CN112430727B - A kind of continuous annealing furnace furnace temperature early warning method and system - Google Patents
A kind of continuous annealing furnace furnace temperature early warning method and system Download PDFInfo
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Abstract
Description
技术领域technical field
本发明涉及冷轧工艺领域,尤其涉及一种连续退火炉(热镀锌机组退火炉)炉温(退火炉温度)预警方法及系统,适用于卧式连续退火炉的炉内数据自动采集存储,并通过基于BP神经网络算法和SPC过程控制图理论模型对炉子稳态进行预警监控。The invention relates to the field of cold rolling technology, in particular to a furnace temperature (annealing furnace temperature) early warning method and system for a continuous annealing furnace (hot-dip galvanizing unit annealing furnace), which is suitable for automatic collection and storage of furnace data in a horizontal continuous annealing furnace, And through the BP neural network algorithm and the SPC process control graph theoretical model, the furnace steady state is early-warned and monitored.
背景技术Background technique
通常,连续退火炉是这样的一种装置,也即在连续退火炉中执行热处理工序,以根据预设温度方案来升高或降低室温或低温金属带的温度,以获得所需的材料特性。连续退火炉主要分为加热段和冷却段,加热段分为预热段(Pre Heating Section)、加热段(Heating Section)和均热段(Soaking Section),冷却段分为缓冷段(Slow CoolingSection)、快冷段(Rapid Cooling Section)、过时效段(OAS)和终冷段(Final CoolingSection)。退火炉的温度控制要根据工艺要求和设定的曲线对带钢进行精确的升温、保温和降温控制,在此过程中还需要确保退火炉的炉温均匀性,它直接决定着产品的质量。Generally, a continuous annealing furnace is a device in which a heat treatment process is performed to raise or lower the temperature of a room or low temperature metal strip according to a preset temperature scheme to obtain desired material properties. The continuous annealing furnace is mainly divided into a heating section and a cooling section. The heating section is divided into a preheating section, a heating section and a soaking section, and the cooling section is divided into a slow cooling section. ), Rapid Cooling Section, Over Aging Section (OAS) and Final Cooling Section. The temperature control of the annealing furnace requires precise heating, heat preservation and cooling control of the strip according to the process requirements and the set curve. In this process, it is also necessary to ensure the uniformity of the furnace temperature of the annealing furnace, which directly determines the quality of the product.
现有技术中专利号为CN201710828269.2的中国专利公开了一种退火炉温度控制方法及系统,通过获取退火炉内M个加热控制区中的每个加热控制区的实际所需加热负载,分别判断M个加热控制区中的每个加热控制区的实际所需加热负载是否小于预设负载阈值,将M个加热控制区中实际所需加热负载小于预设负载阈值的每个加热控制区所对应的燃气流量控制器均保持在第一恒定流量,并控制同一加热控制区内的各个烧嘴进行同时间歇性点燃。The Chinese patent with the patent number of CN201710828269.2 in the prior art discloses a method and system for controlling the temperature of an annealing furnace. Determine whether the actual required heating load of each heating control zone in the M heating control zones is less than the preset load threshold value, and set the actual required heating load of each heating control zone in the M heating control zones less than the preset load threshold value. The corresponding gas flow controllers are all maintained at the first constant flow rate, and control each burner in the same heating control zone to ignite intermittently at the same time.
现有技术中专利号为CN201310109424.7的中国专利公开了一种辊底式退火炉温度控制方法,包括六个步骤,第一步,计算工艺给定的升温曲线对应的产品加热段出口的目标温度;第二步,计算钢坯温度;第三步,计算钢坯到加热段段末的剩余加热时间;第四步,对加热温度进行前馈控制,以步骤二计算的钢坯温度为起点,利用步骤三计算的钢坯剩余加热时间t,以工艺曲线作为炉温输入,按照给定的时间步长Δt,预报钢坯到达加热段段末的温度T,然后求出预报温度和目标温度的偏差,进而求出钢坯所在段的前馈温度控制量;第五步,利用加热段出口位置钢坯温度跟踪模型的计算结果和与钢坯在该处的目标温度的偏差,确定加热温度的反馈控制量;第六步,对辊底炉加热段进行“前馈+反馈”的温度设定控制。The Chinese patent with the patent number of CN201310109424.7 in the prior art discloses a temperature control method for a roller hearth annealing furnace, which includes six steps. The first step is to calculate the target of the product heating section outlet corresponding to a given heating curve of the process. temperature; the second step, calculating the billet temperature; the third step, calculating the remaining heating time from the billet to the end of the heating section; the fourth step, performing feedforward control on the heating temperature, taking the billet temperature calculated in step two as the starting point, using step three Calculate the remaining heating time t of the billet, take the process curve as the furnace temperature input, predict the temperature T of the billet reaching the end of the heating section according to the given time step Δt, and then obtain the deviation between the predicted temperature and the target temperature, and then obtain the billet. The feedforward temperature control amount of the section where it is located; the fifth step, use the calculation result of the billet temperature tracking model at the outlet of the heating section and the deviation from the target temperature of the billet at that place to determine the feedback control amount of the heating temperature; sixth step, to The heating section of the roller hearth furnace carries out "feedforward + feedback" temperature setting control.
上述现有技术中,按照工艺曲线对炉温进行监控仅能做超限报警,无法做到预警与劣化趋势提醒,现有技术已不能满足高生产要求的产线。In the above-mentioned prior art, monitoring the furnace temperature according to the process curve can only provide an over-limit alarm, but cannot provide an early warning and a deterioration trend reminder, and the prior art can no longer meet the production line with high production requirements.
发明内容SUMMARY OF THE INVENTION
本发明的目的在于提供一种连续退火炉(热镀锌机组退火炉)炉温预警方法及系统,将传统报警方式与SPC过程控制统计及神经网络预测算法方式相结合,为热镀锌机组连续退火炉提供一种全新的温度健康监控及预警方法。The purpose of the present invention is to provide a furnace temperature early warning method and system for a continuous annealing furnace (annealing furnace for hot-dip galvanizing units), which combines traditional alarm methods with SPC process control statistics and neural network prediction algorithm methods to provide continuous hot-dip galvanizing units. The annealing furnace provides a new method of temperature health monitoring and early warning.
