The Internet of Things technology has created a new model for the management of trash cans in urban communities. It will overturn the traditional methods of trash management and improve the efficiency of environmental protection departments. The community intelligent environmental trash can system consists of back-end services, front-end pages, and data layers. Realize effective management and real-time monitoring. On this basis, the thesis designs a residential community waste sorting collection system based on the Internet of Things, in order to provide reference and practical application for the residential community to make the first management decision of waste sorting collection link. The garbage classification and collection system in residential quarters based on the Internet of Things can be extended from developed family communities to urban family communities, and finally cover rural families, thus accelerating the construction of a resource-saving and environment-friendly society in China and promoting the sustainable development of the environment and economy.

The water resources carrying capacity (WRCC) lays a foundational basis for long-term coordinated water resource governance. This work builds evaluation logic around the Driving-Pressure-State-Impact-Response (DPSIR) framework. Weighted TOPSIS and geographical detector tools are jointly applied to quantify spatial-temporal WRCC disparities within the Jialu River Basin, alongside extraction of core driving forces during 2010-2022. The results showed that (1) the WRCC exhibited a unique spatial gradient across the basin’s four cities. Zhengzhou outperformed other regions for most surveyed years, with its WRCC score fluctuating between 0.28 and 0.59 and hitting a maximum of 0.59. Kaifeng maintained a chronically weak carrying capacity, its values sitting at 0.10-0.33 and falling under 0.20 for most time points. Xuchang recorded growth starting in 2019, and its annual evaluation indices remained between 0.35 and 0.41 in subsequent years. Zhoukou followed a downward trajectory instead; its initial high value of 0.55 gradually dropped to 0.14-0.28. The average WRCC across the entire basin hovered between 0.18 and 0.35, with most annual averages staying under 0.30. These intercity gaps construct a fixed spatial hierarchy: Zhengzhou > Zhoukou > Xuchang > Kaifeng. (2) Quantitative factor detection makes clear that socioeconomic human activities dominate the basin’s uneven WRCC distribution. R&D expenditure and urbanization rate carry the strongest explanatory power, with respective q statistics of 0.58 and 0.57. Natural background conditions exert far weaker regulating effects. Two typical natural endowment indicators, precipitation and groundwater reserves, only generate tiny q values of 0.11 and 0.09. The large gap between human and natural factor explanatory degrees confirms that human socioeconomic construction acts as the primary trigger for regional WRCC spatial divergence. (3) Factor interaction analysis showed that bivariate enhancement was the primary interaction type (70.53%), followed by nonlinear enhancement (21.05%) and nonlinear weakening (8.42%). The mean q value of the interactive effects reached 0.58, which is 45.0% higher than the average value of the single independent factors, suggesting prominent multi-factor synergistic effects.

The water resources carrying capacity (WRCC) lays a foundational basis for long-term coordinated water resource governance. Based on the Driving–Pressure–State–Impact–Response (DPSIR) framework, this study constructed a WRCC evaluation system containing 21 indicators and adopted a combined weighting method integrating entropy weight and coefficient of variation. Weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and geographical detector tools were jointly applied to quantify spatial-temporal WRCC disparities within the Jialu River Basin, alongside extraction of core driving forces during 2010–2022. Marked spatial disparities existed across administrative units, with basin-average WRCC ranging from 0.18–0.35. Zhengzhou maintained relatively high carrying levels, Kaifeng stayed chronically low, Xuchang improved after 2019, while Zhoukou experienced an overall decline, forming a relatively stable spatial pattern: Zhengzhou > Zhoukou > Xuchang > Kaifeng. Socioeconomic factors stood among the major drivers of spatial divergence. R&D expenditure and urbanization rate exhibited the highest explanatory capacity, with respective q statistics of 0.58 and 0.57. In contrast, natural factors including precipitation and groundwater reserves showed limited impacts, with q values of only 0.11 and 0.09. Factor interaction analysis showed that bivariate enhancement was the primary interaction type (70.53%), followed by nonlinear enhancement (21.05%) and nonlinear weakening (8.42%). The mean q value of the interactive effects reached 0.58, which was 45.0% higher than that of individual factors, suggesting prominent multi-factor synergistic effects. These results deliver empirical evidence for differentiated watershed regulation and cross-jurisdictional water–ecological coordination, and offer actionable governance insights for densely urbanized plain tributary basins with intense human–water conflicts.