An Internet-based intelligent supervision and evaluation system and method

Abstract

This invention belongs to the technical field of data analysis platforms and specifically discloses an internet-based smart supervision and evaluation system and method. The smart supervision and evaluation system is used to evaluate and optimize a smart education platform. The smart education platform is used to exchange teaching data from schools in at least one region and build a cloud resource network through the cloud. The system has the following advantages: The smart supervision and evaluation system utilizes the internet, big data, cloud computing, and artificial intelligence technologies to optimize the functionality of the education platform through three core components: a smart education capability assessment unit to improve data processing efficiency and resource integration, a two-level education hub assessment unit to enhance data security, and a key engineering assessment unit to improve teaching quality. These components collectively improve the efficiency of educational resource utilization, enhance security, optimize decision support, and promote educational modernization.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis platforms, and in particular to an Internet-based intelligent supervision and evaluation system and method. Background Art

[0002] With the development of modern information technologies such as the internet, big data, cloud computing, and artificial intelligence, their application in smart education platforms has significantly advanced the modernization of education. The integration and utilization of these technologies not only enhances the integration of educational resources and service efficiency, but also promotes the popularization and effectiveness of education. Smart education platforms have become a prime example of the powerful application potential of modern information technology in education, demonstrating the future direction of educational development. However, traditional technologies are unable to simultaneously enhance the platform's security and management capabilities while improving teaching quality, resulting in deficiencies in educational data security and platform stability.

[0003] To this end, an Internet-based intelligent supervision and evaluation system and method are proposed to solve the above-mentioned problems. Summary of the Invention

[0004] The present invention aims to provide an Internet-based intelligent supervision and evaluation system and method to solve or improve at least one of the above-mentioned technical problems.

[0005] In view of this, a first aspect of the present invention is to provide an Internet-based intelligent supervision and evaluation system.

[0006] A second aspect of the present invention is to provide a method.

[0007] The first aspect of the present invention provides an Internet-based smart supervision and evaluation system, which is used to evaluate and optimize a smart education platform, which is used to interact with the teaching data of schools in at least one region and build a cloud resource network through the cloud; the smart supervision and evaluation system includes: a smart education capability evaluation unit, which optimizes the teaching data in the cloud resource network and obtains multiple management data sets used by the smart education platform to manage the teaching data; obtains the data management index of the smart education platform through the management data set; a two-level education center evaluation unit, which optimizes the teaching data in the cloud resource network and obtains multiple interactive data sets used by the smart education platform to interact with the teaching data; obtains the interactive safety index of the smart education platform through the interactive data set; a key improvement project evaluation unit, which optimizes the teaching data in the cloud resource network and obtains multiple teaching quality data sets related to school configuration in the teaching data; obtains the teaching quality index of the school through the teaching quality data set; an education application system unit, which is used to optimize the teaching process related to the smart education platform and the school.

[0008] In any of the above technical solutions, the two-level education hub evaluation unit includes: a first data acquisition module for acquiring the interactive data set from the teaching data; wherein the interactive data set includes a digital base user hub coefficient, a data base data hub coefficient, a data base application hub coefficient, a data base resource hub coefficient and a school-level education hub coefficient; a first numbering evaluation module for converting the interactive data set into a parameter for calculating a shared platform security entry coefficient and labeling it; the shared platform security entry coefficient is calculated using the following formula Wherein, Rcf is the shared platform security entry coefficient used to characterize the interactive security index; F z Including F1, F2, F3, F4 and F5, and S z Including S1, S2, S3, S4 and S5, F1 is the digital base user hub coefficient, and the weight of the digital base user hub coefficient in the shared platform security entrance coefficient is S1; F2 is the data base data hub coefficient, and the weight of the data base data hub coefficient in the shared platform security entrance coefficient is S2; F3 is the data base application hub coefficient, and the weight of the data base application hub coefficient in the shared platform security entrance coefficient is S3; F4 is the data base resource hub coefficient, and the weight of the data base resource hub coefficient in the shared platform security entrance coefficient is S4; F5 is the school-level education hub coefficient, and the weight of the school-level education hub coefficient in the shared platform security entrance coefficient is S5.

[0009] In any of the above technical solutions, the key improvement project evaluation unit includes: a second data acquisition module, used to obtain the teaching quality data set from the teaching data; wherein, the teaching quality data set includes data on outstanding personnel trained by backbone teachers, data on teacher popularization in the entire district, data on personnel with outstanding training results, student face registration data and student actual school entry data; a second numbering evaluation module, used to convert the teaching quality data set into parameters for calculating the teaching quality index and label them.

[0010] In any of the above technical solutions, the teaching quality index includes an excellent teacher range index and a basic environment construction safety index for characterizing the configuration of the school;

[0011] The excellent teacher range index is obtained by the following formula: Wherein, Tz is the index of excellent teacher range; G1 is the number of excellent personnel trained by backbone teachers; G2 is the number of personnel with excellent training results; G3 is the standard for evaluating excellent teachers; G1∩G2 is the concentration range of excellent teachers; n is a positive integer;

[0012] The basic environment construction safety index is obtained by the following formula: Among them, Kd is the basic environment construction security index; D3 is the number of student faces registered; and D is the number of students actually entering the school.

[0013] In any of the above technical solutions, the key improvement project evaluation unit further obtains the smart security index of the smart education platform through the multiple teaching quality data sets; the smart security index is obtained by the following formula: Wherein, Hx is the smart security index; Improve the entrance safety factor for teacher literacy; is the safety factor of the entrance to the smart learning space; is the safety factor of the entrance to the basic environment construction; a is the weight of the safety factor of the entrance to the teacher quality improvement in the smart security index; b is the weight of the safety factor of the entrance to the smart learning space in the smart security index; d is the weight of the safety factor of the entrance to the basic environment construction in the smart security index; G is the total number of faculty and staff; F1 is a type of security incident that occurred; F n is all types of security events; Fj1 is a type of unsafe event that occurs; Fj n For all types of unsafe incidents.

[0014] In any of the above technical solutions, the key improvement project evaluation unit also includes: a security evaluation unit, which evaluates the current overall security level of the smart education platform based on the basic environment construction safety index, the smart security index and the shared platform security entry coefficient.

[0015] In any of the above technical solutions, the smart education capability assessment unit includes: a third data acquisition module, used to acquire a management data set from the teaching data; wherein, the management data set includes a unified authentication system data set, a data exchange integration data set, a data governance and management data set, and a data scenario display data set; a third numbering assessment module, used to convert the management data set into a parameter for calculating the data set management area coefficient and label it.

[0016] In any of the above technical solutions, the dataset management area coefficient is obtained by the following formula: Among them, Wil is the dataset management area coefficient used to characterize the data management index; n is a positive integer; Q1 is the unified authentication system dataset; Q2 is the data exchange integration dataset; Q3 is the data governance and management dataset; Q4 is the data scenario display dataset; Q4∩Q3∩Q2∩Q1 is the data management key area.

[0017] In any of the above technical solutions, the smart education capability evaluation unit further includes: a management evaluation module, which evaluates the current data management level of the smart education platform according to the data set management area coefficient.