为实现上述目的,本发明采用以下技术方案:To achieve the above object, the present invention adopts the following technical solutions:
本申请第一个方面提供了一种连续退火炉炉温预警方法,包括:A first aspect of the present application provides a continuous annealing furnace temperature warning method, including:
实时采集退火炉内各炉段设定的工艺点位的工艺参数,所述设定的工艺点位包括氧气含量、氢气含量、炉内气氛的露点、炉压、目标板温、辊轴张力、钢卷卷号、炉温、钢卷规格、机组速度、板温、废气中的一种或更多种,优选为至少两种或更多种;The process parameters of the process points set in each furnace section in the annealing furnace are collected in real time, and the set process points include oxygen content, hydrogen content, dew point of the furnace atmosphere, furnace pressure, target plate temperature, roller tension, One or more, preferably at least two or more, of coil number, furnace temperature, coil specification, unit speed, plate temperature, and exhaust gas;
将采集到的数据传递给SPC系统,所述SPC系统将采集到的数据按照工艺点位的类别进行分组,各分组独立地设置其样本采集时间段,对每个样本采集时间段的数据按照其工艺点位类别对应的预设的样本采集方法进行处理,形成单个的样本;The collected data is transmitted to the SPC system, and the SPC system groups the collected data according to the category of the process point, and each group independently sets its sample collection time period, and the data of each sample collection time period is based on its The preset sample collection method corresponding to the process point category is processed to form a single sample;
对所有的样本进行数据质量检查,仅保留机组正常运行时段内的数据,剔除故障时段内的数据,对正常运行时段少数缺漏数据进行插补,形成样本集合;Data quality inspection is carried out on all samples, only the data in the normal operation period of the unit is retained, the data in the fault period is excluded, and a few missing data in the normal operation period are interpolated to form a sample set;
对样本集合进行特征提取,计算预设时间内样本的平均值和标准差;Perform feature extraction on the sample set, and calculate the average and standard deviation of the samples within a preset time;
根据第一判异规则对样本集合进行统计判定,若判定结果为超限异常,则触发超限报警;否则,根据第二判异规则对样本集合进行统计判定,若判定结果为波动异常,表明退火炉存在劣化趋势,非稳定情况出现,则触发劣化趋势预警;Statistical judgment is made on the sample set according to the first discrimination rule. If the judgment result is abnormal overrun, an overrun alarm will be triggered; otherwise, the sample set will be statistically judged according to the second discrimination rule. If the judgment result is abnormal fluctuation, it indicates that The annealing furnace has a deterioration trend, and if an unstable situation occurs, a deterioration trend warning is triggered;
基于BP神经网络算法,预测退火炉内各炉段的炉温变化情况。Based on the BP neural network algorithm, the furnace temperature change of each furnace section in the annealing furnace is predicted.
优选地,所述预设的样本采集方法包括样本采集时间段的平均值、样本采集时间段的最大值中的一种或更多种。Preferably, the preset sample collection method includes one or more of the average value of the sample collection period and the maximum value of the sample collection period.
优选地,所述各设定工艺点位的辊轴张力包括开卷机张力、清洗段张力、入口活套张力、退火炉预热段张力、退火炉加热段入口张力、退火炉加热段出口张力、退火炉均热段张力、退火炉缓冷段张力、退火炉快冷段张力、锌锅段张力、中央活套张力、平整机入口张力、平整机出口张力、拉矫机张力、后处理段张力、出口活套张力、圆盘剪段张力、卷取机张力中的几种或更多种。Preferably, the roller tension at each set process point includes the tension of the uncoiler, the tension of the cleaning section, the tension of the inlet looper, the tension of the preheating section of the annealing furnace, the inlet tension of the heating section of the annealing furnace, the outlet tension of the heating section of the annealing furnace, Tension of soaking section of annealing furnace, tension of slow cooling section of annealing furnace, tension of fast cooling section of annealing furnace, tension of zinc pot section, tension of central looper, entrance tension of skin passer, exit tension of skin passer, tension of tension leveler, post-treatment Several or more of segment tension, exit looper tension, disc shear segment tension, and coiler tension.
优选地,所述钢卷规格包括钢卷的厚度、宽度、卷号、钢种中的一种或更多种。Preferably, the steel coil specification includes one or more of the thickness, width, coil number, and steel type of the steel coil.
优选地,所述废气包括颗粒物、SO2、NOX、O2、以及废气温度中的一种或几种,其中,x为NOx中O与N的原子数比值。Preferably, the exhaust gas includes one or more of particulate matter, SO 2 , NO X , O 2 , and exhaust gas temperature, where x is the atomic ratio of O to N in NOx.
优选地,在对炉火内的工艺参数进行实时采集之前,所述预警方法还包括:设定各炉段的各工艺点位的标准值,该标准值为其对应的工艺点位的健康度评价的参考范围。Preferably, before the process parameters in the furnace are collected in real time, the early warning method further includes: setting a standard value of each process point of each furnace section, and the standard value is the health degree of the corresponding process point. Reference range for evaluation.
优选地,所述第一判异规则包括:若预设时间内样本的平均值超出了其预设的异常阈值,判为异常点;如果当前时刻之前的一预设时间段内的异常点的个数超过预设值,则判定为超限异常。Preferably, the first discrimination rule includes: if the average value of the samples in a preset time exceeds its preset abnormal threshold, it is determined as an abnormal point; if the abnormal point in a preset time period before the current moment is abnormal If the number exceeds the preset value, it will be judged as an over-limit abnormality.
更优选地,所述预设的异常阈值可以根据健康状态下的设备运行状态信号值的各自分布模式计算获得,例如电机扭矩信号一般服从正态分布,取其均值加三倍标准差作为异常阈值。More preferably, the preset abnormal threshold value can be calculated and obtained according to the respective distribution patterns of the equipment operating state signal values in the healthy state. For example, the motor torque signal generally obeys a normal distribution, and the mean value plus three standard deviations is taken as the abnormal threshold value. .
