CN-121685223-B - AI-based dynamic generation and distribution system and method for fused medium labor education content

CN121685223BCN 121685223 BCN121685223 BCN 121685223BCN-121685223-B

Abstract

The application relates to the technical field of education content generation and distribution, and discloses a system and a method for dynamically generating and distributing an AI-based fused medium labor education content. The method corresponds to the system. The method realizes dynamic and intelligent closed loop iteration of labor education content generation and distribution, completes quantitative setting of parameters, enables propagation efficiency judgment to be more accurate, provides reliable data support for subsequent analysis, improves accuracy of potential audience feature extraction by a hierarchical clustering suboptimal data screening mode, enables adjustment of content and a platform to be more targeted, enables propagation combination to be highly adaptive to propagation and behavior characteristics of student groups, and effectively improves group touch rate, participation and collaborative propagation effects of the labor education content.

Inventors

CUI CAN
Kang Meilin
SU HUIXIANG
PENG CONG

Assignees

湖南科技职业学院
湖北宇树文化传媒有限公司

Dates

Publication Date: 20260505
Application Date: 20260209

Claims (8)

1. An AI-based dynamic generation and distribution system for fused-media labor education content, the system comprising: The system comprises a response module, a transmission module and a control module, wherein the response module is configured to respond to an input labor education target and output a transmission combination, the transmission combination comprises labor education content and a transmission platform, wherein the labor education content is obtained through an AI content generation technology based on the labor education target, and the medium form of the labor education content comprises pictures, texts and/or videos; the feedback module is configured to acquire feedback data corresponding to different propagation combinations, wherein the feedback data is used for representing the realization condition of a labor education target, screen the feedback data based on a preset threshold range to obtain sub-optimal gear data, screen the sub-optimal gear data to obtain sub-optimal feedback data, and the sub-optimal feedback data is used for mapping potential audience groups, wherein the screening of the sub-optimal feedback data comprises the following steps: inputting a completion parameter corresponding to the labor education target to a preset large language model, and constructing a first prompt word through the large language model, wherein the first prompt word is used for definitely feeding back the acquisition type of the data; According to the first prompt word, collecting feedback data generated by different propagation combinations on corresponding propagation platforms, and performing association calculation on the collected feedback data and completion parameters to obtain the completion conditions corresponding to the propagation combinations; inputting the completion situation to a large language model, and constructing a second prompting word through the large language model, wherein the second prompting word is used for defining audience feature dimensions corresponding to different completion situations; performing cluster analysis on the feedback data according to audience feature dimensions defined by the second prompting words to obtain a plurality of feedback data clusters corresponding to different audience groups; The feedback data clusters in each sub-optimal gear are independently ordered according to the completion condition, the data clusters at the head end and the tail end of the ordering in each sub-optimal gear are removed, and feedback data corresponding to the rest data clusters are defined as sub-optimal feedback data; An adjustment module configured to determine a potential audience population based on the suboptimal feedback data and adjust a propagation combination based on the potential audience population to optimize the labor education content dynamic generation and distribution, wherein adjusting the propagation combination includes adjusting the labor education content and/or the propagation platform, the operation of the adjustment module including: receiving suboptimal feedback data, and extracting potential audience group characteristics associated with the data, wherein the potential audience group characteristics at least comprise preferences of an audience on a medium form, content interest focuses and interactive behavior habits; Adjusting labor education content based on the potential audience group characteristics without adding a labor education target; Synchronizing adjusts the propagation platform based on the potential audience group characteristics, the adjusting including at least switching to a propagation platform with higher potential audience group liveness; And combining the adjusted labor education content and the propagation platform, outputting a new propagation combination, realizing the optimization of dynamic generation and distribution of the labor education content, and waiting for the next round of feedback data acquisition and iteration.
2. The AI-based fused media labor education content dynamic generation and distribution system of claim 1 wherein the operation of the response module comprises: Acquiring labor education targets, wherein the labor education targets comprise education targets and real operation targets; Based on the labor education target, calling a preset AI content generation platform to output labor education content; determining a propagation platform based on the media form of the labor education content and the completion parameter; And combining the labor education content and the corresponding propagation platform to output the propagation combination.
