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Design of photoelectric opposite-beam grain flow monitoring device based on BP neural network

During the working process of the rice-wheat combine harvester, the irregular accumulation of grains on the scraper of the scraper-type grain elevator will affect the final measurement accuracy. In this paper, a scraper type combine harvester grain flow monitoring device is designed. In this paper, the input capture function of STM32 single-chip microcomputer is used to obtain the high-level signal output by the array photoelectric opposite-beam sensor, and the grain accumulation information on the elevator scraper is obtained; the discrete element simulation software is used to analyze the grain accumulation model on the scraper, and the calculation formula of grain flow is established; the principle of BP neural network is used to improve the monitoring accuracy of grain flow. The indoor bench test and field dynamic performance test were carried out. The results show that the opposite-beam grain flow monitoring device designed in this paper operates stably, and the maximum relative error of the field harvesting test flow monitoring is 4.12%, which meets the actual needs of combine harvester grain flow monitoring, which provides a technical basis for field grain flow monitoring.