import streamlit as st
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import plotly.express as px
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import plotly.graph_objects as go
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import pandas as pd
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import numpy as np
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import joblib
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import os
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from datetime import datetime
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from app.services.parameter_adjustment_service import ParameterAdjustmentAdvisor
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# 页面函数定义
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def show_extruder_parameter_adjustment():
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# 页面标题
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st.title("挤出机参数调节建议")
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# 添加操作指引
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with st.expander("📖 操作指引", expanded=True):
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st.markdown("""
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欢迎使用挤出机参数调节建议功能！本功能可以根据您输入的米重数据和当前参数，为您提供科学合理的参数调整建议。
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**操作步骤：**
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1. 选择一个已训练好的模型
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2. 输入米重标准值、上下限和当前挤出机参数
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3. 输入当前实际米重测量值
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4. 点击"计算调节建议"按钮
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5. 查看系统生成的参数调整建议
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**注意事项：**
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- 请确保输入的参数值在设备允许的范围内
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- 建议根据实际生产情况调整模型参数
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- 历史调节记录可在页面底部查看
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""")
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# 初始化会话状态
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if 'adjustment_history' not in st.session_state:
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st.session_state['adjustment_history'] = []
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# 1. 模型选择区域
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with st.expander("🔍 模型选择", expanded=True):
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# 创建模型目录（如果不存在）
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model_dir = "saved_models"
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os.makedirs(model_dir, exist_ok=True)
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# 获取所有已保存的模型文件
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model_files = [f for f in os.listdir(model_dir) if f.endswith('.joblib')]
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model_files.sort(reverse=True) # 最新的模型排在前面
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if not model_files:
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st.warning("尚未保存任何模型，请先训练模型并保存。")
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return
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# 模型选择下拉框
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selected_model_file = st.selectbox(
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"选择已保存的模型",
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options=model_files,
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help="选择要用于预测的模型文件"
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)
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# 加载并显示模型信息
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if selected_model_file:
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model_path = os.path.join(model_dir, selected_model_file)
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model_info = joblib.load(model_path)
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# 显示模型基本信息
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st.subheader("📊 模型信息")
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info_cols = st.columns(2)
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with info_cols[0]:
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st.metric("模型类型", model_info['model_type'])
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st.metric("创建时间", model_info['created_at'].strftime('%Y-%m-%d %H:%M:%S'))
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st.metric("使用稳态数据", "是" if model_info.get('use_steady_data', False) else "否")
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with info_cols[1]:
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st.metric("R² 得分", f"{model_info['r2_score']:.4f}")
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st.metric("均方误差 (MSE)", f"{model_info['mse']:.6f}")
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st.metric("均方根误差 (RMSE)", f"{model_info['rmse']:.6f}")
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# 显示模型特征
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st.write("🔑 模型使用的特征:")
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st.code(", ".join(model_info['features']))
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# 如果是深度学习模型，显示序列长度
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if 'sequence_length' in model_info:
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st.metric("序列长度", model_info['sequence_length'])
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# 保存模型信息到会话状态
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st.session_state['selected_model'] = model_info
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st.session_state['selected_model_file'] = selected_model_file
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# 2. 参数输入区域
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st.subheader("📝 参数输入")
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# 2.1 米重标准值、上下限输入
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with st.expander("⚖️ 米重标准与上下限", expanded=True):
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weight_cols = st.columns(3)
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with weight_cols[0]:
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standard_weight = st.number_input(
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"标准米重",
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key="standard_weight",
