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Process.py
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import pandas as pd
import json
from logconfig import *
from self_func import cal_comp, WaterTempBase_summer, WaterTempBase_winter, DeltaT
pd.set_option('display.max_columns', None)
class Process():
def __init__(self, mq_mess):
self.mq_mess_dict = mq_mess
self.entityName = self.mq_mess_dict['entityName']
self.asid = self.mq_mess_dict['asId']
self.coolHeatMode = self.mq_mess_dict['coolHeatMode']
self.baseWaterTemp = self.mq_mess_dict['baseWaterTemp']
# ----------关键变量信息-------------
logging.error('key values'.center(50, '='))
self.dfConfig = pd.DataFrame(self.mq_mess_dict["dfConfig"])
# self.dfConfig['locations'] = self.dfConfig['locations'].map(lambda x: json.loads(x)[2])
# 温湿度的配置信息
print('dfConfig:\n%s' % self.dfConfig)
# 超标点最近3个时刻的温湿度df
self.dfTp = self.dfTp_preprocess()
print('dfTp:\n%s' % self.dfTp)
# 其他点位的送风温度df
self.df_supplyTemp = pd.DataFrame(self.mq_mess_dict['supplyTemp'])
print('supplyTemp:\n%s' % self.df_supplyTemp)
# 冷站最近3个时刻综合负载率和综合出水温度的df
self.df_ratio = self.dfWtp_preprocess()
print('df_ratio:\n%s' % self.df_ratio)
def dfTp_preprocess(self):
'''
对原始数据进行预处理，整理超标温度最近3个时刻数据的df格式
:return:
'''
dict_dfTp = self.mq_mess_dict["dfTp"].copy()
list_dfTp = []
for i in dict_dfTp.keys():
df = pd.DataFrame(dict_dfTp[i])
df.sort_values(by='time', ascending=False, inplace=True)
list_dfTp.append(df)
df_concat = pd.concat(list_dfTp, keys=[int(i) for i in dict_dfTp.keys()], axis=1)
return df_concat
def dfWtp_preprocess(self):
'''
对原始数据预处理，形成冷站最近3个时刻各冷机数据df，再进一步处理成冷站的负载率以及综合出水温度的df
数据预处理，得到该冷站各个冷机的meterid的集合set_meter_id;
得到处理后的冷站加权负载和综合水温df_ratio
:return:
'''
dict_dfWtp = self.mq_mess_dict["dfWtp"].copy()
list_dfWtp = []
for i in dict_dfWtp.keys():
list_dfWtp.append(pd.DataFrame(dict_dfWtp[i]))
self.dfWtp = pd.concat(list_dfWtp, keys=[int(i) for i in dict_dfWtp.keys()], axis=1)
set_meter_id = set([int(i) for i in dict_dfWtp.keys()])
df_ratio = self.dfWtp.apply(cal_comp, axis=1, args=(set_meter_id,))
return df_ratio
# ------------进行逻辑分析------------
def classifyer_summer(self):
'''
超标温度点问题分类——分锅函数
:return:
'''
# 当前冷站出水温度
wtp = self.df_ratio['ratio_2'].mean()
wtp = round(wtp, 2)
# 最小送风温度
min_su_tp = self.df_supplyTemp['tp'].min()
# # #如果部分空调机组全新风或近似全新风，送风温度可能比水温还低，全局最小送风温度将不能反映冷冻水处理空气的最佳效果
if min_su_tp < wtp + 1:
min_su_tp = wtp + 1
# # #如果只开一个空调末端，其送风温度将成为最小的温度，无法真实反映末端正常的可达到的最低送风温度。
min_su_tp = min(min_su_tp, wtp + 5)
# 采集时间
list_time = []
# 超标类型
list_type = []
# 送风温度
list_su_tp_mean = []
# 全局最小送风温度
list_min_su_tp = []
# 回风温度
list_re_tp = []
# 当前水温
list_wtp = []
# 文本
list_text = []
for id in self.dfConfig.su_meterId:
# 当前送风温度(最近3个点的平均送风温度)
su_tp_mean = self.dfTp[(id, 'tp')].mean()
su_tp_mean = round(su_tp_mean, 2)
# 当前回风温度(最近时刻的回温度)
reid = self.dfConfig[self.dfConfig['su_meterId'] == id]['re_meterId'].iloc[0]
re_tp = self.dfTp[(reid, 'tp')].iloc[0]
# 当前时间
time = self.dfTp[(id, 'time')].max()
# 第1类超标问题
if (su_tp_mean - wtp > min_su_tp - wtp + DeltaT) and (re_tp - su_tp_mean <= 7):
type = 1
