Information management method based on school management cloud service platform

Abstract

The present invention relates to the field of school information management technology, and in particular to an information management method based on a school management cloud service platform. The method comprises the following steps: obtaining business pain point heat map data by locating the business pain point of the school management cloud service platform; reconstructing the potential process according to the business pain point heat map data to obtain process reconstruction solution data; reorganizing the business process according to the process reconstruction solution data to obtain a business process map; analyzing the data quality standard requirements of various systems and departments of the school according to the business process map to obtain data standard specification data. The present invention comprehensively solves the problems of insufficient system integration and data sharing, data integration and consistency faced by the school management cloud service platform through the innovative step ideas of business panoramic scanning and process optimization, cross-departmental collaborative processing and process simulation, semantic service orchestration and service scheduling analysis, metadata lineage reasoning and cross-source knowledge fusion.

Description

Technical Field

[0001] The present invention relates to the technical field of school information management, and in particular to an information management method based on a school management cloud service platform. Background Art

[0002] In the early stages of the gradual maturity of cloud computing technology, school management systems were mainly single, independently deployed software systems, with data stored in their own databases. There were data island problems, making it difficult to achieve data sharing and system integration. With the rise of cloud computing and the concept of service-oriented, school management systems have gradually migrated to cloud service platforms, providing more flexible and scalable solutions. However, due to the different standards and inconsistent data formats among various service providers, system integration and data sharing are still challenging. In order to solve the problems of data integration and consistency, the school management cloud service platform began to introduce the concepts of data standardization and master data management. However, due to the lack of unified data standards and management mechanisms, as well as insufficient data quality monitoring, it is still difficult to achieve complete consistency and accuracy of data.

[0003] Although the information management methods of the school management cloud service platform are constantly developing and evolving, there are still some shortcomings in terms of insufficient system integration and data sharing, data integration and consistency:

[0004] 1. Inconsistent standards: Inconsistent data standards among different service providers lead to differences in data format, field definition, etc., affecting the smooth progress of system integration and data sharing.

[0005] 2. Data quality issues: Due to different data sources and insufficient quality monitoring, the data in the school management cloud service platform often have quality problems, including duplication, incompleteness, inaccuracy, etc., making it difficult to ensure data integration and consistency.

[0006] 3. Lack of a unified management mechanism: There is currently a lack of a unified data management mechanism and specifications, resulting in a lack of effective data interaction and sharing mechanisms between systems, limiting the efficiency and effectiveness of system integration and information management. Summary of the invention

[0007] Based on this, it is necessary to provide an information management method based on a school management cloud service platform to solve at least one of the above technical problems.

[0008] To achieve the above purpose, an information management method based on a school management cloud service platform includes the following steps:

[0009] Step S1: locate the business pain points of the school management cloud service platform to obtain the business pain point heat map data; reconstruct the potential process according to the business pain point heat map data to obtain the process reconstruction solution data; reorganize the business process according to the process reconstruction solution data to obtain the business process map;

[0010] Step S2: Analyze the data quality standard requirements of various systems and departments of the school according to the business process map to obtain data standard specification data; perform unified data standard processing according to the data standard specification data to obtain data standard documents;

[0011] Step S3: Perform master data source governance analysis according to the data standard document to obtain master data governance process data; perform master data integration processing according to the master data governance process data to obtain master data integration rule data; perform master data warehouse management analysis according to the master data integration rule data to obtain the master data warehouse;

[0012] Step S4: classify data service requirements according to the main data warehouse to obtain a data service requirement list; perform service orchestration modeling according to the data service requirement list to obtain a semantic service orchestration model; perform service scheduling analysis according to the service clustering model to obtain service orchestration scheduling rules; perform data service bus connection according to the service orchestration scheduling rules to obtain a data service directory;

[0013] Step S5: perform metadata specification collection according to the data service catalog to obtain metadata; perform metadata governance analysis according to the data service catalog and metadata to obtain metadata management process data; perform metadata lineage reasoning analysis according to the metadata to obtain a metadata lineage knowledge graph; perform metadata lineage traceability impact analysis according to the metadata lineage knowledge graph to obtain a metadata lineage graph; perform metadata governance according to the metadata management process data and the metadata lineage graph to obtain a metadata warehouse;

[0014] Step S6: Perform cross-source knowledge fusion based on the main data warehouse and the metadata warehouse to obtain cross-source knowledge fusion data; perform multi-dimensional knowledge linking on the cross-source knowledge fusion data to obtain a linked knowledge graph; perform knowledge reasoning and knowledge expansion based on the linked knowledge graph to obtain an extended knowledge graph; visualize the school domain knowledge ontology based on the extended knowledge graph to obtain visualized knowledge graph data; perform school information management operations based on the business process graph, data service directory and visualized knowledge graph data.

[0015] The present invention locates the business pain points, so that the school can clearly understand the problems and deficiencies in the current management process, and intuitively display them in the form of a heat map. According to the pain point heat map, the school can carry out targeted process reconstruction, optimize key processes, and improve management efficiency and benefits. The business process map finally generated provides a clear process framework and guidance for the school's management work. Based on the business process map, the school can analyze the data quality requirements of each business process and formulate corresponding data standards and specifications. This will effectively improve the quality and consistency of school data and lay the foundation for subsequent data application and analysis. Through the master data source governance analysis, the school can clarify the source and management responsibility of the master data, and formulate the master data governance process to ensure the quality and consistency of the master data. The master data integration processing integrates and unifies the master data scattered in various systems to form a unified master data view of the school, thereby improving the sharing and utilization efficiency of data. Data service demand classification can help schools understand the data requirements of various business systems, and develop and provide corresponding data services according to the requirements. The semantic service orchestration model can flexibly combine and orchestrate different data services according to business needs to form a composite service that meets specific business scenarios. The generation of the data service catalog provides the school with a clear data service view, making it easier for users to find and use the required data services. Metadata collection and governance can help schools establish a sound metadata management system to ensure the accuracy, completeness and consistency of metadata. The metadata lineage diagram intuitively displays the lineage relationship between data, making it easier for users to understand the flow and scope of data. The construction of the metadata warehouse centrally stores and manages the metadata scattered in various systems to form a unified metadata view of the school, providing an important infrastructure for the school's data governance and application. Knowledge cross-source fusion integrates and associates knowledge from different sources to form a richer and more complete knowledge network. The generation of linked knowledge graphs provides a basis for subsequent knowledge reasoning and application. The extended knowledge graph continuously expands the coverage and depth of the knowledge graph through knowledge reasoning and knowledge expansion. Visual knowledge graph data displays the knowledge graph in a graphical way, making it easier for users to understand and use knowledge. Ultimately, schools can perform more intelligent and efficient information management operations based on business process graphs, data service catalogs and visual knowledge graph data to improve the school's informatization level.

[0016] Therefore, the present invention provides an information management method based on the school management cloud service platform, which comprehensively solves the problems of insufficient system integration and data sharing, data integration and consistency faced by the school management cloud service platform through innovative steps of business panoramic scanning and process optimization, cross-departmental collaborative processing and process simulation, semantic service orchestration and service scheduling analysis, metadata lineage reasoning and knowledge cross-source fusion. These steps not only improve management efficiency, but also ensure the consistency and accuracy of data, and at the same time provide deeper knowledge management and decision support for school management.

[0017] Preferably, step S1 comprises the following steps:

[0018] Step S11: obtaining business panoramic map data by performing a business panoramic scan on the school management cloud service platform; performing business pain point mining based on the business panoramic map data to obtain business pain point heat map data;

[0019] Step S12: Acquire school management business process data; perform process value evaluation on the school management business process data according to the business pain point heat map data to obtain a process value matrix, wherein the process value matrix includes high-importance and high-potential process data and low-importance and low-potential process data;

[0020] Step S13: Generate a process optimization plan for the high-importance and high-potential process data to obtain process optimization blueprint data; perform process reconstruction processing on the low-importance and low-potential process data to obtain process reconstruction plan data;

[0021] Step S14: Perform cross-departmental collaborative processing according to the process optimization blueprint data and the process reconstruction solution data to obtain a cross-departmental collaborative matrix;

[0022] Step S15: Perform process simulation on the process optimization blueprint data and the process reconstruction solution data according to the cross-departmental collaboration matrix to obtain a process simulation report; perform process performance indicator evaluation based on the process simulation report to obtain a process performance indicator library;

[0023] Step S16: Reorganize the business process map according to the business panoramic map data, process optimization blueprint data, process reconstruction plan data, cross-departmental collaboration matrix and process performance indicator library to obtain a business process map.

[0024] The present invention can clearly identify the problems and improvement opportunities in the current business operation by conducting a comprehensive business scan and pain point mining on the school management cloud service platform, and provide direction and focus for subsequent process optimization. By conducting a value assessment on the school management business process data and classifying it according to the importance and optimization potential of the process, the priority and resource investment of process optimization can be determined in a targeted manner to improve the efficiency and effect of optimization. By optimizing the design of high-importance and high-potential processes and reconstructing low-importance and low-potential processes, it is possible to simplify low-value processes, rationally allocate optimization resources, and maximize the overall benefits of process optimization while fully mining the value of the process. By cross-departmental collaborative processing of optimization plans and reconstruction plans, departmental barriers can be broken, end-to-end process effects can be fully considered, and the feasibility and efficiency of process optimization at the execution level can be ensured. Through process simulation and performance indicator evaluation, the effect of the optimization plan can be predicted, the optimization results can be quantified, quantitative standards and continuous optimization mechanisms for process management can be established, and the normalization of process optimization can be achieved. By restructuring the business process map based on the previous steps, we can form a business process management system that connects all departments, runs through the entire process, and continuously optimizes, providing school management with an efficient, refined, quantifiable, and optimizable business operation framework.

