Csg Power Generation Co., Ltd.

Rufei He

Hao Zhang

Yu Gong

Xiaoyi Wang

Man Chen

Mingxuan Yang

Yaxiong Yu

Xiaobo Qiu

Yumin Peng

Xiong Dai

Jun She

Dehua Li

Mingliang Yao

Xianbin Guo

Yanming Gao

Yong Lu

Ming Xu

Yulin Han

Tao Liu

Kai Lin

Liqun Sun

Miaogeng Wang

Jianhui Li

Zhipeng Lv

Haifeng Guo

Hanlong Wang

Zhenglin Xiang

Jianqiu Li

A method and device for smoke or fire recognition, a computer device and a storage medium are disclosed. The method includes: acquiring a to-be-recognized image in a smoke or fire monitoring region; recognizing a smoke or fire suspected region in the to-be-recognized image according to the to-be-recognized image, including recognizing a smoke or fire suspected region in a visible light image on the basis of colors, and recognizing a smoke or fire suspected region in an infrared image on the basis of brightness; and inputting the to-be-recognized image including the smoke or fire suspected region into a preset model, and recognizing a smoke or fire state in the to-be-recognized image according to an output result of the preset model, the preset model being obtained by training based on the visible light image pre-marked with a smoke or fire state or the infrared image pre-marked with a smoke or fire state.

Method and device for smoke or fire recognition, computer apparatus, and storage medium

The present disclosure provides a self-adaptive test method for an intelligent prediction algorithm of analog measured values. Firstly, an event recording sequence, an analog measurement point ID and an analog measurement point alarm value are read from a time sequence event record table, an analog measurement point table and an alarm threshold table. Next, operation records of a normal operation state of a unit within a statistical cycle are acquired to form historical statistics of measured values of the analog measurement point based on switching value signals. Then, simulated measured values of the analog measurement point with time scales are calculated based on the historical statistics, the analog measurement point alarm value and an analog measurement point current measured value. Finally, sensitivity is calculated; and an alarm is sent to remind a technician to adjust the algorithm when the sensitivity is greater than a threshold.

Self-adaptive test method for intelligent prediction algorithm of analog measured values

The present disclosure provides a self-adaption faster-than-real-time method for estimating a working condition start-up state of a unit. Firstly, an event recording sequence, an analog measurement point ID, an analog measurement point first-level alarm value and a history scatter point distribution record are read from a time sequence event record table, an analog measurement point table, an alarm threshold table and a historical scatter record table. A measured value slope of the analog measurement point is calculated according to an event recording relative time. Then, an abnormal state estimated value is calculated based on the history scatter point distribution record, the analog measurement point first-level alarm value and the measured value slope of the analog measurement point. Finally, a pre-warning is sent to remind a watchman to perform a preventive operation when the abnormal state estimated value is above the threshold.

Method for self-adaption faster-than-real-time working condition start-up state prediction and estimation of unit

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Guangzhou, China

Rufei He