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

Abstract

The invention relates to a data processing method and system, in particular to a vocational education multi-source data federation treatment method and system, and belongs to the technical field of vocational education informatization and big data. Then constructing a knowledge graph in a triple form and mapping the knowledge graph to a multi-dimensional tensor space; secondly, multi-source data compatibility is analyzed and evaluated through topological features, and feature fusion maintaining topological invariants is executed; finally, knowledge reasoning and service are provided based on a quantum probability field model, the complex relation expression ability is improved through tensor representation, the heterogeneous data fusion quality is guaranteed through topology durability, the uncertainty in knowledge service is processed by introducing the quantum probability theory, and the reliability of the knowledge reasoning and service is improved. The problems of multi-source heterogeneous, complex relation, insufficient service accuracy and the like of vocational education data are effectively solved, and a comprehensive data federation governance solution is provided for the vocational education field.

Description

Technical Field

[0001] The present invention relates to a data processing method and system, and in particular to a method and system for federally managing multi-source data in vocational education, and belongs to the field of vocational education informatization and big data technology. Background Art

[0002] Vocational education data is multi-source, heterogeneous, and dynamic. Data is distributed across multiple entities, including schools, enterprises, and platforms, creating numerous data silos. This data includes student information, course materials, teaching plans, internships, training, employment information, and other diverse formats, making it difficult to effectively integrate and utilize.

[0003] Existing data governance approaches primarily utilize centralized architectures such as data warehouses or data lakes. This approach requires copying data from source systems to central storage, increasing data storage costs and raising issues such as data consistency and privacy. Furthermore, traditional knowledge graph construction methods, primarily based on triple structures, have limited expressive power and struggle to capture the complex, multidimensional relationships within vocational education data.

[0004] With the development of artificial intelligence and big data, new technologies such as federated learning and knowledge graphs have emerged. However, their application in vocational education still faces numerous challenges: first, the effective integration of heterogeneous data; second, the expression of complex relationships; and third, the provision of personalized knowledge services. Therefore, there is an urgent need for a federated governance approach for vocational education data that can effectively integrate multi-source heterogeneous data, mine complex relationships, and provide precise services. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for the federal governance of multi-source vocational education data, aiming to solve the problems of multi-source heterogeneity, complex relationships, and insufficient service accuracy of vocational education data, and to maximize the mining and utilization of data value.

[0006] The present invention proposes a method for federated governance of multi-source vocational education data, including: Acquire multi-source heterogeneous vocational education data and perform data preprocessing, including: Obtain authorized Internet data and vocational education data from paper documents through web crawlers and OCR technology, and perform natural language processing and structured extraction on unstructured documents; Clean, reduce noise, regularize the structure and link entities of the acquired data to generate data in a standard format; Construct a tensor topology knowledge graph, including: Based on 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. Map the initial knowledge graph 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; Perform multi-scale topological analysis on the tensor network and extract topological feature descriptors; Perform multi-source data feature fusion, including: Based on topological feature descriptors, the feature compatibility of multi-source data is evaluated and the feature fusion strategy is determined; According to the feature fusion strategy, a feature fusion operation that maintains topological invariants is performed to generate a fused feature representation; Provide knowledge services driven by quantum probability fields, including: Based on the fusion feature representation, a quantum probability field model is constructed to map user queries into quantum initial states; According to the quantum evolution rules, knowledge reasoning is performed to obtain the probability distribution of the reasoning results; Based on the inference results, personalized knowledge responses are generated to provide users with search, discovery and push services.

[0007] Preferably, the acquisition of multi-source heterogeneous vocational education data and data preprocessing specifically include: Configure the data source collection component and select web crawlers, OCR recognition, database interfaces, or ETL tools based on the data source type; Conduct quality assessment of collected data, including completeness, accuracy, and consistency checks; Remove erroneous data, outliers, and redundant information through data cleaners; Unify field naming and data formats of different data sources through data structure regularizer; Improve data quality by filtering out interference information through data noise reducer; The entity linker identifies and links different representations of the same entity to establish a unique entity identifier.

[0008] Preferably, the construction of the tensor topology knowledge graph specifically includes: Identify core entities in the education field from standard format data, including institutions, teachers, students, courses, assignments, and practical training information; Extract explicit and implicit relationships between entities to form a relationship set; Construct an initial knowledge graph with triples as the basic unit, and the triples are in the form of <entity, relationship, entity>; Define a multidimensional feature space, including basic attribute dimension, relationship feature dimension, spatiotemporal context dimension, and knowledge semantic dimension; Map entities in the knowledge graph into multi-dimensional tensor nodes in the feature space; Mapping the relationship between entities into tensor transformation operators in the feature space; Integrate spatiotemporal context information to form a complete tensor network representation.

[0009] Preferably, the multi-scale topological analysis of the tensor network specifically includes: Based on the semantic similarity in the education field, a distance metric function of the tensor network is constructed; Generate a multi-scale filter sequence to capture topological features of different granularities; Construct a simplicial complex at each scale to form a filter complex sequence; Calculate the persistent homology groups of each dimension and obtain topological invariants; Generate persistent barcodes to visualize the lifecycle of topological features; Extract connected components, cyclic structures and hole features to form topological feature descriptors; Calculate the stability index of topological features and evaluate the reliability of features.

[0010] Preferably, the performing of multi-source data feature fusion specifically includes: Calculate the similarity matrix of topological features of multi-source data; Analyze the structural compatibility of multi-source data based on topological invariants; Based on feature similarity and structural compatibility, the features were divided into high compatibility group and low compatibility group; For highly compatible feature groups, a connection-based fusion is performed to preserve the common topological structure; For low-compatibility feature groups, selective fusion is performed to retain the dominant topological features; Perform quality assessment on the fusion results to verify the preservation of topological invariants; Adjust the fusion parameters and iteratively optimize the fusion results.

[0011] Preferably, the constructing of the quantum probability field model specifically includes: Define a Hilbert space representing knowledge states; Mapping the fused feature representation into a quantum state vector; Mapping knowledge relations into quantum evolution operators; Constructing quantum superposition states that represent the possibility of multiple paths of reasoning; Design quantum state evolution rules to simulate the knowledge reasoning process; Determine quantum measurement strategies for obtaining inference results; Establish a mapping relationship between quantum measurement results and knowledge representation.

