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Research on the strategy of locating abnormal data in IOT management platform based on improved modified particle swarm optimization convolutional neural network algorithm
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  • Yuan Liu,
  • Qiang Li,
  • Di Cai,
  • Weizhi Lu
Yuan Liu
State Grid Henan Information & Telecommunication Company

Corresponding Author:[email protected]

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Qiang Li
State Grid Henan Information & Telecommunication Company
Author Profile
Di Cai
State Grid Henan Information & Telecommunication Company
Author Profile
Weizhi Lu
State Grid Henan Information & Telecommunication Company
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Abstract

The IOT management platform is used to handle and transmit data from many types of power system terminal devices. The current IOT management platform has a low data processing efficiency and a high mistake rate when it comes to finding anomalous data. Furthermore, the effective selection and optimum decision of the convolutional neural network’s structural parameters has a significant impact on prediction performance. Based on this, the paper proposes a decision algorithm for locating anomalous data in an IOT integrated management platform using a convolutional neural network (CNN) and a global optimization decision of key structural parameters of a convolutional neural network using an improved particle swarm optimization (APSO) algorithm. First, an index model is created to determine if the data retrieved from the IOT management platform is anomalous or not. Second, the structure of the convolutional neural network-based decision method for finding anomalous data is examined. Following that, an enhanced particle swarm optimization technique is developed to optimize the structural parameters of the convolutional neural network, and an APSO-CNN with improved performance for anomalous data localization is generated. Finally, the established algorithm’s correctness, feasibility, and efficacy were evaluated using the Adam optimizer. The results reveal that the established APSO-CNN-based decision algorithm for anomaly data localization offers considerable benefits in terms of accuracy and running time, with extremely interesting application potential.
12 Jan 2023Submitted to The Journal of Engineering
18 Jan 2023Submission Checks Completed
18 Jan 2023Assigned to Editor
31 Jan 2023Reviewer(s) Assigned
10 Feb 2023Review(s) Completed, Editorial Evaluation Pending
24 Feb 2023Editorial Decision: Revise Major
03 Mar 20231st Revision Received
04 Mar 2023Submission Checks Completed
04 Mar 2023Assigned to Editor
09 Mar 2023Review(s) Completed, Editorial Evaluation Pending
09 Mar 2023Editorial Decision: Revise Minor
17 Mar 20232nd Revision Received
18 Mar 2023Submission Checks Completed
18 Mar 2023Assigned to Editor
24 Mar 2023Review(s) Completed, Editorial Evaluation Pending
25 Mar 2023Editorial Decision: Revise Minor
28 Mar 20233rd Revision Received
29 Mar 2023Submission Checks Completed
29 Mar 2023Assigned to Editor
30 Mar 2023Review(s) Completed, Editorial Evaluation Pending
30 Mar 2023Editorial Decision: Accept