List <int> MoveWindowFunc(double[] samples, int width, int interval)//滑窗法进行筛选
{
int index = 0;
List <int> list = new List <int>();
do
{
double[] temp_samp = new double[width];//临时样本
bool isQualified = false;
for (int i = 0; i < width; i++)
{
temp_samp[i] = samples[index + i];
}
double means = temp_samp.Mean();
for (int i = 0; i < width; i++)
{
temp_samp[i] -= means;
}
ParaVec paraVec = ComputeParaVec(temp_samp);
if (paraVec.tao1 < -1.95)//-1.95 -2.89 -3.45
{
isQualified = true;
}
if (index >= 300)
{
;
}
if (isQualified)///如果窗口合格
{
for (int j = 0; j < width; j++)
{
if (!list.Contains(j + index))
{
list.Add(j + index);
}
}
}
index += interval;
if (index + width > samples.Count())
{
break;
}
}while (true);
return(list);
}
private void OnCompute(object sender, RoutedEventArgs args)
{
try
{
ParaList = ReadXml(@"..\..\ParaXml.xml"); //读取xml文档
FileStream file = new FileStream("file.txt", FileMode.Create); ///创建文件用于存放稳态工况数据
StreamWriter writer = new StreamWriter(file);
writer.WriteLine("创建日期" + DateTime.Now.ToString());
writer.Flush();
List <DateTime> timeList = new List <DateTime>(); //存放稳态工况的时间点
string strStart = ((DateTime)StartDate.SelectedDate).ToString("yyyy-MM-dd"); ///开始时间字符串
string strEnd = ((DateTime)EndDate.SelectedDate).ToString("yyyy-MM-dd"); //结束时间字符串
DateTime loopTime = (DateTime)StartDate.SelectedDate; ///开始时间,用于循环
DateTime EndTime = (DateTime)EndDate.SelectedDate; //结束时间
////从参数表中取出参数详细信息
DataTable measure_dt = QueryData("select id,name,saveindex from tb_nx_measure");
string strSql = "select cytime";
foreach (ParameterSetting childParameter in ParaList)///查找存储索引并构建字符串
{
var re = from h in measure_dt.AsEnumerable()
where h.Field <string>("name") == childParameter.name
select Convert.ToInt32(h.Field <object>("saveindex"));
if (re == null)
{
throw new Exception("结果为null");
}
if (re.Count() != 1)
{
throw new Exception("未找到指定参数");
}
foreach (int d in re)
{
childParameter.saveindex = d;
strSql += ",V";
strSql += d.ToString();
}
}
strSql = strSql + " from tb_nx_his_run where cytime between '" + strStart + "' and '" + strEnd + "'"; //完成对字符串的构建
DataTable data_dt = QueryData(strSql); ///查询指定时间内的数据
TimeSpan Windowspan = new TimeSpan(0, ParaList[0].time, 0); ///窗口宽度/min
TimeSpan Scannspan = new TimeSpan(0, int.Parse(扫描间隔.Text), 0); ///扫描间隔/min
do
{
DateTime tempEndTime = loopTime + Windowspan;
var re2 = from h in data_dt.AsEnumerable()
let tempTime = Convert.ToDateTime(h.Field <string>("cytime"))
where tempTime >= loopTime && tempTime < tempEndTime
select h;
bool isQualified = true; //先假设该窗口合格
//单纯的稳态判别方式
//for(int index=0;index<ParaList.Count();index++)
//{
// double max = re2.Max(x => Convert.ToDouble(x.Field<object>(index+1)));
// double min = re2.Min(x => Convert.ToDouble(x.Field<object>(index+1)));
// if(ParaList[index].type==0)
// {
// if (Math.Abs(max - min) > ParaList[index].value)
// {
// isQualified = false;
// break;
// }
// }
// if (ParaList[index].type == 1)
// {
// if (Math.Abs(max - min)/max > ParaList[index].value)
// {
// isQualified = false;
// break;
// }
// }
//}
///DF检验判别方式
double[] samples = new double[re2.Count()];//新建指定宽度窗口内的样本
for (int index = 0; index < re2.Count(); index++)
{
samples[index] = Convert.ToDouble(re2.ElementAt(index).Field <object>(1));
}
ParaVec paraVec = ComputeParaVec(GenerateDataFromData(samples));
if (paraVec.tao1 > -2.86)
{
isQualified = false;
}
if (isQualified)///如果窗口合格
{
foreach (var d in re2)
{
DateTime temp_time = Convert.ToDateTime(d.Field <string>("cytime"));
if (!timeList.Contains(temp_time))
{
timeList.Add(temp_time);
}
}
}
loopTime += Scannspan; ///将循环时间增加一个扫面时间间隔
if (loopTime > (EndTime - Windowspan)) ///如果循环时间不足以进行下一次的计算，则推出循环
{
break;
}
}while (true);
writer.WriteLine("共" + timeList.Count + "条记录,占比" + timeList.Count + "/" + data_dt.Rows.Count + "=" + ((double)timeList.Count / data_dt.Rows.Count));
//foreach (DateTime date in timeList)
//{
// var re3 = from h in data_dt.AsEnumerable()
// let tempTime = Convert.ToDateTime(h.Field<string>("cytime"))
// where tempTime == date
// select Convert.ToDouble(h.Field<object>(1));
// foreach (double d in re3)
// {
// writer.WriteLine(date.ToString("yyyy-MM-dd HH:mm:ss") + "\t" + d.ToString());
