To realize high-accuracy posture measurement in large scale, a posture measurement system is proposed in this study based on a six-laser multilateral method, mainly composed of six laser tracers and three reflectors—a different number of laser tracers tracks each mirror. But in this state, the system is positive definite, and it cannot achieve the system parameters by self-calibration. To solve this problem, a stepwise calibration method to obtain the system parameters by three-step calibration is presented. Simulations and experiments are carried out to verify the effectiveness of the proposed method. The experimental results show that the posture errors are distributed over [−13.6″, 6.6″] when the measurement range is [5 m, 7 m], which shows that the new measurement system has a high posture measurement accuracy in large scale.

Currently, there are three fundamental theoretical issues that need to be addressed in the research field of the Archimedes Spiral Wind Blade (ASWB): (1) the existing description of the generation principle of the ASWB’s surface is qualitative, which needs to be formally described using mathematical tools; (2) in the published literature, no studies were found that attempt to fully describe the surface of the ASWB using mathematical tools; (3) in the published literature, no clear definition of the ASWB can be found. The first and second of the above three problems are relatively easy to solve, whereas the ultimate solution to the third problem requires long-term communication and discussion among researchers from various countries. Therefore, this paper focuses primarily on the first of the above problems, namely, to describe the intermediate process of transforming an irregular planar figure into the 3D surface of an ASWB in a standardized manner using mathematical tools including a polar coordinate system, planar and spatial Cartesian coordinate systems, the curve equation of an Archimedes spiral, differential equations, and so on. For the second problem, this paper proposes an alternative approach: namely, conjecturing that the ASWB surface can be approximated by an infinite number of 3D Archimedes spirals, and an example with a finite number of such spirals is provided. For the third problem, this paper can currently only suggest that the precise definition of the ASWB should be based on a standardized description of the generation principle of the ASWB surface, or, alternatively, on an accurate geometric description of the shape of the ASWB surface. The scientific contribution of this paper lies in proposing, for the first time from a purely geometric perspective (independent of aerodynamics), three fundamental theoretical problems concerning ASWB, along with preliminary alternative ideas toward their solution.

Long baseline multiparameter measurement exhibits pronounced temporal dependence, operating condition variation, and coupling among heterogeneous parameters, making normal fluctuations difficult to distinguish from true anomalies and complicating anomaly localization and source tracing. To address these issues, a digital twin-assisted condition aware contrastive reconstruction method is proposed. First, a condition aware temporal encoding and reconstruction network explicitly incorporates nominal speed, motion direction, and motion stage into normal behavior learning, while contrastive regularization enhances same condition compactness and inter condition separability in the latent space. Then, reconstruction deviation, latent space deviation, and deviation in parameter relationships are fused into a joint anomaly score, and dominant anomalous parameters and corresponding measurement chains are identified through contribution analysis. In addition, physically verified normal samples continuously update the normal behavior baseline through physical-virtual feedback. Experiments were conducted on an 80 m long baseline platform with 63 synchronized measurement parameters and seven representative anomaly types. The proposed method achieves an F1-score of 95.77±1.02% and a false positive rate of 2.39±0.24%, outperforming five published anomaly detection baselines while maintaining stable performance across nine operating speeds and different motion conditions. The average event detection rate reaches 97.35%, with onset-time and onset-position errors of 0.030±0.018 s and 11.82±6.98 mm, respectively. Under gradual distribution drift, feedback updating improves the F1-score from 91.62% to 95.32% and reduces the false positive rate from 5.90% to 2.82%. The inference time is only 4.36 ms per window, demonstrating potential for long-term online anomaly analysis and providing a new approach for long baseline multiparameter measurement.