工业质检的“99%陷阱”：YOLO26站岗、Qwen3-VL-Seg坐诊，闭环质检的完整架构

# -*- coding: utf-8 -*-
"""
工业质检闭环系统 - 伪代码框架 (演示版)
模块：边缘YOLO26检测、云端Qwen3-VL-Seg分析、闭环进化、样本生成、边缘部署
作者：AI小怪兽
"""

import time
import random
import json
from typing import Dict, List, Tuple

# =========================== 配置模块 ===========================
CONFIG = {
    "edge": {
        "model_path": "yolo26n.pt",
        "device": "cpu",                 # 边缘设备常用 CPU / RKNN
        "conf_thres": 0.25,
        "iou_thres": 0.45,
        "img_size": 640,
        "max_latency_ms": 15
    },
    "vlm": {
        "model_name": "Qwen3-VL-4B-FP8",  # 量化版，用于边缘侧或近端
        "cloud_model": "Qwen3-VL-8B",     # 云端完整版
        "endpoint": "http://vlm-service/api/v1/predict",
        "max_latency_ms": 100,            # 异步容忍
        "confidence_thresholds": {
            "direct_pass": 0.95,
            "low_confidence": 0.70
        }
    },
    "flywheel": {
        "replay_interval_hours": 24,       # 每日回流
        "retrain_trigger_samples": 50,     # 累积修正标签数量阈值
        "shadow_test_days": 3,
        "a_b_test_ratio": 0.01
    },
    "generator": {
        "diffusion_model": "stabilityai/stable-diffusion-2-1",
        "lora_adapter": "defect-lora.pt",
        "quality_filters": {
            "ssim_threshold": 0.7,
            "discriminator_threshold": 0.8
        }
    }
}


# =========================== 边缘检测器 ===========================
class EdgeDetector:
    """YOLO26 边缘端检测器，模拟实时推理"""
    def __init__(self, config):
        self.config = config
        self.model = None  # 实际中加载 YOLO26
        self.latency = 0
        print("[Edge] Initialized YOLO26 detector.")

    def load_model(self):
        # 模拟加载模型
        self.model = "yolo26n loaded"
        print("[Edge] Model loaded.")

    def predict(self, image: np.ndarray) -> Dict:
        """
        模拟推理过程，返回检测结果
        Returns:
            {
                "boxes": [[x1,y1,x2,y2,conf,cls], ...],
                "latency_ms": float
            }
        """
        start = time.perf_counter()
        # 模拟推理
        time.sleep(random.uniform(0.008, 0.012))  # 9-12ms
        # 生成模拟检测结果
        num_dets = random.randint(0, 5)
        boxes = []
        for _ in range(num_dets):
            conf = random.uniform(0.3, 0.98)
            cls = random.randint(0, 5)
            x1 = random.randint(0, 600)
            y1 = random.randint(0, 600)
            x2 = x1 + random.randint(20, 100)
            y2 = y1 + random.randint(20, 100)
            boxes.append([x1, y1, x2, y2, conf, cls])
        latency = (time.perf_counter() - start) * 1000
        self.latency = latency
        return {"boxes": boxes, "latency_ms": latency}


# =========================== 云端大模型分析器 ===========================
class CloudAnalyzer:
    """Qwen3-VL-Seg 云端/边侧分析器，支持 LoRA 微调"""
    def __init__(self, config):
        self.config = config
        self.model = None
        self.is_finetuned = False  # 是否已进行场景微调

    def finetune(self, dataset_path: str, epochs=5):
        """使用 LoRA 对场景进行微调"""
        print(f"[Cloud] Start LoRA fine-tuning on dataset {dataset_path}")
        # 模拟微调过程
        time.sleep(2)  # 模拟耗时
        self.is_finetuned = True
        print("[Cloud] Fine-tuning completed.")

    def analyze_roi(self, roi_image: np.ndarray, bbox: Tuple) -> Dict:
        """
        对可疑 ROI 进行深度分析，返回结构化诊断报告
        Returns:
            {
                "defect_type": str,
                "severity": float,
                "possible_causes": [str],
                "suggestions": [str],
                "mask": np.array  # 分割掩码
            }
        """
        if not self.is_finetuned:
            print("[Cloud] Warning: model not fine-tuned for this scene, performance may degrade.")
        # 模拟 VLM 推理延迟（80-150ms）
        time.sleep(random.uniform(0.08, 0.12))
        # 生成模拟报告
        defects = ["划痕", "气泡", "凹陷", "毛刺", "裂纹"]
        causes = ["保压时间不足", "模具磨损", "温度过高", "材料批次差异"]
        report = {
            "defect_type": random.choice(defects),
            "severity": round(random.uniform(0.5, 0.95), 2),
            "possible_causes": random.sample(causes, 2),
            "suggestions": ["调整参数", "清洁模具", "更换材料"],
            "mask": None  # 实际应为掩码数组
        }
        return report


