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The transportation of wind fields plays a crucial role in the dispersion and distribution of air pollutants, and accurate wind speed prediction is essential for assessing pollutant concentrations. In this study, we constructed a Stacking ensemble learning model using three models, namely Random Forest, LightGBM, and XGBoost, as base learners, and the LASSO regression model as the meta-learner to optimize wind speed data forecasting for Zhengzhou City’s urban area using WRF. Firstly, based on Pearson correlation coefficients, we selected meteorological variables that have a significant impact on near-surface wind speed and derived historical lagged features. Bayesian TPE was utilized for hyperparameter tuning and model building. Finally, the performance of the trained model was evaluated by comparing it with ground observation data. The results showed that compared to the WRF model, Random Forest, LightGBM, and XGBoost effectively reduced forecast errors and significantly improved wind speed predictions. Both LightGBM and XGBoost demonstrated similar performance in the correction models across the 11 stations and outperformed Random Forest. The Stacking method integrated the advantages of the base learners and exhibited improvement capabilities over individual models, highlighting the potential of machine learning in enhancing localized and accurate weather forecasting.

As a powerful measure to deepen the supply-side structural reform, tax reduction and fee reduction policies play an important role in promoting the high quality development of small and medium-sized enterprises(SMEs).This paper selects 582 SMEs listed on China's A-share market from 2018 to 2021 to measure their high quality development level and explore the policy effect of the new round of tax reduction and fee reduction policies implemented since 2018.The results show that the reduction and exemption of value-added tax(VAT) and social insurance premiums are conducive to improving the high quality development level of SMEs, and the impact of income tax reduction on the high quality development level of enterprises is not significant, and the income tax burden between enterprises is quite different. Therefore, this paper puts forward some policy suggestions to further implement of the tax and fee reduction policies, consolidate the achievements of the existing tax and fee reduction policy, improve the tax structure of enterprise VAT and social insurance premiums, implement differentiated income tax reduction and exemption policy, and guide the high quality development of SMEs.

According to the World Meteorological Organization, “record” greenhouse gas concentrations and heat accumulation have pushed the planet into uncharted territory, driving global warming. We need to figure out how to mitigate global warming by maximizing the overall benefits of forests based on the optimal deforestation period. This provides a basis for decision-making for forest managers. At the micro level, we only consider the carbon sequestration capacity of individual trees. AHP was used to determine the more important influencing factors. The S-curve model was fitted by single-factor and multi-factor analysis to obtain the carbon sequestration saturation point. It should be cut down at the saturation point of carbon sequestration. At the macro level, we consider the carbon sequestration capacity of forests. Therefore, from the economics perspective, we construct a carbon sequestration benefit model based on the optimal rotation period and deduce the optimal and maximum carbon sequestration benefits.

In combination with features such as intensive labor and speech in the customer service report field, this paper discusses the design of a customer service report system based on artificial intelligence automatic speech recognition technology and big data text classification technology. The proposed system realizes functions like a flat IVR menu, quick transcription and input of work orders, dynamic tracking of failure hotspots, automatic classification and accumulation of the knowledge base, speech emotion detection and real-time supervision of service quality, and it can improve the user experience and reduce the labor strengths of customer service staff. The automatically accumulated knowledge base can further assist with feedback to resolve the difficult problem that the emerging intelligent network Q&A and intelligent robots rely on a manually summarized knowledge base.

Aiming at aerospace remote sensing technology, this paper proposes a dualsatellite integrated intelligent reconnaissance decision-making model, and establishes a doublesatellite system. One satellite is used for “general investigation” and one satellite is used for “detailed investigation”. The target is carried out by general investigation load. Track identification provides guidance for detailed inspection loads, thereby improving the accuracy, real-time, flexibility and intelligence of space remote sensing. The focus is on three aspects: investigation model design, key algorithm research and semi-physical simulation verification. The reconnaissance model design completes the conceptual design and process demonstration of the model. The key algorithm research part completes the research of the fast detection based on deep learning and the key algorithm of load imaging strategy decision in the system. The satellite semi-physical simulation verification is carried out by constructing the semi-physical simulation verification system.