{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-52:features","from_node_id":"-331:data"},{"to_node_id":"-52:instruments","from_node_id":"-312:data"},{"to_node_id":"-783:input_1","from_node_id":"-52:data"},{"to_node_id":"-41:features","from_node_id":"-64:data"},{"to_node_id":"-783:input_2","from_node_id":"-64:data"},{"to_node_id":"-52:user_functions","from_node_id":"-726:functions"},{"to_node_id":"-41:user_factor_data","from_node_id":"-783:data_1"}],"nodes":[{"node_id":"-331","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"#_amt = amount\n#_ret = ret_sim(close, open)\n#big_order_ret = np.prod(1 + _ret*where(_amt >= _amt.sort_values(ascending=False).iloc[np.int(240*0.4-1)], 1, 0))\n#######财务因子##########\n# 净资产收益率\nfs_roe_0\t\n# 企业自由现金流\nfs_free_cash_flow_0\t\n# 每股收益\nfs_eps_0\t\n# 归属母公司股东净利润季度环比增长率\nfs_net_profit_qoq_0\t\nclose_0*avg_turn_0+close_1*avg_turn_1+close_2*avg_turn_2\n\n#######估值因子##########\n# 动态市盈率\npe_ttm_0\t\n# 市值\nmarket_cap_0\n\n#######股东因子##########\n# 户均持股比例\nsh_holder_avg_pct_0\t\t\n\n\navg_amount_0/avg_amount_20\n\nrank(sum(amount_0*sign(max(open_0+close_0-high_0-low_0,close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0)-0.1),20))\n\n#######Beta值因子##########\t\n# 上证50\nbeta_sse50_5_0\n# 上证综指\nbeta_szzs_5_0\t\n\nreturn_20\n\nrank_return_20\n\nrank_avg_amount_20\n\n\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-331"}],"output_ports":[{"name":"data","node_id":"-331"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-312","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-12-31","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-312"}],"output_ports":[{"name":"data","node_id":"-312"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-52","module_id":"BigQuantSpace.feature_extractor_1m.feature_extractor_1m-v2","parameters":[{"name":"start_date","value":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-12-31","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"20","type":"Literal","bound_global_parameter":null},{"name":"workers","value":"20","type":"Literal","bound_global_parameter":null},{"name":"parallel_mode","value":"集群","type":"Literal","bound_global_parameter":null},{"name":"table_1m","value":"level2_bar1m_CN_STOCK_A","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-52"},{"name":"features","node_id":"-52"},{"name":"user_functions","node_id":"-52"}],"output_ports":[{"name":"data","node_id":"-52"}],"cacheable":true,"seq_num":7,"comment":"V2","comment_collapsed":true},{"node_id":"-41","module_id":"BigQuantSpace.factorlens.factorlens-v1","parameters":[{"name":"title","value":"因子分析: {factor_name}","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-12-31","type":"Literal","bound_global_parameter":null},{"name":"rebalance_period","value":"2","type":"Literal","bound_global_parameter":null},{"name":"delay_rebalance_days","value":0,"type":"Literal","bound_global_parameter":null},{"name":"rebalance_price","value":"close_0","type":"Literal","bound_global_parameter":null},{"name":"stock_pool","value":"全市场","type":"Literal","bound_global_parameter":null},{"name":"quantile_count","value":"10","type":"Literal","bound_global_parameter":null},{"name":"commission_rate","value":"0.0002","type":"Literal","bound_global_parameter":null},{"name":"returns_calculation_method","value":"累乘","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"沪深300","type":"Literal","bound_global_parameter":null},{"name":"drop_new_stocks","value":60,"type":"Literal","bound_global_parameter":null},{"name":"drop_price_limit_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_st_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_suspended_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"normalization","value":"True","type":"Literal","bound_global_parameter":null},{"name":"neutralization","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22displayValue%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22displayValue%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"metrics","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22displayValue%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"factor_coverage","value":0.5,"type":"Literal","bound_global_parameter":null},{"name":"user_data_merge","value":"inner","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features","node_id":"-41"},{"name":"user_factor_data","node_id":"-41"}],"output_ports":[{"name":"data","node_id":"-41"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-64","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"big_order_ret","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-64"}],"output_ports":[{"name":"data","node_id":"-64"}],"cacheable":false,"seq_num":9,"comment":"","comment_collapsed":true},{"node_id":"-726","module_id":"BigQuantSpace.feature_extractor_user_function.feature_extractor_user_function-v1","parameters":[{"name":"name","value":"ret_sim","type":"Literal","bound_global_parameter":null},{"name":"func","value":"def 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outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-783"},{"name":"input_2","node_id":"-783"},{"name":"input_3","node_id":"-783"}],"output_ports":[{"name":"data_1","node_id":"-783"},{"name":"data_2","node_id":"-783"},{"name":"data_3","node_id":"-783"}],"cacheable":true,"seq_num":5,"comment":"因子值越大越好","comment_collapsed":false}],"node_layout":"<node_postions><node_position Node='-331' Position='-544,-760,200,200'/><node_position Node='-312' Position='-870,-649,200,200'/><node_position Node='-52' Position='-552,-556,200,200'/><node_position Node='-41' Position='-546,-134,200,200'/><node_position Node='-64' Position='-223,-484,200,200'/><node_position Node='-726' Position='-212,-663,200,200'/><node_position Node='-783' Position='-525,-324,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2023年1月2日 15:09
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


