基于Dijkstra算法的煤矿矿井水水泵动态调度方法OA
Dynamic Scheduling Method of Coal Mine Water Pump Based on Dijkstra Algorithm
由于传统的"避峰就谷"控制策略,无法根据涌水量大小调整水泵开启数据,导致水泵不能根据实时水位和涌水量变化进行调度,进而增加了调度成本.因此,提出基于Dijkstra算法的煤矿矿井水水泵动态调度方法.该方法基于数据层、平台层、应用层和网络层建立SSM轻量级框架搭建数字孪生排水系统,通过改进的"避峰就谷"控制策略对数字孪生排水系统进行控制,并结合涌水量大小实现水泵开启数调控.同时利用灰狼优化—差分进化—支持向量机(GWO-DE-SVM)模型预测涌水量,将水泵调度问题转换为求解电费最小值问题,基于改进的控制策略和全天涌水量构建水泵调度数学模型,并采用Dijkstra算法求解最小耗电方案,由此反推水泵启停运行情况,从而实现煤矿矿井水水泵动态调度.结果表明,GWO-DE-SVM预测的绝对误差和相对误差较小,实际值471.6941 m3/h时,GWO-DE-SVM模型预测值绝对误差为3.9344 m3/h、相对误差为0.0083%.采用改进策略调度后,总泵时为44 h、电费为15 541.86元,相较于传统的"避峰就谷"控制策略分别降低5 h和445.46元.由此得出,所提调度方法可提升煤矿矿井水水泵动态调度效果,降低了调度成本.
Because of the traditional control strategy of"avoiding peaks and taking valleys",the pump start-up data can not be adjusted according to the water inflow,which leads to the pump can not be scheduled according to the real-time water level and water inflow changes,thus increasing the scheduling cost.Therefore,a dynamic dispatching method of mine water pump based on Dijkstra algorithm is proposed.Based on the data layer,platform layer,application layer and network layer,the SSM light-weight framework is established to build the digital twin drainage system,and the digital twin drainage system is controlled by the improved"avoiding peaks and valleys"control strategy,and the number of pumps is adjusted according to the water inflow.At the same time,the grey wolf optimization-differential evolution-support vector machine(GWO-DE-SVM)model is used to predict the water inflow,and the problem of pump scheduling is transformed into the problem of solving the minimum electrici-ty charge.Based on the improved control strategy and the all-day water inflow,the mathematical model of pump scheduling is constructed,and the Dijkstra algorithm is used to solve the minimum power consumption scheme,so that the start-stop opera-tion of pumps can be reversed,and the dynamic scheduling of water pumps in coal mines can be realized.The results show that the absolute error and relative error of GWO-DE-SVM prediction are small.When the actual value is 471.6941 m3/h,the abso-lute error and relative error of GWO-DE-SVM model prediction value are 3.9344 m3/h and 0.0083%.After adopting the im-proved strategy,the total pump time is 44 hours,and the electricity bill is 15 541.86 yuan,which is 5 hours and 445.46 yuan lower than the traditional"avoiding peaks and valleys"control strategy.Therefore,the proposed scheduling method can im-prove the dynamic scheduling effect of mine water pumps and reduce the scheduling cost.
杨晓娟;李进;李华
中煤西安设计工程有限责任公司,陕西,西安 710000中煤西安设计工程有限责任公司,陕西,西安 710000北京达美盛软件股份有限公司,北京 100000
信息技术与安全科学
数字孪生动态调度涌水量预测避峰就谷SVM
digital twindynamic schedulingwater inflow predictionavoiding peaks and valleysSVM
《微型电脑应用》 2026 (5)
10-14,5
国家自然科学基金(61673128)
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