequences. Persistent homology is a core tool in topological data analysis, capable of revealing the topological characteristics of data at different scales. The system calculates persistent homology groups for each dimension (typically 0, 1, or 2-dimensional), generating persistent barcodes to visualize the lifecycle of topological features. In the barcode, each bar represents a topological feature (such as a connected component, cyclic structure, or hole), and the length of the bar reflects the stability of the feature.

[0198] The feature extraction component 54 extracts topological feature descriptors from persistent cohomology results. The system focuses on three main types of topological features: connected components (0-dimensional cohomology), cyclic structures (1-dimensional cohomology), and void features (2-dimensional and higher cohomology). Connected components reflect the disjoint substructure of the data, such as knowledge clusters from different professional fields; cyclic structures reflect circular dependencies in the data, such as cyclic preconditions between courses; and void features may indicate information gaps in the knowledge system. The system calculates the number, size, and distribution of these features to form topological feature descriptors and evaluates their stability.

[0199] Feature fusion module 6 is used to perform multi-source data feature fusion while preserving topological invariants. This module includes a compatibility evaluation component 61, a fusion strategy generation component 62, and an execution control component 63.

[0200] The compatibility assessment component 61 first calculates the similarity matrix of topological features from the multi-source data. As mentioned earlier, the system uses Wasserstein distance to calculate the similarity between persistent barcodes:

[0201] ,

[0202] in, For barcodes and Wasserstein distance between them; for and The set of all possible joint distributions; inf is the infimum (minimum); For point and The Euclidean distance between them; The power of the distance (usually 1 or 2); For the point under the joint distribution γ Infinite element; Integral Indicates in Spatial integral. This distance measures the minimum amount of work required to transform one barcode into another. In practical calculations, systems typically use the case where p=1, i.e., the Wasserstein-1 distance (also known as the bulldozer distance), which has a more efficient calculation method.

[0203] Then, the component analyzes the structural compatibility of multi-source data based on topological invariants. The system focuses on topological invariants, such as the Betti number and Euler characteristic number, which reflect the essential topological structure of the data. When the topological invariants of two data sources are similar, it indicates that they have similar structural characteristics and are suitable for direct fusion.

[0204] The fusion strategy generation component 62 divides features and formulates fusion strategies based on the compatibility assessment results. The system divides features into high-compatibility groups and low-compatibility groups, and adopts connection-based fusion and selection-based fusion strategies respectively. Connection-based fusion preserves common topological structures and is suitable for high-compatibility data; selection-based fusion preserves dominant topological features and is suitable for low-compatibility data.

[0205] In practice, the system uses a compatibility threshold of 0.7 as the dividing standard. This threshold was determined based on extensive experimental verification and strikes a balance between maintaining the integrity of the data structure and allowing for appropriate fusion. Setting the threshold too high will excessively restrict fusion, while setting it too low may lead to inappropriate fusion operations that damage the data structure.

[0206] The execution control component 63 is responsible for performing the fusion operation and evaluating the quality of the results. For connection-based fusion, the system identifies common structures in different features as connection points to construct fusion features:

[0207] ,

[0208] in, These are the features resulting from the connection-based fusion. For the first Characteristics of a data source ; Number of data sources; For a common structure; This is the fusion function.

[0209] For selective fusion, the system selects the dominant feature based on the feature importance index:

[0210] ,

[0211] in, Features resulting from selective fusion; The most important feature is the one with the highest importance. Feature importance is determined by multiple factors, including data completeness, timeliness, and applicability. Systems typically weight these factors to form a comprehensive importance index.

[0212] After fusion, the system evaluates the quality of the results and verifies the preservation of key topological invariants. Evaluation metrics include information retention rate, structural consistency, and anomaly detection rate. If the evaluation results are unsatisfactory, the system adjusts the fusion parameters and iteratively optimizes until the preset quality standards are met (information retention rate ≥ 90%, structural consistency ≥ 85%, anomaly detection rate ≤ 5%).

[0213] The quantum reasoning module 7 is used to construct quantum probability field models and perform knowledge reasoning. This module includes a quantum model construction component 71, a query mapping component 72, a quantum evolution component 73, and a result measurement component 74.