[0018] The second aspect of the present invention provides a method, which is implemented through the Internet-based smart supervision and evaluation system in any of the above-mentioned technical solutions, and the method includes the following steps: S101, obtaining the teaching data contained in the cloud resource network, and dividing the teaching data into the management data set, the interaction data set and the teaching quality data set; S102, calculating and obtaining the data management index for evaluating the smart education platform through the management data set, calculating and obtaining the interaction safety index for evaluating the smart education platform through the interaction data set, and calculating and obtaining the teaching quality index for evaluating the smart education platform through the teaching quality data set; S103, optimizing the teaching process related to the smart education platform and the school based on the data management index, the interaction safety index and the teaching quality index.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The Smart Supervision and Evaluation System leverages the internet, big data, cloud computing, and artificial intelligence technologies to address the shortcomings of traditional education platforms in improving teaching quality and simultaneously enhancing security management. The system implements its functions through three core components: the Smart Education Capacity Assessment Unit optimizes cloud resources and obtains data management indices through managed data sets, effectively improving data processing efficiency and resource integration capabilities; the two-level Education Hub Assessment Unit enhances the security management of the education platform through interactive data sets, ensuring data security and integrity; and the Key Improvement Project Assessment Unit focuses on teaching data related to school configuration to improve teaching quality. Together, these assessment units improve the efficiency of educational resource utilization, strengthen data security, and optimize educational decision-making support, significantly advancing the level of educational modernization and demonstrating the future direction of education development.

[0021] Additional aspects and advantages of embodiments according to the present invention will become apparent in the following description or may be learned through practice of embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0023] Figure 1 It is a schematic diagram of the structure of the present invention;

[0024] Figure 2 Build a large platform of Internet and education for this invention, and create a digital base target map for smart education in districts and schools;

[0025] Figure 3 Build a large educational resource pool for the present invention and optimize the educational resource supply service target map;

[0026] Figure 4 The invention realizes the goal map of full coverage of digital campus and standardization of smart campus construction;

[0027] Figure 5 Provide innovative teaching, management, evaluation and service application diagrams for this invention;

[0028] Figure 6 Create a national-level smart education demonstration zone target map for this invention;

[0029] Figure 7 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0030] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0032] See also Figure 1-Figure 7 , the following describes an Internet-based intelligent supervision and evaluation system and method according to some embodiments of the present invention.

[0033] The embodiment of the first aspect of the present invention proposes an Internet-based intelligent supervision and evaluation system. In some embodiments of the present invention, such as Figures 1-6 As shown, the smart supervision and evaluation system is used to evaluate and optimize the smart education platform. The smart education platform is used to exchange teaching data from schools in at least one region and build a cloud resource network through the cloud. The smart supervision and evaluation system includes:

[0034] The smart education capability assessment unit optimizes the teaching data in the cloud resource network and obtains multiple management data sets used by the smart education platform to manage teaching data; it obtains the data management index of the smart education platform through the management data sets.

[0035] The two-level education hub evaluation unit optimizes the teaching data in the cloud resource network and obtains multiple interactive data sets of interactive teaching data of the smart education platform; and obtains the interactive security index of the smart education platform through the interactive data sets.

[0036] Focus on improving the engineering evaluation unit, optimizing the teaching data in the cloud resource network, and obtaining multiple teaching quality data sets related to the school configuration in the teaching data; obtain the school's teaching quality index through the teaching quality data set.

[0037] The education application system unit is used to optimize the teaching processes related to the smart education platform and schools.

[0038] The present invention provides an Internet-based intelligent supervision and evaluation system that can integrate teaching data from schools in different regions, including students' academic performance, teachers' teaching behaviors, classroom interactions, etc. By summarizing and analyzing these data, the system can provide a comprehensive teaching quality assessment; the system builds a cloud resource network through the cloud, which not only stores teaching data, but also enables real-time processing and analysis of data. In this way, educational decision makers and managers can obtain feedback on teaching situations in real time and make adjustments or improvements quickly; the system automatically performs supervision and evaluation tasks, and evaluates teachers' teaching effectiveness and students' learning progress through preset standards and algorithms. This automated process greatly improves the efficiency and fairness of the evaluation; based on the analysis results, the system can generate customized optimization suggestions to help schools and educational institutions improve teaching strategies and methods. These suggestions are based on big data analysis and are more scientific and accurate.

[0039] The system collects teaching activity data in real time through an interface connected to the school's teaching management system. This data is then synchronized to the cloud server to ensure the immediate update and integrity of the information; in the cloud server, the collected data is processed and analyzed. Using data analysis technologies (such as machine learning algorithms), the system can identify key trends and potential problems in the teaching process; based on the analysis results, the system automatically evaluates the teaching quality and generates detailed feedback reports. These reports are provided to school administrators and teachers to help them understand the effectiveness of current teaching and point out directions for improvement; the system can also optimize the allocation and utilization of cloud resources according to teaching needs, such as dynamically allocating teaching content and auxiliary resources according to students' learning needs.

[0040] The capability assessment unit is responsible for analyzing data flows and structures within the cloud resource network, identifying bottlenecks and areas of inefficiency in data processing. Based on these analyses, the unit proposes optimization strategies, such as improving data storage formats, adjusting data synchronization mechanisms, or optimizing data access paths. The unit is able to access multiple management data sets within the smart education platform, which may include student performance data, teacher teaching activity data, and course content update data. Through these data sets, the unit can comprehensively monitor and evaluate the use and effectiveness of teaching resources. By analyzing the management data sets, the capability assessment unit generates a data management index, which reflects the comprehensive performance of the smart education platform in data processing and management. The level of the index directly affects the decision support system of the education platform, providing management with intuitive indicators for operation and management.

[0041] Using data analysis techniques such as data mining and machine learning algorithms, the Capacity Assessment Unit conducts in-depth analysis of teaching data. These techniques help identify patterns, trends, and potential problems, providing a scientific basis for optimization. Based on the results of data analysis, the unit can dynamically adjust the allocation of cloud resources. For example, if data access demand increases in a certain region, the unit will automatically increase the data cache of servers in that area to improve data access speed and reduce latency. The education platform's data management index is regularly updated to reflect the latest data management status. This index provides a quantitative evaluation standard for the platform's continuous improvement and also serves as part of a feedback mechanism, helping managers adjust strategies and resources in a timely manner.

[0042] The two-tiered education hub evaluation unit monitors and analyzes teaching data stored and transmitted within the cloud resource network, optimizing data flow and storage structure. This includes adjusting data distribution, optimizing data access paths, and improving data processing efficiency. It also collects and integrates multiple interactive datasets from the smart education platform, including student interaction records with the teaching platform, user behavior data from the learning management system, and communication exchanges between teachers and students. Based on this collected interaction data, the evaluation unit calculates and generates an interaction security index. This index assesses the security of data interaction within the education platform, including data integrity, confidentiality, and availability.

[0043] Use data analysis technologies (such as big data analysis and machine learning) to process and analyze teaching interaction data. Through these technologies, the unit can identify efficiency bottlenecks, security risk points and improvement opportunities in the teaching process; based on the data analysis results, the unit dynamically adjusts the cloud resource configuration to ensure the efficiency of data storage and processing. For example, more frequent backups and faster access strategies are implemented for frequently accessed data to ensure high data availability and rapid response; continuous security assessments are conducted on interactive data, including monitoring data access patterns, detecting potential unauthorized access, and preventing data leakage risks. Based on these assessments, an interactive security index is generated and necessary security measures are implemented, such as encrypting sensitive data, enhancing authentication and access control, etc.; the interactive security index provides educational institutions with a quantitative security performance indicator, which helps management adjust security policies and resource allocation based on actual security performance. This feedback-based mechanism ensures that the education platform can continuously adapt to new security threats and challenges.

