A Method of Ideological and Political Teaching Resource Accuracy Scheduling and Control Based on MVC Framework

The currently used resource scheduling methods are sorted according to the priority of demand objects, resulting in large computing load, long response delay, low load balance and waste of computing resources on some nodes. Aiming at the above problems, this paper proposes a method of scheduling and controlling ideological and political teaching resources based on MVC framework. After building the MVC framework of Ideological and political teaching resource scheduling, the controller is used to collect the teaching resource scheduling information under the framework. After the establishment of the ideological and political resources scheduling model, the Q-learning algorithm combined with the deep neural network is used to solve the model to achieve the effective scheduling and control of resources. In the experiment of scheduling method, the method under MVC framework reduces the scheduling response time by about 36.85%, improves the load balance, and makes full use of scheduling computing resources.

I. Introduction

Ideological and political courses are a key course to implement the fundamental task of Building Morality and cultivating people, and its role can not be replaced. At this stage, in the context of the Internet +, a series of high and new technologies represented by the construction and sharing of Internet resources have developed by leaps and bounds, completely changing the traditional teaching methods of Ideological and political education [1]. Network teaching resources have the characteristics to autonomy, cooperation, interaction and inquiry. They are rich in content and short in time. Setting open-ended questions for students to discuss in groups is conducive to the cultivation of students' ability of autonomy, self-reliance and self-improvement. The demand subject of Ideological and political course resources is the extensive recipient of network information content. Network teaching resources use new technical means to present teaching content [2]. In recent years, the construction and development of network teaching resources is developing rapidly, but the blind development and construction has led to a large number of rich, but scattered, disordered, disordered and difficult to use teaching resources on the Internet. Literature [3] uses the improved multi-objective cuckoo search algorithm to schedule the resources in the network, which improves the global search ability of resources. Reference [4] proposed a scheduling method based on deep reinforcement learning to reduce the disorder in the process of resource scheduling and improve the communication efficiency in the UAV flight group, aiming at the resource scheduling problem in the UAV Communication Network under the large-scale MIMO technology. In reference [5]. considering the problems of large query load and low processing efficiency of nodes in complex networks, graph theory is used to establish the relationship between network information resources and determine the priority principle of resource planning. The particle swarm optimization algorithm is used to fuse the information processing process and schedule the cluster information to maximize the use of network resources. When the above existing resource scheduling methods are actually applied to teaching resource scheduling, they can not meet the requirements of Ideological and political teaching for resources, and have obvious defects.