import sys
import os
import pandas as pd
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from ollama import Client
import numpy as np
import re
import torch
import torch.nn as nn
# LSTM模型类
class LSTMModel(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super(LSTMModel, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
out, _ = self.lstm(x, (h0, c0))
out = self.fc(out[:, -1, :])
return out
# 向量相似度计算工具
class VectorSimilarity:
@staticmethod
def cosine_similarity(vec1, vec2):
if len(vec1) == 0 or len(vec2) == 0:
return 0
dot_product = np.dot(vec1, vec2)
norm_vec1 = np.linalg.norm(vec1)
norm_vec2 = np.linalg.norm(vec2)
if norm_vec1 == 0 or norm_vec2 == 0:
return 0
return dot_product / (norm_vec1 * norm_vec2)
@staticmethod
def euclidean_distance(vec1, vec2):
return np.linalg.norm(vec1 - vec2)
@staticmethod
def combined_similarity(vec1, vec2):
cosine = VectorSimilarity.cosine_similarity(vec1, vec2)
euclidean = VectorSimilarity.euclidean_distance(vec1, vec2)
if euclidean > 0:
euclidean_sim = 1 / (1 + euclidean)
else:
euclidean_sim = 1
return 0.7 * cosine + 0.3 * euclidean_sim
# 知识库处理类
class KnowledgeBase:
def __init__(self):
self.texts = {}
self.vectors = {}
self.metadata = {}
self.similarity_threshold = 0.3
self.file_paths = {}
def load_from_excel(self, file_path, kb_type):
try:
df = pd.read_excel(file_path)
vector_cols = [col for col in df.columns if col.startswith('向量化特征_')]
if not vector_cols:
raise ValueError("未找到向量化特征列")
self.texts[kb_type] = []
self.vectors[kb_type] = []
self.metadata[kb_type] = []
for _, row in df.iterrows():
text = str(row.get('文本块', ''))
if not text:
continue
vector = row[vector_cols].values.astype(np.float32)
metadata = {
'来源': row.get('来源', ''),
'类别': row.get('类别', ''),
}
self.texts[kb_type].append(text)
self.vectors[kb_type].append(vector)
self.metadata[kb_type].append(metadata)
self._calculate_similarity_threshold(kb_type)
self.file_paths[kb_type] = file_path
return len(self.texts[kb_type])
except Exception as e:
print(f"加载知识库失败: {e}")
return 0
def _calculate_similarity_threshold(self, kb_type):
if not self.vectors[kb_type] or len(self.vectors[kb_type]) < 2:
return
similarities = []
for i in range(min(100, len(self.vectors[kb_type]))):
for j in range(i + 1, min(100, len(self.vectors[kb_type]))):
sim = VectorSimilarity.combined_similarity(self.vectors[kb_type][i], self.vectors[kb_type][j])
similarities.append(sim)
if similarities:
self.similarity_threshold = max(0.2, np.mean(similarities) * 0.7)
print(f"相似度阈值: {self.similarity_threshold:.4f}")
def search(self, query_vector, kb_type, top_k=10):
if kb_type not in self.vectors or not self.vectors[kb_type]:
return []
results = []
for i, vector in enumerate(self.vectors[kb_type]):
sim = VectorSimilarity.combined_similarity(query_vector, vector)
if sim >= self.similarity_threshold:
results.append((i, sim))
results.sort(key=lambda x: x[1], reverse=True)
return results[:top_k]
def add_entry(self, kb_type, text, source, category, vector):
if kb_type not in self.texts:
self.texts[kb_type] = []
self.vectors[kb_type] = []
self.metadata[kb_type] = []
self.texts[kb_type].append(text)
self.vectors[kb_type].append(vector)
self.metadata[kb_type].append({
'来源': source,
'类别': category
})
self.save_to_excel(kb_type)
def save_to_excel(self, kb_type):
if kb_type not in self.file_paths:
return
file_path = self.file_paths[kb_type]
df = pd.DataFrame({
'文本块': self.texts[kb_type],
**{f'向量化特征_{i}': [vec[i] for vec in self.vectors[kb_type]] for i in range(len(self.vectors[kb_type][0]))},
'来源': [meta['来源'] for meta in self.metadata[kb_type]],
'类别': [meta['类别'] for meta in self.metadata[kb_type]]
})
df.to_excel(file_path, index=False)
def edit_entry(self, kb_type, index, text, source, category, vector):
if kb_type in self.texts and 0 <= index < len(self.texts[kb_type]):
self.texts[kb_type][index] = text
self.vectors[kb_type][index] = vector
self.metadata[kb_type][index] = {
'来源': source,
'类别': category
}
self.save_to_excel(kb_type)
# AI处理线程
class AIWorker(QThread):
thinking_signal = pyqtSignal(str) # 发送思考过程
answer_signal = pyqtSignal(str) # 发送最终回答
finish_signal = pyqtSignal()
def __init__(self, client, query, knowledge_base, kb_type):
super().__init__()
self.client = client
self.query = query
self.knowledge_base = knowledge_base
self.kb_type = kb_type
def run(self):
try:
# 发送思考过程开始的信号
self.