[0012] Preferably, the provision of knowledge services driven by quantum probability fields specifically includes: Receive and parse user queries, extract query intent and key elements; Map the query to an initial state in a quantum probability field; Adjust quantum state evolution parameters based on user historical behavior and interest preferences; Perform quantum evolution to generate inference results containing multiple path possibilities; Generate a candidate set of knowledge responses based on the probability distribution of the reasoning results; Optimize the content and form of knowledge responses based on user context and receptiveness; Provide comprehensive knowledge services including search services, knowledge discovery services and personalized push services.

[0013] Preferably, the provision of search services specifically includes: Query the corresponding knowledge graph node data based on keywords; Obtain the entity's neighbor node information and display the associated knowledge network; Calculate the similarity between the nodes in the query result set and the query node; Generate a recommended list of search results based on similarity sorting; Based on user feedback, similarity calculation parameters are dynamically adjusted to optimize search results.

[0014] Preferably, the provision of knowledge discovery services and knowledge push services specifically includes: Identify implicit association patterns in knowledge graphs through cluster analysis; Identify key knowledge nodes and key relationships based on node centrality and edge weights; Analyze knowledge evolution trends and predict potential knowledge development directions; Build user interest profiles, including knowledge preferences, learning styles, and ability levels; Generate personalized recommendation lists based on user interest profiles and knowledge association networks; Optimize the content and timing of knowledge push based on timeliness and relevance; Collect user interaction feedback and continuously optimize push strategies and content.

[0015] The vocational education multi-source data federation management system implementing the method is characterized by including: The data acquisition module is used to obtain multi-source heterogeneous data of vocational education through web crawlers, OCR technology and database interfaces; Data preprocessing module, used to clean, reduce noise, regularize structure and entity link the acquired data; The knowledge graph construction module is used to construct the initial knowledge graph in the form of triples; Tensor representation module, used to map the knowledge graph to a multi-dimensional tensor space to form a tensor network representation; Topology analysis module, used to perform multi-scale topological analysis on tensor networks and extract topological feature descriptors; Feature fusion module, used to perform multi-source data feature fusion while maintaining topological invariants; Quantum reasoning module, used to build quantum probability field models and perform knowledge reasoning; Knowledge service module, 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.

[0016] This paper adopts tensor topology knowledge graph fusion technology to unify the representation of multi-source heterogeneous data through knowledge graphs, and then upgrades it to tensor network representation. It achieves efficient fusion through topological feature analysis and provides precise services by using knowledge reasoning driven by quantum probability fields. It has the following beneficial effects: 1. Efficient integration of multi-source heterogeneous data. By leveraging various collection methods, including OCR technology and web crawlers, combined with standardized data pre-processing, vocational education data scattered across different systems can be effectively integrated, breaking down data silos.

[0017] 2. Improved ability to express complex relationships. Using a multidimensional tensor space representation, it can express more complex multidimensional relationships than traditional triple knowledge graphs, making it particularly suitable for the complex knowledge structure intertwined between theory and practice in vocational education scenarios.

[0018] 3. Enhanced heterogeneous data fusion quality. A feature fusion mechanism based on topological persistence focuses on the essential structural characteristics of the data rather than its surface features, achieving effective fusion of heterogeneous data while maintaining topological invariants.

[0019] 4. Optimized the accuracy of knowledge services. The knowledge reasoning and service mechanism driven by quantum probability fields can handle the uncertainty and multi-path possibilities in educational scenarios, providing users with more personalized knowledge services.

[0020] 5. Support data federation governance. Under the premise of protecting data security and privacy, realize data value sharing through the federation governance mechanism, promote school-enterprise collaboration and the integration of industry and education. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of the method for federal management of multi-source data in vocational education according to the present invention; Figure 2 Schematic diagram of the structure of the data preprocessing module in the present invention; Figure 3 A schematic diagram of the tensor topology knowledge graph constructed in the present invention; Figure 4 This is a flowchart of topological feature extraction and fusion in the present invention; Figure 5 Schematic diagram of the quantum probability field reasoning model in the present invention; Figure 6 This is a structural diagram of the vocational education multi-source data federation management system of the present invention. DETAILED DESCRIPTION

[0022] Please refer to Figure 1 - Figure 6 The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0023] like Figure 1 As shown, the present invention provides a method for federal governance of multi-source data in vocational education, including obtaining multi-source heterogeneous data in vocational education and performing data preprocessing, constructing a tensor topological knowledge graph, performing multi-source data feature fusion, and providing knowledge services driven by quantum probability fields.

[0024] The acquisition and preprocessing of multi-source heterogeneous data of vocational education is the basic link of the entire system. In one embodiment of the present invention, Figure 2 As shown, first, raw data is acquired through a variety of acquisition methods, and then a series of preprocessing operations are performed to convert the heterogeneous data into a standard format.

[0025] Specifically, the data acquisition process includes: using web crawlers to capture authorized vocational education-related information from the internet, such as course descriptions, teaching plans, and industry standards; using optical character recognition (OCR) technology to identify and extract text from paper documents such as lesson plans, student files, and transcripts; and directly accessing structured data from school academic systems and corporate training systems through database interfaces. For example, the web crawler system utilizes a distributed crawler architecture, configured to crawl 3-5 layers deep and 1-3 times per second to avoid excessive pressure on target websites. Natural language processing and structured data extraction are performed on unstructured documents.

[0026] Data preprocessing steps include data cleaning, noise reduction, structural regularization, and entity linking. Data cleaning primarily addresses errors, missing values, and outliers in the data. For example, for student performance data, the system detects and corrects scores outside the normal range (0-100 points). Missing scores are estimated and filled based on the student's performance in similar courses. Noise reduction primarily targets noise in OCR recognition results. Through morphological operations and adaptive threshold filtering, recognition accuracy is increased from the original 85% to over 95%.

[0027] Structuring data is crucial for addressing inconsistent data formats across multiple sources. In this example, the system defines a unified data model, including entity types (e.g., student, teacher, course, etc.) and attribute sets (e.g., ID, name, time, etc.). For similar data from different sources, the system converts it into a unified model using attribute mapping rules. For example, course information from different schools may use different field names (e.g., Course Name / Course Title / CourseName). The system maps these fields to the standard field "Course Name."