// }
//}
//writer.Flush();
//writer.Close();
//file.Close();
foreach (DataRow row in data_dt.Rows)
{
DateTime tempTime = Convert.ToDateTime(row.Field <string>("cytime"));//取出时间
string temp_str = tempTime.ToString("yyyy-MM-dd HH:mm:ss");
for (int i = 1; i < data_dt.Columns.Count; i++)
{
temp_str = temp_str + "\t" + row.Field <object>(i).ToString();
}
if (timeList.Contains(tempTime))
{
writer.WriteLine(temp_str);
}
else
{
writer.WriteLine();
}
writer.Flush();
}
file.Close();
MessageBox.Show("共" + timeList.Count + "条记录,占比" + timeList.Count + "/" + data_dt.Rows.Count + "=" + ((double)timeList.Count / data_dt.Rows.Count));
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
//一阶含常数无趋势自回归DF检验
ParaVec ComputeParaVec(double[] samples)
{
int mCount = samples.Count();
//定义矩阵系数
double a11 = 0;
double a12 = 0;
double a13 = 0;
double a21 = 0;
double a22 = 0;
double a23 = 0;
double a31 = 0;
double a32 = 0;
double a33 = 0;
///定义向量
double b1 = 0;
double b2 = 0;
double b3 = 0;
///定义解向量
double c1 = 0;
double c2 = 0;
double c3 = 0;
for (int index = 1; index < mCount; index++)
{
a11++;
a12 += samples[index - 1];
a13 += (index + 1);
a22 += samples[index - 1] * samples[index - 1];
a23 += (index + 1) * samples[index - 1];
a33 += (index + 1) * (index + 1);
b1 += samples[index];
b3 += (index + 1) * samples[index];
b2 += samples[index - 1] * samples[index];
}
a21 = a12;
a31 = a13;
a32 = a23;
ParaVec paraVec = new ParaVec();//新建参数向量矩阵
//情形1
{
double[,] aMatrix = new double[1, 1];
aMatrix[0, 0] = a22;
double[] bMatrix = new double[1];
bMatrix[0] = b2;
double[] cMatrix = new double[1];
cMatrix = Matrix.Multiply(aMatrix.Inverse(), bMatrix);//计算参数向量
paraVec.fai1 = cMatrix[0];
double squre = aMatrix.Inverse()[0, 0];
double s2t = 0;
for (int index = 1; index < mCount; index++)
{
paraVec.sigma21 += Math.Pow((samples[index] - paraVec.fai1 * samples[index - 1]), 2);
}
s2t = paraVec.sigma21 + samples[0] * samples[0];
paraVec.sigma21 /= mCount - 1;
s2t /= mCount - 1;
paraVec.tao1 = (paraVec.fai1 - 1) / Math.Sqrt(s2t * squre);
paraVec.rou1 = samples.Count() * (paraVec.fai1 - 1);
}
//情形2
{
double[,] aMatrix = new double[2, 2];
aMatrix[0, 0] = a11;
aMatrix[0, 1] = a12;
aMatrix[1, 0] = a21;
aMatrix[1, 1] = a22;
double[] bMatrix = new double[2];
bMatrix[0] = b1;
bMatrix[1] = b2;
double[] cMatrix = new double[2];
cMatrix = Matrix.Multiply(aMatrix.Inverse(), bMatrix);//计算参数向量
paraVec.miu2 = cMatrix[0];
paraVec.fai2 = cMatrix[1];
paraVec.sigma22 = 0;
double[] xishu = new double[] { 0, 1 };
double squre = xishu.Multiply(aMatrix.Inverse()).Multiply(xishu.Transpose())[0];
double s2t = 0;
for (int index = 1; index < mCount; index++)
{
paraVec.sigma22 += Math.Pow((samples[index] - paraVec.miu2 - paraVec.fai2 * samples[index - 1]), 2);
// squre += Math.Pow(samples[index - 1], 2);
}
s2t = paraVec.sigma22 + (samples[0] - paraVec.miu2) * (samples[0] - paraVec.miu2);
paraVec.sigma22 /= mCount - 1;
s2t /= mCount - 1;
paraVec.tao2 = (paraVec.fai2 - 1) / Math.Sqrt(s2t * squre);
paraVec.rou2 = samples.Count() * (paraVec.fai2 - 1);
}
//情形3
{
double[,] aMatrix = new double[3, 3];
aMatrix[0, 0] = a11;
aMatrix[0, 1] = a12;
aMatrix[0, 2] = a13;
aMatrix[1, 0] = a21;
aMatrix[1, 1] = a22;
aMatrix[1, 2] = a23;
aMatrix[2, 0] = a31;
aMatrix[2, 1] = a32;
aMatrix[2, 2] = a33;
double[] bMatrix = new double[3];
bMatrix[0] = b1;
bMatrix[1] = b2;
bMatrix[2] = b3;
double[] cMatrix = new double[3];
cMatrix = Matrix.Multiply(aMatrix.Inverse(), bMatrix);//计算参数向量
paraVec.miu3 = cMatrix[0];
paraVec.fai3 = cMatrix[1];
paraVec.beta3 = cMatrix[2];
double[] xishu = new double[] { 0, 1, 0 };
double squre = xishu.Multiply(aMatrix.Inverse()).Multiply(xishu.Transpose())[0];
double s2t = 0;
for (int index = 1; index < mCount; index++)
{
paraVec.sigma23 += Math.Pow((samples[index] - paraVec.miu3 - paraVec.beta3 * (index + 1) - paraVec.fai3 * samples[index - 1]), 2);
// squre += Math.Pow(samples[index - 1], 2);
}
s2t = paraVec.sigma23 + (samples[0] - paraVec.miu3) * (samples[0] - paraVec.miu3);
paraVec.sigma23 /= mCount - 1;
s2t /= mCount - 1;
paraVec.tao3 = (paraVec.fai3 - 1) / Math.Sqrt(s2t * squre);
paraVec.rou3 = samples.Count() * (paraVec.fai3 - 1);
}
return(paraVec);
}