资源型城市电炉产业的经济效益估计  

Economic Benefit Estimation of Electric Furnace Industry in Resource-based Cities

作　　者：赵敏 ZHAO Min(Industry,Commerce and Information Bureau of Hainan Tibetan Autonomous Prefecture Hainan Tibetan Autonomous Prefecture,Hainan Tibetan Autonomous Prefecture 813099,China)

机构地区：[1]青海省海南藏族自治州工业商务和信息化局,青海海南藏族自治州813099

出　　处：《工业加热》2023年第11期66-70,共5页Industrial Heating

摘　　要：由于资源型城市中,电炉产业的经济效益估计过程中,指标数据过多且复杂,导致资源型城市的电炉产业经济效益估计精度不高。设计一种新的估计模型。搭建科学、公平、全面的电炉产业经济效益指标体系,并对指标数据做标准化处理。设计用于电炉产业经济效益估计的改进卷积神经网络模型,优化该模型的学习速率、激活函数、训练参数,将指标数据输入训练好的模型中,得到电炉产业经济效益估计结果。实验结果表明:该模型的收敛性好,且估计精度始终保持在96%,估计耗时平均值为0.66 s,可以实现快速精准估计资源型城市电炉产业经济效益的目标,具有较好的实用价值。In resource-based cities,there are too many and complex index data in the process of estimating the economic benefits of the electric furnace industry,which leads to the low accuracy of the economic benefits estimation of the electric furnace industry in resource-based cities.A new estimation model is designed.Establish a scientific,fair and comprehensive economic benefit index system for the electric furnace industry,and standardize the index data.An improved convolution neural network model is designed to estimate the economic benefits of the electric furnace industry.The learning rate,activation function and training parameters of the model are optimized.The index data are input into the trained model to obtain the estimated results of the economic benefits of the electric furnace industry.The experimental results show that the model has good convergence,and the estimation accuracy is always kept at 96%,and the average estimated time consumption is 0.66s.It can achieve the goal of rapid and accurate estimation of the economic benefits of the electric furnace industry in resource-based cities,and has good practical value.

关 键 词：资源城市 电炉产业经济 效益估计 指标数据标准化 改进卷积神经网络 学习速率 激活函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]