[0028] Entity linking addresses the problem of inconsistent representations of the same entity across different data sources. The system uses an entity matching algorithm based on attribute similarity. It first calculates entity attribute vectors and then measures the similarity between entities using cosine similarity. When the similarity exceeds a preset threshold (preferably set to 0.85), the entities are identified as identical and a link is established. In complex cases, the system also incorporates the entity relationship network for additional judgment, improving linking accuracy.

[0029] After preprocessing, the system converts all data into a standardized JSON format, laying the foundation for subsequent knowledge graph construction. JSON was chosen because it is lightweight, easy to read, write, and parse, making it suitable as a data exchange format between different modules.

[0030] like Figure 3 As shown, tensor topology knowledge graph construction is one of the core innovations of the present invention, which includes three key steps: initial knowledge graph construction, tensor network representation and topological feature extraction.

[0031] First, an initial knowledge graph is constructed based on the preprocessed standard format data. The system extracts entities and relationships from the data to form a set of triples. In vocational education scenarios, typical entities include institutions (schools, enterprises), personnel (students, teachers), content (courses, knowledge points), etc.; relationships include teaching (teacher-course), learning (student-course), inclusion (course-knowledge point), etc. Through entity relationship mining, the system not only identifies relationships explicitly expressed 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 the interaction data between teachers and students, the strength of the teacher-student relationship can be inferred.

[0032] Secondly, the initial knowledge graph is mapped to a multidimensional tensor space to form a tensor network representation. This step is a leap from a flat 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 (type, strength), time dimensions (creation time, update time), and spatial dimensions (physical location, virtual environment). Each entity in the graph is then represented as a tensor node in this space, and relationships are represented as tensor edges connecting the nodes.

[0033] For example, a tensor representation of a course entity can include information from multiple dimensions, including course attributes (name, credits, difficulty, etc.), temporal attributes (terms offered, duration), and spatial attributes (location, online platform). This multidimensional representation can more comprehensively capture the characteristics and environment of the entity than a traditional two-dimensional graph.

[0034] Finally, multiscale topological analysis is performed on the tensor network to extract topological feature descriptors. Topological data analysis focuses on the shape and structural characteristics of the data and can discover essential patterns in the data. The system first defines a distance metric function based on the similarity between tensors. It then generates a multiscale filter sequence, constructs simplicial complexes at different scales, and computes persistent homology groups.

[0035] In actual implementation, the system uses the following distance measurement function to calculate the similarity between entities: , in, Representing an entity and entities The distance metric between and Represent two entities in the knowledge graph, Representing an entity In the The eigenvalues ​​on the feature dimensions, Representing an entity In the The eigenvalues ​​on the feature dimensions, Indicates the The weight coefficient of each feature dimension is used to adjust the importance of different features in distance calculation. Represents the total number of features. This 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: , in, Indicates the distance threshold The simplicial complex constructed under Indicates a dimensional simplex, given by Vertices, Represents a vertex and The distance between Is the preset distance threshold, if and only if the distance between any two vertices does not exceed the threshold When these vertices can form a simplex. By adjusting the threshold from small to large ,forming a series of nested simplicial complexes for subsequent persistent homology analysis.,The system usually selects 10-20 evenly distributed thresholds,covering the range from the minimum effective distance to the maximum,effective distance.

[0036] By computing the homology groups of these simplicial complexes, the system obtains persistent homology information, reflecting the topological characteristics of the data at different scales. These characteristics are encoded as persistent barcodes, visually showing the birth and death of topological features. The system focuses on long-lasting bars, which represent stable structural features in the data.

[0037] Ultimately, 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.

[0038] like Figure 4 As shown, multi-source data feature fusion is a key step in solving the data island problem. The present invention innovatively adopts a feature fusion mechanism based on topological persistence to ensure that the essential structural characteristics of the data are maintained during the fusion process.

[0039] 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 the Wasserstein distance (also known as the bulldozer distance), which is a metric suitable for comparing persistent barcodes: . in, For barcode and Wasserstein distance between them; for and The set of all possible joint distributions of ; represents the infimum (minimum value); Indicates a point and The Euclidean distance between is a power of the distance (usually 1 or 2); In the joint distribution Lower point differential element of Indicates Integral over space. This distance measures the minimum amount of work required to transform one barcode into another.

[0040] Secondly, based on topological invariants, the system analyzes the structural compatibility of multi-source data and divides the features into high compatibility groups and low compatibility groups. The high compatibility group refers to a group of features with similar topological structures that can be directly fused; the low compatibility group refers to a group of features with large structural differences that require special processing. Preferably, the system uses a similarity threshold of 0.7 as the division standard: 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 and can strike a balance between maintaining the integrity of the data structure and allowing moderate fusion.

[0041] The system then applies different fusion strategies to different compatibility groups. For highly compatible feature groups, a connection-based fusion strategy is used to preserve common topological structures. Specifically, the system identifies common structures among different features and uses this as a basis for fusion, ensuring the preservation of key topological features. For less compatible feature groups, a selective fusion strategy is used to retain dominant topological features based on their importance, avoiding structural distortion caused by forced fusion.

[0042] 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 quite different (low compatibility), the system will select a more representative evaluation system as the dominant one based on factors such as the completeness, timeliness and applicability of the evaluation data.

[0043] Finally, the system evaluates the quality of the fusion results to verify the preservation of topological invariants. Evaluation metrics include information retention rate, structural consistency, and anomaly detection rate.

[0044] The information retention rate calculation formula is: in, represents the information retention rate, Indicates the A feature set of source data, Represents the fused feature set Representing a feature set The amount of information contained (calculated by feature entropy), Indicates the number of data sources. This metric measures the degree of preservation of the original information during fusion. A higher value indicates less information loss.

[0045] The formula for calculating structural consistency is: , in, Indicates structural consistency, Indicates the The topology of the source data (represented by persistent barcodes), represents the topological structure after fusion, Represents the similarity between two topological structures (measured by the normalized Wasserstein distance: ,in yes and The Wasserstein distance, is the maximum Wasserstein distance observed in the dataset), Indicates the number of data sources. This indicator measures the consistency of the topological structure of the fusion result and the original data. A higher value indicates better structure preservation.