[0025] Preferably, step S3 comprises the following steps:

[0026] Step S31: identify master data according to the data standard document to obtain a master data object list; define master data attributes for each master data object in the master data object list to obtain a master data attribute dictionary; sort out the master data source for each master data object in the master data object list to obtain a master data source mapping diagram;

[0027] Step S32: Perform master data quality diagnosis according to the master data source mapping diagram to obtain a master data quality diagnosis report; perform master data governance process planning according to the master data quality diagnosis report to obtain master data governance process data;

[0028] Step S33: Formulate master data integration rules based on the master data attribute dictionary and the master data governance process data to obtain master data integration rule data;

[0029] Step S34: Analyze the master data synchronization mechanism according to the master data source mapping diagram and the preset master data management platform to obtain the master data synchronization solution; analyze the master data service interface according to the master data synchronization solution to obtain the master data service interface specification data;

[0030] Step S35: construct a master data warehouse according to the preset master data management platform, master data integration rules, master data synchronization solution and master data service interface specification data to obtain a master data warehouse.

[0031] By identifying master data objects, defining master data attributes, and combing master data sources, the present invention can fully understand the key data assets of the school, clarify the business meaning, technical characteristics and sources of the data, and lay the foundation for subsequent master data governance. Through master data quality diagnosis, the accuracy, completeness, consistency and other quality status of the current master data can be objectively evaluated, and data quality problems can be found; on this basis, the master data governance process can be planned, and data quality improvement measures can be designed in a targeted manner, and the organizational mode and division of responsibilities of data governance can be formulated to ensure the orderly development of data governance. According to the master data attributes and governance process, master data integration rules are formulated to guide the standardized integration of master data from different sources and formats, improve data consistency and availability, and reduce data redundancy and errors. By analyzing the synchronization mechanism and service interface between the master data source and the management platform, an efficient and automated data transmission and exchange solution can be designed to ensure real-time synchronization and sharing of master data between different systems, and improve the timeliness and accessibility of data. Based on the above specifications and rules, a master data warehouse is constructed to realize the centralized storage, management and application of master data, provide consistent, authoritative and high-quality master data services, support various business applications and data analysis decisions of the school, and improve the efficiency of business operations and management.

[0032] Preferably, step S4 comprises the following steps:

[0033] Step 41: construct the service architecture according to the main data warehouse to obtain the data service bus architecture data; obtain the data service demand data of each business system of the school; perform service classification processing on the data service demand data to obtain a data service demand list;

[0034] Step 42: Define the service interface according to the data service requirement list to obtain data service interface specification data; register and discover the service according to the data service requirement list to obtain service registration and discovery data;

[0035] Step 43: Perform semantic service orchestration processing according to the data service requirement list and the service registration and discovery data to obtain a semantic service orchestration model; perform service scheduling analysis according to the service clustering model to obtain service orchestration scheduling rules;

[0036] Step 44: Generate a data service catalog based on the data service bus architecture data, data service interface specification data, service registration and discovery data, and service orchestration and scheduling rules to obtain a data service catalog.

[0037] The present invention builds a unified data service platform for schools by constructing a data service bus architecture, providing data access and sharing channels for various business systems; the compilation of the data service demand list clarifies the specific data requirements of various business systems. The definition of data service interface specification data ensures the standardization and consistency of data exchange between different services, and improves the efficiency and reliability of data exchange; the establishment of service registration and discovery mechanism facilitates service consumers to find and use the required services, and promotes collaboration and interoperability between services. The construction of the semantic service orchestration model enables schools to flexibly combine and orchestrate different data services according to business needs to form composite services that meet specific business scenarios; service orchestration scheduling rules ensure the execution order and efficiency of services and optimize the operating performance of services. The generation of the data service directory provides a clear data service view for the school, showing the school's available data services and their interface specifications, access methods and other information; this will facilitate users to find and use the required data services, promote the sharing and utilization of data resources, and enhance the school's data service capabilities.

[0038] Preferably, step S5 comprises the following steps:

[0039] Step S51: Generate metadata standards and specifications according to the data service directory to obtain metadata standards and specifications data; collect metadata according to the metadata standards and specifications data to obtain metadata; perform metadata quality assessment on the metadata to obtain a metadata quality assessment report;

[0040] Step S52: Perform metadata storage analysis based on metadata standard specification data and metadata quality assessment report to obtain metadata storage architecture diagram; perform metadata management process analysis based on metadata standard specification data to obtain metadata management process data;

[0041] Step S53: define the metadata service interface based on the metadata management process data to obtain metadata service interface specification data;

[0042] Step S54: performing metadata lineage reasoning analysis based on the metadata to obtain a metadata lineage knowledge graph; performing metadata lineage traceability impact analysis based on the metadata lineage knowledge graph to obtain a metadata lineage graph; performing metadata semantic mapping based on the metadata lineage graph to obtain a metadata semantic mapping table;

[0043] Step S55: construct a metadata warehouse based on the metadata storage architecture diagram, metadata management process data, metadata service interface specification data, metadata lineage graph, and metadata semantic mapping table to obtain a metadata warehouse.

[0044] The present invention provides a unified specification and standard for the metadata management of the school through the establishment of metadata standard specifications, ensuring the consistency and accuracy of metadata; metadata collection collects relevant metadata of various systems and business fields of the school, providing a basis for subsequent metadata management and application. Metadata quality assessment can help schools identify problems in metadata and provide a basis for subsequent metadata cleaning and governance. The design of the metadata storage architecture diagram defines the storage method and structure of metadata to ensure the security and accessibility of metadata. The analysis of the metadata management process clarifies the various links and responsible persons of metadata management to ensure the standardization and efficiency of metadata management. The definition of metadata service interface specification data provides a unified interface standard for application programs to access and use metadata, making it convenient for applications to obtain and use metadata information. Metadata lineage reasoning analysis can help schools understand the lineage relationship between data, such as the source, conversion process and final destination of data. The metadata lineage diagram intuitively shows the lineage relationship between data, making it convenient for users to understand the flow and scope of influence of data. Metadata semantic mapping associates metadata with business terms to facilitate users to understand the meaning and purpose of metadata. The construction of the metadata warehouse will centrally store and manage the metadata scattered in various systems, forming a unified metadata view of the school. This will greatly improve the accessibility and manageability of metadata and provide an important infrastructure for the school's data governance and application.

[0045] Preferably, step S54 includes the following steps:

[0046] Step S541: perform metadata collection and analysis on the metadata to obtain metadata analysis records; perform metadata dependency identification based on the metadata analysis records to obtain a metadata dependency matrix; perform metadata lineage reasoning based on the metadata dependency matrix to obtain a metadata lineage knowledge graph; perform metadata impact analysis based on the metadata lineage knowledge graph to obtain a metadata impact analysis report;

[0047] Step S542: performing metadata traceability query according to the metadata lineage knowledge graph and the metadata impact analysis report to obtain metadata traceability query results;

[0048] Step S543: Perform metadata lineage visualization according to the metadata lineage knowledge graph and the metadata traceability query result to obtain a metadata lineage visualization model;

[0049] Step S544: performing metadata lineage pattern mining based on the metadata lineage knowledge graph to obtain a metadata lineage pattern library; performing metadata lineage anomaly detection based on the metadata lineage pattern library to obtain a metadata lineage anomaly report; performing lineage quality assessment based on the metadata lineage anomaly report to obtain metadata lineage quality score data;

[0050] Step S545: Generate a metadata lineage graph based on the metadata lineage knowledge graph, the metadata lineage visualization model, the metadata lineage pattern library, the metadata lineage anomaly report, and the metadata lineage quality score data to obtain a metadata lineage graph.

[0051] Through metadata collection and analysis, the present invention allows schools to convert metadata into a machine-understandable format, which is convenient for subsequent analysis and processing. Metadata dependency identification and lineage reasoning can help schools understand the dependencies and flows between data, and provide important references for data governance and application. Metadata impact analysis can evaluate the impact of data changes on downstream systems and businesses, and help schools to control risks and provide decision support. Metadata traceability query can help users quickly find the source and historical change records of data, which is convenient for data tracking and problem troubleshooting. The metadata lineage visualization model can display complex data lineage relationships in a graphical way, which is convenient for users to intuitively understand the relationship between data and improve data management efficiency. Metadata lineage pattern mining can discover the laws and patterns in data lineage relationships, such as common data flow paths, data conversion rules, etc. These patterns can be used to automate data governance tasks, such as data quality detection, data lineage management, etc. Metadata lineage anomaly detection can identify anomalies in data lineage relationships, such as data interruption, data circulation, etc., to help schools discover and solve data problems in a timely manner. Lineage quality assessment can quantify the quality level of data lineage and provide a reference for the school's data governance work. The metadata lineage graph finally generated will become an important tool for school data governance, helping schools understand the relationship between data, conduct data traceability, impact analysis, and anomaly detection, and improve the school's data management level.

[0052] Preferably, step S6 comprises the following steps:

[0053] Step 61: Identify and collect knowledge sources based on the main data warehouse and the metadata warehouse to obtain an original knowledge base; extract knowledge from the original knowledge base and represent knowledge to obtain a structured knowledge base; perform cross-source fusion on the structured knowledge base to obtain cross-source fusion data;

[0054] Step 62: Perform multi-dimensional knowledge linking on the cross-source knowledge fusion data to obtain a linked knowledge graph; perform knowledge reasoning and knowledge expansion based on the linked knowledge graph to obtain an extended knowledge graph;

[0055] Step 63: construct the school domain knowledge ontology according to the extended knowledge graph to obtain the ontology enhanced knowledge graph; perform external knowledge semantic mapping on the ontology enhanced knowledge graph to obtain the semantic knowledge network; perform knowledge graph visualization on the semantic knowledge network to obtain visualized knowledge graph data;

[0056] Step 64: Perform school information management tasks based on the business process graph, data service directory, and visualized knowledge graph data.

