A cloud platform system for production-teaching integration talent management

Abstract

This invention relates to the field of talent management, and more particularly to a cloud platform system for talent management in the integration of industry and education. The system includes a recording module for collecting research information from several schools and key requirements from several enterprises; an information processor for preprocessing the research information, generating corresponding processed data, and selecting content keywords from the processed data; a machine learning module for learning from the processed data based on a standard sampling rate and content keywords, and deriving several preferred and alternative results corresponding to the required keywords; and a recommendation module for displaying these preferred and alternative results. Through machine learning, the system improves the speed and accuracy of research information dissemination, and recommends corresponding preferred and alternative results to enterprises based on their needs, further integrating industry and education and enabling the dissemination of large volumes of research information.

Description

Technical Field

[0001] This invention relates to the field of talent management, and in particular to a cloud platform system for talent management that integrates industry and education. Background Technology

[0002] Industry-education integration refers to vocational schools actively establishing professional industries based on their established majors, closely integrating industry with teaching, supporting and promoting each other, and transforming schools into industrial operating entities that integrate talent cultivation, scientific research, and technological services, forming a seamless school-enterprise integration model. However, the existing industry-education integration is not perfect in terms of interconnection, communication, and information sharing. Its integration process is cumbersome, information cannot be shared, and it cannot meet the needs of deep industry-education integration, seriously affecting communication between schools and enterprises. Therefore, utilizing network cloud computing for rapid data processing and improving communication between enterprises and universities has become an important issue.

[0003] Chinese Patent Application Publication No.: CN113254833A. This invention applies to the field of data processing technology and provides a method and service system for information push based on industry-education integration. The method includes: receiving user information of users within user groups uploaded by various distributed data nodes, and generating group feature parameters corresponding to each distributed data node based on the user information; importing the object feature parameters and group feature parameters of the target object into various user recommendation matching algorithms to calculate the matching degree between the target object and the user group; identifying user groups with a matching degree greater than a preset recommendation threshold as target groups of the target object; selecting target user information associated with the target object from the target group, and pushing the target user information to the target object's device. This invention ensures the association between user information and the target object while also considering the group characteristics between user information, thus improving the accuracy of user information push and enabling large-scale user information push, thereby increasing the efficiency of information push.

[0004] Chinese Patent Publication No. CN114884701B discloses a new era vocational education industry-education integration governance system and method, which includes a campus terminal, a server terminal, and an enterprise terminal. This system and method is user-friendly, allowing for real-time monitoring and facilitating timely understanding of information changes, thus enhancing communication and integration between enterprises and schools. By viewing enterprise needs, future development directions, and job benefits posted by personnel, students can improve their understanding of society and plan their future career paths. It also helps schools optimize students' future education, enhances the campus's ability to cultivate talent, and prevents campus knowledge from lagging behind societal development. Through cloud computing, data is quickly filtered, facilitating students' independent learning of necessary knowledge and planning for future work directions, locations, and industries, while also preventing confusion among students entering the workforce.

[0005] However, the above methods have the following problems: the management system has low efficiency and low intelligence. Summary of the Invention

[0006] To address this, the present invention provides a cloud platform system for talent management in the integration of industry and education, in order to overcome the problems of low management efficiency and low intelligence in existing technology management systems.

[0007] To achieve the above objectives, the present invention provides a cloud platform system for talent management through industry-education integration, comprising: The recording module is used to collect research information from several schools and keyword requirements from several companies. An information processor, connected to the recording module, is used to preprocess the scientific research information, generate corresponding processed data, and select content keywords from the processed data. A machine learning module, connected to the information processor, is used to learn the content keywords based on a standard sampling rate and the content keywords, and to derive several preferred and alternative results corresponding to the required keywords; A recommendation module, which is connected to the machine learning module, is used to display several of the preferred results and the alternative results; The research information includes several keywords from research results and academic papers; The preprocessing involves fitting the scientific research information to generate several processed data sets with a sampling rate of the standard sampling rate, and generating corresponding content keywords based on the processed data. The fitting process involves extracting content keywords from the research findings and title keywords from the academic papers based on the company's needs. The standard sampling rate is the sampling rate that the machine learning module can recognize.

[0008] Furthermore, the recording module includes: The cloud platform is used to collect research information from several schools and keyword requirements from several enterprises. A storage device, connected to the cloud platform, is used to store the research information and the required keywords; A conversion program, connected to the recording module, is used to convert the scientific research information into corresponding processing information.

[0009] Furthermore, the information processor includes: A receiver is used to receive the processing information, the requirement keywords, and the secondary learning instructions; A basic processor, connected to the receiver, is used to perform the preprocessing on the processed information; The preprocessing involves the basic processor filtering out keywords from the processed information as content keywords.

