CN-121809424-B - Personalized service configuration method and system for education intelligent cloud platform

CN121809424BCN 121809424 BCN121809424 BCN 121809424BCN-121809424-B

Abstract

The invention discloses a personalized service configuration method and a personalized service configuration system for an educational intelligent cloud platform, and relates to the technical field of service configuration. The personalized service configuration method of the educational intelligent cloud platform comprises the steps of obtaining correction image data and writing interaction data of each writing task of each student user of the educational intelligent cloud platform, analyzing the correction image data of each writing task based on a pre-trained correction semantic analysis model to obtain writing transition characteristic values of corresponding student users, and analyzing feedback correction characteristic values of the corresponding student users based on the writing interaction data of each writing task of each student user.

Inventors

ZHU XIWEI
Bian Jihai
LI JIAMING
Lv Panpan
YE YANFEI
ZHANG LIQIAO
YANG JIYIN

Assignees

中慧云启科技集团有限公司
贵州电子信息职业技术学院

Dates

Publication Date: 20260508
Application Date: 20260312

Claims (5)

1. The personalized service configuration method of the educational intelligent cloud platform is characterized by comprising the following steps of: obtaining correction image data of each writing task of each student user of the education intelligent cloud platform and writing interaction data; Based on the correction image data of each writing task of each student user of the set education intelligent cloud platform, and by combining with a pre-trained correction semantic analysis model, the writing transition characteristic values of the corresponding student users are analyzed, wherein the correction transition characteristic values specifically comprise: inputting correction image data of each writing task of each student user of the set education intelligent cloud platform into a pre-trained correction semantic analysis model, and analyzing correction evaluation feature sets of the corresponding student users, wherein the correction evaluation feature sets comprise correction evolution feature sets and annotation evolution feature sets, and the correction evaluation feature sets specifically comprise: Extracting the correction evaluation text information and the correction labeling text information of the corresponding writing tasks based on correction image data of each writing task of each student user of the set education intelligent cloud platform in a correction recognition sub-network in the correction semantic analysis model; extracting a correction evolution characteristic set of each student user based on correction evaluation text information of each writing task of each student user of the set education intelligent cloud platform in a correction evaluation sub-network in the correction semantic analysis model; Extracting annotation evolution feature sets of corresponding student users based on the annotation text information of each writing task of each student user of the set education intelligent cloud platform in an annotation sub-network in the annotation semantic analysis model; Analyzing writing transition characteristic values of corresponding student users based on the correction evaluation characteristic set of each student user of the set education intelligent cloud platform; Based on the writing interaction data of each writing task of each student user of the education intelligent cloud platform, the writing interaction data comprises a submitting time length value, a feedback immersion time length value, a feedback response difference value, an iteration submitting ratio and a pause peak value ratio, and the feedback study and repair characteristic values of the corresponding student users are analyzed, wherein the method specifically comprises the following steps: Based on the writing interaction data of each writing task of each student user of the education intelligent cloud platform, analyzing a writing response characteristic set of the corresponding writing task, wherein the writing response characteristic set comprises writing efficacy characteristic values and feedback research characteristic values; Performing time sequence analysis on the writing response characteristic set of each writing task of each student user of the education intelligent cloud platform to obtain a feedback study characteristic value of the corresponding student user, wherein the method comprises the following specific steps: Analyzing the writing efficacy characteristic value of the corresponding writing task based on the submission duration value and the iterative submission ratio of each writing task of each student user of the education intelligent cloud platform; based on the feedback immersion time length value, the feedback response difference value and the pause peak value ratio of each writing task of each student user of the education intelligent cloud platform, analyzing the feedback research and reading characteristic value of the corresponding writing task; based on the writing transition characteristic value and the feedback study characteristic value, corresponding service configuration processing is carried out on each student user setting the education intelligent cloud platform, and the method specifically comprises the following steps: Normalizing the writing transition characteristic value and the feedback researching characteristic value of each student user of the education intelligent cloud platform; The writing transition characteristic value and the feedback study characteristic value of each student user of the normalized set education intelligent cloud platform are respectively judged and analyzed with a plurality of preset service configuration adjustment intervals; and based on the judgment and analysis result, corresponding service configuration measures are adopted for each student user setting the education intelligent cloud platform.
2. The personalized service configuration method of the educational intelligent cloud platform according to claim 1, wherein the correction image data specifically comprises a pixel value and a two-dimensional coordinate of each pixel point in the correction image, and the correction semantic analysis model comprises a correction identification sub-network, a correction evaluation sub-network and a correction labeling sub-network.
3. The personalized service configuration method of the educational intelligent cloud platform according to claim 1, wherein the modifying and evaluating sub-network comprises an evaluating input layer, an evaluating feature extraction layer and a task sequence association output layer, and the specific steps of extracting modifying and evolution feature sets of each student user setting the educational intelligent cloud platform are as follows: Receiving correction evaluation text information of each writing task of each student user of the education intelligent cloud platform in an evaluation input layer of the correction evaluation sub-network, and preprocessing; in an evaluation feature extraction layer of the correction evaluation sub-network, extracting an evaluation feature vector of a corresponding writing task based on correction evaluation text information of each writing task of each student user of the preprocessed set education intelligent cloud platform; And extracting the correction evolution characteristic set of each student user of the education intelligent cloud platform based on the set evaluation characteristic vector of each writing task of each student user of the education intelligent cloud platform at the task sequence association output layer of the correction evaluation sub-network.
4. The personalized service configuration method of the educational intelligent cloud platform according to claim 1, wherein the specific steps of the time sequence analysis are as follows: comprehensively analyzing the characteristic values of the writing efficacy and the feedback research and reading characteristic values of each writing task of each student user of the set education intelligent cloud platform to obtain the initial feedback research and repairing characteristic values of the corresponding writing tasks; Based on the initial feedback study characteristic value of each writing task of each student user of the education intelligent cloud platform, the study fluctuation characteristic value, the study track direction characteristic value and the study balance characteristic value of the corresponding student user are analyzed, and comprehensive analysis is performed to obtain the feedback study characteristic value of the corresponding student user.
5. A personalized service configuration system of an educational intelligent cloud platform, configured to implement the personalized service configuration method of the educational intelligent cloud platform according to any one of claims 1-4, comprising: the data acquisition module is used for acquiring correction image data and writing interaction data of each writing task of each student user of the education intelligent cloud platform; The writing transition analysis module is used for analyzing writing transition characteristic values of corresponding student users based on correction image data of each writing task of each student user of the set education intelligent cloud platform and combining a pre-trained correction semantic analysis model; the feedback study analysis module is used for analyzing the feedback study characteristic values of the corresponding student users based on the writing interaction data of each writing task of each student user of the set education intelligent cloud platform; And the service configuration feedback module is used for carrying out corresponding service configuration processing on each student user of the set education intelligent cloud platform based on the writing transition characteristic value and the feedback study characteristic value.

