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CN118627691B - Construction method and system of intelligent public training platform based on vocational education - Google Patents

Construction method and system of intelligent public training platform based on vocational education Download PDF

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CN118627691B
CN118627691B CN202411098729.7A CN202411098729A CN118627691B CN 118627691 B CN118627691 B CN 118627691B CN 202411098729 A CN202411098729 A CN 202411098729A CN 118627691 B CN118627691 B CN 118627691B
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information
training
workshop
personnel
public
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CN118627691A (en
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丁天霞
黄清宇
唐德敏
李瑾雯
于翔
周远非
熊庆
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Chengdu Vocational and Technical College of Industry
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Chengdu Vocational and Technical College of Industry
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Abstract

The invention provides a construction method and a construction system of an intelligent public training platform based on vocational education, and relates to the technical field of construction of the public training platform. And (3) carrying out historical classification information training by using a class analysis model, and realizing personnel allocation optimization of a training workshop. Meanwhile, the comprehensive scoring information of each practical training workshop is sent to a strategy optimization model public practical training platform to optimize workshop personnel through real-time task feedback information and comprehensive scoring information, personnel allocation information of the practical training workshops obtained through optimization is uploaded to the public practical training platform, and the public practical training platform is controlled to complete personnel allocation, so that the public practical training platform information of final allocation personnel is obtained.

Description

Construction method and system of intelligent public training platform based on vocational education
Technical Field
The invention relates to the technical field of practical training platform construction, in particular to a construction method and a construction system of an intelligent public practical training platform based on vocational education.
Background
With the continuous development of modern education concepts and the increasing maturity of practice technologies, the demands of the education and training field for practice teaching are increasing. However, the conventional practice teaching mode has the problems of uneven resource allocation, low efficiency, opaque information and the like, and brings certain challenges to education and training work. In order to solve the problems, an efficient and intelligent public training platform needs to be established, and effective allocation and management of resources such as teachers, students and training bases can be achieved, so that the quality and efficiency of practical teaching activities are improved.
The traditional practice teaching management system generally depends on manual scheduling and management, and has the problems of unbalanced resource allocation, unreasonable task allocation, untimely information transmission and the like. There is a need for a public training platform that can achieve reasonable matching of teachers, students and training bases, improve the utilization efficiency of teaching resources, and improve the overall quality of practical teaching activities.
Disclosure of Invention
The invention aims to provide a construction method and a system of an intelligent public training platform based on vocational education, so as to solve the problems. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
In a first aspect, the present application provides a method for constructing a public training platform, including:
Acquiring first information and second information, wherein the first information comprises teacher course information and training personnel identity information, the second information comprises workshop information of a training base, and the workshop information of the training base comprises workshop position information, workshop equipment information and maximum accommodation number information of a workshop;
The first information and the second information are sent to a cloud network to construct a public practical training platform, wherein the first information and the second information are sent to a category analysis model to be analyzed and matched, and the public practical training platform is constructed based on personnel allocation information of each practical training workshop obtained through analysis, and the public practical training platform can check the platform of the workshop of which each practical training workshop belongs in real time;
the method comprises the steps that a preset task to be processed in each training workshop is sent to a corresponding training workshop for processing, and feature extraction and comprehensive analysis are carried out on task allocation information and task completion information acquired in real time, so that comprehensive scoring information of each training workshop is obtained;
and sending the comprehensive scoring information of each practical training workshop to a strategy optimization model public practical training platform for workshop personnel optimization, uploading personnel allocation information of the practical training workshops obtained by optimization to the public practical training platform, and controlling the public practical training platform to complete personnel allocation to obtain the public practical training platform information of final allocation personnel.
In a second aspect, the present application further provides a system for constructing a public training platform, including:
the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring first information and second information, the first information comprises teacher course information and training personnel identity information, the second information comprises workshop information of a training base, and the workshop information of the training base comprises workshop position information, workshop equipment information and maximum accommodation number information of a workshop;
The analysis unit is used for sending the first information and the second information to a cloud network for constructing a public training platform, wherein the first information and the second information are sent to a category analysis model for analysis matching, and the public training platform is constructed based on personnel allocation information of each training workshop obtained through analysis, and the public training platform can be used for checking the platform of the workshop of which each training workshop belongs in real time;
The scoring unit is used for sending the tasks to be processed in each preset training workshop to the corresponding training workshop for processing, and extracting and comprehensively analyzing the characteristics of the task distribution information and the task completion information acquired in real time to obtain comprehensive scoring information of each training workshop;
The optimization unit is used for sending the comprehensive scoring information of each practical training workshop to a strategy optimization model public practical training platform to optimize workshop personnel, uploading personnel allocation information of the practical training workshops obtained through optimization to the public practical training platform, controlling the public practical training platform to complete personnel allocation, and obtaining the public practical training platform information of final allocation personnel.
