Vocational college big data center platform architecture system, method and equipment

Abstract

The invention relates to the technical field of data management, in particular to a vocational college big data center platform architecture system, method and equipment. The system comprises a data storage module used for obtaining to-be-integrated data and storing the to-be-integrated data to a tree directory structure to obtain stored hierarchical data, and each node in the tree directory structure corresponds to a directory; the data archiving module is used for acquiring to-be-archived data, comparing the to-be-archived data with the hierarchical data, determining a matched target directory of the to-be-archived data in the tree directory structure, and storing the to-be-archived data to the target directory; the data analysis module is used for analyzing the hierarchical data to obtain a data analysis result under each directory, and the data analysis result comprises a historical development condition and a future improvement direction; and the system management module is used for acquiring access data of the hierarchical data and dynamically adjusting the access authority of the hierarchical data based on the access data. According to the invention, the college data can be accurately and efficiently managed.

Description

Technical Field

[0001] The present application relates to the field of data management technology, and in particular to a big data center platform architecture system, method and equipment for vocational colleges. Background Art

[0002] With the rapid development of information technology, educational informatization has become an important means to improve the quality of education and promote educational equity. Various colleges and universities have strengthened their informatization construction and built big data center platforms to integrate, store and analyze various types of educational data to support teaching, scientific research and management decisions. This trend has not only promoted the transformation of educational methods, but also improved the utilization efficiency of educational resources. However, related technologies mostly use manual management of school data, which is not only inefficient and difficult to cope with the processing needs of massive data, but also prone to errors, resulting in inconsistency and inaccuracy of data, which in turn affects the scientificity and effectiveness of teaching, scientific research and management decisions. Summary of the invention

[0003] In order to solve the problem of low accuracy of school data management in the existing technology, the present application provides a vocational school big data center platform architecture system, method and equipment.

[0004] In the first aspect, this application provides a vocational school big data center platform architecture system, which adopts the following technical solutions: A big data center platform architecture system for vocational schools, comprising: a data storage module, a data archiving module, a data analysis module and a system management module; The data storage module is used to obtain the data to be integrated, and store the data to be integrated in a tree directory structure to obtain stored hierarchical data, wherein each node in the tree directory structure corresponds to a directory; The data archiving module is used to obtain the data to be archived, compare the data to be archived with the hierarchical data, determine the target directory that matches the data to be archived in the tree directory structure, and store the data to be archived in the target directory; The data analysis module is used to analyze the hierarchical data to obtain data analysis results under each directory, and the data analysis results include historical development status and future improvement directions; The system management module is used to obtain access data of the hierarchical data and dynamically adjust the access rights of the hierarchical data based on the access data.

[0005] By adopting the above technical solution, the data storage module uses a tree directory structure to store data, which can clearly display the hierarchical relationship and classification of data, making it easier to manage and search data. The data archiving module can accurately find the target directory of the data to be archived in the tree directory structure by comparing the data to be archived and the hierarchical data, ensuring the correct archiving of the data. Analyzing the hierarchical data can accurately reveal the inherent laws and trends of the data, understand the historical development of data under each directory, and then propose future improvement directions to promote the continuous improvement and enhancement of colleges and universities. The system management module can obtain the access data of the hierarchical data and dynamically adjust the access rights of the hierarchical data based on the access data to ensure the security and compliance of the data. This application realizes the accurate and efficient management of college data.

[0006] In a preferred example, the present application may be further configured as follows: the data storage module includes a cluster analysis unit and a data storage unit; The cluster analysis unit is used to perform multi-step splitting on the data to be integrated using a split hierarchical clustering algorithm, and record the splitting process and splitting results, wherein the splitting results include the classification clusters obtained in each step of the splitting; The data storage unit is used to construct a tree directory structure according to the splitting process, and store the classification clusters in the splitting results in the corresponding directories in the tree directory structure in a one-to-one matching manner.

