基于变分模态分解联合小波分析的颗粒肥流量微波信号测量研究

Particle Fertilizer Flow Microwave Signal Measurement Based on Variational Mode Decomposition and Wavelet Analysis

摘要:

针对精准农业中颗粒肥料质量流量难以精确测量的难题，提出了一种基于微波传感器采集颗粒肥料流量信号的预处理算法。首先通过仿真排肥管道结构，确定了在管道长度为400mm、直径为30mm的条件下，颗粒肥料在管道内的碰撞对速度的影响最小，并根据仿真结果搭建了颗粒肥料质量流量采集平台。通过对采集的流量信号进行分析，提出了一种结合变分模态分解（VMD）与离散小波变换（DWT）的复合去噪算法。试验结果表明该算法处理后的信号信噪比提高了7.3757dB，均方根误差降低了57%，从而显著提升了颗粒肥料质量流量的测量精度。最后实际车载试验表明，经该算法处理后的流量信号，最大相对误差为13.84%，最小相对误差为1.36%。该试验平台与算法为精准施肥提供了可靠的技术支持。

Abstract:

The accurate measurement of granular fertilizer mass flow rate presents a significant challenge for achieving precision fertilization. Due to the complex dynamics of granular flows, traditional measurement methods often struggle to handle the resulting signals, which are typically characterized by significant noise and non-stationarity. To address this issue, a novel pre-processing algorithm for granular fertilizer flow signals acquired by a microwave sensor was proposed. Initially, a model of the fertilizer discharge pipeline was constructed and simulation experiments were conducted. These simulations determined that under conditions of a 400mm pipeline length and 30mm diameter, the impact of particle collisions within the pipeline on velocity was minimized. Based on these simulation results, a signal acquisition platform for granular fertilizer mass flow was established. Through analysis of the acquired flow signals, a hybrid denoising algorithm combining variational mode decomposition (VMD) and discrete wavelet transform (DWT) was innovatively proposed. Experimental results demonstrated that after processing with this algorithm, the signal-to-noise ratio of the signal was increased by 7.3757dB, and the root mean square error was decreased by 57%. The algorithm effectively separated the noise component from the signal, thereby significantly improving the measurement accuracy of the granular fertilizer mass flow rate. Finally, real-world vehicle tests were conducted. The processed flow signals exhibited a maximum relative error of 13.84% and a minimum relative error of 1.36%. The results confirmed that the experimental platform developed and the proposed denoising algorithm provided reliable technical support for precision fertilization.