RESEARCH ON DATA-DRIVEN URBAN INTELLIGENT MONITORING AND OLD CITY RECONSTRUCTION

As the global urbanization process is further accelerating, the number of urban people is also steadily increasing. With the continuous growth of the urban population, the use of various functional facilities in the city is gradually becoming saturated, which seriously restricts the development of the city. Therefore, real-time perception and prediction of the operational status of various functional urban facilities, as well as environmental safety monitoring in the city, are of great significance for improving the functionality and livability of the city. In complex urban environments, multi-source data is interrelated, and the noise reduction of multi-source data and the integration of this correlation still face great challenges. At the same time, how to apply urban intelligent monitoring in the reconstruction of old cities is rarely mentioned. Based on the above problems, this paper proposes a data noise reduction model based on a wavelet algorithm and combines it with the Macroscopic Fundamental Diagram (MFD) multi-source data fusion structure proposed in this paper to provide a theoretical basis for the construction of an urban intelligent monitoring model. Then, the author constructed a data-driven urban safety environment monitoring model and a multi-source data-based urban congestion monitoring model, which have good experimental results and certain practical value. At the end of the paper, the author briefly discussed the application of the smart city monitoring model in the reconstruction of old cities, hoping to provide certain guidance for further integration in the future.