优选地,所述第二判异规则包括:采用自定义的控制界限值,包括第一控制界限、第二控制界限和第三控制界限,第一控制界限为预设时间内样本的标准差的3倍,第二控制界限为所述标准差的2倍,第三控制界限为所述标准差的1倍;根据以下规则进行数据趋势分析,只要符合任意一条规则,则判定为波动异常;Preferably, the second discrimination rule includes: using a self-defined control limit value, including a first control limit, a second control limit and a third control limit, and the first control limit is the standard deviation of the sample within a preset time. 3 times, the second control limit is 2 times the standard deviation, and the third control limit is 1 time the standard deviation; data trend analysis is carried out according to the following rules, as long as any one of the rules is met, it is determined that the fluctuation is abnormal;
规则1:一个样本落在预设的第一控制界限以外;Rule 1: A sample falls outside the preset first control limit;
规则2:连续K个样本落在中心线的同一侧;Rule 2: consecutive K samples fall on the same side of the centerline;
准则3:连续K个样本递增或递减;Criterion 3: consecutive K samples are incremented or decremented;
规则4:连续K个样本中相邻样本上下交替;Rule 4: Adjacent samples in consecutive K samples alternate up and down;
准则5:连续K+1个样本中有K个样本落在中心线同一侧的第二控制界限以外;Criterion 5: K samples in consecutive K+1 samples fall outside the second control limit on the same side of the centerline;
准则6:连续K+1个样本中有K个样本落在中心线同一侧的第三控制界限以外;Criterion 6: K samples in consecutive K+1 samples fall outside the third control limit on the same side of the centerline;
准则7:连续K个样本落在中心线同两侧的第三控制界限之内;Criterion 7: consecutive K samples fall within the third control limit on the same side of the centerline;
准则8:连续K个样本落在中心线两侧且无一在第三控制界限以内;Criterion 8: consecutive K samples fall on both sides of the centerline and none of them are within the third control limit;
其中,K为正整数。Among them, K is a positive integer.
优选地,基于BP神经网络算法,预测退火炉内各炉段的炉温变化情况,包括:Preferably, based on the BP neural network algorithm, predict the furnace temperature change of each furnace section in the annealing furnace, including:
从样本集合中选取样本训练集;Select a sample training set from the sample set;
构建BP神经网络模型,BP神经网络模型包括输入层、隐藏层及输出层,相邻层之间各神经元进行全连接,每层各神经元之间无连接;设置模型参数,包括:设置输入层输入信号的选择,设置隐含层的层数以及各层的节点数,设置输出层的节点数、学习速率、传递函数、动量因子、最大训练次数和最小精度;Build a BP neural network model. The BP neural network model includes an input layer, a hidden layer and an output layer. The neurons between adjacent layers are fully connected, and there is no connection between the neurons in each layer. Set the model parameters, including: setting the input Layer input signal selection, set the number of hidden layers and the number of nodes in each layer, set the number of nodes in the output layer, learning rate, transfer function, momentum factor, maximum training times and minimum precision;
利用现有的样本训练集对BP神经网络模型进行训练,包括:按前向传播方向进行,从输入层至隐含层至输出层的方向,得到各神经元的输出值,直至得到最后输出层的输出值,若输出层的输出值与期望输出值不符,则按反向传播方向进行,根据输出层的实际输出和期望输出值之间的误差,按照设定的学习速率来调整神经元之间的连接权值;正向输出计算和反向权值修改交替进行,直到网络输出的误差小于预设的最小精度,或进行到预先设定的最大训练次数为止,以确定当前的炉温变化趋势的预测模型;Use the existing sample training set to train the BP neural network model, including: proceeding in the forward propagation direction, from the input layer to the hidden layer to the output layer, to obtain the output value of each neuron, until the final output layer is obtained If the output value of the output layer does not match the expected output value, proceed in the direction of reverse propagation. According to the error between the actual output of the output layer and the expected output value, adjust the neuron according to the set learning rate. The connection weights between the two; forward output calculation and reverse weight modification are alternately performed until the error of the network output is less than the preset minimum accuracy, or until the preset maximum training times are performed to determine the current furnace temperature change Predictive models of trends;
根据已经训练完成的BP神经网络模型,将样本集合输入到BP神经网络模型的输入层,经过神经网络的处理,得到当前的炉温变化趋势的预测值。According to the BP neural network model that has been trained, the sample set is input into the input layer of the BP neural network model, and the predicted value of the current furnace temperature change trend is obtained through the processing of the neural network.
更优选地,所述BP神经网络模型的学习速率选取范围在0.01-0.8之间,动量因子的选取范围在0-1之间、且比学习速率大。More preferably, the selection range of the learning rate of the BP neural network model is between 0.01-0.8, the selection range of the momentum factor is between 0-1, and is larger than the learning rate.
进一步地,所述BP神经网络模型的学习速率优选为0.06,动量因子优选为0.95。Further, the learning rate of the BP neural network model is preferably 0.06, and the momentum factor is preferably 0.95.
更优选地,所述BP神经网络模型的传递函数为 More preferably, the transfer function of the BP neural network model is
本申请第二个方面提供了一种连续退火炉炉温预警系统,包括:A second aspect of the present application provides a continuous annealing furnace temperature warning system, including:
数据采集模块,用于实时采集退火炉内各炉段设定的工艺点位的工艺参数,所述设定的工艺点位包括氧气含量、氢气含量、炉内气氛的露点、炉压、目标板温、辊轴张力、钢卷卷号、炉温、钢卷规格、机组速度、板温、废气中的一种或更多种;优选为至少两种或更多种;The data acquisition module is used to collect the process parameters of the process points set in each furnace section in the annealing furnace in real time, and the set process points include oxygen content, hydrogen content, dew point of the furnace atmosphere, furnace pressure, target plate One or more of temperature, roll tension, coil number, furnace temperature, coil specification, unit speed, plate temperature, exhaust gas; preferably at least two or more;
样本生成模块,用于将采集到的数据按照工艺点位的类别进行分组,各分组独立地设置其样本采集时间段,对每个样本采集时间段的数据按照其工艺点位类别对应的预设的样本采集方法进行处理,形成单个的样本;The sample generation module is used to group the collected data according to the category of the process point, each group independently sets its sample collection time period, and the data of each sample collection time period is preset according to its process point category. The sample collection method is processed to form a single sample;
数据质量检查模块,用于对所有的样本进行数据质量检查,仅保留机组正常运行时段内的数据,剔除故障时段内的数据,对正常运行时段少数缺漏数据进行插补,形成样本集合;The data quality inspection module is used to check the data quality of all samples. Only the data in the normal operation period of the unit is retained, the data in the fault period is excluded, and a few missing data in the normal operation period are interpolated to form a sample set;
特征提取模块,用于对样本集合进行特征提取,计算预设时间内样本的平均值和标准差;The feature extraction module is used to perform feature extraction on the sample set, and calculate the average and standard deviation of the samples within a preset time;
超限判断报警模块,用于根据第一判异规则对样本集合进行统计判定,若判定结果为超限异常,则触发超限报警;The over-limit judgment alarm module is used to perform statistical judgment on the sample set according to the first judgment rule, and if the judgment result is an over-limit abnormality, an over-limit alarm is triggered;
劣化趋势判断预警模块,用于在样本集合无超限异常的情况下,根据第二判异规则对样本集合进行统计判定,若判定结果为波动异常,表明退火炉存在劣化趋势,非稳定情况出现,则触发劣化趋势预警;The deterioration trend judgment and early warning module is used to perform statistical judgment on the sample set according to the second discrimination rule when the sample set has no abnormality exceeding the limit. If the judgment result is abnormal fluctuation, it indicates that the annealing furnace has a deterioration trend and an unstable situation occurs , then trigger the deterioration trend warning;
基于BP神经网络算法的炉温预测模块,用于基于BP神经网络算法,对退火炉内各炉段的炉温变化情况进行预测。The furnace temperature prediction module based on the BP neural network algorithm is used to predict the furnace temperature change of each furnace section in the annealing furnace based on the BP neural network algorithm.
