In the face of increasing environmental challenges, carbon emissions from industrial parks have become a global focal point, particularly as electricity consumption serves as a major source of carbon emissions that requires effective management. Despite proactive efforts by governments and industry stakeholders to transition industrial parks toward cleaner production methods, traditional energy management systems exhibit significant limitations in data collection, real-time monitoring, and intelligent analysis, making it difficult to meet the urgent demands for carbon reduction. To address these challenges, this study proposes a carbon data management approach for industrial parks based on digital twin technology and develops an intelligent system that integrates monitoring, environmental surveillance, energy management, and carbon emission monitoring. The system supports efficient energy-saving and carbon-reducing decision making by real-time collection of energy consumption data. By incorporating Building Information Modeling (BIM) and Internet of Things (IoT) technologies, the system facilitates the integration and visualization of multi-source data, significantly enhancing the transparency of carbon data. The results of the carbon reduction validation system demonstrate that the application of this platform and its associated facilities can significantly reduce carbon emissions in the park, providing robust support for the transition of industrial parks toward low-carbon and sustainable development.

High-altitude environments characterized by low air pressure, hypoxia, and strong solar radiation have a significant impact on human thermal comfort; however, existing thermal comfort theories and evaluation models are primarily developed under low-altitude climatic conditions, and their applicability in plateau regions remains limited. With the acceleration of urbanization and the increase in residential, tourism, and occupational activities in high-altitude areas, systematically reviewing the research progress on thermal comfort in such environments is of great practical significance. This study combines systematic literature retrieval and bibliometric analysis, based on the Web of Science Core Collection and China National Knowledge Infrastructure (CNKI) databases, to analyze relevant studies published since 2001. Using CiteSpace, research hotspots, collaboration networks, and evolutionary trends are visualized. The results indicate that current research hotspots mainly focus on physiological responses and thermal adaptation mechanisms under low-pressure and hypoxic conditions, thermal comfort regulation strategies for high-altitude buildings and environments, and the applicability and modification of conventional thermal comfort models. Emerging trends include multi-environmental factor coupling analysis, adaptive model development, region-specific building design approaches, and health-oriented comprehensive evaluation frameworks. The findings provide valuable references for building thermal environment design, regional revision of thermal comfort evaluation standards, and policy-making in high-altitude regions.

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection.