A labor education evaluation intelligence system

Abstract

The present invention discloses a labor education evaluation intelligent system, which relates to the technical field of labor education intelligent evaluation. The system includes an authority management unit, a labor education management unit, a score service unit and a data monitoring unit. The labor education management unit provided by the present invention obtains the labor education course data of the students, inputs the labor education evaluation data according to the labor education intelligent evaluation authority and the labor education course data of the students, and uses the natural language processing method to generate the labor education semester evaluation results from the labor education evaluation data, thereby avoiding the strong subjectivity of the traditional evaluation method and improving the fairness and accuracy of the evaluation results.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent evaluation of labor education, and particularly relates to a smart system for labor education evaluation. Background Art

[0002] With the continuous development of educational technology, the traditional labor education model can no longer meet the needs of modern education. In order to better cultivate students' labor concepts and practical abilities, labor education has gradually become an important part of modern education. At the same time, with the continuous development of artificial intelligence technology, its application in the labor education evaluation system has also become possible.

[0003] The existing labor education evaluation systems mainly evaluate students' labor performance through teachers. This evaluation method has the disadvantage of strong subjectivity, which will affect the fairness and accuracy of the evaluation results. In addition, the existing labor education evaluation systems also lack a comprehensive evaluation of students' labor concepts and practical abilities, and cannot fully stimulate students' labor enthusiasm. At the same time, the existing labor education evaluation systems also lack digital management of students' labor performance and cannot achieve dynamic tracking and management of students' labor performance. Therefore, the existing labor education evaluation systems can no longer meet the needs of modern education, and there is an urgent need for a new labor education evaluation system to solve these problems. Summary of the Invention

[0004] In view of the above deficiencies in the prior art, the present invention provides a smart system for labor education evaluation.

[0005] In order to achieve the above invention purpose, the technical solution adopted by the present invention is:

[0006] A smart system for labor education evaluation includes a permission management unit, a labor education management unit, an integral service unit, and a data monitoring unit;

[0007] The permission management unit is used to construct a permission management model according to a skew heap and determine the intelligent evaluation permission for labor education by using the permission management model;

[0008] The labor education management unit is used to set the labor education semester plan scores of students, obtain the labor education course data of students, input labor education evaluation data according to the intelligent evaluation permission for labor education and the labor education course data of students, and generate a labor education semester evaluation result by using natural language processing;

[0009] The integral service unit is used to generate the labor education integral of students according to the labor education course data of students, and generate a score compliance result of students according to the labor education semester plan scores of students and the labor education integral of students;

[0010] The data monitoring unit is used to visualize the semester evaluation results of labor education and the score compliance results of students for real-time viewing.

[0011] Furthermore, in the permission management unit, a permission management model is constructed based on a skew heap, expressed as:

[0012]

[0013] Where: is the constructor of the permission management model, is the node value of the current labor education intelligent evaluation permission, is the set of all node values of the labor education intelligent evaluation permission, is an empty set, is the function to generate the node value of the labor education intelligent evaluation permission, is the left child node value of the labor education intelligent evaluation permission, is the set of all left child node values of the labor education intelligent evaluation permission.

[0014] Furthermore, the labor education management unit includes a labor education course module, a labor education plan module, and a labor education evaluation module;

[0015] The labor education course module is used to obtain the labor education course data of students;

[0016] The labor education plan module is used to set the semester plan scores of students' labor education;

[0017] The labor education evaluation module is used to input labor education evaluation data according to the labor education intelligent evaluation permission and the labor education course data of students, and generate semester evaluation results of labor education by using the natural language processing method based on the semester plan scores of labor education and the labor education evaluation data.

[0018] Furthermore, in the labor education course module, to obtain the labor education course data of students, specifically:

[0019] Render the course information to the student side in a waterfall flow manner to obtain the registration data on the student side. The course information includes course time, course location, course instructor, course cover, and detailed description;

[0020] Set the registration conditions for the labor education course and screen the registration data on the student side based on the registration conditions of the labor education course;

[0021] Issue a check-in code to the students who pass the registration screening to obtain the check-in data on the student side, and obtain the labor education course data of students based on the check-in data on the student side.