The beneficial effects of the invention are as follows:
According to the method, first information and second information such as teacher course information, training personnel identity information and workshop information of a training base are obtained and sent to a cloud network for public training platform construction. Meanwhile, the collected information is matched and analyzed by using the category analysis model, a reasonable personnel allocation scheme can be built for each training workshop according to historical data and pattern recognition technology, and the utilization efficiency and teaching quality of training resources are further improved.
And secondly, the method utilizes the strategy optimization model to optimize workshop personnel, and can automatically redistribute and optimize the workshop personnel according to the comprehensive scoring information of each training workshop collected in real time. By applying optimization algorithms such as genetic algorithm, a better personnel allocation scheme can be quickly searched, so that the efficiency and quality of practice teaching activities are improved. In addition, through the information of the public practical training platform updated in real time, each practical training workshop can timely check and control the platform of the workshop to which the practical training workshop belongs, and instantaneity and flexibility of practical teaching activities are improved. The public training platform construction method provided by the invention can effectively solve the problems of uneven resource allocation, low efficiency and the like in the traditional practice teaching mode.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for constructing a public training platform according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a construction system of the public training platform according to an embodiment of the present invention.
The marks in the figure: 701. an acquisition unit; 702. an analysis unit; 703. a scoring unit; 704. an optimizing unit; 7021. a first processing subunit; 7022. a first identification subunit; 7023. a second identification subunit; 7024. a second processing subunit; 7025. a third processing subunit; 7026. a fourth processing subunit; 7027. a first update subunit; 7028. a second update subunit; 7031. a fifth processing subunit; 7032. a sixth processing subunit; 7033. a first scoring subunit; 7034. a second scoring subunit; 7041. a first optimization subunit; 7042. a second optimization subunit; 7043. and a third optimization subunit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1:
The embodiment provides a method for constructing a public training platform.
Referring to fig. 1, the method is shown to include steps S1, S2, S3 and S4.
Step S1, acquiring first information and second information, wherein the first information comprises teacher course information and training personnel identity information, the second information comprises workshop information of a training base, and the workshop information of the training base comprises workshop position information, workshop equipment information and maximum accommodation number information of a workshop;
It will be appreciated that the teacher lesson information in this step includes the personal information of the teacher and the lesson information they are responsible for. The teacher's personal information includes name, job number, contact, etc., while the course information may include the name, time, place, etc. of the course taught. The training personnel identity information includes information about students, teaching aids or other staff engaged in the training activities. Such information includes personal information of students (name, number, grade, etc.), personal information of teaching aid (name, work number, contact, etc.), and the like. The workshop information of the training base refers to the workshop information in the training base related to the training activities. Including location information of the workshops (e.g., building number, floor, room number), device information of the workshops (e.g., device type, number, status), and maximum number of persons accommodated in the workshops. Through obtaining teacher course information, training personnel identity information and workshop information of a training base, training resources can be well integrated and planned, and effective utilization of the resources is ensured.
Step S2, the first information and the second information are sent to a cloud network for constructing a public training platform, wherein the first information and the second information are sent to a category analysis model for analysis and matching, and the public training platform is constructed based on personnel allocation information of each training workshop obtained through analysis, and the public training platform can check a platform of the corresponding workshop in real time for each training workshop;
It can be understood that the cloud server is used for building a back-end system required by the public training platform, and the class analysis model is used for analyzing and matching information of teachers, students and training bases. The training platform is provided for relevant users in the form of web pages or mobile application programs, and the users can view and manage the information of the training workshops according to the authority and the requirements of the users. The information is sent to the cloud network to construct the practical training platform, so that teachers, students and practical training bases can access the platform through the network at any time and any place, real-time checking and management are realized, and the real-time performance and convenience of work are improved. In this step, step S2 includes step S21, step S22, step S23, step S24, and step S25.
