Research On Edge Computing Task Scheduling Strategy Based On Attention Mechanism

In the context of the Industrial Internet,with the integration and digitization of manufacturing and production processes,the industrial environment is accompanied by more and more data.While data is generally of value,artificial intelligence and deep learning methods respectively provide huge optimization potentials for machine learning data development,such as improving efficiency,flexibility,and personalization of production processes.Through condition monitoring,anomaly detection,preventive maintenance or improvement,and data-driven process modeling,task status can be identified faster and the autonomy of industrial production models can be increased.This provides predictive maintenance of production equipment and improves the monitoring model of industrial processes.In the context of edge computing,due to the lack of bandwidth resources and the instability of the network itself,task scheduling based on the deep neural network model may have a higher delay,thereby affecting the user experience.Moreover,due to its own complexity and scale,the deep neural network model has certain requirements on the storage and computing capabilities of the device,and cannot be directly deployed on the device with insufficient resources.Therefore,how to meet the needs of users for low latency,high precision and high cost performance based on deep neural network model applications has become the core issue of task scheduling.In this paper,the SK model based on the attention mechanism is improved on the fault classification task,and a new model SK-ECA is proposed.This model not only retains the multi-convolution kernel feature information processing capability of the SK model,but also draws on the fast one of ECA.The characteristic of dimensional convolution improves the performance index of the original SK model in the fault classification task.The dual-pool feature fusion experiment based on the SK-ECA model has absorbed the ability of global average pooling to summarize global information,and also has the feature of global maximum pooling to highlight important features,further tapping the potential of the model and expanding into seven attention mechanisms model.At the same time,for fault classification tasks,a neural network model based on the attention mechanism is used to combine the convenience of edge devices with the computing power of edge nodes.A series of studies have been carried out on the problem of edge computing task scheduling,and an edge-oriented approach is proposed.The equipment's fault classification task scheduling strategy.This strategy takes into account the excellent computing performance of edge nodes and the convenience of mobile devices by coordinating edge computing nodes and devices,taking into account user needs and model advantages,and completing the dynamic deployment of the attention mechanism model on the edge and device ends,and The classification tasks are scheduled on demand,so as to take advantage of the deep attention mechanism model,improve the efficiency of task scheduling,and meet the needs of users.Experimental results show that the selected optimal scheduling model has 28% higher classification accuracy than the original SK model,an average recall rate 26% higher than the original SK model,F score 26% higher than the original SK model,and reasoning time is higher than the original SK model.The SK model is reduced by 36%.