[0022] Further, render the course information to the student side in a waterfall flow manner, specifically as follows:

[0023] Obtain the course browsing data of the student side, expressed as:

[0024]

[0025] Wherein: is the pixel difference of the scrolling wheel distance within the user time period, is the number of milliseconds elapsed after the page has finished loading, is the pixel value of the height where the scrolling wheel is located after milliseconds,

[0026] Take the derivative of the course browsing data of the student side to obtain the conditional data of course browsing, expressed as:

[0027]

[0028] Wherein: is the conditional data of course browsing;

[0029] When the conditional data of course browsing is equal to 0, set the number of milliseconds elapsed after the page has finished loading as , and take the left and right limits of :

[0030]

[0031]

[0032] Wherein: is the left limit value, is the right limit value, is infinitely approaching from the left side, is infinitely approaching from the right side;

[0033] When it is monitored that the left limit value is equal to the right limit value, request the course information from the backend based on throttling limit and add it to the waterfall flow queue;

[0034] Set the throttling function, expressed as:

[0035]

[0036] Wherein: is the throttling value, is the throttling process within the time period, The number of times the left limit value equals the right limit value within a time period;

[0037] Build a model for requesting course information from the backend, expressed as:

[0038]

[0039] Where: Is the number of pages of backend data, Is the number of data requests, Is the th course on the

[0040] Use the model for requesting course information from the backend to add the course information to the waterfall queue, and use a throttling function to control the waterfall queue to render the course information to the student side.

[0041] Furthermore, in the labor education plan module, the labor education semester plan scores of students include public welfare service labor scores, labor theory learning scores, daily life labor scores, production on-the-job labor scores, and social practice scores.

[0042] Furthermore, in the labor education evaluation module, use natural language processing methods to generate labor education semester evaluation results, specifically:

[0043] Perform Chinese word segmentation on the labor education evaluation data to obtain the segmentation path with the highest probability, expressed as:

[0044]

[0045] Where: Is the segmentation path with the highest conditional probability in the segmentation path, Is the path of the labor education evaluation data segmentation corresponding to when selecting the maximum value, Is the conditional probability of the segmentation path, Is all paths of the labor education evaluation data segmentation, Is the labor education evaluation data;

[0046] Calculate the conditional probability of the segmentation path according to Bayes' formula, expressed as:

[0047]

[0048] Where: Is the normalized labor education evaluation data, Is the probability model of the segmentation path, Is always 1;

[0049] Use the N-gram model to build the probability model of the segmentation path, expressed as:

[0050]

[0051] Wherein: is the total number of segmentation paths, is the number of the segmentation path, is at the given previous segmentation paths, the th segmentation path appears probability;

[0052] Based on the Viterbi algorithm, calculate the optimal part-of-speech tag sequence and its probability at each segmentation position, so as to perform part-of-speech tagging on the evaluation words in the segmentation path, expressed as:

[0053]

[0054] Wherein: is the part-of-speech tag corresponding to the evaluation word, is the initial probability vector of the part-of-speech tag, is the part-of-speech tag as the initial probability of the first word of the sentence, is the number of part-of-speech tags, is the transition probability matrix between part-of-speech tags, is from the part-of-speech tag transition to the part-of-speech tag probability, is the part-of-speech tag to the emission probability matrix of the word, is the segmentation path with the largest conditional probability in the segmentation path in the th word;

[0055] Based on the part-of-speech tagging, perform standardization and feature extraction, and use the HMM model in natural language processing to generate text, so as to generate the evaluation results of the labor education semester.

