Personalized smart learning management and cognitive influence data pushing method and system

Abstract

The invention provides a personalized smart learning management and cognitive influence data pushing method and system, and the method comprises the steps: receiving business data, sent by a business application end, of a target user for integrated business management, and carrying out the standardization processing and storage, the business application end carries out user portraying according to the standardized business data to obtain learning condition analysis result data, whether learning condition early warning information is output or not is judged, and a personalized learning resource matching request is generated according to the learning condition analysis result data; and searching personalized learning resource data of the personalized learning resource matching request in a local resource knowledge base, so that the business application end pushes the personalized learning resource data as cognitive influence data to the target user. According to the application, the integration of business functions and resources related to smart learning can be effectively realized, the comprehensiveness of learning management functions and the data calling efficiency can be effectively improved, the individuation and pertinence of learning resource pushing can be effectively improved, and the user experience can be effectively improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method and system for personalized intelligent learning management and cognitive impact data push. Background Art

[0002] The continuous development and application of new-generation information technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence have promoted the transformation of traditional education to intelligent education, that is, based on the campus network, advanced technologies and innovative means are comprehensively and deeply applied in the field of education (education management, education teaching, education research, etc.). By building a series of hardware devices, databases, and software systems, it promotes the transformation of teaching models and the improvement of teaching quality. Intelligent learning is one of the core elements of the intelligent education system, and it plays an important role in stimulating students' learning motivation, improving learning effects, enhancing learning experiences, and transforming the talent cultivation model.

[0003] Currently, many universities have started the construction related to intelligent learning and achieved certain results. In particular, a large amount of funds have been invested in the construction of the informatization hardware environment. However, the intelligent learning software system developed based on the data middle platform is not yet perfect, and most of them adopt a chimney-style construction mode, resulting in the failure to truly integrate hardware devices, basic software, application software, etc. Students need to switch back and forth between different systems, which is likely to cause problems such as system fragmentation, difficult data exchange, low business integration degree, and ineffective sharing of educational resources. It is inconvenient for students to use, and there is a phenomenon of "built but not used". In addition, the learning big data reflecting students' learning processes, learning behaviors, learning states, learning attitudes, learning habits, etc. cannot be effectively collected, aggregated, processed, statistically analyzed, and mined due to problems such as system islands and data islands, so the tracking and portrait description of students' comprehensive behavior data cannot be realized, and the personalized learning needs of students cannot be met.

[0004] Based on this, there is an urgent need to design a method that can further improve the personalization and pertinence of learning resource push on the basis of ensuring the comprehensiveness of intelligent learning management functions. Summary of the Invention

[0005] In view of this, the embodiments of this application provide a method and system for personalized intelligent learning management and cognitive impact data push to eliminate or improve one or more defects existing in the prior art.

[0006] One aspect of this application provides a method for personalized intelligent learning management and cognitive impact data push that can be implemented by a data middle platform, including:

[0007] Receiving the service data of a target user for integrated service management sent by a service application end, performing standardized processing on the service data to obtain corresponding standardized service data, and storing the standardized service data in a preset service topic library;

[0008] Send all the standardized service data corresponding to the target user to the service application end, so that the service application end can perform user profiling on the target user based on the standardized service data corresponding to the target user to obtain the corresponding learning situation analysis result data, determine whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generate a corresponding personalized learning resource matching request according to the learning situation analysis result data;

[0009] Receive the personalized learning resource matching request sent by the service application end, search for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base, and send the personalized learning resource data as the cognitive impact data for the target user to the service application end, so that the service application end can push the cognitive impact data to the target user.

[0010] In some embodiments of the present application, in the personalized intelligent learning management and cognitive impact data push method that can be implemented by the data middle platform, the service data includes at least one of teaching management data, scientific research management data, campus card management data, and questionnaire management data;

[0011] Correspondingly, the receiving of the service data of the target user for the integrated service management sent by the service application end, the standardization processing of the service data to obtain the corresponding standardized service data, and the storage of the standardized service data into the preset service theme library include:

[0012] Receive the service data of the target user for the integrated service management sent by the service application end;

[0013] If the service data contains the teaching management data, perform standardization processing on the teaching management data to obtain the corresponding standardized teaching management data, and store the standardized teaching management data into the teaching theme library in the preset service theme library;

[0014] If the service data contains the scientific research management data, perform standardization processing on the scientific research management data to obtain the corresponding standardized scientific research management data, and store the standardized scientific research data into the scientific research theme library in the preset scientific research theme library;

[0015] If the service data contains the campus card management data, perform standardization processing on the campus card management data to obtain the corresponding standardized campus card management data, and store the standardized campus card data into the campus card theme library in the preset service theme library;

[0016] If the business data contains the questionnaire management data, the questionnaire management data is standardized to obtain the corresponding standardized questionnaire management data, and the standardized questionnaire data is stored in the questionnaire theme library of the preset business theme library.

[0017] In some embodiments of the present application, in the personalized intelligent learning management and cognitive impact data push method that can be implemented by the data middle platform, the learning situation analysis result data consists of user portrait data; wherein, the user portrait data includes at least two of student basic attribute data, cultivation plan completion progress data, information on courses taken, trend data of usual test scores, classroom performance data, course grade ranking and distribution data, library borrowing data, daily consumption data, library access data, dormitory time distribution data, leave application and approval data, thesis and publication data, academic activity participation data, scientific research project participation data, and psychological measurement result data.

[0018] In some embodiments of the present application, the personalized intelligent learning management and cognitive impact data push method that can be implemented by the data middle platform further includes:

[0019] Periodically retrieve learning resource data from the school resource library and store the learning resource data in the resource knowledge library, where the resource knowledge library is used for digital storage, incremental maintenance, and update of at least two of subject resource data, library resource data, academic resource data, campus news, psychological knowledge data, external links, and application software.

[0020] The second aspect of the present application provides a personalized intelligent learning management and cognitive impact data push method that can be implemented by the business application end, including:

[0021] Receive the business data sent by the target user for integrated business management based on the unified identity authentication login interface, and send the business data of the target user for integrated business management to the data middle platform, so that the data middle platform standardizes the business data to obtain the corresponding standardized business data, stores the standardized business data in the preset business theme library, and sends out the standardized business data corresponding to the target user;

[0022] Receive the standardized business data corresponding to the target user sent by the data middle platform, perform user portrait on the target user according to the standardized business data corresponding to the target user to obtain the corresponding learning situation analysis result data, judge whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generate a corresponding personalized learning resource matching request according to the learning situation analysis result data;

[0023] Send the personalized learning resource matching request to the data center, so that the data center can search for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base and send out the personalized learning resource data;

[0024] Receive the personalized learning resource data sent by the data center and push the personalized learning resource data to the target user.

