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- ##修订电池的状态码,处理对象为清洗后并合并清洗后合并gps的数据
- import pandas as pd
- import numpy as np
- ##修订电池的状态码,充电soc的变化率最小值设定c_soc_dif_p,充电时间的最小值设定c_order_delta,
- ##静置soc的变化率最小值设定s_soc_dif_p,静置时间的最小值设定s_order_delta,
- def data_status(df_merge, c_soc_dif_p=0.05, c_order_delta=300, s_order_delta=300):
- ##构建状态的flag
- df_merge=df_merge.sort_values(["time"],ascending = [True])
- df_merge["flag"]="d"
- df_merge["flag"][df_merge["pack_crnt"] < -1]="c"
- df_merge["flag"][(df_merge["pack_crnt"]<0.5) & (df_merge["pack_crnt"]>-0.5)]="s"
- ##构建连续的flag标记为flag_block
- df_merge['flag_block']=(df_merge["flag"].shift(1) != df_merge["flag"]).astype(int).cumsum()
- ##针对充电,按照条件筛选
- ##首先统计在每个flag区间上的soc和开始结束时间
- df_merge_c=df_merge[df_merge["flag"]=="c"]
- df_merge_c_soc_b=df_merge_c[["pack_soc","sn","flag_block"]].groupby(["sn","flag_block"]).first()
- df_merge_c_soc_e=df_merge_c[["pack_soc","sn","flag_block"]].groupby(["sn","flag_block"]).last()
- df_merge_c_time_b=df_merge_c[["time","sn","flag_block"]].groupby(["sn","flag_block"]).first()
- df_merge_c_time_e=df_merge_c[["time","sn","flag_block"]].groupby(["sn","flag_block"]).last()
- frames=[df_merge_c_soc_b,df_merge_c_soc_e,df_merge_c_time_b,df_merge_c_time_e]
- df_merge_c_choice = pd.concat(frames, axis=1, join='inner')
- df_merge_c_choice=df_merge_c_choice.reset_index()
- df_merge_c_choice.columns=["sn","charge_block","soc_first","soc_last","time_first","time_last"]
- df_merge_c_choice["soc_dif_p"]=(df_merge_c_choice["soc_last"]-df_merge_c_choice["soc_first"])/df_merge_c_choice["soc_first"]
- df_merge_c_choice["order_delta"]=pd.to_timedelta(pd.to_datetime(df_merge_c_choice["time_last"])-pd.to_datetime(df_merge_c_choice["time_first"])).dt.total_seconds()
- df_merge_c_choice["order_delta_h"]=round(df_merge_c_choice["order_delta"]/3600,2)
- df_merge_c_choice["rate"]=(df_merge_c_choice["soc_last"]-df_merge_c_choice["soc_first"])/df_merge_c_choice["order_delta_h"]
- df_merge_c_choice_result1=df_merge_c_choice[(df_merge_c_choice["soc_dif_p"]>=c_soc_dif_p)&(df_merge_c_choice["order_delta"]>=c_order_delta)&(df_merge_c_choice["rate"]>0.1)]
- df_merge_c_choice_result2=df_merge_c_choice[(df_merge_c_choice["soc_dif_p"]>=c_soc_dif_p)&(df_merge_c_choice["order_delta"]>=c_order_delta)&(df_merge_c_choice["rate"]<=0.1)]
- df_merge_c21=df_merge_c[(df_merge_c["sn"].isin(df_merge_c_choice_result1["sn"]))&(df_merge_c["flag_block"].isin (df_merge_c_choice_result1["charge_block"])) ]
- df_merge_c22=df_merge_c[(df_merge_c["sn"].isin(df_merge_c_choice_result2["sn"]))&(df_merge_c["flag_block"].isin (df_merge_c_choice_result2["charge_block"])) ]
- ##更新电池的状态码
- df_merge["bms_sta"]=3
- df_merge["bms_sta"][df_merge_c21.index]=21
- df_merge["bms_sta"][df_merge_c22.index]=22
- ##针对静置,按条件筛选
- ##首先统计在每个flag区间上的soc和开始结束时间
- df_merge_s=df_merge[df_merge["flag"]=="s"]
- df_merge_s_soc_b=df_merge_s[["pack_soc","sn","flag_block"]].groupby(["sn","flag_block"]).first()
- df_merge_s_soc_e=df_merge_s[["pack_soc","sn","flag_block"]].groupby(["sn","flag_block"]).last()
- df_merge_s_time_b=df_merge_s[["time","sn","flag_block"]].groupby(["sn","flag_block"]).first()
- df_merge_s_time_e=df_merge_s[["time","sn","flag_block"]].groupby(["sn","flag_block"]).last()
- frames=[df_merge_s_soc_b,df_merge_s_soc_e,df_merge_s_time_b,df_merge_s_time_e]
- df_merge_s_choice = pd.concat(frames, axis=1, join='inner')
- df_merge_s_choice=df_merge_s_choice.reset_index()
- df_merge_s_choice.columns=["sn","charge_block","soc_first","soc_last","time_first","time_last"]
- df_merge_s_choice["soc_dif_p"]=(df_merge_s_choice["soc_last"]-df_merge_s_choice["soc_first"])/df_merge_s_choice["soc_first"]
- df_merge_s_choice["order_delta"]=pd.to_timedelta(pd.to_datetime(df_merge_s_choice["time_last"])-pd.to_datetime(df_merge_s_choice["time_first"])).dt.total_seconds()
- df_merge_s_choice_result=df_merge_s_choice[df_merge_s_choice["order_delta"]>=s_order_delta]
- df_merge_s2=df_merge_s[(df_merge_s["sn"].isin(df_merge_s_choice_result["sn"]))&(df_merge_s["flag_block"].isin (df_merge_s_choice_result["charge_block"])) ]
- ##更新充电的状态码
- df_merge["bms_sta"][df_merge_s2.index]=0
- df_merge=df_merge.drop(['flag', 'flag_block'], axis=1, inplace=False)
- return df_merge
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