[0046] The formula for calculating the anomaly detection rate is: in, represents the anomaly detection rate, represents the set of abnormal features detected in the fusion result, represents the complete feature set after fusion, Representing a collection The number of elements in the feature distribution is denoted by . Anomalous features are detected by counting outliers in the feature distribution. The local outlier factor (LOF) algorithm is used to calculate the anomaly score for each feature point. Anomalies are considered when the score exceeds a preset threshold (typically 2.0). This metric measures the proportion of anomalies in the fusion result; lower values ​​indicate higher fusion quality.

[0047] The system requires an information retention rate of no less than 90%, 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 iterate until the preset quality standards are met.

[0048] like Figure 5 As shown, the knowledge service driven by quantum probability field is another innovation of the present invention. By introducing quantum probability theory, the system can handle the uncertainty in knowledge reasoning and provide more intelligent and personalized services.

[0049] First, the system constructs a quantum probability field model. A Hilbert space representing knowledge states is defined, fusion feature representations are mapped into quantum state vectors, and knowledge relationships are mapped into quantum evolution operators. In this embodiment, the system uses a finite-dimensional Hilbert space, where the dimensionality depends on the type and number of knowledge entities. Typically, a medium-sized vocational education knowledge base (containing approximately 1,000 entities and 5,000 relationships) corresponds to a Hilbert space dimension of approximately 100-200, which achieves good representation within the limits of computing resources.

[0050] The quantum state vector is represented as follows: , in, is the quantum state vector, representing the state of the system; is the basic knowledge state (corresponding to a single entity or relationship), constituting an orthogonal basis of the Hilbert space; is the corresponding complex amplitude, indicating the state The weight of is the total number of basic states; sum Indicates that all The complex amplitude must satisfy the normalization condition ,in Represents the probability of measuring the corresponding state.

[0051] Knowledge relations are represented as quantum operators acting on quantum state vectors: , in, For the relationship The corresponding quantum operator; From the state Transition to state The complex magnitude of From the state To status The projection operator of For all state pairs The sum of . It must satisfy the unitary property, that is ,in express The conjugate transpose of represents the identity matrix. This constraint ensures that the normalized properties of the quantum state remain unchanged during the evolution.

[0052] Secondly, the system receives and parses user queries, extracts query intent and key elements, and maps the query into an initial state in the quantum probability field. For example, when a user searches for the matching degree between CNC technology courses and company job requirements, the system identifies CNC technology courses and company job requirements as key entities, uses matching degree as the relationship type, and constructs the corresponding initial quantum state.

[0053] The system then 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 preferences. For example, if it finds that the user is more concerned with practical skills than theoretical knowledge, the system will increase the weight of the state related to the practical training content. This adjustment is achieved by modifying the transfer amplitude in the quantum evolution operator: , in, is the adjusted user-specific quantum operator; is a standard relational operator; For adjustment items based on user preferences, the preferred adjustment range is within the range of ±0.2 to maintain the rationality of the overall evolution.

[0054] Next, the system performs quantum evolution to generate inference results containing multiple path possibilities: , in, is the final quantum state after evolution; A user-specific quantum evolution operator; is the initial quantum state. The final quantum state contains multiple possible inference results and their probabilities.

[0055] During quantum evolution, the system simultaneously considers multiple possible reasoning paths, something that traditional deterministic reasoning cannot. For example, when analyzing the compatibility between courses and positions, the system considers both direct correlations (direct correspondence between course content and job requirements) and indirect correlations (correlations established through intermediate knowledge points or competency elements).

[0056] 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 based on the user's context and receptiveness. The system provides three types of knowledge services: search services (based on keyword queries), knowledge discovery services (exploring implicit associations), and knowledge push services (personalized recommendations).

[0057] In the search service, the system queries the corresponding knowledge graph node data based on keywords, simultaneously obtaining information about the entity's neighboring nodes and displaying the associated knowledge network. Query results are sorted by similarity to form a recommendation list. The system also collects user feedback on search results and dynamically adjusts similarity calculation parameters to optimize the future search experience.

[0058] In the knowledge discovery service, the system uses cluster analysis to identify implicit association patterns within the knowledge graph. Preferably, the system uses a spectral-based clustering algorithm, first constructing a similarity matrix, then calculating its eigenvectors, and then dividing the nodes into clusters based on the eigenvectors. By analyzing relationships within and between clusters, the system can uncover potential knowledge patterns and trends within the data. For example, the system might discover significant overlap between the curriculum systems of CNC technology and industrial robotics, providing data support for curriculum integration.

[0059] In the knowledge push service, the system builds a user interest profile, including knowledge preferences, learning style, and ability level, based on which a personalized recommendation list is generated. The system adopts 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 on the timeliness and relevance of the recommended content: , in, For content For users Recommendation score; For content With users relevance of the interest (normalized value in the range 0-1); For content Timeliness (usually a normalized value calculated based on the time of publication, with higher scores for more recent publications); For content With recommended list diversity (usually calculated as the average distance to already recommended content); 、 、 is the weight parameter, which controls the importance of the three factors and satisfies The weight parameter is usually set to 、 、 , which can be adjusted dynamically according to specific scenarios.

[0060] like Figure 6As shown, the present invention also provides a vocational education multi-source data federation 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.

[0061] The data acquisition module 1 is used to acquire multi-source heterogeneous vocational education data through various methods. The module includes a web crawler component 11, an OCR recognition component 12, a database interface component 13, and an ETL tool component 14.

[0062] The web crawler component 11 is responsible for crawling vocational education-related information from the internet. In this embodiment, this component utilizes a distributed architecture and supports customized configuration for different websites, including crawl depth, frequency, and content filtering rules. For example, for authoritative information sources such as education department websites, the system assigns higher crawling priority and more comprehensive content coverage; for unstructured information sources such as industry forums, the system prioritizes crawling specific sections and themes.

[0063] The OCR recognition component 12 processes paper documents, including lesson plans, test papers, and student files. It integrates image preprocessing, text recognition, and layout analysis, adapting to document images of varying quality and format. The system optimally utilizes a deep learning model for text recognition, specifically optimized for specialized terminology and symbols within the vocational education field, achieving recognition accuracy exceeding 95%.

[0064] The 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 educational and training management systems, enabling rapid connection establishment and extraction of structured data.

[0065] The ETL tool component 14 is responsible for extracting, transforming, and loading data, handling complex data migration tasks. This component supports scheduled task settings and can automatically synchronize data updates according to a predetermined plan.