[0056] The present invention has the following beneficial effects:

[0057] (1) The labor education management unit provided by the present invention inputs labor education evaluation data by obtaining the labor education course data of students and according to the labor education intelligent evaluation authority and the labor education course data of students, and uses natural language processing to generate the evaluation results of the labor education semester from the labor education evaluation data, avoiding the disadvantages of strong subjectivity in the traditional evaluation method and improving the fairness and accuracy of the evaluation results;

[0058] (2) In the labor education plan module of the present invention, the semester plan scores of students' labor education are set as public welfare service labor scores, labor theory learning scores, daily life labor scores, production internship labor scores, and social practice scores, and then the compliance status of each score is obtained, which can comprehensively focus on the comprehensive evaluation of students' public welfare service labor, labor theory learning, daily life labor, production internship labor, and social practice, and can fully stimulate students' labor enthusiasm and improve the quality and effect of labor education;

[0059] (3) By setting up a data monitoring unit, the present invention visualizes the labor education semester evaluation results and the students' score compliance results for real-time viewing, realizes the dynamic tracking and management of students' labor performance, facilitates teachers and education administrators to understand and master students' labor performance, and promptly discovers problems and takes measures;

[0060] (4) The labor education evaluation intelligent system of the present invention uses natural language processing to generate labor education semester evaluation results from labor education evaluation data, realizes the intelligence of the labor education evaluation process, improves the evaluation efficiency, and reduces the work burden of teachers and education administrators. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic structural diagram of a labor education evaluation intelligent system. DETAILED DESCRIPTION OF THE INVENTION

[0062] The following describes the specific implementation manners of the present invention to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation manners. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are within the scope of protection.

[0063] As Figure 1 shown, a labor education evaluation intelligent system includes a permission management unit, a labor education management unit, an integral service unit, and a data monitoring unit.

[0064] In an optional embodiment of the present invention, the permission management unit is used to construct a permission management model according to a single left-leaning tree and determine the labor education intelligent evaluation permissions using the permission management model.

[0065] In the permission management unit of the present invention, the permission management model is constructed according to a single left-leaning tree, which is expressed as:

[0066]

[0067] Wherein: is the constructor function of the permission management model, is the node value of the current intelligent evaluation permission for labor education, is the set of all node values of the intelligent evaluation permission for labor education, is an empty set, is a function for generating the node value of the intelligent evaluation permission for labor education, is the left child node value of the intelligent evaluation permission for labor education, is the set of all left child node values of the intelligent evaluation permission for labor education.

[0068] In an alternative embodiment of the present invention, the labor education management unit is used to set the semester plan score of the student's labor education, obtain the labor education course data of the student, input the labor education evaluation data according to the intelligent evaluation permission of labor education and the labor education course data of the student, and generate the semester evaluation result of labor education by using the natural language processing method.

[0069] The labor education management unit includes a labor education course module, a labor education plan module, and a labor education evaluation module.

[0070] The labor education course module is used to obtain the labor education course data of the student. In the labor education course module of the present invention, the labor education course data of the student is obtained as follows:

[0071] Render the course information to the student side in a waterfall flow manner to obtain the registration data of the student side. The course information includes course time, course location, course leader, course cover, and detailed description.

[0072] In the present invention, the course information is rendered to the student side in a waterfall flow manner as follows:

[0073] Obtain the course browsing data of the student side, expressed as:

[0074]

[0075] where: is the pixel difference of the scrolling pulley distance within the user time period, is the number of milliseconds elapsed after the page is loaded, is the pixel value of the height where the pulley is located after milliseconds,

[0076] Take the derivative of the course browsing data of the student side to obtain the conditional data of the course browsing, expressed as:

[0077]

[0078] where: is the conditional data of the course browsing.

[0079] When the conditional data for course browsing is equal to 0, let the number of milliseconds elapsed after the page finishes loading at this time be , for Take the left and right limits:

[0080]

[0081]

[0082] Among them: is the left limit value, is the right limit value, is infinitely approaching , is infinitely approaching .

[0083] When it is monitored that the left limit value is equal to the right limit value, request course information from the backend based on throttling limits and add it to the waterfall flow queue.

[0084] Set the throttling function, expressed as:

[0085]

[0086] Among them: is the throttling value, is the throttling process within the is the number of times the left limit value is equal to the right limit value within the

[0087] Build a model for requesting course information from the backend, expressed as:

[0088]

[0089] Among them: is the number of pages of backend data, is the number of data requests, is the th course on the

[0090] Use the model for requesting course information from the backend to add course information to the waterfall flow queue, and use the throttling function to control the waterfall flow queue to render the course information to the student side.

[0091] Set the registration conditions for labor education courses, and screen the registration data on the student side based on the registration conditions for labor education courses.

