An intelligent planning method and system based on learning feedback

Abstract

This invention relates to the field of intelligent software planning, and discloses an intelligent planning method and system based on learning feedback. The method, used in an intelligent planning system, includes: S1, obtaining feedback collection results based on learned software feedback intelligence; S2, performing cluster analysis on the feedback collection results to obtain intelligent analysis results; S3, establishing a standardized user requirement table based on the intelligent analysis results; S4, selecting sub-requirement items from the standardized user requirement table; and S5, parsing the sub-requirement items to obtain a planning scheme. In overseas software development projects, market demands change rapidly. Software engineers need to focus their main efforts on market demands to gradually free themselves from complex product development meetings and improve team efficiency. Therefore, this application can significantly improve the work efficiency of R&D teams.

Description

Technical Field

[0001] This invention relates to the field of intelligent planning in software, and more particularly to an intelligent planning method and system based on learning feedback. Background Technology

[0002] For software development projects targeting overseas markets, market demands change rapidly. Software engineers need to focus their main efforts on market needs, gradually freeing themselves from tedious product development meetings to improve team efficiency. This is especially true for educational software, which operates in multiple countries with significantly different requirements, necessitating flexible solutions to the development team's daily challenges.

[0003] Therefore, the industry needs to design an intelligent planning method and system based on learning feedback to solve the above problems, namely, to improve team work efficiency. Summary of the Invention

[0004] This invention provides an intelligent planning method and system based on learning feedback, which can significantly improve the work efficiency of R&D teams.

[0005] To achieve the above objectives, this invention provides an intelligent planning method based on learning feedback. The method is used in an intelligent planning system and includes: S1, obtaining feedback collection results based on feedback information from learning software; S2, performing cluster analysis on the feedback collection results to obtain intelligent analysis results; S3, establishing a standardized user requirement table based on the intelligent analysis results; S4, selecting sub-requirement items from the standardized user requirement table; and S5, parsing the sub-requirement items to obtain a planning scheme.

[0006] Optionally, step S1 includes: S11, collecting learning software feedback information from users in multiple countries within a preset time interval, and obtaining feedback collection results.

[0007] Optionally, step S2 includes: S21, performing text semantic-based clustering analysis on the feedback collection results according to preset clustering rules to obtain intelligent analysis results.

[0008] Optionally, step S3 includes: S31, parsing the intelligent analysis results and extracting multiple overseas user demand keywords; S32, converting the multiple overseas user demand keywords into standard demand items according to preset mapping rules; S33, aggregating the multiple standard demand items into a preset table structure template to generate a standardized user demand table.

[0009] Optionally, step S5 includes: identifying the user's country and user tags corresponding to each sub-requirement item; obtaining the local hot software with the highest matching degree to the user tags, using the user's country as a limit; identifying the interface style set corresponding to the first front-end interface based on the first front-end interface of the local hot software, and intelligently generating a second front-end interface based on the interface style set.

[0010] Optionally, step S5 further includes: replacing the original front-end interface of the learning software with a second front-end interface, thus transforming the learning software into local learning software carrying the second front-end interface; obtaining change comparison information of the learning software transformed into local learning software relative to the original learning software, and decomposing the change comparison information into multiple sub-change units, wherein each sub-change unit corresponds to a judgment task and is in a pending judgment state; changing the sub-change unit corresponding to the learning software when the judgment task receives an agreement instruction, and keeping the learning software unchanged when the judgment task receives a rejection instruction; after all multiple sub-change units have completed receiving the agreement / rejection instructions, all multiple sub-change units are in a completed judgment state, obtaining a planning scheme from the learning software and thus completing intelligent planning, wherein the back-end architecture of the planning scheme is the same as the back-end architecture of the learning software. In the above scheme, replacing the original front-end interface of the learning software with a second front-end interface is equivalent to keeping the software logic unchanged throughout the process. Further, the agreement / rejection instructions can be implemented by the user or by an intelligent agent. Furthermore, the acquisition of learning software is transformed into comparison information of changes between the local learning software and the learning software, that is, the differences or distinctions between the learning software and the local learning software. This can be identified by a large AI model, and the specific method is known to engineers in the art.

[0011] Optionally, embodiments of the present invention propose an intelligent planning system, which is used to implement the learning feedback-based intelligent planning method as described in any of the above embodiments. Furthermore, the intelligent planning system can significantly improve the work efficiency of the R&D team.

[0012] In the proposed solution, the system first collects feedback from users in different countries regarding the learning software over a specific time period. Next, it performs text-based semantic clustering analysis on this feedback to identify common themes and questions. Then, the system transforms these analysis results into standardized requirement items and integrates them into a standardized user requirement table. Afterward, the system filters these requirements, comparing them with historical requirements and calculating priorities to select one or two most critical sub-requirements from the standardized requirement table. Finally, the selected sub-requirements are analyzed to generate a specific planning scheme. During this process, the generated modification scheme is broken down into multiple small units, each requiring review and approval before implementation, ultimately forming a software planning scheme with an unchanged backend architecture and only a localized frontend interface.