[0010] Furthermore, the recommendation module includes: A transmitter is used to select the preferred result and generate a secondary learning instruction corresponding to the enterprise's required keywords, which is then returned to the base processor. An interactive interface, connected to the transmitter, is used to display the learning results of the machine learning module.

[0011] Furthermore, for a single user, the research information of several schools is entered into the recording module, transmitted to the conversion program, and processed information is generated. The receiver receives the processed information, and the basic processor preprocesses the processed information according to the required keywords.

[0012] Furthermore, the preprocessing involves the basic processor generating corresponding content keywords based on the processing information, fitting the processing information, extracting content keywords of the research results and title keywords of the academic papers according to enterprise needs, generating subject data with a sampling rate of the standard sampling rate, and standardizing the obtained subject data to generate the processed data. The standardization process involves dividing the subject data according to the standard sampling rate.

[0013] Furthermore, the machine learning module includes a corresponding requirements analysis model. When the machine learning module receives the processed data, the processed data is processed through the requirements analysis model to generate several corresponding preferred results. The demand analysis model is generated by training based on the processed data.

[0014] Furthermore, the interactive interface displays several of the preferred results. When any preferred result is selected, the transmitter generates a secondary learning instruction and returns it to the information processor.

[0015] Furthermore, upon receiving the secondary learning instruction, the basic processor performs secondary preprocessing on the processing information based on the optimization result and the scientific research information to generate corresponding secondary processing data, which is then transmitted to the machine learning module.

[0016] Furthermore, when the machine learning module receives the secondary processing data, the demand analysis model learns from the secondary processing data, generates several alternative results, and transmits them to the interactive interface corresponding to the recommendation module.

[0017] Compared with existing technologies, this invention utilizes a recording module to collect research information from several schools and demand keywords from several enterprises; an information processor to preprocess the research information, generate corresponding processed data, and select content keywords from the processed data; a machine learning module to learn from the processed data based on a standard sampling rate and content keywords, and derive several preferred and alternative results corresponding to the demand keywords; and a recommendation module to display these preferred and alternative results. Through machine learning, the speed and accuracy of research information dissemination are improved, and corresponding preferred and alternative results are recommended to enterprises based on their needs, further integrating industry and education and realizing the dissemination of large-scale research information.

[0018] Furthermore, by setting up a conversion program to transform scientific research information into corresponding processed information, the accuracy of machine learning is improved, and the speed of identifying scientific research information is further increased.

[0019] Furthermore, by setting up a receiver to receive and process information, demand keywords, and secondary learning instructions, the error rate of identifying scientific research information is reduced, thereby improving the accuracy of job recommendations between enterprises and students.

[0020] Furthermore, by setting up an interactive interface to display learning results, enterprises can easily select scientific research information, and a large amount of scientific research information can be pushed out, thus further integrating industry and education.

[0021] Furthermore, by preprocessing the research information, the accuracy of data processing in the demand analysis model was improved, which in turn improved the accuracy of identifying research information.

[0022] Furthermore, by setting up a demand analysis model, the system enables rapid processing of data, thereby accurately generating student recommendation results suitable for enterprises and improving the speed of scientific research information dissemination.

[0023] Furthermore, by setting secondary learning instructions, secondary learning can be carried out quickly, increasing the company's selection of talent and improving the speed of scientific research information dissemination. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the cloud platform system structure for talent management in industry-education integration according to the present invention; Figure 2 This is a schematic diagram of the recording module structure of the present invention; Figure 3 This is a schematic diagram of the information processor structure of the present invention; Figure 4 This is a schematic diagram of the recommended module structure of the present invention. Detailed Implementation

[0025] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0026] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0027] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0028] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0029] Please see Figure 1 As shown, it is a schematic diagram of the cloud platform system structure for talent management through industry-education integration of the present invention, including: The recording module is used to collect research information from several schools and keyword requirements from several companies. An information processor, connected to a recording module, is used to preprocess scientific research information, generate corresponding processed data, and select keywords from the processed data. The machine learning module, connected to the information processor, learns from the processed data based on the standard sampling rate and content keywords, and derives several preferred and alternative results corresponding to the required keywords. The recommendation module, which is connected to the machine learning module, is used to display several preferred results and alternative results; Among them, research information includes several keywords of research results and academic papers; Preprocessing involves fitting the scientific research information to generate several processed data points with a sampling rate of the standard sampling rate, and generating corresponding content keywords based on the processed data. The fitting method extracts content keywords from scientific research results and title keywords from academic papers based on the needs of enterprises. The standard sampling rate is the sampling rate that the machine learning module can recognize.

