智能爆破分析数据

通过在爆破现场采集孔距、台阶高度、钻孔精度、炮孔直径等数据，分析计算得出爆破的最佳抗线、最佳爆破位置及炸药用量，得到不同爆破现场的爆破优化方案，最终输出一个爆破效果预测分析报告和爆破优化设计，实现模型自动绘制成爆破块度分布曲线，便于进行爆破块度分布的量化分析，极大的优化了爆破安全管理模式，增强了爆破管理的准确性和安全性。1.数据来源
通过在爆破现场采集孔距、台阶高度、钻孔精度、炮孔直径、爆破掘进尺寸、开挖断面面积等数据；
2.数据处理
2.1在爆破模型中输入孔距D、炸药单耗q、台阶高度h、钻孔精度φ、爆破掘进尺寸L、开挖断面面积S、炮孔直径d等数据，系统载入数据后匹配至模型中对现场爆破进行模拟演示，对级配优化、爆破振动优化、爆破飞石优化、爆堆形态优化以及其他目标的优化，计算出最佳抗线l、爆破位置及炸药用量Q，最终输出一个爆破效果预测分析报告和爆破优化设计。
2.2爆破优化设计方案中，最佳抗线和炸药用量通过以下公式计算得出：
（1）最佳抗线l=nd（n=25~35），其中n为项目系数，该系数是爆破前、在项目现场进行爆破测试确定，不同项目的系数不同，样例数据中n=30；
（2）炸药用量Q=qLS，其中q为炸药单耗，L为爆破掘进尺寸，S为开挖断面面积；
3.数据应用
根据上述分析计算，得到不同爆破现场的爆破优化方案，实现模型自动绘制成爆破块度分布曲线，并输出爆破效果预测分析报告，便于进行爆破块度分布的量化分析，极大的优化了爆破安全管理模式，增强了爆破管理的准确性和安全性。

By collecting data such as hole spacing, bench height, drilling accuracy, borehole diameter and other parameters at blasting sites, the optimal burden, optimal blasting position and explosive dosage are obtained through analysis and calculation, so as to develop blasting optimization schemes for different blasting sites. Finally, a blasting effect prediction and analysis report and blasting optimization design are output, and the model can automatically generate blasting fragmentation distribution curves to facilitate quantitative analysis of blasting fragmentation distribution, greatly optimizing the blasting safety management mode and improving the accuracy and safety of blasting management.

1. Data Source
Collect data including hole spacing, bench height, drilling accuracy, borehole diameter, blasting excavation size, excavation cross-sectional area and other parameters at blasting sites;

2. Data Processing
2.1 Input data such as hole spacing D, specific explosive consumption q, bench height h, drilling accuracy φ, blasting excavation size L, excavation cross-sectional area S, borehole diameter d into the blasting model. After loading the data, the system matches them into the model to simulate on-site blasting, optimizes grading, blasting vibration, flying rock, muck pile morphology and other targets, calculates the optimal burden l, blasting position and explosive dosage Q, and finally outputs a blasting effect prediction and analysis report and blasting optimization design.
2.2 The optimal burden and explosive dosage in the blasting optimization design are calculated via the following formulas:
(1) Optimal burden l = nd, where n ranges from 25 to 35. The project coefficient n is determined by on-site blasting tests before blasting, and differs across various projects. In the sample data, n=30;
(2) Explosive dosage Q = qLS, where q is the specific explosive consumption, L is the blasting excavation size, and S is the excavation cross-sectional area;

3. Data Application
Based on the above analysis and calculation, blasting optimization schemes for different blasting sites are obtained. The model can automatically generate blasting fragmentation distribution curves and output blasting effect prediction and analysis reports, which facilitates quantitative analysis of blasting fragmentation distribution, greatly optimizes the blasting safety management mode, and enhances the accuracy and safety of blasting management.