[0013] In summary, software development projects in overseas markets face rapidly changing market demands. Software engineers need to focus their main efforts on these demands to gradually reduce their workload from tedious product development meetings and improve team efficiency. This is especially true for learning software, which spans multiple countries in overseas markets with significant differences in needs across these countries, requiring flexible solutions to the R&D team's daily challenges. Therefore, this application proposes an intelligent planning method and system based on learning feedback, which can significantly improve the work efficiency of R&D teams. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort. The realization of the purpose, functional features, and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings.

[0016] Figure 1 This is a schematic diagram of the first process of an intelligent planning method based on learning feedback provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of an intelligent planning method based on learning feedback provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the third process of an intelligent planning method based on learning feedback provided in an embodiment of the present invention. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] In this specification and the appended claims, there may be multiple ways of expressing the same technical feature or technical term, such as using a superordinate generalization, a subordinate limitation, or a synonym substitution. Those skilled in the art can clearly understand the substantially the same technical meaning referred to by different expressions based on their professional knowledge and in conjunction with the overall content of the specification and the drawings. The differences in different expressions are only reflected in the diversity of words and do not constitute a substantial modification or limitation to the technical solution, nor will they affect the certainty of the scope of protection of this patent claim or the full disclosure of the technical content of the specification.

[0019] This application provides an intelligent planning method based on learning feedback. The executing entity of the intelligent planning method based on learning feedback includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the intelligent planning method based on learning feedback can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0020] Reference Figures 1 to 3 The diagram shown illustrates a flowchart of an intelligent planning method based on learning feedback, according to an embodiment of the present invention. In this embodiment, an intelligent planning method based on learning feedback is used in an intelligent planning system. The method includes: S1, obtaining feedback collection results based on feedback information from learning software; S2, performing cluster analysis on the feedback collection results to obtain intelligent analysis results; S3, establishing a standardized user requirement table based on the intelligent analysis results; S4, selecting sub-requirement items from the standardized user requirement table; and S5, parsing the sub-requirement items to obtain a planning scheme.

[0021] In one embodiment, step S1 includes: S11, collecting learning software feedback information from users in multiple countries within a preset time interval, and obtaining feedback collection results.

[0022] In one embodiment, step S2 includes: S21, performing text semantic-based clustering analysis on the feedback collection results according to preset clustering rules to obtain intelligent analysis results.

[0023] In one embodiment, step S3 includes: S31, parsing the intelligent analysis results and extracting multiple overseas user demand keywords; S32, converting the multiple overseas user demand keywords into standard demand items according to preset mapping rules; and S33, aggregating the multiple standard demand items into a preset table structure template to generate a standardized user demand table. In the above scheme, step S32, converting multiple overseas user demand keywords into standard demand items according to preset mapping rules, achieves the job matching effect corresponding to the multiple overseas user demand keywords.

[0024] In one embodiment, step S5 includes: identifying the user's country and user tags corresponding to each sub-requirement item; obtaining the local trending software with the highest matching degree to the user tags, limited by the user's country; identifying the interface style set corresponding to the first front-end interface based on the first front-end interface of the local trending software; and intelligently generating a second front-end interface based on the interface style set. In the above scheme, identifying the user's country and user tags corresponding to each sub-requirement item can be based on user age group or user occupation attributes, etc. Further, the local trending software can be shopping software, chat software, video software, etc., and those skilled in the art can identify specific local trending software based on local conditions. Further, identifying the interface style set corresponding to the first front-end interface based on the first front-end interface of the local trending software and intelligently generating a second front-end interface based on the interface style set means migrating the local culture corresponding to the local trending software to the second front-end interface.

[0025] In one embodiment, step S5 further includes: replacing the original front-end interface of the learning software with a second front-end interface, thereby transforming the learning software into local learning software carrying the second front-end interface; obtaining change comparison information of the learning software transformed into local learning software relative to the learning software, and decomposing the change comparison information into multiple sub-change units, wherein each sub-change unit corresponds to a judgment task and is in a pending judgment state; changing the sub-change unit corresponding to the learning software when the judgment task receives an agreement instruction, and keeping the learning software unchanged when the judgment task receives a rejection instruction; after all multiple sub-change units have completed receiving the agreement / rejection instructions, all multiple sub-change units are in a completed judgment state, obtaining a planning scheme derived from the learning software and thus completing intelligent planning, wherein the back-end architecture of the planning scheme is the same as the back-end architecture of the learning software. In the above scheme, replacing the original front-end interface of the learning software with a second front-end interface is equivalent to keeping the software logic unchanged throughout the process. Furthermore, the agreement / rejection instructions can be implemented by the user or by an intelligent agent. Furthermore, the acquisition of learning software is transformed into comparison information of changes between the local learning software and the learning software, that is, the differences or distinctions between the learning software and the local learning software. This can be identified by a large AI model, and the specific method is known to engineers in the art.

[0026] In one embodiment, this invention proposes an intelligent planning system for implementing the learning feedback-based intelligent planning method as described in any of the above embodiments. Furthermore, the intelligent planning system can significantly improve the work efficiency of R&D teams.