Step S21, preset historical classification information is sent to a class analysis model as a training set to be trained, wherein the historical classification information is mapped with a preset label according to a natural language processing technology to obtain label information in the historical classification information, and the historical classification information comprises workshop information, teacher course information and training personnel identity information of a historic training base;
It can be understood that the preset labels comprise a teacher label, a student label and a training base label, and the teacher label comprises the professional field, teaching experience and the like; the student's labels may include profession, achievements, etc.; the tags of the training base may include locations, equipment conditions, etc. The step is to map the history classification information with a preset label by using a natural language processing technology. The text information is converted into a machine-understandable form and corresponds to a preset label, so that a training set is constructed.
Step S22, label information in the history classification information is sent to a convolution kernel for recognition training, and a trained convolution kernel is obtained;
It will be appreciated that this step builds and trains a convolution kernel model by using a deep learning framework. And (3) taking the label information in the history classification information as training data, and inputting the training data into a convolution kernel model for training. By means of a back propagation algorithm, the model will automatically adjust the parameters to extract the valid features in the data to the greatest extent. The trained convolution kernel has good feature extraction capability, can extract more representative and distinguishing features from the original data, and provides a better basis for subsequent classification and recognition tasks.
Step S23, the first information and the second information are sent to a trained convolution kernel to carry out label identification, and label information corresponding to the first information and the second information is obtained;
It will be appreciated that this step sends the prepared first and second information to the trained convolution kernel model for tag identification. The convolution kernel model has good feature extraction and classification capability through the training of the historical classification information. By feeding new information into the model, the model will be able to identify the tag information to which each piece of information corresponds. Based on the trained convolution kernel model, the automatic label identification of the first information and the second information can be realized without manual intervention. Thus, the working efficiency and the accuracy can be improved.
Step S24, the label information corresponding to the first information and the second information is sent to a K-means clustering model for clustering, and at least two clustering clusters are obtained;
it will be appreciated that this step performs clustering by sending the prepared tag information to the K-means cluster model. Wherein, by assigning data points into K clusters, the distance between each data point and the center point of the cluster to which it belongs is minimized. And further, the information can be effectively clustered, and the information with similar characteristics or attributes is classified into one type. This helps to understand the relationships and similarities between information, allowing for better subsequent data analysis and decision making.
And S25, matching the first information and the second information of the same cluster, wherein personnel allocation is performed on the first information and the second information of the same cluster according to a preset allocation formula, and personnel allocation information of each training workshop is obtained.
It can be understood that the step is performed by performing personnel allocation on the first information and the second information successfully matched according to a preset allocation formula, and performing allocation according to an allocation scheme with the highest allocation score, wherein the preset allocation formula is as follows:
wherein, Representing the allocation score of each person's allocation information,AndIs a weight parameter that is used to determine the weight of the object,The degree of association between students and workshops is calculated according to the prior art, and is not described in detail herein,The association degree between students and workshops is calculated according to the prior art, and is not described in detail herein.
It is understood that in this step, step S25 is followed by step S26, step S27 and step S28.
Step S26, uploading personnel allocation information of each training workshop into a database for storage, and obtaining a database containing the personnel allocation information;
It can be appreciated that the step can be used for centralized management of data by storing personnel allocation information in a database, so that data loss or confusion caused by scattered storage in different places is avoided.
Step S27, uploading the database containing the personnel allocation information to a module of a public practical training webpage, and updating the public practical training webpage based on the personnel allocation information to obtain practical training allocation webpages of all personnel;
It will be appreciated that this step is accomplished by uploading a database containing personnel allocation information into the module of the public training web page. The method is realized by a back-end server, the back-end server is connected with a database, and data in the database is provided for a front-end webpage through an interface. Next, the public training web page is updated based on the personnel allocation information in the database. This includes presenting the training distribution of teachers and students on the web page and ensuring the real-time and accuracy of the web page.
And step S28, inserting the practical training distribution webpage based on the preset webpage authority of each person to obtain a public practical training platform with the webpage authority, wherein the practical training distribution webpage is updated based on the update of the database.