[0007] By adopting the above technical solution, the clustering analysis unit adopts a split hierarchical clustering algorithm to process the integrated data. Multi-step splitting can ensure that the data is subdivided into more specific categories, which helps to improve the accuracy and precision of data classification. Similar data in the splitting results are stored in the same directory for easy management and search. The data storage unit constructs a tree directory structure according to the splitting process, and matches the classification clusters generated by the clustering analysis unit to the corresponding directories in the tree directory structure one by one. The matching storage method ensures the accuracy and consistency of the data and improves the efficiency of data storage.

[0008] In a preferred example, the present application may be further configured as: the data archiving module directory label construction unit and the data archiving unit; The directory label construction unit is used to extract features from the data stored in each directory in the tree-shaped directory structure to obtain a feature label for each directory; The data archiving unit is used to extract features of the data to be archived to obtain features to be archived, match the features to be archived with feature labels of each directory to obtain feature matching results, and determine a target directory based on the feature matching results.

[0009] By adopting the above technical solution, the data archiving module extracts the data features of each directory in the tree directory structure through the directory label construction unit to form a feature label, and the data archiving unit extracts the features of the data to be archived and matches them with the feature labels of each directory, thereby accurately determining the target directory and achieving high efficiency and accuracy in data archiving.

[0010] In a preferred example, the present application may be further configured as follows: the feature matching result includes the feature matching degree between the feature to be archived and the feature label of each directory; The data archiving unit is used to sort the directories in the tree-shaped directory structure from high to low according to the feature matching degree to obtain a directory list, and select a preset number of directories from the directory list as candidate directories; match the data to be archived with the data stored in each directory in the candidate directory to obtain a data matching result, and determine the directory with the highest matching degree between the stored data and the data to be archived as the target directory based on the data matching result.

[0011] By adopting the above technical solution, the data archiving module calculates the feature matching degree between the features to be archived and the feature labels of each directory in the tree directory structure, and sorts them accordingly to obtain a directory list. After further screening out a preset number of candidate directories, the data level matching is performed, and finally the directory with the highest matching degree with the data to be archived is determined as the target directory. This process comprehensively considers the feature level and the specific data level, thereby improving the accuracy of data archiving.

[0012] In a preferred example, the present application can be further configured as follows: the data analysis module is used to perform time series analysis on the data under the target leaf directory to obtain the historical development of the target leaf directory, and the target leaf directory is any leaf directory in the tree directory structure; based on the historical development of each leaf directory under the tree directory structure, determine the future improvement direction.

[0013] By adopting the above technical solution, the data analysis module can accurately depict the historical development of data under the directory by performing time series analysis on the target leaf directory data in the tree directory structure, and then based on the comprehensive analysis of the historical data of each leaf directory in the entire tree directory structure, it can scientifically predict and determine the future improvement direction.

[0014] In a preferred example, the present application may be further configured as follows: the historical development of the target leaf directory includes the sub-historical development of each indicator; the future improvement direction includes the indicator to be improved; The data analysis module is used to extract the sub-historical development of the target indicator under each leaf directory, and determine whether the target indicator is an indicator to be improved based on the sub-historical development under each leaf directory, thereby obtaining all the indicators to be improved under each leaf directory. The target indicator is any indicator under any leaf directory.

[0015] By adopting the above technical solution, the data analysis module can carefully extract and compare the sub-historical development of the target indicators under each leaf directory, accurately identify which indicators are to be improved, and comprehensively summarize the indicators to be improved under each leaf directory. This in-depth analysis capability helps to accurately locate the problem and provides solid data support for formulating targeted improvement measures and optimization strategies.

[0016] In a preferred example, the present application may be further configured as follows: the system management module includes an on-campus management unit and an off-campus management unit; The campus management unit is used to obtain the identity information of each person and the corresponding initial access rights, and when the identity information of any person changes, adjust the initial access rights of the corresponding person based on the identity information change; The off-campus management unit is used to obtain initial off-campus access rights and access data of the classified data, determine the danger level of each off-campus access user based on the access data, and dynamically adjust the initial off-campus access rights of the off-campus access user based on the danger level.