[0057] Through the knowledge source identification and collection, the present invention allows schools to collect relevant knowledge from various systems and business fields to form an original knowledge base. Knowledge extraction and representation convert unstructured knowledge into a structured form, which is convenient for subsequent knowledge fusion and application. Knowledge cross-source fusion integrates and associates knowledge from different sources to form a unified knowledge view. Multidimensional knowledge links can associate and integrate entities and relationships in the knowledge graph to form a richer and more complete knowledge network. Knowledge reasoning can infer new knowledge based on existing knowledge and expand the coverage of the knowledge graph. The construction of the school domain knowledge ontology can standardize and systematize the school's unique knowledge to form the core framework of the school's knowledge base. External knowledge semantic mapping can associate school knowledge with external knowledge bases to expand the breadth and depth of school knowledge. Knowledge graph visualization can display the knowledge graph in a graphical way, which is convenient for users to understand and use knowledge. Based on business process graphs, data service catalogs and visualized knowledge graph data, schools can perform more intelligent and efficient information management operations, such as intelligent search, intelligent question and answer, and intelligent recommendation. This will greatly improve the school's level of informatization and provide more intelligent support for school management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flowchart of the steps of the information management method based on the school management cloud service platform;

[0059] Figure 2 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0060] Figure 3 for Figure 1 Detailed implementation steps of step S5;

[0061] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0062] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0063] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0064] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0065] To achieve this, please refer to Figures 1 to 3 , an information management method based on a school management cloud service platform, comprising the following steps:

[0066] Step S1: locate the business pain points of the school management cloud service platform to obtain the business pain point heat map data; reconstruct the potential process according to the business pain point heat map data to obtain the process reconstruction solution data; reorganize the business process according to the process reconstruction solution data to obtain the business process map;

[0067] Step S2: Analyze the data quality standard requirements of various systems and departments of the school according to the business process map to obtain data standard specification data; perform unified data standard processing according to the data standard specification data to obtain data standard documents;

[0068] Step S3: Perform master data source governance analysis according to the data standard document to obtain master data governance process data; perform master data integration processing according to the master data governance process data to obtain master data integration rule data; perform master data warehouse management analysis according to the master data integration rule data to obtain the master data warehouse;

[0069] Step S4: classify data service requirements according to the main data warehouse to obtain a data service requirement list; perform service orchestration modeling according to the data service requirement list to obtain a semantic service orchestration model; perform service scheduling analysis according to the service clustering model to obtain service orchestration scheduling rules; perform data service bus connection according to the service orchestration scheduling rules to obtain a data service directory;

[0070] Step S5: perform metadata specification collection according to the data service catalog to obtain metadata; perform metadata governance analysis according to the data service catalog and metadata to obtain metadata management process data; perform metadata lineage reasoning analysis according to the metadata to obtain a metadata lineage knowledge graph; perform metadata lineage traceability impact analysis according to the metadata lineage knowledge graph to obtain a metadata lineage graph; perform metadata governance according to the metadata management process data and the metadata lineage graph to obtain a metadata warehouse;

[0071] Step S6: Perform cross-source knowledge fusion based on the main data warehouse and the metadata warehouse to obtain cross-source knowledge fusion data; perform multi-dimensional knowledge linking on the cross-source knowledge fusion data to obtain a linked knowledge graph; perform knowledge reasoning and knowledge expansion based on the linked knowledge graph to obtain an extended knowledge graph; visualize the school domain knowledge ontology based on the extended knowledge graph to obtain visualized knowledge graph data; perform school information management operations based on the business process graph, data service directory and visualized knowledge graph data.

[0072] The present invention locates the business pain points, so that the school can clearly understand the problems and deficiencies in the current management process, and intuitively display them in the form of a heat map. According to the pain point heat map, the school can carry out targeted process reconstruction, optimize key processes, and improve management efficiency and benefits. The business process map finally generated provides a clear process framework and guidance for the school's management work. Based on the business process map, the school can analyze the data quality requirements of each business process and formulate corresponding data standards and specifications. This will effectively improve the quality and consistency of school data and lay the foundation for subsequent data application and analysis. Through the master data source governance analysis, the school can clarify the source and management responsibility of the master data, and formulate the master data governance process to ensure the quality and consistency of the master data. The master data integration processing integrates and unifies the master data scattered in various systems to form a unified master data view of the school, thereby improving the sharing and utilization efficiency of data. Data service demand classification can help schools understand the data requirements of various business systems, and develop and provide corresponding data services according to the requirements. The semantic service orchestration model can flexibly combine and orchestrate different data services according to business needs to form a composite service that meets specific business scenarios. The generation of the data service catalog provides the school with a clear data service view, making it easier for users to find and use the required data services. Metadata collection and governance can help schools establish a sound metadata management system to ensure the accuracy, completeness and consistency of metadata. The metadata lineage diagram intuitively displays the lineage relationship between data, making it easier for users to understand the flow and scope of data. The construction of the metadata warehouse centrally stores and manages the metadata scattered in various systems to form a unified metadata view of the school, providing an important infrastructure for the school's data governance and application. Knowledge cross-source fusion integrates and associates knowledge from different sources to form a richer and more complete knowledge network. The generation of linked knowledge graphs provides a basis for subsequent knowledge reasoning and application. The extended knowledge graph continuously expands the coverage and depth of the knowledge graph through knowledge reasoning and knowledge expansion. Visual knowledge graph data displays the knowledge graph in a graphical way, making it easier for users to understand and use knowledge. Ultimately, schools can perform more intelligent and efficient information management operations based on business process graphs, data service catalogs and visual knowledge graph data to improve the school's informatization level.

[0073] Therefore, the present invention provides an information management method based on the school management cloud service platform, which comprehensively solves the problems of insufficient system integration and data sharing, data integration and consistency faced by the school management cloud service platform through innovative steps of business panoramic scanning and process optimization, cross-departmental collaborative processing and process simulation, semantic service orchestration and service scheduling analysis, metadata lineage reasoning and knowledge cross-source fusion. These steps not only improve management efficiency, but also ensure the consistency and accuracy of data, and at the same time provide deeper knowledge management and decision support for school management.

[0074] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of the information management method based on the school management cloud service platform of the present invention. In this example, the information management method based on the school management cloud service platform includes the following steps:

[0075] Step S1: locate the business pain points of the school management cloud service platform to obtain the business pain point heat map data; reconstruct the potential process according to the business pain point heat map data to obtain the process reconstruction solution data; reorganize the business process according to the process reconstruction solution data to obtain the business process map;

[0076] In the embodiment of the present invention, the school information department organizes relevant personnel to conduct a survey on the school management cloud service platform, find out the pain points in the platform, such as process redundancy, data inconsistency, etc., and draw them into a business pain point heat map. Then, according to the pain point heat map, the process in the platform is reconstructed, such as simplifying process steps, optimizing process links, etc., to form a process reconstruction plan. Finally, according to the reconstructed process, the business process map is redrawn to show the overall business process of the school management cloud service platform.

[0077] Step S2: Analyze the data quality standard requirements of various systems and departments of the school according to the business process map to obtain data standard specification data; perform unified data standard processing according to the data standard specification data to obtain data standard documents;

[0078] In the embodiment of the present invention, according to the business process map, the requirements of each process for data quality, such as data format, data encoding, etc., are analyzed, and corresponding data standard specifications are formulated. Then, the data of each system and department are uniformly processed to make it conform to the data standard specifications and form a data standard document.

[0079] Step S3: Perform master data source governance analysis according to the data standard document to obtain master data governance process data; perform master data integration processing according to the master data governance process data to obtain master data integration rule data; perform master data warehouse management analysis according to the master data integration rule data to obtain the master data warehouse;

[0080] In the embodiment of the present invention, according to the data standard document, the school's master data source is subject to governance analysis, such as determining the source of the master data, management responsibility, etc., and formulating a master data governance process. Then, according to the governance process, the master data in each system is integrated and processed, such as data cleaning, data merging, etc., to form master data integration rules. Finally, according to the integration rules, a master data warehouse is constructed to centrally store and manage the school's master data.

[0081] Step S4: classify data service requirements according to the main data warehouse to obtain a data service requirement list; perform service orchestration modeling according to the data service requirement list to obtain a semantic service orchestration model; perform service scheduling analysis according to the service clustering model to obtain service orchestration scheduling rules; perform data service bus connection according to the service orchestration scheduling rules to obtain a data service directory;

[0082] In an embodiment of the present invention, the needs of various business systems for data services are analyzed based on the data in the main data warehouse, such as querying student information, querying course information, etc., and are classified and sorted to form a data service demand list. Then, based on the demand list, service orchestration modeling is performed, for example, multiple atomic services are combined into a composite service, and a semantic service orchestration model is formed. At the same time, based on the service clustering model, the call relationship and dependency relationship of the service are analyzed, and service orchestration scheduling rules are formulated. Finally, according to the scheduling rules, the various services are connected in series to form a data service directory.

[0083] Step S5: perform metadata specification collection according to the data service catalog to obtain metadata; perform metadata governance analysis according to the data service catalog and metadata to obtain metadata management process data; perform metadata lineage reasoning analysis according to the metadata to obtain a metadata lineage knowledge graph; perform metadata lineage traceability impact analysis according to the metadata lineage knowledge graph to obtain a metadata lineage graph; perform metadata governance according to the metadata management process data and the metadata lineage graph to obtain a metadata warehouse;

[0084] In the embodiment of the present invention, metadata of the service, such as service name, service description, data table structure, etc., is collected according to the data service directory, and a metadata set is formed. Then, metadata governance analysis is performed according to the data service directory and the metadata set, such as determining the management process and storage method of the metadata, and forming metadata management process data. At the same time, lineage reasoning analysis is performed on the metadata set, such as analyzing the dependency relationship and flow relationship between the data, to form a metadata lineage knowledge graph and a metadata lineage graph. Finally, a metadata warehouse is constructed according to the metadata management process data and the metadata lineage graph to centrally store and manage the school's metadata.

[0085] Step S6: Perform cross-source knowledge fusion according to the main data warehouse and the metadata warehouse to obtain cross-source knowledge fusion data; perform multi-dimensional knowledge linking on the cross-source knowledge fusion data to obtain a linked knowledge graph; perform knowledge reasoning and knowledge expansion according to the linked knowledge graph to obtain an extended knowledge graph; perform school domain knowledge ontology visualization according to the extended knowledge graph to obtain visualized knowledge graph data; perform school information management operations based on the business process graph, the data service directory and the visualized knowledge graph data;

[0086] In an embodiment of the present invention, knowledge cross-source fusion is performed based on the data in the main data warehouse and the metadata warehouse, for example, the knowledge in the data dictionary, business rules, process documents, etc. is associated and integrated to form knowledge cross-source fusion data. Then, multi-dimensional knowledge links are performed on the fused data, for example, entities are linked to entities, entities to relationships, and relationships to relationships to form a linked knowledge graph. Next, based on the linked knowledge graph, knowledge reasoning and knowledge expansion are performed, for example, new knowledge is inferred based on existing knowledge, new entities, relationships, and attributes are added, etc., to form an extended knowledge graph. Finally, based on the extended knowledge graph, the school domain knowledge ontology is constructed and visualized to form visualized knowledge graph data. The school can perform school information management operations, such as intelligent search, intelligent question and answer, etc., based on the business process graph, data service directory, and visualized knowledge graph data.