3. The AI-based fused media labor education content dynamic generation and distribution system of claim 2 wherein the operation of the feedback module comprises: Collecting feedback data corresponding to different propagation combinations, and determining corresponding completion conditions, wherein the collection of the feedback data corresponds to the completion parameters; Sequencing the feedback data based on the completion condition, and dividing target completion grades corresponding to the feedback data by combining the completion parameters of the labor education targets; and eliminating feedback data of which the target completion level is in the first gear and the last gear to obtain sub-optimal gear data.
4. The AI-based fused media labor education content dynamic generation and distribution system of claim 3 wherein the completion parameter is an acquisition index for quantifying labor education propagation efficiency; Aiming at educational objectives, the acquisition index at least comprises effective play amount and content stay time duty ratio; Aiming at the real operation targets, the acquisition indexes at least comprise the number of active participation persons and the number of group linkage participation records.
5. The AI-based dynamic generation and distribution system of fused media labor education content as claimed in claim 3, wherein the potential audience group characteristics further comprise student group linkage behavior characteristics, wherein the student group linkage behavior characteristics at least comprise student group gathering scene identifications including at least bedroom, class or community; the adjustment module adjusts the propagation combination based on the student group linkage behavior characteristics, specifically increases group cooperative guidance when labor education content is generated, and carries out directional pushing according to group aggregation time periods, and opens a group sharing entrance on the propagation platform.
6. An AI-based dynamic generation and distribution method of labor education content for molten media, applied to the AI-based dynamic generation and distribution system of labor education content for molten media as claimed in any one of claims 1 to 5, comprising: Outputting a propagation combination in response to an input labor education target, wherein the propagation combination comprises labor education content and a propagation platform, the labor education content is obtained through an AI content generation technology based on the labor education target, and the medium form of the labor education content comprises pictures, texts and/or videos; The method comprises the steps of acquiring feedback data corresponding to different propagation combinations, wherein the feedback data are used for representing the realization condition of a labor education target, screening the feedback data based on a preset threshold range to obtain sub-optimal gear data, and screening the sub-optimal gear data to obtain sub-optimal feedback data, wherein the sub-optimal feedback data are used for mapping potential audience groups, and the screening of the sub-optimal feedback data comprises the following steps: inputting a completion parameter corresponding to the labor education target to a preset large language model, and constructing a first prompt word through the large language model, wherein the first prompt word is used for definitely feeding back the acquisition type of the data; According to the first prompt word, collecting feedback data generated by different propagation combinations on corresponding propagation platforms, and performing association calculation on the collected feedback data and completion parameters to obtain the completion conditions corresponding to the propagation combinations; inputting the completion situation to a large language model, and constructing a second prompting word through the large language model, wherein the second prompting word is used for defining audience feature dimensions corresponding to different completion situations; performing cluster analysis on the feedback data according to audience feature dimensions defined by the second prompting words to obtain a plurality of feedback data clusters corresponding to different audience groups; The feedback data clusters in each sub-optimal gear are independently ordered according to the completion condition, the data clusters at the head end and the tail end of the ordering in each sub-optimal gear are removed, and feedback data corresponding to the rest data clusters are defined as sub-optimal feedback data; Determining a potential audience group based on the suboptimal feedback data, and adjusting a propagation combination based on the potential audience group to optimize the dynamic generation and distribution of the labor education content, wherein the adjustment of the propagation combination comprises adjustment of the labor education content and/or a propagation platform, and the operation of the adjustment module comprises: receiving suboptimal feedback data, and extracting potential audience group characteristics associated with the data, wherein the potential audience group characteristics at least comprise preferences of an audience on a medium form, content interest focuses and interactive behavior habits; Adjusting labor education content based on the potential audience group characteristics without adding a labor education target; Synchronizing adjusts the propagation platform based on the potential audience group characteristics, the adjusting including at least switching to a propagation platform with higher potential audience group liveness; And combining the adjusted labor education content and the propagation platform, outputting a new propagation combination, realizing the optimization of dynamic generation and distribution of the labor education content, and waiting for the next round of feedback data acquisition and iteration.
7. An AI-based dynamic generation and distribution of molten media labor education content comprising at least one processor, at least one memory, and a data bus; the processor and the memory complete communication with each other through the data bus; The memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the AI-based fused media labor education content dynamic generation and distribution method of claim 6.
8. A storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the AI-based dynamic generation and distribution method of molten media labor education content of claim 6.

