Research on Cross-domain Data Fusion and Co-optimization Strategy of Smart City Management Software
In order to enhance the collaborative capability of smart cities in multi-domain resource scheduling and dynamic governance, we construct cross-domain data fusion and optimization strategies for transportation, energy, security and other domains, and study the standardized modeling of heterogeneous data from multiple sources, the edge-cloud collaborative fusion mechanism and the optimization method of deep reinforcement learning under multi-intelligent body system. We analyze the security guarantee effectiveness of federated learning, differential privacy and other techniques in data sharing, and propose a system architecture that can realize low-latency response and efficient collaboration in complex urban scenarios. The constructed model significantly improves the decision-making efficiency and robustness of the system under the actual operation data of multiple cities, and effectively alleviates the problems of data silo and policy mismatch in traditional urban management.
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