[0017] By adopting the above technical solution, the system management module realizes dynamic management of the identity information and access rights of campus personnel through the on-campus management unit, ensuring that the rights are consistent with the identities. At the same time, the off-campus management unit dynamically adjusts the rights of off-campus access users according to access data and risk assessment, effectively improving the security and flexibility of data access, and providing a more reasonable and secure access environment for users inside and outside the campus.

[0018] In the second aspect, this application provides a method for the architecture of a big data center platform for vocational schools, which adopts the following technical solutions: A big data center platform architecture method for vocational schools, including: Acquire the data to be integrated, and store the data to be integrated in a tree-shaped directory structure to obtain stored hierarchical data, wherein each node in the tree-shaped directory structure corresponds to a directory; Acquire the data to be archived, and compare the data to be archived with the hierarchical data, determine the target directory that matches the data to be archived in the tree directory structure, and store the data to be archived in the target directory; Analyze the hierarchical data to obtain data analysis results under each directory, wherein the data analysis results include historical development status and future improvement directions; Access data of the hierarchical data is obtained, and access rights of the hierarchical data are dynamically adjusted based on the access data.

[0019] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: one or more processors; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the vocational school big data center platform architecture method as described in the second aspect.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the vocational school big data center platform architecture method as described in the second aspect.

[0021] In summary, this application includes the following beneficial technical effects: In this application, the data storage module uses a tree directory structure to store data, which can clearly display the hierarchical relationship and classification of data, making it easier to manage and search data. The data archiving module can accurately find the target directory of the data to be archived in the tree directory structure by comparing the data to be archived and the hierarchical data, ensuring the correct archiving of data. Analyzing hierarchical data can accurately reveal the inherent laws and trends of the data, understand the historical development of data under each directory, and then propose future improvement directions to promote the continuous improvement and enhancement of colleges and universities. The system management module can ensure the security and compliance of data by obtaining the access data of hierarchical data and dynamically adjusting the access rights of hierarchical data based on the access data. This application realizes the accurate and efficient management of college data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a structural diagram of a vocational school big data center platform architecture system provided by an embodiment of the present application; Figure 2 It is a flow chart of a big data center platform architecture method for vocational schools provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is combined with Figure 1 -Attached Figure 3 This application is described in further detail.

[0024] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0027] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0028] The present application embodiment provides a vocational school big data center platform architecture system, such as Figure 1 As shown, the system includes a data storage module, a data archiving module, a data analysis module and a system management module.

[0029] The data storage module is used to obtain the data to be integrated and store the data to be integrated in a tree directory structure to obtain stored hierarchical data. Each node in the tree directory structure corresponds to a directory.

[0030] Specifically, the data to be integrated can be collected through data interfaces or manual import by school administrators, and the data source of the data to be integrated can be teaching, scientific research or management system data within the school. The data storage module constructs a tree directory structure, which can be constructed through a clustering algorithm. Each node in the tree directory structure corresponds to a directory for storing related data. The data to be integrated is stored according to the tree directory structure to form hierarchical data.

[0031] The data archiving module is used to obtain the data to be archived, compare the data to be archived with the classified data, determine the target directory that matches the data to be archived in the tree directory structure, and store the data to be archived in the target directory.

[0032] Specifically, the data to be archived is newly generated data. By using feature extraction and similarity calculation, the data to be archived is compared with the stored hierarchical data, and the directory with the highest similarity is determined as the matching target directory.

[0033] The data analysis module is used to analyze the hierarchical data to obtain the data analysis results under each directory. The data analysis results include historical development status and future improvement directions.

[0034] Specifically, the hierarchical data is analyzed in time series to obtain the time series analysis results, and the time series analysis results of each indicator are integrated to obtain the historical development of each leaf directory, including the good or bad trend of each indicator. For example, for a positive indicator, such as student grades, the larger the grade data, the better the trend of change; for a negative indicator, such as the number of student violations, the smaller the number of violations, the better the trend of change. The historical development of each sub-directory at the same level and belonging to the same parent directory is integrated to determine the future improvement direction of each sub-directory under the parent directory.