def m4_func_bigquant_run(df, close, op):
    res = close.pct_change()
    res.iloc[0] = close.iloc[0] / op.iloc[0] - 1
    return res

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m5_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    print(input_1)
    print(input_2)
    df = input_1.read() 
    f = input_2.read()[0]
    df[f] = df[f] * -1
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1)
# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m5_post_run_bigquant_run(outputs):
    return outputs


m1 = M.input_features.v1(
    features="""#_amt = amount
#_ret = ret_sim(close, open)
#big_order_ret = np.prod(1 + _ret*where(_amt >= _amt.sort_values(ascending=False).iloc[np.int(240*0.4-1)], 1, 0))
#######财务因子##########
# 净资产收益率
fs_roe_0	
# 企业自由现金流
fs_free_cash_flow_0	
# 每股收益
fs_eps_0	
# 归属母公司股东净利润季度环比增长率
fs_net_profit_qoq_0	
close_0*avg_turn_0+close_1*avg_turn_1+close_2*avg_turn_2

#######估值因子##########
# 动态市盈率
pe_ttm_0	
# 市值
market_cap_0

#######股东因子##########
# 户均持股比例
sh_holder_avg_pct_0		


avg_amount_0/avg_amount_20

rank(sum(amount_0*sign(max(open_0+close_0-high_0-low_0,close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0)-0.1),20))

#######Beta值因子##########	
# 上证50
beta_sse50_5_0
# 上证综指
beta_szzs_5_0	

return_20

rank_return_20

rank_avg_amount_20


"""
)

m2 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2022-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m9 = M.input_features.v1(
    features='big_order_ret',
    m_cached=False
)

m4 = M.feature_extractor_user_function.v1(
    name='ret_sim',
    func=m4_func_bigquant_run
)

m7 = M.feature_extractor_1m.v2(
    instruments=m2.data,
    features=m1.data,
    user_functions=m4.functions,
    start_date='2021-01-01',
    end_date='2022-12-31',
    before_start_days=20,
    workers=20,
    parallel_mode='集群',
    table_1m='level2_bar1m_CN_STOCK_A'
)

m5 = M.cached.v3(
    input_1=m7.data,
    input_2=m9.data,
    run=m5_run_bigquant_run,
    post_run=m5_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m8 = M.factorlens.v1(
    features=m9.data,
    user_factor_data=m5.data_1,
    title='因子分析: {factor_name}',
    start_date='2021-01-01',
    end_date='2022-12-31',
    rebalance_period=2,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=10,
    commission_rate=0.0002,
    returns_calculation_method='累乘',
    benchmark='沪深300',
    drop_new_stocks=60,
    drop_price_limit_stocks=True,
    drop_st_stocks=True,
    drop_suspended_stocks=True,
    normalization=True,
    neutralization=[],
    metrics=['因子表现概览', '因子分布', '因子行业分布', '因子市值分布', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '因子值最大/最小股票', '表达式因子值', '多因子相关性分析'],
    factor_coverage=0.5,
    user_data_merge='inner'
)

[2023-01-02 14:59:33.173754] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-01-02 14:59:33.186469] INFO: moduleinvoker: 命中缓存

[2023-01-02 14:59:33.188876] INFO: moduleinvoker: input_features.v1 运行完成[0.015133s].

[2023-01-02 14:59:33.196600] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-01-02 14:59:33.202340] INFO: moduleinvoker: 命中缓存

[2023-01-02 14:59:33.203718] INFO: moduleinvoker: instruments.v2 运行完成[0.007117s].

[2023-01-02 14:59:33.207397] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-01-02 14:59:33.224756] INFO: moduleinvoker: input_features.v1 运行完成[0.017358s].

