Digital economy analysis method and device based on micro-isolation technology

Abstract

The present application relates to a digital economy analysis method and device based on micro-isolation technology. The method includes: obtaining digital learning data, learner information and learning activity information in a preset teaching area; dividing the digital learning data into at least one data pool, and micro-isolating the digital learning data in each data pool; obtaining the learning path of each learner in different learning activities; modeling the learning path to predict the learning direction of each learner for different learning activities; obtaining the emotional feedback information of learners for different learning activities on the learning platform; obtaining the emotional demand information of each learner for different learning activities; obtaining the digital economy data in the preset teaching area according to the learning direction and emotional demand information of each learner; obtaining the analysis results of educational decision-making according to the digital economy data. This method can solve the problem that the user portrait of electric vehicle charging stations is not accurate enough and the portrayal of user charging behavior is not detailed enough.

Description

Technical Field

[0001] The present application relates to the field of micro-isolation technology, and in particular to a digital economy analysis method and device based on micro-isolation technology. Background Art

[0002] Digital economy refers to a new economic form that is based on digital technology, takes data as the key element, takes modern information networks as the main carrier, takes the integrated application of information and communication technology and the digital transformation of all factors as the important driving force, and promotes a more unified fairness and efficiency. With the rapid development of the digital economy, digital education, as an important part of it, is also growing stronger. In order to ensure the steady development of digital education, relevant personnel need to analyze it to formulate subsequent development plans. Traditional digital education analysis methods are mainly based on the isolation of IP addresses. This method assumes that the resources to be accessed have a certain network location. However, with the widespread application of cloud computing, the prevalence of remote office and BYOD, IP addresses are no longer uniquely determined, which makes the isolation method based on IP addresses limited in terms of security.

[0003] Therefore, a new technology is needed to meet the security needs in the development of digital education and to cope with the challenges brought by cloud computing and mobile office. Summary of the invention

[0004] Based on this, it is necessary to provide a digital economic analysis method and device based on micro-isolation technology that can solve the problems of inaccurate user portraits of electric vehicle charging stations and insufficiently detailed portrayals of user charging behaviors in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a digital economy analysis method based on micro-isolation technology. The method comprises:

[0006] Obtain digital learning data, learner information and learning activity information in the preset teaching area;

[0007] According to the learner information and the learning activity information, the digital learning data is divided into at least one data pool, and the digital learning data in each data pool is micro-isolated;

[0008] According to the digital learning data after micro-isolation, obtain the learning path of each learner in different learning activities;

[0009] Modeling the learning paths to predict each learner's learning direction for different learning activities;

[0010] Use natural language processing technology to obtain learners' emotional feedback information on different learning activities on the learning platform;

[0011] Performing sentiment analysis on the sentiment feedback information to obtain each learner's sentiment demand information for different learning activities;

[0012] According to the learning direction and the emotional demand information of each learner, digital economic data in a preset teaching area is obtained; specifically including: according to the learning direction and the emotional demand information of each learner, a preset teaching area related to the learning activity is determined, and digital economic data in the preset teaching area is obtained; wherein the digital economic data refers to indicators and information related to education, including the allocation of educational resources, students' learning outcomes and the operation of educational institutions;

[0013] Based on the digital economic data, analytical results for educational decision-making are obtained.

[0014] In one embodiment, the micro-isolation method includes file system isolation, database isolation or cloud storage isolation; the digital learning data includes evaluation data and other data; the digital learning data is divided into at least one data pool according to the learner information and the learning activity information, and the digital learning data in each data pool is micro-isolated, including:

[0015] According to the learner information and the learning activity information, the digital learning data is classified into at least one data pool;

[0016] Get the learner's learning identifier;

[0017] Determining access rights of each learner to the data pool according to the learning identifier;

[0018] Use file system isolation, database isolation, or cloud storage isolation to isolate the evaluation data and other data in the data pool;

[0019] Among them, file system isolation: data is stored in different folders or paths, and only learners with corresponding permissions can access it;

[0020] Database isolation: At the database level, data isolation is achieved through different tables, views or query permissions;

[0021] Cloud storage isolation: In cloud services, use different storage buckets or access policies to isolate data;

[0022] According to the preset audit monitoring mechanism, the data operation status of at least one data pool is monitored in real time.

[0023] In one embodiment, the use of any one of file system isolation, database isolation or cloud storage isolation to isolate the evaluation data and other data in the data pool includes:

[0024] Use any one of file system isolation, database isolation or cloud storage isolation to set a first micro-isolation firewall and a second micro-isolation firewall for each data pool; wherein the first micro-isolation firewall is located at the entrance or boundary of the data pool, and the second micro-isolation firewall is located inside the data pool;

[0025] Obtaining a data type of the evaluation data, the data type including evaluation indicators, scoring information, evaluation feedback, and evaluation history records; wherein the first micro-isolation firewall is used to isolate the evaluation indicators and the other data; and the second micro-isolation firewall is used to further isolate the scoring information, the evaluation feedback, and the evaluation history records;

[0026] Obtain first isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall of the current data pool;

[0027] According to the first isolation feedback information, when at least one of the scoring information, the evaluation feedback, and the evaluation history record is detected in the first micro-isolation firewall, second isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall in other data pools is obtained;

[0028] According to at least one item of the second isolation feedback information, obtaining a proportion of at least one of the rating information, the evaluation feedback, and the evaluation history record detected in the first micro-isolation firewall;

[0029] When the situation ratio reaches a preset ratio threshold, the digital learning data is reclassified into at least one data pool.

