高电压强磁场环境噪声及各种扰动直接影响电气信号的准确检测，干扰电气信号的时频特征分析。为滤除噪声及扰动分量，提出了一种基于小波分频阈值与分频阈值函数的电气信号自适应降噪方法。首先，对信号进行小波包分解，将最后两层小波包树节点系数按频率大小排序，分别估算不同频段的节点系数阈值（分频阈值）。其次，通过相邻层节点系数能量之比构造降噪调节系数，对分频阈值进行自适应优化。再次，通过降噪调节系数构造融合软硬阈值优点的改进阈值函数（分频阈值函数），实现不同节点阈值函数的自适应调整。最后，重构小波包树得到降噪后的信号。Simulink仿真和高压强磁下的实测录波信号降噪结果表明该方法在白噪声的干扰下能有效去除尖峰、毛刺等影响，降噪效果显著，信号波形还原度好，在电气信号检测中具有一定的应用价值。

Noise and various disturbances in the high voltage and strong magneticfield environment directly impact the accurate detection of electrical signals, as well as interfere with the time-frequency characteristics of these signals. To mitigate noise and disturbance components, an adaptive noise reduction method is proposed based on wavelet frequency division threshold and frequency division threshold function. Firstly, following wavelet packet decomposition of the signal, the node coefficients from the final two layers of the wavelet packet tree are arranged in order of frequency magnitude, and the node coefficient thresholds for different frequency bands (frequency division thresholds) are estimated. Secondly, the noise reduction adjustment coefficient is constructed by the ratio of energy of adjacent layer node coefficients, and the frequency division threshold is adaptively optimized. Subsequently, an improved threshold function (frequency division threshold function) is constructed by incorporating the benefits of both soft and hard thresholds, utilizing the noise reduction adjustment coefficient to achieve adaptive adjustment of threshold functions of different nodes. Finally, the denoised signal is obtained by reconstructing the wavelet packet tree. Simulink simulation and noise reduction results of the measured recorded waveform signal under high-voltage strong magnetism show that the method can effectively remove some effects, such as spikes, burrs, etc. The noise reduction effect is significant and the signal waveform restoration is excellent. The method holds certain application value in electrical signal detection.