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@@ -93,9 +93,9 @@ def diag_cal():
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if df_feishu_sta.empty:#飞书中没有该sn记录故障的新增
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if df_feishu_sta.empty:#飞书中没有该sn记录故障的新增
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df_Diag_cal_early_unfix = df_Diag_Cal_new#如果为新出故障，则直接记录在df_diag_frame中
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df_Diag_cal_early_unfix = df_Diag_Cal_new#如果为新出故障，则直接记录在df_diag_frame中
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else:
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else:
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- df_Diag_cal_later = df_Diag_Cal_new.loc[df_Diag_Cal_new['start_time'] > max(df_feishu_sta['start_time'])]#故障表中故障时间晚于飞书记录时间的新增
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+ df_Diag_cal_later = df_Diag_Cal_new.loc[pd.to_datetime(df_Diag_Cal_new['start_time']) > max(pd.to_datetime(df_feishu_sta['start_time']))]#故障表中故障时间晚于飞书记录时间的新增
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df_Diag_cal_early = pd.concat([df_Diag_Cal_new,df_Diag_cal_later,df_Diag_cal_later]).drop_duplicates(subset=['product_id','code','start_time'],keep=False)#故障表中故障时间早于飞书记录时间
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df_Diag_cal_early = pd.concat([df_Diag_Cal_new,df_Diag_cal_later,df_Diag_cal_later]).drop_duplicates(subset=['product_id','code','start_time'],keep=False)#故障表中故障时间早于飞书记录时间
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- df_feishu_sta_latest = df_feishu_sta.loc[df_feishu_sta['start_time'] == max(df_feishu_sta['start_time'])]#飞书中该SN下的最新故障
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+ df_feishu_sta_latest = df_feishu_sta.loc[pd.to_datetime(df_feishu_sta['start_time']) == max(pd.to_datetime(df_feishu_sta['start_time']))]#飞书中该SN下的最新故障
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df_feishu_diag_unfix = (df_feishu_sta_latest['advice'] == '需正常返仓') | (df_feishu_sta_latest['advice'] == '需紧急返仓')
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df_feishu_diag_unfix = (df_feishu_sta_latest['advice'] == '需正常返仓') | (df_feishu_sta_latest['advice'] == '需紧急返仓')
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if any(df_feishu_diag_unfix):
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if any(df_feishu_diag_unfix):
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df_Diag_cal_early_unfix = df_Diag_Cal_new
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df_Diag_cal_early_unfix = df_Diag_Cal_new
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