[0030] In one embodiment, the use of natural language processing technology to obtain learners' emotional feedback information on different learning activities on the learning platform includes:

[0031] Obtaining interactive information of different learning activities of learners on the learning platform, wherein the interactive information includes the frequency and type of learning activities;

[0032] Determining the learner's platform learning habits according to the learning activity frequency and the learning activity type;

[0033] Obtaining the matching degree between the platform learning habits and the preset learning habit standards;

[0034] When the matching degree reaches a preset matching degree threshold, emotional feedback information of learners' different learning activities on the learning platform is obtained.

[0035] In one embodiment, the process of obtaining the preset learning habit standard includes:

[0036] After the digital learning data is micro-isolated, obtaining the customer complaint rate and error rate of the current micro-isolation method;

[0037] When both the customer complaint rate and the error rate meet the preset conditions, obtaining the learner's access rights and access time periods for different learning activities on the learning platform based on the digital learning data after micro-isolation;

[0038] A process of obtaining a preset learning habit standard according to the access permission and the access period.

[0039] In one embodiment, after obtaining the customer complaint rate and error rate of micro-segmentation, the method further includes:

[0040] When at least one of the customer complaint rate and the error rate does not meet the expected conditions, the current micro-isolation mode is adjusted to cloud storage isolation.

[0041] In one embodiment, the method further comprises:

[0042] Get the age distribution data of all learners;

[0043] Obtaining a correlation between the age distribution data and the micro-isolation method;

[0044] According to the learning direction and emotional needs of each learner, analyzing the correlation between the age group of the learner and the micro-isolation method;

[0045] When the correlation reaches a preset correlation threshold, a corresponding micro-isolation method is determined according to the age of each learner.

[0046] In a second aspect, the present application also provides a digital economy analysis device based on micro-isolation technology. The device includes:

[0047] An acquisition module, used to acquire digital learning data, learner information and learning activity information in a preset teaching area;

[0048] A processing module, configured to classify the digital learning data into at least one data pool according to the learner information and the learning activity information, and to micro-isolate the digital learning data in each data pool;

[0049] The processing module is further used to obtain the learning path of each learner in different learning activities according to the digital learning data after micro-isolation;

[0050] A modeling module, used to model the learning path and predict the learning direction of each learner for different learning activities;

[0051] The processing module is also used to obtain learners’ emotional feedback information on different learning activities on the learning platform using natural language processing technology;

[0052] The processing module is further used to perform sentiment analysis on the sentiment feedback information to obtain the sentiment demand information of each learner for different learning activities;

[0053] The processing module is further used to obtain digital economic data in a preset teaching area according to the learning direction and the emotional demand information of each learner; specifically including: determining a preset teaching area related to the learning activity according to the learning direction and the emotional demand information of each learner, and obtaining digital economic data in the preset teaching area; wherein the digital economic data refers to indicators and information related to education, including the allocation of educational resources, students' learning outcomes and the operation of educational institutions;

[0054] The analysis module is used to obtain analysis results of educational decisions based on the digital economic data.

[0055] In a third aspect, the present application further provides a computer device. The memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0056] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when the computer program is executed by a processor.

[0057] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0058] The digital economy analysis method and device based on micro-isolation technology can better protect the security of data and prevent data leakage and malicious attacks by dividing digital learning data into different data pools through micro-isolation technology. According to the digital learning data after micro-isolation, the learning path of each learner in different learning activities is obtained, the learning path is modeled and the learning direction of the learner is predicted, which helps to provide personalized learning paths and suggestions for each learner. By using natural language processing technology to obtain learners' emotional feedback information and perform sentiment analysis, the emotional needs of each learner for different learning activities can be understood, so as to better meet the needs of learners. According to the learners' learning direction and emotional needs information, the digital economy data in the preset teaching area is obtained and analyzed, which can provide data support for educational decision-making and help educational institutions better plan and adjust teaching strategies. This technology can cope with the challenges brought by cloud computing and mobile office, meet the security needs in the development of digital education, and help promote the steady development of digital education. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 This is an application environment diagram of a digital economy analysis method based on micro-isolation technology in one embodiment;

[0061] Figure 2 A flowchart of a digital economy analysis method based on micro-isolation technology in one embodiment;

[0062] Figure 3 A schematic diagram of a flow chart of a digital economy analysis method based on micro-isolation technology in another embodiment;

[0063] Figure 4 It is a structural block diagram of a digital economy analysis device based on micro-isolation technology in one embodiment;

[0064] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] The digital economy analysis method based on micro-isolation technology provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0067] In an exemplary embodiment, Figure 2 As shown in the figure, a digital economy analysis method based on micro-isolation technology is provided. Figure 1 The server in the example is used to illustrate, including the following steps S202 to S216. Among them:

[0068] Step S202, obtaining digital learning data, learner information and learning activity information in a preset teaching area.

[0069] Specifically, digital learning data may include data on students' academic performance, learning progress, learning habits, etc.; learner information may include information on students' personal information, learning background, learning needs, etc.; learning activity information may include information on students' learning behavior, learning time, and use of learning resources, etc. By obtaining this information, digital education analysts can better understand students' learning situations, develop more personalized teaching plans, and improve teaching effectiveness. At the same time, this information can also be used for teaching research and teaching management to promote the development and improvement of education. It should be noted that in the process of obtaining this information, students' privacy and personal information security need to be protected.

[0070] Step S204: classify the digital learning data into at least one data pool according to the learner information and the learning activity information, and micro-isolate the digital learning data in each data pool.

[0071] Specifically, digital learning data are divided into data pools according to learner information and learning activity information: This means that digital learning data will be classified into different data pools according to learners' personal characteristics, learning preferences, learning history, etc., as well as the type, time, and location of the learning activities they participate in. Each data pool can represent a specific learner group or learning activity category.

[0072] Micro-segmentation of digital learning data in each data pool: Micro-segmentation is a data security technology that aims to isolate sensitive data into specific areas or environments to limit access and use of that data. Here, micro-segmentation of digital learning data in each data pool can help protect learners' privacy and data security. By dividing digital learning data into different data pools and micro-segmenting each data pool, access to a specific data pool can be restricted, and only authorized personnel or systems can access and process the data in that data pool.