[0025] In some embodiments of the present application, in the personalized intelligent learning management and cognitive impact data push method that can be implemented by the business application end, the business data includes at least one of teaching management data, scientific research management data, campus card management data, and questionnaire management data.

[0026] In some embodiments of the present application, in the personalized intelligent learning management and cognitive impact data push method that can be implemented by the business application end, the learning situation analysis result data is composed of user portrait data; among them, the user portrait data includes at least two of student basic attribute data, progress of training plan completion data, information on courses taken, trend data of usual test scores, classroom performance data, course grade ranking and distribution data, library borrowing data, daily consumption data, library access data, dormitory time distribution data, leave application and approval data, thesis and publication data, academic activity participation data, scientific research project participation data, and psychological measurement result data;

[0027] Correspondingly, the determination of whether to output a learning situation warning message for the target user based on the learning situation analysis result data includes:

[0028] Based on a one-to-one relationship table between various types of user portrait data and various requirement rule data defined, determine whether there is any type of user portrait data in the learning situation analysis result data that does not meet the corresponding requirement rule data. If so, generate a warning message for the target user for the user portrait data that does not meet the corresponding requirement rule data.

[0029] The third aspect of the present application further provides a data center, including:

[0030] A standardization and business theme storage module, configured to receive the business data of the target user for integrated business management sent by the business application end, perform standardization processing on the business data to obtain the corresponding standardized business data, and store the standardized business data in a preset business theme library;

[0031] A data sending module, configured to send all the standardized service data corresponding to the target user to the service application end, so that the service application end performs user profiling on the target user according to the standardized service data corresponding to the target user to obtain corresponding learning situation analysis result data, determines whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generates a corresponding personalized learning resource matching request according to the learning situation analysis result data;

[0032] A material knowledge calling module, configured to receive the personalized learning resource matching request sent by the service application end, search for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base, and send the personalized learning resource data as the cognitive influence data for the target user to the service application end, so that the service application end pushes the cognitive influence data to the target user.

[0033] The fourth aspect of this application further provides a service application end, including: an integrated service management module, a personal profiling module, and an intelligent push module;

[0034] The integrated service management module is configured to receive the service data for integrated service management sent by the target user based on the unified identity authentication login interface, and send the service data for integrated service management of the target user to the data middle platform, so that the data middle platform performs standardized processing on the service data to obtain corresponding standardized service data, stores the standardized service data in a preset service topic library, and sends out the standardized service data corresponding to the target user;

[0035] The personal profiling module includes: a profile presentation unit and a learning situation warning unit;

[0036] Among them, the profile presentation unit is configured to receive the standardized service data corresponding to the target user sent by the data middle platform, and perform user profiling on the target user according to the standardized service data corresponding to the target user to obtain corresponding learning situation analysis result data;

[0037] The learning situation warning unit is configured to determine whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generate a corresponding personalized learning resource matching request according to the learning situation analysis result data;

[0038] The intelligent push module includes: a resource retrieval unit and a resource push unit;

[0039] Among them, the resource retrieval unit is used to send the personalized learning resource matching request to the data center, so that the data center can search for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base and send out the personalized learning resource data;

[0040] The resource push unit is used to receive the personalized learning resource data sent by the data center and push the personalized learning resource data to the target user.

[0041] The fifth aspect of this application also provides a personalized intelligent learning management system, including: a data center and a business application terminal connected by communication;

[0042] The data center is used to execute the personalized intelligent learning management and cognitive impact data push method that can be implemented by the data center provided in the first aspect, and the business theme library and the resource knowledge base are preset locally in the data center;

[0043] The business application terminal is used to execute the personalized intelligent learning management and cognitive impact data push method that can be implemented by the business application terminal provided in the second aspect;

[0044] Among them, the business application terminal includes: an integrated business management module, a personal portrait module, and an intelligent push module;

[0045] The integrated business management module is used to receive the business data sent by the target user for integrated business management based on the unified identity authentication login interface, and send the business data of the target user for integrated business management to the data center, so that the data center can perform standardization processing on the business data to obtain the corresponding standardized business data, store the standardized business data in the preset business theme library and send out the standardized business data corresponding to the target user;

[0046] The personal portrait module includes: a portrait presentation unit and a learning situation warning unit;

[0047] Among them, the portrait presentation unit is used to receive the standardized business data corresponding to the target user sent by the data center, and perform user portrait on the target user according to the standardized business data corresponding to the target user to obtain the corresponding learning situation analysis result data;

[0048] The learning situation warning unit is used to judge whether to output the learning situation warning information for the target user based on the learning situation analysis result data, and generate the corresponding personalized learning resource matching request according to the learning situation analysis result data;

[0049] The intelligent push module includes: a resource retrieval unit and a resource push unit;

[0050] Among them, the resource retrieval unit is used to send the personalized learning resource matching request to the data middleware, so that the data middleware searches for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base and sends out the personalized learning resource data;

[0051] The resource push unit is used to receive the personalized learning resource data sent by the data middleware and push the personalized learning resource data to the target user.

[0052] The sixth aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the personalized intelligent learning management and cognitive impact data push method executed by the data middleware, or implements the personalized intelligent learning management and cognitive impact data push method executed by the business application end.

[0053] The seventh aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the personalized intelligent learning management and cognitive impact data push method executed by the data middleware provided in the first aspect, or implements the personalized intelligent learning management and cognitive impact data push method executed by the business application end provided in the second aspect.

[0054] The personalized intelligent learning management and cognitive impact data push method provided by this application receives the business data of the target user for integrated business management sent by the business application end, performs standardized processing on the business data to obtain the corresponding standardized business data, and stores the standardized business data in a preset business theme library; sends all the standardized business data corresponding to the target user to the business application end, so that the business application end can perform user profiling on the target user according to the standardized business data corresponding to the target user to obtain the corresponding learning situation analysis result data, determines whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generates a corresponding personalized learning resource matching request according to the learning situation analysis result data; receives the personalized learning resource matching request sent by the business application end, searches for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base, and sends the personalized learning resource data as the cognitive impact data for the target user to the business application end, so that the business application end can push the cognitive impact data to the target user, which can effectively realize the integration of business functions and resources related to intelligent learning, can effectively improve the comprehensiveness of learning management functions and data call efficiency, can effectively improve the personalization and pertinence of learning resource push, and can effectively improve the user experience.