[0066] The data preprocessing module 2 is used to clean, reduce noise, regularize the structure and entity link the acquired data to generate standard format data. This module includes a data cleaner 21, a data structure regularizer 22, a data noise reducer 23 and an entity linker 24.

[0067] The data cleaner 21 is responsible for handling erroneous values, missing values, and outliers in the data. The system employs different cleaning strategies for different types of data. For example, for numerical data (such as grades and ratings), the system detects outliers and corrects them based on statistical distribution. For text data, the system performs spelling checks and standardizes the format. For missing values, the system employs methods such as mean / median filling, similar record filling, or predictive model filling, depending on the data type.

[0068] 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 that contains different representations of common fields and their standard mappings. For example, a field representing a student's name might be named "student_name," "name," "name," "stu_name," and so on in different systems. The system maps these different representations to the standard field "student_name."

[0069] The data denoiser 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 results, the system uses morphological operations and adaptive threshold filtering to remove recognition noise. In a typical vocational education document processing case, noise reduction increased the accuracy of text information extraction from an initial 78% to 96%.

[0070] The entity linker 24 is used to identify and link different representations of the same entity. This component first extracts the key attributes of the entity, constructs a feature vector, and then calculates the similarity between entities. In this embodiment, the system uses the weighted Jaccard coefficient to calculate the similarity of text attributes: , Among them, A and B represent the attribute sets of two entities. Represents the weight of attribute x. For different attribute types, the system selects an appropriate similarity calculation method and weights multiple similarity scores to form a final similarity. When the similarity exceeds a preset threshold (usually set at 0.85), the system determines that they are the same entity and establishes a link.

[0071] 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 relationship identification component 32, and a triple construction component 33.

[0072] 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 uses a combination of domain dictionaries and named entity recognition technology to accurately identify entities and their types in text. Preferably, for structured data, the system extracts entity information directly from the data schema; for unstructured data, natural language processing techniques are used to extract entities.

[0073] The relationship identification component 32 is responsible for identifying various relationships between entities. The present invention divides relationships into two categories: explicit relationships and implicit relationships. Explicit relationships are relationships that are clearly marked in the data, such as students registering for courses, teachers teaching, etc., which can be directly extracted from the data structure. Implicit relationships need to be inferred through data analysis, such as knowledge dependencies between courses, cooperative relationships between students, etc. The system uses a combination of rule reasoning and statistical analysis to mine implicit relationships. For example, by analyzing the similarity of course content, the system can infer the knowledge association between courses; by analyzing the co-occurrence pattern of student homework and exams, the system can discover potential cooperative relationships between students.

[0074] The triple construction component 33 organizes the extracted entities and relationships 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 contextual information such as time and space to each triple to enhance the integrity of the knowledge representation. Preferably, the system uses a graph database (such as Neo4j) to store the knowledge graph, supporting efficient graph-structured querying and analysis.

[0075] The tensor representation module 4 is used to map the knowledge graph to a multi-dimensional tensor space to form a tensor network representation. This module includes a feature space definition component 41, an entity tensorization component 42, a relationship tensorization component 43, and a tensor network construction component 44.

[0076] The feature space definition component 41 is responsible for designing the coordinate system of the multidimensional feature space. In this embodiment, the system defines a multidimensional feature space that includes entity attribute dimensions, relationship feature dimensions, time dimensions, and space dimensions. For example, for a course entity, its feature space may contain 10 to 20 dimensions, covering various aspects of information, such as basic course information (title, credits, etc.), content characteristics (keywords, difficulty, etc.), and spatiotemporal attributes (semester, location).

[0077] The entity tensorization component 42 maps entities in the knowledge graph to tensor nodes in the feature space. Specifically, each entity is represented as a multidimensional tensor, where each dimension of the tensor corresponds to a different coordinate axis in the feature space. For example, a course entity may be represented as a three-order tensor, where the three dimensions correspond to attribute features, time features, and space features. In mathematical representation, the entity tensor can be expressed as: , in, is the tensor representation of the entity; It is the eigenvalue in multiple dimensions, and each subscript corresponds to a feature dimension.

[0078] The relation tensorization component 43 represents relations in the knowledge graph as tensor transformation operators. In traditional knowledge graphs, relations are simply represented as connections between entities. However, in tensor representation, relations are modeled as tensor transformations that can capture more complex interaction patterns. For example, the professor relation can be represented as a transformation operator that maps the teacher tensor to the course tensor: , in, It is a tensor operator representing the professor relationship, which reflects the mapping relationship of how teacher characteristics affect course characteristics.

[0079] The tensor network construction component 44 integrates the tensorized entities and relationships into a complete tensor network. This component first connects the tensor nodes according to the topology of the knowledge graph, then organizes the hierarchy according to the domain ontology, and finally integrates the sub-networks to form a complete tensor network. The system also optimizes the network, including removing low-strength connections, merging highly similar nodes, and supplementing implicit transitive relationships, to improve the quality and efficiency of the network.

[0080] The topology analysis module 5 is used to perform multi-scale topological analysis on the tensor network and extract topological feature descriptors. The module includes a distance measurement component 51, a complex construction component 52, a persistent homology calculation component 53 and a feature extraction component 54.

[0081] 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 based on domain knowledge. As mentioned above, the distance function is expressed as: , in Representing an entity and The distance between and Indicates that they are The value of the feature dimension, Indicates the The weight of the feature, Represents the total number of features. In practical applications, the system typically determines weights through a combination of expert experience and data analysis. For example, when analyzing course associations, the weight of content relevance features is typically set to 0.5-0.7, while the weight of external features such as time and location is set to 0.2-0.3.

[0082] The complex construction component 52 is responsible for constructing simplicial complexes at multiple scales. The system selects a series of increasing distance thresholds , construct a sequence of nested simplicial complexes: , in, Indicates that the threshold The simplicial complex constructed under the following conditions contains all vertices whose distances do not exceed The threshold sequence typically covers the range from the minimum effective distance to the maximum effective distance, evenly distributed on a logarithmic scale. In a typical vocational education data analysis case, the system selects 10 to 15 threshold points, covering the distance range of 0.1 to 1.0.