thinking_signal.emit("<b>知识库检索中...</b>")
# 模拟生成查询向量
query_vector = np.random.rand(len(self.knowledge_base.vectors[self.kb_type][0]) if self.knowledge_base.vectors[self.kb_type] else 50)
# 执行搜索
search_results = self.knowledge_base.search(query_vector, self.kb_type, top_k=5)
if not search_results:
self.
thinking_signal.emit("<p style=\"color:#888888;\">未找到匹配的知识库内容</p>")
thinking_process = "无"
else:
# 构建HTML格式的思考过程
thinking_html = "<p><b>匹配到的知识库内容：</b></p><ul>"
for i, (idx, sim) in enumerate(search_results):
text = self.knowledge_base.texts[self.kb_type][idx]
metadata = self.knowledge_base.metadata[self.kb_type][idx]
thinking_html += f"<li><b>匹配项 {i + 1} (相似度: {sim:.4f})</b><br>"
thinking_html += f"<span style=\"color:#6c757d;\">来源: {metadata['来源']} | 类别: {metadata['类别']}</span><br>"
# 提取前300个字符作为摘要，并保留段落结构
summary = text[:300] + ('...' if len(text) > 300 else '')
# 将换行符转换为<br>标签
summary =
summary.replace('\n', '<br>')
thinking_html += f"{
summary}</li><br>"
thinking_html += "</ul>"
self.
thinking_signal.emit(
thinking_html)
# 构建LLM提示词中的思考过程
thinking_process = "以下是知识库中的相关参考资料：\n"
for i, (idx, sim) in enumerate(search_results):
text = self.knowledge_base.texts[self.kb_type][idx]
metadata = self.knowledge_base.metadata[self.kb_type][idx]
thinking_process += f"\n[{i + 1}] 相似度: {sim:.4f}\n"
thinking_process += f"来源: {metadata['来源']} | 类别: {metadata['类别']}\n"
# 提取前200个字符作为摘要
summary = text[:200] + ('...' if len(text) > 200 else '')
thinking_process += f"{
summary}\n\n"
# 构建提示词
prompt = f"""
你是一位专业的涂装工程师。用户问题：{self.query}。
{
thinking_process}
请根据上述资料提供准确、专业的回答。
如果资料不足，请补充涂装领域的通用知识和最佳实践。
请确保回答条理清晰，适当分段分点。
"""
# 发送开始生成回答的信号
self.answer_signal.emit("<b>正在生成回答...</b>")
# 调用模型生成回答
answer_html = ""
stream = self.client.chat(
model='deepseek-r1:1.5b',
messages=[{"role": "user", "content": prompt}],
stream=True
)
for chunk in stream:
content = chunk['message']['content']
answer_html += content
# 处理回答内容，添加分段分点格式
formatted_answer = self._format_answer(answer_html)
self.answer_signal.emit(formatted_answer)
except Exception as e:
self.answer_signal.emit(f"<p style=\"color:#FF0000;\">[Error] {str(e)}</p>")
finally:
self.finish_signal.emit()
def _format_answer(self, answer):
# 简单的文本格式化处理
# 将段落分隔（空行）转换为<p>标签
paragraphs = answer.split('\n\n')
formatted = ""
for para in paragraphs:
para = para.strip()
if not para:
continue
# 处理列表项（如果以数字+点或短横线开头）
if re.match(r'^\d+\.', para):
# 有序列表
if not formatted.endswith('</ol>'):
formatted += '<ol>'
else:
formatted = formatted[:-5] # 移除最后的</ol>标签以便继续添加
# 提取序号和内容
match = re.match(r'^(\d+\.)\s*(.*)', para)
if match:
formatted += f'<li><b>{match.group(1)}</b> {match.group(2)}</li>'
else:
formatted += f'<li>{para}</li>'
formatted += '</ol>'
elif re.match(r'^[-*•]', para):
# 无序列表
if not formatted.endswith('</ul>'):
formatted += '<ul>'
else:
formatted = formatted[:-5] # 移除最后的</ul>标签