It can be understood that this step allocates corresponding web page rights to each person according to preset rules. For example, a teacher may have authority to view all practical training workshops, and a student may only view the practical training workshops allocated by the teacher, wherein according to preset webpage authority, functions and information display of different users can be flexibly controlled, so that the safety and privacy of information are ensured. The practical training distribution webpage is updated based on the updating of the database, so that the information displayed on the webpage is always up to date, and the real-time performance and accuracy of the information are improved.
Step S3, sending a preset task to be processed in each training workshop to a corresponding training workshop for processing, and carrying out feature extraction and comprehensive analysis on task allocation information and task completion information acquired in real time to obtain comprehensive scoring information of each training workshop;
It can be understood that in the step, the task processing efficiency of the training workshop can be improved by distributing tasks in real time and monitoring the processing condition, and the problems can be found and solved in time. Through comprehensive scoring information, the working efficiency and the task completion condition of each practical training workshop can be objectively evaluated, and references are provided for improvement and optimization of practical training work. Based on feature extraction and comprehensive analysis, and based on scoring information, the working efficiency, task completion condition and the like of the training workshop can be reflected, references are provided for subsequent optimization and improvement, and step S3 comprises step S31, step S32, step S33 and step S34.
Step S31, sending a preset task to be processed in each practical training workshop to the corresponding practical training workshop, and acquiring personnel feedback information, wherein the personnel feedback information is real-time feedback information when a person processes the task, the real-time feedback information comprises the completion state, the completion time and the task processing problem of the task, and the task to be processed comprises task content, task responsible personnel and task deadline;
it can be understood that the system can monitor the processing progress of the task in real time by acquiring the real-time feedback information, discover the problems in time and take corresponding measures. The real-time feedback information can help task responsible personnel to know the progress situation of the task in time, so that the efficiency and accuracy of task processing are improved.
Step S32, the real-time feedback information is sent to a feature extraction module for feature extraction, wherein the task data information to be processed comprises the completion state, the completion time and each word or phrase of the task processing problem, and feature word recognition is carried out according to a natural language processing technology, so that feature information of the real-time feedback information is obtained;
It will be appreciated that this step will recognize and analyze each word or phrase in the real-time feedback information through natural language processing techniques and feature extraction modules. The method can help the system to understand the meaning of the feedback information and extract key features in the feedback information, so that data support is provided for subsequent comprehensive analysis and decision.
Step S33, converting the characteristic information of the real-time feedback information according to a preset scoring standard, calculating the real-time scores of all the personnel in the workshops, and performing square error calculation on the real-time scores of all the personnel in each workshop to obtain the real-time score of each workshop;
It can be understood that this step scores the feature information extracted from the real-time feedback information by a preset scoring criterion. These scoring criteria are dependent on the nature and requirements of the task, such as the score for completion status, the score for completion time, and the score for treatment problems. Through the scoring of the characteristic information and the calculation of the real-time scoring of the workshop, the performance of each person in task processing and the execution condition of the whole workshop can be objectively evaluated. The calculation of the variance can help analyze the differences in task processing among personnel in the plant to provide targeted training and improvement.
And step S34, sorting the real-time scores of all workshops, wherein the real-time scores are arranged in a sequence from small to large, and comprehensive score information of all practical training workshops is obtained.
It will be appreciated that this step can help the manager to learn about the differences in task processing performance between different workshops by scoring the ranking, thereby making corresponding adjustments and improvements, and better making data-driven decisions.
And S4, sending the comprehensive scoring information of each practical training workshop to a strategy optimization model public practical training platform for workshop personnel optimization, uploading personnel allocation information of the practical training workshops obtained through optimization to the public practical training platform, and controlling the public practical training platform to complete personnel allocation to obtain the public practical training platform information of final allocation personnel.
It can be understood that personnel can be more reasonably distributed through personnel distribution optimization of the strategy optimization model in the step, and the overall training efficiency and quality are improved. The optimized personnel allocation can reduce the waste of resources, such as avoiding overload of certain personnel and ensuring that each personnel has tasks to be executed, and in the step, the step S4 comprises the steps S41, S42 and S43.