[0083] The persistent homology computation component 53 computes the persistent homology group of a simplicial complex sequence. Persistent homology is a core tool for topological data analysis, revealing topological features at different scales. The system computes persistent homology groups for each dimension (typically 0, 1, and 2) and generates a persistent barcode, which visualizes the lifecycle of topological features. In the barcode, each bar represents a topological feature (such as a connected component, a cyclic structure, or a hole), and the length of the bar reflects the stability of the feature.

[0084] The feature extraction component 54 extracts topological feature descriptors from the persistent homology results. The system focuses on three main types of topological features: connected components (0-dimensional homology), cyclic structures (1-dimensional homology), and hole features (2-dimensional and higher homology). Connected components reflect disjoint substructures in the data, such as clusters of knowledge in different professional fields; cyclic structures reflect cyclic dependencies in the data, such as cyclic pre-requisite relationships between courses; and hole features may indicate information gaps in the knowledge system. The system calculates the number, size, and distribution of these features to form a topological feature descriptor and evaluates its stability.

[0085] The feature fusion module 6 is used to perform multi-source data feature fusion while maintaining topology invariants. The module includes a compatibility assessment component 61 , a fusion strategy generation component 62 , and an execution control component 63 .

[0086] The compatibility evaluation component 61 first calculates the similarity matrix of the topological features of the multi-source data. As mentioned above, the system uses Wasserstein distance to calculate the similarity between persistent barcodes: , in, For barcode and Wasserstein distance between them; for and The set of all possible joint distributions of ; inf is the infimum (minimum); for point and The Euclidean distance between is a power of the distance (usually 1 or 2); is the point under the joint distribution γ differential element of Indicates The integral over space. This distance measures the minimum amount of work required to transform one barcode into another. In practical calculations, the system typically uses the case of p = 1, which is the Wasserstein-1 distance (also known as the bulldozer distance), which has a more efficient calculation method.

[0087] The component then analyzes the structural compatibility of multi-source data based on topological invariants. The system focuses on topological invariants, such as Betti numbers and Euler characteristic numbers, which reflect the essential topological structure of the data. When the topological invariants of two data sources are similar, they indicate that they have similar structural characteristics and are suitable for direct fusion.

[0088] Based on the compatibility assessment results, the fusion strategy generation component 62 divides features and formulates a fusion strategy. The system divides features into high- and low-compatibility groups, employing either a connection-based fusion or a selective fusion strategy, respectively. Connection-based fusion preserves common topological structures and is suitable for highly compatible data; selective fusion preserves dominant topological features and is suitable for low-compatibility data.

[0089] In actual implementation, the system uses a compatibility threshold of 0.7 as the classification criterion. This threshold, determined through extensive experimental validation, strikes a balance between maintaining data structure integrity and allowing for moderate fusion. Setting the threshold too high can overly restrict fusion, while setting it too low can lead to inappropriate fusion operations that destroy the data structure.

[0090] The execution control component 63 is responsible for executing the fusion operation and evaluating the quality of the result. For connection-based fusion, the system identifies common structures in different features as connection points and constructs fusion features: , in, It is the feature after connection fusion; For the The characteristics of the data source, ; is the number of data sources; For common structure; is the fusion function.

[0091] For selective fusion, the system selects the dominant features based on feature importance metrics: , in, It is the feature after selective fusion; The most important feature. Feature importance is determined by multiple factors, including data completeness, timeliness, and scope of application. The system typically weights these factors to form a comprehensive importance index.

[0092] After fusion is complete, the system evaluates the quality of the results to verify the preservation of key topological invariants. Evaluation metrics include information retention, structural consistency, and anomaly detection rate. If the evaluation results are unsatisfactory, the system adjusts the fusion parameters and iterates until the preset quality standards are met (information retention ≥ 90%, structural consistency ≥ 85%, anomaly detection ≤ 5%).

[0093] The quantum reasoning module 7 is used to construct a quantum probability field model 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.

[0094] The quantum model building component 71 is responsible for defining the knowledge state space and constructing a quantum representation. The system first defines a finite-dimensional Hilbert space representing the knowledge state, then maps the fused feature representation into a quantum state vector and the knowledge relationship into a quantum evolution operator. As mentioned above, the quantum state vector is represented as: , in, is the quantum state vector, representing the state of the system; is the basic knowledge state (corresponding to a single entity or relationship), forming an orthogonal basis of the Hilbert space; is the corresponding complex amplitude, indicating the state The weight of is the total number of basic states; sum Indicates that all The complex amplitude must satisfy the normalization condition ,in Represents the probability of measuring the corresponding state.

[0095] Basic knowledge status The method of obtaining is to vectorize the features of nodes and edges of the knowledge graph. Specifically, the system first extracts all entities and relationships from the knowledge graph and constructs an entity-relationship dictionary. Then, for each dictionary element , define a unit vector As their corresponding foundational knowledge states, these vectors form an orthonormal basis of the Hilbert space. For example, in In the 3-dimensional Hilbert space, you can choose , of which only The components are 1 and the rest are 0. Complex amplitude The acquisition method is achieved by mapping the fusion features onto the quantum state.

[0096] The system adopts a relevance-based mapping method. The specific steps are as follows: First, calculate the relevance score between the query or current context and each basic knowledge state. , and then converted to probability amplitude by normalization: , in, is a relevance score calculated based on cosine similarity or other relevance metrics, is the phase angle, which can be set based on the relationship type or context information. Usually, to simplify the calculation, ,Right now Take real numbers. This mapping method ensures the normalization condition of the quantum state ,while preserving the correlation information of the original fusion features.

[0097] Knowledge relations are represented as quantum operators: , in, Indicates the slave state Transition to state The complex magnitude of From the ground state to the ground state The projection operator, matrix The unitary property must be satisfied to ensure the normalization of the quantum state. Operator element The specific calculation method is to first construct a relationship strength matrix by analyzing the relationship strength and type between nodes in the knowledge graph. ,in Represents a slave node To Node The relationship strength is then converted to Convert to a unitary matrix ,make sure .

[0098] 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 into quantum states in Hilbert space. For example, if a user queries the relationship between CNC technology and intelligent manufacturing, the system maps CNC technology and intelligent manufacturing into corresponding entity states and constructs an appropriate initial superposition state.