# 提取标记和内容
match = re.match(r'^[-*•]\s*(.*)', para)
if match:
formatted += f'<li>{match.group(1)}</li>'
else:
formatted += f'<li>{para}</li>'
formatted += '</ul>'
else:
# 普通段落
formatted += f'<p>{para}</p>'
return formatted
# 主窗口
class PaintChatWindow(QWidget):
def __init__(self):
super().__init__()
self.knowledge_base = KnowledgeBase()
self.client = Client(host="http://localhost:11435")
self.current_kb_type = None
self.init_ui()
def init_ui(self):
self.setWindowTitle("涂装知识助手")
self.setGeometry(100, 100, 1200, 800)
# 主布局
main_layout = QVBoxLayout()
# 顶部状态栏
status_bar = QWidget()
status_layout = QHBoxLayout(status_bar)
self.status_label = QLabel("知识库未加载")
self.status_label.setStyleSheet("color: #888888; font-size: 12px; padding: 5px;")
status_layout.addWidget(self.status_label)
main_layout.addWidget(status_bar)
# 知识库选择区域
kb_selection_layout = QHBoxLayout()
kb_types = ["涂料信息库", "涂装设备库", "涂装工艺库", "涂装环境库", "行业标准与法规库"]
for kb_type in kb_types:
btn = QPushButton(kb_type)
btn.setStyleSheet("""
QPushButton {
background-color: #6c757d;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #5a6268; }
""")
btn.clicked.connect(lambda _, t=kb_type: self.select_knowledge_base(t))
kb_selection_layout.addWidget(btn)
main_layout.addLayout(kb_selection_layout)
# 中间内容区域 - 分为思考过程和最终回答两栏
content_splitter = QSplitter(Qt.Horizontal)
# 思考过程区域
self.
thinking_area = QTextEdit()
self.
thinking_area.setReadOnly(True)
self.
thinking_area.setStyleSheet("""
QTextEdit {
background-color: #f8f9fa;
font-family: SimHei, sans-serif;
font-size: 14px;
padding: 15px;
border: 1px solid #e9ecef;
border-radius: 4px;
}
""")
self.
thinking_area.setHtml("<b>思考过程将显示在这里...</b>")
content_splitter.addWidget(self.
thinking_area)
# 最终回答区域
self.answer_area = QTextEdit()
self.answer_area.setReadOnly(True)
self.answer_area.setStyleSheet("""
QTextEdit {
background-color: #ffffff;
font-family: SimHei, sans-serif;
font-size: 14px;
padding: 15px;
border: 1px solid #e9ecef;
border-radius: 4px;
}
""")
self.answer_area.setHtml("<b>回答将显示在这里...</b>")
content_splitter.addWidget(self.answer_area)
# 设置两栏的初始大小比例
content_splitter.setSizes([400, 800])
main_layout.addWidget(content_splitter)
# 底部控制区域
bottom_layout = QHBoxLayout()
# 文件加载区域
file_layout = QVBoxLayout()
self.file_label = QLabel("未选择知识库文件")
self.file_label.setStyleSheet("color: #6c757d; font-size: 12px;")
file_layout.addWidget(self.file_label)
self.load_btn = QPushButton("加载知识库")
self.load_btn.setStyleSheet("""
QPushButton {
background-color: #6c757d;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #5a6268; }
""")
self.load_btn.clicked.connect(self.load_knowledge_base)
file_layout.addWidget(self.load_btn)
bottom_layout.addLayout(file_layout, 1)
# 输入区域
input_layout = QVBoxLayout()
self.input_box = QTextEdit()
self.input_box.setMaximumHeight(60)
self.input_box.setPlaceholderText("输入您的问题...")