[0214] The quantum model building component 71 is responsible for defining the knowledge state space and constructing quantum representations. The system first defines a finite-dimensional Hilbert space representing the knowledge state, then maps the fused feature representations to quantum state vectors, and maps knowledge relations to quantum evolution operators. As mentioned earlier, the quantum state vector is represented as:

[0215] ,

[0216] in, Let be a quantum state vector, representing the state of the system; These basic knowledge states (corresponding to individual entities or relations) form an orthogonal basis of the Hilbert space; The corresponding complex amplitude represents the state. The weights; The total number of basic states; summation. Indicates all A linear combination of fundamental states. Complex amplitudes must satisfy a normalization condition. ,in This represents the probability of obtaining the corresponding state through measurement.

[0217] Basic knowledge status The acquisition method involves vectorizing the features of nodes and edges in the knowledge graph. Specifically, the system first extracts all entities and relations from the knowledge graph to construct an entity-relation dictionary. Then, for each dictionary element... Define a unit vector As their corresponding fundamental knowledge states, these vectors constitute an orthonormal basis for the Hilbert space. For example, in In the Wichbert space, one can choose Only the first one One component is 1, and the rest are 0. Complex amplitude The method for obtaining this is achieved by mapping the fused features onto quantum states.

[0218] The system employs a relevance-based mapping method, with the following specific steps: First, calculate the relevance score between the query or current context and each basic knowledge state. Then, it is converted into a probability magnitude through normalization:

[0219] ,

[0220] in, The relevance score is calculated based on cosine similarity or other relevance measures. It is the phase angle, which can be set based on the relationship type or context information. Typically, to simplify calculations, let... ,Right now Take real numbers. This mapping method ensures the normalization condition of the quantum state. At the same time, it retains the correlation information of the original fusion features.

[0221] Knowledge relationships are represented as quantum operators:

[0222] ,

[0223] in, Indicates from state Transition to state The complex amplitude, Indicates from the ground state to ground state The projection operator, matrix It must satisfy the unitary property to ensure the normalization of quantum states. Operator elements This is determined by analyzing the strength and type of relationships between nodes in the knowledge graph. The specific calculation method is as follows: First, construct a relationship strength matrix. ,in Indicates from node To the node The strength of the relationship is then determined using matrix exponentiation or other unitization methods. Convert to unitary matrix ,make sure .

[0224] The query mapping component 72 converts user queries into quantum initial states. The system first parses the query, extracts key entities and relationships, and then maps them to quantum states in Hilbert space. For example, when a user queries the association between CNC technology and intelligent manufacturing, the system maps CNC technology and intelligent manufacturing to corresponding entity states, constructing an appropriate initial superposition state.

[0225] Quantum Evolution Component 73 performs knowledge reasoning based on quantum evolution rules. The system designs time evolution rules to simulate the propagation process of quantum states in the knowledge graph. Mathematically, this is expressed as:

[0226] ,

[0227] in, It is the system Hamiltonian, representing the structure and dynamic characteristics of the knowledge network. This refers to the evolution time parameter. In practical implementations, the system typically employs discrete-time evolution, approximating the continuous evolution process through iterative application of quantum gate operations. Preferably, the number of evolution steps is set to 10-20 steps, achieving a balance between computational efficiency and inference depth.

[0228] The result measurement component 74 performs quantum measurements to obtain inference results. The system designs appropriate measurement bases to measure the evolved quantum states, obtaining the probability distributions of various possible outcomes. These results reflect the possibilities of different knowledge paths, providing users with diverse inference results. The system also performs post-processing on the measurement results, filtering high-probability results and eliminating noise to form the final inference result set.

[0229] The knowledge service module 8 is used to provide search, knowledge discovery, and push services based on reasoning results. This module includes a search service component 81, a knowledge discovery service component 82, and a knowledge push service component 83.

[0230] Search service component 81 provides keyword-based knowledge graph query functionality. Users input keywords or entity IDs, and the system queries the corresponding knowledge graph node data and neighbor node information, displaying the associated knowledge network. Query results are sorted by relevance to form a recommendation list. The system supports multiple query modes, including exact search, fuzzy search, and semantic search, to meet the needs of different scenarios.

[0231] Preferably, the system uses an improved version of the PageRank algorithm to calculate node importance, as shown in the following formula:

[0232] ,

[0233] in, Represents a node PageRank value, This is the damping factor (usually set to 0.85). Indicates link to The node, Represents a node The number of outgoing chains, It is a link to The total number of nodes. The system will combine this importance metric with query relevance to calculate the final ranking score.