[0035] The system management module is used to obtain access data of the hierarchical data and dynamically adjust the access rights of the hierarchical data based on the access data.

[0036] Specifically, it is used to configure initial access rights for each person based on their identity as an on-campus person, configure the same initial off-campus access rights for off-campus personnel, obtain access data of off-campus personnel for graded data, judge the dangerousness of each off-campus access user based on the access data, and adjust the corresponding initial off-campus access rights for off-campus access users with higher risks by reducing the permissions.

[0037] In this embodiment, the data storage module uses a tree directory structure to store data, which can clearly display the hierarchical relationship and classification of data, and facilitate data management and search. The data archiving module can accurately find the target directory of the data to be archived in the tree directory structure by comparing the data to be archived and the hierarchical data, ensuring the correct archiving of the data. Analyzing the hierarchical data can accurately reveal the inherent laws and trends of the data, understand the historical development of data under each directory, and then propose future improvement directions to promote the continuous improvement and improvement of the school. The system management module can ensure the security and compliance of the data by obtaining the access data of the hierarchical data and dynamically adjusting the access rights of the hierarchical data based on the access data. This application realizes the accurate and efficient management of school data.

[0038] In a possible implementation of an embodiment of the present application, the data storage module includes a clustering analysis unit and a data storage unit; the clustering analysis unit is used to use a split hierarchical clustering algorithm to perform multi-step splitting on the integrated data, and record the splitting process and splitting results, and the splitting results include the classification clusters obtained in each step of the splitting; the data storage unit is used to construct a tree directory structure according to the splitting process, and match the classification clusters in the splitting results one by one and store them in the corresponding directories in the tree directory structure.

[0039] In this embodiment, the clustering analysis unit is used to split the data to be integrated using a split hierarchical clustering algorithm. The technician can determine the algorithm parameters based on actual experience, including the termination conditions of the split (such as reaching a preset number of clusters or the similarity within the cluster reaching a preset threshold), which is not specifically limited in this embodiment. The clustering analysis unit executes a split hierarchical clustering algorithm to split the data to be integrated in multiple steps. In each step of the split, the current cluster is split into multiple new clusters, and the split process and split results are recorded. The split process includes starting from the data to be integrated, the various clusters into which the data to be integrated is divided in the first step of the split, and the further splitting of the various clusters obtained in the first step of the split in the second step, etc. The split results include the classification clusters and feature descriptions obtained in each step of the split, and the classification clusters include the data classified into this cluster.

[0040] The data storage unit can display the feature description of each cluster on the platform display interface, and receive the directory title determined and input by the technician based on the feature description. It can also directly extract key features as directory titles based on the feature description. According to the splitting process and the directory titles formed by the clusters, a tree directory structure can be constructed. A node in the tree directory structure represents a directory (corresponding to a cluster), and the edge in the tree directory structure represents the splitting process. Each child node (subdirectory) belonging to the same parent node (parent directory) represents the data obtained by splitting the cluster corresponding to the parent node. The data of each cluster in the splitting result is stored in the corresponding directory, which can be stored in the form of files. Indexes can also be created for the stored data for quick retrieval and query.

[0041] In this embodiment, the clustering analysis unit uses a split hierarchical clustering algorithm to process the integrated data. Multi-step splitting can ensure that the data is subdivided into more specific categories, which helps to improve the accuracy and precision of data classification. Similar data in the splitting results are stored in the same directory for easy management and search. The data storage unit constructs a tree directory structure according to the splitting process, and matches the classification clusters generated by the clustering analysis unit to the corresponding directories in the tree directory structure one by one. The matching storage method ensures the accuracy and consistency of the data and improves the efficiency of data storage.

[0042] A possible implementation method of the embodiment of the present application includes a data archiving module directory label construction unit and a data archiving unit; the directory label construction unit is used to extract features of the data stored in each directory in the tree directory structure to obtain a feature label of each directory; the data archiving unit is used to extract features of the data to be archived to obtain features to be archived, match the features to be archived with the feature labels of each directory to obtain feature matching results, and determine the target directory based on the feature matching results.