[0087] Preferably, step S1 comprises the following steps:

[0088] Step S11: obtaining business panoramic map data by performing a business panoramic scan on the school management cloud service platform; performing business pain point mining based on the business panoramic map data to obtain business pain point heat map data;

[0089] Step S12: Acquire school management business process data; perform process value evaluation on the school management business process data according to the business pain point heat map data to obtain a process value matrix, wherein the process value matrix includes high-importance and high-potential process data and low-importance and low-potential process data;

[0090] Step S13: Generate a process optimization plan for the high-importance and high-potential process data to obtain process optimization blueprint data; perform process reconstruction processing on the low-importance and low-potential process data to obtain process reconstruction plan data;

[0091] Step S14: Perform cross-departmental collaborative processing according to the process optimization blueprint data and the process reconstruction solution data to obtain a cross-departmental collaborative matrix;

[0092] Step S15: Perform process simulation on the process optimization blueprint data and the process reconstruction solution data according to the cross-departmental collaboration matrix to obtain a process simulation report; perform process performance indicator evaluation based on the process simulation report to obtain a process performance indicator library;

[0093] Step S16: Reorganize the business process map according to the business panoramic map data, process optimization blueprint data, process reconstruction plan data, cross-departmental collaboration matrix and process performance indicator library to obtain a business process map.

[0094] The present invention can clearly identify the problems and improvement opportunities in the current business operation by conducting a comprehensive business scan and pain point mining on the school management cloud service platform, and provide direction and focus for subsequent process optimization. By conducting a value assessment on the school management business process data and classifying it according to the importance and optimization potential of the process, the priority and resource investment of process optimization can be determined in a targeted manner to improve the efficiency and effect of optimization. By optimizing the design of high-importance and high-potential processes and reconstructing low-importance and low-potential processes, it is possible to simplify low-value processes, rationally allocate optimization resources, and maximize the overall benefits of process optimization while fully mining the value of the process. By cross-departmental collaborative processing of optimization plans and reconstruction plans, departmental barriers can be broken, end-to-end process effects can be fully considered, and the feasibility and efficiency of process optimization at the execution level can be ensured. Through process simulation and performance indicator evaluation, the effect of the optimization plan can be predicted, the optimization results can be quantified, quantitative standards and continuous optimization mechanisms for process management can be established, and the normalization of process optimization can be achieved. By restructuring the business process map based on the previous steps, we can form a business process management system that connects all departments, runs through the entire process, and continuously optimizes, providing a business operation framework that is efficient, refined, quantifiable, and optimizable for school management.

[0095] In the embodiment of the present invention, the school information department organizes relevant personnel to conduct a comprehensive investigation and analysis of the school management cloud service platform, including the functional modules, business processes, data flows, user roles and other aspects of the platform. Finally, a business panoramic map of the school management cloud service platform is drawn, and the pain points existing in the platform are identified according to the survey results, such as process redundancy, inconsistent data, poor user experience, etc., and a business pain point heat map is formed. The existing management process documents of the school are collected, and analyzed in combination with the business pain point heat map to evaluate the importance and optimization potential of each process. For example, the teaching management process may be evaluated as a high-importance and high-potential process, while the library borrowing process may be evaluated as a low-importance and low-potential process. Finally, a process value matrix is ​​formed, and the processes are divided into four categories: high-importance and high-potential, high-importance and low-potential, low-importance and high-potential, and low-importance and low-potential. For high-importance and high-potential processes, such as the teaching management process, relevant department personnel are organized to discuss and analyze, propose process optimization plans, and form a process optimization blueprint. For example, the traditional paper teaching process can be changed to an online process to simplify the process steps and improve work efficiency. For low-importance and low-potential processes, such as the library borrowing process, process reconstruction is carried out to simplify the process and reduce management costs. According to the process optimization blueprint and process reconstruction plan, the departments and roles involved in the process are analyzed, and a cross-departmental coordination mechanism is formulated, such as establishing a cross-departmental communication platform, clarifying departmental responsibilities and interfaces, etc. The process simulation software is used to simulate the process optimization plan and process reconstruction plan to evaluate the effectiveness and feasibility of the plan. According to the simulation results, the plan is adjusted and optimized, and process performance indicators are formulated, such as process processing time, process error rate, etc., for subsequent process monitoring and evaluation. Based on the business panoramic map, process optimization blueprint, process reconstruction plan, cross-departmental coordination matrix and process performance indicator library, the school's business process map is reorganized to form a new and optimized business process map, which is used as an important reference for school management.

[0096] Preferably, step S2 comprises the following steps:

[0097] Step S21: perform data asset inventory processing on various systems and departments of the school according to the business process map to obtain a data asset catalog; perform data quality assessment on the data asset catalog to obtain a data quality report;

[0098] Step S22: Perform data standardization requirements analysis based on the data quality report and the business process map to obtain a data standardization requirements document; perform data standardization formulation based on the data standardization requirements document to obtain data standard specification data;

[0099] Step S23: perform standardized data modeling according to the data standard specification data to obtain a standardized data model; perform data standard mapping on the standardized data model to obtain a data standard mapping table; perform standard data interface specification processing according to the data standard mapping table to obtain standard data interface specification data;

[0100] Step S24: Performing data standard testing on the data standard specification data, the standardized data model, the data standard mapping table, and the standard data interface specification data to obtain a data standard testing report;

[0101] Step S25: Generate a data standard document according to the data standard specification data, the standardized data model, the data standard mapping table, the standard data interface specification data and the data standard test report to obtain a data standard document.

[0102] Through data asset inventory, the present invention enables schools to fully understand the data resources they own, including information such as data type, source, storage location, etc. The establishment of a data asset catalog provides a basis for subsequent data governance and application, and is also convenient for schools to manage and maintain data assets. Based on data quality reports and business process maps, schools can analyze the needs of various business processes for data standardization and formulate corresponding data standardization requirements documents; this document will guide subsequent data standardization work to ensure data consistency, accuracy and completeness, thereby improving data availability and value. Standardized data modeling can organize and store data according to unified specifications, facilitating data sharing and exchange; the data standard mapping table defines the data correspondence between different systems or departments to ensure data consistency between different systems; the establishment of standard data interface specification data provides a unified interface standard for data exchange between different systems, improving the efficiency and reliability of data exchange. Data standard testing can verify the effectiveness and feasibility of data standards, ensure that data standards meet actual business needs and can effectively solve data quality problems; test reports can help schools discover defects in data standards and make corrections and improvements in a timely manner. The data standard document finally generated will become an important normative document for school data management, guiding the school in data collection, storage, processing and application; this will effectively improve the school's data management level, promote the effective use of data resources, and provide reliable data support for the school's decision-making and management.

[0103] In the embodiment of the present invention, the data of each system and department of the school are counted, including the type, format, storage location, access rights and other information of the data, and a data asset catalog is formed. At the same time, the data in the data asset catalog is evaluated for quality, such as the integrity, accuracy, consistency and timeliness of the data, and a data quality report is formed. According to the data quality report and the business process map, the requirements of each business process for data standardization, such as data format, data encoding, data definition, etc., are analyzed, and a data standardization requirements document is formed. According to the requirements document, data standard specifications are formulated, such as student information data standards, course information data standards, etc., to clarify the format, encoding, definition and other specifications of the data. According to the data standard specifications, the data is standardized and modeled, such as establishing a student information data model, a course information data model, etc., to define the attributes, relationships and constraints of the data. At the same time, a data standard mapping table is formulated to clarify the data correspondence between different systems or departments, such as mapping the student number in the academic affairs system with the student number in the student affairs system. Finally, a standard data interface specification is formulated to define the interface standard for data exchange, such as interface type, data format, transmission protocol, etc. Test the data standard specifications, standardized data models, data standard mapping tables, and standard data interface specifications to verify their effectiveness and feasibility. For example, test whether the data complies with the standard specifications, whether the data model can meet business needs, whether the data mapping is accurate, etc., and form a data standard test report. Based on the data standard specifications, standardized data models, data standard mapping tables, standard data interface specifications, and data standard test reports, form the final data standard document and publish it for implementation as a normative document for school data management.

[0104] Preferably, step S3 comprises the following steps:

[0105] Step S31: Identify master data according to the data standard document to obtain a master data object list; define master data attributes for each master data object in the master data object list to obtain a master data attribute dictionary; sort out the master data source for each master data object in the master data object list to obtain a master data source mapping diagram;

[0106] Step S32: Perform master data quality diagnosis according to the master data source mapping diagram to obtain a master data quality diagnosis report; perform master data governance process planning according to the master data quality diagnosis report to obtain master data governance process data;

[0107] Step S33: Formulate master data integration rules based on the master data attribute dictionary and the master data governance process data to obtain master data integration rule data;

[0108] Step S34: Analyze the master data synchronization mechanism according to the master data source mapping diagram and the preset master data management platform to obtain the master data synchronization solution; analyze the master data service interface according to the master data synchronization solution to obtain the master data service interface specification data;

[0109] Step S35: construct a master data warehouse according to the preset master data management platform, master data integration rules, master data synchronization solution and master data service interface specification data to obtain a master data warehouse.

