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Aiming to the problem that semi-characters seriously affect character recognition accuracy in the process of automatic meter reading, we proposed a new method to recognize characters automatically which is based on improved BP neural network. First, we preprocess the image with morphology and locate the character area by combining projecting method and the characteristics of the gray transition, then we scan the image to segment the characters, at last, we recognize the characters using improved BP neural network. Experiments turn out that the algorithm can not only recognize the complete characters but also incomplete characters.

Given the traditional use of sensors of temperature, humidity, smoke to identify the fire hazard, this design adds the image recognition algorithm based on OpenCV to identify fire through capturing images with camera, and caculates the probability of fire hazard according to weight of four factors that influence fire occurrence. The weight is determined by integrated multifactor probability model. Integrated fieldbus Redundancy technology is used in transmission of data on Ethernet and CAN. The practice has showed that the system achieved stability, high reliability, fast, and accuracy. The alarm is given rapidly when fire hazard happens.