Design of power line safety operation and maintenance monitoring system based on cloud computing

2021 ◽  
Vol 19 (3) ◽  
pp. 242
Author(s):  
Haiying Wang
Keyword(s):  
Cloud Computing ◽  
Monitoring System ◽  
Power Line ◽  
Operation And Maintenance ◽  
Safety Operation

Development of a Comprehensive Monitoring System Based on Cloud Computing and XMPP

2016 ◽  
Vol 25 ◽  
pp. 98-107
Author(s):  
Yun Sen Wang
Keyword(s):  
Cloud Computing ◽  
Monitoring System ◽  
Safety Monitoring ◽  
System Structure ◽  
Network System ◽  
Actual Situation ◽  
Virtual Sensor ◽  
System A ◽  
Operation And Maintenance ◽  
Definition Of

According to the actual situation of the coexistence of multi-sensor and multi-protocol in Mine Monitoring System, a comprehensive monitoring system of network based on XMPP protocol was carried out in this paper. The characteristics of the network system structure and the scheme of virtual sensor gateway were described in detail. Moreover, a session protocol based on the XMPP protocol was proposed and the definition of API was given. Functional structure and software structure of the whole system were designed based on Zend Framework. By using the virtual server cloud technology of VMware, as well as the centralized storage method of SAN, the background monitoring server group was built. After testing, the system greatly met the requirements of safety monitoring, which also possessed the advantage of easy implement, simple operation and maintenance, and great expansibility.

A Remote Monitoring System Based on Measuring Instrument Cloud

2014 ◽  
Vol 1014 ◽  
pp. 525-529 ◽  
Author(s):  
Jun Zhang ◽  
Yuan Sheng Liu ◽  
Lin Xue
Keyword(s):  
Cloud Computing ◽  
Monitoring System ◽  
Environmental Parameters ◽  
Mobile Terminal ◽  
Measuring Instrument ◽  
Remote Monitoring System ◽  
Front End ◽  
Remote Lab ◽  
Computing Center ◽  
Network Equipment

A remote lab room monitoring system based on measuring instrument cloud is presented in this paper. The system consists of cloud computing center, embedded front-end acquisition hardware, mobile terminal device, and network equipment. Measuring service software system located in cloud computing center sends control commands to front-end acquisition hardware and collects acquisition data. Then processing, analysis and integration of signals are completed in the cloud computing framework. Finally, all the information can be interacted with mobile terminals by Web service. The system can monitor environmental parameters of lab rooms located in different campuses, such as temperature, humidity, light, smoke, and flooding. When environmental parameters exceed the preset thresholds, light and audio alarms will be immediately switched on and the administrator will be noticed on Web management page.

Seepage behavior assessment of earth-rock dams based on Bayesian network

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110586
Author(s):  
Lu He ◽  
Shijun Wang ◽  
Yanchang Gu ◽  
Qiong Pang ◽  
Yunxing Wu ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Monitoring System ◽  
Dynamic Bayesian Networks ◽  
Behavior Assessment ◽  
Extended Model ◽  
Scoring Method ◽  
Seepage Failure ◽  
Cause Of Failure ◽  
Safety Operation ◽  
Monitoring Information

Seepage behavior assessment is an important part of the safety operation assessment of earth-rock dams, because of insufficient intelligent analysis of monitoring information, abnormal phenomena or measured values are often ignored or improperly processed. To improve the intelligent performance of the monitoring system, this article has established an assessment framework covering project quality, maintenance status, monitoring data analysis, and on-site inspection based on the relevant norms of seepage safety assessment of earth-rock dams and the expert survey scoring method, and the Leaky Noisy-OR Gate extended model were used to determine the probability of events, and the dynamic and static Bayesian networks used to assess the possibility of seepage failure of earth-rock dams and diagnose the most likely cause of failure. The function of static and dynamic Bayesian networks to assess the seepage behavior of earth-rock dams, abnormal measured values, and causes of anomalies can make up for the limitations of reservoir management personnel and monitoring system in seepage failure experience and seepage knowledge of earth-rock dams and enable better handling of abnormal phenomena and monitoring information, making the monitoring system more intelligent.

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