优选地,所述各设定工艺点位的辊轴张力包括开卷机张力、清洗段张力、入口活套张力、退火炉预热段张力、退火炉加热段入口张力、退火炉加热段出口张力、退火炉均热段张力、退火炉缓冷段张力、退火炉快冷段张力、锌锅段张力、中央活套张力、平整机入口张力、平整机出口张力、拉矫机张力、后处理段张力、出口活套张力、圆盘剪段张力、卷取机张力中的几种或更多种。Preferably, the roller tension at each set process point includes the tension of the uncoiler, the tension of the cleaning section, the tension of the inlet looper, the tension of the preheating section of the annealing furnace, the inlet tension of the heating section of the annealing furnace, the outlet tension of the heating section of the annealing furnace, Tension of soaking section of annealing furnace, tension of slow cooling section of annealing furnace, tension of fast cooling section of annealing furnace, tension of zinc pot section, tension of central looper, entrance tension of skin passer, exit tension of skin passer, tension of tension leveler, post-treatment Several or more of segment tension, exit looper tension, disc shear segment tension, and coiler tension.
优选地,所述钢卷规格包括钢卷的厚度、宽度、卷号、钢种中的一种或更多种。Preferably, the steel coil specification includes one or more of the thickness, width, coil number, and steel type of the steel coil.
优选地,所述废气包括颗粒物、SO2、NOX、O2、以及废气温度中的一种或几种,其中,x为NOx中O与N的原子数比值。Preferably, the exhaust gas includes one or more of particulate matter, SO 2 , NO X , O 2 , and exhaust gas temperature, where x is the atomic ratio of O to N in NOx.
优选地,所述基于BP神经网络算法的炉温预测模块,包括:Preferably, the furnace temperature prediction module based on BP neural network algorithm includes:
BP神经网络模型构建模块,用于设置输入层输入信号的选择,设置隐含层的层数以及各层的节点数,设置输出层的节点数、学习速率、传递函数、动量因子、最大训练次数和最小精度;The BP neural network model building module is used to set the selection of the input signal of the input layer, the number of layers of the hidden layer and the number of nodes of each layer, the number of nodes of the output layer, the learning rate, the transfer function, the momentum factor, and the maximum number of training times. and minimum precision;
BP神经网络模型训练模块,用于利用现有的样本训练集对BP神经网络模型进行训练,得到当前的炉温变化趋势的预测模型;The BP neural network model training module is used to train the BP neural network model by using the existing sample training set to obtain the prediction model of the current furnace temperature change trend;
炉温变化趋势预测模块,用于根据已经训练完成的BP神经网络模型,将样本集合输入到BP神经网络模型的输入层,经过神经网络的处理,得到当前的炉温变化趋势的预测值。The furnace temperature change trend prediction module is used to input the sample set into the input layer of the BP neural network model according to the BP neural network model that has been trained. After processing by the neural network, the predicted value of the current furnace temperature change trend is obtained.
与现有技术相比,本发明的技术方案具有以下有益效果:Compared with the prior art, the technical scheme of the present invention has the following beneficial effects:
本申请提供了一种连续退火炉炉温预警方法及系统,将传统报警方式与SPC过程控制统计及BP神经网络预测算法方式相结合,生成了一套基于SPC控制理论稳态下的温度预测的全新预警方式。本申请的技术方案在做到超限报警的同时还可以对炉子健康度做趋势预测,如递增、递减、混乱、规律性波动等,同时采用BP神经网络算法,对炉温进行预测。运用本方法的预警系统可以辅助操作人员,在出现问题时,提前报警并及时发现故障点,争取早发现、早干预,避免造成因查找故障点而错过故障最佳处理时间。The present application provides a furnace temperature early warning method and system for a continuous annealing furnace, which combines the traditional alarm method with SPC process control statistics and BP neural network prediction algorithm method to generate a set of temperature prediction based on SPC control theory under steady state. A new way of warning. The technical solution of the present application can make trend prediction of furnace health degree, such as increasing, decreasing, chaotic, regular fluctuation, etc. while achieving over-limit alarm, and at the same time use BP neural network algorithm to predict furnace temperature. The early warning system using this method can assist the operator to give an alarm in advance and find the fault point in time when there is a problem, so as to strive for early detection and early intervention, so as to avoid missing the best processing time for the fault due to finding the fault point.