S41, performing binary coding on personnel allocation information of all practical training workshops to obtain codes corresponding to the personnel allocation information of all practical training workshops;
It will be appreciated that this step is accomplished by converting the personnel assignment information for each of the training workshops to a binary code. For example, each person's task allocation, work schedule, etc. may be converted into a corresponding binary sequence. The binary code can facilitate processing and operation of a computer, particularly when personnel allocation optimization is performed in an optimization algorithm, a large amount of personnel allocation information can be efficiently processed, and complex personnel allocation information can be effectively compressed by the binary code, so that storage space and transmission bandwidth are saved.
Step S42, randomly combining all codes, performing fitness calculation on the obtained initial population, performing selection operation based on the calculated fitness value, and taking the codes with the fitness greater than a preset first threshold as father individuals of cross variation;
It will be appreciated that this step forms the initial population by randomly combining all the codes of the training plant. The combination of codes forms an initial state of a population, the initial state is used for a subsequent genetic algorithm optimization process, the initial population is formed by random combination codes, the diversity of the population can be kept, the situation that the population falls into a local optimal solution is avoided, and the fitness calculation and the selection operation can help to screen out individuals with higher fitness, so that the convergence rate of an optimization algorithm is accelerated. The calculation formula of the fitness is as follows:
Wherein P represents the fitness, AndAll of which represent the weight coefficient,Represents the comprehensive score of the training workshop,Indicating the completion of the task,And representing teacher course information.
And step S43, repeatedly crossing and mutating the parent individuals until all the child individuals are larger than a preset second threshold value, and taking the finally obtained child as a personnel allocation scheme of a final training workshop.
It will be appreciated that this step performs a crossover operation on the parent individuals to produce new offspring individuals. The crossover operation can adopt methods of single-point crossover, multi-point crossover and the like, and the chromosomes of the two parent individuals are crossed and interchanged to generate new offspring individuals. And carrying out mutation operation on crossed offspring individuals, and introducing new mutation genes. The mutation operation can increase the diversity of the population, is beneficial to avoiding sinking into a local optimal solution, increases the diversity of the population through the crossover and mutation operation, is beneficial to avoiding sinking into the local optimal solution, and improves the global searching capability of an optimization algorithm. Repeated crossing and mutation processing is beneficial to stabilizing the convergence of the algorithm, reduces the unstable phenomenon of the algorithm in the iterative process, and improves the robustness of the algorithm.
Example 2:
As shown in fig. 2, the present embodiment provides a system for constructing a public training platform, where the system includes an acquisition unit 701, an analysis unit 702, a scoring unit 703, and an optimization unit 704.
An obtaining unit 701, configured to obtain first information and second information, where the first information includes teacher course information and training personnel identity information, the second information includes workshop information of a training base, and the workshop information of the training base includes workshop position information, workshop equipment information and maximum number of people accommodated in a workshop;
The analysis unit 702 is configured to send the first information and the second information to a cloud network for constructing a public training platform, wherein the first information and the second information are sent to a category analysis model for analysis and matching, and construct the public training platform based on personnel allocation information of each training workshop obtained by analysis, and the public training platform is a platform capable of checking the affiliated workshop in real time for each training workshop;
The analysis unit 702 includes a first processing subunit 7021, a first recognition subunit 7022, a second recognition subunit 7023, a second processing subunit 7024, and a third processing subunit 7025.
The first processing subunit 7021 is configured to send preset historical classification information as a training set to a class analysis model for training, where the historical classification information is mapped with a preset label according to a natural language processing technology to obtain label information in the historical classification information, where the historical classification information includes workshop information of a historic training base, teacher course information, and training personnel identity information;
The first recognition subunit 7022 is configured to send the tag information in the history classification information to a convolution kernel for recognition training, so as to obtain a trained convolution kernel;
a second recognition subunit 7023, configured to send the first information and the second information to the trained convolution kernel to perform tag recognition, so as to obtain tag information corresponding to the first information and the second information;
The second processing subunit 7024 is configured to send the tag information corresponding to the first information and the second information to a K-means clustering model for clustering, so as to obtain at least two clusters;
The third processing subunit 7025 is configured to match the first information and the second information of the same cluster, where personnel allocation is performed on the first information and the second information of the same cluster according to a preset allocation formula, so as to obtain personnel allocation information of each training workshop.
The third processing subunit 7025 further includes a fourth processing subunit 7026, a first update subunit 7027, and a second update subunit 7028.
