def OutterChecker(predictGroups, SD, ED, HRZID, Code, downGasAmount,
upGasAmount):
# 基于起始和终止日期，建立合适的时间索引和DataFrame
validIndex = pd.date_range(SD, ED, freq='1min')
validDataFrame = pd.DataFrame(index=validIndex)
# 创建数据库连接
conn = MySQLConnector()
conn.openConnector()
# 查询该控制周期内燃气流量序列
SQL_Gas = u'SELECT create_time,瞬时流量 FROM bd_xinkou_2 WHERE create_time>"%s" and create_time<"%s"' % (
SD, ED)
conn.cursor.execute(SQL_Gas)
realGasSeries = np.array(conn.cursor.fetchall())
if len(realGasSeries) == 0:
return 1, [1], np.random.randn(len(validIndex)), np.random.randn(
len(validIndex))
realGasDataFrame = pd.DataFrame(np.array(realGasSeries[:, 1],
dtype=np.float32),
index=realGasSeries[:, 0],
columns=[u'瞬时流量'])
realGasDataFrame = realGasDataFrame.resample('1min').mean()
validDataFrame[u'瞬时流量'] = realGasDataFrame[u'瞬时流量']
realGasMean = realGasSeries[:, 1].mean()
# 查询换热站一次回温度
SQL_HFBT = u'SELECT create_time,一次回温度 FROM bd_xinkou_hrz WHERE project_sub_station_id=%d and code="%s" and create_time>"%s" and create_time<"%s"' % (
HRZID, Code, SD, ED)
conn.cursor.execute(SQL_HFBT)
realHFBTArray = np.array(conn.cursor.fetchall())
if len(realHFBTArray) == 0:
return 1, [1], np.random.randn(len(validIndex)), np.random.randn(
len(validIndex))
realHFBTSeries = pd.Series(np.array(realHFBTArray[:, 1], dtype=np.float32),
index=realHFBTArray[:, 0],
name=u'一次回温度')
realHFBTSeries = realHFBTSeries.resample('1min').mean()
validDataFrame[u'一次回温度'] = realHFBTSeries
# 确定预测组的恰当索引
try:
assert realGasMean > downGasAmount
assert realGasMean < upGasAmount
predictGroupsIndex = realGasMean // 100 - downGasAmount // 100
except AssertionError:
if realGasMean <= downGasAmount:
predictGroupsIndex = 0
else:
predictGroupsIndex = -1
targetPredict = predictGroups[predictGroupsIndex]
validDataFrame[u'预测回水温度'] = targetPredict
validDataFrame = validDataFrame.fillna(method='ffill').fillna(
method='bfill')
score = evalueHFBT(validDataFrame[u'瞬时流量'].as_matrix().ravel(),
validDataFrame[u'一次回温度'].as_matrix().ravel(),
validDataFrame[u'预测回水温度'].as_matrix().ravel())
GasDis = [1]
print u'在该控制周期内实际耗费燃气量为：%.1f' % realGasMean
return score, GasDis, validDataFrame[u'一次回温度'].as_matrix().ravel(
), validDataFrame[u'预测回水温度'].as_matrix().ravel()
def readData(self):
# 建立对数据库的连接
conn = MySQLConnector()
conn.openConnector()
# ++++++++++++++ 查询锅炉状态 +++++++++++++++++++++++++++++++++++
# 从SQL语句中分析所需要抓取的字段名模式
columnsPattern = re.compile("SELECT DISTINCT (.*?) FROM")
columnsStr = re.findall(columnsPattern, self._QueryBoilerState)[0]
columns = columnsStr.strip().split(',')
columns = [(c.strip())[2:] for c in columns]
# 对字段名进行修正
if len(self._BoilerColumnsMap()) != 0:
columns = [self._BoilerColumnsMap()[c] for c in columns]
# 游标执行查询锅炉状态数据的语句
conn.cursor.execute(self._QueryBoilerState)