reason = '当前送风温度远大于出水温度；该点位流量不足，送风温度设定偏高或当前时刻末端可能发生故障。'
# 第2类超标问题
elif wtp < WaterTempBase_summer + 1.0:
type = 2
reason = '出水温度已经在7±1℃范围，监测点依然超标；当前时刻冷负荷较大，末端能力不足。'
# 第4类问题
elif str(wtp) == 'nan':
type = 4
reason = '可能没开机或其他原因(离线)。'
# 第3类超标问题
else:
type = 3
reason = '冷站出水温度较高；当前设定温度较高。'
list_time.append(time)
list_type.append(type)
list_su_tp_mean.append(su_tp_mean)
list_min_su_tp.append(min_su_tp)
list_re_tp.append(re_tp)
list_wtp.append(wtp)
list_text.append(reason)
df = pd.DataFrame({'locations': self.dfConfig['locations'], 'meterId': self.dfConfig['re_meterId'],
'time': list_time, 'type': list_type, 'su_tp_mean': list_su_tp_mean,
'global_min_su_tp': list_min_su_tp, 're_tp': list_re_tp, 'wtp': list_wtp, 'reason': list_text})
logging.error('超标诊断结果'.center(50, '='))
logging.error('\n%s' % df)
# 返回数据-整合
dict_send = self.mq_mess_dict.copy()
del dict_send['dfConfig']
del dict_send['dfTp']
del dict_send['dfWtp']
del dict_send['supplyTemp']
dict_send["results"] = df.to_dict(orient='list')
dict_send = json.dumps(dict_send, ensure_ascii=False)
logging.error('Data sent to mq'.center(50, '='))
logging.error(dict_send)
return dict_send
def classifyer_winter(self):
'''
超标温度点问题分类——分锅函数
:return:
'''
# 当前冷站出水温度
wtp = self.df_ratio['ratio_2'].mean()
wtp = round(wtp, 2)
# 最大送风温度
max_su_tp = self.df_supplyTemp['tp'].max()
# # # 类似地
max_su_tp = max(max_su_tp, wtp - 5)
# 采集时间
list_time = []
# 超标类型
list_type = []
# 送风温度
list_su_tp_mean = []
# 回风温度
list_re_tp = []
# 当前水温
list_wtp = []
# 文本
list_text = []
for id in self.dfConfig.su_meterId:
# 当前送风温度(最近3个点的平均送风温度)
su_tp_mean = self.dfTp[(id, 'tp')].mean()
# 当前回风温度(最近时刻的回温度)
reid = self.dfConfig[self.dfConfig['su_meterId'] == id]['re_meterId'].iloc[0]
re_tp = self.dfTp[(reid, 'tp')].iloc[0]
# 当前时间
time = self.dfTp[(id, 'time')].max()
# 第1类超标问题
if su_tp_mean < max_su_tp - DeltaT:
type = 1
reason = '当前送风温度远小于出水温度；该点位流量不足，送风温度设定偏低或当前时刻末端可能发生故障。'
elif wtp > WaterTempBase_winter - 1:
type = 2
reason = '出水温度已经达到冬季标准水温，监测点依然超标；当前时刻冷负荷较大，末端能力不足。'
else:
type = 3
reason = '冷站出水温度较低。'
list_time.append(time)
list_type.append(type)
list_su_tp_mean.append(su_tp_mean)
list_re_tp.append(re_tp)
list_wtp.append(wtp)
list_text.append(reason)
df = pd.DataFrame({'locations': self.dfConfig['locations'], 'meterId': self.dfConfig['re_meterId'],
'time': list_time, 'type': list_type, 'su_tp_mean': list_su_tp_mean, 're_tp': list_re_tp,
'wtp': list_wtp, 'reason': list_text})
logging.error('超标诊断结果'.center(50, '='))
logging.error('\n%s' % df)
# 返回数据-整合
dict_send = self.mq_mess_dict.copy()
del dict_send['dfConfig']
del dict_send['dfTp']
del dict_send['dfWtp']
del dict_send['supplyTemp']
dict_send["results"] = df.to_dict(orient='list')
dict_send = json.dumps(dict_send, ensure_ascii=False)
logging.error('Data sent to mq'.center(50, '='))
logging.error(dict_send)
return dict_send
def classifyer(self):
'''
根据夏季冬季的模式不同，区分
:return:
'''
if self.coolHeatMode == 0:
dict_send = self.classifyer_summer()
elif self.coolHeatMode == 1:
dict_send = self.classifyer_winter()
else:
raise ValueError('!!!制冷制热模式输出错误!!!')
return dict_send