[0110] By identifying master data objects, defining master data attributes, and combing master data sources, the present invention can fully understand the key data assets of the school, clarify the business meaning, technical characteristics and sources of the data, and lay the foundation for subsequent master data governance. Through master data quality diagnosis, the accuracy, completeness, consistency and other quality status of the current master data can be objectively evaluated, and data quality problems can be found; on this basis, the master data governance process can be planned, and data quality improvement measures can be designed in a targeted manner, and the organizational mode and division of responsibilities of data governance can be formulated to ensure the orderly development of data governance. According to the master data attributes and governance process, master data integration rules are formulated to guide the standardized integration of master data from different sources and formats, improve data consistency and availability, and reduce data redundancy and errors. By analyzing the synchronization mechanism and service interface between the master data source and the management platform, an efficient and automated data transmission and exchange solution can be designed to ensure real-time synchronization and sharing of master data between different systems, and improve the timeliness and accessibility of data. Based on the above specifications and rules, a master data warehouse is constructed to realize the centralized storage, management and application of master data, provide consistent, authoritative and high-quality master data services, support various business applications and data analysis decisions of the school, and improve the efficiency of business operations and management.

[0111] In an embodiment of the present invention, according to the data standard document, the core master data objects of the school, such as students, teachers, courses, organizations, etc., are identified, and a list of master data objects is formed. For each master data object, its attributes and characteristics are defined, such as the student number, name, gender, major, etc., to form a master data attribute dictionary. At the same time, the distribution of each master data object in each system is sorted out, for example, student data may be distributed in the teaching system, the student work system, the library system, etc., to form a master data source mapping diagram. According to the master data source mapping diagram, the quality of the master data in each system is diagnosed, such as checking the integrity, accuracy, consistency and timeliness of the data, and forming a master data quality diagnosis report. According to the diagnosis report, the master data governance process is formulated to clarify the goals, scope, steps and responsible persons of the master data governance, such as formulating data cleaning rules, data merging rules, data verification rules, etc. Based on the master data attribute dictionary and the master data governance process, the master data integration rules are formulated, such as defining how to merge student data in different systems and how to handle data conflicts. According to the master data source mapping diagram and the preset master data management platform, analyze the master data synchronization mechanism, such as real-time synchronization, scheduled synchronization or manual synchronization, and formulate a master data synchronization plan. According to the synchronization plan, define the master data service interface specifications, such as interface type, data format, transmission protocol, etc., to facilitate application programs to access and use master data. According to the preset master data management platform, master data integration rules, master data synchronization plan and master data service interface specifications, build a master data warehouse to centrally store and manage the master data in each system to form a unified master data view of the school.

[0112] Preferably, step S4 comprises the following steps:

[0113] Step 41: construct the service architecture according to the main data warehouse to obtain the data service bus architecture data; obtain the data service demand data of each business system of the school; perform service classification processing on the data service demand data to obtain a data service demand list;

[0114] Step 42: Define the service interface according to the data service requirement list to obtain data service interface specification data; register and discover the service according to the data service requirement list to obtain service registration and discovery data;

[0115] Step 43: Perform semantic service orchestration processing according to the data service requirement list and the service registration and discovery data to obtain a semantic service orchestration model; perform service scheduling analysis according to the service clustering model to obtain service orchestration scheduling rules;

[0116] Step 44: Generate a data service catalog based on the data service bus architecture data, data service interface specification data, service registration and discovery data, and service orchestration and scheduling rules to obtain a data service catalog.

[0117] The present invention builds a unified data service platform for schools by constructing a data service bus architecture, providing data access and sharing channels for various business systems; the compilation of the data service demand list clarifies the specific data requirements of various business systems. The definition of data service interface specification data ensures the standardization and consistency of data exchange between different services, and improves the efficiency and reliability of data exchange; the establishment of service registration and discovery mechanism facilitates service consumers to find and use the required services, and promotes collaboration and interoperability between services. The construction of the semantic service orchestration model enables schools to flexibly combine and orchestrate different data services according to business needs to form composite services that meet specific business scenarios; service orchestration scheduling rules ensure the execution order and efficiency of services and optimize the operating performance of services. The generation of the data service directory provides a clear data service view for the school, showing the school's available data services and their interface specifications, access methods and other information; this will facilitate users to find and use the required data services, promote the sharing and utilization of data resources, and enhance the school's data service capabilities.

[0118] As an example of the present invention, refer to Figure 2 As shown, in this example, step S4 includes:

[0119] Step 41: construct the service architecture according to the main data warehouse to obtain the data service bus architecture data; obtain the data service demand data of each business system of the school; perform service classification processing on the data service demand data to obtain a data service demand list;

[0120] In the embodiment of the present invention, according to the architecture and function of the main data warehouse, the data service bus architecture is designed, such as adopting SOA architecture, microservice architecture or API gateway, and defining the components and interfaces of the data service bus. At the same time, the data service requirements of various business systems of the school are collected, such as querying student information, querying course information, submitting grades, etc., and the requirements are classified and sorted to form a data service requirement list.

[0121] Step 42: Define the service interface according to the data service requirement list to obtain data service interface specification data; register and discover the service according to the data service requirement list to obtain service registration and discovery data;

[0122] In the embodiment of the present invention, according to the data service requirement list, the interface specification of each service is defined, such as the interface name, parameter type, return value type, etc., and the data service interface specification data is formed. At the same time, a service registration and discovery mechanism is established, such as using a service registration center or a service directory, so that service consumers can find and use the required services.

[0123] Step 43: Perform semantic service orchestration processing according to the data service requirement list and the service registration and discovery data to obtain a semantic service orchestration model; perform service scheduling analysis according to the service clustering model to obtain service orchestration scheduling rules;

[0124] In the embodiment of the present invention, semantic service orchestration processing is performed based on the data service demand list and service registration and discovery data, such as combining multiple atomic services into a composite service, such as combining a student information query service and a grade query service into a student comprehensive information query service. At the same time, based on the service clustering model, the service call relationship and dependency relationship are analyzed, and service orchestration scheduling rules are formulated, such as defining the execution order of services, concurrency control, etc.

[0125] Step 44: Generate a data service catalog based on the data service bus architecture data, the data service interface specification data, the service registration and discovery data, and the service orchestration and scheduling rules to obtain a data service catalog;

[0126] In an embodiment of the present invention, a data service directory is generated based on data service bus architecture data, data service interface specification data, service registration and discovery data, and service orchestration and scheduling rules, including, for example, information such as service name, service description, interface specification, access method, etc., to facilitate users to find and use the required data services.

[0127] Preferably, step S43 includes the following steps:

[0128] Step 431: Perform service dependency analysis based on the data service requirement list and the service registration and discovery data to obtain service dependency data; perform service clustering optimization modeling based on the service dependency data to obtain a service clustering model;

[0129] Step 432: Obtain historical service call records; perform service process mining based on service registration and discovery data and historical service call records to obtain service process data;

[0130] Step 433: Design a service orchestration template based on SPModel to obtain a service orchestration template; perform service orchestration semantic enhancement on the service orchestration template to obtain a semantic service orchestration model;

[0131] Step 434: Perform service scheduling strategy optimization modeling based on the service clustering model and service process data to obtain a service scheduling strategy model;

[0132] Step 435: Perform service orchestration scheduling simulation according to the semantic service orchestration model and the service scheduling strategy model to obtain service orchestration scheduling simulation data; generate service orchestration scheduling rules according to the service orchestration scheduling simulation data to obtain service orchestration scheduling rules.

[0133] Through service dependency analysis, the present invention can help schools understand the dependency relationship between various services, identify key services and potential risk points; service clustering optimization modeling can group services with similar functions or strong correlation, facilitate unified management and scheduling, and improve service operation efficiency. Service process mining can help schools discover potential patterns and rules in the service call process, such as common service call paths, service call frequencies, etc. This information can be used to optimize service orchestration and scheduling strategies and improve the response speed and reliability of services. Designing service orchestration templates based on SPModel can standardize the process and structure of service orchestration and improve the efficiency and maintainability of service orchestration. Semantic enhancement can make the service orchestration model more intelligent, automatically match and call services according to semantic information, and improve the flexibility and adaptability of service orchestration. Service scheduling strategy optimization modeling can formulate more efficient service scheduling strategies based on service clustering models and service process data, such as dynamically adjusting service resource allocation according to factors such as service importance and load conditions to ensure stable operation and performance of services. Service orchestration scheduling simulation can simulate the actual operation of service orchestration and scheduling, evaluate the effects of different strategies, and optimize and adjust according to the simulation results. The service orchestration and scheduling rules finally generated will guide the actual operation of service orchestration and scheduling, ensuring the reliability and efficiency of the service.

[0134] In an embodiment of the present invention, the data service requirement list and service registration and discovery data are analyzed to identify the dependency relationship between each service, for example, a service may depend on the data or functions provided by other services, and form service dependency data. According to the service dependency data, service clustering optimization modeling is performed to group services with similar functions or strong correlations, for example, student information query service, grade query service and course query service are clustered into a student service group. Historical service call records are collected, such as recording service call time, caller, call parameters and other information. According to the service registration and discovery data and historical service call records, service process mining is performed to discover potential patterns and rules in the service call process, such as common service call paths, service call frequencies, etc., and form service process data. Design a service orchestration template based on SPModel (Service Process Model), such as defining the process, steps, input and output information of service orchestration. Semantic enhancement is performed on the service orchestration template, such as adding semantic descriptions of services and semantic relationships between services, to form a semantic service orchestration model. Based on the service clustering model and service process data, the service scheduling strategy optimization modeling is performed. For example, according to factors such as service priority, load, and resource requirements, service scheduling strategies and rules are formulated, such as giving priority to high-priority services and evenly distributing service loads, to form a service scheduling strategy model. Using the service orchestration and scheduling simulation tool, based on the semantic service orchestration model and service scheduling strategy model, the actual operation of service orchestration and scheduling is simulated, such as simulating the service call process, resource consumption, response time, etc., and forming service orchestration scheduling simulation data. Based on the simulation data, the effects of different scheduling strategies are evaluated, and optimization and adjustment are performed, and finally the service orchestration scheduling rules are generated to guide the actual operation of the service.

