Batch task scheduling in cloud manufacturing has dynamic, real-time characteristic and the presence of big data concurrency and exchange requirements, while traditional workshop tasks scheduling models and algorithms can't fit. In order to effectively save the time and reduce the cost of workshop production, an optimization model is put forward at first. And then improved cooperative particle swarm optimization algorithm with fast convergence and strong ability to avoid local optimization is used to solve the tasks scheduling problems. At last simulation experiment analysis results prove its effectiveness.

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Edge computing has strong real-time and big data interaction processing requirements. The long scheduling time and load imbalance among edge nodes and edge servers are the key problems of edge computing. The current cloud computing scheduling algorithms all have balance problems between algorithm complexity and performance, and cannot fundamentally solve the contradiction. It is a feasible method to use the deep learning model to train the scheduled data to achieve a direct prediction of the scheduling results. This paper mainly studies from two aspects, one is to obtain more accurate training data from the perspective of researching optimal scheduling algorithms, and the other is to improve the training speed from the perspective of improving the deep learning model. At first, an improved chaotic bat swarm algorithm is put forward. It introduces chaotic factors and second-order oscillation mechanisms to improve the speed of update and dynamic parameter mechanisms. Subsequently, the long short-term memory network deep learning model is trained with the historical data by the improved algorithm. The experimental results show that the improved learning model can achieve the purpose of quickly predicting the scheduling result.INDEX TERMS Edge computing, collaborative scheduling, task scheduling, second-order oscillation mechanisms, improved chaotic bat algorithm, LSTM.

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In the field of continuous hand‐gesture trajectory recognition, aiming at the problems of existing a lot of noise for handwriting trajectories, and difficult to segment multiple continuous hand gestures accurately, a long short‐term memory‐based dynamic probability (DP‐LSTM) method is proposed. Firstly, obtain the classification result for each sub‐period in the whole time period by using LSTM; secondly, cluster the classification results by non‐maximum suppression for trajectory algorithm to eliminate interference of invalid subsets; Finally, the end point of the valid trajectory is obtained according to the characteristics of the probability change, thus realising dynamic trajectory segmentation and recognition. In order to evaluate the performance of the DP‐LSTM, this method is evaluated by using an Arabic numerals gesture database. The experiments show that the DP‐LSTM has a high recognition rate for continuous hand gestures and can recognise its in real time.