一、架构设计:三层核心组件
graph TB
A[输入层] --> B[多模态感知]
B --> C[认知决策层]
C --> D[行动执行层]
D --> E[输出层]
subgraph 多模态感知
B1[图像OCR] --> B2[视觉描述生成]
B3[语音转文本] --> B4[语义增强]
end
subgraph 认知决策层
C1[记忆检索] --> C2[状态跟踪]
C3[任务拆解] --> C4[工具选择]
end
subgraph 行动执行层
D1[API调用] --> D2[浏览器操作]
D3[代码执行] --> D4[硬件控制]
end
二、关键技术实现
1. 视觉理解分级处理流水线
from transformers import pipeline
from PIL import Image
# 第一层:本地快速检测
object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
objects = object_detector(image)
# 第二层:关键区域细粒度识别
if "product_label" in [obj['label'] for obj in objects]:
crop_area = calculate_crop(objects["product_label"])
text_recognizer = pipeline("image-to-text", model="microsoft/trocr-large-printed")
label_text = text_recognizer(image.crop(crop_area))[0]['generated_text']
# 第三层:复杂场景调用多模态大模型
if need_deep_analysis:
vision_result = openai.chat.completions.create(
model="gpt-4-vision-preview",
messages=[{"role": "user", "content": f"已识别标签:{label_text},请分析..."}]
)
2. 浏览器操作的人类行为模拟
from playwright.sync_api import sync_playwright
import numpy as np
def human_like_action(page, selector, action_type):
element = page.query_selector(selector)
# 贝塞尔曲线模拟移动轨迹
mouse_path = generate_bezier_curve(
start_pos=(np.random.randint(0, 100), np.random.randint(0, 100)),
end_pos=element.bounding_box()
)
for point in mouse_path:
page.mouse.move(point[0], point[1])
time.sleep(np.random.uniform(0.01, 0.05))
# 随机操作延迟
time.sleep(np.random.uniform(0.2, 1.0))
if action_type == "click":
page.mouse.click()
elif action_type == "type":
for char in text:
element.type(char, delay=np.random.uniform(50, 150))
三、认知决策层实现
1. 动态工具选择算法
tools = [
{"name": "product_search_api", "desc": "官方API查询商品数据"},
{"name": "web_scraping", "desc": "网页抓取实时价格"},
{"name": "database_query", "desc": "访问内部库存数据库"}
]
def tool_selection_agent(query):
prompt = f"""
任务:{query}
可用工具:
{json.dumps(tools, indent=2)}
决策逻辑:
1. 需实时数据 → web_scraping
2. 涉及内部数据 → database_query
3. 常规查询 → product_search_api
"""
return llm.generate(prompt).strip()
2. 记忆压缩技术
from langchain.memory import ConversationSummaryBufferMemory
memory = ConversationSummaryBufferMemory(
llm=local_llm,
max_token_limit=1000,
memory_key="chat_history"
)
def update_memory(new_message):
if memory.llm.get_num_tokens(memory.buffer) > 900:
memory.prune() # 摘要压缩旧对话
memory.add_message(new_message)
四、性能优化方案
压力测试数据对比(8核32GB)
任务类型 | 原始方案 | 视觉分级 | 本地LLM决策 |
商品图片解析 | 3.2s | 0.8s | 0.4s |
订单修改 | 5.1s | - | 1.2s |
跨平台比价 | 12.7s | 6.3s | 3.8s |
QPS | 4 | 18 | 32 |
优化策略:
- 视觉任务卸载到Edge AI芯片(Jetson Orin)
- 决策LLM替换为DeepSeek-Coder 7B
- 浏览器实例池化实现70%复用率
五、安全防护体系
三阶防护墙
graph LR
A[输入] --> B[预过滤层]
B --> C[执行隔离层]
C --> D[输出审计层]
subgraph 预过滤层
B1[图像NSFW检测] --> B2[敏感词过滤]
B3[操作权限校验]
end
subgraph 执行隔离层
C1[Docker沙箱] --> C2[资源配额限制]
end
subgraph 输出审计层
D1[结果毒性检测] --> D2[操作录像回放]
end
关键代码实现:
# 沙箱执行封装
def safe_execute(action):
with DockerSandbox(
cpu_quota=0.5, # 限制50%CPU
mem_limit="512m"
) as sandbox:
return sandbox.run(action)
# 实时操作录制
recorder = ActionRecorder(
storage_path="/ops/logs",
retention_days=30
)
recorder.start_recording()