A fourth processing subunit 7026, configured to upload personnel allocation information of each training workshop into a database for storing, to obtain a database containing personnel allocation information;
A first updating subunit 7027, configured to upload the database containing personnel allocation information to a module of a public training webpage, and update the public training webpage based on the personnel allocation information to obtain a training allocation webpage of all personnel;
And a second updating subunit 7028, configured to insert the training distribution webpage based on the preset webpage authority of each person, so as to obtain a public training platform for distributing webpage authorities, where the training distribution webpage updates the webpage based on the update of the database.
The scoring unit 703 is configured to send a preset task to be processed in each training workshop to a corresponding training workshop for processing, and perform feature extraction and comprehensive analysis on task allocation information and task completion information acquired in real time to obtain comprehensive scoring information of each training workshop;
wherein the scoring unit 703 comprises a fifth processing subunit 7031, a sixth processing subunit 7032, a first scoring subunit 7033, and a second scoring subunit 7034.
A fifth processing subunit 7031, configured to send a preset task to be processed in each training workshop to a corresponding training workshop, and obtain personnel feedback information, where the personnel feedback information is real-time feedback information when a person processes the task, the real-time feedback information includes a completion status of the task, a completion time, and a task processing problem, and the task to be processed includes task content, a task responsible person, and a task deadline;
A sixth processing subunit 7032, configured to send the real-time feedback information to a feature extraction module for feature extraction, where, according to a natural language processing technology, feature word recognition is performed on each word or phrase that includes a task completion state, a completion time, and a task processing problem in the task data information to be processed, so as to obtain feature information of the real-time feedback information;
the first scoring subunit 7033 is configured to convert the feature information of the real-time feedback information according to a preset scoring standard, calculate real-time scores of all people in the workshops, and calculate a square error of the real-time scores of all people in each workshop as the real-time score of each workshop;
And a second scoring subunit 7034, configured to rank the real-time scores of all workshops, where the real-time scores are ranked in order from small to large, so as to obtain comprehensive score information of all training workshops.
And the optimizing unit 704 is configured to send the comprehensive score information of each training workshop to a public training platform of a strategy optimization model for workshop personnel optimization, upload personnel allocation information of the training workshops obtained by the optimization to the public training platform, and control the public training platform to complete personnel allocation, so as to obtain public training platform information of final allocation personnel.
Wherein the optimization unit 704 comprises a first optimization subunit 7041, a second optimization subunit 7042, and a third optimization subunit 7043.
The first optimizing subunit 7041 is configured to perform binary coding on personnel allocation information of all training workshops to obtain codes corresponding to the personnel allocation information of all training workshops;
the second optimization subunit 7042 is configured to randomly combine all the codes, perform fitness calculation on the obtained initial population, perform selection operation based on the fitness value obtained by calculation, and use the code with the fitness greater than a preset first threshold as a parent individual of the cross mutation;
And a third optimization subunit 7043, configured to repeatedly perform crossover and mutation processing on the parent individuals until all child individuals are greater than a preset second threshold, and use the finally obtained child as a personnel allocation scheme of the final training workshop.
It should be noted that, regarding the system in the above embodiment, the specific manner in which the respective modules perform the operations has been described in detail in the embodiment regarding the method, and will not be described in detail herein.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims (8)

1. The construction method of the intelligent public training platform based on the vocational education is characterized by comprising the following steps of:
Acquiring first information and second information, wherein the first information comprises teacher course information and training personnel identity information, the second information comprises workshop information of a training base, and the workshop information of the training base comprises workshop position information, workshop equipment information and maximum accommodation number information of a workshop;
The first information and the second information are sent to a cloud network to construct a public practical training platform, wherein the first information and the second information are sent to a category analysis model to be analyzed and matched, and the public practical training platform is constructed based on personnel allocation information of each practical training workshop obtained through analysis, and the public practical training platform can check the platform of the workshop of which each practical training workshop belongs in real time;
the method comprises the steps that a preset task to be processed in each training workshop is sent to a corresponding training workshop for processing, and feature extraction and comprehensive analysis are carried out on task allocation information and task completion information acquired in real time, so that comprehensive scoring information of each training workshop is obtained;
the comprehensive scoring information of each practical training workshop is sent to a strategy optimization model public practical training platform for workshop personnel optimization, personnel allocation information of the practical training workshops obtained through optimization is uploaded to the public practical training platform, and the public practical training platform is controlled to complete personnel allocation, so that public practical training platform information of final allocation personnel is obtained;
The method comprises the steps of sending the comprehensive scoring information of each training workshop to a strategy optimization model public training platform for workshop personnel redistribution to obtain optimized personnel distribution information of the training workshops, and comprises the following steps:
binary coding is carried out on the personnel allocation information of all the training workshops, and codes corresponding to the personnel allocation information of all the training workshops are obtained;
randomly combining all codes, performing fitness calculation on the obtained initial population, performing selection operation based on the calculated fitness value, and taking the codes with fitness larger than a preset first threshold as father individuals with cross variation;
And repeatedly crossing and mutating the parent individuals until all the child individuals are larger than a preset second threshold value, and taking the finally obtained child as a personnel allocation scheme of a final training workshop.