self.input_box.setStyleSheet("""
QTextEdit {
border: 1px solid #ced4da;
border-radius: 4px;
padding: 8px;
font-family: SimHei, sans-serif;
font-size: 14px;
}
""")
input_layout.addWidget(self.input_box)
self.send_btn = QPushButton("提问")
self.send_btn.setStyleSheet("""
QPushButton {
background-color: #007bff;
color: white;
font-size: 14px;
padding: 8px;
border-radius: 4px;
}
QPushButton:hover { background-color: #0069d9; }
""")
self.send_btn.clicked.connect(self.send_message)
input_layout.addWidget(self.send_btn)
bottom_layout.addLayout(input_layout, 3)
# 添加、查看和编辑按钮
action_layout = QHBoxLayout()
self.add_btn = QPushButton("添加知识库内容")
self.add_btn.setStyleSheet("""
QPushButton {
background-color: #28a745;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #218838; }
""")
self.add_btn.clicked.connect(self.add_knowledge_entry)
action_layout.addWidget(self.add_btn)
self.view_btn = QPushButton("查看知识库内容")
self.view_btn.setStyleSheet("""
QPushButton {
background-color: #17a2b8;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #138496; }
""")
self.view_btn.clicked.connect(self.view_knowledge_entries)
action_layout.addWidget(self.view_btn)
self.edit_btn = QPushButton("编辑知识库内容")
self.edit_btn.setStyleSheet("""
QPushButton {
background-color: #ffc107;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #e0a800; }
""")
self.edit_btn.clicked.connect(self.edit_knowledge_entry)
action_layout.addWidget(self.edit_btn)
bottom_layout.addLayout(action_layout, 2)
main_layout.addLayout(bottom_layout)
self.setLayout(main_layout)
def select_knowledge_base(self, kb_type):
self.current_kb_type = kb_type
if kb_type in self.knowledge_base.texts:
self.status_label.setText(f"当前知识库: {kb_type} ({len(self.knowledge_base.texts[kb_type])} 条记录)")
self.status_label.setStyleSheet("color: #28a745; font-size: 12px; padding: 5px;")
else:
self.status_label.setText(f"请加载 {kb_type} 知识库")
self.status_label.setStyleSheet("color: #dc3545; font-size: 12px; padding: 5px;")
def load_knowledge_base(self):
if not self.current_kb_type:
QMessageBox.warning(self, "警告", "请先选择知识库类型")
return
file_path, _ = QFileDialog.getOpenFileName(
self, f"选择 {self.current_kb_type} 文件", "", "Excel Files (*.xlsx *.xls)"
)
if file_path:
self.file_label.setText(f"已加载: {os.path.basename(file_path)}")
self.status_label.setText("正在加载知识库...")
self.status_label.setStyleSheet("color: #007bff; font-size: 12px; padding: 5px;")
# 在单独线程中加载知识库
QTimer.singleShot(0, lambda: self._load_knowledge_base_thread(file_path, self.current_kb_type))
def _load_knowledge_base_thread(self, file_path, kb_type):
try:
count = self.knowledge_base.load_from_excel(file_path, kb_type)
if count > 0:
self.status_label.setText(f"{kb_type} 已加载 ({count} 条记录)")
self.status_label.setStyleSheet("color: #28a745; font-size: 12px; padding: 5px;")
self.