[0044] The evaluation unit is responsible for monitoring and optimizing teaching data stored in the cloud resource network. It analyzes data usage patterns and access frequency, optimizing data storage structures and processing flows to improve data processing efficiency and access speed. It also collects and integrates teaching quality datasets related to each school's configuration. This data may include multi-dimensional information such as classroom interaction, student performance, teacher evaluations, and course resource usage. These datasets help the evaluation unit gain a comprehensive understanding of each school's teaching environment and resource allocation. Based on the analyzed teaching quality datasets, the evaluation unit calculates a teaching quality index for each school. This index reflects the school's overall performance in the teaching process and is used to quantify the school's teaching effectiveness.

[0045] Data integration tools are used to aggregate teaching data from multiple sources into a cloud platform, ensuring data integrity and timeliness. Data mining and machine learning algorithms are used to conduct in-depth data analysis to identify key trends and potential issues in teaching. Based on the data analysis results, cloud resource configuration is automatically adjusted, such as increasing storage capacity for high-demand data or optimizing data backup and recovery strategies, to ensure the high availability and security of critical teaching data. Quantitative models are used to score and weight the collected teaching data, and a teaching quality index is calculated based on the school's specific teaching objectives and educational standards. This index helps school management and education authorities understand the actual effectiveness of teaching activities and guide future adjustments to teaching strategies. The teaching quality index provides regular feedback to relevant education managers and school leaders. Based on this feedback, schools can implement targeted improvement measures, such as strengthening teacher training, updating teaching resources, or adjusting teaching methods, to achieve continuous improvement in teaching quality.

[0046] The Education Application System unit analyzes existing teaching processes to identify bottlenecks and inefficient links. It then proposes improvement measures, such as streamlining operations, automating routine tasks, and reconfiguring teaching steps, to improve the overall efficiency of the teaching process. This unit manages the resource allocation of the smart education platform, dynamically allocating and optimizing resources based on actual teaching needs. For example, cloud storage resources and computing power are automatically adjusted based on course load and student interaction data. The system unit continuously monitors the quality of teaching activities, collecting feedback and evaluation results. Using this data, the unit can evaluate the effectiveness of teaching strategies and recommend necessary adjustments. The Education Application System unit provides data-driven decision support, helping educators and administrators develop more scientific education strategies and decisions by analyzing educational data.

[0047] The educational application system unit integrates educational data from different sources and systems, including student performance, teacher feedback, and course content usage. Using data mining and analysis tools, the system conducts in-depth analysis of this data to identify patterns and associations; using automation tools and AI algorithms, the educational application system unit automatically performs various teaching management tasks, such as course scheduling, learning progress tracking, and performance evaluation. These technologies reduce the administrative burden on teachers, allowing them to focus more on teaching and student interaction; the system unit collects feedback information from the teaching process in real time and adjusts teaching resources and strategies based on this information. This rapid iterative improvement mechanism ensures that teaching activities can adapt to student needs and changes in the educational environment in a timely manner; the educational application system unit uses cloud computing infrastructure to support data storage, processing, and analysis. This allows educational resources to be flexibly expanded and ensures high availability and reliability of the system.

[0048] In summary, the smart supervision and evaluation system proposed in the present invention utilizes the Internet, big data, cloud computing and artificial intelligence technologies to solve the shortcomings of traditional education platforms in improving teaching quality and simultaneously enhancing security management performance. The system implements its functions through three core components: the smart education capability evaluation unit optimizes cloud resources and obtains data management indexes by managing data sets, effectively improving data processing efficiency and resource integration capabilities; the two-level education hub evaluation unit enhances the security management of the education platform through interactive data sets to ensure data security and integrity; the key improvement engineering evaluation unit focuses on teaching data related to school configuration to improve teaching quality. These evaluation units jointly improve the efficiency of the use of educational resources, strengthen data security, optimize educational decision-making support, and thus significantly improve the level of education modernization, demonstrating the future development direction of education.

[0049] In any of the above embodiments, the two-level education hub assessment unit includes:

[0050] The first data acquisition module is used to obtain the interactive data set from the teaching data; wherein the interactive data set includes the digital base user hub coefficient, the data base data hub coefficient, the data base application hub coefficient, the data base resource hub coefficient and the school-level education hub coefficient.

[0051] The first numbering evaluation module is used to convert the interactive data set into parameters for calculating the shared platform security entry coefficient and label them; the shared platform security entry coefficient is calculated using the following formula:

[0052]

[0053] Wherein, Rcf is the shared platform security entry coefficient used to characterize the interactive security index; F z Including F1, F2, F3, F4 and F5, and S z Including S1, S2, S3, S4 and S5, F1 is the digital base user hub coefficient, and the weight of the digital base user hub coefficient in the shared platform security entrance coefficient is S1; F2 is the data base data hub coefficient, and the weight of the data base data hub coefficient in the shared platform security entrance coefficient is S2; F3 is the data base application hub coefficient, and the weight of the data base application hub coefficient in the shared platform security entrance coefficient is S3; F4 is the data base resource hub coefficient, and the weight of the data base resource hub coefficient in the shared platform security entrance coefficient is S4; F5 is the school-level education hub coefficient, and the weight of the school-level education hub coefficient in the shared platform security entrance coefficient is S5.

[0054] In this embodiment, the first data acquisition module is responsible for collecting key interactive data sets from the education platform's extensive database. These data sets include the digital base user hub coefficient, the data base data hub coefficient, the data base application hub coefficient, the data base resource hub coefficient, and the school-level education hub coefficient, covering key aspects of user activity, data flow, application usage, and resource allocation within the education platform.

[0055] The first data acquisition module extracts the required data from various systems of the education platform (such as learning management systems, online resource libraries, etc.) through preset queries and algorithms. The extracted data is integrated into an interactive data set in a unified format for further analysis and evaluation.

[0056] The first numbering evaluation module converts the acquired interaction data set into parameters that can be used for further calculations and numbers these parameters. The purpose of numbering is to standardize the data format and simplify the subsequent processing process, ensuring the accuracy and consistency of the data during calculation and analysis.

[0057] The first numbering and evaluation module converts the various coefficients in the interactive dataset into numerical parameters, which are used to calculate the shared platform's security entry coefficient. This conversion process may include normalization, standardization, and other mathematical processing to ensure the scientific and practical validity of the parameters. The converted parameters are then numbered to create an organized parameter library. This step is intended to improve data processing efficiency and facilitate tracking, comparison, and reference of these parameters within the system.

[0058] F1-Digital Base User Centrality Factor: Indicates the security of user interaction, such as authentication strength, protection of user data, etc.

[0059] F2-Data Base Data Centrality Factor: Indicates the security of data stored on the platform, including data encryption, backup and anti-leakage measures.

[0060] F3-Data Base Application Central Factor: involves the security of the application, such as the application's handling of sensitive data, the application's security audit and update mechanism.

[0061] F4-Data base resource hub coefficient: Focus on the security of infrastructure resources, such as the secure configuration and monitoring of servers and network facilities.

[0062] F5-School-level Education Hub Coefficient: Pays special attention to the management and security of educational resources and data at the campus level.

[0063] Security-related data is collected from various monitoring points, reflecting the security status of users, data, applications, resources, and education hubs. The weights of each coefficient (S1, S2, S3, S4, S5) are pre-set based on their importance in ensuring the overall security of the platform. The allocation of weights may be based on historical data, security needs analysis, or risk assessment. The overall security entry index is calculated by multiplying each coefficient by its corresponding weight and summing the results. This index provides a quantitative measure to help managers understand the current security status of the platform. Based on the resulting security entry index, managers can evaluate the effectiveness of existing security measures and make adjustments when necessary to ensure that the platform's security is always in optimal condition.