2. The method for constructing an intelligent public training platform based on vocational education according to claim 1, wherein the step of sending the first information and the second information to a category analysis model for analysis matching comprises the steps of:
Transmitting preset historical classification information as a training set to a class analysis model for training, wherein the historical classification information is mapped with a preset label according to a natural language processing technology to obtain label information in the historical classification information, and the historical classification information comprises workshop information of a historic training base, teacher course information and training personnel identity information;
The label information in the history classification information is sent to a convolution kernel for identification training, and a trained convolution kernel is obtained;
The first information and the second information are sent to a trained convolution kernel to carry out tag identification, and tag information corresponding to the first information and the second information is obtained;
the label information corresponding to the first information and the second information is sent to a K-means clustering model for clustering, and at least two clustering clusters are obtained;
and matching the first information and the second information of the same cluster, wherein the personnel allocation information of each training workshop is obtained by carrying out personnel allocation on the first information and the second information of the same cluster according to a preset allocation formula.
3. The method for constructing an intelligent public training platform based on vocational education according to claim 1, wherein the method for constructing the public training platform based on the personnel allocation information of each training workshop obtained by analysis, wherein the public training platform is a platform capable of checking the workshops of each training workshop in real time, comprises the following steps:
Uploading personnel allocation information of each training workshop to a database for storage to obtain a database containing personnel allocation information;
uploading the database containing the personnel allocation information to a module of a public practical training webpage, and updating the public practical training webpage based on the personnel allocation information to obtain practical training allocation webpages of all personnel;
And inserting the practical training distribution webpage based on the preset webpage authority of each person to obtain a public practical training platform for distributing the webpage authority, wherein the practical training distribution webpage is updated based on the update of the database.
4. The method for constructing an intelligent public training platform based on vocational education according to claim 1, wherein the method comprises the steps of sending the task to be processed in each preset training workshop to the corresponding training workshop for processing, and extracting and comprehensively analyzing characteristics of task allocation information and task completion information acquired in real time, and the method comprises the following steps:
Transmitting a preset task to be processed in each practical training workshop to a corresponding practical training workshop, and acquiring personnel feedback information, wherein the personnel feedback information is real-time feedback information when a person processes the task, the real-time feedback information comprises a task completion state, a task completion time and a task processing problem, and the task to be processed comprises task content, task responsible personnel and task deadline;
The real-time feedback information is sent to a feature extraction module for feature extraction, wherein the task data information to be processed comprises the completion state, the completion time and each word or phrase of the task processing problem, and feature word recognition is carried out according to a natural language processing technology to obtain feature information of the real-time feedback information;
converting the characteristic information of the real-time feedback information according to a preset scoring standard, calculating the real-time scores of all the personnel in the workshops, and performing square error calculation on the real-time scores of all the personnel in each workshop to obtain the real-time score of each workshop;
and sequencing the real-time scores of all workshops, wherein the real-time scores are arranged in the order from small to large, and comprehensive score information of all practical training workshops is obtained.