[2023-01-02 14:59:33.234891] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[8.6e-05s].

[2023-01-02 14:59:33.525603] INFO: moduleinvoker: feature_extractor_1m.v2 开始运行..

[2023-01-02 14:59:33.622781] INFO: 高频特征抽取-分钟到日频: 【集群模式】, instruments:5135, workers:20, 并发数:20

[2023-01-02 14:59:33.647415] WARNING: AI: 当前可运行1个高级AI任务，购买资源获取更多高级AI任务位[url="https://bigquant.com/account/big_member/?from=navigation" style="display: inline-block;padding: 5px 7px;border-radius: 2px;background: #F0BC41;color: white"]购买高级AI任务位[/url]

[2023-01-02 14:59:33.649661] INFO: AI: 开始并行运算, remote_run=True, workers=1 ..

[2023-01-02 14:59:33.651782] INFO: AI: [ParallelEx(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

[2023-01-02 14:59:33.796555] INFO: cached.v2.031f457c: 任务状态: Pending

[2023-01-02 14:59:43.823715] INFO: cached.v2.031f457c: 任务状态: Running

[2023-01-02 15:00:03.886537] INFO: cached.v2.031f457c: 任务状态: Succeeded

[2023-01-02 15:00:03.891267] INFO: AI: [ParallelEx(n_jobs=1)]: Done   1 out of   1 | elapsed:   30.2s remaining:    0.0s

[2023-01-02 15:00:04.097735] INFO: cached.v2.152ae3d4: 任务状态: Pending

[2023-01-02 15:00:14.131768] INFO: cached.v2.152ae3d4: 任务状态: Running

[2023-01-02 15:00:34.196246] INFO: cached.v2.152ae3d4: 任务状态: Succeeded

[2023-01-02 15:00:34.200495] INFO: AI: [ParallelEx(n_jobs=1)]: Done   2 out of   2 | elapsed:  1.0min remaining:    0.0s

[2023-01-02 15:00:34.402806] INFO: cached.v2.273667a6: 任务状态: Pending

[2023-01-02 15:00:44.433397] INFO: cached.v2.273667a6: 任务状态: Running

[2023-01-02 15:01:04.494574] INFO: cached.v2.273667a6: 任务状态: Succeeded

[2023-01-02 15:01:04.498198] INFO: AI: [ParallelEx(n_jobs=1)]: Done   3 out of   3 | elapsed:  1.5min remaining:    0.0s

[2023-01-02 15:01:04.657985] INFO: cached.v2.3944b7f4: 任务状态: Pending

[2023-01-02 15:01:14.688482] INFO: cached.v2.3944b7f4: 任务状态: Running

[2023-01-02 15:01:34.777849] INFO: cached.v2.3944b7f4: 任务状态: Succeeded

[2023-01-02 15:01:34.782044] INFO: AI: [ParallelEx(n_jobs=1)]: Done   4 out of   4 | elapsed:  2.0min remaining:    0.0s

[2023-01-02 15:01:34.956931] INFO: cached.v2.4b51a25e: 任务状态: Pending

[2023-01-02 15:01:44.988774] INFO: cached.v2.4b51a25e: 任务状态: Running

[2023-01-02 15:02:05.050424] INFO: cached.v2.4b51a25e: 任务状态: Succeeded

[2023-01-02 15:02:05.054690] INFO: AI: [ParallelEx(n_jobs=1)]: Done   5 out of   5 | elapsed:  2.5min remaining:    0.0s

[2023-01-02 15:02:05.231644] INFO: cached.v2.5d5c7ae6: 任务状态: Pending

[2023-01-02 15:02:15.263004] INFO: cached.v2.5d5c7ae6: 任务状态: Running

[2023-01-02 15:02:35.324325] INFO: cached.v2.5d5c7ae6: 任务状态: Succeeded

[2023-01-02 15:02:35.328387] INFO: AI: [ParallelEx(n_jobs=1)]: Done   6 out of   6 | elapsed:  3.0min remaining:    0.0s

[2023-01-02 15:02:35.482448] INFO: cached.v2.6f682302: 任务状态: Pending

[2023-01-02 15:02:45.518382] INFO: cached.v2.6f682302: 任务状态: Running

[2023-01-02 15:03:05.583855] INFO: cached.v2.6f682302: 任务状态: Succeeded

[2023-01-02 15:03:05.587884] INFO: AI: [ParallelEx(n_jobs=1)]: Done   7 out of   7 | elapsed:  3.5min remaining:    0.0s

