The ecological and environmental problems of arid zones have become an urgent global concern. Current research on ecological risk is based mainly on the dominant functions of land use, with a primary focus on land use landscape projections and less consideration of potential risks to ecosystems, system resilience and interactions between nature and future sustainable development. In this study, a potential–connectivity–resilience ecological risk assessment model based on the SDGs was constructed using multisource data to spatially quantify indicators at the grid scale in the Turpan and Hami regions of Xinjiang, China. This model was used as a basis for studying ecological risk in arid zones from a production–living–ecological space (PLES) perspective. The results revealed that, during the period 2000–2020, PLESs in the Turpan and Hami regions presented significant spatial similarity, with an increasing trend in overall risk. The production space in the Turpan and Hami regions showed a parabolic trend of increasing and then decreasing, whereas the living space and ecological space in the Hami region showed continuous linear upward trends. The state of ecological security in the Turpan and Hami regions is gradually deteriorating, and comprehensive ecological protection and restoration measures are urgently needed to rationally allocate the structure and layout of the production-–living-–ecological space. The study of ecological risk from a PLES perspective not only helps in fully understanding the development trend of the arid zone; it also provides new ideas and methods for evaluating regional ecological environmental safety and scientific references for formulating regional sustainable development, ecological risk prevention and control and the rational allocation of resources.

One of the key conditions for achieving superior regional growth is ensuring the harmonious development of both the layout and functions of territorial space. Territorial space, which includes production space, living space, and ecological space, serves as a critical system and venue for economic, cultural, and social activities in a region. The harmonized growth of production–living–ecological space (PLES) is essential for attaining sustainable development goals. Research on PLES offers a fresh perspective on promoting sustainable development of the spatial domain and the sustainable use of resources. However, studies on PLE functions in ecologically fragile areas are lacking. Therefore, in this study, which adopts a PLES perspective, land-use data are used to classify land according to the dominant functions of production, ecology, and living. Integration of point-of-interest (POI) data with socio-economic data was established to spatially describe indicators at the grid level and create a scoring system for PLES indicators in ecologically fragile areas. Finally, the entropy weight method, holistic assessment methods, coupling coordination degree model (CCDM), and geodetector were employed to explore the coupling coordination relationships and factors influencing PLESs in the Turpan–Hami Basin from 2010 to 2020. The results indicate that the Turpan–Hami Basin consists predominantly of potential ecological space, mainly in the central and northern regions, which are characterized by the Gobi Desert and bare rock landforms. Over the past decade, the PLES framework has seen a notable rise in the allocation of residential and ecological areas, whereas the portion dedicated to production spaces has diminished. The overall coupling coordination degree (CCD) of PLES in the Turpan–Hami Basin is at a coordinated level and gradually increasing. The most significant impact on the degree of PLES coupling coordination is exerted by population and natural factors. The research findings provide theoretical support for the sustainable utilization of resources in the Turpan–Hami Basin and other ecologically fragile areas while also offering scientific evidence to promote the coupling coordination of PLES, thereby contributing to high-quality regional development.

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments.