Real-time online monitoring method and device for cleaning losses of grain combines

Abstract

Disclosed are a real-time online monitoring method and a device for cleaning losses of grain combines. A symmetrical piezoelectric crystal sensor is mounted at an outlet of a cleaning screen; cleaning matters flow out through the outlet of the cleaning screen; the sensor obtains impact signals of the cleaning matters and sends the impact signals into a band-pass electric charge amplifier circuit which converts the signals into voltage signals with certain amplitude; a signal compensation circuit performs signal adjustment, eliminates strong interference signals and enhances identification of grain signals; a band-pass filtering circuit and a sensitivity adjusting circuit are applied to separate vibrating noise and grain signals respectively; and a pulse shaping circuit is used for shaping separated vibrating noise and grain signals into standard pulse signals which can be executed by a processor; an online monitoring pattern includes: the processor collects grain cleaning loss signals in real time and counts and processes the signals; and a data inquiring pattern includes: the processor calls out the total amount of loss grains in a time interval for evaluating operation quality conditions. According to the real-time online monitoring method and the device for cleaning losses of grain combine harvesters, interference signals can be weakened, grain impact signals is enhanced, the straw identification accuracy is improved, and the test reliability is improved.

Description

technical field [0001] The invention relates to a performance testing technology of a grain combine harvester, in particular to a real-time on-line monitoring method for the cleaning loss of the grain combine harvester and a real-time on-line monitoring device for the cleaning loss of the grain combine harvester used in the method. Background technique [0002] With the rapid development of agricultural equipment, the application scale, harvesting performance and degree of automation of combine harvesters have become important indicators of modern agriculture. A combine harvester with good performance and a high degree of automation should be able to monitor the amount of grain loss in real time and automatically adjust the operating status, so as to ensure the maximum harvesting efficiency without exceeding the allowable loss amount. At present, when most combine harvesters in our country are operating, they rely on the operator's personal experience to adjust the operating...

AI Technical Summary

Problems solved by technology

This method of operation requires high driving skills of the operator, and is labor-intensive. It is difficult to reduce the failure rate, and the operation speed and efficiency are low.

Sometimes, in pursuit of operating efficiency, when the moisture content of the stalks is low, a large number of broken stalks will be generated during the threshing process, which will increase the cleaning load, reduce the cleaning quality, and the cleaning loss rate is high, which greatly reduces the operating cost. quality

For a long time, the monitoring of this index has been using manual counting and manual calculation of the loss rate. The disadvantages are: low work efficiency, large errors, and inability to guide the harvesting operation of the unit in real time

[0003] Compared with the stalks and grasses, there are very few grains in the discharge of the harvester's cleaning screen. The conditions of the harvester's work in the field are complex, coupled with the influence of machine vibration and other strong interference signals, making the identification and extraction of grain signals extremely difficult. Difficult, the signal of grain loss detected by the sensor is very weak, which brings great difficulties to the research

At present, domestic research has not seen methods and devices with good monitoring performance; foreign research sensors have poor adaptability to strong interference from domestic harvester vibration and noise, and the reliability of monitoring data is not high

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