[0030] The system utilizes a setting and recording module to collect research information from several schools and key requirements from several enterprises. An information processor preprocesses the research information, generating corresponding processed data and selecting content keywords from it. A machine learning module learns from the processed data based on a standard sampling rate and content keywords, deriving several preferred and alternative results corresponding to the required keywords. A recommendation module displays these preferred and alternative results. Through machine learning, the system improves the speed and accuracy of research information delivery, and recommends corresponding preferred and alternative results to enterprises based on their needs, further integrating industry and education and enabling the delivery of large volumes of research information.

[0031] Please see Figure 2 As shown, it is a schematic diagram of the recording module structure of the present invention, including: The cloud platform is used to collect research information from several schools and keyword requirements from several enterprises. The storage device, connected to the cloud platform, is used to store scientific research information and required keywords; The conversion program, which is connected to the recording module, is used to convert scientific research information into corresponding processing information.

[0032] By setting up a conversion program, scientific research information is transformed into corresponding processed information, which improves the accuracy of machine learning and further increases the speed of identifying scientific research information.

[0033] Please see Figure 3 As shown, it is a schematic diagram of the information processor structure of the present invention, including: The receiver is used to receive and process information, requirement keywords, and secondary learning instructions; The basic processor, connected to the receiver, is used to preprocess the information to be processed; In this process, the preprocessing stage involves the basic processor filtering out keywords from the processed information to serve as content keywords.

[0034] In practice, different selections of content keywords result in different standard resolutions. Specifically, the resolution of title keywords < the resolution of abstract keywords < the resolution of text content keywords, and the sampling rate of preferred results < the sampling rate of alternative results.

[0035] By setting up a receiver to receive and process information, demand keywords, and secondary learning instructions, the error rate of identifying scientific research information is reduced, thereby improving the accuracy of job recommendations between enterprises and students.

[0036] Please see Figure 4 As shown, it is a schematic diagram of the recommended module structure of the present invention, including: The transmitter is used to select the best results and generate secondary learning instructions corresponding to the enterprise's required keywords, which are then returned to the base processor. An interactive interface, connected to the transmitter, is used to display the learning results of the machine learning module.

[0037] By setting up an interactive interface to display learning results, enterprises can easily select scientific research information, and a large amount of scientific research information can be pushed out, further integrating industry and education.

[0038] Specifically, for a single user, the research information of several schools is entered into the recording module, transmitted to the conversion program, and processed information is generated. The receiver receives the processed information, and the basic processor preprocesses the processed information according to the required keywords.

[0039] By preprocessing scientific research information, the accuracy of data processing in the demand analysis model was improved, further enhancing the accuracy of identifying scientific research information.

[0040] Specifically, preprocessing involves the basic processor generating corresponding content keywords based on the processed information, fitting the processed information, extracting content keywords from research results and title keywords from academic papers according to enterprise needs, and generating subject data with a sampling rate of the standard sampling rate. The obtained subject data is then standardized to generate processed data. The standardization process involves dividing the subject data according to a standard sampling rate.

[0041] In practice, the sampling rate is generally set between 1500 samples / minute and 1700 samples / minute; Preferably, setting the sampling rate to 1600 / minute yields better results for the analysis of scientific research information, and for the system described in this application, the corresponding generation effect is optimal.

[0042] In practice, content keywords can refer to the matching degree and matching speed of content keywords, which will not be elaborated here.

[0043] Specifically, the machine learning module includes a corresponding requirements analysis model. When the machine learning module receives data for processing, it uses the requirements analysis model to generate several optimal results. The demand analysis model is generated by training based on the processed data.

[0044] By setting up a demand analysis model, the system can quickly process data and accurately generate student recommendations suitable for enterprises, thereby improving the speed of scientific research information dissemination.

[0045] Specifically, the interactive interface displays several preferred results. When any preferred result is selected, the transmitter generates a secondary learning instruction and returns it to the information processor.

[0046] Specifically, when the receiver receives the secondary learning instruction, the basic processor performs secondary preprocessing on the processed information based on the optimization results and scientific research information, generates corresponding secondary processed data, and transmits it to the machine learning module.

[0047] By setting up secondary learning instructions, secondary learning can be carried out quickly, increasing the company's selection of talent and improving the speed of scientific research information dissemination.