[0073] The purpose of this is to better manage and protect digital learning data and ensure data security and compliance. At the same time, by dividing the data into different data pools, the data can be analyzed and utilized according to different needs and purposes, thereby providing more targeted learning support and services.

[0074] Step S206, obtaining the learning path of each learner in different learning activities based on the digital learning data after micro-isolation.

[0075] Specifically, in data processing and analysis, micro-segmentation is a method of segmenting data into smaller, independent parts or data pools. Through micro-segmentation, large-scale data sets can be divided into smaller, more manageable and analytical units. Learning paths refer to the steps, sequence, and process that learners go through when completing learning activities. By analyzing digital learning data after micro-segmentation, the learning path taken by each learner in different learning activities can be determined. This means that the specific learning process of each learner in different learning activities can be tracked, including the learning resources they accessed, the tasks they completed, the discussions they participated in, etc. By analyzing these learning paths, each learner's learning habits, preferences, and learning style can be understood.

[0076] For example, by observing the learning path, you may find that a learner is more inclined to read the relevant materials first, then practice, and finally participate in the discussion in a certain learning activity, while another learner may directly enter the practice phase and then look up the relevant materials.

[0077] In summary, obtaining the learning path of each learner in different learning activities based on the digital learning data after micro-isolation can help to gain a deeper understanding of learners' learning behaviors and preferences, and provide a basis for personalized learning and teaching optimization.

[0078] Step S208: Model the learning path to predict each learner's learning direction for different learning activities.

[0079] Specifically, the learner's learning path can be represented as a series of steps or stages, and then data analysis and machine learning algorithms can be used to build a model that can predict the learning direction that learners may take in future learning activities based on their previous learning paths and behaviors.

[0080] For example, if a learner has shown a strong interest in a topic in past learning activities and has spent a lot of time on related resources, the model may predict that the learner will continue to delve deeper into the topic in future learning activities.

[0081] By predicting where learners are headed, educators or systems can provide personalized recommendations, resources, and support to meet each learner’s unique needs and interests. This helps improve learning outcomes and learner engagement.

[0082] Step S210, using natural language processing technology to obtain learners' emotional feedback information on different learning activities on the learning platform.

[0083] Specifically, natural language processing technologies can be used to analyze textual content such as comments, feedback, and discussions posted by learners on learning platforms. These technologies can identify sentiment tendencies in texts, such as positive, negative, or neutral, and extract relevant keywords and phrases to represent specific aspects of sentiment.

[0084] By obtaining learners' emotional feedback, educators or platform developers can understand learners' feelings and attitudes towards different learning activities. This helps to identify problems, challenges or positive experiences encountered by learners in the learning process, so as to take corresponding improvement measures or provide more targeted support.

[0085] For example, if learners express negative emotional feedback during a learning activity, such as confusion, frustration, or boredom, educators can intervene in time to provide additional guidance, resources, or adjust the design of the learning activity. On the contrary, if learners express positive emotional feedback, such as excitement, satisfaction, or a sense of achievement, these effective learning activities can be further strengthened and promoted.

[0086] Using natural language processing technology to obtain learners' emotional feedback information can provide valuable insights for the optimization of learning platforms and personalized learning, thereby improving learners' experience and learning outcomes.

[0087] Step S212, performing sentiment analysis on the sentiment feedback information to obtain each learner's sentiment demand information for different learning activities.

[0088] Specifically, sentiment analysis is a natural language processing technology that can automatically identify and understand the emotional tendencies in texts. By performing sentiment analysis on learners' emotional feedback information, the emotional needs of each learner for different learning activities can be determined.

[0089] For example, learners may express positive emotions such as liking, excitement, or satisfaction towards a learning activity, which indicates that they have a high interest and need for the learning activity. On the contrary, if learners express negative emotions such as confusion, frustration, or boredom towards a learning activity, then the learning activity may need to be improved to meet their needs.

[0090] By obtaining information about each learner's emotional needs for different learning activities, educators or platform developers can better understand learners' learning experience and expectations. This helps optimize the design and content of learning activities and provide learning resources and support that better meet learners' needs.

[0091] At the same time, emotional needs information can also be used for personalized learning. According to the emotional needs of each learner, the learning platform can provide them with personalized recommendations, feedback, and guidance to improve learners' participation and learning effects.

[0092] It should be noted that the results of sentiment analysis are based on the inference of text content, which may be subjective and inaccurate. Therefore, when interpreting and applying sentiment demand information, it is necessary to comprehensively consider the feedback of multiple learners and combine it with other data sources for analysis and verification.

[0093] Step S214, obtaining digital economic data in a preset teaching area according to the learning direction and emotional demand information of each learner.

[0094] Specifically, by modeling and predicting the learners' learning paths, we can understand the learning direction of each learner in different learning activities. We use natural language processing technology to perform sentiment analysis on the learners' emotional feedback information on the learning platform to obtain each learner's emotional needs for different learning activities. Based on each learner's learning direction and emotional needs information, we determine the preset teaching area related to these learning activities and obtain the digital economy data in the area.

[0095] Digital economy data can include various digital economy indicators related to the teaching area, such as Internet usage, e-commerce sales data, digital payment usage, etc. These data can come from government statistical agencies, industry reports, market research companies, etc. By obtaining digital economy data in the preset teaching area, we can understand the development of the digital economy, market trends, consumer behavior and other information in the area. These data can be used to further optimize teaching content and learning resources to better meet the needs of learners and combine them with the actual economic environment and industry needs. At the same time, these data can also provide references for educational institutions and policymakers on digital economy education and training to cultivate talents that adapt to the development of the digital economy.