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value=5.20,
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min_value=0.01,
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max_value=10.0,
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step=0.0001,
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format="%.4f",
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help="输入目标米重标准值"
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)
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st.caption("单位: Kg/m")
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with weight_cols[1]:
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upper_limit = st.number_input(
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"米重上限",
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key="upper_limit",
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value=5.46,
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min_value=standard_weight,
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max_value=10.0,
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step=0.0001,
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format="%.4f",
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help="输入米重允许的上限值"
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)
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st.caption("单位: Kg/m")
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with weight_cols[2]:
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lower_limit = st.number_input(
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"米重下限",
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key="lower_limit",
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value=5.02,
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min_value=0.01,
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max_value=standard_weight,
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step=0.0001,
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format="%.4f",
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help="输入米重允许的下限值"
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)
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st.caption("单位: Kg/m")
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# 2.2 挤出机当前参数输入
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with st.expander("🔧 挤出机当前参数", expanded=True):
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param_cols = st.columns(3)
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with param_cols[0]:
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current_screw_speed = st.number_input(
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"螺杆转速",
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key="current_screw_speed",
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value=230.0,
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min_value=0.0,
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max_value=500.0,
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step=0.1,
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help="输入当前螺杆转速"
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)
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st.caption("单位: rpm")
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current_head_pressure = st.number_input(
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"机头压力",
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key="current_head_pressure",
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value=0.26,
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min_value=0.0,
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max_value=500.0,
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step=1.0,
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help="输入当前机头压力"
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)
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st.caption("单位: bar")
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current_process_speed = st.number_input(
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"流程主速",
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key="current_process_speed",
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value=6.6,
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min_value=0.0,
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max_value=300.0,
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step=0.1,
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help="输入当前流程主速"
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)
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st.caption("单位: m/min")
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with param_cols[1]:
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current_screw_temperature = st.number_input(
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"螺杆温度",
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key="current_screw_temperature",
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value=79.9,
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min_value=0.0,
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max_value=300.0,
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step=1.0,
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help="输入当前螺杆温度"
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)
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st.caption("单位: °C")
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current_rear_barrel_temperature = st.number_input(
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"后机筒温度",
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key="current_rear_barrel_temperature",
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value=79.9,
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min_value=0.0,
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max_value=300.0,
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step=1.0,
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help="输入当前后机筒温度"
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)
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st.caption("单位: °C")
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with param_cols[2]:
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current_front_barrel_temperature = st.number_input(
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"前机筒温度",
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key="current_front_barrel_temperature",
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value=80.1,