[0234] The knowledge discovery service component 82 is responsible for mining implicit relationship patterns in the knowledge graph. The system discovers important knowledge patterns in the data through methods such as cluster analysis, centrality analysis, and trend analysis. In cluster analysis, the system preferentially uses the spectral clustering algorithm. First, it constructs a node similarity matrix S, calculates its Laplacian matrix L=DS (where D is the degree matrix), and then calculates the eigenvectors of L. Based on these eigenvectors, the nodes are divided into different clusters. By analyzing the relationships within and between clusters, the system can discover implicit patterns in the data.

[0235] For example, in a curriculum analysis case of a vocational school, the system discovered cross-professional knowledge association groups through clustering, identified core competency modules shared by multiple majors, and provided data support for major development and curriculum optimization.

[0236] Centrality analysis is used to identify key nodes and relationships in knowledge networks. The system calculates various centrality metrics, such as degree centrality (number of connections), betweenness centrality (path mediator roles), and eigenvector centrality (considering neighbor importance). These metrics help identify core concepts and key relationships in a knowledge system.

[0237] Trend analysis identifies knowledge evolution trends by comparing knowledge graphs at different points in time. The system tracks changes in entities and relationships, including additions, deletions, and attribute changes, thereby inferring the development direction of domain knowledge.

[0238] The knowledge recommendation service component 83 provides personalized knowledge recommendation functionality. This component first constructs a user interest profile, including knowledge preferences, learning styles, and ability levels. The user profile is built based on historical interaction data and is dynamically updated as user behavior changes. Then, the system generates a personalized recommendation list based on the user profile and the knowledge association network.

[0239] The recommendation process employs a hybrid recommendation strategy, combining content recommendation and collaborative filtering:

[0240] ,

[0241] in, Content For users Recommended score, Relevance (value range 0-1) Indicates timeliness (value range 0-1), Indicates the list of recommended items Diversity (value range O-1), These are weight parameters that satisfy... In vocational education knowledge delivery, all three factors are important: relevance ensures that the recommended content is relevant to the user's major and interests; timeliness ensures that the content reflects the latest industry developments; and diversity prevents information silos and helps users broaden their knowledge. Systems are typically set up... It can be dynamically adjusted according to specific application scenarios. This recommendation strategy ensures the relevance of recommended content while also taking into account the timeliness and diversity of content, thus avoiding the information cocoon effect.

[0242] Relevance of content to user interests The calculation method is as follows:

[0243] ,

[0244] in, Content The feature vector is generated by extracting the topic, keywords, and semantic features of the content. Indicates user Interest vectors are generated by analyzing users' historical interaction records and explicit preferences. This represents the cosine similarity between two vectors, with values ​​ranging from -1 to 1. A higher value indicates a higher correlation. To ensure the calculated result is within the range of [0, 1], the cosine similarity is normalized in the actual implementation.

[0245] Timeliness of content The calculation method is as follows:

[0246] ,

[0247] in, Indicates the current timestamp. Content The publication or update timestamp, The time interval (usually in days) that represents the content. This is the timeliness decay coefficient, which controls the rate at which timeliness decays over time. It is usually set to 0.05-0.1 and adjusted according to the timeliness requirements of different types of content. This formula is an exponential decay function, where the timeliness of new content is close to 1 and gradually decreases over time, reflecting the characteristic that new content is usually more valuable.

[0248] Diversity of content and recommended lists The calculation method is as follows:

[0249] ,

[0250] in, This indicates a list of content that has been recommended to the user. This represents a content item in the list. Content With content The similarity is usually calculated using cosine similarity. Content The maximum similarity with any content in the recommended list. This formula measures the content's... The degree of difference from previously recommended content; a higher value indicates greater diversity, helping to avoid homogenization and providing more comprehensive knowledge coverage. Through a weighted combination of these three indicators, the system can ensure content relevance while also considering timeliness and diversity, providing users with more balanced and personalized knowledge recommendations. The system will also dynamically adjust the weight parameters based on user feedback to continuously optimize the recommendation effect.

[0251] In addition, the system will collect user feedback on recommended content, including clicks, dwell time, and collection behavior, to continuously optimize the recommendation algorithm and parameters and improve the accuracy of the recommendation service.

[0252] Application Interface Module 9 is used to receive user requests and return system responses, serving as the portal for the system's external services. This module includes API Gateway Component 91, Authentication and Authorization Component 92, Request Routing Component 93, and Response Formatting Component 94.