Description

AI-based dynamic generation and distribution system and method for fused medium labor education content Technical Field The application relates to the technical field of education content generation and distribution, in particular to a system and a method for dynamically generating and distributing a fused medium labor education content based on AI. Background In the current fusion media technology and labor education fusion application, the generation and distribution system of the labor education content still has a plurality of technical defects, and is difficult to adapt to the actual development requirements of the labor education. The existing system can not generate the adapted picture, text and video content according to the labor education target, the combination of the transmission platform and the content medium form lacks reasonable basis, and the problem of mismatching of the content and the transmission channel is easy to occur. Meanwhile, the system only performs basic acquisition on the feedback data after propagation, does not perform fine screening and mining on the data, cannot identify effective data capable of mapping potential audience groups, and performs optimization adjustment only by means of full quantity or optimal feedback data, so that the optimization direction lacks pertinence. In addition, the existing system does not have a design for adjusting the propagation combination based on the characteristics of potential audience groups, cannot realize the dynamic adaptation optimization of labor education contents and a propagation platform, is difficult to form a complete closed loop for content generation, distribution, feedback and adjustment, finally influences the realization effect of the labor education target, and is difficult to ensure the propagation efficiency and the education effect. Disclosure of Invention The application aims to provide an AI-based dynamic generation and distribution system and method for fused medium labor education content, which are used for solving the technical problems that in the prior art, the design of adjusting the propagation combination based on the characteristics of potential audience groups is not available, the dynamic adaptation and optimization of the labor education content and the propagation platform are not available, the complete closed loop of content generation, distribution, feedback and adjustment is difficult to form, the realization effect of the labor education target is finally affected, and the propagation efficiency and education effect are difficult to guarantee. In order to achieve the above object, the present application provides a dynamic generation and distribution system of AI-based molten medium labor education content, the system comprising: A response module configured to output a propagation combination including labor education content and a propagation platform in response to the inputted labor education target, wherein the labor education content is obtained through an AI content generation technique based on the labor education target, and a medium form thereof includes a graphic and/or a video; the feedback module is configured to acquire feedback data corresponding to different propagation combinations, wherein the feedback data is used for representing the realization condition of the labor education target; An adjustment module configured to determine a potential audience population based on the suboptimal feedback data and adjust a propagation combination based on the potential audience population to optimize the labor education content dynamic generation and distribution, wherein adjusting the propagation combination includes adjusting the labor education content and/or the propagation platform. Preferably, the operation of the response module includes: acquiring a labor education target, wherein the labor education target comprises an education target and an actual operation target, and the labor education target is provided with corresponding completion parameters, and the completion parameters are used for quantifying the realization condition of the labor education target; Based on the labor education target, calling a preset AI content generation platform to output labor education content; determining a propagation platform based on the media form of the labor education content and the completion parameter; And combining the labor education content and the corresponding propagation platform to output the propagation combination. Preferably, the operation of the feedback module includes: Collecting feedback data corresponding to different propagation combinations, and determining corresponding completion conditions, wherein the collection of the feedback data corresponds to the completion parameters; Sequencing the feedback data based on the completion condition, and dividing target completion grades corresponding to the feedback data by combining the completion parameters of the labor educat