CAJ | 학술논문

为降低田间振动干扰对谷物产量检测精度的影响，同时增加测产系统的实用性，设计了一种基于 CAN总线技术、无线通信技术以及计算机网络技术的新型谷物智能测产系统。系统包括车载子系统和远程监测子系统2个部分，实现了谷物产量的现场监测、产量图绘制、远程监控与收获作业管理等功能。车载部分设计了弧形冲量传感器，提出了机械减振和双板差分方法来降低收割机振动对谷物流量测量的影响，采用数字阈值滤波的方法来提高谷物产量的测量精度，并建立了总产量和单位面积产量的数学模型。田间动态试验结果表明双板回归差分方式滤除干扰的效果优于直接差分，其最大测产误差为8.03%，测产平均误差为3.27%，最大测产误差比直接差分方式降低了7.12个百分点，最后绘制了试验地块的产量分布图。另外，系统的远程监控部分开发了界面友好的收获作业管理系统，实现了谷物产量的远程监测与管理。系统总体运行性能良好，满足了测产需要。
위강저전간진동간우대곡물산량검측정도적영향，동시증가측산계통적실용성，설계료일충기우 CAN총선기술、무선통신기술이급계산궤망락기술적신형곡물지능측산계통。계통포괄차재자계통화원정감측자계통2개부분，실현료곡물산량적현장감측、산량도회제、원정감공여수획작업관리등공능。차재부분설계료호형충량전감기，제출료궤계감진화쌍판차분방법래강저수할궤진동대곡물류량측량적영향，채용수자역치려파적방법래제고곡물산량적측량정도，병건립료총산량화단위면적산량적수학모형。전간동태시험결과표명쌍판회귀차분방식려제간우적효과우우직접차분，기최대측산오차위8.03%，측산평균오차위3.27%，최대측산오차비직접차분방식강저료7.12개백분점，최후회제료시험지괴적산량분포도。령외，계통적원정감공부분개발료계면우호적수획작업관리계통，실현료곡물산량적원정감측여관리。계통총체운행성능량호，만족료측산수요。
Since grain yield in farmland has spatial variability, and the size of production can reflect the growth and management situation of grain, it is necessary to obtain accurate information on spatial distribution of production for implementing precision agriculture. However, it is still lacking of yield monitoring systems that are suitable for grain combine harvester and field conditions in China. The current developed systems in China mostly cannot reduce the vibration from the harvester, and tend to produce a large error in dynamic measurement of production. Therefore, in this study, a new type of intelligent grain yield monitoring system was developed in order to minimize the influence of the field vibration on accuracy of grain yield monitoring system and improve its practicality. The system included a remote monitoring subsystem based on computer networking technology and a vehicle-mounted subsystem based on controller area network (CAN) bus technology. The remote monitoring subsystem could realize on-site yield measurement, yield mapping, remote monitoring and harvest management. The vehicle-mounted subsystem consisted of industrial computer, CAN bus module, general packet radio service (GPRS), GPS receiver module and a variety of signal sensors. It could detect grain yield, generate yield map and remote wireless communication. Meanwhile, it collected impulse sensor data, elevator shaft speed, grain moisture, harvester travel speed and cutting width to establish mathematical model and measured the grain yield accurately. In addition, it also could get information on geographical location from GPS receiver to draw grain yield distribution map. Moreover, through the GPRS network, it sent the data to a remote personal computer (PC) for processing and displaying. The vehicle-mounted subsystem here adopted mechanical denoising method and double plates differential method to reduce the influence of harvester vibration on measurement accuracy, but the minor differences in output signals between pre-plate and rear-plate of the impact sensor could be observed, which might be caused by difference in installation location of the two plate bracket in fixed end distance and the different force on the sensitive beam resulted from mechanical vibration of the combine. For this reason, a regression difference method was proposed, by which the vibration signal of rear plate approximated the vibration signal of first board before difference processing. In the subsystem, digital threshold filtering was used to improve the estimating accuracy of grain yield, and the filtered data was used for fitting mathematical models of total yield and yield of per unit area. Field test results showed that by regression difference method, the average error of the yield estimate was 3.27%and the maximum error was 8.03%, which was reduced by 7.12%compared with the direct difference method. It suggested that the regression difference method was superior to the direct difference method in eliminating vibration interference. The remote subsystem developed a friendly interface, which realized the remote monitoring and managing grain harvest. The system had a good performance to meet the needs of yield measurement in China.