淮河流域夏玉米生育阶段需水量及农业干旱时空特征

基于淮河流域110个气象站点1961—2015年日尺度气象资料,利用Penman-Monteith (P-M)公式和作物系数法,分析夏玉米各生育阶段需水量时空变化特征,以作物水分亏缺指数(Crop Water Deficit Index, CWDI)为农业干旱评价指标,揭示夏玉米生育期干旱时空演变规律,并利用四大类33种分布函数拟合水分亏缺指数序列,建立最优概率分布模型,估算淮河流域夏玉米各生育阶段干旱发生概率。结果表明:1)近55年来,夏玉米生育期需水量均呈明显下降趋势,空间分布均大致呈现流域中北部较高而西南部和东南部较低的特征;2)夏玉米各生育阶段水分亏缺指数无明显趋势变化,除拔节—抽雄期外,其余生育阶段流域整体上均呈现水分亏缺状态,且流域北部相对于南部水分亏缺严重;3)夏玉米生育期中,播种—出苗期和抽雄—乳熟期干旱发生概率最大,除播种—出苗期特旱发生概率在30%～50%之间,各生育阶段不同等级干旱概率均大致在20%以内。

淮河上游地区夏玉米生长降水关键期内旱涝致灾降水阈值研究

基于淮河上游地区11个气象站点1959—2015年降水资料,利用夏玉米气候产量与生育期降水序列进行相关性分析,确定降水影响夏玉米生长的关键时段,定义为夏玉米生长降水关键期,并采用21种分布函数对降水关键期内降水序列进行拟合,选取K-S和A-D方法进行拟合优度检验,建立最优概率分布模型,再基于降水概率分位数法定量化设计夏玉米各级旱涝的降水阈值R,并验证指标的合理性。结果表明:(1)淮河上游地区夏玉米抽雄-成熟生育时段及前一旬降水量对夏玉米产量影响显著;(2)各站点夏玉米生长降水关键期内降水序列的最优概率分布模型差异明显;(3) 11个站点夏玉米各级旱涝灾害的降水阈值地区差异较大,利用泰森多边形法确定整个淮河上游地区夏玉米生长降水关键期内旱涝致灾降水阈值分别是:重旱R<73 mm,旱73 mm≤R<138 mm,正常138 mm≤R<337 mm,涝337 mm≤R<475 mm,重涝R≥475 mm。

淮河流域蚌埠闸以上地区夏玉米生长水分关键期干旱致灾降水阈值

基于淮河流域蚌埠闸以上地区66个气象站点1961—2015年气象资料,利用夏玉米气候产量与生育期内水分盈亏指数进行了相关性分析,确定夏玉米生长水分关键期,并利用28种分布函数对水分关键期内降水量序列进行了拟合,K-S和A-D进行了拟合优度检验,建立了最优概率分布模型,再基于降水概率分位数法定量化设计夏玉米各级干旱的致灾降水阈值R,通过典型干旱年份和站点验证了指标的合理性。结果表明:(1)淮河流域蚌埠闸以上地区夏玉米抽雄—成熟阶段是夏玉米生长的水分关键期;(2)各站点夏玉米生长水分关键期内降水序列的最优概率分布模型差异明显;(3)66个站点夏玉米干旱致灾的降水阈值地区差异较大,但各级阈值空间分布大致相同,均呈现北部至南部依次增加的趋势,其中西部的桐柏和南部的霍山最高,利用泰森多边形法确定整个研究区夏玉米生长水分关键期内干旱致灾降水阈值分别是:轻旱147≤R<172mm,中旱118≤R<147mm,重旱89≤R<118mm,特旱R<89mm。

An improved cross entropy algorithm for steelmaking-continuous casting production scheduling with complicated technological routes

In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to optimize the SCC production scheduling(SCCPS) problem. Based on the CE method, a matrix encoding scheme was proposed and a backward decoding method was used to generate a reasonable schedule. To describe the distribution of the solution space, a probability distribution model was built and used to generate individuals. In addition, the probability updating mechanism of the probability distribution model was proposed which helps to find the optimal individual gradually. Because of the poor stability and premature convergence of the standard cross entropy(SCE) algorithm, the improved cross entropy(ICE) algorithm was proposed with the following improvements: individual generation mechanism combined with heuristic rules, retention mechanism of the optimal individual, local search mechanism and dynamic parameters of the algorithm. Simulation experiments validate that the CE method is effective in solving the SCCPS problem with complicated technological routes and the ICE algorithm proposed has superior performance to the SCE algorithm and the genetic algorithm(GA).