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# ============================================================
# Task: sdwpf_operating_state_classification
# ============================================================
# 任务目标:
# - 根据 SDWPF 风机的静态属性和历史 SCADA 数据,预测风机的运行状态类别。
# - 这是 episode-level 分类任务:每个 episode_id 对应一个静态样本。
#
# 核心语义:
# - sample 是 wind_turbine episode_id。
# - inputs 是模型能看到的静态属性和历史时序数据。
# - prediction.target.column = target_class_id 是模型要预测的类别标签。
# - target_class_id / target_class_name 不能作为输入字段,否则会产生直接标签泄漏。
# ============================================================
task:
id: sdwpf_operating_state_classification
type: episode_classification
description: >
Classify each wind turbine timestamp into one of four operating states
(normal, low_power, stopped, anomalous) based on active power output relative
to rated power. 134 turbines, single wind farm.
inherit_general: true
sample:
primary_key: episode_id # ID 用于连接 labels.csv、splits.csv 和静态属性表。
inputs:
tabular_static:
source: turbine_metadata # 指向 data_refs.tabular_static 中的静态属性表。
fields:
# - x
# - y
- elevation
- mean_power
- std_power
- max_power
- capacity_factor_pct
- mean_wind_speed
time_series:
- fields:
- Wspd
- Wdir
- Etmp
- Itmp
- Ndir
- Pab1
- Pab2
- Pab3
- Prtv
- T2m
- Sp
- RelH
- Wspd_w
- Wdir_w
- Tp
# - Patv
source: sdwpf_scada # 指向 data_refs.time_series 中定义的底层 SDWPF h5 文件。
window:
length: 2016 # 历史窗口长度为 6 个任务时间步。
unit: 10min # 每步 10 分钟,因此模型看到过去 1 小时。
graph:
source: turbine_proximity
prediction:
field: target_class_id
leakage:
forbidden_input_columns:
- target_class_id
- target_class_name
- episode_id # 主键只用于 join / indexing
- fd_subset # 数据子集标记,不能作为输入。
- Patv #数据泄露
data_refs:
tabular_static:
turbine_metadata:
file: SDWPF/input_multimodal/meta_features.csv
id_column: episode_id
time_series:
sdwpf_scada:
graph:
turbine_proximity:
file: SDWPF/input_multimodal/node_level_task_graphs/turbine_proximity/turbine_proximity.csv