5. Construction system based on intelligent public real standard platform of vocational education, its characterized in that includes:
the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring first information and second information, the first information comprises teacher course information and training personnel identity information, the second information comprises workshop information of a training base, and the workshop information of the training base comprises workshop position information, workshop equipment information and maximum accommodation number information of a workshop;
The analysis unit is used for sending the first information and the second information to a cloud network for constructing a public training platform, wherein the first information and the second information are sent to a category analysis model for analysis matching, and the public training platform is constructed based on personnel allocation information of each training workshop obtained through analysis, and the public training platform can be used for checking the platform of the workshop of which each training workshop belongs in real time;
The scoring unit is used for sending the tasks to be processed in each preset training workshop to the corresponding training workshop for processing, and extracting and comprehensively analyzing the characteristics of the task distribution information and the task completion information acquired in real time to obtain comprehensive scoring information of each training workshop;
The optimization unit is used for sending the comprehensive scoring information of each practical training workshop to a strategy optimization model public practical training platform to optimize workshop personnel, uploading personnel allocation information of the practical training workshops obtained by optimization to the public practical training platform, and controlling the public practical training platform to complete personnel allocation to obtain the public practical training platform information of final allocation personnel;
wherein the optimizing unit includes:
The first optimizing subunit is used for binary coding the personnel allocation information of all the practical training workshops to obtain codes corresponding to the personnel allocation information of all the practical training workshops;
The second optimizing subunit is used for randomly combining all codes, carrying out fitness calculation on the obtained initial population, carrying out selection operation on the basis of the calculated fitness value, and taking the codes with the fitness larger than a preset first threshold value as father individuals of the cross variation;
And the third optimization subunit is used for repeatedly intersecting and mutating the parent individuals until all the child individuals are larger than a preset second threshold value, and taking the finally obtained child as a personnel allocation scheme of a final training workshop.
6. The system for constructing an intelligent public training platform based on vocational education according to claim 5, wherein the analysis unit comprises:
The first processing subunit is used for sending preset historical classification information to a class analysis model as a training set for training, wherein the historical classification information is mapped with a preset label according to a natural language processing technology to obtain label information in the historical classification information, and the historical classification information comprises workshop information of a historic training base, teacher course information and training personnel identity information;
the first recognition subunit is used for sending the label information in the history classification information into a convolution kernel for recognition training to obtain a trained convolution kernel;
the second identification subunit is used for sending the first information and the second information to the trained convolution kernel to carry out tag identification, so as to obtain tag information corresponding to the first information and the second information;
the second processing subunit is used for sending the label information corresponding to the first information and the second information to a K-means clustering model for clustering to obtain at least two clustering clusters;
And the third processing subunit is used for matching the first information and the second information of the same cluster, wherein the personnel allocation information of each training workshop is obtained by carrying out personnel allocation on the first information and the second information of the same cluster according to a preset allocation formula.
7. The system for constructing an intelligent public training platform based on vocational education according to claim 5, wherein the analysis unit further comprises:
the fourth processing subunit is used for uploading the personnel allocation information of each training workshop into a database for storage to obtain a database containing the personnel allocation information;
The first updating subunit is used for uploading the database containing the personnel allocation information into a module of a public practical training webpage, and updating the public practical training webpage based on the personnel allocation information to obtain practical training allocation webpages of all personnel;
And the second updating subunit is used for inserting the practical training allocation webpage based on the preset webpage authority of each person to obtain a public practical training platform for allocating the webpage authority, wherein the practical training allocation webpage is updated based on the updating of the database.
8. The system for constructing an intelligent public training platform based on vocational education according to claim 5, wherein the scoring unit comprises:
The fifth processing subunit is used for sending a preset task to be processed in each practical training workshop to the corresponding practical training workshop, and acquiring personnel feedback information, wherein the personnel feedback information is real-time feedback information when a person processes the task, the real-time feedback information comprises the completion state, the completion time and the task processing problem of the task, and the task to be processed comprises task content, task responsible personnel and task deadline;
A sixth processing subunit, configured to send the real-time feedback information to a feature extraction module for feature extraction, where, according to a natural language processing technology, feature word recognition is performed on each word or phrase in the task data information to be processed, where the task data information includes a task completion state, a task completion time and a task processing problem, so as to obtain feature information of the real-time feedback information;
the first scoring subunit is used for converting the characteristic information of the real-time feedback information according to a preset scoring standard, calculating the real-time scores of all the personnel in the workshops, and performing square error calculation on the real-time scores of all the personnel in each workshop to serve as the real-time score of each workshop;
And the second scoring subunit is used for sequencing the real-time scores of all workshops, wherein the real-time scores are arranged in the order from small to large, and comprehensive scoring information of all practical training workshops is obtained.
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