[0064] Furthermore, the user hub coefficient of the digital base: establish a unified multi-level education organization and user system in the entire district, connect the Smart Education China Pass and the National Education Trusted Digital Identity Authentication to realize the real-name authentication of the national smart education digital public service system, support WeChat, DingTalk, and APP QR code scanning joint login, provide more convenient login services, connect to the authoritative data sources of the teacher information management system and the student information management system, and provide a unified address book interface, user authentication, and single sign-on service capabilities for various educational applications at all levels.

[0065] Data base application hub coefficient: By building a unified application open platform and application supermarket for the entire district, unified aggregation and multi-level decentralized management of applications at all levels and types can be achieved, and ultimately full application coverage can be achieved with the online learning space as the link, building a new regional education application ecosystem.

[0066] Data base resource center: In view of the scattered entrances and multiple sources of high-quality education and teaching resources from different manufacturers and sources, as well as different navigation and search entrances, we will create a large resource pool with basic education and teaching metadata standards, introduce intelligent technology, and build a resource interconnection system to achieve unified navigation, unified retrieval, and unified recommendation of high-quality education resources.

[0067] School-level education hub coefficient: By building a collaborative development environment with service integration, data integration, and equipment integration as core functions, each campus subsystem can open up its business capabilities simply and quickly, build a campus capability center, lay business circulation channels, open up every business link, realize school business restructuring and process reengineering, and comprehensively improve the level of smart campus construction.

[0068] In any of the above embodiments, the key improvement project assessment unit includes:

[0069] The second data acquisition module is used to obtain the teaching quality data set from the teaching data; wherein, the teaching quality data set includes data on outstanding personnel trained by backbone teachers, data on popularization of teachers in the whole district, data on personnel with outstanding training results, student face registration data and data on students' actual entry into school.

[0070] The second numbering evaluation module is used to convert the teaching quality data set into parameters for calculating the teaching quality index and label them.

[0071] In this embodiment, the second data acquisition module is responsible for collecting key teaching quality related data from the education system for further analysis. The data types include:

[0072] Data on the training of outstanding backbone teachers: collects information on the training and achievements of outstanding teachers, reflecting the effectiveness of teacher training programs.

[0073] District-wide teacher coverage data: Statistics on the coverage and participation of teachers in each school district are used to assess the level of teacher education coverage.

[0074] Data on personnel with outstanding training results: Collect information on personnel with outstanding performance in various training programs for analysis of training effectiveness.

[0075] Student face registration data: Biometric data about student identity and attendance, used to monitor and improve student attendance.

[0076] Student attendance data: real-time collection of student attendance data for attendance statistics and security monitoring.

[0077] The second numbering evaluation module converts the collected teaching quality data set into parameters for evaluating and calculating the teaching quality index, and labels them, converting the teaching quality data into quantitative indicators. Each indicator reflects a different aspect of teaching quality. The parameters are systematically numbered to facilitate reference and tracking in analysis and reports.

[0078] The conversion process for the second numbered assessment module includes data cleaning, standardization, and normalization to ensure data consistency and comparability. Using the converted parameters, a comprehensive teaching quality index is calculated using a pre-defined formula. This index comprehensively reflects information on multiple dimensions, including teacher training effectiveness, educational accessibility, and student attendance.

[0079] In any of the above embodiments, the teaching quality index includes an excellent teacher range index and a basic environment construction safety index for characterizing the school configuration.

[0080] The excellent teacher range index is obtained by the following formula:

[0081]

[0082] Among them, Tz is the range index of excellent teachers; G1 is the number of excellent personnel trained by backbone teachers; G2 is the number of personnel with excellent training results; G3 is the standard for evaluating excellent teachers; G1∩G2 is the concentration range of excellent teachers; n is a positive integer.

[0083] The basic environment construction safety index is obtained through the following formula:

[0084]

[0085] Among them, Kd is the basic environment construction security index; D3 is the number of student face registrations; and D is the number of students actually entering the school.

[0086] In this example, the Excellent Teacher Coverage Index assesses the proportion of teachers recognized as excellent among the total number of teachers in a school, as well as the influence these teachers have on teaching and student development. By measuring the distribution of excellent teachers, the index also indirectly reflects the effectiveness of the school's teacher training and professional development programs. The system regularly collects data from the teacher evaluation system and school personnel files, including teachers' titles, awards, student evaluations, and teaching achievements. The Excellent Teacher Coverage Index is calculated by calculating the percentage of teachers recognized as excellent among the total number of teachers. A high proportion indicates that the school has high teacher quality and a good environment for teacher development.

[0087] The Infrastructure Safety Index assesses a school's infrastructure safety, including building safety, fire safety, and laboratory safety. It also evaluates the preparedness and effectiveness of schools in disaster prevention, mitigation, and emergency response. Regular campus safety inspections provide the data needed for this assessment, including facility maintenance, the frequency and effectiveness of safety drills, and accident records. The Infrastructure Safety Index is calculated by analyzing safety inspection results and accident records. A higher index indicates that a school excels in infrastructure safety management and is able to effectively prevent and address potential safety issues.

[0088] The comprehensive evaluation indicator (Tz) for outstanding teachers measures the number of teachers who simultaneously meet multiple criteria for outstanding teachers. This includes those considered key teachers, teachers with excellent training results, and teachers who meet the criteria for exemplary evaluation. Multi-dimensional evaluation criteria: G1 (Number of outstanding personnel trained by key teachers) assesses teachers' performance in professional development and capacity building; G2 (Number of personnel with excellent training results) focuses on teachers' performance in various types of educational training; and G3 (Criteria for Demonstrating Excellent Teacher Evaluation) may include a range of performance and quality criteria, such as classroom teaching effectiveness, student evaluations, and peer reviews.

[0089] The intersection operation (G1∩G2) first identifies teachers who meet both G1 and G2 criteria, meaning they are both core teachers and have performed well in training. This step is accomplished through data querying and matching. The intersection operation (G3∩(G1∩G2)) further identifies teachers who not only meet the first two criteria but also meet the G3 criteria for excellent teachers. This means these teachers meet high standards in teaching practice, training performance, and overall evaluation. The Excellent Teacher Range Index (Tz) calculates the number of teachers who ultimately meet all of the above criteria. This index reflects the degree of excellence in the quality of a school's teachers; a higher index indicates a school with more well-rounded and excellent teachers.

[0090] By comparing the number of students D who actually enter the school with the number D3 of students registered by the security system (such as a facial recognition system), the index kd can reflect the recognition efficiency and accuracy of the school security system. A kd index greater than 1 may indicate that students have entered the school without being recognized by the system, indicating a potential blind spot or loophole in the system. A kd index less than or equal to 1 indicates that all students entering the school were captured by the facial registration system, demonstrating efficient monitoring and recording capabilities.

[0091] The actual number of students entering the school D: This is usually recorded by the campus entrance and exit monitoring system. Every time a student passes through the gate, the system automatically records an entry. The number of student face registrations D3: Through the face recognition system, identity verification and recording are performed when students enter the school. Every student who is successfully identified will be recorded by the system. Comparing the data recorded in the school gate monitoring system and the face recognition system, it is ensured that every student entering the campus has a corresponding face recognition record. KD index, through This index directly reflects the coverage rate of students entering the school by the facial recognition system. The KD index allows school security management to assess the effectiveness of the facial recognition system and identify any students entering the school that have not been captured by the system. An abnormal KD index (e.g., significantly greater than 1) indicates the need to investigate possible system failures or to implement additional security measures, such as enhancing the accuracy of the facial recognition system or adding more monitoring points.