[0055] Additional advantages, objects, and features of this application will be partially described below and will become partially apparent to those of ordinary skill in the art after studying the following. Or they can be learned through the practice of this application. The objects and other advantages of this application can be achieved and obtained through the structures specifically pointed out in the specification and the drawings.

[0056] Those skilled in the art will understand that the objects and advantages that can be achieved by this application are not limited to the above specific descriptions, and the above and other objects that this application can achieve will be more clearly understood according to the following detailed description. Brief Description of the Drawings

[0057] The drawings described herein are used to provide a further understanding of this application, form a part of this application, and do not limit this application. The components in the drawings are not drawn to scale, but are only for showing the principles of this application. For the convenience of showing and describing some parts of this application, the corresponding parts in the drawings may be enlarged, that is, they may become larger relative to other components in the exemplary device actually manufactured according to this application. In the drawings:

[0058] Figure 1 It is the first flow schematic diagram of the first personalized intelligent learning management and cognitive impact data push method in an embodiment of this application.

[0059] Figure 2 It is the second process schematic diagram of the first personalized intelligent learning management and cognitive impact data push method in an embodiment of this application.

[0060] Figure 3 It is the first process schematic diagram of the second personalized intelligent learning management and cognitive impact data push method in an embodiment of this application.

[0061] Figure 4 It is the second process schematic diagram of the second personalized intelligent learning management and cognitive impact data push method in an embodiment of this application.

[0062] Figure 5 It is the structural schematic diagram of the data middle platform in an embodiment of this application.

[0063] Figure 6 It is the structural schematic diagram of the business application end in an embodiment of this application.

[0064] Figure 7 It is the logic block diagram of the personalized intelligent learning management system in an application example of this application. Detailed implementation manners

[0065] To make the objectives, technical solutions and advantages of this application clearer and more understandable, the following further elaborates on this application in combination with the implementation manners and the drawings. Herein, the illustrative implementation manners of this application and their descriptions are used to explain this application, but do not limit this application.

[0066] Herein, it also needs to be noted that, in order to avoid obscuring this application due to unnecessary details, only the structures and / or processing steps closely related to the solution of this application are shown in the drawings, while other details less related to this application are omitted.

[0067] It should be emphasized that the term "including / containing" when used in this text refers to the existence of features, elements, steps or components, but does not exclude the existence or addition of one or more other features, elements, steps or components.

[0068] Herein, it also needs to be noted that, if not specifically stated, the term "connection" in this text can not only refer to direct connection, but also represent indirect connection with an intermediate.

[0069] In the following, the embodiments of this application will be described with reference to the drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0070] The construction and application in the field of intelligent learning have developed rapidly. On the one hand, a new type of intelligent learning environment is constructed through the deep integration of information technology, providing a more immersive and intelligent learning platform for students in the new era and promoting the transformation of teaching methods. This is prominently manifested in the construction of intelligent classrooms characterized by openness, interactivity, collaboration, etc., mainly including electronic class signs, intelligent control systems, multi-screen teaching display systems, interactive teaching systems, environmental intelligent monitoring systems, classroom live recording and broadcasting systems, etc., forming a learning-centered intelligent learning space environment to support interactive, collaborative, and flipped teaching of courses. On the other hand, an online learning platform covering functions such as teaching resource management, massive open online courses (MOOCs) teaching, online and offline hybrid teaching, small private online courses (SPOCs), data collection and analysis, etc. is constructed to realize coherent learning before class, during class, and after class, and cultivate students' active learning and autonomous learning abilities.

[0071] In order to design a way that can further improve the personalization and pertinence of learning resource push on the basis of ensuring the comprehensiveness of intelligent learning management functions, the embodiments of this application respectively provide a first personalized intelligent learning management and cognitive impact data push method, a data middle platform for executing the first personalized intelligent learning management and cognitive impact data push method, a second personalized intelligent learning management and cognitive impact data push method, a business application end for executing the second personalized intelligent learning management and cognitive impact data push method, a personalized intelligent learning management system including the business application end and the data middle platform, as well as physical devices and computer-readable storage media.

[0072] Specific details are described in detail through the following embodiments.

[0073] Based on this, the embodiments of this application provide a first personalized intelligent learning management and cognitive impact data push method that can be implemented by devices such as a data middle platform. Refer to Figure 1 , the first personalized intelligent learning management and cognitive impact data push method specifically includes the following contents:

[0074] Step 100: Receive the business data of the target user for integrated business management sent by the business application end, perform standardization processing on the business data to obtain the corresponding standardized business data, and store the standardized business data in a preset business theme library.

[0075] In one or more embodiments of this application, the target user may refer to a user authorized to use the personalized intelligent learning management system, which may be a student, a teacher, or an administrator, etc.

[0076] It can be understood that the business application end may be a type of client device.

[0077] Step 200: Send all the standardized service data corresponding to the target user to the service application end, so that the service application end can perform user profiling on the target user based on the standardized service data corresponding to the target user to obtain corresponding learning situation analysis result data, determine whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generate a corresponding personalized learning resource matching request according to the learning situation analysis result data.

[0078] Step 300: Receive the personalized learning resource matching request sent by the service application end, search for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base, and send the personalized learning resource data as the cognitive impact data for the target user to the service application end, so that the service application end can push the cognitive impact data to the target user.

[0079] In one or more embodiments of the present application, the cognitive impact data refers to searching for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base.

[0080] It can be understood that the service application end may specifically refer to a client device provided with an application program, etc.

[0081] As can be seen from the above description, the first personalized intelligent learning management and cognitive impact data pushing method provided by the embodiments of the present application can effectively realize the integration of business functions and resources related to intelligent learning, can effectively improve the comprehensiveness of learning management functions and data calling efficiency, can effectively improve the personalization and pertinence of learning resource pushing, and can effectively improve the user experience.