[0135] Preferably, step S5 comprises the following steps:

[0136] Step S51: Generate metadata standards and specifications according to the data service directory to obtain metadata standards and specifications data; collect metadata according to the metadata standards and specifications data to obtain metadata; perform metadata quality assessment on the metadata to obtain a metadata quality assessment report;

[0137] Step S52: Perform metadata storage analysis based on metadata standard specification data and metadata quality assessment report to obtain metadata storage architecture diagram; perform metadata management process analysis based on metadata standard specification data to obtain metadata management process data;

[0138] Step S53: define the metadata service interface based on the metadata management process data to obtain metadata service interface specification data;

[0139] Step S54: performing metadata lineage reasoning analysis based on the metadata to obtain a metadata lineage knowledge graph; performing metadata lineage traceability impact analysis based on the metadata lineage knowledge graph to obtain a metadata lineage graph; performing metadata semantic mapping based on the metadata lineage graph to obtain a metadata semantic mapping table;

[0140] Step S55: construct a metadata warehouse based on the metadata storage architecture diagram, metadata management process data, metadata service interface specification data, metadata lineage graph, and metadata semantic mapping table to obtain a metadata warehouse.

[0141] The present invention provides a unified specification and standard for the metadata management of the school through the establishment of metadata standard specifications, ensuring the consistency and accuracy of metadata; metadata collection collects relevant metadata of various systems and business fields of the school, providing a basis for subsequent metadata management and application. Metadata quality assessment can help schools identify problems in metadata and provide a basis for subsequent metadata cleaning and governance. The design of the metadata storage architecture diagram defines the storage method and structure of metadata to ensure the security and accessibility of metadata. The analysis of the metadata management process clarifies the various links and responsible persons of metadata management to ensure the standardization and efficiency of metadata management. The definition of metadata service interface specification data provides a unified interface standard for application programs to access and use metadata, making it convenient for applications to obtain and use metadata information. Metadata lineage reasoning analysis can help schools understand the lineage relationship between data, such as the source, conversion process and final destination of data. The metadata lineage diagram intuitively shows the lineage relationship between data, making it convenient for users to understand the flow and scope of influence of data. Metadata semantic mapping associates metadata with business terms to facilitate users to understand the meaning and purpose of metadata. The construction of the metadata warehouse will centrally store and manage the metadata scattered in various systems, forming a unified metadata view of the school. This will greatly improve the accessibility and manageability of metadata and provide an important infrastructure for the school's data governance and application.

[0142] As an example of the present invention, refer to Figure 3 As shown, in this example, step S5 includes:

[0143] Step S51: Generate metadata standards and specifications according to the data service directory to obtain metadata standards and specifications data; collect metadata according to the metadata standards and specifications data to obtain metadata; perform metadata quality assessment on the metadata to obtain a metadata quality assessment report;

[0144] In the embodiment of the present invention, metadata standards and specifications are formulated based on the service information and data information contained in the data service directory, such as defining the format, content, and naming rules of metadata. According to the metadata standards and specifications, metadata, such as service name, service description, data table structure, data field meaning, etc., are collected from the data service directory and other related systems to form a metadata set. The metadata set is quality evaluated, such as checking the completeness, accuracy, consistency, and timeliness of the metadata, and a metadata quality evaluation report is formed.

[0145] Step S52: Perform metadata storage analysis based on metadata standard specification data and metadata quality assessment report to obtain metadata storage architecture diagram; perform metadata management process analysis based on metadata standard specification data to obtain metadata management process data;

[0146] In the embodiment of the present invention, according to the metadata standard specification and the metadata quality assessment report, the metadata storage architecture is designed, for example, a relational database, a NoSQL database or a graph database is used to store metadata, and a metadata storage architecture diagram is formed. At the same time, according to the metadata standard specification, the metadata management process is analyzed, such as the collection, storage, update, and release of metadata, and metadata management process data is formed.

[0147] Step S53: define the metadata service interface based on the metadata management process data to obtain metadata service interface specification data;

[0148] In the embodiment of the present invention, metadata service interface specifications, such as interface type, data format, transmission protocol, etc., are defined based on metadata management process data to facilitate application programs to access and use metadata.

[0149] Step S54: performing metadata lineage reasoning analysis based on the metadata to obtain a metadata lineage knowledge graph; performing metadata lineage traceability impact analysis based on the metadata lineage knowledge graph to obtain a metadata lineage graph; performing metadata semantic mapping based on the metadata lineage graph to obtain a metadata semantic mapping table;

[0150] In the embodiment of the present invention, lineage reasoning analysis is performed on the metadata data set, such as analyzing the dependency relationship and flow relationship between the data, to form a metadata lineage knowledge graph. According to the lineage knowledge graph, the impact of data changes on downstream systems and businesses is analyzed, and a metadata lineage graph is formed. At the same time, the metadata is associated with business terms, such as mapping the data table name to the business entity name, to form a metadata semantic mapping table.

[0151] Step S55: constructing a metadata warehouse based on the metadata storage architecture diagram, metadata management process data, metadata service interface specification data, metadata lineage diagram, and metadata semantic mapping table to obtain a metadata warehouse;

[0152] In an embodiment of the present invention, a metadata warehouse is constructed based on the metadata storage architecture diagram, metadata management process data, metadata service interface specification data, metadata lineage graph and metadata semantic mapping table, and the metadata in each system is centrally stored and managed to form a unified metadata view of the school.

[0153] Preferably, step S54 includes the following steps:

[0154] Step S541: perform metadata collection and analysis on the metadata to obtain metadata analysis records; perform metadata dependency identification based on the metadata analysis records to obtain a metadata dependency matrix; perform metadata lineage reasoning based on the metadata dependency matrix to obtain a metadata lineage knowledge graph; perform metadata impact analysis based on the metadata lineage knowledge graph to obtain a metadata impact analysis report;

[0155] Step S542: performing metadata traceability query according to the metadata lineage knowledge graph and the metadata impact analysis report to obtain metadata traceability query results;

[0156] Step S543: Perform metadata lineage visualization according to the metadata lineage knowledge graph and the metadata traceability query result to obtain a metadata lineage visualization model;

[0157] Step S544: performing metadata lineage pattern mining based on the metadata lineage knowledge graph to obtain a metadata lineage pattern library; performing metadata lineage anomaly detection based on the metadata lineage pattern library to obtain a metadata lineage anomaly report; performing lineage quality assessment based on the metadata lineage anomaly report to obtain metadata lineage quality score data;

[0158] Step S545: Generate a metadata lineage graph based on the metadata lineage knowledge graph, the metadata lineage visualization model, the metadata lineage pattern library, the metadata lineage anomaly report, and the metadata lineage quality score data to obtain a metadata lineage graph.

[0159] Through metadata collection and analysis, the present invention allows schools to convert metadata into a machine-understandable format, which is convenient for subsequent analysis and processing. Metadata dependency identification and lineage reasoning can help schools understand the dependencies and flows between data, and provide important references for data governance and application. Metadata impact analysis can evaluate the impact of data changes on downstream systems and businesses, and help schools to control risks and provide decision support. Metadata traceability query can help users quickly find the source and historical change records of data, which is convenient for data tracking and problem troubleshooting. The metadata lineage visualization model can display complex data lineage relationships in a graphical way, which is convenient for users to intuitively understand the relationship between data and improve data management efficiency. Metadata lineage pattern mining can discover the laws and patterns in data lineage relationships, such as common data flow paths, data conversion rules, etc. These patterns can be used to automate data governance tasks, such as data quality detection, data lineage management, etc. Metadata lineage anomaly detection can identify anomalies in data lineage relationships, such as data interruption, data circulation, etc., to help schools discover and solve data problems in a timely manner. Lineage quality assessment can quantify the quality level of data lineage and provide a reference for the school's data governance work. The metadata lineage graph finally generated will become an important tool for school data governance, helping schools understand the relationship between data, conduct data traceability, impact analysis, and anomaly detection, and improve the school's data management level.

[0160] In an embodiment of the present invention, the collected metadata is parsed, for example, the data table structure, the meaning of the data field, the ETL script, etc. are parsed, and a metadata parsing record is formed. According to the parsing record, the dependency between metadata, such as the dependency between data tables, the dependency between data fields, etc., is identified, and a metadata dependency matrix is ​​formed. According to the dependency matrix, metadata lineage reasoning is performed, such as inferring the source, conversion process and final destination of the data, and a metadata lineage knowledge graph is formed. According to the lineage knowledge graph, the impact of data changes on downstream systems and businesses is analyzed, and a metadata impact analysis report is formed. According to the metadata lineage knowledge graph and the metadata impact analysis report, the user can perform metadata traceability query, such as querying the source of a certain data field, querying the dependency of a certain data table, etc., and obtain the metadata traceability query result. According to the metadata lineage knowledge graph and the metadata traceability query result, the metadata lineage relationship is displayed in a graphical manner, for example, using nodes and edges to represent data entities and data relationships, and forming a metadata lineage visualization model. Pattern mining is performed on the metadata lineage knowledge graph, such as discovering common data flow paths, data conversion rules, etc., and forming a metadata lineage pattern library. According to the pattern library, perform anomaly detection on metadata lineage, such as detecting abnormal situations such as data interruption and data circulation, and generate metadata lineage anomaly reports. According to the anomaly reports, evaluate the quality level of metadata lineage, such as evaluating the integrity, accuracy and consistency of lineage, and generate metadata lineage quality score data. According to the metadata lineage knowledge graph, metadata lineage visualization model, metadata lineage pattern library, metadata lineage anomaly reports and metadata lineage quality score data, generate the final metadata lineage graph and use it as an important tool for data governance.

[0161] Preferably, step S6 comprises the following steps:

[0162] Step 61: Identify and collect knowledge sources based on the main data warehouse and the metadata warehouse to obtain an original knowledge base; extract knowledge from the original knowledge base and represent knowledge to obtain a structured knowledge base; perform cross-source fusion on the structured knowledge base to obtain cross-source fusion data;

[0163] Step 62: Perform multi-dimensional knowledge linking on the cross-source knowledge fusion data to obtain a linked knowledge graph; perform knowledge reasoning and knowledge expansion based on the linked knowledge graph to obtain an extended knowledge graph;

[0164] Step 63: construct the school domain knowledge ontology according to the extended knowledge graph to obtain the ontology enhanced knowledge graph; perform external knowledge semantic mapping on the ontology enhanced knowledge graph to obtain the semantic knowledge network; perform knowledge graph visualization on the semantic knowledge network to obtain visualized knowledge graph data;

[0165] Step 64: Perform school information management tasks based on the business process graph, data service directory, and visualized knowledge graph data.

