3results about How to "Reduce establishment" patented technology

The present invention relates to an online calibration method and system for a coal gasification device. A calibration model of a coal gasification device is constructed in advance, and calibration data required for calibrating the coal gasification device is obtained. Finally, the obtained calibration data is input into the calibration model of the coal gasification device to obtain a calibration result of the coal gasification device, and a calibration report for online processing is automatically generated, thereby reducing the time cost of manual calibration, improving the calibration efficiency and accuracy of the coal gasification device, and reducing the preparation and verification time of the calibration report of the coal gasification device.

The invention relates to the technical field of spatial modeling and three-dimensional point cloud data processing, in particular to a point cloud model optimization method and a base station point selection method based on point cloud model feature extraction. The method comprises the steps of firstly performing down-sampling on a point cloud model, and then for each point, acquiring a unit normal vector of a fitting plane of the point and a neighborhood point of the point as a unit normal vector of the point; performing normal vector optimization on each point in the simplified point cloud; and calculating the feature measurement of each point in combination with the optimized unit normal vector of each point in the simplified point cloud, and selecting the points with the feature measurement in a set feature interval to form a feature point cloud. According to the method, data simplification of the point cloud model is realized under the condition that the original features of the point cloud model are reserved.

This invention discloses a dynamic quality analysis and optimization system method based on the ultimate computing power of a development model, belonging to the field of software engineering process measurement and quality prediction optimization technology. It addresses the difficulties in quality decision-making caused by inconsistent and untraceable multi-source process data, static defect analysis, and the unstable and easily reversed linkage prediction of productivity and defect escape rate. The method unifies the reading of project process data and standardizes, completes, and weights the data according to a data dictionary to form a unified dataset, automatically generating and versioning the stored documents. It statistically analyzes defect severity and weighted defect quantity within a rolling time window, calculating the defect density sequence and Cpk. A two-layer linkage prediction model is constructed, with gating corrections for productivity and defect escape rate. Satisfaction regression and Monte Carlo output distribution results are used for quality decision-making.