[0082] In order to further improve the effectiveness and applicability of integrated business management in personalized intelligent learning management and cognitive impact data pushing, in the first personalized intelligent learning management and cognitive impact data pushing method provided by the embodiments of the present application, the service data includes at least one of teaching management data, scientific research management data, campus card management data, and questionnaire management data; in a specific example, the service data may include teaching management data, scientific research management data, campus card management data, and questionnaire management data.

[0083] Correspondingly, referring to Figure 2 , step 100 in the first personalized intelligent learning management and cognitive impact data pushing method specifically includes the following contents:

[0084] Step 110: Receive the service data of the target user for integrated business management sent by the service application end.

[0085] Step 120: If the service data contains the teaching management data, perform standardization processing on the teaching management data to obtain corresponding standardized teaching management data, and store the standardized teaching management data in the teaching theme library of a preset service theme library.

[0086] Step 130: If the service data contains the scientific research management data, perform standardization processing on the scientific research management data to obtain corresponding standardized scientific research management data, and store the standardized scientific research data in the scientific research theme library of a preset scientific research theme library.

[0087] Step 140: If the service data contains the one-card pass management data, perform standardization processing on the one-card pass management data to obtain corresponding standardized one-card pass management data, and store the standardized one-card pass data in the one-card pass theme library of a preset service theme library.

[0088] Step 150: If the service data contains the questionnaire management data, perform standardization processing on the questionnaire management data to obtain corresponding standardized questionnaire management data, and store the standardized questionnaire data in the questionnaire theme library of a preset service theme library.

[0089] That is to say, the service theme library accesses the massive service data generated by the integrated service management subsystem, and performs full-process dynamic collection, storage, cleaning, integration, and processing on it, so as to build a unified data storage center, provide data support for upper-layer applications, and give full play to the data value. It can be divided into a teaching theme library, a scientific research theme library, a one-card pass theme library, and a questionnaire theme library.

[0090] In order to further improve the application reliability and comprehensiveness of the learning situation analysis result data in personalized intelligent learning management and cognitive influence data push, in the first personalized intelligent learning management and cognitive influence data push method provided in the embodiments of the present application, the learning situation analysis result data is composed of user portrait data; among them, the user portrait data includes at least two of student basic attribute data, cultivation plan completion progress data, information on courses taken, change trend data of usual test scores, classroom performance data, course grade ranking and distribution data, book borrowing data, daily consumption data, library access data, dormitory time distribution data, leave application and cancellation data, paper and work publication data, academic activity participation data, scientific research project participation data, and psychological measurement result data.

[0091] In order to further improve the application effectiveness and comprehensiveness of learning resource data in personalized intelligent learning management and cognitive impact data push, in the first personalized intelligent learning management and cognitive impact data push method provided in the embodiment of the present application, step 010 may be included before and after step 300, see Figure 2 Taking step 010 executed before step 300 as an example, step 010 in the personalized smart learning management and cognitive impact data push method specifically includes the following contents:

[0092] Step 010: Periodically retrieve learning resource data from the school resource library and store the learning resource data in the resource knowledge base, wherein the resource knowledge base is used for digital storage, incremental maintenance and updating of at least two of subject resource data, library resource data, academic resource data, campus information, psychological knowledge data, external links and application software.

[0093] In one example, the resource knowledge base connects to the school's existing resource library and maintains the resource library to digitally store, incrementally maintain, timely update, and uniformly manage educational resources such as subject resources (electronic textbooks, micro-courses, MOOCs, courseware, cases, mind maps, question banks, test paper libraries, etc.), library resources (electronic books, etc.), academic resources (literature, etc.), campus information, psychological knowledge, external links, application software, etc., thereby building a rich and comprehensive resource knowledge base that supports classification and tagging according to business topics, storage structures, etc., to facilitate student use and sharing.

[0094] The present application also provides a second personalized intelligent learning management and cognitive impact data push method that can be implemented by a business application terminal and other devices, see Figure 3 The second personalized intelligent learning management and cognitive impact data push method specifically includes the following contents:

[0095] Step 400: Based on the unified identity authentication login interface, the business data for integrated business management sent by the target user is received, and the business data for integrated business management of the target user is sent to the data middle platform, so that the data middle platform standardizes the business data to obtain corresponding standardized business data, stores the standardized business data in a preset business theme library and sends the standardized business data corresponding to the target user.

[0096] Step 500: Receive the standardized business data corresponding to the target user sent by the data center, create a user profile for the target user based on the standardized business data corresponding to the target user to obtain corresponding learning situation analysis result data, determine whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generate a corresponding personalized learning resource matching request based on the learning situation analysis result data;

[0097] Step 600: Send the personalized learning resource matching request to the data center, so that the data center searches for personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base and sends out the personalized learning resource data;

[0098] Step 700: Receive the personalized learning resource data sent by the data center and push the personalized learning resource data to the target user.

[0099] It can be understood that during the interaction between the business application end and the data center, Step 400 is executed before Step 100, Step 500 is executed after Step 200, Step 600 is executed before Step 300, and Step 700 is executed after Step 300.

[0100] As can be seen from the above description, the second personalized intelligent learning management and cognitive impact data push method provided by the embodiments of the present application can effectively integrate the business functions and resources related to intelligent learning, can effectively improve the comprehensiveness of learning management functions and data call efficiency, can effectively improve the personalization and pertinence of learning resource push, and can effectively improve the user experience.

[0101] In order to further improve the effectiveness and applicability of integrated business management in personalized intelligent learning management and cognitive impact data push, in the second personalized intelligent learning management and cognitive impact data push method provided by the embodiments of the present application, the business data includes at least one of teaching management data, scientific research management data, campus card management data, and questionnaire management data; in a specific example, the business data may include teaching management data, scientific research management data, campus card management data, and questionnaire management data.

[0102] In order to further improve the application reliability and comprehensiveness of the learning situation analysis result data in personalized intelligent learning management and cognitive impact data push, in the second personalized intelligent learning management and cognitive impact data push method provided in the embodiments of the present application, the learning situation analysis result data is composed of user portrait data; wherein, the user portrait data includes at least two of student basic attribute data, progress data of the completion of the training plan, information of the courses taken, data on the changing trend of usual test scores, data on classroom performance, data on course grade ranking and distribution, data on library borrowing, data on daily consumption, data on library access, data on the distribution of staying time in the dormitory, data on leave application and cancellation, data on the publication of papers and works, data on participation in academic activities, data on participation in scientific research projects, and psychological measurement result data.