[0092] In any of the above embodiments, the key improvement project evaluation unit further obtains the smart security index of the smart education platform through multiple teaching quality data sets; the smart security index is obtained by the following formula:

[0093]

[0094] Among them, Hx is the smart security index; Improve entrance safety factor for teacher literacy; Safety factor for the entrance to the smart learning space; is the safety factor of the entrance to the basic environment construction; a is the weight of the safety factor of the entrance to the teacher quality improvement in the smart security index; b is the weight of the safety factor of the entrance to the smart learning space in the smart security index; d is the weight of the safety factor of the entrance to the basic environment construction in the smart security index; G is the total number of faculty and staff; F1 is a type of security incident that occurred; F n is all types of security events; Fj1 is a type of unsafe event that occurs; Fj n For all types of unsafe incidents.

[0095] In this embodiment, the Smart Security Index is an indicator that comprehensively evaluates the effectiveness of schools using smart technologies to ensure campus safety. It includes but is not limited to the performance evaluation of access control systems (such as facial recognition access control), monitoring systems, emergency response systems, and data protection measures. The evaluation unit collects and analyzes multiple data sets related to smart security, such as student facial registration data, actual school entry data, security incident reports, response time records, etc., as well as the effectiveness of data security and privacy protection measures of the education platform. The index is used to guide schools' decisions in smart security technology investment and policy formulation, evaluate the effectiveness of existing measures, and identify areas that need improvement or updating.

[0096] By connecting with the school security system and education platform, relevant security data is collected in real time, such as the use of the access control system, the operating status of surveillance cameras, the handling results of security incidents, etc.; data analysis techniques (such as statistical analysis, machine learning algorithms) are used to conduct in-depth analysis of the collected data to identify weaknesses and risk points in security management; the analysis process may include pattern recognition (such as identifying abnormal access behavior), trend analysis (such as the time and location distribution of security incidents) and performance evaluation (such as the mean and variance of emergency response time); based on the analysis results, the smart security index is calculated, which may be a weighted score, with each component assigned a weight according to its contribution to overall security; the calculation of the index will also take into account the synergistic effect of various security measures, that is, how different systems interact to improve overall security; the smart security index will be reported to school administrators and security teams on a regular basis as a benchmark for evaluating the performance of the smart security system; based on the feedback from the index, the school can optimize or upgrade its smart security system and policies, such as strengthening data encryption measures, improving the coverage of the monitoring system or improving emergency response protocols.

[0097] Specifically, the classification of safe and unsafe incidents in smart learning spaces is generally based on the nature of the incident, its impact on the learning environment, and the potential threat to students, teachers, or educational data. Smart learning spaces include all educational activities conducted through digital devices and online platforms, which may involve student information systems, online learning management systems, interactive applications, etc. These include but are not limited to the following:

[0098] Safety incidents and unsafe incidents are divided into:

[0099] Security incidents are those that are effectively managed and result in no damage or minimal impact; these incidents may not escalate into more serious issues due to timely response. For example, unauthorized access is quickly detected and blocked, or systems are automatically updated to prevent security vulnerabilities.

[0100] Unsafe incidents: These are events that result in data leakage, system damage, functional interruption, or specific harm to users. These incidents may occur due to poor management, technical defects, or external attacks. Examples include data leakage, malware attacks, phishing attacks, or identity theft.

[0101] Security incident types include data security incidents, access control incidents, network security incidents, application security incidents, and physical security incidents;

[0102] Data security incidents include data leakage and data corruption; data leakage: unauthorized access or release of sensitive data such as student personal information, transcripts, etc.; data corruption: damage or loss of data due to system failure or external attack.

[0103] Access control events include unauthorized access attempts and privilege abuse. Unauthorized access attempts: unauthorized users are detected trying to access protected resources. Privilege abuse: users abuse authorized access rights to perform illegal operations.

[0104] Network security incidents include network intrusion and distributed denial of service (DDoS) attacks; among them, network intrusion: hackers break into the system through network security vulnerabilities; distributed denial of service (DDoS) attacks: attack the learning platform's servers through a large number of requests, causing system overload and service interruption.

[0105] Application security incidents include malware infection and cross-site scripting (XSS); malware infection: the learning management system or other educational applications are infected by malware or viruses; cross-site scripting (XSS): attackers implant malicious scripts on online education platforms to capture user information or destroy page content.

[0106] Physical security incidents: Although most smart learning spaces are online, physical security incidents such as device theft or loss or theft of physical data storage media (e.g., hard drives, USB drives) should also be considered important security incidents.

[0107] In any of the above embodiments, the key improvement project assessment unit further includes:

[0108] The security evaluation unit assesses the current overall security level of the smart education platform based on the basic environment construction security index, smart security index and the shared platform security entry coefficient.

[0109] In this embodiment, the basic environment construction safety index measures the security of school infrastructure such as buildings and fire protection systems; the smart security index evaluates security issues unique to smart education systems, such as data security and network security; the shared platform security entry coefficient focuses on the access control and data exchange security of the shared education resource platform; the overall security level assessment uses these indices to comprehensively evaluate the security status of the smart education platform and provide an overall security score or level.

[0110] The security assessment unit automatically collects relevant security data from various security monitoring systems, including but not limited to intrusion detection logs, access control logs, and user and device activity records; updates each security index in real time to ensure that the latest data is used to reflect the current security situation; each index may have its own calculation formula, involving weighting the severity and frequency of different security events; the security assessment unit combines these independent security indices into a comprehensive security score or grade based on preset standards and algorithms; it may use arithmetic mean, weighted average or other statistical methods to combine these indices, and the specific method depends on the importance of each index in the overall security strategy; generates security assessment reports regularly or as needed to provide decision support for managers, and the report includes a comprehensive security score, performance in key security areas, and improvement suggestions; based on the assessment results, the security assessment unit recommends adjustments to security policies and measures, and after implementing improvement measures, continues to monitor their effectiveness and makes further adjustments when necessary to ensure that the security of the system is always in the best condition.

[0111] In any of the above embodiments, the smart education capability assessment unit includes:

[0112] The third data acquisition module is used to acquire a management data set from teaching data; wherein, the management data set includes a unified authentication system data set, a data exchange integration data set, a data governance and management data set, and a data scenario display data set.

[0113] The third numbering evaluation module is used to convert the management data set into parameters for calculating the management area coefficient of the data set and label them.

[0114] In this embodiment, the third data acquisition module is responsible for collecting the following data sets from multiple systems and services of the smart education platform:

[0115] Unified authentication system data set: Contains user authentication and access control information, which is key to ensuring the security and access management of the platform.

[0116] Data Exchange Integration Dataset: Covers information on data exchange within and outside the platform, focusing on data fluidity and integration efficiency.

[0117] Data governance and management data sets: including information such as data quality, data lifecycle management, data permissions and policies, with the core being the overall data governance structure.

[0118] Data scenario presentation dataset: involves data visualization and reporting, and how to convert data into actionable insights and presentations.

[0119] The third data acquisition module automatically extracts the required data from various systems by connecting to the platform's database and APIs. The collected data is standardized and integrated into a unified format for further analysis and evaluation.