[0096] Step S216, obtaining analysis results of educational decisions based on digital economic data.

[0097] Specifically, digital economic data can include various indicators and information related to education, such as the allocation of educational resources, students' learning outcomes, the operation of educational institutions, etc. By analyzing these data, we can understand the trends, problems and opportunities in the field of education.

[0098] For example, by analyzing student learning outcomes data, we can find out which teaching methods and curriculum designs are more effective, thus providing a basis for educational decision-making. By analyzing the allocation of educational resources, we can find out which regions or schools need more support and investment. Obtaining analytical results for educational decision-making can help educational decision-makers formulate more informed policies and strategies to improve the quality of education, promote educational equity, and promote educational innovation. These analytical results can provide strong support and guidance for the formulation of educational policies, the planning of educational projects, and the management of educational institutions.

[0099] In the method of the above embodiment, the digital learning data is divided into different data pools through micro-isolation technology, which can better protect the security of the data and prevent data leakage and malicious attacks. According to the digital learning data after micro-isolation, the learning path of each learner in different learning activities is obtained, the learning path is modeled and the learning direction of the learner is predicted, which helps to provide each learner with personalized learning paths and suggestions. By using natural language processing technology to obtain learners' emotional feedback information and perform sentiment analysis, the emotional needs of each learner for different learning activities can be understood, so as to better meet the needs of learners. According to the learners' learning direction and emotional needs information, the digital economy data in the preset teaching area is obtained and analyzed, which can provide data support for educational decision-making and help educational institutions better plan and adjust teaching strategies. This technology can cope with the challenges brought by cloud computing and mobile office, meet the security needs in the development of digital education, and help promote the steady development of digital education.

[0100] In an exemplary embodiment, Figure 3 As shown, the micro-isolation method includes file system isolation, database isolation or cloud storage isolation; digital learning data includes evaluation data and other data; digital learning data is divided into at least one data pool according to learner information and learning activity information, and the digital learning data in each data pool is micro-isolated, including:

[0101] Step S302, classifying the digital learning data into at least one data pool according to the learner information and the learning activity information;

[0102] Step S304, obtaining the learner's learning identifier;

[0103] Step S306, determining the access rights of each learner to the data pool according to the learning identifier;

[0104] Step S308, isolating the evaluation data and other data in the data pool by using any one of file system isolation, database isolation or cloud storage isolation;

[0105] Step S310: monitor the data operation status of at least one data pool in real time according to a preset audit monitoring mechanism.

[0106] Specifically, based on learner information and learning activity information, digital learning data is classified and stored in different data pools. In this way, data of the same type or from the same source can be managed centrally. In order to ensure the correctness and security of the data, the system needs to identify each learner. The learning identifier (such as student number, user name, etc.) is used to uniquely identify a learner. Based on the learning identifier, the system can determine each learner's access rights to the data pool. This means that different learners may see different data or perform different operations, depending on their permission settings.

[0107] There are several ways to implement micro-isolation:

[0108] File system isolation: Store data in different folders or paths so that only learners with corresponding permissions can access them.

[0109] Database isolation: At the database level, data isolation is achieved through different tables, views, or query permissions.

[0110] Cloud storage isolation: In cloud services, use different storage buckets or access policies to isolate data.

[0111] In this embodiment, through the preset audit monitoring mechanism, the system can monitor the data operation status of the data pool in real time, including operations such as data access, modification, and deletion, to ensure the integrity and security of the data.

[0112] In an exemplary embodiment, the evaluation data and other data of the data pool are isolated by using any one of file system isolation, database isolation or cloud storage isolation, including:

[0113] Using any one of file system isolation, database isolation or cloud storage isolation, a first micro-isolation firewall and a second micro-isolation firewall are respectively set for each data pool; wherein the first micro-isolation firewall is located at the entrance or boundary of the data pool, and the second micro-isolation firewall is located inside the data pool;

[0114] The data type of the evaluation data is obtained, and the data type includes evaluation indicators, scoring information, evaluation feedback, and evaluation history records; wherein the first micro-isolation firewall is used to isolate the evaluation indicators and other data; and the second micro-isolation firewall is used to further isolate the scoring information, evaluation feedback, and evaluation history records;

[0115] Obtain first isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall in the current data pool;

[0116] According to the first isolation feedback information, when at least one of the rating information, the evaluation feedback, and the evaluation history record is detected in the first micro-isolation firewall, second isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall in other data pools is obtained;

[0117] According to at least one second isolation feedback information, obtaining a proportion of at least one of the scoring information, the evaluation feedback, and the evaluation history records detected in the first micro-isolation firewall;

[0118] When the situation ratio reaches a preset ratio threshold, the digital learning data is reclassified into at least one data pool.

[0119] Specifically, the settings of the micro-isolation firewall are:

[0120] The first micro-isolation firewall is located at the entrance or boundary of the data pool. Its main function is to preliminarily screen and isolate the data entering the data pool.

[0121] The second micro-isolation firewall is located inside the data pool and provides more detailed data isolation and security management.

[0122] Division and isolation of data types:

[0123] Based on the data type, the evaluation data is subdivided into evaluation indicators, rating information, evaluation feedback and evaluation history.

[0124] The first micro-isolation firewall is used to isolate evaluation indicators from other data to ensure the security and independence of evaluation indicators.

[0125] Second micro-isolation firewall: used to further isolate scoring information, evaluation feedback, and evaluation history. These data may involve learners' personal performance and privacy, and therefore require stricter isolation measures.

[0126] Acquisition and processing of isolation feedback information:

[0127] The system will obtain the isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall in the current data pool.

[0128] First isolation feedback information: reflects the isolation status of the first micro-isolation firewall, including whether there is unisolated data.

[0129] If at least one of the rating information, evaluation feedback, and evaluation history is detected in the first micro-isolation firewall, the system will further obtain isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall in other data pools.