[0103] Correspondingly, referring to Figure 4 , step 500 in the second personalized intelligent learning management and cognitive impact data push method specifically includes the following contents:

[0104] Step 510: Receive the standardized business data corresponding to the target user sent by the data center, and perform user portrait on the target user according to the standardized business data corresponding to the target user to obtain the corresponding learning situation analysis result data.

[0105] Step 520: Based on the one-to-one relationship table between various types of user portrait data defined and various requirement rule data, determine whether there is any type of user portrait data in the learning situation analysis result data that does not meet the corresponding requirement rule data. If so, generate a warning message for the target user for the user portrait data that does not meet the corresponding requirement rule data.

[0106] Step 530: Generate a corresponding personalized learning resource matching request according to the learning situation analysis result data.

[0107] The present application also provides a data center for executing all or part of the content in the first personalized intelligent learning management and cognitive impact data push method. Referring to Figure 5 , the data center specifically includes the following contents:

[0108] The standardization and business theme storage module 10 is used to receive the business data of the target user for integrated business management sent by the business application end, perform standardization processing on the business data to obtain the corresponding standardized business data, and store the standardized business data in a preset business theme library.

[0109] The data sending module 20 is configured to send all the standardized service data corresponding to the target user to the service application end, so that the service application end performs user profiling on the target user according to the standardized service data corresponding to the target user to obtain corresponding learning situation analysis result data, determines whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generates a corresponding personalized learning resource matching request according to the learning situation analysis result data.

[0110] The material knowledge calling module 30 is configured to receive the personalized learning resource matching request sent by the service application end, search for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base, and send the personalized learning resource data as the cognitive influence data for the target user to the service application end, so that the service application end pushes the cognitive influence data to the target user.

[0111] The embodiment of the data middle platform provided in this application can specifically be used to execute the processing flow of the embodiment of the first personalized intelligent learning management and cognitive influence data pushing method in the above embodiment. Its functions will not be elaborated here and can refer to the detailed description of the first personalized intelligent learning management and cognitive influence data pushing method embodiment.

[0112] The part of the data middle platform for the first personalized intelligent learning management and cognitive influence data pushing can be executed in the server or completed in the client device. Specifically, it can be selected according to the processing capacity of the client device and the limitations of the user usage scenario, etc. This application does not make a limitation in this regard. If all operations are completed in the client device, the client device may further include a processor for the specific processing of the first personalized intelligent learning management and cognitive influence data pushing.

[0113] The above client device may have a communication module (i.e., communication unit) and can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, it may also include a server of an intermediate platform, such as a server of a third-party server platform having a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster composed of multiple servers, or a server structure of a distributed device.

[0114] Any suitable network protocol can be used for communication between the above-mentioned server and the client device, including network protocols that have not been developed as of the filing date of this application. The network protocol can, for example, include TCP / IP protocol, UDP / IP protocol, HTTP protocol, HTTPS protocol, etc. Of course, the network protocol can also, for example, include RPC protocol (Remote Procedure Call Protocol) and REST protocol (Representational State Transfer) used on top of the above-mentioned protocols, etc.

[0115] From the above description, it can be seen that the data middle platform provided by the embodiments of this application can effectively implement the integration of business functions and resources related to intelligent learning, can effectively improve the comprehensiveness of learning management functions and data call efficiency, can effectively improve the personalization and pertinence of learning resource push, and can effectively improve the user experience.

[0116] This application also provides a business application end for executing all or part of the content in the second personalized intelligent learning management and cognitive impact data push method. Refer to Figure 6 , the business application end specifically includes: an integrated business management module 40, a personal portrait module 50, and an intelligent push module 60;

[0117] The integrated business management module 40 is used to receive the business data sent by the target user for integrated business management based on the unified identity authentication login interface, and send the business data of the target user for integrated business management to the data middle platform, so that the data middle platform performs standardized processing on the business data to obtain the corresponding standardized business data, stores the standardized business data in a preset business theme library, and issues the standardized business data corresponding to the target user;

[0118] The personal portrait module 50 includes: a portrait presentation unit 51 and a learning situation warning unit 52;

[0119] Among them, the portrait presentation unit 51 is used to receive the standardized business data corresponding to the target user sent by the data middle platform, and perform user portrait on the target user according to the standardized business data corresponding to the target user to obtain the corresponding learning situation analysis result data;

[0120] The learning situation warning unit 52 is used to judge whether to output the learning situation warning information for the target user based on the learning situation analysis result data, and generate the corresponding personalized learning resource matching request according to the learning situation analysis result data;

[0121] The intelligent push module 60 includes: a resource retrieval unit 61 and a resource push unit 62;

[0122] Among them, the resource retrieval unit 61 is configured to send the personalized learning resource matching request to the data center, so that the data center searches for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base and sends out the personalized learning resource data;

[0123] The resource push unit 62 is configured to receive the personalized learning resource data sent by the data center and push the personalized learning resource data to the target user.

[0124] The embodiment of the service application end provided in this application can specifically be used to execute the processing flow of the embodiment of the second personalized intelligent learning management and cognitive impact data push method in the above embodiment. Its functions will not be elaborated here and can refer to the detailed description of the embodiment of the second personalized intelligent learning management and cognitive impact data push method.

[0125] The part of the service application end for performing the second personalized intelligent learning management and cognitive impact data push can be completed in the client device. Specifically, it can be selected according to the processing ability of the client device and the limitations of the user usage scenario, etc. This application does not make a limitation in this regard. If all operations are completed in the client device, the client device may further include a processor for the specific processing of the second personalized intelligent learning management and cognitive impact data push.

[0126] As can be seen from the above description, the service application end provided in the embodiment of this application can effectively implement the integration of business functions and resources related to intelligent learning, can effectively improve the comprehensiveness of learning management functions and the data call efficiency, can effectively improve the personalization and pertinence of learning resource push, and can effectively improve the user experience.

[0127] Based on at least one of the above embodiments of the first personalized intelligent learning management method, data center, second personalized intelligent learning management method, and service application end, this application further provides a personalized intelligent learning management system, and the personalized intelligent learning management system specifically includes the following:

[0128] A data center and a service application end that are communicatively connected;

[0129] The data center is configured to execute the first personalized intelligent learning management and cognitive impact data push method, and the business theme library and the resource knowledge base are preset locally in the data center;

[0130] The service application end is configured to execute the second personalized intelligent learning management and cognitive impact data push method.

