The Chinese government has long attached great importance to rural revitalization. The harmony and stability in rural areas are the fundamental guarantees of rural sustainable economic growth and farmers’ prosperity, and they rely on effective rural governance. Taking the Fu’an City in Fujian Province of China as an example, this paper analyzes the rural governance problems arising from the implementation of the rural revitalization strategy with the method of dynamic games with incomplete information after data collection by on-the-spot investigation and file inquiry, etc. The results show that the “solicitation” behavior of village B to increase its own income does not maximize the income of village B and village N; Even under the optimal state of income distribution is derived through the game model, the income distribution between the two villages is still unfair to village N, the investor of the “Waterlands Resort”. Therefore, in order to solve the rural governance problems caused by the distribution of benefits between village B and village N, government subsidies, property rights protection, village rules and regulations are required. Besides, the leading role of rural grassroots organizations should be given full play, and villagers’ self-governing system needs to be improved.
The fault-diagnosis and recovery strategy of the electric distribution network were discussed. The procedure of the hybrid genetic – particle swarm optimization algorithm, together with a practical example, was also introduced.
Time series follow the basic principles of mathematical statistics and can provide a set of scientifically based dynamic data processing methods. Using this method, various types of data can be approximated by corresponding mathematical models, and then, the internal structure and complex characteristics of the data can be understood essentially, so as to achieve the purpose of predicting its development trend. This paper mainly studies the combined forecasting model based on the time series model and its application. First, the application prospects and research status of the combined forecasting model, the source of time series analysis, and the status of research development at home and abroad are given, and the purpose and significance of the research topic and the research content are summarized. Then, the paper gives the relevant theories about the ARIMA model and the basic principles of model recognition and explains the method of time series smoothing. Finally, the paper uses the ARIMA model to identify and fit the time series data and then the gray forecast model to fit and predict the time series data. Finally, by assigning reasonable weights and combining these methods, a combined forecasting model is proposed and carried out.
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