tempBoilerState = np.array(conn.cursor.fetchall())
# 对该数据集中日期时间进行修正
modifiedTime = [tools_DateTimeTrans(i) for i in tempBoilerState[:, 0]]
BoilerData = pd.DataFrame(np.array(tempBoilerState[:, 1:], np.float32),
index=modifiedTime,
columns=columns[1:])
# ++++++++++++++ 查询锅炉状态 +++++++++++++++++++++++++++++++++++
# ++++++++++++++ 查询天气状态 +++++++++++++++++++++++++++++++++++
# 从SQL语句中分析所需要抓取的字段名模式
columnsStr = re.findall(columnsPattern, self._QueryWeatherState)[0]
columns = columnsStr.strip().split(',')
columns = [c.strip()[2:] for c in columns]
# 对字段名进行修正
if len(self._WeatherColumnsMap()) != 0:
columns = [self._WeatherColumnsMap()[c] for c in columns]
# 游标执行查询天气状态数据的语句
conn.cursor.execute(self._QueryWeatherState)
tempWeatherState = np.array(conn.cursor.fetchall())
modifiedTime = [tools_DateTimeTrans(i) for i in tempWeatherState[:, 0]]
WeatherData = pd.DataFrame(np.array(tempWeatherState[:, 1:],
np.float32),
index=modifiedTime,
columns=columns[1:])
# ++++++++++++++ 查询天气状态 +++++++++++++++++++++++++++++++++++
# ++++++++++++++ 查询室内状态 +++++++++++++++++++++++++++++++++++
dev_ids = self._CollectInHomeDeviceID()
dev_DataFrames = []
for k, dev_id in enumerate(dev_ids):
# 生成每一个设备对应的SQL语句
individualDevSQL = self._QueryInHomeState % (dev_id)
# 从SQL语句中抽取所抽取的字段
columnsStr = re.findall(columnsPattern, individualDevSQL)[0]
# 对字段名称进行修正
columns = columnsStr.strip().split(',')
columns = [c.strip()[2:] for c in columns]
if len(self._InHomeColumnsMap()) != 0:
columns = [self._InHomeColumnsMap()[c] for c in columns]
columns = [u'%d#传感器%s' % (k, c) for c in columns]
# 游标执行查询室内状态数据的语句
conn.cursor.execute(individualDevSQL)
tempInHomeState = np.array(conn.cursor.fetchall())
try:
modeifiedTime = [
tools_DateTimeTrans(i) for i in tempInHomeState[:, 0]
]
InHomeData = pd.DataFrame(np.array(tempInHomeState[:, 1:],
np.float32),
index=modeifiedTime,
columns=columns[1:])
dev_DataFrames.append(InHomeData)
except IndexError:
print u'%d#室内传感器无数据' % k
continue
# ++++++++++++++ 查询室内状态 +++++++++++++++++++++++++++++++++++
# ++++++++++++++ 查询换热站状态 +++++++++++++++++++++++++++++++++
if self._QueryHRZState != False:
# 从SQL语句中分析所需要抓取的字段名模式
columnsStr = re.findall(columnsPattern, self._QueryHRZState)[0]
columns = columnsStr.strip().split(',')
columns = [c.strip()[2:] for c in columns]
# 对字段名进行修正
if len(self._HRZColumnsMap()) != 0:
columns = [self._HRZColumnsMap()[c] for c in columns]
# 游标执行查询天气状态数据的语句
conn.cursor.execute(self._QueryHRZState)
tempHRZState = np.array(conn.cursor.fetchall())
modifiedTime = [tools_DateTimeTrans(i) for i in tempHRZState[:, 0]]
HRZData = pd.DataFrame(np.array(tempHRZState[:, 1:], np.float32),
index=modifiedTime,
columns=columns[1:])
else:
HRZData = pd.DataFrame()
totalData = self.concatData(BoilerData, WeatherData, dev_DataFrames,
HRZData)
return totalData