[0131] To further illustrate at least one of the embodiments of the above-mentioned first-generation intelligent learning management method, data middle platform, second-generation intelligent learning management method, and business application end, the present application also provides a specific application example of a personalized intelligent learning management system. Relying on the current mature learning environments such as intelligent classrooms and network conditions, the present application designs a personalized intelligent learning management system composed of two parts: a wisdom learning space and a data middle platform, which realizes learning-centered, highlights the cultivation of abilities, meets personalized needs, and supports the construction of an intelligent campus in colleges and universities. This system integrates and fuses common student functions such as teaching, scientific research, and campus card through a unified login entrance to achieve one-stop business handling; centrally collects and manages the generated business data through the data middle platform to realize learning situation diagnosis and analysis; realizes the co-construction, sharing, and common use of high-quality educational resources by establishing a comprehensive resource knowledge base; based on learning big data, through functions such as setting student portraits, learning situation warnings, and intelligent resource push, realizes data-driven personalized learning, influences students' learning thinking and learning cognition, promotes students' autonomous learning, and improves learning effects.

[0132] See Figure 7 , a personalized intelligent learning system with cognitive influence proposed in the application example of the present application includes two components: a wisdom learning space and a data middle platform in implementation. The wisdom learning space is a business application end for college student users, and is set with three subsystems: integrated business management, personal portrait, and intelligent push, to realize the integrated handling of services such as teaching, scientific research, campus card, and questionnaire survey, and provide intelligent functions such as portrait presentation, learning situation warning, resource retrieval, and push; the data middle platform is a basic support platform for providing unified data management and services, and is set with two modules: a business theme library and a resource knowledge base, to ensure that data empowers services.

[0133] Specifically, the core improvements of the personalized intelligent learning management system are as follows:

[0134] (1) Business integration and fusion. Set up an integrated business management subsystem, abandon function stacking, integrate the most commonly used and practical services for students, and achieve one-stop handling.

[0135] (2) Resource integration and fusion. Set up an educational resource knowledge base, integrate resources in aspects such as subject knowledge, library, scientific research, and academics, and realize resource sharing.

[0136] (3) Data integration and fusion. Set up a data middle platform to uniformly collect, store, process, and manage the whole-process business data to achieve data support.

[0137] (4) Personalized learning. Set up intelligent function services such as student portraits, learning situation analysis, learning situation warnings, and intelligent resource push to achieve personalized learning.

[0138] Based on this, the following describes the application examples of this application in detail from the two components of Zhixue Space and Data Middle Platform:

[0139] Component 1: Smart Learning Space

[0140] College student users can enter the Smart Learning Space by logging in through unified identity authentication and set up three subsystems: integrated business management, personal portrait, and smart push.

[0141] 1. Integrated business management subsystem

[0142] The integrated business management subsystem integrates various commonly used and practical information services into one platform. Students can log in to handle various business operations in one stop, enhancing their sense of gain in intelligent construction. There are four subsystems: teaching management, scientific research management, one-card management, and questionnaire management.

[0143] (1) Teaching management: Set up functions such as training program management, student information, course selection management, examination results, leave management, pre-class preparation, classroom interaction, homework, online testing, and online Q&A. The generated data is uniformly stored in the teaching theme library of the business theme library module of the data center.

[0144] (2) Scientific research management: Set up functions such as academic achievement management, scientific research project management, and academic activities. The generated data is uniformly stored in the scientific research theme library of the data middle platform business theme library.

[0145] (3) One-card management: Based on the one-card service generally provided by universities, functions such as book borrowing, recharge consumption, and access control card swiping are set up. The generated data is uniformly stored in the one-card theme library of the business theme library module of the data middle platform.

[0146] (4) Questionnaire management: Set up functions such as questionnaire surveys and result statistics, embed mature templates such as mental health assessments, and support custom templates and indicators. The generated data is uniformly stored in the questionnaire theme library of the business theme library module of the data middle platform.

[0147] 2. Personal portrait subsystem

[0148] The standardized business data processed in the business theme library of the data center is accessed. The personal portrait subsystem presents student portraits through information-based, labeled, and visualized data characterization, and issues warnings based on the results of academic situation analysis. Two subsystems, portrait presentation and academic situation warning, are set up.

[0149] (1) Image Presentation: Based on the learning big data at all stages and in all aspects, deeply depict the learning image from the perspective of students, analyze and diagnose the learning situation information, including dimensions such as students' basic attributes, progress of completion of training programs, information on courses taken, trends in changes in usual test scores, classroom performance, course grade rankings and distributions, library borrowing situations, daily consumption situations, library access situations, dormitory time distributions, leave application and cancellation situations, publication of papers and works, participation in academic activities, participation in scientific research projects, psychological measurement results, etc., and support students to customize and present their personal images according to their own needs, to support students to evaluate their academic status in a timely and accurate manner and identify learning problems that are difficult to realize under the traditional teaching mode.

[0150] (2) Learning Situation Early Warning: Based on big data technology, according to the task completion situation, knowledge mastery situation, achievement degree of teaching objectives, learning behavior trends, changes in learning attitudes, and learning state reflected in the student image, realize learning situation early warning through customizing early warning conditions.

[0151] 3. Intelligent Push Sub-system

[0152] Based on the results of image description and learning situation analysis, according to the resource knowledge base stored in the data center, the intelligent push sub-system realizes automatic push of learning resources, supports students' personalized inquiry learning, and sets up 2 sub-systems: resource retrieval and resource push. At the same time, the learning data of the pushed resources, the behavior data of retrieving resources, etc. can be fed back to the personal image sub-system for further analysis of the effectiveness and quality of resource push.

[0153] (1) Resource Retrieval: Students can search for learning resources in multiple dimensions according to different structural types such as documents, pictures, videos, audios, links, etc. and different disciplines such as computer science, communication, physics, etc., and support online viewing and downloading.

[0154] (2) Resource Push: Based on the educational resource knowledge base in the data center, according to the statistical analysis results of the student image, for students' interest items and difficult and weak items, accurately match learning resources through tags, keywords, etc. and conduct systematic automatic push, positively influencing students' learning cognition, consciously conducting intensive learning, actively and efficiently learning, and ensuring personalized services.