[0130] Second isolation feedback information: provides information on the isolation status in other data pools.

[0131] Assessment of situation proportions and reclassification of data:

[0132] Based on the acquired second isolation feedback information, the system calculates the proportion of cases in which at least one of the rating information, evaluation feedback, and evaluation history records is detected in the first micro-isolation firewall.

[0133] If this ratio reaches the preset threshold, it means that the data isolation is not effective and there may be security risks or data leakage risks. In this case, the system will reclassify the digital learning data into at least one data pool to adjust the isolation strategy and ensure data security and compliance.

[0134] In this embodiment, the implementation details of micro-isolation are further refined, including setting up double micro-isolation firewalls, performing detailed isolation according to data types, obtaining isolation feedback information, and re-dividing data according to feedback information. These measures together enhance the security and privacy protection of digital learning data.

[0135] In an exemplary embodiment, natural language processing technology is used to obtain emotional feedback information of learners on different learning activities on the learning platform, including:

[0136] Obtain learners' interactive information on different learning activities on the learning platform, including the frequency and type of learning activities;

[0137] Determine learners’ platform learning habits based on the frequency and type of learning activities;

[0138] Obtain the matching degree between the platform learning habits and the preset learning habit standards;

[0139] When the matching degree reaches a preset matching degree threshold, the emotional feedback information of the learners on different learning activities on the learning platform is obtained.

[0140] Specifically, the frequency of learning activities refers to the number of times or frequency that learners participate in learning activities on the learning platform, for example, the number of times learners log into the platform every day, the number of times they complete tasks, etc.

[0141] The learning activity type describes the specific learning activities that learners perform on the platform, such as watching videos, completing exercises, participating in discussions, etc.

[0142] Determine platform learning habits: Based on the collected learning activity frequency and type, learners’ platform learning habits can be analyzed. For example, learners may prefer to watch videos rather than do exercises, or have specific preferences in learning time (such as learning better at night).

[0143] Evaluate the matching degree: After the learner's platform learning habits are obtained, the next step is to compare them with the preset learning habit standards. These standards may be based on educational psychology, learning science or other theories, and are designed to determine which learning habits are effective and efficient.

[0144] Match: This is a quantitative indicator used to measure the similarity or degree of conformity between the learner's learning habits and the preset standards.

[0145] Obtaining emotional feedback information: When the match between a learner’s platform learning habits and the preset standards reaches or exceeds a certain preset threshold, the learner’s learning habits can be considered effective or worthy of attention.

[0146] In this case, the system can further obtain learners’ emotional feedback information on different learning activities on the learning platform through natural language processing technology. This may involve analyzing learners’ comments, feedback, discussions and other textual content on the platform to understand their emotional attitudes towards learning activities (such as satisfaction, dissatisfaction, confusion, excitement, etc.).

[0147] In this embodiment, this process aims to better understand learners' learning needs and preferences by analyzing their platform learning habits and emotional feedback, thereby optimizing the design and functions of the learning platform and improving learners' learning experience and results.

[0148] In an exemplary embodiment, the process of obtaining the preset learning habit standard includes:

[0149] After the digital learning data has been micro-segregated, obtain the customer complaint rate and error rate of the current micro-segregation method;

[0150] When both the customer complaint rate and the error rate meet the preset conditions, the learners’ access rights and access time periods for different learning activities on the learning platform are obtained based on the digital learning data after micro-isolation;

[0151] The process of obtaining preset standards of study habits based on access rights and access periods.

[0152] Specifically, the customer complaint rate refers to the proportion of cases where learners complain about the micro-isolation method. Complaints may involve problems such as learning inconvenience, limited data access, and privacy leakage caused by the micro-isolation method. The error rate refers to the proportion of cases where errors or accidents occur in the actual application of the micro-isolation method. For example, misjudgment of learners' access rights, resulting in incorrect data isolation, etc. These two indicators are used to evaluate the effectiveness and acceptability of the current micro-isolation method.

[0153] In actual applications, certain thresholds for customer complaint rate and error rate may be set. Only when both indicators are lower than or equal to these thresholds, the current micro-segmentation method is considered acceptable. After confirming that the micro-segmentation method is effective and acceptable, the next step is to analyze the digital learning data after micro-segmentation. This data contains information about learners' access rights and access time periods for different learning activities on the learning platform.

[0154] Among them, access rights reflect what data or operations a learner can access. For example, a learner may only have the permission to view specific course content, but not edit or download it. Access time describes the time when a learner is active on the learning platform. For example, a learner may prefer to study in the evening or on weekends.

[0155] Specifically, once the learner's access rights and access period information are obtained, the preset learning habit standards can be formulated or adjusted by combining the theoretical and practical experience in educational psychology, learning science and other fields. These standards may include recommended learning time, optimal learning time, appropriate learning activity combination, etc., aiming to help learners improve learning efficiency and quality.

[0156] In this embodiment, this process aims to develop or adjust the preset learning habit standards by evaluating the effectiveness and acceptance of micro-isolation methods and analyzing learners' access rights and access time periods. These standards will provide important references for subsequent learning recommendations, platform optimization, etc.

[0157] In an exemplary embodiment, after obtaining the customer complaint rate and error rate of micro-segmentation, the following further includes:

[0158] When at least one of the customer complaint rate and the error rate does not meet the expected conditions, the current micro-isolation method will be adjusted to cloud storage isolation.

[0159] Specifically, after obtaining the customer complaint rate and error rate of micro-segmentation, if at least one of these two indicators does not meet the expected conditions, it means that the current micro-segmentation method may have problems or deficiencies. In this case, the system or administrator will consider adjusting the current micro-segmentation method to ensure the security of digital learning data and the learner experience.