[0099] The quantum evolution component 73 performs knowledge reasoning based on quantum evolution rules. The system designs time evolution rules to simulate the propagation of quantum states in the knowledge graph. Mathematically, this is expressed as: , in, is the system Hamiltonian, which represents the structure and dynamic characteristics of the knowledge network. is the evolution time parameter. In practical implementations, the system typically uses discrete-time evolution, approximating the continuous evolution process by iteratively applying quantum gate operations. Preferably, the number of evolution steps is set to 10-20 to achieve a balance between computational efficiency and inference depth.

[0100] The result measurement component 74 performs quantum measurements and obtains inference results. The system designs an appropriate measurement basis to measure the evolved quantum state, obtaining a probability distribution for each possible result. These results reflect the likelihood of different knowledge paths, providing users with diverse inference results. The system also post-processes the measurement results, filtering out high-probability results and eliminating noise to form the final inference result set.

[0101] The knowledge service module 8 is used to provide search, knowledge discovery and push services based on the inference results. The module includes a search service component 81, a knowledge discovery service component 82 and a knowledge push service component 83.

[0102] The search service component 81 provides keyword-based knowledge graph query functionality. Users enter a keyword or entity ID, 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 precise, fuzzy, and semantic queries, to meet the needs of different scenarios.

[0103] Preferably, the system uses an improved version of the PageRank algorithm to calculate node importance, and the formula is as follows: , in, Representation node PageRank value, is the damping factor (usually set to 0.85), Indicates link to Node, Representation node The number of outgoing links, is linked to The system will combine this importance index and query relevance to calculate the final ranking score.

[0104] The knowledge discovery service component 82 is responsible for mining implicit association patterns within the knowledge graph. The system uses methods such as cluster analysis, centrality analysis, and trend analysis to discover important knowledge patterns within the data. For cluster analysis, the system preferably employs a spectral clustering algorithm. This algorithm first constructs a node similarity matrix S, calculates its Laplace matrix L = DS (where D is the degree matrix), and then calculates the eigenvectors of L. Based on these eigenvectors, the nodes are grouped into clusters. By analyzing relationships within and between clusters, the system can discover implicit patterns within the data.

[0105] For example, in a course analysis case of a vocational college, the system discovered cross-disciplinary knowledge association groups through clustering and identified core competency modules shared by multiple disciplines, providing data support for professional construction and course optimization.

[0106] Centrality analysis is used to identify key nodes and relationships within a knowledge network. The system calculates various centrality metrics, such as degree centrality (number of connections), betweenness centrality (path intermediary role), and eigenvector centrality (accounting for neighbor importance). These metrics help identify core concepts and key relationships within a knowledge system.

[0107] Trend analysis compares knowledge graphs at different points in time to identify knowledge evolution trends. The system tracks changes in entities and relationships, including additions, deletions, and attribute changes, to infer the direction of domain knowledge development.

[0108] The knowledge push service component 83 provides personalized knowledge recommendation functionality. This component first constructs a user interest profile, including knowledge preferences, learning style, and ability level. This user profile is built based on historical interaction data and dynamically updated as user behavior changes. The system then generates a personalized recommendation list based on the user profile and the knowledge association network.

[0109] The recommendation process adopts a hybrid recommendation strategy that combines content recommendation and collaborative filtering: , in, Display content For users The recommendation score, Indicates the degree of relevance (value range 0-1), Indicates timeliness (value range 0-1), Indicates the recommended list Diversity (value range O-1), is a weight parameter that satisfies In the push of vocational education knowledge, these three factors are very important: relevance ensures that the recommended content is related to the user's profession and interests; timeliness ensures that the content reflects the latest developments in the industry; and diversity prevents information cocoons and helps users broaden their knowledge. The system usually sets , which can be dynamically adjusted according to specific application scenarios. This recommendation strategy ensures the relevance of recommended content while taking into account the timeliness and diversity of the content, avoiding the information cocoon effect.

[0110] The relevance of content to user interests The calculation method is as follows: , in, Display content The feature vector is generated by extracting the topic, keywords and semantic features of the content; Represents a user The interest vector is generated by analyzing the user's historical interaction records and explicit preferences. Indicates the cosine similarity of two vectors, with a value range of [-1, 1]. A larger value indicates a higher correlation. To ensure that the calculation result is within the range of [0, 1], the cosine similarity is normalized in the actual implementation: Timeliness of content The calculation method is as follows: , in, Indicates the current timestamp, Display content The release or update timestamp, Indicates the time interval of the content (usually in days), The timeliness decay coefficient controls the rate at which timeliness decays over time. It is typically set between 0.05 and 0.1, and is adjusted based on the timeliness requirements of different content types. This formula is an exponential decay function. The timeliness of new content approaches 1 and gradually decreases over time, reflecting the fact that new content is generally more valuable as a reference.

[0111] Diversity of content and recommended lists The calculation method is as follows: , in, Represents a list of content recommended to the user. Represents an item in a list. Display content and content The similarity is usually calculated using cosine similarity; Display content The maximum similarity with any content in the recommended list. This formula measures the content The degree of difference from already recommended content. A larger value indicates greater diversity, helping to avoid homogeneity in recommended content and providing more comprehensive knowledge coverage. Through a weighted combination of these three indicators, the system ensures content relevance while also balancing timeliness and diversity, providing users with more balanced and personalized knowledge recommendations. The system also dynamically adjusts weighting parameters based on user feedback to continuously optimize recommendation effectiveness.

[0112] In addition, the system will also collect user feedback on recommended content, including clicks, dwell time, collections and other behaviors, and continuously optimize the recommendation algorithm and parameters to improve the accuracy of the recommendation service.

[0113] The application interface module 9 is used to receive user requests and return system responses, and is the portal for the system to provide external services. This module includes an API gateway component 91, an authentication and authorization component 92, a request routing component 93, and a response formatting component 94.

[0114] The API Gateway component 91 provides a unified interface entry point to handle all external requests. The system supports both REST API 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 the RESTful specification and supports standard HTTP methods (GET, POST, PUT, DELETE, etc.) and status codes.

[0115] The authentication and authorization component 92 is responsible for identity verification and permission control. The system uses a JWT (JSON Web Token)-based authentication mechanism and supports multiple authentication methods, including account and password, third-party login, and API key. Permission control uses the RBAC (Role-Based Access Control) model, assigning different operation permissions based on user role.

[0116] The request routing component 93 forwards requests to the appropriate service module based on the request type and parameters. The system uses a service registration and discovery mechanism to support 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.