[0155] Part Two: Data Center

[0156] The data center is a support platform that provides functions such as data collection and aggregation, data integration and integration, data cleaning and governance, and centralized data management, provides learning big data for statistical analysis and upper-layer applications, effectively exerts the value of data assets, and sets up 2 modules: business theme library and resource knowledge base.

[0157] 1. Business Theme Library

[0158] By accessing the massive business data generated by the integrated business management subsystem, and dynamically collecting, storing, cleaning, integrating, and processing it throughout the process, a unified data storage center can be constructed to provide data support for upper-layer applications, giving full play to the value of data. It can be divided into teaching theme libraries, scientific research theme libraries, one-card theme libraries, and questionnaire theme libraries.

[0159] 2. Resource knowledge base

[0160] By accessing the existing school resource libraries and maintaining the resource libraries, educational resources such as subject resources (e.g., e-textbooks, micro-courses, MOOCs, courseware, cases, mind maps, question banks, test paper libraries, etc.), library resources (e.g., e-books, etc.), academic resources (e.g., literature materials, etc.), campus news, psychological knowledge, external links, application software, etc. are digitally stored, incrementally maintained, updated in a timely manner, and uniformly managed, thereby constructing a rich and comprehensive resource knowledge base, supporting classification and tagging according to business themes, storage structures, etc., and facilitating students' utilization and sharing.

[0161] A personalized intelligent learning management system for cognitive impact provided by the application example of this application aims to improve the intelligent campus learning experience for students. First, an integrated business management subsystem is set up to integrate the most commonly used student services such as teaching, scientific research, one-card, and questionnaires, eliminating problems such as information silos and multiple logins between systems. Second, an educational resource knowledge base is set up to comprehensively integrate learning resources, break down the barriers between disciplines and regions, eliminate the problem of closed "knowledge warehouses", improve the utilization rate of learning resources, and truly realize the sharing of learning resources. Third, relying on the data middle platform, it aggregates the explicit and implicit learning behavior data generated by application systems, facilitating the standardization and exchange sharing of raw data between different services, supporting data-driven precise profiling and mining analysis, and giving full play to the value of data assets. Fourth, services such as student profiling, learning situation analysis, learning situation warning, and intelligent resource push are set up to meet the needs of personalized learning and differentiated learning, and affect students' learning cognition and improve learning effects.

[0162] The embodiment of this application also provides an electronic device, which may include a processor, a memory, a receiver, and a transmitter. The processor is used to execute the first personalized intelligent learning management and cognitive impact data push method or the second personalized intelligent learning management and cognitive impact data push method mentioned in the above embodiments. The processor and the memory can be connected through a bus or other means. Taking the connection through the bus as an example. The receiver can be connected to the processor and the memory in a wired or wireless manner.

[0163] The processor may be a Central Processing Unit (CPU). The processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., such as chips, or combinations of the above types of chips.

[0164] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the first personalized intelligent learning management and cognitive impact data push method or the second personalized intelligent learning management and cognitive impact data push method in the embodiments of the present application. By running the non-transitory software programs, instructions, and modules stored in the memory, the processor executes various functional applications and data processing of the processor, that is, implements the first personalized intelligent learning management and cognitive impact data push method or the second personalized intelligent learning management and cognitive impact data push method in the above method embodiments.

[0165] The memory may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created by the processor, etc. In addition, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include but are not limited to the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof.

[0166] The one or more modules are stored in the memory and, when executed by the processor, execute the first personalized intelligent learning management and cognitive impact data push method or the second personalized intelligent learning management and cognitive impact data push method in the embodiments.

[0167] In some embodiments of the present application, the user equipment may include a processor, a memory, and a transceiver unit. The transceiver unit may include a receiver and a transmitter. The processor, the memory, the receiver, and the transmitter may be connected through a bus system. The memory is used to store computer instructions, and the processor is used to execute the computer instructions stored in the memory to control the transceiver unit to transmit and receive signals.

[0168] As an implementation manner, the functions of the receiver and the transmitter in the present application can be considered to be implemented by a transceiver circuit or a dedicated transceiver chip, and the processor can be considered to be implemented by a dedicated processing chip, a processing circuit or a general-purpose chip.

[0169] As another implementation manner, a general-purpose computer can be considered to be used to implement the server provided in the embodiments of the present application. That is, the program codes for implementing the functions of the processor, the receiver and the transmitter are stored in the memory, and the general-purpose processor implements the functions of the processor, the receiver and the transmitter by executing the codes in the memory.

[0170] The embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of implementing the foregoing first personalized intelligent learning management and cognitive influence data pushing method or the second personalized intelligent learning management and cognitive influence data pushing method are realized. The computer-readable storage medium may be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium well known in the technical field.

[0171] Those of ordinary skill in the art should understand that the various exemplary components, systems, and methods described in connection with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Specifically, whether to execute in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium or transmitted through a data signal carried in a carrier wave on a transmission medium or a communication link.

[0172] It should be clear that the present application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, the detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order between the steps after understanding the spirit of the present application.

[0173] In this application, features described and / or illustrated for one embodiment can be used in the same or a similar manner in one or more other embodiments, and / or combined with the features of other embodiments or replace the features of other embodiments.

[0174] The foregoing are only the preferred embodiments of this application and are not intended to limit this application. For those skilled in the art, various modifications and variations can be made to the embodiments of this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. A personalized intelligent learning management and cognitive impact data push method, characterized in that, it includes: Receiving business data of a target user for integrated business management sent by a business application end, performing standardization processing on the business data to obtain corresponding standardized business data, and storing the standardized business data in a preset business theme library; Sending all the standardized business data corresponding to the target user to the business application end, so that the business application end performs user profiling on the target user according to the standardized business data corresponding to the target user to obtain corresponding learning situation analysis result data, determining whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generating a corresponding personalized learning resource matching request according to the learning situation analysis result data; Receiving the personalized learning resource matching request sent by the business application end, searching for personalized learning resource data corresponding to the personalized learning resource matching request in a local resource knowledge library, and sending the personalized learning resource data as cognitive impact data for the target user to the business application end, so that the business application end pushes the cognitive impact data to the target user.