thinking_area.setHtml(f"<b>{kb_type} 加载成功</b>: 共{count}条知识条目")
self.answer_area.setHtml("<b>回答将显示在这里...</b>")
else:
self.status_label.setText(f"{kb_type} 加载失败")
self.status_label.setStyleSheet("color: #dc3545; font-size: 12px; padding: 5px;")
except Exception as e:
self.status_label.setText(f"{kb_type} 加载错误: {str(e)}")
self.status_label.setStyleSheet("color: #dc3545; font-size: 12px; padding: 5px;")
def send_message(self):
user_input = self.input_box.toPlainText().strip()
if not user_input:
return
if not self.current_kb_type:
QMessageBox.warning(self, "警告", "请先选择知识库类型")
return
if self.current_kb_type not in self.knowledge_base.texts or not self.knowledge_base.texts[self.current_kb_type]:
QMessageBox.warning(self, "警告", f"请先加载 {self.current_kb_type} 知识库")
return
# 清空之前的回答和思考过程
self.
thinking_area.setHtml(f"<b>用户问题:</b> {user_input}<br><br><b>思考过程:</b>")
self.answer_area.setHtml("<b>正在生成回答...</b>")
self.input_box.clear()
# 创建AI处理线程
self.ai_worker = AIWorker(self.client, user_input, self.knowledge_base, self.current_kb_type)
self.ai_worker.
thinking_signal.connect(self.update_
thinking)
self.ai_worker.answer_signal.connect(self.update_answer)
self.ai_worker.finish_signal.connect(self.on_ai_finished)
self.ai_worker.start()
def update_
thinking(self, message):
# 将思考过程追加到思考区域
cursor = self.
thinking_area.textCursor()
cursor.movePosition(QTextCursor.End)
cursor.insertHtml(f"<br><br>{message}")
self.
thinking_area.setTextCursor(cursor)
self.
thinking_area.ensureCursorVisible()
def update_answer(self, message):
# 更新回答区域
self.answer_area.setHtml(message)
def on_ai_finished(self):
pass
def add_knowledge_entry(self):
if not self.current_kb_type:
QMessageBox.warning(self, "警告", "请先选择知识库类型")
return
dialog = QDialog(self)
dialog.setWindowTitle(f"添加 {self.current_kb_type} 条目")
layout = QVBoxLayout()
text_label = QLabel("文本块:")
text_input = QTextEdit()
layout.addWidget(text_label)
layout.addWidget(text_input)
source_label = QLabel("来源:")
source_input = QLineEdit()
layout.addWidget(source_label)
layout.addWidget(source_input)
category_label = QLabel("类别:")
category_input = QLineEdit()
layout.addWidget(category_label)
layout.addWidget(category_input)
vector_label = QLabel("向量化特征 (以逗号分隔):")
vector_input = QLineEdit()
layout.addWidget(vector_label)
layout.addWidget(vector_input)
button_box = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)
button_box.accepted.connect(dialog.accept)
button_box.rejected.connect(dialog.reject)
layout.addWidget(button_box)
dialog.setLayout(layout)
if dialog.exec_() == QDialog.Accepted:
text = text_input.toPlainText()
source = source_input.text()
category = category_input.text()
vector_str = vector_input.text()
try:
vector = np.array([float(x) for x in vector_str.split(',')], dtype=np.float32)
self.knowledge_base.add_entry(self.current_kb_type, text, source, category, vector)
QMessageBox.information(self, "成功", "条目已添加到知识库")
except ValueError:
QMessageBox.warning(self, "错误", "向量化特征输入无效，请输入有效的浮点数，以逗号分隔")
def view_knowledge_entries(self):
if not self.current_kb_type:
QMessageBox.warning(self, "警告", "请先选择知识库类型")
return
if self.current_kb_type not in self.knowledge_base.texts or not self.knowledge_base.texts[self.current_kb_type]:
QMessageBox.warning(self, "警告", f"请先加载 {self.current_kb_type} 知识库")
return
dialog = QDialog(self)
dialog.setWindowTitle(f"{self.current_kb_type} 内容")
layout = QVBoxLayout()
list_widget = QListWidget()