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min_value=0.0,
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max_value=300.0,
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step=1.0,
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help="输入当前前机筒温度"
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)
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st.caption("单位: °C")
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current_head_temperature = st.number_input(
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"机头温度",
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key="current_head_temperature",
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value=95.1,
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min_value=0.0,
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max_value=300.0,
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step=1.0,
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help="输入当前机头温度"
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)
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st.caption("单位: °C")
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# 2.3 当前实际米重测量值输入
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with st.expander("📏 当前实际米重", expanded=True):
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actual_weight = st.number_input(
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"当前实际米重",
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key="actual_weight",
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value=5.115,
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min_value=0.01,
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max_value=10.0,
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step=0.0001,
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format="%.4f",
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help="输入当前实际测量的米重值"
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)
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st.caption("单位: Kg/m")
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# 3. 计算调节建议
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st.subheader("🚀 计算调节建议")
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# 添加迭代调整选项
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use_iterative_adjustment = st.checkbox("🔄 使用迭代调整", value=False,
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help="启用迭代调整，自动优化参数直到预测米重满足偏差要求")
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# 迭代调整参数设置
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max_iterations = 5
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tolerance = 0.5
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if use_iterative_adjustment:
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st.write("### 迭代调整参数设置")
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iter_cols = st.columns(2)
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max_iterations = iter_cols[0].number_input("最大迭代次数", min_value=1, max_value=20, value=5, step=1)
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tolerance = iter_cols[1].number_input("允许偏差百分比(%)", min_value=0.1, max_value=5.0, value=0.5, step=0.1)
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if st.button("📊 计算调节建议", key="calculate_adjustment"):
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# 参数验证
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validation_errors = []
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if standard_weight <= 0:
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validation_errors.append("标准米重必须大于0")
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if upper_limit <= standard_weight:
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validation_errors.append("米重上限必须大于标准米重")
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if lower_limit >= standard_weight:
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validation_errors.append("米重下限必须小于标准米重")
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if current_screw_speed <= 0:
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validation_errors.append("螺杆转速必须大于0")
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if current_process_speed <= 0:
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validation_errors.append("流程主速必须大于0")
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if actual_weight <= 0:
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validation_errors.append("实际米重必须大于0")
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if validation_errors:
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st.error("参数输入错误：")
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for error in validation_errors:
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st.error(f"- {error}")
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else:
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with st.spinner("正在计算调节建议..."):
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# 初始化参数调节建议器
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adjustment_advisor = ParameterAdjustmentAdvisor()
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# 准备初始参数
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initial_params = {
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'real_time_weight': actual_weight,
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'standard_weight': standard_weight,
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'upper_limit': upper_limit,
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'lower_limit': lower_limit,
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'current_screw_speed': current_screw_speed,
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'current_process_speed': current_process_speed,
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'current_screw_temperature': current_screw_temperature,
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'current_rear_barrel_temperature': current_rear_barrel_temperature,
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'current_front_barrel_temperature': current_front_barrel_temperature,
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'current_head_temperature': current_head_temperature,
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'current_head_pressure': current_head_pressure
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}
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# 根据是否启用迭代调整执行不同逻辑