[0120] The third numbering and evaluation module converts management data sets into specific evaluation parameters. These parameters will be used to calculate the performance coefficient of the data management area and, in turn, assess the efficiency and effectiveness of data management. To ensure data consistency and traceability, the converted parameters will be numbered. Predefined logic and formulas are used to convert the collected data sets into quantitative parameters. This may include calculating data quality indicators, integration efficiency, governance compliance, etc. A systematic numbering scheme ensures that each parameter is uniquely identified, facilitating reference in subsequent analysis and decision-making.

[0121] In any of the above embodiments, the dataset management area coefficient is obtained by the following formula:

[0122]

[0123] Among them, Wil is the dataset management area coefficient used to characterize the data management index; n is a positive integer; Q1 is the unified authentication system dataset; Q2 is the data exchange integration dataset; Q3 is the data governance and management dataset; Q4 is the data scenario display dataset; Q4∩Q3∩Q2∩Q1 is the key area of ​​data management.

[0124] In this example, the unified authentication system dataset (Q1) involves data management for user authentication and authorization, focusing on security and access control. The data exchange and integration dataset (Q2) covers the exchange and integration efficiency of data between internal or external systems. The data governance and management dataset (Q3) includes data quality control, compliance, storage, and lifecycle management. The data scenario display dataset (Q4) focuses on the visual presentation of data and the user interface, specifically how to effectively transform data into information that is easy to understand and operate. By identifying areas that meet all of these key dataset requirements, these areas are defined as key areas of data management, indicating that these areas excel in multiple data management aspects.

[0125] Collect relevant data from four key data sets from different systems and services of the smart education platform; analyze each data set to confirm the data points that meet the set standards and requirements in their respective fields; identify key areas (intersection operation): perform the intersection operation Q4∩Q3∩Q2∩Q1 to identify data points that meet the requirements of all four key areas at the same time. The identification of these data points reflects the platform's integration and coordination capabilities in data management; calculate the data set management area coefficient Wil based on the scope or number of identified data management key areas. It may be calculated through simple counting or more complex algorithms (such as weighted scoring), depending on the importance of the data point and its impact on the operation of the platform; use the data set management area coefficient to evaluate the overall data management efficiency and effectiveness of the smart education platform; based on the evaluation results, adjust the data management strategy to optimize data integration, governance, security and user experience.

[0126] In any of the above embodiments, the smart education capability assessment unit further includes:

[0127] The management evaluation module evaluates the current data management level of the smart education platform based on the data set management area coefficient.

[0128] In this embodiment, the management assessment module uses the dataset management area coefficient (wil) to evaluate the platform's performance in key data management areas such as unified authentication, data exchange integration, data governance and management, and data scenario-based display; through this assessment, the unit not only detects the current data management effect, but also identifies potential problems and improvement opportunities in the data management process; the data management area coefficient is used as a comprehensive performance indicator to reflect the smart education platform's ability to process, maintain and utilize data; this indicator provides the platform with a clear data management quality benchmark to help decision makers understand and optimize data assets.

[0129] The management evaluation module regularly collects necessary data from the data management system of the smart education platform, which involves operations and outputs in multiple key areas; the data is pre-processed using advanced data processing and analysis technologies to ensure that the data used in the evaluation is accurate and up-to-date; using the calculation method of the dataset management area coefficient (such as the intersection operation described above), this unit calculates the current coefficient value; this coefficient is analyzed and compared with historical data to evaluate the improvement or degradation of data management capabilities; based on the evaluation results, the management evaluation module generates detailed reports, which include analysis of data management capabilities, performance in key areas, and recommended improvement measures.

[0130] By continuously monitoring and evaluating data management performance, the management assessment module helps the smart education platform optimize its data processing processes and improve data security, accessibility, and utilization efficiency. Open and transparent data management assessment results increase user trust in the platform, especially in the processing of sensitive data and the protection of personal privacy. The assessment results provide a basis for improvement, enabling the platform to take action on identified weaknesses and continuously improve its data management standards and performance.

[0131] The reports provide management with data-driven insights, enabling them to make more informed decisions on data policies, investments, and technology upgrades.

[0132] Furthermore, the smart supervision and evaluation system also includes a cloud computing platform;

[0133] The dataset management area Wil is calculated based on the unified authentication system dataset, data exchange integration dataset, data governance and management dataset, and data scenario display dataset.

[0134] The cloud computing platform calculates the shared platform security entry coefficient Rcf based on the digital base user center coefficient, data base data center coefficient, data base application center coefficient, data base resource center coefficient and school-level education center coefficient.

[0135] The cloud computing platform calculates the range of excellent teachers Tz based on the teacher quality improvement project data set, and calculates the basic environment construction safety factor Kd based on the basic environment construction improvement project data set.

[0136] The cloud computing platform calculates the smart security coefficient Hx based on the teacher literacy improvement project data set, the smart learning space improvement project data set, and the basic environment construction improvement project data set.

[0137] In any of the above embodiments, Figure 2 As shown, the smart education platform mentioned in the present invention is specifically:

[0138] User access layer: provides multiple ways (such as Web, App, PC client) for users to access the platform.

[0139] Unified user identity management: Manage user identities to ensure access control and data security.

[0140] The smart learning environment includes resource management, teaching middle platform, resource application and capability middle platform; among them, resource management: includes public resource library, professional resource library, course resource library, experimental resource library, etc.; teaching middle platform: provides teaching support such as lesson plan center, interactive teaching, classroom management, learning evaluation, etc.; resource application: involves integrated teaching, classroom interactive software, resource sharing and communication and other application services; capability middle platform: includes unified message push, artificial intelligence, real-time analysis and processing, data calculation and other technical services.

[0141] Education and teaching evaluation: including teaching resource data management, curriculum construction data management, etc., used to evaluate and optimize teaching quality and resource application efficiency.

[0142] Platform operation and maintenance management: responsible for the daily operation and maintenance of the system to ensure the stable operation of the system.

[0143] In this embodiment, unified user identity management ensures that all user access passes strict authentication, maintaining system security. The smart learning environment supports teacher teaching and student learning through comprehensive management and application of educational resources, improving resource utilization efficiency and teaching effectiveness. Educational and teaching evaluation monitors educational quality and resource usage through data analysis, providing support for educational decision-making. Platform operation and maintenance management ensures the efficient operation of the platform, handles system failures, and optimizes system performance. Various educational resources and user data are integrated to provide rich teaching and learning resources. Teaching resources and strategies are adjusted based on real-time feedback to achieve personalized learning and teaching. Big data and artificial intelligence technologies are used to analyze the effectiveness of educational activities and optimize teaching processes and resource allocation.

[0144] In any of the above embodiments, Figure 3 As shown in the figure, the data flow and information processing process of the smart education platform are as follows:

[0145] 1. Basic information:

[0146] Public resources: include a wide range of educational resources, such as open course materials, teaching videos, etc.

[0147] Educational affairs engine: involves the school's educational affairs management system, such as course scheduling, grade management, etc.

[0148] Marketing and enrollment engine: used for school marketing and enrollment activities.

[0149] Funding Engine: Responsible for managing the school's finances and cash flow.

[0150] 2. Multi-person intelligent service support:

[0151] Teacher Intelligent Assistance: Provides intelligent support for teachers during the teaching process, such as lesson plan recommendations and student performance analysis.

