基于AI的干熄焦精准控制数据

数据内容：
工艺参数：循环气体温度、压力、流量；焦炭冷却速率；锅炉入口/出口烟气成分（O₂、CO、CO₂等）。
设备状态：电机电流、振动频率；除尘系统运行参数；提升机与装入装置的定位数据。
焦炭质量指标：挥发分、抗碎强度（M40）、耐磨强度（M10）、水分含量等实验室检测结果。
环境数据：排放烟气中的SO₂、NOx浓度；噪音及颗粒物监测值。
数据特征：
多源异构性：包含传感器时序数据（高频率采样）、图像（红外热成像）、文本日志及实验室离散数据。
强动态关联：循环气体流量与焦炭冷却速率呈非线性耦合，需通过AI建模解耦。

Data Content:
Process parameters: circulating gas temperature, pressure, flow rate; coke cooling rate; flue gas composition at boiler inlet and outlet (O₂, CO, CO₂, etc.).
Equipment status: motor current, vibration frequency; operating parameters of the dust removal system; positioning data of the hoist and charging device.
Coke quality indicators: laboratory test results including volatile matter, crushing strength (M40), wear resistance (M10), moisture content, etc.
Environmental data: SO₂ and NOₓ concentrations in emitted flue gas; noise and particulate matter monitoring values.

Data Characteristics:
1. Multi-source heterogeneity: Contains high-frequency sampled sensor time-series data, infrared thermal imaging images, text logs and laboratory discrete data.
2. Strong dynamic correlation: The circulating gas flow rate and coke cooling rate are non-linearly coupled, which requires decoupling via AI modeling.