基于STM32的城市地下空间智能防汛监控系统设计OA

To address the issues of high false alarm rates caused by water wave disturbances and the limited computing power of edge nodes in urban underground space flood monitoring,an STM32-based heterogeneous data fusion adaptive measurement and control sys-tem is designed.Methodologically,a dynamic weighting model based on spatio-temporal statistical features is first constructed.This model adaptively assigns weights according to the long-term variance and short-term deviation of sensor data to suppress random errors from single sources at the root.Secondly,to overcome the lack of a Floating Point Unit(FPU)in microcontrollers,a Q15 format-based fixed-point fuzzy Kalman filtering algorithm is proposed.By utilizing a 2D lookup table to dynamically adjust the measurement noise co-variance matrix,complex floating-point matrix operations are reconstructed as efficient integer shift operations.Experimental results indi-cate that within the 10～40 mm effective measurement range,the maximum absolute error is controlled within 1.65 mm,and the corre-sponding maximum relative error does not exceed 5.5%,while the goodness of linear fit is improved to above 0.98.Compared with the standard floating-point filtering algorithm,the single operation time of the fixed-point algorithm is reduced from 820 μs to 68 μs,impro-ving the computational efficiency by approximately 12.05 times,and the step response convergence time is shortened to 200 ms.In con-clusion,the proposed method effectively addresses the challenge of high-precision real-time filtering for low-cost edge nodes,signifi-cantly improving the robustness and response speed of flood monitoring in complex fluid environments.However,limited by experimental conditions,this study only verifies the effectiveness of the algorithm on a small-scale model,providing technical support for future large-depth field monitoring and multi-node collaborative perception.