[0117] The response formatter 94 converts the processing results into a response format that complies with 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.

[0118] Through the collaborative work of the above modules, the vocational education multi-source data federation 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 the teaching, management and decision-making of vocational education.

[0119] The technical solution of the present invention is not limited to the above embodiments. Any non-substantial changes, modifications, additions or substitutions made by those skilled in the art on the basis of the present invention belong to the same technical solution as the present invention.

Claims

1. The federated governance method for multi-source vocational education data is characterized by: include: Acquire multi-source heterogeneous vocational education data and perform data preprocessing, including: Obtain authorized Internet data and vocational education data from paper documents through web crawlers and OCR technology, and perform natural language processing and structured extraction on unstructured documents; Clean, reduce noise, regularize the structure and link entities of the acquired data to generate data in a standard format; Construct a tensor topology knowledge graph, including: Based on 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. Map the initial knowledge graph 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; Perform multi-scale topological analysis on the tensor network and extract topological feature descriptors; Perform multi-source data feature fusion, including: Based on topological feature descriptors, the feature compatibility of multi-source data is evaluated and the feature fusion strategy is determined; According to the feature fusion strategy, a feature fusion operation that maintains topological invariants is performed to generate a fused feature representation; Provide knowledge services driven by quantum probability fields, including: Based on the fusion feature representation, a quantum probability field model is constructed to map user queries into quantum initial states; According to the quantum evolution rules, knowledge reasoning is performed to obtain the probability distribution of the reasoning results; Based on the inference results, personalized knowledge responses are generated to provide users with search, discovery and push services.

2. The method for federal management of multi-source vocational education data according to claim 1 is characterized in that: The acquisition of multi-source heterogeneous vocational education data and data preprocessing specifically include: Configure the data source collection component and select web crawlers, OCR recognition, database interfaces, or ETL tools based on the data source type; Conduct quality assessment of collected data, including completeness, accuracy, and consistency checks; Remove erroneous data, outliers, and redundant information through data cleaners; Unify field naming and data formats of different data sources through data structure regularizer; Improve data quality by filtering out interference information through data noise reducer; The entity linker identifies and links different representations of the same entity to establish a unique entity identifier.

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

4. The method for federal management of multi-source vocational education data according to claim 1 is characterized in that: The multi-scale topological analysis of the tensor network specifically includes: Based on the semantic similarity in the education field, a distance metric function of the tensor network is constructed; Generate a multi-scale filter sequence to capture topological features of different granularities; Construct a simplicial complex at each scale to form a filter complex sequence; Calculate the persistent homology groups of each dimension and obtain topological invariants; Generate persistent barcodes to visualize the lifecycle of topological features; Extract connected components, cyclic structures and hole features to form topological feature descriptors; Calculate the stability index of topological features and evaluate the reliability of features.

5. The method for federal management of multi-source vocational education data according to claim 1 is characterized in that: The performing of multi-source data feature fusion specifically includes: Calculate the similarity matrix of topological features of multi-source data; Analyze the structural compatibility of multi-source data based on topological invariants; Based on feature similarity and structural compatibility, the features were divided into high compatibility group and low compatibility group; For highly compatible feature groups, a connection-based fusion is performed to preserve the common topological structure; For low-compatibility feature groups, selective fusion is performed to retain the dominant topological features; Perform quality assessment on the fusion results to verify the preservation of topological invariants; Adjust the fusion parameters and iteratively optimize the fusion results.

6. The method for federal management of multi-source vocational education data according to claim 1 is characterized in that: The construction of the quantum probability field model specifically includes: Define a Hilbert space representing knowledge states; Mapping the fused feature representation into a quantum state vector; Mapping knowledge relations into quantum evolution operators; Constructing quantum superposition states that represent the possibility of multiple paths of reasoning; Design quantum state evolution rules to simulate the knowledge reasoning process; Determine quantum measurement strategies for obtaining inference results; Establish a mapping relationship between quantum measurement results and knowledge representation.

7. The method for federal management of multi-source vocational education data according to claim 1 is characterized in that: The provision of knowledge services driven by quantum probability fields specifically includes: Receive and parse user queries, extract query intent and key elements; Map the query to an initial state in a quantum probability field; Adjust quantum state evolution parameters based on user historical behavior and interest preferences; Perform quantum evolution to generate inference results containing multiple path possibilities; Generate a candidate set of knowledge responses based on the probability distribution of the reasoning results; Optimize the content and form of knowledge responses based on user context and receptiveness; Provide comprehensive knowledge services including search services, knowledge discovery services and personalized push services.

8. The method for federal management of multi-source vocational education data according to claim 7 is characterized in that: The provision of search services specifically includes: Query the corresponding knowledge graph node data based on keywords; Obtain the entity's neighbor node information and display the associated knowledge network; Calculate the similarity between the nodes in the query result set and the query node; Generate a recommended list of search results based on similarity sorting; Based on user feedback, similarity calculation parameters are dynamically adjusted to optimize search results.

9. The method for federal management of multi-source vocational education data according to claim 7, characterized in that: The provision of knowledge discovery services and knowledge push services specifically includes: Identify implicit association patterns in knowledge graphs through cluster analysis; Identify key knowledge nodes and key relationships based on node centrality and edge weights; Analyze knowledge evolution trends and predict potential knowledge development directions; Build user interest profiles, including knowledge preferences, learning styles, and ability levels; Generate personalized recommendation lists based on user interest profiles and knowledge association networks; Optimize the content and timing of knowledge push based on timeliness and relevance; Collect user interaction feedback and continuously optimize push strategies and content.

10. A vocational education multi-source data federation management system implementing the method according to any one of claims 1 to 9, characterized in that: include: The data acquisition module is used to obtain multi-source heterogeneous data of vocational education through web crawlers, OCR technology and database interfaces; Data preprocessing module, used to clean, reduce noise, regularize structure and entity link the acquired data; The knowledge graph construction module is used to construct the initial knowledge graph in the form of triples; Tensor representation module, used to map the knowledge graph to a multi-dimensional tensor space to form a tensor network representation; Topology analysis module, used to perform multi-scale topological analysis on tensor networks and extract topological feature descriptors; Feature fusion module, used to perform multi-source data feature fusion while maintaining topological invariants; Quantum reasoning module, used to build quantum probability field models and perform knowledge reasoning; Knowledge service module, 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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