[0152] Student intelligent assistance: Provide students with intelligent services such as learning suggestions and learning progress tracking.

[0153] Smart Family Assistance: Smart services provided to parents, such as feedback on students’ learning progress and homework tutoring suggestions.

[0154] 3. Continuous optimization of innovative operating models:

[0155] Intelligent Operation: Automate and optimize the operational processes of the education platform to improve efficiency.

[0156] Experience upgrade: Continuously improve the user interface and interactive experience to enhance user satisfaction.

[0157] User Growth: Expand your user base by optimizing marketing and enrollment strategies.

[0158] Intellectual property and brand building: Strengthen intellectual property protection and enhance the value of education brands.

[0159] 4. Real-time operation and maintenance support:

[0160] Proactive service: Proactively monitor the system and resolve potential problems in a timely manner.

[0161] Intelligent service: Use artificial intelligence technology to provide intelligent customer service and technical support.

[0162] Personalized service: Provide customized services according to the specific needs of different users.

[0163] In any of the above embodiments, Figure 4 As shown in FIG, the detailed structure of the educational technology ecosystem mentioned in the present invention, specifically involving the various services and functions of the smart education platform, is as follows:

[0164] The central node, specifically the Education Bureau, represents the core or management center of the educational technology platform and is responsible for overall supervision and policy formulation.

[0165] Peripheral service nodes, where nodes are divided into different service and management categories according to their functions, including but not limited to:

[0166] Basic education management, specifically basic education: covers daily teaching activities and the management of basic educational resources; basic education management: focuses on the operation of educational infrastructure and administrative management.

[0167] Professional education services: including the development and delivery of professional courses, such as vocational education and specific skills training.

[0168] Educational information services include digital resource services: providing digital learning materials and resources; educational big data services: using big data technology to conduct educational analysis and decision support; and cloud computing services: supporting the data processing and storage needs of educational platforms.

[0169] Educational evaluation and development: including student performance evaluation, education quality monitoring and research on the application of new technologies in education.

[0170] Smart education solutions: specifically artificial intelligence applications: applying AI technology in education to improve learning and teaching outcomes; personalized learning plans: customizing learning plans based on students' individual needs and abilities.

[0171] In this embodiment, the arrows in the diagram for connections and processes may represent the direction of data flow, information exchange, or service provision, showing the interdependence and collaborative relationship between various services; the technical and strategic levels are that the various nodes and connections shown in the diagram reflect an integrated education strategy, which aims to achieve modernization and intelligence in education through technology integration and service innovation.

[0172] In any of the above embodiments, Figure 5 The figure shows an overview of the functional modules of the smart education platform mentioned in this invention, which shows the main functions and services provided by the platform. These functional modules are divided into four main categories: basic teaching management, course management, progress management and intelligent services. The following is a description of the specific functions under each category:

[0173] Basic teaching management includes integrated login, three shields, precise teaching, and synchronous and synchronous announcements. Among them, integrated login: users can access all teaching resources and services through a single login, simplifying the login process and enhancing the user experience. Three shields: This may refer to providing three layers of security protection to ensure the security of user data and privacy. Precision teaching: Using data analysis to achieve accurate teaching pairing, ensuring that teaching content matches students' learning needs and abilities. Synchronous and synchronous announcements: Ensure that all users can obtain the latest teaching-related announcements and information in real time, keeping information synchronized.

[0174] Course management includes intelligent teacher assistance, intelligent test generation, quick course search, and real-time classroom feedback. Intelligent teacher assistance provides teachers with intelligent tools and resources to help them design and manage courses more efficiently. Intelligent test generation automatically generates tests based on teaching objectives and student learning progress, improving the efficiency and quality of assessments. Quick course search enables users to quickly find the course resources they need, improving learning efficiency. Real-time classroom feedback instantly collects and analyzes student feedback during class, providing teachers with real-time teaching support.

[0175] Progress management includes teacher attendance management, automatic class time calculation, and comprehensive control. Teacher attendance management automatically records teacher attendance, helping management monitor attendance and course quality. Automatic class time calculation automatically calculates each teacher's teaching time and student learning time, ensuring that teaching activities meet prescribed time standards. Comprehensive control provides a comprehensive management view, allowing administrators to fully monitor the progress and quality of educational activities.

[0176] Intelligent services include after-school resource sharing, student performance and evaluation, and after-school service platform interaction. After-school resource sharing provides a platform for teachers and students to share teaching resources after class, enhancing the continuity and depth of learning. Student performance and evaluation automatically collect and analyze student performance and evaluations, helping teachers and schools understand student learning outcomes. After-school service platform interaction provides a platform to promote after-school interaction between teachers and students, strengthening teacher-student relationships and the continuity of learning.

[0177] In any of the above embodiments, Figure 6 As shown in the figure, the information flow and decision-making architecture of the smart education platform mentioned in this invention particularly emphasizes the transmission path of information from data sources to decision makers. Specifically, the figure describes the information flow between three main data sources (smart data A, smart data B, smart data C) and three user roles (management, application, and monitoring). This architecture supports flexible data processing and information distribution, ensuring that each role can obtain the required information to support its functions and decisions. The following is a detailed description of each part:

[0178] Data sources, including intelligent data A, B, and C, represent data of different types or sources, which may include student learning data, teaching resource data, operational data, etc. Each data source has certain intelligent features, such as automated data collection and real-time analysis.

[0179] User roles include management, application, and monitoring. Management: Management roles focus on the operation and resource management of the entire platform, requiring the integration of various data to formulate policies and management decisions. Application: Application roles may be teachers or instructional designers, requiring access to specific teaching and learning data to optimize teaching activities and learning experiences. Monitoring: Monitoring roles are responsible for overseeing the security and stability of the system and require real-time operational data to ensure the normal operation of the platform.

[0180] Information flow includes bidirectional information flow and information integration. Regarding bidirectional information flow, the dashed and solid arrows in the diagram illustrate the flow of information within the system. Dashed lines may indicate on-demand or non-real-time information flow, while solid lines represent regular or real-time data flow. Information integration: The blue oval represents the information integration point, where information from different data sources is aggregated and processed for provision to different user roles.

[0181] The embodiment of the second aspect of the present invention proposes a method, which is implemented by the Internet-based intelligent supervision and evaluation system in any of the above embodiments, such as Figure 7 As shown, the method includes the following steps:

[0182] S101, obtaining teaching data contained in a cloud resource network, and dividing the teaching data into a management data set, an interaction data set, and a teaching quality data set;

[0183] S102, calculating and obtaining a data management index for evaluating the smart education platform using the management data set, calculating and obtaining an interaction security index for evaluating the smart education platform using the interaction data set, and calculating and obtaining a teaching quality index for evaluating the smart education platform using the teaching quality data set;

[0184] S103, optimize the teaching process related to the smart education platform and the school based on the data management index, the interactive security index and the teaching quality index.

[0185] The present invention proposes a method, S101, for acquiring and segmenting teaching data: extracting all currently available teaching data from a cloud resource network, which may include but is not limited to student performance data, course content data, teacher interaction data, etc.; segmenting the acquired teaching data into three main data sets according to their nature and purpose: a management data set, an interaction data set, and a teaching quality data set; using automated tools to extract data from different data sources, such as a learning management system (LMS), a student information system (SIS), etc.; and fluidly allocating the data to corresponding sets according to the purpose and source of the data, with each set focusing on supporting specific evaluation and decision-making functions.

