Research on 3D Visualization of Safety Monitoring and Early Warning for Steel Continuous Casting Scenarios

doi:10.16182/j.issn1004731x.joss.24-0327

Abstract

Abstract:

In order to improve the visualization and integration of production safety monitoring and fault warning, a three-dimensional (3D) visualization model architecture for whole-process industrial production safety monitoring and early warning for steel continuous casting scenarios was designed. By using 3ds Max and Unity3D, a multi-dimensional and multi-scale model was built, and functional modules such as visualization display and multi-level early warning for safety monitoring data were developed. By combining WebGL technology and Node.js runtime environment, the visualization of whole-process industrial production safety monitoring based on Web terminal was realized. The alarm threshold determination method for whole-process industrial production safety monitoring parameters was analyzed, and a single-parameter evaluation early warning model and a multi-parameter coupling early warning model were proposed. The early warning level and standard were clarified, and the feasibility and effectiveness of the established model were verified by simulation analysis.

Key words: safety monitoring, multi-parameter coupling, multi-level early warning, 3D visualization, steel continuous casting

CLC Number:

TP391.9

Zhang Wei, Sheng Wei, Cao Yidan, Zhao Tingsheng. Research on 3D Visualization of Safety Monitoring and Early Warning for Steel Continuous Casting Scenarios[J]. Journal of System Simulation, 2025, 37(8): 1991-2003.

Figures/Tables 14

Fig. 1

Table 1

Fig. 2

Fig. 3

Table 2

Classification of single-parameter evaluation early warning levels for safety monitoring

评价指标	预警等级	颜色
$δ i$ ≤25%	良好	绿
25%＜ $δ i$ ≤50%	轻警	黄
50%＜ $δ i$ ≤75%	中警	橙
$δ i$ ＞75%	重警	红

Table 2

Table 3

Classification of multi-parameter coupling early warning levels for safety monitoring

评价指标	预警等级	颜色
$I n$ ≤0.25	良好	绿
0.25＜ $I n$ ≤0.5	轻警	黄
0.5＜ $I n$ ≤0.75	中警	橙
$I n$ ＞0.75	重警	红

Table 3

Fig. 4

Fig. 5

Table 4

Table 5

Fig. 6

Fig. 7

Table 6

Table 7

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监测指标	监测参数	传感器类型
人员行为	人员位置	电子围栏
人员行为	是否佩戴劳保护品	视频监控
钢包及钢包回转台	钢水浇注温度	温度传感器
	钢包回转台加速度	加速度传感器
	钢包回转台应变	应变计
中间包	钢液流速	位移传感器
结晶器及二次冷却装置	结晶器铜板温度	温度传感器
	冷却水温度	温度传感器
	冷却水速度	液体流量传感器
	振动频率	开关传感器
拉坯及传送装置	拉坯速度	速度测试仪
拉坯及传送装置	坯壳厚度	超声波测厚仪
作业环境	CO浓度	CO传感器
	H₂浓度	氢气检测仪
	环境气压	气压传感器
	环境温度	温度传感器

监测参数	参数权重	数据条数
钢包回转台加速度	0.129	471 413
CO浓度	0.228	54 655
H₂浓度	0.210	55 246
钢包回转台应变	0.081	4 849
环境温度	0.189	4 480
环境气压	0.163	5 057

监测参数	样本均值	标准差	95%置信区间
回转台加速度/(m/s²)	0.692	0.006	[0.681, 0.703]
CO浓度/(μg/m³)	1 883.75	17.5	[1 852.5, 1 918.75]
H₂浓度/(μg/m³)	34.375	0.804	[32.857, 36.071]
回转台应变/με	34.953	4.649	[26.160, 44.208]
环境温度/℃	24.950	0.228	[24.471, 25.377]
环境气压/kPa	101.043	0.037	[100.968, 101.116]

预警方法	平均预测时间/ms	平均训练时间/s
本文	15	—
RF	773	16.48
FFNN	25	3.20
SVM	28	7.84

性能	传统监测形式	本文监测形式
终端访问	组态软件	PC、Web、手机
模型形式	文本、二维图形	三维立体模型
虚拟漫游	不可漫游	可漫游与人机交互
动画渲染	无动画效果	具备动画渲染效果
数据展示	单一数据显示	数据动态可视化
实时性	数据更新滞后	实时监测
扩展性	功能固定不易扩展	可定制型强