[0160] Specifically, if the customer complaint rate or error rate does not meet the preset conditions (i.e. exceeds the acceptable threshold), this indicates that the current micro-isolation method may have caused inconvenience or security risks to learners. Therefore, as a countermeasure, the system will adjust the current micro-isolation method to cloud storage isolation.

[0161] Cloud storage isolation is a way to store data in the cloud and ensure data security and privacy through access control and permission management. By storing data in the cloud and leveraging the powerful features and security measures of cloud services, data can be better protected from unauthorized access and leakage. At the same time, cloud storage isolation also provides greater flexibility and scalability, making it easy to adjust storage space and access rights according to needs.

[0162] In this embodiment, when the customer complaint rate and error rate of micro-isolation do not meet the expected conditions, the system will adjust the current micro-isolation mode to cloud storage isolation to ensure the security of digital learning data and learner experience. This adjustment is based on consideration of learner needs and feedback, as well as the emphasis on data security and privacy protection.

[0163] In an exemplary embodiment, the method further comprises:

[0164] Get the age distribution data of all learners;

[0165] Obtain the correlation between age distribution data and micro-isolation methods;

[0166] Analyze the correlation between learners’ age groups and micro-isolation methods based on each learner’s learning direction and emotional needs;

[0167] When the correlation reaches the preset correlation threshold, the corresponding micro-isolation method is determined according to the age of each learner.

[0168] Specifically, the age distribution data of all learners is obtained in order to understand the overall age composition of learners on the learning platform. By collecting and analyzing the age data of learners, the distribution ratio of different age groups can be obtained, providing basic data for subsequent analysis.

[0169] Obtain the correlation between age distribution data and micro-segmentation methods: In this step, the system or management personnel will study the relationship between the age distribution of learners and different micro-segmentation methods (such as file system isolation, database isolation, and cloud storage isolation). The purpose is to find out which micro-segmentation method is more suitable for learners of which age group and why.

[0170] According to each learner's learning direction and emotional needs, analyze the correlation between the learner's age group and the micro-isolation method: In addition to age factors, learners' learning direction and emotional needs are also important considerations for choosing micro-isolation methods. For example, learners of certain age groups may prefer to use file system isolation, while learners of other age groups may prefer cloud storage isolation. At the same time, learners' learning directions (such as mathematics, literature, programming, etc.) may also affect their preference for micro-isolation methods. Therefore, the system needs to take these factors into consideration to more accurately determine the correlation between learners' age groups and micro-isolation methods.

[0171] When the correlation reaches the preset correlation threshold, the corresponding micro-isolation method is determined according to the age of each learner: the system will set a correlation threshold. When the correlation between learners of a certain age group and a certain micro-isolation method reaches or exceeds this threshold, the system will automatically select the most appropriate micro-isolation method for the learners of that age group. The purpose of this is to ensure that each learner can get the data isolation method that best suits him / her, thereby improving learning effects and satisfaction.

[0172] Micro-isolation methods include file system isolation, database isolation, or cloud storage isolation: This step clarifies the specific options for micro-isolation methods, namely file system isolation, database isolation, and cloud storage isolation. This means that when choosing a micro-isolation method, the system will make decisions among these three methods to best meet the needs and expectations of learners.

[0173] In this embodiment, the selection of micro-isolation methods is optimized by comprehensively considering the learner's age, learning direction and emotional needs, as well as the characteristics and advantages of different micro-isolation methods. This is intended to provide each learner with the most suitable data isolation method for them, thereby improving the learning effect and platform usage experience.

[0174] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0175] Based on the same inventive concept, the embodiment of the present application also provides a digital economy analysis device based on micro-isolation technology for implementing the digital economy analysis method based on micro-isolation technology involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the digital economy analysis device based on micro-isolation technology provided below can refer to the limitations of the digital economy analysis method based on micro-isolation technology above, and will not be repeated here.

[0176] In an exemplary embodiment, Figure 4 As shown, a digital economy analysis device based on micro-isolation technology is provided, including: an acquisition module 402, used to acquire digital learning data, learner information and learning activity information in a preset teaching area;

[0177] A processing module 404 is used to classify the digital learning data into at least one data pool according to the learner information and the learning activity information, and to micro-isolate the digital learning data in each data pool;

[0178] The processing module 404 is further used to obtain the learning path of each learner in different learning activities based on the digital learning data after micro-isolation;

[0179] A modeling module 406 is used to model the learning path and predict the learning direction of each learner for different learning activities;

[0180] The processing module 404 is also used to obtain emotional feedback information of learners on different learning activities on the learning platform using natural language processing technology;

[0181] The processing module 404 is also used to perform sentiment analysis on the sentiment feedback information to obtain the sentiment demand information of each learner for different learning activities;

[0182] The processing module 404 is further used to obtain digital economic data in a preset teaching area according to the learning direction and emotional demand information of each learner; specifically, it includes: determining a preset teaching area related to the learning activity according to the learning direction and emotional demand information of each learner, and obtaining digital economic data in the preset teaching area; wherein the digital economic data refers to indicators and information related to education, including the allocation of educational resources, students' learning outcomes and the operation of educational institutions;

[0183] The analysis module 408 is used to obtain analysis results of educational decisions based on digital economic data.

[0184] In an exemplary embodiment, the micro-isolation method includes file system isolation, database isolation, or cloud storage isolation; the digital learning data includes evaluation data and other data;

[0185] The processing module 404 is further used to classify the digital learning data into at least one data pool according to the learner information and the learning activity information;

[0186] The acquisition module 402 is also used to acquire the learning identifier of the learner;

[0187] The processing module 404 is further used to determine the access rights of each learner to the data pool according to the learning identifier;

[0188] The processing module 404 is further used to isolate the evaluation data and other data in the data pool by using any of file system isolation, database isolation or cloud storage isolation; wherein, file system isolation: storing data in different folders or paths, and only learners with corresponding permissions can access; database isolation: at the database level, data isolation is achieved through different tables, views or query permissions; cloud storage isolation: in cloud services, different storage buckets or access policies are used to isolate data;

[0189] The processing module 404 is further configured to monitor the data operation status of at least one data pool in real time according to a preset audit monitoring mechanism.