附图说明Description of drawings
构成本申请的一部分附图用来提供对本申请的进一步理解,本申请的示意性实施例及其说明用于解释本申请,并不构成对本申请的不当限定。在附图中:The accompanying drawings constituting a part of the present application are used to provide further understanding of the present application, and the schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the attached image:
图1是本申请的连续退火炉炉温预警方法的流程示意图;Fig. 1 is the schematic flow sheet of the continuous annealing furnace furnace temperature early warning method of the present application;
图2是本申请的一种SPC模型设计示意图;Fig. 2 is a kind of SPC model design schematic diagram of the present application;
图3是本申请实施例的退火炉SPC非稳态劣化下钢卷退火温度1的变化曲线图(目标温度830℃);Fig. 3 is the variation curve diagram of the
图4是本申请实施例的退火炉SPC非稳态劣化下钢卷退火温度2的变化曲线图(目标温度830℃);Fig. 4 is the change curve diagram of the
图5是本申请实施例的退火炉SPC稳态健康下钢卷退火温度的变化曲线图(目标温度810℃);Fig. 5 is the variation curve of the annealing temperature of the coil under the steady-state health of the SPC of the annealing furnace of the embodiment of the present application (
图6是本申请实施例的退火炉SPC稳态健康下钢卷退火温度的变化曲线图(目标温度835℃)。FIG. 6 is a graph showing the variation of the annealing temperature of the coil under the steady state health of the SPC of the annealing furnace according to the embodiment of the present application (
具体实施方式Detailed ways
为使本发明的目的、技术方案及效果更加清楚、明确,以下参照附图并举实例对本发明进一步详细说明。应当理解,此处所描述的具体实施例仅用以解释本发明,并不用于限定本发明。In order to make the objectives, technical solutions and effects of the present invention clearer and clearer, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.
需要说明的是,本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象,而不必用于描述特定的顺序或先后次序,应该理解这样使用的数据在适当情况下可以互换。此外,术语“包括”和“具有”以及它们的任何变形,意图在于覆盖不排他的包含,例如,包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元,而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It should be understood that data so used may be interchanged under appropriate circumstances. Furthermore, the terms "comprising" and "having", and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.
参阅图1所示,本申请的一种连续退火炉炉温预警方法,包括:Referring to Figure 1, a method for early warning of a continuous annealing furnace temperature of the present application includes:
实时采集退火炉内各炉段设定的工艺点位的工艺参数,所述设定的工艺点位包括氧气含量、氢气含量、炉内气氛的露点、炉压、目标板温、辊轴张力、钢卷卷号、炉温、钢卷规格、机组速度、板温、废气中的一种或更多种;优选为至少两种或更多种;The process parameters of the process points set in each furnace section in the annealing furnace are collected in real time, and the set process points include oxygen content, hydrogen content, dew point of the furnace atmosphere, furnace pressure, target plate temperature, roller tension, One or more of coil number, furnace temperature, coil specification, unit speed, plate temperature, exhaust gas; preferably at least two or more;
将采集到的数据传递给SPC系统,所述SPC系统将采集到的数据按照工艺点位的类别进行分组,各分组独立地设置其样本采集时间段,对每个样本采集时间段的数据按照其工艺点位类别对应的预设的样本采集方法进行处理,形成单个的样本;The collected data is transmitted to the SPC system, and the SPC system groups the collected data according to the category of the process point, and each group independently sets its sample collection time period, and the data of each sample collection time period is based on its The preset sample collection method corresponding to the process point category is processed to form a single sample;
对所有的样本进行数据质量检查,仅保留机组正常运行时段内的数据,剔除故障时段内的数据,对正常运行时段少数缺漏数据进行插补,形成样本集合;Data quality inspection is carried out on all samples, only the data in the normal operation period of the unit is retained, the data in the fault period is excluded, and a few missing data in the normal operation period are interpolated to form a sample set;
对样本集合进行特征提取,计算预设时间内样本的平均值和标准差;Perform feature extraction on the sample set, and calculate the average and standard deviation of the samples within a preset time;
根据第一判异规则对样本集合进行统计判定,若判定结果为超限异常,则触发超限报警;否则,根据第二判异规则对样本集合进行统计判定,若判定结果为波动异常,表明退火炉存在劣化趋势,非稳定情况出现,则触发劣化趋势预警;Statistical judgment is made on the sample set according to the first discrimination rule. If the judgment result is abnormal overrun, an overrun alarm will be triggered; otherwise, the sample set will be statistically judged according to the second discrimination rule. If the judgment result is abnormal fluctuation, it indicates that The annealing furnace has a deterioration trend, and if an unstable situation occurs, a deterioration trend warning is triggered;
基于BP神经网络算法,预测退火炉内各炉段的炉温变化情况。Based on the BP neural network algorithm, the furnace temperature change of each furnace section in the annealing furnace is predicted.
实施例:Example:
炉子健康度的预警本质是各炉段重要指标的预警,因此首先要确认各炉段的重要参数和合理范围,具体内容见表1,表1中主要从工艺参数、质量相关因素判断来设计各炉段的健康度评价方案。The nature of the early warning of furnace health is the early warning of important indicators of each furnace section. Therefore, the important parameters and reasonable ranges of each furnace section should be confirmed first. The specific content is shown in Table 1. In Table 1, the design of each furnace is mainly based on process parameters and quality-related factors. Furnace section health evaluation scheme.
表1各炉段健康度评价方案Table 1 Health evaluation scheme of each furnace section
冷轧退火炉电气控制单元有两部分,分别为DCS与PLC。确认需求工艺点位的数据地址并自动采集这部分工艺点位。参见表2,这部分工艺点位可以包括氧气含量、氢气含量、炉内气氛的露点、炉压、目标板温、辊轴张力、钢卷卷号、炉温、钢卷规格、机组速度、板温和废气。The electrical control unit of the cold rolling annealing furnace has two parts, namely DCS and PLC. Confirm the data address of the required process points and automatically collect this part of the process points. See Table 2, this part of the process points can include oxygen content, hydrogen content, dew point of furnace atmosphere, furnace pressure, target plate temperature, roll tension, coil number, furnace temperature, coil size, unit speed, plate Gentle exhaust.
表2重要工艺点位及其对应的地址表Table 2 Important process points and their corresponding addresses
现场部署SPC系统,将采集到的数据传递给SPC系统,所述SPC系统的主要工作为SPC样本区间调整,设置样本区间过短极易形成频繁误报,设置样本区间过大则发挥不出预警的提前性。本申请的所述SPC系统将采集到的数据按照工艺点位的类别进行分组,各分组独立地设置其样本采集时间段,对每个样本采集时间段的数据按照其工艺点位类别对应的预设的样本采集方法进行处理,形成单个的样本。参阅表3所示。The SPC system is deployed on site and the collected data is transmitted to the SPC system. The main job of the SPC system is to adjust the SPC sample interval. If the sample interval is too short, it is easy to cause frequent false alarms. If the sample interval is too large, the warning will not work. advance. The SPC system of the present application groups the collected data according to the category of the process point, each group independently sets its sample collection time period, and the data of each sample collection time period is based on the prediction corresponding to the category of the process point. The designed sample collection method is processed to form a single sample. See Table 3.