[0043] In this embodiment, the directory label construction unit can use a feature extraction algorithm (such as a statistical method or a machine learning algorithm) to extract features from the data stored in the tree directory structure, use the extracted features as feature labels of the corresponding directory, and associate the generated feature labels with the corresponding directory for storage. When the data in the directory changes, the feature labels are updated in a timely manner to ensure the accuracy and effectiveness of the feature labels.

[0044] The data archiving unit reads the data to be archived, applies the same feature extraction algorithm as the target label construction unit to extract features from the data to be archived, and uses the extracted features as the features to be archived. The extracted features to be archived are matched with the directory labels corresponding to each directory in the tree directory structure. The matching algorithm can be a distance-based matching algorithm (such as Euclidean distance, Manhattan distance, etc.) or a similarity-based matching algorithm (such as cosine similarity, Jaccard similarity, etc.), which is not limited in this embodiment, so as to obtain the similarity between the features to be archived and the directory labels of each directory, and use the similarity as the feature matching degree. The feature matching result includes the feature matching degree of the features to be archived and the directory labels of each directory. Optionally, a directory with the highest feature matching degree with the features to be archived can be used as the target directory.

[0045] In this embodiment, the data archiving module extracts the data features of each directory in the tree directory structure through the directory label construction unit to form a feature label, and the data archiving unit extracts the features of the data to be archived and matches them with the feature labels of each directory, thereby accurately determining the target directory and achieving high efficiency and accuracy in data archiving.

[0046] In a possible implementation manner of an embodiment of the present application, the feature matching result includes the feature matching degree between the feature to be archived and the feature label of each directory; a data archiving unit is used to sort the directories in the tree directory structure from high to low according to the feature matching degree to obtain a directory list, and select a preset number of directories from the directory list as candidate directories; the data to be archived is matched with the data stored in each directory in the candidate directory to obtain a data matching result, and based on the data matching result, the directory with the highest matching degree between the stored data and the data to be archived is determined as the target directory.

[0047] In this embodiment, the directory list is arranged in descending order according to the degree of matching, reflecting the similarity between the feature to be archived and each directory label. The technician can flexibly set a preset number according to business needs, indicating that the first preset number of directories are selected from the sorted directory list as the candidate directories. For each directory in the candidate directory, the data stored therein is read, and the matching degree of the data to be archived and the data in each directory in the candidate directory is calculated using a data matching algorithm (which can be expressed as the number of matched data points or similarity scores). According to the data matching results, the one with the highest matching degree in the candidate directory is selected as the target directory.

[0048] Among them, feature matching is to evaluate the relevance between data by calculating the similarity between the data to be archived and the directory label. The feature label is a summary of the data stored in the directory, which can reflect the core information and uniqueness of the data. By sorting and screening the directories, the directories that are most similar to the core data of the data to be archived can be given priority, thereby reducing unnecessary matching calculations and improving archiving efficiency. Data matching is to further compare the specific differences between the data to be archived and the data stored in the selected directory on the basis of feature matching. Data matching can provide more detailed and accurate matching results, which helps to determine the target directory that is most suitable for storing the data to be archived. Through the comprehensive consideration of feature matching and data matching, both the overall similarity of the data (feature matching degree) and the matching degree of specific data are considered, thereby improving the accuracy and reliability of data archiving.

[0049] In this embodiment, the data archiving module calculates the feature matching degree between the features to be archived and the feature labels of each directory in the tree directory structure, and sorts them accordingly to obtain a directory list. After further screening out a preset number of candidate directories, a data-level match is performed, and finally the directory with the highest matching degree with the data to be archived is determined as the target directory. This process comprehensively considers the feature level and the specific data level, thereby improving the accuracy of data archiving.

[0050] A possible implementation method of an embodiment of the present application is that the data analysis module is used to perform time series analysis on the data under the target leaf directory to obtain the historical development of the target leaf directory, where the target leaf directory is any leaf directory in the tree directory structure; and determine the future improvement direction based on the historical development of each leaf directory under the tree directory structure.

