# A Scraper Conveyor Coal Flow Monitoring Method Based on Speckle Structured Light Data
## 5 Application Example and Analysis of Scraper Conveyor Monitoring
### Accuracy Analysis of Coal Quantity Calculation with PDS Algorithm  
This article uses the C++ language in Visual Studio software to compile the algorithm under the Ubuntu system, the processor is i7-12700K, the graphics card model is NVIDIA GeForce RTX 3080, and the system memory is 32 G. The compiled algorithm is installed on an industrial computer with model GK4000 underground for testing. Figure 12 shows the comparison of the effects of different simplified algorithms on the original coal point cloud data. Line (1) in Figure 12 indicates that the point cloud data simplification rate is 30%, and line (2) in the figure indicates that the simplification rate is 60%. In particular, the color that appears in the point cloud data graph in this article represents the height of the point cloud data. Blue is the lowest, green is next, yellow is next, and red is the highest. It can be seen from the figure that the simplification effect of the four simplification algorithms is similar in general. However, the distribution of point cloud data processed by the bounding box grid method and the PDS algorithm is more uniform. Compared with other methods, the PDS algorithm does not give up the elevation of each point cloud data, so the point cloud data simplified by the PDS algorithm has the point cloud height information closest to the original data. To compare whether the point cloud data before and after simplification is distorted, this paper conducts modeling analysis on ten consecutive frames of point cloud data. Figure 13 shows the point cloud data constructed from the original data and the point cloud model after 60% simplification. The coal blocks share six images from entering the monitoring area to leaving the monitoring area, which is consistent with the sampling rate set in this paper. Figure 13e is the computational mathematical model constructed in this paper. It can be seen from Figure 13c,f that the simplified point cloud data in this paper are constructed by the Delaunay triangulation method. The overall difference is not large, which shows that the simplified method in this paper does not damage the internal relationship of the model. To quantitatively calculate the improved accuracy of the PDS algorithm proposed in this paper and the same type of grid simplification algorithm, this paper compares random grid simplification, median grid simplification, mean grid simplification, and the PDS algorithm in this paper. We collected 3000 continuous point cloud data and measured the corresponding coal volume data and compared the algorithms using the accuracy calculation method of Equation (10). Table 2 shows the accuracy of the final  
Figure 12: Comparison of simplification effects of different algorithms: (**a**) Raw point cloud; (**b**) Random simplification; (**c**) Curvature simplification; (**d**) Bounding Box Grid; (**e**) PDS. volume for different simplification methods. Where \(y_{p}\) is the predicted volume and \(y_{r}\) is the real volume. \[accuracy=\frac{\left|y_{p}-y_{r}\right|}{y_{r}} \tag{10}\]  
Figure 13: Original and simplified models: (**a**) Image; (**b**) Raw point cloud; (**c**) Original model; (**d**) Simplified point cloud; (**e**) Computational model; (**f**) Simplified model. It can be seen from Table 2 that the PDS algorithm proposed in this paper has the best accuracy under different simplified precisions. The mean simplification algorithm is next, and the random simplification is similar to the median simplification. The superiority of the PDS algorithm is that it considers the density relationship in the range of point cloud numbers in the process of simplification. The processing time of the above methods is basically the same. The processing time of one frame of data without simplification is 17.6 s, but when the simplification rate is 70%, the processing time is only 0.81 s. This fully shows that it is very necessary to simplify the coal point cloud data. When the simplification rate reaches 80%, the processing time is 1.45 s and the accuracy also reaches 93.42%.