期刊:
Mobile Information Systems,2022年2022 ISSN:1574-017X
作者机构:
[Song, Hua] Wuhan Polytech Univ, Sch Art & Design, Wuhan 430048, Hubei, Peoples R China.
摘要:
Visual target tracking technology has always been one of the hotspots in the field of computer vision. After analyzing the above two problems and introducing real-time tracking, this article makes corresponding improvements to the visual target tracking structure of the Internet of Things. Based on this point, this article finally brings together related theories such as the Internet of Things and supply chain, starting from practical problems, inspecting the current situation of the supply chain of Chinese art product design companies, and proposing the necessity of establishing a systemic risk indicator system to use in the product supply chain; this article uses the HHM method to identify the risk factors of the artwork in the Internet of Things environment and promotes a multiangle risk analysis tailored to its own characteristics in the product supply chain. According to controllable risks and uncontrollable risks, combined with the structural level of the Internet of Things system, risks are divided into detection risk layer, network layer (information layer), application layer risk, and other risks. This article combines the above-mentioned visual target tracking technology with the relationship between the Internet of Things supply chains, uses the G1 method and the entropy weight method to determine the risk indicators for the subject and purpose of the risk weight, and classifies the risk indicators to propose risk control measures.
作者机构:
[Li, Jing] Wuhan Polytech Univ, Sch Art & Design, Wuhan 430048, Hubei, Peoples R China.
通讯机构:
[Li, Jing] W;Wuhan Polytech Univ, Sch Art & Design, Wuhan 430048, Hubei, Peoples R China.
关键词:
Central Junggar area;T-R sequence. Sangonghe Formation;depression lake basin;shallow delta
摘要:
As an important part of the urban-rural fringe area, vegetable greenhouse cultivation not only plays an important role in the supply of urban fresh vegetables, but also promotes the development of the local economy to a large extent. However, when vegetable greenhouse cultivation is in the process of vegetable production, pesticide environmental pollution problem is very severe. With the development of society, people's environmental protection concepts have been continuously strengthened and the requirements for the ecological environment have been continuously improved. During the greenhouse planting period, in view of the pest problem of vegetables, how to select the types of pesticides and use the appropriate amount of pesticides has become a major problem. To this end, this paper studies and designs an intelligent spraying management system for vegetable greenhouses based on image recognition, which can help vegetable farmers automatically identify vegetable diseases and insect pests in a timely and accurate manner and control the pests through the intelligent spray system. Taking tomato pest identification and diagnosis as an example, the effective pest identification rate of the system reaches 89%. The intelligent vegetable planting box designed in this paper can increase the rationality of the use of pesticides, thereby reducing the pollution of the ecological environment.
作者机构:
[He, Fangqiuzi] Wuhan Polytech Univ, Sch Art & Design, Wuhan 430074, Peoples R China.;[Xu, Junfeng] Wuhan Maritime Commun Res Inst, Wuhan 430074, Peoples R China.;[Zhong, Jinglin] Univ Calgary, Dept Math, Calgary, AB T2N IV4, Canada.;[Chen, Guang] China Univ Geosci Wuhan, Sch Mech Engn & Elect Informat, Wuhan 430074, Peoples R China.;[Peng, Shixin] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构:
[Peng, Shixin] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
关键词:
power materials warehouse;wireless sensor network;topology control;Internet of Things;data collection
摘要:
In order to realize the intelligent management of a power materials warehouse, the Internet of Things based on wireless sensor networks (WSNs) is a promising effective solution. Considering the limited battery capacity of sensor nodes, the optimization of the topology control and the determination of the amount of collected data are critical for prolonging the survival time of WSNs and increasing the satisfaction of the warehouse supplier. Therefore, in this paper, an optimization problem on sensor association and acquisition data satisfaction is proposed, and the subproblem of the sensor association is modeled as the knapsack problem. To cope with it, the block coordinate descent method is used to obtain the suboptimal solution. A sensor association scheme based on the ant colony algorithm (ACO) is proposed, and the upper and lower bounds of this optimization problem are also obtained. After this, a cluster head selection algorithm is given to find the optimal cluster head. Finally, the experimental simulations show that the algorithms proposed in this paper can effectively improve the energy utilization of WSNs to ensure the intelligent management of a power materials warehouse.