Seeing is a key factor affecting the image quality of astronomical observations and can be quantitatively described by the Fried parameter $r_0$. The larger the $r_0$ value (in unit of cm), the better the seeing conditions. Currently, daytime seeing measurements are primarily conducted using the Solar Differential Image Motion Monitor (SDIMM) or the spectral ratio method. In this work, we propose a neural network model for estimating daytime $r_0$. The experimental results of the training set and the test set show that this model can currently estimate $r_0$ with an accuracy exceeding $99\%$. Using this model, we estimate the $r_0$ of the Huairou Solar Observing Station (HSOS) in 22 consecutive years from 1989 to 2010. The median $r_0$ of HSOS in 22 consecutive years was around 2.5 cm, and the best seeing condition was in April and September of one year. This result confirmed the long-term stability of seeing conditions. In addition, we conducted an error analysis comparing the seeing measured by SDIMM and the results obtained by the spectral ratio method both under domeless and domed conditions. The results indicate a significant correlation between the SDIMM results and the spectral ratio method results, with first-order fitting coefficients of 2.2 and 2.9, respectively.

The precise monitoring of solar flares holds significant scientific value for space mission safety, communication security, and space environment forecasting. The H$\alpha$ line has long been utilized as a tool to extract information about the structure and dynamics of the solar chromosphere and is crucial for observing solar activities such as prominences and flares. However, ground-based H$\alpha$ observations are susceptible to cloud interference, which significantly reduces data reliability and complicates the effective separation of genuine flare signals from cloud modulation effects. To address this challenge, our study proposes a dual-band brightness ratio method tailored to the SMAT configuration, leveraging synchronous observation data from the Huairou SMAT at two wavelengths (photospheric 5324 Å and chromospheric 6562.8 Å). Observational data validation demonstrates that this method can effectively characterize true chromospheric brightness variations. In real observational data, the reconstructed brightness curve successfully recovered the brightness peak of an M1.5 class flare, with the peak position aligning well with the X-ray flux peak. This method enhances the accuracy of flare monitoring under cloudy conditions for SMAT, providing a promising pathway for high-reliability ground-based solar activity observations with this telescope.