[0253] API Gateway Component 91 provides a unified interface entry point to handle all external requests. The system supports both RESTAPI and GraphQL interfaces; the former is suitable for simple data operations, while the latter is suitable for complex graph data queries. The interface design follows RESTful specifications and supports standard HTTP methods (GET, POST, PUT, DELETE, etc.) and status codes.

[0254] The authentication and authorization component 92 is responsible for authentication and access control. The system uses a JWT (JSON Web Token)-based authentication mechanism and supports multiple authentication methods, including username and password, third-party login, and API keys. Access control adopts the RBAC (Role-Based Access Control) model, assigning different operation permissions according to user roles.

[0255] The request routing component 93 forwards the request to the appropriate service module based on the request type and parameters. The system employs a service registration and discovery mechanism, supporting dynamic service expansion and load balancing. For complex requests, the system may need to call multiple service modules; the request routing component coordinates these calls to ensure complete request processing.

[0256] The response formatting component 94 converts the processing results into a response format conforming to the API specification and returns it to the client. The system supports multiple data formats, including JSON, XML, and CSV, and dynamically selects the response format based on the Accept parameter in the request header. For responses containing knowledge graph data, the system also supports a dedicated graphical visualization format for easy client display.

[0257] Through the collaborative work of the above modules, the vocational education multi-source data federated governance system of the present invention can effectively integrate vocational education multi-source data, construct high-dimensional knowledge representation, provide accurate knowledge services, and provide strong support for vocational education teaching, management and decision-making.

[0258] The technical solutions of the present invention are not limited to the above embodiments. Any non-substantial changes, modifications, additions or substitutions made by those skilled in the art based on the present invention shall be considered as the same technical solutions as the present invention.

Claims

1. A multi-source data federated governance method for vocational education, characterized in that: include: Acquire multi-source heterogeneous vocational education data and perform data preprocessing, including: We acquire authorized internet data and vocational education data from paper documents using web crawlers and OCR technology, and perform natural language processing and structure extraction on unstructured documents. The acquired data is cleaned, noise reduced, structurally regularized, and entity linked to generate standard format data; Constructing a tensor topological knowledge graph, including: Based on standard format data, an initial knowledge graph in the form of triples is constructed, with nodes in the knowledge graph representing entities and edges in the knowledge graph representing binary relations between entities. The initial knowledge graph is mapped to a multidimensional tensor space to form a tensor network representation, where entities are represented as tensor nodes and relations are represented as tensor edges; Perform multi-scale topology analysis on tensor networks to extract topological feature descriptors; The multi-scale topology analysis of tensor networks specifically includes: Based on semantic similarity in the education field, a distance metric function for tensor networks is constructed. Generate multi-scale filter sequences to capture topological features at different granularities; Simple complexes are constructed at each scale to form a filtered complex sequence; Calculate the persistent homology groups for each dimension to obtain topological invariants; Generate persistent barcodes and visualize the lifecycle of topological features; Extract connected components, cycle structures, and hole features to form a topological feature descriptor; Calculate the stability index of topological features and evaluate the reliability of the features; Performing feature fusion of multi-source data includes: Based on topological feature descriptors, the feature compatibility of multi-source data is evaluated, and feature fusion strategies are determined. Based on the feature fusion strategy, perform a feature fusion operation that preserves topological invariants to generate a fused feature representation; Provides quantum probability field-driven knowledge services, including: Based on the fusion feature representation, a quantum probability field model is constructed to map user queries to quantum initial states; the construction of the quantum probability field model specifically includes: Define a Hilbert space to represent knowledge states; The fused feature representation is mapped to a quantum state vector; Map knowledge relationships to quantum evolution operators; Construct quantum superposition states representing the possibilities of multi-path reasoning; Design quantum state evolution rules to simulate the knowledge reasoning process; Determine the quantum measurement strategy to obtain the inference results; Establish a mapping relationship between quantum measurement results and knowledge representation; According to the rules of quantum evolution, knowledge reasoning is performed to obtain the probability distribution of the reasoning results; Based on the reasoning results, personalized knowledge responses are generated to provide users with search, discovery, and push services.