[0027] This application proposes an intelligent planning method based on learning feedback. Its core objective is to address the low R&D efficiency in overseas software development projects caused by rapidly changing market demands and significant cross-border differences. This method, through a data-driven automated process, helps technical teams free themselves from tedious meetings and focus their energy on real market needs, thereby significantly improving team efficiency. It is particularly suitable for learning software that needs to quickly adapt to multi-national market environments. This method originates from the collection of extensive feedback intelligence. The system collects various feedback from users in multiple countries regarding learning software within a preset time period, forming preliminary feedback collection results. Next, in the intelligent analysis phase, the system performs in-depth semantic clustering analysis on these textual feedback results according to preset clustering rules, thereby identifying common themes and core issues in user feedback and obtaining intelligent analysis results. Based on this, the system extracts multiple overseas user demand keywords by parsing the intelligent analysis results and uses preset mapping rules to convert these keywords into standardized demand items, ultimately aggregating and generating a standardized user demand table with a clear structure and uniform format. To ensure efficient allocation of R&D resources, the system will filter out the most noteworthy sub-requirements from this standardized requirements list. This filtering process combines deduplication and priority assessment. First, it determines whether there is semantic overlap between new and historical requirements to remove redundancy. Then, it substitutes the remaining standard requirements into a preset priority formula for calculation and sorting, and finally selects at most two sub-requirements with the highest priority as the key development directions for the current cycle.

[0028] In summary, software development projects in overseas markets face rapidly changing market demands. Software engineers need to focus their main efforts on these demands to gradually reduce their workload from tedious product development meetings and improve team efficiency. This is especially true for learning software, which spans multiple countries in overseas markets with significant differences in needs across these countries, requiring flexible solutions to the R&D team's daily challenges. Therefore, this application proposes an intelligent planning method and system based on learning feedback, which can significantly improve the work efficiency of R&D teams.

[0029] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0030] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions, but such implementations should not be considered beyond the scope of this invention.

[0031] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is only a logical functional division, and there may be other division methods in actual implementation. For example, units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0032] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0033] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent planning method based on learning feedback, characterized in that, The method is used in an intelligent planning system, and the method includes: S1, Obtain feedback collection results based on the feedback information from the learning software; S2, perform cluster analysis on the feedback collection results to obtain intelligent analysis results; S3, based on intelligent analysis results, establishes a standardized user requirement table; S4, select sub-requirement items from the standardized user requirement table; S5 analyzes the sub-requirement items to obtain the planning scheme.

2. The intelligent planning method based on learning feedback according to claim 1, characterized in that, The steps in S1 include: S11 collects learning software feedback information from users in multiple countries within a preset time interval and obtains the feedback collection results.

3. The intelligent planning method based on learning feedback according to claim 2, characterized in that, The steps in S2 include: S21, perform text semantic-based clustering analysis on the feedback collection results according to preset clustering rules to obtain intelligent analysis results.

4. The intelligent planning method based on learning feedback according to claim 3, characterized in that, The steps in S3 include: S31 analyzes the intelligent analysis results and extracts multiple keywords related to overseas user needs; S32, according to the preset mapping rules, converts multiple overseas user demand keywords into standard demand items; S33 aggregates multiple standard requirement items into a preset table structure template to generate a standardized user requirement table.

5. The intelligent planning method based on learning feedback according to claim 4, characterized in that, The steps in S4 include: S41, determine whether there is semantic overlap between the standard requirement entries in the standardized user requirement table and the historical requirement entries in the historical user requirement table; S42, after deleting standard requirement items with semantic overlap, substitute each standard requirement item in the standardized user requirement table into the preset priority formula to obtain the priority ranking result. S43. Based on the priority sorting results, select at most two sub-requirement items with the highest priority.

6. The intelligent planning method based on learning feedback according to claim 5, characterized in that, The steps in S5 include: Identify the user's country and user tags corresponding to each sub-requirement item; Based on the user's country, retrieve the local trending apps that best match the user's tags; Based on the first front-end interface of the local hot software, identify the set of interface styles corresponding to the first front-end interface, and intelligently generate the second front-end interface according to the set of interface styles. The original front-end interface of the learning software is replaced with a second front-end interface, so that the learning software is transformed into a local learning software that carries the second front-end interface. The changes of the learning software to the local learning software are compared with the learning software. The comparison information of the changes is broken down into multiple sub-change units, each of which corresponds to a judgment task sheet and is in a state of pending judgment. When the task order receives an acceptance instruction, the corresponding sub-change unit of the learning software is changed; when the task order receives a rejection instruction, the learning software remains unchanged. After all the sub-change units have received the consent / negation instructions, all the sub-change units are in the judgment completion state, obtain the planning scheme from the learning software and then complete the intelligent planning. The backend architecture of the planning scheme is the same as the backend architecture of the learning software.

7. An intelligent planning system, characterized in that, The intelligent planning system is used to implement the intelligent planning method based on learning feedback as described in any one of claims 1 to 6.

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