煤矿井下局扇系统监测分析数据集合

1、加工工具：（1）基础处理：采用Excel完成数据录入、字段整理（如按“主/备局扇”“运行状态/电参数/风速”分类）与可视化查看，确保数据初步规整；（2）深度处理：使用Python（集成pandas库）开展数据清洗（剔除重复采集的运行状态记录、分类标注不同类型参数）与逻辑核验，提升数据可用性。2、核心处理规则：（1）参数分类：按“数据类型”（二进制变量/无符号16位值/32 位浮点数IEEE 754）与“监测维度”（状态/时间/电参数/风速）双重归类，如“运行状态”归为二进制变量-状态维度，“总运行时间”归为无符号16位值-时间维度；（2）异常标记：对超出合理范围的数值（如电机电压＜700V 或＞750V）、逻辑矛盾的状态（如主备局扇同时显示“运行”）进行标记，便于后续核查。

1. Processing Tools:
(1) Basic Processing: Use Excel to complete data entry, field organization (e.g., classified by "main/standby local fan", "operating status/electrical parameters/wind speed"), and visual review, to ensure preliminary data tidying.
(2) Advanced Processing: Use Python (with the pandas library integrated) to conduct data cleaning (remove duplicate collected operating status records, classify and label different types of parameters) and logical verification, so as to improve data availability.
2. Core Processing Rules:
(1) Parameter Classification: Carry out dual classification based on "data type" (binary variable/unsigned 16-bit value/32-bit floating point number compliant with IEEE 754 standard) and "monitoring dimension" (status/time/electrical parameters/wind speed). For example, "operating status" is classified as binary variable - status dimension, and "total operating time" is classified as unsigned 16-bit value - time dimension.
(2) Anomaly Marking: Mark values that exceed reasonable ranges (e.g., motor voltage <700V or >750V) and logically inconsistent statuses (e.g., both the main and standby local fans displaying "operating") to facilitate subsequent verification.