[0051] In this embodiment, the tree directory structure includes a root directory, an intermediate directory and a leaf directory. The directory corresponding to the leaf node is the leaf directory, the directory corresponding to the root node is the root directory, and the directory corresponding to the intermediate node between the root node and the leaf node is the intermediate directory. Each leaf directory stores multiple indicators and indicator data corresponding to each indicator. For example, the indicator can be a high-level math score, and the corresponding indicator data is a specific score.

[0052] The data analysis module performs time series analysis on the indicator data corresponding to each indicator under each leaf directory, and the obtained time series analysis result includes the time series analysis result of each indicator. Based on the time series analysis results corresponding to each indicator under the target leaf directory, the historical development of the target leaf directory can be determined, including the change rates of positive and negative indicators, where the positive and negative indicators can be pre-set and marked. Positive indicators are indicators with larger data values, indicating better development, such as grades, and negative indicators are indicators with larger data values, indicating worse development, such as the number of violations within a period. The change rate of any indicator = (current period quantity - base period quantity) / base period quantity × 100%, where the current period quantity is the data with the latest timestamp of the indicator, and the base period quantity is the data with the earliest timestamp of the indicator. For positive indicators, a change rate greater than zero indicates that the indicator is developing positively, and a change rate less than zero indicates that the positive indicator is developing negatively; for negative indicators, a change rate greater than zero indicates that the indicator is developing negatively, and a change rate less than zero indicates that the negative indicator is developing positively. Therefore, the change rate and development direction (positive development / negative development) of each indicator under the target leaf directory can be obtained, and the change rate and development direction of each indicator under the target leaf directory are used as the sub-historical development of the corresponding indicator.

[0053] Furthermore, future improvement directions include indicators to be improved. According to the splitting results, different leaf directories may correspond to different students or classes. The same indicators may exist in different leaf directories, but the corresponding data acquisition objects are different. For any indicator, extract the sub-historical development of the indicator in each leaf directory. In one possible case, the sub-historical development of the indicator in each leaf directory is positive, and it is determined that the indicator does not need to be improved. In another possible case, the sub-historical development of the indicator in each leaf directory has positive development and negative development. The directory information where the negative development of the indicator is located can be extracted as an improvement indicator. For example, a student's performance in a certain subject has declined compared to other students. The student's performance can be used as an indicator to be improved. In another possible case, the sub-historical development of the indicator in each leaf directory is negative. The indicator can be used as an improvement indicator so that school administrators can make rectifications based on actual experience.

[0054] In this embodiment, the data analysis module can accurately depict the historical development of data under the directory by performing time series analysis on the target leaf directory data in the tree directory structure, and then based on the comprehensive analysis of the historical data of each leaf directory in the entire tree directory structure, it can scientifically predict and determine the future improvement direction.

[0055] A possible implementation method of the embodiment of the present application is that the historical development of the target leaf directory includes the sub-historical development of each indicator; the future improvement direction includes the indicators to be improved; the data analysis module is used to extract the sub-historical development of the target indicator under each leaf directory, and determine whether the target indicator is an indicator to be improved based on the sub-historical development under each leaf directory, thereby obtaining all the indicators to be improved under each leaf directory, and the target indicator is any indicator under any leaf directory.

[0056] In this embodiment, the data analysis module can carefully extract and compare the sub-historical development of the target indicator under each leaf directory, accurately identify which indicators are to be improved, and comprehensively summarize the indicators to be improved under each leaf directory. This in-depth analysis capability helps to accurately locate the problem and provides solid data support for formulating targeted improvement measures and optimization strategies.

[0057] In a possible implementation manner of an embodiment of the present application, the system management module includes an on-campus management unit and an off-campus management unit; the on-campus management unit is used to obtain the identity information of each person and the corresponding initial access rights, and when the identity information of any person changes, the initial access rights of the corresponding person are adjusted based on the change in identity information; the off-campus management unit is used to obtain the initial off-campus access rights and access data of graded data, determine the dangerousness of each off-campus access user based on the access data, and dynamically adjust the initial off-campus access rights of the off-campus access users based on the dangerousness.