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if use_iterative_adjustment and 'selected_model' in st.session_state:
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# 使用迭代调整
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iterative_result = adjustment_advisor.iterative_adjustment(
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initial_params=initial_params,
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model_info=st.session_state['selected_model'],
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max_iterations=max_iterations,
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tolerance=tolerance
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)
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# 使用迭代调整的最终结果
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adjustment_result = iterative_result['final_result']
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iteration_history = iterative_result['iteration_history']
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converged = iterative_result['converged']
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total_iterations = iterative_result['total_iterations']
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else:
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# 正常计算调整建议
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adjustment_result = adjustment_advisor.calculate_adjustment(
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real_time_weight=actual_weight,
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standard_weight=standard_weight,
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upper_limit=upper_limit,
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lower_limit=lower_limit,
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current_screw_speed=current_screw_speed,
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current_process_speed=current_process_speed,
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current_screw_temperature=current_screw_temperature,
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current_rear_barrel_temperature=current_rear_barrel_temperature,
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current_front_barrel_temperature=current_front_barrel_temperature,
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current_head_temperature=current_head_temperature
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)
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# 使用选中的模型预测调整后的米重
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predicted_weight = None
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if 'selected_model' in st.session_state:
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selected_model_info = st.session_state['selected_model']
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predicted_weight = adjustment_advisor.predict_weight(
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model_info=selected_model_info,
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screw_speed=adjustment_result['new_screw_speed'],
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head_pressure=current_head_pressure,
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process_speed=adjustment_result['new_process_speed'],
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screw_temperature=current_screw_temperature,
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rear_barrel_temperature=current_rear_barrel_temperature,
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front_barrel_temperature=current_front_barrel_temperature,
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head_temperature=current_head_temperature
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)
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# 将预测结果添加到调整结果中
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adjustment_result['predicted_weight'] = predicted_weight
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# 初始化迭代历史（如果未使用迭代调整）
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iteration_history = None
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converged = None
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total_iterations = None
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# 保存到历史记录
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history_record = {
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'timestamp': datetime.now(),
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'model_file': st.session_state.get('selected_model_file', '未知模型'),
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'standard_weight': standard_weight,
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'upper_limit': upper_limit,
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'lower_limit': lower_limit,
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'actual_weight': actual_weight,
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'current_screw_speed': current_screw_speed,
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'current_process_speed': current_process_speed,
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'current_screw_temperature': current_screw_temperature,
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'current_rear_barrel_temperature': current_rear_barrel_temperature,
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'current_front_barrel_temperature': current_front_barrel_temperature,
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'current_head_temperature': current_head_temperature,
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'adjustment_result': adjustment_result,
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'use_iterative_adjustment': use_iterative_adjustment,
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'iteration_history': iteration_history
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}
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# 添加到会话状态的历史记录
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if 'adjustment_history' not in st.session_state:
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st.session_state['adjustment_history'] = []
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st.session_state['adjustment_history'].append(history_record)
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# 限制历史记录数量
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if len(st.session_state['adjustment_history']) > 100:
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st.session_state['adjustment_history'] = st.session_state['adjustment_history'][-100:]
