MONITORING METHODS AND THEIR APPLICATION OF FLOWING WATER POLLUTION BASED ON INTELLIGENT VISION

ZENG Xiangji; YAN Feng; LI Yonggang; PAN Yan; YANG Jingya; TAN Xiangtian

doi:10.13205/j.hjgc.202311014

Volume 41 Issue 11

Nov. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(11): 78-83,122

ZENG Xiangji, YAN Feng, LI Yonggang, PAN Yan, YANG Jingya, TAN Xiangtian. MONITORING METHODS AND THEIR APPLICATION OF FLOWING WATER POLLUTION BASED ON INTELLIGENT VISION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 78-83,122. doi: 10.13205/j.hjgc.202311014

Citation:

ZENG Xiangji, YAN Feng, LI Yonggang, PAN Yan, YANG Jingya, TAN Xiangtian. MONITORING METHODS AND THEIR APPLICATION OF FLOWING WATER POLLUTION BASED ON INTELLIGENT VISION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 78-83,122. doi: 10.13205/j.hjgc.202311014

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MONITORING METHODS AND THEIR APPLICATION OF FLOWING WATER POLLUTION BASED ON INTELLIGENT VISION

doi: 10.13205/j.hjgc.202311014

1. CINF Engineering Co., Ltd., Changsha 410019, China;
2. School of Automation, Central South University, Changsha 410083, China

Received Date: 2022-07-05
Available Online: 2023-12-25

Abstract

Abstract

All kinds of pollutants are inevitably produced in the production process of industrial and mining enterprises, among which the discharge of water pollutants has been always an important work of monitoring and prevention. The traditional monitoring methods adopted by current industrial and mining enterprises, such as video or online equipment monitoring, are often weak in adaptability to the randomness, contingency, and uncertainty of sudden water pollution accidents, and have problems such as low efficiency, high cost, and poor accuracy. Combined with the time/space continuum image information self-check method to analyze the water image online, a universal water pollution monitoring method was proposed, and a dynamic water pollution monitoring system based on intelligent vision was developed, to realize the efficient and accurate qualitative judgment of the pollution state. After the industrial and mining enterprises put the system into use, the operation and maintenance were simple. Compared with the traditional manual video pollution monitoring method, the pollution identification accuracy was increased by 13%, the effective recognition rate was more than 99%, the average pollution identification time was reduced by 3 to 5 hours, the rapid response of sudden water pollution accidents was realized, and the incidence of environmental protection accidents was greatly reduced. This method can effectively reduce the labor intensity of personnel, and save enterprise operating cost.
- dynamic water,
- dynamic optimization,
- deep learning,
- dynamic weighting,
- image analysis

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References(26)

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