[0190] In an exemplary embodiment, the processing module 404 is further configured to use any one of file system isolation, database isolation, or cloud storage isolation to respectively set a first micro-isolation firewall and a second micro-isolation firewall for each data pool; wherein the first micro-isolation firewall is located at the entrance or boundary of the data pool, and the second micro-isolation firewall is located inside the data pool;

[0191] The acquisition module 402 is further used to obtain the data type of the evaluation data, the data type includes evaluation indicators, scoring information, evaluation feedback and evaluation history records; wherein the first micro-isolation firewall is used to isolate the evaluation indicators and other data; the second micro-isolation firewall is used to further isolate the scoring information, evaluation feedback and evaluation history records;

[0192] The acquisition module 402 is further used to obtain first isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall in the current data pool;

[0193] The processing module 404 is further configured to obtain second isolation feedback information of the first micro-isolated firewall and the second micro-isolated firewall in other data pools when at least one of the rating information, the evaluation feedback, and the evaluation history record is detected in the first micro-isolated firewall according to the first isolation feedback information;

[0194] The processing module 404 is further configured to obtain a proportion of at least one of the scoring information, the evaluation feedback, and the evaluation history records detected in the first micro-isolation firewall according to at least one second isolation feedback information;

[0195] The processing module 404 is further configured to reclassify the digital learning data into at least one data pool when the situation ratio reaches a preset ratio threshold.

[0196] In an exemplary embodiment, the acquisition module 402 is further used to acquire interactive information of different learning activities of learners on the learning platform, where the interactive information includes the frequency and type of learning activities;

[0197] The processing module 404 is further used to determine the platform learning habits of the learner based on the learning activity frequency and the learning activity type;

[0198] The processing module 404 is also used to obtain the matching degree between the platform learning habit and the preset learning habit standard;

[0199] The processing module 404 is further configured to obtain emotional feedback information of learners on different learning activities on the learning platform when the matching degree reaches a preset matching degree threshold.

[0200] In an exemplary embodiment, the acquisition module 402 is further used to obtain the customer complaint rate and error rate of the current micro-isolation mode after the digital learning data passes through the micro-isolation;

[0201] The processing module 404 is further used to obtain the learner's access rights and access time periods for different learning activities on the learning platform according to the digital learning data after micro-isolation when both the customer complaint rate and the error rate meet the preset conditions;

[0202] The processing module 404 is also used to obtain the preset learning habit standard according to the access permission and the access period.

[0203] In an exemplary embodiment, the processing module 404 is further configured to adjust the current micro-isolation mode to cloud storage isolation when at least one of the customer complaint rate and the error rate does not meet the expected conditions.

[0204] In an exemplary embodiment, the processing module 404 is further used to obtain age distribution data of all learners;

[0205] The processing module 404 is further used to obtain the correlation between the age distribution data and the micro-isolation method;

[0206] The processing module 404 is further used to analyze the correlation between the age group of the learners and the micro-isolation method according to the learning direction and emotional needs of each learner;

[0207] The processing module 404 is further configured to determine a corresponding micro-isolation method according to the age of each learner when the correlation reaches a preset correlation threshold.

[0208] Each module in the digital economy analysis device based on micro-isolation technology can be implemented in whole or in part through software, hardware, and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0209] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store digital learning data, learner information and learning activity information. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a digital economic analysis method based on micro-isolation technology is implemented.

[0210] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0211] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above method when executing the computer program.

[0212] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0213] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0214] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0215] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0216] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0217] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A digital economy analysis method based on micro-isolation technology, characterized in that: include: Obtain digital learning data, learner information and learning activity information in the preset teaching area; The micro-isolation method includes file system isolation, database isolation or cloud storage isolation; The digital learning data includes evaluation data and other data; the digital learning data is divided into at least one data pool according to the learner information and the learning activity information, and the digital learning data in each data pool is micro-isolated, including: According to the learner information and the learning activity information, the digital learning data is classified into at least one data pool; Get the learner's learning identifier; Determining access rights of each learner to the data pool according to the learning identifier; Use file system isolation, database isolation, or cloud storage isolation to isolate the evaluation data and other data in the data pool, including: Use any one of file system isolation, database isolation or cloud storage isolation to set a first micro-isolation firewall and a second micro-isolation firewall for each data pool; wherein the first micro-isolation firewall is located at the entrance or boundary of the data pool, and the second micro-isolation firewall is located inside the data pool; Obtaining a data type of the evaluation data, the data type including evaluation indicators, scoring information, evaluation feedback, and evaluation history records; wherein the first micro-isolation firewall is used to isolate the evaluation indicators and the other data; and the second micro-isolation firewall is used to further isolate the scoring information, the evaluation feedback, and the evaluation history records; Obtain first isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall of the current data pool; According to the first isolation feedback information, when at least one of the scoring information, the evaluation feedback, and the evaluation history record is detected in the first micro-isolation firewall, second isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall in other data pools is obtained; According to at least one item of the second isolation feedback information, obtaining a proportion of at least one of the rating information, the evaluation feedback, and the evaluation history record detected in the first micro-isolation firewall; When the proportion of the situation reaches a preset proportion threshold, reclassifying the digital learning data into at least one data pool; Among them, file system isolation: data is stored in different folders or paths, and only learners with corresponding permissions can access it; Database isolation: At the database level, data isolation is achieved through different tables, views or query permissions; Cloud storage isolation: In cloud services, use different storage buckets or access policies to isolate data; According to the preset audit monitoring mechanism, the data operation status of at least one data pool is monitored in real time; According to the digital learning data after micro-isolation, obtain the learning path of each learner in different learning activities; Modeling the learning paths to predict each learner's learning direction for different learning activities; Use natural language processing technology to obtain learners' emotional feedback information on different learning activities on the learning platform; Performing sentiment analysis on the sentiment feedback information to obtain each learner's sentiment demand information for different learning activities; According to the learning direction and the emotional demand information of each learner, digital economic data in a preset teaching area is obtained; specifically including: according to the learning direction and the emotional demand information of each learner, a preset teaching area related to the learning activity is determined, and digital economic data in the preset teaching area is obtained; wherein the digital economic data refers to indicators and information related to education, including the allocation of educational resources, students' learning outcomes and the operation of educational institutions; Based on the digital economic data, analytical results for educational decision-making are obtained.

