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Under the background of the promotion of the Olympic effect of the 2022 Beijing Winter Olympics and the in-depth implementation of the national ice and snow industry's strategy of "expanding from the south to the west and expanding from the east," China's ice and snow industry has ushered in a new period of development. By applying literature and logical analysis, etc., with in-depth analysis of various subjective and objective supplies for the integration of China's ice-snow industry and ecocultural tourism in Hunan Province, it is considered to be driven by the bidding and preparation effect of Beijing Winter Olympic Games in 2022. The supply capacity of ice-snow industry venues, professional facilities, and public services are gradually strengthened, and the soft power of construction of the ice-snow industry needs to be coordinated and improved. The problem of safety is prominent and it is urgent to build a safety prevention and emergency management of ice-snow tourism.The integration of ice-snow sports and ecotourism industry has gradually became a prototype, but the integration point, line, and surface still need to be further strengthened and expanded. Accordingly, this paper puts forward a method to realize the transformation and upgrading of the commercial operation mode of the integration of ice-snow industry and ecological culture tourism, comprehensively to improve the ecological innovation ability of ice-snow tourism public service, to create multi-industry ice-snow industry cluster area, and to construct the basic framework system of risk prevention and control and management with multisubject participation and other relevant strategies. [ABSTRACT FROM AUTHOR]

Since the limitation of carbon emissions, China's photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department's decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting. [ABSTRACT FROM AUTHOR]