[0092] Release a check-in code to the students who have passed the registration screening to obtain the check-in data on the student side, and obtain the labor education course data of the students based on the check-in data on the student side.

[0093] The labor education plan module is used to set the labor education semester plan scores of students. In the labor education plan module of the present invention, the labor education semester plan scores of students include public welfare service labor scores, labor theory learning scores, daily life labor scores, production internship labor scores and social practice scores.

[0094] The labor education evaluation module is used to input labor education evaluation data according to the labor education intelligent evaluation authority and the labor education course data of students, and generate a labor education semester evaluation result by using the natural language processing method based on the labor education semester plan scores and the labor education evaluation data.

[0095] In the labor education evaluation module of the present invention, the labor education semester evaluation result is generated by using the natural language processing method, specifically:

[0096] Perform Chinese word segmentation on the labor education evaluation data to obtain the segmentation path with the highest probability, expressed as:

[0097]

[0098] Where: Is the segmentation path with the highest conditional probability in the segmentation path, Is the path of the labor education evaluation data segmentation corresponding to when selecting the maximum value, Is the conditional probability of the segmentation path, Is all the paths of the labor education evaluation data segmentation, Is the labor education evaluation data.

[0099] Calculate the conditional probability of the segmentation path according to the Bayesian formula, expressed as:

[0100]

[0101] Where: Is the normalized labor education evaluation data, Is the probability model of the segmentation path, Is always 1.

[0102] Use the N-gram model to construct the probability model of the segmentation path, expressed as:

[0103]

[0104] Where: Is the total number of segmentation paths, Is the number of the segmentation path, Is at the given previous The probability of the th segmentation path appearing under a segmentation path.

[0105] Based on the Viterbi algorithm, calculate the optimal part-of-speech tag sequence and its probability at each segmentation position to perform part-of-speech tagging on the evaluation words in the segmentation path, expressed as:

[0106]

[0107] Where: is the part-of-speech tag corresponding to the evaluation word, is the initial probability vector of the part-of-speech tag, is the part-of-speech tag as the initial probability of the first word in the sentence, is the number of part-of-speech tags, is the transition probability matrix between part-of-speech tags, is from the part-of-speech tag to the part-of-speech tag probability, is the part-of-speech tag to the emission probability matrix of the word, is the segmentation path with the maximum conditional probability in the segmentation path in the th word.

[0108] Based on part-of-speech tagging, perform standardization and feature extraction, and use the HMM (Hidden Markov Model) model in natural language processing to generate text to generate the evaluation results of the labor education semester.

[0109] In an optional embodiment of the present invention, the integral service unit is used to generate the labor education integral of the student according to the labor education course data of the student, and generate the score compliance result of the student according to the planned score of the student's labor education semester and the labor education integral of the student.

[0110] In an optional embodiment of the present invention, the data monitoring unit is used to visualize the evaluation results of the labor education semester and the score compliance results of the students for real-time viewing.

[0111] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0112] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction means that implements the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0114] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

[0115] Those of ordinary skill in the art will realize that the embodiments described herein are for helping readers understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various specific deformations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.

