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College student dormitory allocation method based on machine learning algorithm

A machine learning, college student technology, applied in the field of intersecting logical relationships, can solve the problem of not having a set of science, and achieve the effect of avoiding differences

Pending Publication Date: 2020-12-15
LIAONING UNIVERSITY OF PETROLEUM AND CHEMICAL TECHNOLOGY
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Problems solved by technology

[0002] For the dormitory allocation of college students, there has been no scientific method in China. At present, almost all universities in China use random allocation, or dormitory allocation in order of names

Method used

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  • College student dormitory allocation method based on machine learning algorithm
  • College student dormitory allocation method based on machine learning algorithm
  • College student dormitory allocation method based on machine learning algorithm

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Embodiment Construction

[0020] The following is attached with the manual Figure 1-2 And embodiment the present invention is described in further detail.

[0021] A method of dormitory allocation for college students based on machine learning algorithms. According to the academic performance data of freshmen and sophomores, a machine learning algorithm is used to establish a prediction model for the trend of college students' grades after the second semester of their junior year, which can predict the next semester of their junior year. The subsequent trend of performance changes.

[0022] The machine learning algorithm includes BP neural network, KNN, local linear regression, and support vector machine.

[0023] According to the classification of students' grades, establish and define the dormitory state, and calculate the expected value of the future change trend of each dormitory state based on the grade change trend prediction model, progress +1, regress -1, and the quantitative value of grade r...

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Abstract

The invention discloses a college student dormitory allocation method based on a machine learning algorithm. The invention belongs to the crossing field of social behavioristics, data science and system science. According to the method, a prediction model of a student score change trend is established mainly through historical score data and dormitory data of college students and through a classical algorithm based on machine learning, including a BP neural network, Logistic regression, local linear regression and a support vector machine, and a data model suitable for the actual situation ofeach college is screened out through model precision comparison; dormitory states are defined according to student score classification, and based on the score change trend prediction model, a conversion score under each dormitory state is calculated; constraint conditions are reasonably set according to actual conditions, the maximum conversion score serves as a target function, the number of dormitories in each state under the maximum target function value is solved through a CPLEX optimization solver, and optimal allocation of the dormitories is achieved.

Description

technical field [0001] The invention belongs to the technical field of intersecting logical relations of social behavior, data science, and system science, and specifically analyzes social behavior based on data science, and finally optimizes and realizes it through system science algorithms. Background technique [0002] For the dormitory allocation of college students, there has been no scientific method in China. At present, almost all universities in China use random allocation or dormitory allocation in order of names. In 2018, Nanjing University took the lead in adopting the method of dormitory allocation according to the interests and hobbies of college students. Although there are many shortcomings, it has already shown that colleges and universities have begun to pay attention to the dormitory life of college students. College students spend most of their time in the dormitory, far more than classroom time. The dormitory environment is very important for personal de...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/20G06N3/08G06N20/00G06N20/10
CPCG06Q10/04G06Q50/205G06N3/084G06N20/00G06N20/10
Inventor 曹宇程旭魏海平朱诗朦刘琳琳题晓颖张国玉程少帅祝金淼
Owner LIAONING UNIVERSITY OF PETROLEUM AND CHEMICAL TECHNOLOGY
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