[0048] Specifically, when the machine learning module receives secondary processing data, the demand analysis model learns from the secondary processing data, generates several alternative results, and transmits them to the interactive interface corresponding to the recommendation module. Example 1:

[0049] The system includes four universities: A (Science and Technology University), B (Political and Law University), C (Aerospace University), D (Maritime University), and F (Political and Law University). Company G's products are related to legal services. The system inputs company keywords and university research information into a recording module. The information processor extracts keywords as "science and technology," "political and law," "aerospace," "maritime," "political and law," and "law." The processed data is then input into a machine learning module. The optimal matching result for Company G is several research achievements and academic papers from "B (Political and Law University)." The transmitter generates a secondary learning instruction and returns it to the information processor. The basic processor performs secondary preprocessing on the processed information based on the optimal result and research information, and then inputs it into the machine learning module. The alternative matching result for Company G after secondary learning is several research achievements and academic papers from "F (Political and Law University)." Example 2:

[0050] The system includes A University of Science and Technology, B University of Political Science and Law, C University of Aeronautics and Astronautics, D Maritime University, and F Academy of Sciences. Company G's products are related to science and technology activities. The system inputs company keywords and university research information into a recording module. The information processor extracts keywords as "science and technology," "political and legal affairs," "aerospace," "maritime," "science," and "technology." The processed data is then input into a machine learning module. The optimal matching result for Company G is several research achievements and academic papers from "A University of Science and Technology." The transmitter generates a secondary learning instruction and returns it to the information processor. The basic processor performs secondary preprocessing on the processed information based on the optimal result and research information, and then inputs it into the machine learning module. The alternative matching result for Company G after secondary learning is several research achievements and academic papers from "F Academy of Sciences."

[0051] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cloud platform system for talent management in industry-education integration, characterized in that, include: The recording module is used to collect research information from several schools and keyword requirements from several companies. An information processor, connected to the recording module, is used to preprocess the scientific research information, generate corresponding processed data, and select content keywords from the processed data. A machine learning module, connected to the information processor, is used to learn the content keywords based on a standard sampling rate and the content keywords, and to derive several preferred and alternative results corresponding to the required keywords; A recommendation module, which is connected to the machine learning module, is used to display several of the preferred results and the alternative results; The research information includes several keywords from research results and academic papers; The preprocessing involves fitting the scientific research information to generate several processed data sets with a sampling rate of the standard sampling rate, and generating corresponding content keywords based on the processed data. The fitting process involves extracting content keywords from the research findings and title keywords from the academic papers based on the company's needs. The standard sampling rate is the sampling rate that the machine learning module can recognize.

2. The cloud platform system for talent management integrating industry and education according to claim 1, characterized in that, The recording module includes: The cloud platform is used to collect research information from several schools and keyword requirements from several enterprises. A storage device, connected to the cloud platform, is used to store the research information and the required keywords; A conversion program, connected to the recording module, is used to convert the scientific research information into corresponding processing information.

3. The cloud platform system for talent management integrating industry and education according to claim 1, characterized in that, The information processor includes: A receiver is used to receive the processing information, the requirement keywords, and the secondary learning instructions; A basic processor, connected to the receiver, is used to perform the preprocessing on the processed information; The preprocessing involves the basic processor filtering out keywords from the processed information as content keywords.

4. The cloud platform system for talent management integrating industry and education according to claim 1, characterized in that, The recommendation module includes: A transmitter is used to select the preferred result and generate a secondary learning instruction corresponding to the enterprise's required keywords, which is then returned to the base processor. An interactive interface, connected to the transmitter, is used to display the learning results of the machine learning module.

5. The cloud platform system for talent management integrating industry and education according to claim 1, characterized in that, For a single user, the research information of several schools is entered into the recording module, transmitted to the conversion program, and processed information is generated. The receiver receives the processed information, and the basic processor preprocesses the processed information according to the required keywords.

6. The cloud platform system for talent management integrating industry and education according to claim 5, characterized in that, The preprocessing involves the basic processor generating corresponding content keywords based on the processing information, fitting the processing information, extracting content keywords from the research results and title keywords from the academic papers according to enterprise needs, generating subject data with a sampling rate of the standard sampling rate, and standardizing the obtained subject data to generate the processed data. The standardization process involves dividing the subject data according to the standard sampling rate.

7. The cloud platform system for talent management integrating industry and education according to claim 6, characterized in that, The machine learning module includes a corresponding requirements analysis model. When the machine learning module receives the processed data, it uses the requirements analysis model to generate several corresponding preferred results. The demand analysis model is generated by training based on the processed data.

8. The cloud platform system for talent management integrating industry and education according to claim 7, characterized in that, The interactive interface displays several preferred results. When any preferred result is selected, the transmitter generates a secondary learning instruction and returns it to the information processor.

9. The cloud platform system for talent management integrating industry and education according to claim 8, characterized in that, The receiver receives the secondary learning instruction, and the basic processor performs secondary preprocessing on the processing information based on the optimization result and the scientific research information to generate corresponding secondary processing data, which is then transmitted to the machine learning module.

10. The cloud platform system for talent management integrating industry and education according to claim 9, characterized in that, When the machine learning module receives the secondary processed data, the demand analysis model learns from the secondary processed data, generates several alternative results, and transmits them to the interactive interface corresponding to the recommendation module.

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