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Network public opinion device scheduling method based on reinforcement learning

A technology of reinforcement learning and network public opinion, applied in biological neural network models, instruments, data processing applications, etc., can solve problems such as only decision-making systems that cannot handle scheduling distributed cluster structures well, and achieve good environmental adaptability and stability. Robustness, the effect of reducing the scheduling error rate

Active Publication Date: 2019-07-23
北京牡丹电子集团有限责任公司数字科技中心
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Problems solved by technology

However, current intelligent decision-making systems cannot handle and schedule distributed cluster structures and behavior-based resources well.

Method used

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  • Network public opinion device scheduling method based on reinforcement learning
  • Network public opinion device scheduling method based on reinforcement learning

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Embodiment Construction

[0035] In the following description, for purposes of illustration rather than limitation, specific details such as specific equipment structures, interfaces, and techniques are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0036] Such as figure 1 As shown, a scheduling method of network public opinion devices based on reinforcement learning, including:

[0037] S1: Build a deep reinforcement learning model;

[0038] S2: Judging the state of the Internet public opinion device according to the deep reinforcement learning model;

[0039] S3: Scheduling an Internet public opinion device to perform an operation behavi...

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Abstract

The invention provides a network public opinion device scheduling method based on reinforcement learning. The method comprises the steps of S1, establishing a deep reinforcement learning model; S2, scheduling a network public opinion device to execute a target operation behavior and recording the operation behavior; S3, judging the state of the network public opinion device according to the deep reinforcement learning model in the step S1 before scheduling in the step S2, and then executing operation by the network public opinion device; S4, storing the state record of the network public opinion device when the execution in the step S3 is completed, and calculating a reward score according to the state record; S5, calculating a loss value according to the reward score obtained in the stepS4 so as to update parameters of the deep reinforcement learning model; and S6, performing network public opinion prediction according to the deep reinforcement learning model updated in the S5.

Description

technical field [0001] The invention belongs to the field of automatic control, and in particular relates to a scheduling method of a network public opinion device based on reinforcement learning. Background technique [0002] In recent years, the Internet has developed rapidly. As the fourth media after TV, radio, and newspapers, it has become an important carrier to reflect public opinion in society. On the other hand, due to the openness and virtuality of the Internet, online public opinion has become more and more complex, and its impact on real life is increasing day by day. Some major online public opinion events often have a greater influence on society. For government departments, public media and large enterprises, how to strengthen timely responses to Internet public opinion has become a major difficulty in Internet public opinion management. The network public opinion device cluster system is to build a network public opinion device with a distributed cluster str...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/00G06N3/04
CPCG06Q10/04G06Q50/01G06N3/044
Inventor 费豪武开智
Owner 北京牡丹电子集团有限责任公司数字科技中心
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