[2023-01-02 15:03:05.759201] INFO: cached.v2.817163b0: 任务状态: Pending

[2023-01-02 15:03:15.791758] INFO: cached.v2.817163b0: 任务状态: Running

[2023-01-02 15:03:35.847659] INFO: cached.v2.817163b0: 任务状态: Succeeded

[2023-01-02 15:03:35.851634] INFO: AI: [ParallelEx(n_jobs=1)]: Done   8 out of   8 | elapsed:  4.0min remaining:    0.0s

[2023-01-02 15:03:36.017913] INFO: cached.v2.937abb74: 任务状态: Pending

[2023-01-02 15:03:46.048027] INFO: cached.v2.937abb74: 任务状态: Running

[2023-01-02 15:04:06.113698] INFO: cached.v2.937abb74: 任务状态: Succeeded

[2023-01-02 15:04:06.118288] INFO: AI: [ParallelEx(n_jobs=1)]: Done   9 out of   9 | elapsed:  4.5min remaining:    0.0s

[2023-01-02 15:04:06.285932] INFO: cached.v2.a585aa36: 任务状态: Pending

[2023-01-02 15:04:16.317266] INFO: cached.v2.a585aa36: 任务状态: Running

[2023-01-02 15:04:36.383289] INFO: cached.v2.a585aa36: 任务状态: Succeeded

[2023-01-02 15:04:36.554648] INFO: cached.v2.b78f4192: 任务状态: Pending

[2023-01-02 15:04:46.588199] INFO: cached.v2.b78f4192: 任务状态: Running

[2023-01-02 15:05:06.658857] INFO: cached.v2.b78f4192: 任务状态: Succeeded

[2023-01-02 15:05:06.842806] INFO: cached.v2.c99de2a8: 任务状态: Pending

[2023-01-02 15:05:16.874065] INFO: cached.v2.c99de2a8: 任务状态: Running

[2023-01-02 15:05:36.937036] INFO: cached.v2.c99de2a8: 任务状态: Succeeded

[2023-01-02 15:05:37.120004] INFO: cached.v2.dba94708: 任务状态: Pending

[2023-01-02 15:05:47.150691] INFO: cached.v2.dba94708: 任务状态: Running

[2023-01-02 15:06:07.216510] INFO: cached.v2.dba94708: 任务状态: Succeeded

[2023-01-02 15:06:07.516643] INFO: cached.v2.edb47ce2: 任务状态: Pending

[2023-01-02 15:06:17.551450] INFO: cached.v2.edb47ce2: 任务状态: Running

[2023-01-02 15:06:37.621558] INFO: cached.v2.edb47ce2: 任务状态: Succeeded

[2023-01-02 15:06:37.820250] INFO: cached.v2.ffd694aa: 任务状态: Pending

[2023-01-02 15:06:47.852979] INFO: cached.v2.ffd694aa: 任务状态: Running

[2023-01-02 15:07:07.917279] INFO: cached.v2.ffd694aa: 任务状态: Succeeded

[2023-01-02 15:07:08.113395] INFO: cached.v2.11e33e5a: 任务状态: Pending

[2023-01-02 15:07:18.142689] INFO: cached.v2.11e33e5a: 任务状态: Running

[2023-01-02 15:07:38.217525] INFO: cached.v2.11e33e5a: 任务状态: Succeeded

[2023-01-02 15:07:38.389775] INFO: cached.v2.23f320ba: 任务状态: Pending

[2023-01-02 15:07:48.418145] INFO: cached.v2.23f320ba: 任务状态: Running

[2023-01-02 15:08:08.482122] INFO: cached.v2.23f320ba: 任务状态: Succeeded

[2023-01-02 15:08:08.672541] INFO: cached.v2.35fb37ca: 任务状态: Pending

[2023-01-02 15:08:18.714594] INFO: cached.v2.35fb37ca: 任务状态: Running

[2023-01-02 15:08:38.789211] INFO: cached.v2.35fb37ca: 任务状态: Succeeded

[2023-01-02 15:08:38.978962] INFO: cached.v2.480da34e: 任务状态: Pending

[2023-01-02 15:08:49.010563] INFO: cached.v2.480da34e: 任务状态: Running

[2023-01-02 15:09:09.071178] INFO: cached.v2.480da34e: 任务状态: Succeeded

[2023-01-02 15:09:09.250408] INFO: cached.v2.5a1890da: 任务状态: Pending

[2023-01-02 15:09:19.281080] INFO: cached.v2.5a1890da: 任务状态: Running