[0186] S102 calculates various security and quality indices: uses management datasets to evaluate the platform's data management capabilities, such as data integration, protection, and governance; uses interaction datasets to evaluate the security of interactions between users and the platform, including the security of data exchange and the strength of user authentication; uses teaching quality datasets to measure the effectiveness and quality of teaching activities; applies specific algorithms and models, such as statistical analysis and machine learning, to each dataset to calculate the corresponding index; these indices serve as key performance indicators (KPIs) to monitor and evaluate the platform's performance in data management, interaction security, and teaching quality.

[0187] S103 Optimize teaching processes: Based on the index obtained from S102, optimize the school-related teaching processes, including curriculum design, teaching methods, student interaction, etc.; implement necessary improvement measures based on the evaluation results to improve teaching efficiency and quality and enhance data security; use the obtained data management, interaction security and teaching quality indexes to provide data support for the formulation of school management and teaching strategies; based on feedback and changes in the index, continuously adjust the teaching and management processes to respond to changes in educational needs and challenges.

[0188] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present disclosure, and should all be included in the scope of protection of the present disclosure.

Claims

1. An Internet-based intelligent supervision and evaluation system, characterized by: The smart supervision and evaluation system is used to evaluate and optimize the smart education platform, which is used to exchange teaching data of schools in at least one region and build a cloud resource network through the cloud; The smart supervision and evaluation system includes: a smart education capability evaluation unit, configured to optimize the teaching data in the cloud resource network and obtain a plurality of management data sets used by the smart education platform to manage the teaching data; and obtain a data management index of the smart education platform from the management data sets, wherein the data management index is used to reflect the comprehensive performance of the smart education platform in data processing and management. A two-level education hub evaluation unit optimizes the teaching data in the cloud resource network and obtains multiple interactive data sets of the teaching data interacted by the smart education platform; obtains the interactive security index of the smart education platform through the interactive data set, and the interactive security index is used to evaluate the data interaction security of the smart education platform, including the integrity, confidentiality and availability of the data; and the two-level education hub evaluation unit includes a first data acquisition module and a first numbering evaluation module; the first data acquisition module is used to obtain the interactive data set in the teaching data; wherein, the interactive data set includes a digital base user hub coefficient, a data base data hub coefficient, a data base application hub coefficient, a data base resource hub coefficient and a school-level education hub coefficient; the first numbering evaluation module is used to convert the interactive data set into a parameter for calculating the shared platform security entry coefficient and label it; the shared platform security entry coefficient is calculated using the following formula: Wherein, Rcf is the shared platform security entry coefficient used to characterize the interactive security index; F z Including F1, F2, F3, F4 and F5, and S z Including S1, S2, S3, S4 and S5, F1 is the digital base user hub coefficient, and the weight of the digital base user hub coefficient in the shared platform security entrance coefficient is S1; F2 is the data base data hub coefficient, and the weight of the data base data hub coefficient in the shared platform security entrance coefficient is S2; F3 is the data base application hub coefficient, and the weight of the data base application hub coefficient in the shared platform security entrance coefficient is S3; F4 is the data base resource hub coefficient, and the weight of the data base resource hub coefficient in the shared platform security entrance coefficient is S4; F5 is the school-level education hub coefficient, and the weight of the school-level education hub coefficient in the shared platform security entrance coefficient is S5; Focus on improving the engineering evaluation unit, optimize the teaching data in the cloud resource network, and obtain multiple teaching quality data sets related to school configuration from the teaching data; obtain the school's teaching quality index through the teaching quality data sets, and the teaching quality index is used to reflect the school's comprehensive performance in the teaching process to quantify the school's teaching effectiveness; The education application system unit is used to optimize the teaching process related to the smart education platform and the school.

2. The intelligent supervision and evaluation system according to claim 1 is characterized in that: The key improvement project assessment units include: A second data acquisition module is configured to acquire the teaching quality data set from the teaching data; wherein the teaching quality data set includes data on outstanding personnel trained by key teachers, district-wide teacher popularization data, data on personnel with outstanding training results, student face registration data, and student actual enrollment data; The second numbering evaluation module is used to convert the teaching quality data set into parameters for calculating the teaching quality index and label them.

3. The intelligent supervision and evaluation system according to claim 2 is characterized in that: The teaching quality index includes an excellent teacher range index and a basic environment construction safety index for characterizing the configuration of the school; The excellent teacher range index is obtained by the following formula: Wherein, Tz is the index of excellent teacher range; G1 is the number of excellent personnel trained by backbone teachers; G2 is the number of personnel with excellent training results; G3 is the standard for evaluating excellent teachers; G1∩G2 is the concentration range of excellent teachers; n is a positive integer; The basic environment construction safety index is obtained by the following formula: Among them, Kd is the basic environment construction security index; D3 is the number of student faces registered; and D is the number of students actually entering the school.

4. The intelligent supervision and evaluation system according to claim 3 is characterized in that: The key improvement project evaluation unit further obtains the smart security index of the smart education platform through the multiple teaching quality data sets; the smart security index is obtained by the following formula: Wherein, Hx is the smart security index; Improve the entrance safety factor for teacher literacy; is the safety factor of the entrance to the smart learning space; is the safety factor of the entrance to the basic environment construction; a is the weight of the safety factor of the entrance to the teacher quality improvement in the smart security index; b is the weight of the safety factor of the entrance to the smart learning space in the smart security index; d is the weight of the safety factor of the entrance to the basic environment construction in the smart security index; G is the total number of faculty and staff; F1 is a type of security incident that occurred; F n is all types of security events; Fj1 is a type of unsafe event that occurs; Fj n For all types of unsafe incidents.

5. The intelligent supervision and evaluation system according to claim 4 is characterized in that: The key improvement project assessment unit also includes: The security evaluation unit evaluates the current overall security level of the smart education platform based on the basic environment construction security index, the smart security index and the shared platform security entry coefficient.

6. The intelligent supervision and evaluation system according to claim 1 is characterized in that: The smart education capability assessment unit includes: A third data acquisition module is configured to acquire a management data set from the teaching data; wherein the management data set includes a unified authentication system data set, a data exchange integration data set, a data governance and management data set, and a data scenario display data set; The third numbering evaluation module is used to convert the management data set into parameters for calculating the management area coefficient of the data set and label them.

7. The intelligent supervision and evaluation system according to claim 6 is characterized in that: The dataset management area coefficient is obtained by the following formula: Among them, Wil is the dataset management area coefficient used to characterize the data management index; n is a positive integer; Q1 is the unified authentication system dataset; Q2 is the data exchange integration dataset; Q3 is the data governance and management dataset; Q4 is the data scenario display dataset; Q4∩Q3∩Q2∩Q1 is the data management key area.

8. The intelligent supervision and evaluation system according to claim 6 is characterized in that: The smart education capability assessment unit further includes: The management evaluation module evaluates the current data management level of the smart education platform according to the data set management area coefficient.

9. A method using the intelligent supervision and evaluation system according to any one of claims 1 to 8, characterized in that: The steps include: S101, obtaining the teaching data contained in the cloud resource network, and dividing the teaching data into the management data set, the interaction data set and the teaching quality data set; S102: Calculating and obtaining the data management index for evaluating the smart education platform using the management data set, calculating and obtaining the interaction safety index for evaluating the smart education platform using the interaction data set, and calculating and obtaining the teaching quality index for evaluating the smart education platform using the teaching quality data set; S103: Optimize the teaching process related to the smart education platform and the school according to the data management index, the interaction security index, and the teaching quality index.

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