2. The personalized intelligent learning management and cognitive impact data push method according to claim 1, characterized in that, the business data includes at least one of teaching management data, scientific research management data, one-card management data, and questionnaire management data; Correspondingly, the receiving business data of a target user for integrated business management sent by a business application end, performing standardization processing on the business data to obtain corresponding standardized business data, and storing the standardized business data in a preset business theme library includes: Receiving business data of a target user for integrated business management sent by a business application end; If the business data contains the teaching management data, performing standardization processing on the teaching management data to obtain corresponding standardized teaching management data, and storing the standardized teaching management data in a teaching theme library in the preset business theme library; If the business data contains the scientific research management data, performing standardization processing on the scientific research management data to obtain corresponding standardized scientific research management data, and storing the standardized scientific research data in a scientific research theme library in the preset scientific research theme library; If the business data contains the one-card management data, performing standardization processing on the one-card management data to obtain corresponding standardized one-card management data, and storing the standardized one-card data in a one-card theme library in the preset business theme library; If the business data contains the questionnaire management data, performing standardization processing on the questionnaire management data to obtain corresponding standardized questionnaire management data, and storing the standardized questionnaire data in a questionnaire theme library in the preset business theme library.

3. The personalized intelligent learning management and cognitive impact data push method according to claim 1, characterized in that, The data of the learning situation analysis results consists of user portrait data; among them, the user portrait data includes at least two of the following: student basic attribute data, progress data of the training plan completion, information of the courses taken, data on the changing trend of usual test scores, data on classroom performance, data on course grade rankings and distributions, data on library borrowing, data on daily consumption, data on library access, data on the distribution of staying time in the dormitory, data on leave application and cancellation, data on the publication of papers and works, data on participation in academic activities, data on participation in scientific research projects, and data on psychological measurement results.

4. The personalized intelligent learning management and cognitive impact data push method according to any one of claims 1 to 3, characterized in that, it further includes: periodically retrieving learning resource data from the school resource library and storing the learning resource data in the resource knowledge base, where the resource knowledge base is used for digitally storing, incrementally maintaining, and updating at least two of subject resource data, library resource data, academic resource data, campus information, psychological knowledge data, external links, and application software.

5. A personalized intelligent learning management and cognitive impact data push method, characterized in that, it includes: receiving service data sent by a target user for integrated service management based on a unified identity authentication login interface, and sending the service data of the target user for integrated service management to a data center, so that the data center performs standardization processing on the service data to obtain corresponding standardized service data, storing the standardized service data in a preset service theme library, and sending out the standardized service data corresponding to the target user; receiving the standardized service data corresponding to the target user sent by the data center, and performing user portrait on the target user according to the standardized service data corresponding to the target user to obtain corresponding learning situation analysis result data, judging whether to output a learning situation warning message for the target user based on the learning situation analysis result data, and generating a corresponding personalized learning resource matching request according to the learning situation analysis result data; sending the personalized learning resource matching request to the data center, so that the data center searches for personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base and sends out the personalized learning resource data; receiving the personalized learning resource data sent by the data center and pushing the personalized learning resource data to the target user.

6. The personalized intelligent learning management and cognitive impact data push method according to claim 5, characterized in that, the service data includes at least one of teaching management data, scientific research management data, one-card management data, and questionnaire management data.

7. The personalized intelligent learning management and cognitive impact data push method according to claim 5, characterized in that, The data of the learning situation analysis results consists of user portrait data; among them, the user portrait data includes at least two of the following: student basic attribute data, progress data of the completion of the training plan, information on the courses taken, data on the changing trend of usual test scores, data on classroom performance, data on the ranking and distribution of course scores, data on library borrowing, data on daily consumption, data on library access, data on the distribution of staying time in the dormitory, data on leave application and cancellation, data on the publication of papers and works, data on participation in academic activities, data on participation in scientific research projects, and data on psychological measurement results; Correspondingly, the judgment on whether to output a learning situation warning message for the target user based on the data of the learning situation analysis results includes: Based on a one-to-one relationship table between various types of user portrait data defined by the user and various requirement rule data, it is judged whether there is any type of user portrait data in the data of the learning situation analysis results that does not meet the corresponding requirement rule data. If so, a warning message for the target user is generated for the user portrait data that does not meet the corresponding requirement rule data.

8. A personalized intelligent learning management system, characterized in that, it includes: A data middle platform and a business application end that are communicatively connected; The data middle platform is used to execute the personalized intelligent learning management and cognitive impact data push method described in any one of claims 1 to 4, and the business theme library and the resource knowledge library are preset locally in the data middle platform; The business application end is used to execute the personalized intelligent learning management and cognitive impact data push method described in any one of claims 5 to 7; Among them, the business application end includes: an integrated business management module, a personal portrait module, and an intelligent push module; The integrated business management module is used to receive the business data sent by the target user for integrated business management based on a unified identity authentication login interface, and send the business data of the target user for integrated business management to the data middle platform, so that the data middle platform performs standardized processing on the business data to obtain the corresponding standardized business data, stores the standardized business data in a preset business theme library, and issues the standardized business data corresponding to the target user; The personal portrait module includes: a portrait presentation unit and a learning situation warning unit; Among them, the portrait presentation unit is used to receive the standardized business data corresponding to the target user sent by the data middle platform, and perform user portrait on the target user according to the standardized business data corresponding to the target user to obtain the corresponding data of the learning situation analysis results; The learning situation warning unit is used to judge whether to output a learning situation warning message for the target user based on the data of the learning situation analysis results, and generate a corresponding personalized learning resource matching request according to the data of the learning situation analysis results; The intelligent push module includes: a resource retrieval unit and a resource push unit; Among them, the resource retrieval unit is used to send the personalized learning resource matching request to the data middle platform, so that the data middle platform can search for the personalized learning resource data corresponding to the personalized learning resource matching request in the local resource knowledge base and send out the personalized learning resource data; The resource pushing unit is used to receive the personalized learning resource data sent by the data middle platform and push the personalized learning resource data to the target user.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the personalized intelligent learning management and cognitive influence data pushing method according to any one of claims 1 to 4, or implements the personalized intelligent learning management and cognitive influence data pushing method according to any one of claims 5 to 7.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by the processor, it implements the personalized intelligent learning management and cognitive influence data pushing method according to any one of claims 1 to 4, or implements the personalized intelligent learning management and cognitive influence data pushing method according to any one of claims 5 to 7.

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