The large-scale application of industrial robots has created a demand for more intelligent and efficient health monitoring, which is more efficiently met by data-driven methods due to the surge in data and the advancement of computing technology. However, applying deep learning methods to industrial robots presents critical challenges such as data collection, application packaging, and the need for customized algorithms. To overcome these difficulties, this paper introduces a Platform of data-driven Health monitoring for IRs (PHIR) that provides a universal framework for manufacturers to utilize deep-learning-based approaches with minimal coding. Real-time data from multiple IRs and sensors is collected through a cloud-edge system and undergoes unified pre-processing to facilitate model training with a large volume of data. To enable code-free development, containerization technology is used to convert algorithms into operators, and users are provided with a process orchestration interface. Furthermore, algorithm research both for sudden fault and long-term aging failure detection is conducted and applied to the platform for industrial robot health monitoring experiments, by which the superiority of the proposed platform, in reality, is proven through positive results.

Rapid urbanization has significantly affected urban ecological environments, necessitating accurate and scientific quality assessments. In this study, we develop an enhanced remote sensing ecological index (WRSEI) for water network cities using Linyi City, China, as a case study. Key innovations include (1) introducing a water–vegetation index to better represent aquatic ecosystems; (2) incorporating nighttime light data to quantify the intensity of human activity; and (3) employing hierarchical PCA to rationally weight ecological endowment and stress indicators. The model’s effectiveness was rigorously validated using independent land use data. The results show that (1) the WRSEI accurately captures Linyi’s “water–city symbiosis” pattern, increasing the assessed ecological quality of water bodies by 15.78% compared to the conventional RSEI; (2) hierarchical PCA provides more ecologically reasonable indicator weights; and (3) from 2000 to 2020, ecological quality exhibited a pattern of “central degradation and peripheral improvement”, driven by urban expansion. This study establishes a validated technical framework for ecological assessment in water-rich cities, offering a scientific basis for sustainable urban management.

The spatial distribution of Ground Control Points (GCPs) is a critical factor affecting the accuracy of UAV photogrammetry in hilly terrain. Existing studies primarily focus on the influence of GCP quantity on accuracy or discuss planar distribution uniformity in flat areas, with limited systematic analysis separating horizontal and vertical distributions as independent dimensions. This study utilizes a DJI Mavic 3E UAV to acquire aerial imagery in a typical hilly area of Mengyin County, Shandong Province, with 27 high-precision GCPs deployed. Four comparative experiments combining random/uniform distributions in both horizontal and vertical dimensions are designed to quantitatively analyze the impact of different distribution patterns on aerial triangulation and mapping accuracy. Results demonstrate that the dual-uniform distribution strategy achieves optimal accuracy, with horizontal RMSE of 0.045 m and vertical RMSE of 0.039 m, representing improvements of 32.8% and 17.0% respectively compared to the random distribution scheme. Furthermore, this paper proposes the Spatial Distribution Balance Index (SDBI), which integrates Planar Uniformity Index (PUI) and Vertical Uniformity Index (VUI) with a terrain-adaptive weighting mechanism. The VUI weight, calibrated as β=0.714 via a Sigmoid nonlinear amplification function (k=15,x0=0.15), enables the SDBI to adaptively reflect terrain sensitivity to vertical control. The enhanced SDBI shows a correlation coefficient of r=−0.93 with final accuracy, validating its effectiveness as a GCP layout optimization and evaluation tool. This research provides a quantifiable technical framework for GCP deployment in UAV photogrammetry in hilly regions.

The spatial distribution of ground control points (GCPs) is a critical factor affecting the accuracy of UAV photogrammetry in hilly terrain. In existing studies on GCP distribution, researchers have largely focused on planar uniformity metrics in flat terrain or on the effects of flight parameters in mountainous areas, with limited attention to the distinct roles of horizontal and vertical placement. In this study, we utilized a consumer-grade RTK-equipped UAV to acquire aerial imagery in a typical hilly area, with 27 high-precision GCPs deployed as a reference dataset. Four comparative experiments combining random/uniform distributions in both horizontal and vertical dimensions were designed to quantitatively analyze the impact of different distribution patterns on aerial triangulation and mapping accuracy. Our results demonstrate that the dual-uniform distribution strategy (i.e., uniform in both planimetric layout and elevation stratification) achieves the highest accuracy among the four tested configurations, with horizontal RMSE of 0.045 m and vertical RMSE of 0.039 m. Furthermore, we propose the Spatial Distribution Balance Index (SDBI), which integrates the Planar Uniformity Index (PUI) and Vertical Uniformity Index (VUI) with a terrain-adaptive weighting mechanism. The VUI weight, exemplified as β = 0.714 for this study area via a Sigmoid nonlinear amplification function (k = 15, x₀ = 0.15), enables the SDBI to adaptively reflect terrain sensitivity to vertical control. The enhanced SDBI exhibits a correlation coefficient of r = −0.93 with final accuracy, validating its effectiveness as a GCP layout evaluation tool. In this study, we establish the SDBI as a diagnostic metric that quantitatively links GCP distribution characteristics to photogrammetric accuracy outcomes, providing both theoretical insights into anisotropic error propagation and practical guidance for deployment design in hilly regions.