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With the development of Internet of things technology, the smart elderly care mode is attracting more and more attention. Based on the questionnaire data of adult residents in some cities of Jiangsu Province, this paper analyzes the multiple linear regression model between the intelligent elderly care mode and its possible influencing factors. And it can be found that for participants aged 59 and under, their educational level, blood type, professional identity and age have a significant impact on intelligent old-age care mode. For participants aged 60 and over, only their educational level and age have a significant impact on the smart old-age care approach. Finally, this paper puts forward some suggestions about the development of industries related to intelligent elderly care to build an integrated intelligent community old-age care pattern, integrated pattern of intelligent home-based care or integrated smart home-based care service center, adjust and perfect the existing elderly care institutions, and improve intelligence old-age care institutions pattern to promote the development of intelligent industry related to smart elderly care.

This paper focuses on the development of regional GDP and proposes a method proposed for forecast of enterprise power consumption data and GDP based on ensemble algorithms. The enterprise power consumption data are used as independent variables and GDP data as dependent variables. A multiple linear regression model is selected as the primary learner for training and its outputs will be sorted into a new dataset of input features to train a secondary learner. The forecast of GDP is thus realized through ensemble learning.

This paper use the super efficiency SBM-DEA model with undesirable outputs to calculate the ecological efficiency of Zhejiang Province from 2001 to 2018, and construct multiple linear regression model that evaluating the influencing factors of ecological efficiency based on it. The ecological efficiency of Zhejiang Province fluctuated and increased during the study period, which was mainly driven by pure technical efficiency. In the multiple regression analysis, it can be concluded that income factors, structural factors and institutional factors have a gradually weakening positive impact on ecological efficiency. This paper argues that Zhejiang Province should make full use of regional advantages, resource advantages and talent advantages on the basis of maintaining the current industrial level. It is advised to vigorously develop high-tech industries, strengthen international communications on knowledge and seek new economic growth points. Finally a higher level of coordination between economic development and ecological protection can be achieved.

As the GDP of lodging and catering industry is generally quarterly or annual, the data volume is very small, so it is difficult to accurately predict the GDP of lodging and catering industry in this city for a city. However, a city can accurately predict the GDP development of the lodging and catering industry through electricity consumption. And the local government can formulate relevant policies to promote the stable development of lodging and catering industry and economy, and find the relationship between electricity consumption and GDP development of this industry, so as to achieve the purpose of low energy consumption and fast economic growth.t. This paper first conducted desensitization and normalization processing on the data, and then used Matlab to forecast the GDP of the lodging and catering industry in X City respectively by using GM (1, N) grey model, multiple linear regression model and BP neural network model. The experimental results show that the GM (1, N) grey model is more accurate and reliable than the other two methods in the case of less data. This paper solves the problem that the GDP of accommodation and catering industry in a single city is inaccurate.