2. The method according to claim 1, characterized in that The use of natural language processing technology to obtain learners' emotional feedback information on different learning activities on the learning platform includes: Obtaining interactive information of different learning activities of learners on the learning platform, wherein the interactive information includes the frequency and type of learning activities; Determining the learner's platform learning habits according to the learning activity frequency and the learning activity type; Obtaining the matching degree between the platform learning habits and the preset learning habit standards; When the matching degree reaches a preset matching degree threshold, emotional feedback information of learners' different learning activities on the learning platform is obtained.

3. The method according to claim 2, characterized in that The process of obtaining the preset study habit standards includes: After the digital learning data is micro-isolated, obtaining the customer complaint rate and error rate of the current micro-isolation method; When both the customer complaint rate and the error rate meet the preset conditions, obtaining the learner's access rights and access time periods for different learning activities on the learning platform based on the digital learning data after micro-isolation; A process of obtaining a preset learning habit standard according to the access permission and the access period.

4. The method according to claim 3, characterized in that After obtaining the customer complaint rate and error rate of the current micro-segmentation method, the following is also included: When at least one of the customer complaint rate and the error rate does not meet the expected conditions, the current micro-isolation mode is adjusted to cloud storage isolation.

5. The method according to claim 1, characterized in that The method further comprises: Get the age distribution data of all learners; Obtaining a correlation between the age distribution data and the micro-isolation method; According to the learning direction and emotional needs of each learner, analyzing the correlation between the age group of the learner and the micro-isolation method; When the correlation reaches a preset correlation threshold, a corresponding micro-isolation method is determined according to the age of each learner.

6. A digital economy analysis device based on micro-isolation technology, characterized in that: The device comprises: An acquisition module, used to acquire digital learning data, learner information and learning activity information in a preset teaching area; The micro-isolation method includes file system isolation, database isolation or cloud storage isolation; the digital learning data includes evaluation data and other data; a processing module is used to classify the digital learning data into at least one data pool according to the learner information and the learning activity information, and micro-isolate the digital learning data in each data pool, including: The processing module is further used to classify the digital learning data into at least one data pool according to the learner information and the learning activity information; The acquisition module is also used to obtain the learning identifier of the learner; The processing module is further used to determine the access rights of each learner to the data pool according to the learning identifier; The processing module is further used to isolate the evaluation data and other data in the data pool by using any one of file system isolation, database isolation or cloud storage isolation, including: The processing module is further used to use any one of file system isolation, database isolation or cloud storage isolation to respectively set a first micro-isolation firewall and a second micro-isolation firewall for each data pool; wherein the first micro-isolation firewall is located at the entrance or boundary of the data pool, and the second micro-isolation firewall is located inside the data pool; The acquisition module is further used to obtain the data type of the evaluation data, the data type including evaluation indicators, scoring information, evaluation feedback and evaluation history records; wherein the first micro-isolation firewall is used to isolate the evaluation indicators and the other data; the second micro-isolation firewall is used to further isolate the scoring information, the evaluation feedback and the evaluation history records; The acquisition module is also used to obtain the first isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall of the current data pool; The processing module is further used to obtain second isolation feedback information of the first micro-isolation firewall and the second micro-isolation firewall in other data pools when at least one of the scoring information, the evaluation feedback and the evaluation history record is detected in the first micro-isolation firewall according to the first isolation feedback information; The processing module is further configured to obtain, based on at least one item of the second isolation feedback information, a proportion of situations in which at least one of the rating information, the evaluation feedback, and the evaluation history record is detected in the first micro-isolation firewall; The processing module is further used to reclassify the digital learning data into at least one data pool when the proportion of the situation reaches a preset proportion threshold; Among them, file system isolation: data is stored in different folders or paths, and only learners with corresponding permissions can access it; Database isolation: At the database level, data isolation is achieved through different tables, views or query permissions; Cloud storage isolation: In cloud services, use different storage buckets or access policies to isolate data; The processing module is further used to monitor the data operation status of at least one data pool in real time according to a preset audit monitoring mechanism; The processing module is further used to obtain the learning path of each learner in different learning activities according to the digital learning data after micro-isolation; A modeling module, used to model the learning path and predict the learning direction of each learner for different learning activities; The processing module is also used to obtain learners’ emotional feedback information on different learning activities on the learning platform using natural language processing technology; The processing module is further used to perform sentiment analysis on the sentiment feedback information to obtain the sentiment demand information of each learner for different learning activities; The processing module is further used to obtain digital economic data in a preset teaching area according to the learning direction and the emotional demand information of each learner; specifically including: determining a preset teaching area related to the learning activity according to the learning direction and the emotional demand information of each learner, and obtaining digital economic data in the preset teaching area; wherein the digital economic data refers to indicators and information related to education, including the allocation of educational resources, students' learning outcomes and the operation of educational institutions; The analysis module is used to obtain analysis results of educational decisions based on the digital economic data.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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