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.
The Internet of Things (IOT) management platform is used to manage and transmit data from a variety of terminal devices in the power system. In terms of detecting abnormal data, the existing IOT management platform has a low data processing efficiency and a high error rate. In addition, the optimal selection and determination of the structural parameters of a convolutional neural network (CNN) have a substantial effect on its prediction performance. On this basis, the paper proposes a decision algorithm for locating anomalous data in an IOT integrated management platform using a CNN and a global optimization decision of key structural parameters of a CNN using an improved particle swarm optimization (APSO) algorithm. Initially, an index model is developed to identify whether the data obtained from the IOT management platform is abnormal. Second, the structure of the CNN‐based anomaly detection approach is investigated. Next, an improved particle swarm optimization approach is designed to optimize the structural parameters of the CNN, and an APSO‐CNN with higher performance for anomalous data localization is constructed. Using the Adam optimizer, the accuracy, feasibility, and efficiency of the established method were assessed. The results demonstrate that the developed APSO‐CNN‐based decision method for anomaly data localization offers significant advantages in terms of precision and execution speed, with potentially intriguing application potential.
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