Development of IoT Cloud Platform Based Intelligent Raising System for Rice Seedlings

Feng, Xiang; Yan, Fang; Liu, Xiaoyu; Jiang, Qiyou

doi:10.1007/s11277-021-08967-2

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…On October 30, 2012, Ali Cloud computer room was disconnected due to power failure, and a large number of users could not access the services deployed on Ali Cloud. On May 27, 2015, due to a variety of reasons, the network services of Ali Cloud Hong Kong computer room were forced to stop, a large number of companies overseas services were forced to stop as well, resulting in huge economic losses [1].…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Energy Security Defense Monitoring Software Based on Cloud Computing

Shang

2022

Security and Communication Networks

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In order to solve the problem of outlier detection of integrated energy security defense monitoring software, an automatic detection algorithm of virtual machine power anomaly in a cloud computing environment is proposed. The method is implemented through three main steps: data preprocessing, pattern recognition, and prediction of virtual machine power anomaly detection model. It is found through experiments that with the increase of node number, the convergent iterations of the model are less and RMSE is lower, but the increase of node number of the hidden layer will lead to a longer model running time. When the number of nodes reaches 100, the test results of the validation set are significantly improved, and the loss function of the validation set is minimal when the number of nodes is less than 30 iterations. Finally, the hidden layer of the model consists of 100 LSTM units, followed by a dense output layer with 1 neuron, and 0.2 loss, retrospection, and foresight equal to 1. Adam optimizer was used to train LSTM and stop it in advance after 50 iteration steps. Its parameters remained default, with a learning rate of 0.001 and attenuation of 0.9. It can be seen that this model can well predict the virtual machine power consumption data and effectively solve the problem of outlier detection of integrated energy security defense monitoring software.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated Energy Security Defense Monitoring Software Based on Cloud Computing

Shang

2022

Security and Communication Networks

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“…(1) Uniformly Distributed Scheduling Strategy. Uniform distribution scheduling is to adopt the idea of uniform, distribute the arbitration time evenly according to the number of exclusive windows in the basic cycle of the system matrix, and add an equally distributed arbitration window after each exclusive window [21]. In addition, in order to prevent node identification confusion caused by the transmission of adjacent periodic messages, a time interval window t gap shall be introduced between adjacent periodic messages.…”

Section: 32mentioning

confidence: 99%

The Function of Remote Monitoring System of a Robot Inverter Based on PLC and Cloud Platform

Yu¹

2022

Journal of Sensors

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In order to improve the communication efficiency of the CAN bus application layer communication protocol of the inverter network monitoring system, this paper proposes a TTCAN scheduling optimization algorithm for the robot inverter remote monitoring system based on PLC and cloud platform. According to the analysis of the communication requirements of the frequency converter monitoring system, this paper designs the application layer protocol of the nodes in the system and establishes the system scheduling matrix. The time triggered can bus protocol (TTCAN) combines the event triggered mechanism with the time triggered mechanism. The hybrid scheduling strategy is used to optimize the system matrix of TTCAN; that is, the hybrid particle swarm optimization algorithm is used for periodic messages. For nonperiodic messages, uniform scheduling strategy and dynamic scheduling algorithm are adopted. The simulation is carried out by MATLAB tools. The simulation results show that the sum of partial minimum transmission time is 1928 and there are multiple optimal individuals through the hybrid particle swarm optimization algorithm. Compared with the traditional genetic algorithm and single particle swarm optimization algorithm, the hybrid algorithm is better than the traditional genetic algorithm and particle swarm optimization algorithm in terms of iteration times and average fitness value. In conclusion, the optimized TTCAN protocol improves the real-time performance, reliability, and bandwidth utilization of the communication network.

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“…Big data not only refers to massive data but also has a variety of structures, including all kinds of structured data and unstructured data (such as video les, audio les, and pictures). In addition, the massive data may also include missing data and incomplete data, which pose severe challenges to data processing [2].…”

Section: Introductionmentioning

confidence: 99%

Video Image Processing Method Based on Cloud Platform Massive Data and Virtual Reality

Ming

Xue

2022

Mathematical Problems in Engineering

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In order to solve the practical problem that the massive video data generated by monitoring equipment cannot be processed efficiently temporarily, this paper proposes a framework for face recognition of massive video data based on distributed environment. Combined with the application features of face recognition, this method designs a strategy for fast reading of massive video data and optimizes the feature data obtained by the cloud platform, so as to speed up the retrieval speed of face features. The results are as follows: the compression rate of the proposed compression method is higher than that of the traditional matrix triple and binary methods, which is increased by about 65%; the data optimization method in this paper greatly reduces the amount of feature data, which is 7.08 times less than that in the nonoptimization state. At the same time, the process of face recognition is reduced from 12.6 seconds to 2.73 seconds, and the time of feature decompression is only 0.75 seconds more than the original; the experiment shows that it takes 10180 seconds for the system to process 200 GB pictures with 9 computing nodes, and the total running time of the system is 4737 seconds longer than that of a single node, accounting for about 5.45% of the total time of a single node system. At the same time, the experimental data show that the system is 8.53 times faster than that of a single node with 9 computing nodes. It is proved that this framework has certain research significance in dealing with massive unstructured data. It not only provides theoretical reference value for the research of massive video processing but also makes a contribution to the actual industry.

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Development of IoT Cloud Platform Based Intelligent Raising System for Rice Seedlings

Cited by 13 publications

References 21 publications

Integrated Energy Security Defense Monitoring Software Based on Cloud Computing

Integrated Energy Security Defense Monitoring Software Based on Cloud Computing

The Function of Remote Monitoring System of a Robot Inverter Based on PLC and Cloud Platform

Video Image Processing Method Based on Cloud Platform Massive Data and Virtual Reality

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