Claims

1. A labor education evaluation intelligent system, characterized by: It includes the authority management unit, labor education management unit, points service unit and data monitoring unit; The authority management unit is used to construct an authority management model based on a one-way left-biased tree, and use the authority management model to determine the authority of intelligent evaluation of labor education; The labor education management unit is used to set the labor education semester plan score of the students, obtain the labor education course data of the students, input the labor education evaluation data according to the labor education intelligent evaluation authority and the labor education course data of the students, and use the natural language processing method to generate the labor education semester evaluation results from the labor education evaluation data; The labor education management unit includes the labor education course module, the labor education plan module and the labor education evaluation module; The labor education course module is used to obtain students' labor education course data; In the labor education course module, the labor education course data of students is obtained, specifically: the course information is rendered to the student side in a waterfall flow to obtain the student side registration data, the course information includes the course time, course location, course person in charge, course cover and detailed description; the registration conditions for the labor education course are set, and the student side registration data is screened based on the registration conditions for the labor education course; the sign-in code is issued to the students who pass the registration screening to obtain the student side sign-in data, and the student's labor education course data is obtained based on the student side sign-in data; Render the course information to the student end in a waterfall flow, specifically: Get the course browsing data of the student side, expressed as: in: is the pixel difference of the distance the user scrolls the wheel during the time period. The number of milliseconds after the page is loaded. for The height pixel value of the pulley after milliseconds, The initial height pixel value of the scroll wheel; The course browsing data on the student side is derived to obtain the conditional data for course browsing, which is expressed as: in: Conditional data for course browsing; When the conditional data of course browsing is equal to 0, let the number of milliseconds after the page is loaded be ,right Take the left and right limits: in: is the left limit value, is the right limit value, for Approaching from the left , for Approaching from the right ; When the left limit value is equal to the right limit value, the course information is requested from the backend based on the throttling limit and added to the waterfall queue; Set the throttling function, expressed as: in: is the throttling value, for Throttling within a time period, for The number of times the left limit value is equal to the right limit value within the time period; Build a model to request course information from the backend, represented as: in: is the number of backend data pages, is the number of data requests, For the Page No. courses; Use the model that requests course information from the backend to add the course information to the waterfall queue, and use the throttling function to control the waterfall queue to render the course information to the student end; The labor education plan module is used to set the labor education semester plan score for students; In the labor education plan module, students' labor education semester plan scores include public service labor scores, labor theory learning scores, daily life labor scores, production post labor scores, and social practice scores; The labor education evaluation module is used to input labor education evaluation data according to the labor education intelligent evaluation authority and the labor education course data of the students, and generate the labor education semester evaluation results by using the natural language processing method according to the labor education semester plan score and the labor education evaluation data; The points service unit is used to generate the labor education points of students according to their labor education course data, and to generate the score achievement results of students according to their labor education semester plan scores and their labor education points; The data monitoring unit is used to visualize the evaluation results of the labor education semester and the students’ score achievement results for real-time viewing.

2. The labor education evaluation intelligent system according to claim 1 is characterized in that: In the permission management unit, the permission management model is constructed according to the one-way left-biased tree, which is expressed as: in: It is the constructor of the permission management model. is the node value of the current labor education intelligent evaluation authority, is the collection of all labor education intelligent evaluation authority node values, is an empty set, It is a function for generating the value of the authority node of labor education intelligent evaluation. is the left child node value of the labor education intelligent evaluation authority. It is the collection of all left child node values ​​of labor education intelligent evaluation authority.

3. The labor education evaluation intelligent system according to claim 1 is characterized in that: In the labor education evaluation module, the natural language processing method is used to generate the evaluation results of the labor education semester, specifically: Perform Chinese word segmentation on the labor education evaluation data to obtain the segmentation path with the highest probability, which is expressed as: in: is the segmentation path with the largest conditional probability among the segmentation paths, The path for segmenting the labor education evaluation data corresponding to selecting the maximum value , is the conditional probability of the segmentation path, For all the paths of segmenting the labor education evaluation data, To provide evaluation data for labor education; The conditional probability of the segmentation path is calculated according to the Bayesian formula, which is expressed as: in: is the normalized labor education evaluation data, is the probability model of the segmentation path, Always 1; The probability model of the segmentation path is constructed using the N-gram model, which is expressed as: in: is the total number of segmentation paths, is the number of the segmentation path, For before a given The first segmentation path The probability of a segmentation path appearing; Based on the Viterbi algorithm, the optimal part-of-speech tag sequence and its probability at each segmentation position are calculated to perform part-of-speech tagging on the evaluation words in the segmentation path, which is expressed as: in: To evaluate the part-of-speech tag corresponding to the word, is the initial probability vector of the part-of-speech tag, Part-of-speech tags As the initial probability of the first word in the sentence, is the number of part-of-speech tags, is the transition probability matrix between part-of-speech tags, From the part-of-speech tag Move to part-of-speech tags The probability of Part-of-speech tags To the emission probability matrix of words, The segmentation path with the largest conditional probability among the segmentation paths Middle words; Standardization and feature extraction are performed based on part-of-speech tagging, and text generation is performed using the HMM model in natural language processing to generate the evaluation results of the labor education semester.

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