[0166] Through the knowledge source identification and collection, the present invention allows schools to collect relevant knowledge from various systems and business fields to form an original knowledge base. Knowledge extraction and representation convert unstructured knowledge into a structured form, which is convenient for subsequent knowledge fusion and application. Knowledge cross-source fusion integrates and associates knowledge from different sources to form a unified knowledge view. Multidimensional knowledge links can associate and integrate entities and relationships in the knowledge graph to form a richer and more complete knowledge network. Knowledge reasoning can infer new knowledge based on existing knowledge and expand the coverage of the knowledge graph. The construction of the school domain knowledge ontology can standardize and systematize the school's unique knowledge to form the core framework of the school's knowledge base. External knowledge semantic mapping can associate school knowledge with external knowledge bases to expand the breadth and depth of school knowledge. Knowledge graph visualization can display the knowledge graph in a graphical way, which is convenient for users to understand and use knowledge. Based on business process graphs, data service catalogs and visualized knowledge graph data, schools can perform more intelligent and efficient information management operations, such as intelligent search, intelligent question and answer, and intelligent recommendation. This will greatly improve the school's level of informatization and provide more intelligent support for school management and decision-making.

[0167] In an embodiment of the present invention, potential knowledge sources, such as data dictionaries, business rules, process documents, etc., are identified based on the data in the main data warehouse and the metadata warehouse, and knowledge collection is performed, such as extracting term definitions in the data dictionary, extracting logical relationships in the business rules, etc., to form an original knowledge base. Knowledge extraction is performed on the original knowledge base, such as extracting knowledge elements such as entities, relationships, and attributes, and knowledge representation is performed, such as representing knowledge in triples or RDF format to form a structured knowledge base. Cross-source fusion is performed on the structured knowledge base, such as associating and integrating knowledge from different knowledge sources to form cross-source fusion data of knowledge. Multi-dimensional knowledge links are performed on the cross-source fusion data of knowledge, such as linking entities with entities, entities with relationships, and relationships with relationships to form a richer and more complete knowledge network, and a linked knowledge graph is generated. Based on the linked knowledge graph, knowledge reasoning is performed, such as inferring new knowledge based on existing knowledge, and knowledge expansion is performed, such as adding new entities, relationships, and attributes to form an extended knowledge graph. Based on the extended knowledge graph, the school domain knowledge ontology is constructed, such as defining school-related concepts, entities, relationships, and attributes, etc., to form an ontology-enhanced knowledge graph. External knowledge semantic mapping is performed on the ontology-enhanced knowledge graph, such as associating school knowledge with external knowledge bases (such as encyclopedia knowledge bases) to form a semantic knowledge network. The knowledge graph of the semantic knowledge network is visualized, such as using a graphical method to display entities, relationships, and attributes in the knowledge graph to form visualized knowledge graph data. Based on the business process graph, data service directory, and visualized knowledge graph data, the school information department develops and deploys school information management applications, such as intelligent search, intelligent question and answer, and intelligent recommendation, to provide intelligent support for school management and decision-making.

[0168] Preferably, step S62 includes the following steps:

[0169] Step 621: Perform social media knowledge linking on the cross-source fusion data to obtain school social media knowledge data; perform spatiotemporal knowledge linking on the cross-source fusion data to obtain school activity spatiotemporal knowledge data;

[0170] Step 622: Generate a linked knowledge graph based on the cross-source knowledge fusion data, the school social media knowledge data, and the school activity spatiotemporal knowledge data to obtain a linked knowledge graph;

[0171] Step 623: perform knowledge quality assessment on the linked knowledge graph to obtain a knowledge quality report; perform heterogeneous knowledge reasoning based on the linked knowledge graph and the knowledge quality report to obtain heterogeneous knowledge reasoning data;

[0172] Step 624: Automatically expand the knowledge of the main data warehouse and the metadata warehouse according to the heterogeneous knowledge reasoning data to obtain knowledge expansion data;

[0173] Step 625: Perform incremental knowledge reasoning based on heterogeneous knowledge reasoning data and knowledge expansion data to obtain an extended knowledge graph.

[0174] Through social media knowledge links, the present invention allows schools to associate information on social media with internal school knowledge, such as associating student activities on social media with student activities in the school, enriching the school's understanding of students. Spatiotemporal knowledge links can associate school activities with time and location information, making it easier to manage and analyze activities. Linking the generation of knowledge graphs integrates and associates knowledge from different sources to form a richer and more complete knowledge network, providing a basis for subsequent knowledge reasoning and application. Knowledge quality assessment can help schools understand the quality of knowledge in knowledge graphs, such as the accuracy, completeness and consistency of knowledge. Heterogeneous knowledge reasoning can be based on different types of knowledge. For example, text knowledge is associated and reasoned with image knowledge to expand the expression and reasoning capabilities of knowledge graphs. Automated knowledge expansion can use the results of heterogeneous knowledge reasoning to automatically add new knowledge to the main data warehouse and metadata warehouse, enrich the school's knowledge base, and improve the completeness and accuracy of knowledge. Incremental knowledge reasoning can continuously update and expand the knowledge graph based on new data and knowledge to maintain the dynamic and timeliness of the knowledge graph. The extended knowledge graph finally generated will become the core platform for school knowledge management, providing intelligent support for school decision-making and management.

[0175] In an embodiment of the present invention, social media knowledge links are performed on cross-source fusion data of knowledge, for example, students' activity information on social media is associated with school enrollment information, grade information, etc., to form school social media knowledge data. At the same time, spatiotemporal knowledge links are performed on cross-source fusion data of knowledge, for example, school activity information is associated with time and location information to form school activity spatiotemporal knowledge data. Based on the cross-source fusion data of knowledge, school social media knowledge data, and school activity spatiotemporal knowledge data, a linked knowledge graph is generated, for example, student information, social media information, activity information, time information, and location information are associated and integrated to form a knowledge network containing multidimensional information. Knowledge quality assessment is performed on the linked knowledge graph, for example, the accuracy, completeness, consistency, and timeliness of knowledge are assessed, and a knowledge quality report is formed. Based on the linked knowledge graph and the knowledge quality report, heterogeneous knowledge reasoning is performed, for example, text knowledge is associated with image knowledge, and structured knowledge is associated with unstructured knowledge and reasoning is performed to form heterogeneous knowledge reasoning data. Based on heterogeneous knowledge reasoning data, the main data warehouse and metadata warehouse are automatically expanded, such as adding inferred new entities, relationships, and attributes to the knowledge base to enrich the school's knowledge base. Based on heterogeneous knowledge reasoning data and knowledge expansion data, incremental knowledge reasoning is performed, such as continuously updating and expanding the knowledge graph based on new data and knowledge, and forming the final extended knowledge graph.

[0176] The present invention locates the business pain points, so that the school can clearly understand the problems and deficiencies in the current management process, and intuitively display them in the form of a heat map. According to the pain point heat map, the school can carry out targeted process reconstruction, optimize key processes, and improve management efficiency and benefits. The business process map finally generated provides a clear process framework and guidance for the school's management work. Based on the business process map, the school can analyze the data quality requirements of each business process and formulate corresponding data standards and specifications. This will effectively improve the quality and consistency of school data and lay the foundation for subsequent data application and analysis. Through the master data source governance analysis, the school can clarify the source and management responsibility of the master data, and formulate the master data governance process to ensure the quality and consistency of the master data. The master data integration processing integrates and unifies the master data scattered in various systems to form a unified master data view of the school, thereby improving the sharing and utilization efficiency of data. Data service demand classification can help schools understand the data requirements of various business systems, and develop and provide corresponding data services according to the requirements. The semantic service orchestration model can flexibly combine and orchestrate different data services according to business needs to form a composite service that meets specific business scenarios. The generation of the data service catalog provides the school with a clear data service view, making it easier for users to find and use the required data services. Metadata collection and governance can help schools establish a sound metadata management system to ensure the accuracy, completeness and consistency of metadata. The metadata lineage diagram intuitively displays the lineage relationship between data, making it easier for users to understand the flow and scope of data. The construction of the metadata warehouse centrally stores and manages the metadata scattered in various systems to form a unified metadata view of the school, providing an important infrastructure for the school's data governance and application. Knowledge cross-source fusion integrates and associates knowledge from different sources to form a richer and more complete knowledge network. The generation of linked knowledge graphs provides a basis for subsequent knowledge reasoning and application. The extended knowledge graph continuously expands the coverage and depth of the knowledge graph through knowledge reasoning and knowledge expansion. Visual knowledge graph data displays the knowledge graph in a graphical way, making it easier for users to understand and use knowledge. Ultimately, schools can perform more intelligent and efficient information management operations based on business process graphs, data service catalogs and visual knowledge graph data to improve the school's informatization level.

[0177] Therefore, the present invention provides an information management method based on the school management cloud service platform, which comprehensively solves the problems of insufficient system integration and data sharing, data integration and consistency faced by the school management cloud service platform through innovative steps of business panoramic scanning and process optimization, cross-departmental collaborative processing and process simulation, semantic service orchestration and service scheduling analysis, metadata lineage reasoning and knowledge cross-source fusion. These steps not only improve management efficiency, but also ensure the consistency and accuracy of data, and at the same time provide deeper knowledge management and decision support for school management.

