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Big-data-based optimized decision arrangement guiding system

A big data and decision-making technology, applied in the field of teaching systems, can solve problems such as heavy workload, insufficient flexibility of output results, and insufficient ability to satisfy individualization

Inactive Publication Date: 2016-12-07
杭州中字软科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to solve the problems of huge workload, heavy mental work, unavoidable data errors and other problems brought about by traditional manual course arrangement, and at the same time solve the high computational complexity of existing automatic course arrangement (NP completely leads to The server is not available), the output is not ideal (approximate algorithm or other models), the output is not flexible enough (fine-tuning or local adjustment), and the ability to meet individual needs is insufficient, so that any school (educational institution or other), any The intersection of factors (space, time, resources, strong ambiguity), and any individual needs can get satisfactory output results

Method used

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  • Big-data-based optimized decision arrangement guiding system
  • Big-data-based optimized decision arrangement guiding system
  • Big-data-based optimized decision arrangement guiding system

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Embodiment Construction

[0011] Such as figure 1 , 2 As shown, the present invention based on big data optimization decision guiding system includes

[0012] A course selection information input module, which is used to input course selection information and initialize the course selection information, and generate first auxiliary decision-making data according to the course selection information;

[0013] A combination classification module, which is used to display the combination course selection information selected by all students, and it generates the second auxiliary decision-making data according to the combination course selection information;

[0014] The shift information input module is used to input the shift information and initialize the shift information, which generates the fifth auxiliary decision-making data according to the shift information;

[0015] A class combination module, which is used to display information about the course selection combination of students in all classes...

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Abstract

The invention discloses a big-data-based optimized decision arrangement guiding system, which comprises a course selecting information input module, a combination classifying module, a class arranging and forming information input module, and a basic course arranging module for generating a basic course arranging timetable according to a preset constraint condition. The basic course arranging timetable includes a period area, a date area, and a course arranging area. The course arranging area comprises a plurality of period units, each of which includes a plurality of optional class units. The number of the period units depends on the number of constraints. The number of optional class units is greater than or equal to the number of basic classes. The invention provides the big-data-based optimized decision arrangement guiding system, which is capable of reasonable arrangement of classes according to different levels, flexible configuration of class size, student composition and combination of students and courses, and excellent matching between the course arrangement and school resources.

Description

technical field [0001] The invention relates to a teaching system, in particular to a teaching system for arranging lessons. Background technique [0002] As early as the 1970s, the course scheduling problem was proved to be an NP-complete problem, that is, the calculation time of the algorithm increased exponentially. This assertion established the theoretical depth of the course scheduling problem. For NP-complete problems, there is currently no general algorithm that can solve them well in mathematics. At present, the main idea of ​​everyone's research on NP-complete problems is how to reduce its computational complexity. That is to use an approximate algorithm instead, and strive to simplify the time to solve the problem from exponential growth to polynomial growth. Combined with the problem of class scheduling and class scheduling, it is to establish a suitable realistic and simple model. Using this simple model can greatly reduce the complexity of the algorithm and f...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/20
CPCG06Q10/04G06Q50/205
Inventor 于学威
Owner 杭州中字软科技有限公司
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