2. The vocational education multi-source data federated governance method according to claim 1, characterized in that, The acquisition of multi-source heterogeneous vocational education data and the subsequent data preprocessing specifically include: Configure the data source acquisition components, and select web crawler, OCR recognition, database interface or ETL tool according to the data source type; Perform quality assessment on the collected data, including checks on completeness, accuracy, and consistency; Remove erroneous data, outliers, and redundant information using a data cleaner; The data structure regularizer unifies field naming and data format across different data sources; Data quality is improved by filtering out interference information through a data noise reduction device; By identifying and linking different representations of the same entity through an entity linker, a unique identifier for the entity is established.

3. The vocational education multi-source data federated governance method according to claim 1, characterized in that, The construction of the tensor topological knowledge graph specifically includes: Identify core entities in the education sector from standard format data, including institutions, teachers, students, courses, assignments, and practical training information; Extract the explicit and implicit relationships between entities to form a relationship set; Construct an initial knowledge graph with triples as the basic unit, where the triples are in the form of <entity, relation, entity>. Define a multidimensional feature space, including the basic attribute dimension, relation feature dimension, spatiotemporal context dimension, and knowledge semantic dimension; Map entities in the knowledge graph to multidimensional tensor nodes in the feature space; Mapping relationships between entities to tensor transformation operators in feature space; Integrate spatiotemporal context information to form a complete tensor network representation.

4. The vocational education multi-source data federated governance method according to claim 1, characterized in that, The process of performing multi-source data feature fusion specifically includes: Calculate the similarity matrix of topological features of multi-source data; Analysis of structural compatibility of multi-source data based on topological invariants; Based on feature similarity and structural compatibility, features are divided into a high compatibility group and a low compatibility group. For highly compatible feature groups, perform connectivity-based fusion to preserve common topology. For low-compatibility feature groups, selective fusion is performed to preserve the dominant topological features; The quality of the fusion results is evaluated to verify the preservation of topological invariants; Adjust the fusion parameters and iteratively optimize the fusion results.

5. The vocational education multi-source data federated governance method according to claim 1, characterized in that, The provision of quantum probability field-driven knowledge services specifically includes: Receive and parse user queries, extracting query intent and key elements; Map the query to the initial state in the quantum probability field; Adjust quantum state evolution parameters based on user history and interests; Perform quantum evolution to generate inference results that include multiple path possibilities; Generate a candidate set of knowledge responses based on the probability distribution of the reasoning results; Optimize the content and format of knowledge responses based on user context and comprehension ability; It provides comprehensive knowledge services, including search services, knowledge discovery services, and personalized push services.

6. The vocational education multi-source data federated governance method according to claim 5, characterized in that, The provision of search services specifically includes: Search for corresponding knowledge graph node data based on keywords; Obtain information about the neighboring nodes of an entity and display the associated knowledge network; Calculate the similarity between nodes in the query result set and the query node; Based on similarity ranking, a search result recommendation list is generated; Based on user feedback, we dynamically adjust the similarity calculation parameters to optimize search results.

7. The vocational education multi-source data federated governance method according to claim 6, characterized in that, The provision of knowledge discovery and knowledge push services specifically includes: Cluster analysis is used to identify implicit association patterns in knowledge graphs; Based on node centrality and edge weight, identify key knowledge nodes and key relationships; Analyze the trends in knowledge evolution and predict potential directions for knowledge development; Build user interest profiles, including knowledge preferences, learning styles, and ability levels; Based on user interest profiles and knowledge association networks, a personalized recommendation list is generated. Optimize the content and timing of knowledge delivery based on timeliness and relevance; Collect user interaction feedback to continuously optimize push strategies and content.

8. A vocational education multi-source data federated governance system implementing the method of any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire multi-source heterogeneous vocational education data through web crawlers, OCR technology, and database interfaces. The data preprocessing module is used to clean, reduce noise, regularize the structure, and link entities in the acquired data. The knowledge graph construction module is used to construct an initial knowledge graph in the form of triples. The tensor representation module is used to map knowledge graphs to multidimensional tensor spaces to form tensor network representations. The topology analysis module is used to perform multi-scale topology analysis on tensor networks and extract topology feature descriptors. The feature fusion module is used to perform feature fusion of multi-source data while preserving topological invariants; The quantum reasoning module is used to construct quantum probability field models and perform knowledge reasoning. The knowledge service module is used to provide search, knowledge discovery, and push services based on reasoning results; The application interface module is used to receive user requests and return system responses.

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