表3 SPC采集区间示意表Table 3 Schematic diagram of SPC collection interval
数据质量检测的目的在于检测输入数据的数据质量是否满足算法需求,数据质量的检查包括两个方面:The purpose of data quality inspection is to detect whether the data quality of the input data meets the requirements of the algorithm. The inspection of data quality includes two aspects:
(1)检查样本数据中是否存在缺失值或者异常值,对于异常值的检测通常采用基于规则的方法,例如信号是否处于某个数值范围内,PLC信号的判断规则等;(1) Check whether there are missing values or outliers in the sample data. The detection of outliers usually adopts a rule-based method, such as whether the signal is within a certain value range, the judgment rules of PLC signals, etc.;
(2)检查样本数据的工况是否符合算法要求,即是否是机组正常运行过程中的数据,需要利用PLC数据中的标志位来进行判断。(2) Check whether the working conditions of the sample data meet the algorithm requirements, that is, whether it is the data during the normal operation of the unit, and it is necessary to use the flag bits in the PLC data to judge.
接着,对样本集合进行特征提取,计算预设时间内样本的平均值和标准差,其中,计算平均值是用于超限异常判断,计算标准差是用于波动异常判断。Next, feature extraction is performed on the sample set, and the average value and standard deviation of the samples within a preset time period are calculated, wherein the calculated average value is used for over-limit abnormality judgment, and the calculated standard deviation is used for fluctuation abnormality judgment.
超限判断报警:Over-limit judgment alarm:
SPC系统会自动计算设定异常阈值。异常阈值可以根据健康状态下的设备运行状态信号值的各自分布模式计算获得,例如电机扭矩信号一般服从正态分布,取其均值加三倍标准差作为异常阈值。若预设时间内样本的平均值超出了其预设的异常阈值,判为异常点;为了降低误报率,可以根据当前时刻之前的一预设时间段内的异常点的个数超过预设值,则判定为超限异常,触发超限报警。The SPC system will automatically calculate and set the abnormal threshold. The abnormal threshold can be calculated and obtained according to the respective distribution patterns of the equipment operating state signal values in the healthy state. For example, the motor torque signal generally obeys a normal distribution, and its mean plus three standard deviations is taken as the abnormal threshold. If the average value of the samples in the preset time exceeds its preset abnormal threshold, it is judged as an abnormal point; in order to reduce the false alarm rate, the number of abnormal points in a preset time period before the current moment can exceed the preset number of abnormal points. value, it is judged to be over-limit abnormality, and an over-limit alarm is triggered.
劣化趋势判断预警:Deterioration trend judgment warning:
当样本集合经超限判断确认在可控范围内的数据将进行劣化趋势判断,判断基于SPC规则。When the sample set is confirmed to be within the controllable range through the over-limit judgment, the deterioration trend will be judged, and the judgment will be based on the SPC rule.
SPC即统计过程控制,是一种被广泛应用的过程控制方法,是一种借助数理统计方法的过程控制工具。它对生产过程进行分析评价,根据反馈信息及时发现系统性因素出现的征兆,并采取措施消除其影响,使过程维持在仅受随机性因素影响的受控状态,以达到控制质量的目的。SPC (Statistical Process Control) is a widely used process control method and a process control tool with the help of mathematical statistics. It analyzes and evaluates the production process, finds the signs of systematic factors in time according to the feedback information, and takes measures to eliminate its influence, so as to maintain the process in a controlled state only affected by random factors, so as to achieve the purpose of quality control.
SPC规则(判异准则)如下:The SPC rules (discrimination criteria) are as follows:
准则1:1个点子落在A区以外(点子越出控制界限)Criterion 1: 1 idea falls outside the A zone (the idea is out of control limits)
准则2:连续9点落在中心线同一侧Rule 2: 9 consecutive points on the same side of the centerline
准则3:连续6点递增或递减Rule 3: 6 points in a row increment or decrement
准则4:连续14点中相邻点子总是上下交替Rule 4: Adjacent points in a row of 14 points always alternate up and down
准则5:连续3点中有2点落在中心线同一侧B区以外Criterion 5: 2 of 3 consecutive points fall outside Zone B on the same side of the centerline
准则6:连续5点中有4点子落在中心线同一侧C区以外Rule 6: 4 out of 5 consecutive points fall outside the C area on the same side of the centerline
准则7:连续15点落在中心线同两侧C区之内Rule 7: 15 consecutive points fall within the C area on the same sides of the centerline
准则8:连续8点落在中心线两侧且无1点在C区中Rule 8: 8 consecutive points on either side of the centerline and no 1 in zone C
其中,C区域为预设的第三控制界限,其大小为预设时间内样本的标准差的1倍;B区域为预设的第二控制界限,其大小为预设时间内样本的标准差的2倍;A区域为预设的第三控制界限,其大小为预设时间内样本的标准差的3倍。Among them, the C area is the preset third control limit, and its size is 1 times the standard deviation of the samples within the preset time; the B area is the preset second control limit, and its size is the standard deviation of the samples within the preset time. 2 times; the A area is the preset third control limit, and its size is 3 times the standard deviation of the samples within the preset time.
参阅图2所示,根据以上规则进行数据趋势分析,当样本集合符合上述任意一条规则,表明退火炉的健康出现劣化,非稳态情况出现,则判定为波动异常,触发劣化趋势预警。Referring to Figure 2, data trend analysis is performed according to the above rules. When the sample set conforms to any one of the above rules, it indicates that the health of the annealing furnace is deteriorating, and the non-steady state occurs, it is judged as abnormal fluctuation, and a deterioration trend warning is triggered.
