With the rapid development in the internet, cloud computing, Internet of Things, mobile internet, and other information technologies, the big data technology is widely used in all aspects of smart city construction. This study analyzes the development status of big data in urban planning and construction and studies the application of the big data technology in smart city construction. This study mainly analyzes and studies the aspects of smart city planning, smart transportation, smart community, smart health care, and smart education and finally puts forward relevant thoughts.
Trusted network is characterized by a large amount of data, abnormal dispersion, and high complexity. Traditional methods are easily affected by trusted network environment, resulting in unreliable mining results. Therefore, a new real-time mining method of trusted network difference data is proposed. Real-time collection of trusted network difference data through history system is performed on the basis of determining the principle of trusted network difference data mining and collecting and extracting the characteristics of difference data. The process of trusted network differential data mining is designed through the artificial bee colony algorithm. According to the process, differential data mining is carried out from three aspects: constructing a trusted network differential data transmission path, updating pheromone, and establishing a differential data transmission path set. The experimental results show that the proposed method can effectively realize the real-time mining of difference data, and the mining accuracy is more accurate.
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<p>The intelligent clonal optimizer (ICO) is a new evolutionary algorithm, which adopts a new cloning and selection mechanism. In order to improve the performance of the algorithm, quasi-opposition-based and quasi-reflection-based learning strategy is applied according to the transition information from exploration to exploitation of ICO to speed up the convergence speed of ICO and enhance the diversity of the population. Furthermore, to avoid the stagnation of the optimal value update, an adaptive parameter method is designed. When the update of the optimal value falls into stagnation, it can adjust the parameter of controlling the exploration and exploitation in ICO to enhance the convergence rate of ICO and accuracy of the solution. At last, an improved intelligent chaotic clonal optimizer (IICO) based on adaptive parameter strategy is proposed. In this paper, twenty-seven benchmark functions, eight CEC 2104 test functions and three engineering optimization problems are used to verify the numerical optimization ability of IICO. Results of the proposed IICO are compared to ten similar meta-heuristic algorithms. The obtained results confirmed that the IICO exhibits competitive performance in convergence rate and accurate convergence.</p>
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