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# 4. 结果展示
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st.success("调节建议计算完成！")
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st.subheader("📋 调节建议结果")
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# 4.1 米重状态
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if adjustment_result['status'] == "正常":
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st.success(f"米重状态: {adjustment_result['status']}")
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else:
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st.warning(f"米重状态: {adjustment_result['status']}")
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# 4.2 偏差信息
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info_cols = st.columns(3)
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info_cols[0].metric("实时米重", f"{adjustment_result['real_time_weight']:.4f} Kg/m")
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info_cols[1].metric("标准米重", f"{adjustment_result['standard_weight']:.4f} Kg/m")
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info_cols[2].metric("偏差百分比", f"{adjustment_result['deviation_percentage']:.2f}%")
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# 4.2.1 模型预测结果
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if adjustment_result['predicted_weight'] is not None:
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st.markdown("### 📈 模型预测结果")
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pred_cols = st.columns(3)
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pred_cols[0].metric("调整后预测米重", f"{adjustment_result['predicted_weight']:.4f} Kg/m")
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# 计算预测偏差
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predicted_deviation = adjustment_result['predicted_weight'] - adjustment_result['standard_weight']
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predicted_deviation_percent = (predicted_deviation / adjustment_result['standard_weight']) * 100
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pred_cols[1].metric("预测偏差", f"{predicted_deviation:.4f} Kg/m")
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pred_cols[2].metric("预测偏差百分比", f"{predicted_deviation_percent:.2f}%")
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# 显示预测效果
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if abs(predicted_deviation_percent) < 0.5:
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st.success("调整后米重预测值接近标准值，调整效果良好！")
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elif abs(predicted_deviation_percent) < 1.0:
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st.info("调整后米重预测值在可接受范围内。")
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else:
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st.warning("调整后米重预测值仍有较大偏差，建议进一步微调。")
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else:
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st.warning("模型预测失败，请检查模型文件或参数。")
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# 4.3 关键调整建议
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st.markdown("### 🔑 关键调整建议")
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st.info(adjustment_result['recommendation'])
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# 4.4 参数调整对比
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st.markdown("### 📊 参数调整对比")
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param_compare_df = pd.DataFrame({
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'参数名称': ['螺杆转速', '流程主速'],
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'当前值': [adjustment_result['current_screw_speed'], adjustment_result['current_process_speed']],
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'建议值': [adjustment_result['new_screw_speed'], adjustment_result['new_process_speed']],
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'调整幅度': [f"{adjustment_result['screw_speed_adjust_percent']:.2f}%",
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f"{adjustment_result['process_speed_adjust_percent']:.2f}%"]
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})
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# 高亮显示调整幅度
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def highlight_adjustment(val):
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if isinstance(val, str) and '%' in val:
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try:
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percent = float(val.strip('%'))
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if percent > 0:
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return 'background-color: #90EE90' # 绿色表示增加
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elif percent < 0:
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return 'background-color: #FFB6C1' # 红色表示减少
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except:
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pass
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return ''
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styled_df = param_compare_df.style.applymap(highlight_adjustment, subset=['调整幅度'])
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st.dataframe(styled_df, use_container_width=True, hide_index=True)
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# 4.5 可视化对比
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=param_compare_df['参数名称'],
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y=param_compare_df['当前值'],
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name='当前值',
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marker_color='blue'
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))
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fig.add_trace(go.Bar(
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x=param_compare_df['参数名称'],
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y=param_compare_df['建议值'],
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name='建议值',
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marker_color='green'
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))
|
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fig.update_layout(
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barmode='group',
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title='参数调整对比',
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yaxis_title='数值',
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height=400
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)