[0058] In this embodiment, the campus management unit collects the identity information of all teachers and students in the school through the campus database or personnel system, including but not limited to name, student number / work number, department / class, position, etc., and presets initial access rights for each campus personnel based on their identity (student, teacher, administrative staff, etc.) and position level. These rights include the right to access, view or modify specific data.

[0059] Regularly check the campus system or personnel system or use real-time data synchronization technology to detect whether there are any changes in identity information, such as job changes, resignation, enrollment, graduation, etc. When identity information changes are detected, the initial access rights of the person will be automatically adjusted based on the correspondence between the preset identity information and the initial access rights.

[0060] The off-campus management unit is used to manage the access rights of off-campus personnel. According to the identity information of off-campus users (such as partners, visitors, parents, etc.), the correspondence between identity information and initial off-campus access rights is preset. The rights include accessing specific web pages, downloading specific files, etc. Access data of off-campus users is collected, including but not limited to access time, access frequency, access content and operation behavior, etc. Access data can be obtained through technical means such as log recording and network monitoring. The collected access data is analyzed to identify abnormal behaviors. Abnormal access behaviors may include: frequent access to sensitive data, attempts to access illegally, or staying on a certain page for a long time, which can be regarded as high-risk behaviors. A risk score is assigned to off-campus users based on the analysis results. The risk score can be calculated based on multiple factors, such as the number of abnormal access behaviors and user type. Optionally, for the number of abnormal access behaviors, the number of abnormal accesses of the user ID can be used as an abnormal access score. For user types, a basic user type score is set for each user type. Optionally, the basic user type score of a partner can be set to 1, the basic user type score of a visitor can be set to 3, and the basic user type score of a parent can be set to 0. For any user ID, the sum of its abnormal access score and the basic user type score is used as the dangerousness score of the user ID.

[0061] The correspondence between the risk score and the risk level is pre-defined. The greater the risk score, the higher the corresponding risk level. For example, for users with higher risk scores, their access rights can be reduced, or even their accounts can be temporarily or permanently banned. For users with lower risk scores, warnings can be issued to indicate abnormal access behaviors.

[0062] In this embodiment, the system management module implements dynamic management of the identity information and access rights of campus personnel through the on-campus management unit, ensuring that the rights are consistent with the identities. At the same time, the off-campus management unit dynamically adjusts the rights of off-campus access users based on access data and risk assessment, effectively improving the security and flexibility of data access, and providing a more reasonable and secure access environment for users inside and outside the campus.

[0063] This application embodiment provides a method for structuring a big data center platform in a vocational school, such as Figure 2 As shown, the method provided in the embodiment of the present application is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S201 to S204, wherein: S201, obtaining data to be integrated, and storing the data to be integrated in a tree directory structure to obtain stored hierarchical data, wherein each node in the tree directory structure corresponds to a directory.

[0064] S202, obtaining the data to be archived, and comparing the data to be archived with the hierarchical data, determining a target directory that matches the data to be archived in the tree directory structure, and storing the data to be archived in the target directory.

[0065] S203, analyzing the hierarchical data to obtain data analysis results under each directory, the data analysis results including historical development status and future improvement directions.

[0066] S204: Obtain access data of the hierarchical data, and dynamically adjust access rights of the hierarchical data based on the access data.

[0067] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0068] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0069] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0070] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0071] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the embodiment of the big data center platform architecture method of the vocational school.

[0072] Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0073] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable storage medium is run on a computer, the computer can execute the contents shown in the aforementioned embodiment of the vocational school big data center platform architecture method.