本申请上述预警方法对传统的SPC进行了改进,主要有以下两点:The above-mentioned early warning method of the present application improves the traditional SPC, mainly in the following two points:
①SPC控制图的作用是反应生产过程的稳定性,但稳定并不能完全体现数据的优良,稳定的输出错误数据也是一种可能,因此本申请的技术方案新增了上下限条件设置功能,在确保数据在可控范围内的同时再分析数据的趋势。① The function of the SPC control chart is to reflect the stability of the production process, but the stability does not fully reflect the quality of the data, and stable output of wrong data is also a possibility. Therefore, the technical solution of the present application adds a function of setting upper and lower limit conditions, in order to ensure Re-analyze the trend of the data while the data is within the controllable range.
②样本个数可调整,因为现场数据变化速率不同,所以需根据工艺情况调节相应数据单个样本的采集个数,这样才能更好的反应现场趋势。如炉温板温差采用30分钟求一次平均值做一个样本,并对样本经过SPC XBar图分析,假如连续6个点位上升即为3个小时内炉温板温差有上升趋势。设置合理的样本才能够发挥SPC的真实效果。②The number of samples can be adjusted. Because the rate of change of the on-site data is different, it is necessary to adjust the number of samples collected for a single sample of the corresponding data according to the process conditions, so as to better reflect the on-site trend. For example, the temperature difference of the furnace temperature plate is averaged once every 30 minutes to make a sample, and the sample is analyzed by SPC XBar chart. If the continuous 6 points rise, it means that the temperature difference of the furnace temperature plate has an upward trend within 3 hours. Only by setting a reasonable sample can the real effect of SPC be exerted.
本申请还可以基于BP神经网络算法,预测退火炉内各炉段的炉温变化情况。The present application can also predict the furnace temperature change of each furnace section in the annealing furnace based on the BP neural network algorithm.
BP神经网络是一种神经网络学习算法。其由输入层、隐藏层、输出层组成的阶层型神经网络,隐藏层可扩展为多层。相邻层之间各神经元进行全连接,而每层各神经元之间无连接,网络按有教师监督的方式进行学习。BP neural network is a neural network learning algorithm. It is a hierarchical neural network composed of an input layer, a hidden layer, and an output layer, and the hidden layer can be extended to multiple layers. Each neuron between adjacent layers is fully connected, but there is no connection between each neuron in each layer, and the network learns in a teacher-supervised manner.
学习过程由信号正向传播与误差的反向回传两个部分组成;正向传播时,输入样本从输入层传入,经各隐藏层依次逐层处理,传向输出层,若输出层输出与期望不符,则将误差作为调整信号逐层反向回传,对神经元之间的连接权矩阵做出处理,使误差减小。经反复学习,最终使误差减小到可接受的范围,最终选取传递函数为学习速率0.06,附加动量因子0.95,具体步骤如下:The learning process consists of two parts: the forward propagation of the signal and the reverse return of the error; in the forward propagation, the input samples are passed in from the input layer, processed layer by layer by each hidden layer, and transmitted to the output layer. If the output layer outputs If it is not in line with expectations, the error is used as an adjustment signal to return back layer by layer, and the connection weight matrix between neurons is processed to reduce the error. After repeated learning, the error is finally reduced to an acceptable range, and the final transfer function is selected as The learning rate is 0.06, and the additional momentum factor is 0.95. The specific steps are as follows:
①从训练样本集合中取出某一样本,把信息输入网络中。① Take a sample from the training sample set and input the information into the network.
②通过各节点间的连接情况正向逐层处理后,得到神经网络的实际输出。②The actual output of the neural network is obtained after forward processing layer by layer through the connection between the nodes.
③计算网络实际输出与期望输出的误差。③ Calculate the error between the actual output of the network and the expected output.
④将误差逐层反向回传至之前各层,并按一定原则将误差信号加载到连接权值上,使整个神经网络的连接权值向误差减小的方向转化。④Reverse the error back to the previous layers layer by layer, and load the error signal onto the connection weight according to a certain principle, so that the connection weight of the entire neural network is transformed in the direction of reducing the error.
⑤对训练样本集合中每一个输入-输出样本对重复以上步骤,直到整个训练样本集合的误差小于预设的精度,或进行到预先设定的次数为止。⑤ Repeat the above steps for each input-output sample pair in the training sample set, until the error of the entire training sample set is less than the preset accuracy, or until the preset number of times.
根据已经训练完成的BP神经网络模型,将样本集合输入到BP神经网络模型的输入层,经过神经网络的处理,得到当前的炉温变化趋势的预测值。According to the BP neural network model that has been trained, the sample set is input into the input layer of the BP neural network model, and the predicted value of the current furnace temperature change trend is obtained through the processing of the neural network.
表4是使用了上述训练得到的BP神经网络模型分别对退火炉非稳态劣化下、稳态健康下的炉温进行预测的预测结果表。Table 4 is a prediction result table of using the BP neural network model obtained by the above training to predict the furnace temperature under unsteady deterioration and steady state health of the annealing furnace, respectively.
表4模型算法结果表Table 4 Model algorithm result table
需要说明的是,表4中的数据为样本的个值,由于样本数量较大,图3~图6中的点为样本在某时刻的均值。It should be noted that the data in Table 4 are the individual values of the samples. Due to the large number of samples, the points in Figures 3 to 6 are the mean values of the samples at a certain time.
图3为退火炉内SPC非稳态劣化下的钢卷退火温度1的变化曲线图,图4为退火炉内SPC非稳态劣化下的钢卷退火温度2的变化曲线图,其中,图3中的目标温度为830℃,图4中的目标温度为830℃。由表4结合图3可以看出,后行材板温偏低。由表4结合图4可以看出,后行材板温偏低大于10℃。Fig. 3 is a graph showing the change of the
图5为退火炉内SPC稳态健康下的钢卷退火温度的变化曲线图,图6为退火炉内SPC稳态健康下的钢卷退火温度的变化曲线图,其中,图5中的目标温度为810℃,图6中的目标温度为835℃。由表4结合图5可以看出,后板波动大,后行材板温较好。由表4结合图6可以看出,后板波动大,后行材板温控制较好。Fig. 5 is the change curve of the annealing temperature of the coil under the steady state of the SPC in the annealing furnace, and Fig. 6 is the change curve of the annealing temperature of the steel coil under the steady state of the SPC in the annealing furnace. Among them, the target temperature in Fig. 5 was 810°C, and the target temperature in Figure 6 was 835°C. It can be seen from Table 4 and Figure 5 that the fluctuation of the rear panel is large, and the temperature of the rear panel is better. It can be seen from Table 4 and Figure 6 that the fluctuation of the rear plate is large, and the temperature of the rear plate is well controlled.