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st.plotly_chart(fig, use_container_width=True)
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# 4.6 迭代调整结果展示
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if use_iterative_adjustment and iteration_history:
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st.markdown("### 🔄 迭代调整历史")
|
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# 显示迭代调整状态
|
if converged:
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st.success(f"✅ 迭代调整成功收敛！经过 {total_iterations} 次迭代，预测米重偏差达到 {tolerance}% 以内。")
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else:
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st.warning(f"⚠️ 迭代调整未收敛！经过 {total_iterations} 次迭代，预测米重偏差仍未达到 {tolerance}% 以内。")
|
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# 显示迭代历史表格
|
iter_history_df = pd.DataFrame(iteration_history)
|
iter_history_df = iter_history_df[[
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'iteration', 'current_screw_speed', 'current_process_speed',
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'adjusted_screw_speed', 'adjusted_process_speed',
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'predicted_weight', 'predicted_deviation_percent'
|
]]
|
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# 格式化表格
|
iter_history_df = iter_history_df.rename(columns={
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'iteration': '迭代次数',
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'current_screw_speed': '调整前螺杆转速',
|
'current_process_speed': '调整前流程主速',
|
'adjusted_screw_speed': '调整后螺杆转速',
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'adjusted_process_speed': '调整后流程主速',
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'predicted_weight': '预测米重',
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'predicted_deviation_percent': '预测偏差百分比(%)'
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})
|
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st.dataframe(iter_history_df, use_container_width=True)
|
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# 迭代调整可视化
|
st.markdown("### 📉 迭代调整效果")
|
|
# 偏差变化趋势图
|
fig_deviation = go.Figure()
|
fig_deviation.add_trace(go.Scatter(
|
x=iter_history_df['迭代次数'],
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y=iter_history_df['预测偏差百分比(%)'],
|
mode='lines+markers',
|
name='预测偏差百分比',
|
line=dict(color='blue', width=2),
|
marker=dict(size=8)
|
))
|
|
# 添加偏差阈值线
|
fig_deviation.add_trace(go.Scatter(
|
x=iter_history_df['迭代次数'],
|
y=[tolerance] * len(iter_history_df),
|
mode='lines',
|
name='允许偏差上限',
|
line=dict(color='red', dash='dash', width=1)
|
))
|
|
fig_deviation.add_trace(go.Scatter(
|
x=iter_history_df['迭代次数'],
|
y=[-tolerance] * len(iter_history_df),
|
mode='lines',
|
name='允许偏差下限',
|
line=dict(color='red', dash='dash', width=1)
|
))
|
|
fig_deviation.update_layout(
|
title='迭代调整偏差变化趋势',
|
xaxis_title='迭代次数',
|
yaxis_title='预测偏差百分比(%)',
|
height=400,
|
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01)
|
)
|
|
st.plotly_chart(fig_deviation, use_container_width=True)
|
|
# 螺杆转速和流程主速变化趋势
|
fig_params = go.Figure()
|
fig_params.add_trace(go.Scatter(
|
x=iter_history_df['迭代次数'],
|
y=iter_history_df['调整前螺杆转速'],
|
mode='lines+markers',
|
name='调整前螺杆转速',
|
line=dict(color='blue', width=2),
|
marker=dict(size=8)
|
))
|
|
fig_params.add_trace(go.Scatter(
|
x=iter_history_df['迭代次数'],
|
y=iter_history_df['调整后螺杆转速'],
|
mode='lines+markers',
|
name='调整后螺杆转速',
|
line=dict(color='green', width=2),
|
marker=dict(size=8)
|
))
|
|
fig_params.add_trace(go.Scatter(
|
x=iter_history_df['迭代次数'],
|
y=iter_history_df['调整前流程主速'],
|
mode='lines+markers',
|
name='调整前流程主速',
|
line=dict(color='orange', width=2),
|
marker=dict(size=8)
|
))
|
|
fig_params.add_trace(go.Scatter(
|
x=iter_history_df['迭代次数'],
|
y=iter_history_df['调整后流程主速'],
|
mode='lines+markers',
|
name='调整后流程主速',
|
line=dict(color='purple', width=2),
|
marker=dict(size=8)
|
))
|
|
fig_params.update_layout(
|
title='参数调整变化趋势',
|
xaxis_title='迭代次数',
|
yaxis_title='数值',
|
height=400,
|
legend=dict(yanchor="top", y=0.99, xanchor="right", x=0.99)
|
)
|
|
st.plotly_chart(fig_params, use_container_width=True)
|
|
# 5. 历史记录展示
|
st.subheader("📚 历史调节记录")
|
|
if 'adjustment_history' in st.session_state and st.session_state['adjustment_history']:
|
# 显示历史记录数量
|
st.write(f"共 {len(st.session_state['adjustment_history'])} 条历史记录")
|
|
# 分页显示
|
page_size = 10
|
total_pages = (len(st.session_state['adjustment_history']) + page_size - 1) // page_size
|
|
page = st.selectbox(
|
"选择页码",
|
options=range(1, total_pages + 1),
|
key="history_page"
|
)
|
|
start_idx = (page - 1) * page_size
|
end_idx = start_idx + page_size
|
paginated_history = st.session_state['adjustment_history'][start_idx:end_idx]
|
|
# 反向显示，最新记录在前面
|
for record in reversed(paginated_history):
|
with st.expander(f"记录时间: {record['timestamp'].strftime('%Y-%m-%d %H:%M:%S')} | 模型: {record['model_file']}"):
|
history_cols = st.columns(3)
|
|
with history_cols[0]:
|
st.write("**米重参数**")
|
st.write(f"- 标准米重: {record['standard_weight']:.4f} Kg/m")
|
st.write(f"- 米重上限: {record['upper_limit']:.4f} Kg/m")
|
st.write(f"- 米重下限: {record['lower_limit']:.4f} Kg/m")
|
st.write(f"- 实际米重: {record['actual_weight']:.4f} Kg/m")
|
|
with history_cols[1]:
|
st.write("**速度参数**")
|
st.write(f"- 螺杆转速: {record['current_screw_speed']:.1f} rpm")
|
st.write(f"- 流程主速: {record['current_process_speed']:.1f} m/min")
|
|
with history_cols[2]:
|
st.write("**温度参数**")
|
st.write(f"- 螺杆温度: {record['current_screw_temperature']:.1f} °C")
|
st.write(f"- 后机筒温度: {record['current_rear_barrel_temperature']:.1f} °C")
|
st.write(f"- 前机筒温度: {record['current_front_barrel_temperature']:.1f} °C")
|
st.write(f"- 机头温度: {record['current_head_temperature']:.1f} °C")
|
|
st.write("**调整建议**")
|
st.write(record['adjustment_result']['recommendation'])
|
else:
|
st.info("暂无历史调节记录")
|
|
# 6. 帮助说明
|
with st.expander("❓ 帮助说明"):
|
st.markdown("""
|
### 功能说明
|
本功能模块用于根据当前米重测量值和挤出机参数，为用户提供科学合理的参数调整建议，以帮助用户将米重控制在标准范围内。
|
|
### 模型选择
|
- 系统会自动读取项目目录中训练好的模型文件
|
- 模型文件需符合系统要求的格式，包含模型参数和训练信息
|
- 建议选择R²得分较高、误差较小的模型
|
|
### 参数输入
|
- 米重标准值：您期望的目标米重值
|
- 米重上下限：允许的米重波动范围
|
- 挤出机当前参数：包括螺杆转速、流程主速、机头压力和挤出机电流
|
- 当前实际米重：实际测量得到的米重值
|
|
### 结果解读
|
- 米重状态：显示当前米重是否在允许范围内
|
- 偏差百分比：当前米重与标准米重的偏差百分比
|
- 关键调整建议：系统给出的主要调整建议
|
- 参数调整对比：详细展示每个参数的当前值、建议值和调整幅度
|
|
### 注意事项
|
1. 请确保输入的参数值准确反映设备当前状态
|
2. 调整建议仅供参考，实际操作时请结合现场经验
|
3. 建议在调整参数后密切观察米重变化
|
4. 定期更新模型以提高建议的准确性
|
""")
|
|
# 页面入口
|
if __name__ == "__main__":
|
show_extruder_parameter_adjustment()
|