[0178] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0179] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An information management method based on a school management cloud service platform, characterized in that: The following steps are involved: Step S1: locate the business pain points of the school management cloud service platform to obtain the business pain point heat map data; Reconstruct potential processes based on the business pain point heat map data to obtain process reconstruction solution data; The business process is reorganized according to the process reconstruction solution data to obtain a business process map, wherein step S1 includes the following steps: Step S11: obtaining business panoramic map data by performing a business panoramic scan on the school management cloud service platform; performing business pain point mining based on the business panoramic map data to obtain business pain point heat map data; Step S12: Acquire school management business process data; perform process value evaluation on the school management business process data according to the business pain point heat map data to obtain a process value matrix, wherein the process value matrix includes high-importance and high-potential process data and low-importance and low-potential process data; Step S13: Generate a process optimization plan for the high-importance and high-potential process data to obtain process optimization blueprint data; perform process reconstruction processing on the low-importance and low-potential process data to obtain process reconstruction plan data; Step S14: Perform cross-departmental collaborative processing according to the process optimization blueprint data and the process reconstruction solution data to obtain a cross-departmental collaborative matrix; Step S15: Perform process simulation on the process optimization blueprint data and the process reconstruction solution data according to the cross-departmental collaboration matrix to obtain a process simulation report; perform process performance indicator evaluation based on the process simulation report to obtain a process performance indicator library; Step S16: reorganize the business process map according to the business panorama map data, process optimization blueprint data, process reconstruction solution data, cross-departmental collaboration matrix and process performance indicator library to obtain a business process map; Step S2: Analyze the data quality standard requirements of various systems and departments of the school according to the business process map to obtain data standard specification data; perform unified data standard processing according to the data standard specification data to obtain data standard documents; Step S3: Perform master data source governance analysis according to the data standard document to obtain master data governance process data; perform master data integration processing according to the master data governance process data to obtain master data integration rule data; perform master data warehouse management analysis according to the master data integration rule data to obtain the master data warehouse; Step S4: classify data service requirements according to the main data warehouse to obtain a data service requirement list; perform service orchestration modeling according to the data service requirement list to obtain a semantic service orchestration model; perform service scheduling analysis according to the service clustering model to obtain service orchestration scheduling rules; perform data service bus connection according to the service orchestration scheduling rules to obtain a data service directory; Step S5: perform metadata specification collection according to the data service catalog to obtain metadata; perform metadata governance analysis according to the data service catalog and metadata to obtain metadata management process data; perform metadata lineage reasoning analysis according to the metadata to obtain a metadata lineage knowledge graph; perform metadata lineage traceability impact analysis according to the metadata lineage knowledge graph to obtain a metadata lineage graph; perform metadata governance according to the metadata management process data and the metadata lineage graph to obtain a metadata warehouse; Step S6: perform cross-source knowledge fusion according to the main data warehouse and the metadata warehouse to obtain cross-source knowledge fusion data; perform multi-dimensional knowledge linking on the cross-source knowledge fusion data to obtain a linked knowledge graph; perform knowledge reasoning and knowledge expansion according to the linked knowledge graph to obtain an extended knowledge graph; perform school domain knowledge ontology visualization according to the extended knowledge graph to obtain visualized knowledge graph data; perform school information management operations based on the business process graph, the data service directory and the visualized knowledge graph data, wherein step S6 includes the following steps: Step S61: Identify and collect knowledge sources based on the main data warehouse and the metadata warehouse to obtain an original knowledge base; extract knowledge from the original knowledge base and represent knowledge to obtain a structured knowledge base; perform cross-source fusion on the structured knowledge base to obtain cross-source fusion data; Step S62: Perform multi-dimensional knowledge linking on the cross-source knowledge fusion data to obtain a linked knowledge graph; perform knowledge reasoning and knowledge expansion based on the linked knowledge graph to obtain an extended knowledge graph, wherein step S62 includes the following steps: Step S621: Perform social media knowledge linking on the cross-source fusion data to obtain school social media knowledge data; perform spatiotemporal knowledge linking on the cross-source fusion data to obtain school activity spatiotemporal knowledge data; Step S622: Generate a linked knowledge graph based on the cross-source knowledge fusion data, the school social media knowledge data, and the school activity spatiotemporal knowledge data to obtain a linked knowledge graph; Step S623: perform knowledge quality assessment on the linked knowledge graph to obtain a knowledge quality report; perform heterogeneous knowledge reasoning based on the linked knowledge graph and the knowledge quality report to obtain heterogeneous knowledge reasoning data; Step S624: Automatically expand the knowledge of the main data warehouse and the metadata warehouse according to the heterogeneous knowledge reasoning data to obtain knowledge expansion data; Step S625: performing incremental knowledge reasoning based on heterogeneous knowledge reasoning data and knowledge expansion data to obtain an extended knowledge graph; Step S63: construct the school domain knowledge ontology according to the extended knowledge graph to obtain the ontology enhanced knowledge graph; perform external knowledge semantic mapping on the ontology enhanced knowledge graph to obtain the semantic knowledge network; perform knowledge graph visualization on the semantic knowledge network to obtain visualized knowledge graph data; Step S64: Perform school information management tasks based on the business process graph, data service directory and visualized knowledge graph data.

2. The information management method based on the school management cloud service platform according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: perform data asset inventory processing on various systems and departments of the school according to the business process map to obtain a data asset catalog; perform data quality assessment on the data asset catalog to obtain a data quality report; Step S22: Perform data standardization requirements analysis based on the data quality report and the business process map to obtain a data standardization requirements document; perform data standardization formulation based on the data standardization requirements document to obtain data standard specification data; Step S23: perform standardized data modeling according to the data standard specification data to obtain a standardized data model; perform data standard mapping on the standardized data model to obtain a data standard mapping table; perform standard data interface specification processing according to the data standard mapping table to obtain standard data interface specification data; Step S24: Performing data standard testing on the data standard specification data, the standardized data model, the data standard mapping table, and the standard data interface specification data to obtain a data standard testing report; Step S25: Generate a data standard document according to the data standard specification data, the standardized data model, the data standard mapping table, the standard data interface specification data and the data standard test report to obtain a data standard document.

3. The information management method based on the school management cloud service platform according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: identify master data according to the data standard document to obtain a master data object list; define master data attributes for each master data object in the master data object list to obtain a master data attribute dictionary; sort out the master data source for each master data object in the master data object list to obtain a master data source mapping diagram; Step S32: Perform master data quality diagnosis according to the master data source mapping diagram to obtain a master data quality diagnosis report; perform master data governance process planning according to the master data quality diagnosis report to obtain master data governance process data; Step S33: Formulate master data integration rules based on the master data attribute dictionary and the master data governance process data to obtain master data integration rule data; Step S34: Analyze the master data synchronization mechanism according to the master data source mapping diagram and the preset master data management platform to obtain the master data synchronization solution; analyze the master data service interface according to the master data synchronization solution to obtain the master data service interface specification data; Step S35: construct a master data warehouse according to the preset master data management platform, master data integration rules, master data synchronization solution and master data service interface specification data to obtain a master data warehouse.

4. The information management method based on the school management cloud service platform according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: constructing a service architecture according to the main data warehouse to obtain data service bus architecture data; obtaining data service demand data of each business system of the school; performing service classification processing on the data service demand data to obtain a data service demand list; Step S42: define the service interface according to the data service requirement list to obtain data service interface specification data; register and discover the service according to the data service requirement list to obtain service registration and discovery data; Step S43: Perform semantic service orchestration processing according to the data service demand list and the service registration and discovery data to obtain a semantic service orchestration model; perform service scheduling analysis according to the service clustering model to obtain service orchestration scheduling rules; Step S44: Generate a data service catalog based on the data service bus architecture data, data service interface specification data, service registration and discovery data, and service orchestration and scheduling rules to obtain a data service catalog.

5. The information management method based on the school management cloud service platform according to claim 4 is characterized in that: Step S43 includes the following steps: Step S431: performing service dependency analysis according to the data service requirement list and the service registration and discovery data to obtain service dependency data; performing service clustering optimization modeling according to the service dependency data to obtain a service clustering model; Step S432: Obtain historical service call records; perform service process mining based on service registration and discovery data and historical service call records to obtain service process data; Step S433: Design a service orchestration template based on SPModel to obtain a service orchestration template; perform service orchestration semantic enhancement on the service orchestration template to obtain a semantic service orchestration model; Step S434: performing service scheduling strategy optimization modeling according to the service clustering model and the service process data to obtain a service scheduling strategy model; Step S435: Perform service orchestration scheduling simulation according to the semantic service orchestration model and the service scheduling strategy model to obtain service orchestration scheduling simulation data; generate service orchestration scheduling rules according to the service orchestration scheduling simulation data to obtain service orchestration scheduling rules.

6. The information management method based on the school management cloud service platform according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: Generate metadata standards and specifications according to the data service directory to obtain metadata standards and specifications data; collect metadata according to the metadata standards and specifications data to obtain metadata; perform metadata quality assessment on the metadata to obtain a metadata quality assessment report; Step S52: Perform metadata storage analysis based on metadata standard specification data and metadata quality assessment report to obtain metadata storage architecture diagram; perform metadata management process analysis based on metadata standard specification data to obtain metadata management process data; Step S53: define the metadata service interface based on the metadata management process data to obtain metadata service interface specification data; Step S54: performing metadata lineage reasoning analysis based on the metadata to obtain a metadata lineage knowledge graph; performing metadata lineage traceability impact analysis based on the metadata lineage knowledge graph to obtain a metadata lineage graph; performing metadata semantic mapping based on the metadata lineage graph to obtain a metadata semantic mapping table; Step S55: construct a metadata warehouse based on the metadata storage architecture diagram, metadata management process data, metadata service interface specification data, metadata lineage graph, and metadata semantic mapping table to obtain a metadata warehouse.

7. The information management method based on the school management cloud service platform according to claim 6 is characterized in that: Step S54 includes the following steps: Step S541: perform metadata collection and analysis on the metadata to obtain metadata analysis records; perform metadata dependency identification based on the metadata analysis records to obtain a metadata dependency matrix; perform metadata lineage reasoning based on the metadata dependency matrix to obtain a metadata lineage knowledge graph; perform metadata impact analysis based on the metadata lineage knowledge graph to obtain a metadata impact analysis report; Step S542: Perform metadata traceability query based on the metadata lineage knowledge graph and the metadata impact analysis report to obtain metadata traceability query results; Step S543: Perform metadata lineage visualization according to the metadata lineage knowledge graph and the metadata traceability query result to obtain a metadata lineage visualization model; Step S544: performing metadata lineage pattern mining based on the metadata lineage knowledge graph to obtain a metadata lineage pattern library; performing metadata lineage anomaly detection based on the metadata lineage pattern library to obtain a metadata lineage anomaly report; performing lineage quality assessment based on the metadata lineage anomaly report to obtain metadata lineage quality score data; Step S545: Generate a metadata lineage graph based on the metadata lineage knowledge graph, the metadata lineage visualization model, the metadata lineage pattern library, the metadata lineage anomaly report, and the metadata lineage quality score data to obtain a metadata lineage graph.

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