A 30-Day Raw Telemetry Dataset Package for an Indoor Ice Rink Refrigeration System

# 30-Day Indoor Ice Rink Telemetry Dataset Package ## 1. Package contents - `raw_30d_telemetry.csv` — raw row-level telemetry archive extracted from ClickHouse. - `minute_30d_from_raw.csv` — minute-aligned intermediate table obtained from the raw archive. - `data_30d_ml_from_raw.csv` — cleaned minute-level analytical table. - `preprocess_ice_rink_30d_raw_telemetry.py` — preprocessing script that reconstructs the minute-level files from the raw archive. - `data_dictionary.csv` — machine-readable description of the released files and columns. - `signals_mapping.csv` — mapping from raw signals to cleaned variables. - `requirements.txt` — minimal Python dependencies required to run the preprocessing script. - `LICENSE.txt` — license notice for the supplementary dataset package. ## 2. Observation window The released package covers the observation window from 2025-03-03 00:00:00 to 2025-04-02 00:00:00 in the Europe/Moscow timezone. The raw archive preserves timestamps with an explicit `+03:00` offset. The intermediate and cleaned minute-level tables use naive minute timestamps consistent with the internal `data.csv` workflow. ## 3. File descriptions - `raw_30d_telemetry.csv` — raw row-level archive restricted to the twenty selected telemetry signals. - `minute_30d_from_raw.csv` — minute-level intermediate table produced by rounding timestamps down to the nearest minute, averaging repeated observations within each minute, pivoting to a wide table, and reconstructing the complete minute calendar. - `data_30d_ml_from_raw.csv` — cleaned minute-level analytical table produced by continuity restoration and range-based plausibility filtering. ## 4. Raw schema The raw archive contains the following columns: - `timestamp` - `device_name` - `measure_name` - `channel` - `phase` - `signal_name` - `value` ## 5. Processing steps 1. Filter the source archive to the twenty signals used in the refrigeration dataset. 2. Round timestamps down to the nearest minute. 3. Average repeated observations within each minute for the same signal. 4. Pivot the signal stream to a wide minute-level table. 5. Aggregate composite power variables for compressors and heating. 6. Restore continuity by forward filling and remove the unresolved leading segment. 7. Apply plausibility filtering for the supervisory setpoint, compressor power, and heating power. ## 6. How to reproduce 1. Install the dependencies listed in `requirements.txt`. 2. Run: ```bash python preprocess_ice_rink_30d_raw_telemetry.py ``` 3. The script recreates `minute_30d_from_raw.csv` and `data_30d_ml_from_raw.csv` from `raw_30d_telemetry.csv`. ## 7. Notes and limitations - The raw file may contain repeated observations within the same minute for the same signal. - The intermediate minute-level table retains missing values and should not be treated as a final analytical dataset. - The cleaned minute-level table is unlabeled and contains no engineered features. - The release is limited to a 30-day window and to twenty selected telemetry signals. ## 8. License and citation The supplementary dataset package is distributed under the CC BY 4.0 license. When reusing the package, cite both the dataset paper and the supplementary dataset package submitted with it.# 30天室内冰场遥测数据集套件 ## 1. 套件内容 - `raw_30d_telemetry.csv` — 从ClickHouse提取的原始行级遥测归档文件。 - `minute_30d_from_raw.csv` — 从原始归档文件获取的按分钟对齐的中间表。 - `data_30d_ml_from_raw.csv` — 经过清洗的分钟级分析表。 - `preprocess_ice_rink_30d_raw_telemetry.py` — 用于从原始归档文件重构分钟级文件的预处理脚本。 - `data_dictionary.csv` — 发布文件与列的机器可读数据字典。 - `signals_mapping.csv` — 原始信号与清洗后变量的映射表。 - `requirements.txt` — 运行预处理脚本所需的最低Python依赖包列表。 - `LICENSE.txt` — 补充数据集套件的许可声明文件。 ## 2. 观测窗口 本次发布的套件覆盖欧洲莫斯科（Europe/Moscow）时区下的2025-03-03 00:00:00至2025-04-02 00:00:00观测窗口。原始归档文件保留了显式带有`+03:00`偏移的时间戳。中间表与清洗后的分钟级表采用与内部`data.csv`工作流一致的朴素分钟时间戳格式。 ## 3. 文件详细说明 - `raw_30d_telemetry.csv` — 仅包含20个选定遥测信号的原始行级归档文件。 - `minute_30d_from_raw.csv` — 分钟级中间表，生成流程为：将时间戳向下取整至最近的分钟，对每分钟内的重复观测值取平均，转换为宽表，并补全完整的分钟时间序列。 - `data_30d_ml_from_raw.csv` — 经过连续性修复与基于范围的合理性过滤后得到的清洗后分钟级分析表。 ## 4. 原始数据模式 原始归档文件包含以下列： - `timestamp` - `device_name` - `measure_name` - `channel` - `phase` - `signal_name` - `value` ## 5. 处理流程 1. 从源归档文件中筛选出制冷数据集所用的20个信号。 2. 将时间戳向下取整至最近的分钟。 3. 对同一信号在每分钟内的重复观测值取平均。 4. 将信号流转换为宽格式的分钟级表。 5. 为压缩机与加热系统聚合复合功率变量。 6. 通过前向填充修复数据连续性，并移除未补全的起始片段。 7. 对监控设定值、压缩机功率与加热功率应用合理性过滤。 ## 6. 复现方法 1. 安装`requirements.txt`中列出的依赖包。 2. 执行以下命令： bash python preprocess_ice_rink_30d_raw_telemetry.py 3. 该脚本将从`raw_30d_telemetry.csv`重构生成`minute_30d_from_raw.csv`与`data_30d_ml_from_raw.csv`。 ## 7. 注意事项与局限性 - 原始文件中同一信号在同一分钟内可能存在重复观测值。 - 分钟级中间表保留了缺失值，不应视为最终分析数据集。 - 清洗后的分钟级表未标注标签，也未包含人工构造的特征。 - 本次发布仅覆盖30天观测窗口与20个选定的遥测信号。 ## 8. 许可与引用 本补充数据集套件采用CC BY 4.0许可协议进行分发。在重新使用该套件时，请同时引用本数据集论文与随其提交的补充数据集套件。