由上述图例和数据可知,预测趋势与实际板温变化趋势大致相同,其预测值与实测值的拟合程度较好。It can be seen from the above legend and data that the predicted trend is roughly the same as the actual plate temperature change trend, and the predicted value fits well with the measured value.
基于上述实施例相同的发明构思,在另一种优选实施例中,本申请还提供了一种连续退火炉炉温预警系统,包括:Based on the same inventive concept of the above embodiments, in another preferred embodiment, the present application also provides a furnace temperature warning system for a continuous annealing furnace, including:
数据采集模块,用于实时采集退火炉内各炉段设定的工艺点位的工艺参数,所述设定的工艺点位包括氧气含量、氢气含量、炉内气氛的露点、炉压、目标板温、辊轴张力、钢卷卷号、炉温、钢卷规格、机组速度、板温、废气中的一种或更多种;优选为至少两种或更多种;The data acquisition module is used to collect the process parameters of the process points set in each furnace section in the annealing furnace in real time, and the set process points include oxygen content, hydrogen content, dew point of the furnace atmosphere, furnace pressure, target plate One or more of temperature, roll tension, coil number, furnace temperature, coil specification, unit speed, plate temperature, exhaust gas; preferably at least two or more;
样本生成模块,用于将采集到的数据按照工艺点位的类别进行分组,各分组独立地设置其样本采集时间段,对每个样本采集时间段的数据按照其工艺点位类别对应的预设的样本采集方法进行处理,形成单个的样本;The sample generation module is used to group the collected data according to the category of the process point, each group independently sets its sample collection time period, and the data of each sample collection time period is preset according to its process point category. The sample collection method is processed to form a single sample;
数据质量检查模块,用于对所有的样本进行数据质量检查,仅保留机组正常运行时段内的数据,剔除故障时段内的数据,对正常运行时段少数缺漏数据进行插补,形成样本集合;The data quality inspection module is used to check the data quality of all samples. Only the data in the normal operation period of the unit is retained, the data in the fault period is excluded, and a few missing data in the normal operation period are interpolated to form a sample set;
特征提取模块,用于对样本集合进行特征提取,计算预设时间内样本的平均值和标准差;The feature extraction module is used to perform feature extraction on the sample set, and calculate the average and standard deviation of the samples within a preset time;
超限判断报警模块,用于根据第一判异规则对样本集合进行统计判定,若判定结果为超限异常,则触发超限报警;The over-limit judgment alarm module is used to perform statistical judgment on the sample set according to the first judgment rule, and if the judgment result is an over-limit abnormality, an over-limit alarm is triggered;
劣化趋势判断预警模块,用于在样本集合无超限异常的情况下,根据第二判异规则对样本集合进行统计判定,若判定结果为波动异常,表明退火炉存在劣化趋势,非稳定情况出现,则触发劣化趋势预警;The deterioration trend judgment and early warning module is used to perform statistical judgment on the sample set according to the second discrimination rule when the sample set has no abnormality exceeding the limit. If the judgment result is abnormal fluctuation, it indicates that the annealing furnace has a deterioration trend and an unstable situation occurs , then trigger the deterioration trend warning;
基于BP神经网络算法的炉温预测模块,用于基于BP神经网络算法,对退火炉内各炉段的炉温变化情况进行预测。The furnace temperature prediction module based on the BP neural network algorithm is used to predict the furnace temperature change of each furnace section in the annealing furnace based on the BP neural network algorithm.
综上所述,本申请提供了一种连续退火炉炉温预警方法及系统,将传统报警方式与SPC过程控制统计及BP神经网络预测算法方式相结合,生成了一套基于SPC控制理论稳态下的温度预测的全新预警方式。本申请的技术方案在做到超限报警的同时还可以对炉子健康度做趋势预测,如递增、递减、混乱、规律性波动等,同时采用BP神经网络算法,对炉温进行预测。运用本方法的预警系统可以辅助操作人员,在出现问题时,提前报警并及时发现故障点,争取早发现、早干预,避免造成因查找故障点而错过故障最佳处理时间。本申请应用于宝钢宝日汽车板冷轧热镀锌机组A08连续退火炉设计,通过SPC判定原理分析不同炉段重点工艺参数的趋势,判断退火炉可能存在的健康隐患,根据BP神经网络算法预测炉温变化,协助管理者决策避免了重大故障的产生,降低了停机次数,提高了生产效率。In summary, the present application provides a continuous annealing furnace temperature warning method and system, which combines the traditional alarm method with the SPC process control statistics and BP neural network prediction algorithm method to generate a set of steady state based on SPC control theory. A new early warning method for temperature forecasting. The technical solution of the present application can make trend prediction of furnace health, such as increasing, decreasing, chaotic, regular fluctuation, etc., while achieving over-limit alarm, and at the same time, the BP neural network algorithm is used to predict furnace temperature. The early warning system using this method can assist the operator to give an alarm in advance and find the fault point in time when there is a problem, strive for early detection and early intervention, and avoid missing the best processing time for the fault due to finding the fault point. This application is applied to the design of the A08 continuous annealing furnace for Baosteel Baori Auto Sheet Cold Rolling and Hot-dip Galvanizing Unit. The trend of key process parameters in different furnace sections is analyzed through the SPC judgment principle, and the possible health hazards in the annealing furnace are judged, and predicted according to the BP neural network algorithm. The change of furnace temperature helps managers make decisions, avoids major failures, reduces the number of downtimes, and improves production efficiency.
以上对本发明的具体实施例进行了详细描述,但其只是作为范例,本发明并不限制于以上描述的具体实施例。对于本领域技术人员而言,任何对本发明进行的等同修改和替代也都在本发明的范畴之中。因此,在不脱离本发明的精神和范围下所作的均等变换和修改,都应涵盖在本发明的范围内。The specific embodiments of the present invention have been described above in detail, but they are only used as examples, and the present invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications and substitutions to the present invention are also within the scope of the present invention. Therefore, equivalent changes and modifications made without departing from the spirit and scope of the present invention should be included within the scope of the present invention.
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