[0074] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0075] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A big data center platform architecture system for vocational schools, characterized in that: include: Data storage module, data archiving module, data analysis module and system management module; The data storage module is used to obtain the data to be integrated, and store the data to be integrated in a tree directory structure to obtain stored hierarchical data, wherein each node in the tree directory structure corresponds to a directory; The data archiving module is used to obtain the data to be archived, compare the data to be archived with the hierarchical data, determine the target directory that matches the data to be archived in the tree directory structure, and store the data to be archived in the target directory; The data analysis module is used to analyze the hierarchical data to obtain data analysis results under each directory, and the data analysis results include historical development status and future improvement directions; The system management module is used to obtain access data of the hierarchical data and dynamically adjust the access rights of the hierarchical data based on the access data.

2. The big data center platform architecture system for vocational schools according to claim 1 is characterized in that: The data storage module includes a cluster analysis unit and a data storage unit; The cluster analysis unit is used to perform multi-step splitting on the data to be integrated using a split hierarchical clustering algorithm, and record the splitting process and splitting results, wherein the splitting results include the classification clusters obtained in each step of the splitting; The data storage unit is used to construct a tree directory structure according to the splitting process, and store the classification clusters in the splitting results in the corresponding directories in the tree directory structure in a one-to-one matching manner.

3. The big data center platform architecture system for vocational schools according to claim 1 is characterized in that: The data archiving module directory label construction unit and data archiving unit; The directory label construction unit is used to extract features from the data stored in each directory in the tree-shaped directory structure to obtain a feature label for each directory; The data archiving unit is used to extract features of the data to be archived to obtain features to be archived, match the features to be archived with feature labels of each directory to obtain feature matching results, and determine a target directory based on the feature matching results.

4. The big data center platform architecture system for vocational schools according to claim 3 is characterized in that: The feature matching result includes the feature matching degree between the feature to be archived and the feature label of each directory; The data archiving unit is used to sort the directories in the tree-shaped directory structure from high to low according to the feature matching degree to obtain a directory list, and select a preset number of directories from the directory list as directories to be selected; The data to be archived is matched with the data stored in each directory of the selected directory to obtain a data matching result, and based on the data matching result, the directory with the highest matching degree between the stored data and the data to be archived is determined as the target directory.

5. The big data center platform architecture system for vocational schools according to claim 1 is characterized in that: The data analysis module is used to perform time series analysis on the data under the target leaf directory to obtain the historical development of the target leaf directory, and the target leaf directory is any leaf directory in the tree directory structure; The future improvement direction is determined based on the historical development of each leaf directory under the tree directory structure.

6. The big data center platform architecture system for vocational schools according to claim 5 is characterized in that: The historical development of the target leaf directory includes the sub-historical development of each indicator; the future improvement direction includes the indicator to be improved; The data analysis module is used to extract the sub-historical development of the target indicator under each leaf directory, and determine whether the target indicator is an indicator to be improved based on the sub-historical development under each leaf directory, thereby obtaining all the indicators to be improved under each leaf directory. The target indicator is any indicator under any leaf directory.

7. The big data center platform architecture system for vocational schools according to claim 1 is characterized in that: The system management module includes an on-campus management unit and an off-campus management unit; The campus management unit is used to obtain the identity information of each person and the corresponding initial access rights, and when the identity information of any person changes, adjust the initial access rights of the corresponding person based on the identity information change; The off-campus management unit is used to obtain initial off-campus access rights and access data of the classified data, determine the danger level of each off-campus access user based on the access data, and dynamically adjust the initial off-campus access rights of the off-campus access user based on the danger level.

8. A method for constructing a big data center platform for vocational schools, characterized in that: include: Acquire the data to be integrated, and store the data to be integrated in a tree-shaped directory structure to obtain stored hierarchical data, wherein each node in the tree-shaped directory structure corresponds to a directory; Acquire the data to be archived, and compare the data to be archived with the hierarchical data, determine the target directory that matches the data to be archived in the tree directory structure, and store the data to be archived in the target directory; Analyze the hierarchical data to obtain data analysis results under each directory, wherein the data analysis results include historical development status and future improvement directions; Access data of the hierarchical data is obtained, and access rights of the hierarchical data are dynamically adjusted based on the access data.

9. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the vocational school big data center platform architecture method as described in claim 8.

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