for i, text in enumerate(self.knowledge_base.texts[self.current_kb_type]):
metadata = self.knowledge_base.metadata[self.current_kb_type][i]
item_text = f"[{i + 1}] 来源: {metadata['来源']} | 类别: {metadata['类别']}\n{text[:200]}"
list_widget.addItem(item_text)
layout.addWidget(list_widget)
dialog.setLayout(layout)
dialog.exec_()
def edit_knowledge_entry(self):
if not self.current_kb_type:
QMessageBox.warning(self, "警告", "请先选择知识库类型")
return
if self.current_kb_type not in self.knowledge_base.texts or not self.knowledge_base.texts[self.current_kb_type]:
QMessageBox.warning(self, "警告", f"请先加载 {self.current_kb_type} 知识库")
return
dialog = QDialog(self)
dialog.setWindowTitle(f"编辑 {self.current_kb_type} 条目")
layout = QVBoxLayout()
index_label = QLabel("请输入要编辑的条目编号 (从1开始):")
index_input = QLineEdit()
layout.addWidget(index_label)
layout.addWidget(index_input)
button_box = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)
button_box.accepted.connect(dialog.accept)
button_box.rejected.connect(dialog.reject)
layout.addWidget(button_box)
dialog.setLayout(layout)
if dialog.exec_() == QDialog.Accepted:
try:
index = int(index_input.text()) - 1
if 0 <= index < len(self.knowledge_base.texts[self.current_kb_type]):
text = self.knowledge_base.texts[self.current_kb_type][index]
metadata = self.knowledge_base.metadata[self.current_kb_type][index]
vector = self.knowledge_base.vectors[self.current_kb_type][index]
edit_dialog = QDialog(self)
edit_dialog.setWindowTitle(f"编辑 {self.current_kb_type} 条目 {index + 1}")
edit_layout = QVBoxLayout()
text_label = QLabel("文本块:")
text_input = QTextEdit()
text_input.setPlainText(text)
edit_layout.addWidget(text_label)
edit_layout.addWidget(text_input)
source_label = QLabel("来源:")
source_input = QLineEdit()
source_input.setText(metadata['来源'])
edit_layout.addWidget(source_label)
edit_layout.addWidget(source_input)
category_label = QLabel("类别:")
category_input = QLineEdit()
category_input.setText(metadata['类别'])
edit_layout.addWidget(category_label)
edit_layout.addWidget(category_input)
vector_label = QLabel("向量化特征 (以逗号分隔):")
vector_input = QLineEdit()
vector_str = ','.join([str(x) for x in vector])
vector_input.setText(vector_str)
edit_layout.addWidget(vector_label)
edit_layout.addWidget(vector_input)
edit_button_box = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)
edit_button_box.accepted.connect(edit_dialog.accept)
edit_button_box.rejected.connect(edit_dialog.reject)
edit_layout.addWidget(edit_button_box)
edit_dialog.setLayout(edit_layout)
if edit_dialog.exec_() == QDialog.Accepted:
new_text = text_input.toPlainText()
new_source = source_input.text()
new_category = category_input.text()
new_vector_str = vector_input.text()
try:
new_vector = np.array([float(x) for x in new_vector_str.split(',')], dtype=np.float32)
self.knowledge_base.edit_entry(self.current_kb_type, index, new_text, new_source, new_category, new_vector)
QMessageBox.information(self, "成功", "条目已更新到知识库")
except ValueError:
QMessageBox.warning(self, "错误", "向量化特征输入无效，请输入有效的浮点数，以逗号分隔")
else:
QMessageBox.warning(self, "错误", "输入的条目编号无效")
except ValueError:
QMessageBox.warning(self, "错误", "请输入有效的整数作为条目编号")
if __name__ == "__main__":
app = QApplication(sys.argv)
app.setFont(QFont("SimHei"))
window = PaintChatWindow()
window.show()
sys.exit(app.exec_())把这段代码里的“相似度”全部修改为“匹配度”，ollama调用窗口11435修改为11434，其余内容不变，完整代码给我
回顾了大一的学习经历，虽然感觉实训部分没有达到预期的效果，但仍然对未来充满希望。计划利用暑假时间补足不足之处，并设定了具体的学习目标。
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