Description

Personalized service configuration method and system for education intelligent cloud platform Technical Field The invention relates to the technical field of service configuration, in particular to a personalized service configuration method and a personalized service configuration system for an educational intelligent cloud platform. Background Along with the development of education informatization and artificial intelligence technology, an education intelligent platform based on cloud computing and big data gradually becomes an important supporting tool for teaching and learning, the education intelligent cloud platform can be connected with multi-terminal equipment through the Internet to provide functions of online writing training, automatic correction, feedback generation, learning resource pushing and the like for students, in a writing training scene, writing tasks submitted by the students can be uploaded through the platform to write images, the platform analyzes and revises the writing images by means of optical character recognition, AI recognition and the like, and the education intelligent cloud platform gradually develops to intelligent, refined and personalized directions, so that the correction efficiency is ensured, meanwhile, the learning requirements of different students can be dynamically adapted, and the education service is promoted to be differentiated by unification. The limitation of the prior art at least includes the following problems that the prior art lacks of tracking the dynamic evolution process of the writing ability of the student user in the continuous task, and is difficult to comprehensively reflect the real writing level of the student, for example, when the score of the student is low due to poor temporary state in a certain task, the prior art tends to directly push the learning service in a low level according to the score, but is difficult to identify the overall progress trend of the student in the continuous writing task for many times, and the service configuration is easy to be mismatched with the actual development direction of the student, so that the personalized learning service configuration measures are difficult to be timely adjusted when the writing ability of the student is improved or reduced. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a personalized service configuration method and a personalized service configuration system for an educational intelligent cloud platform, which solve the problem that the prior art is difficult to track writing evolution, so that service configuration is not matched with student development. The personalized service configuration method for the educational intelligent cloud platform comprises the following steps of obtaining correction image data and writing interaction data of each writing task of each student user of the educational intelligent cloud platform, analyzing writing transition characteristic values of corresponding student users based on the correction image data of each writing task of each student user of the educational intelligent cloud platform and combining a pre-trained correction semantic analysis model, analyzing feedback correction characteristic values of corresponding student users based on the writing interaction data of each writing task of each student user of the educational intelligent cloud platform, and carrying out corresponding service configuration processing on each student user of the educational intelligent cloud platform based on the writing transition characteristic values and the feedback correction characteristic values. Further, the correction image data specifically comprises a pixel value and a two-dimensional coordinate of each pixel point in the correction image, and the correction semantic analysis model comprises a correction identification sub-network, a correction evaluation sub-network and a correction labeling sub-network. The method comprises the specific steps of setting the characteristic value of the writing transition of each student user of the education intelligent cloud platform, inputting correction image data of each writing task of each student user of the education intelligent cloud platform into a pre-trained correction semantic analysis model, analyzing correction evaluation characteristic sets of corresponding student users, including correction evolution characteristic sets and annotation evolution characteristic sets, and analyzing the characteristic value of the writing transition of each student user of the education intelligent cloud platform based on the correction evaluation characteristic sets of each student user of the education intelligent cloud platform. The method comprises the specific steps of analyzing and setting the correction evaluation characteristic set of each student user of the education intelligent cloud platform, extracting correction evaluation text information and correction la