# =========================== 数据飞轮（闭环进化）===========================
class DataFlywheel:
    """管理修正标签回流、增量训练、A/B测试"""
    def __init__(self, config):
        self.config = config
        self.correction_buffer = []  # 存储修正样本
        self.model_version = 1
        self.shadow_model = None

    def collect_correction(self, roi_image, predicted_label, corrected_label, context):
        """收集人工复核的修正标签"""
        sample = {
            "image": roi_image,
            "pred": predicted_label,
            "correct": corrected_label,
            "context": context,  # 光照、温度等元数据
            "timestamp": time.time()
        }
        self.correction_buffer.append(sample)
        print(f"[Flywheel] Collected correction sample, buffer size: {len(self.correction_buffer)}")
        if len(self.correction_buffer) >= self.config["retrain_trigger_samples"]:
            self.trigger_retrain()

    def trigger_retrain(self):
        """触发增量训练（LoRA 微调）"""
        print("[Flywheel] Triggering incremental training...")
        # 实际：将 buffer 数据打包上传至云端训练平台
        # 训练新模型，进行 shadow 部署
        self.shadow_model = "yolo26n_v2"  # 模拟新模型
        self.start_shadow_test()

    def start_shadow_test(self):
        """启动 shadow 模式 A/B 测试"""
        print(f"[Flywheel] Shadow testing started for {self.config['shadow_test_days']} days.")
        # 模拟并行运行
        # 收集指标，对比新旧模型
        pass

    def promote_shadow(self):
        """如果 shadow 模型表现更优，切换流量"""
        print("[Flywheel] Shadow model promoted to production.")
        self.model_version += 1
        # 实际：更新边缘端模型


# =========================== 样本生成器 ===========================
class SampleGenerator:
    """扩散模型生成合成缺陷样本"""
    def __init__(self, config):
        self.config = config

    def generate_defects(self, base_image, defect_description, num_samples=100):
        """
        使用扩散模型生成缺陷样本
        Returns: list of generated images
        """
        print(f"[Generator] Generating {num_samples} synthetic defects with '{defect_description}'")
        # 模拟生成过程
        generated = []
        for i in range(num_samples):
            # 实际调用 diffusers 库
            fake_img = np.zeros((640,640,3), dtype=np.uint8)  # 模拟
            generated.append(fake_img)
        # 质量筛选
        filtered = self.quality_filter(generated)
        return filtered

    def quality_filter(self, images):
        """PSNR/SSIM + 判别器过滤"""
        # 模拟过滤，保留 70%
        keep = images[:int(len(images)*0.7)]
        print(f"[Generator] Quality filter passed {len(keep)} images.")
        return keep


# =========================== 主流程 ===========================
def main_loop():
    # 初始化各模块
    edge = EdgeDetector(CONFIG["edge"])
    edge.load_model()

    cloud = CloudAnalyzer(CONFIG["vlm"])
    # 场景微调（实际中在项目开始时执行）
    cloud.finetune("scene_dataset/", epochs=3)

    flywheel = DataFlywheel(CONFIG["flywheel"])
    generator = SampleGenerator(CONFIG["generator"])

    # 模拟产线连续检测
    frame_id = 0
    while frame_id < 100:  # 模拟100帧
        frame_id += 1
        # 1. 边缘检测
        det_result = edge.predict(None)  # 模拟图像
        boxes = det_result["boxes"]
        print(f"Frame {frame_id}: detected {len(boxes)} objects, latency {det_result['latency_ms']:.2f}ms")

        # 2. 分流处理
        for box in boxes:
            x1, y1, x2, y2, conf, cls = box
            if conf > CONFIG["vlm"]["confidence_thresholds"]["direct_pass"]:
                # 直接放行
                print(f"  Object {cls} with conf {conf:.2f}: DIRECT PASS")
            elif conf < CONFIG["vlm"]["confidence_thresholds"]["low_confidence"]:
                # 直接判为缺陷，送入隔离区
                print(f"  Object {cls} with conf {conf:.2f}: DEFECT, sent to quarantine")
            else:
                # 模糊区，裁剪 ROI 发送到云端分析
                roi = np.zeros((100,100,3))  # 模拟ROI
                print(f"  Object {cls} with conf {conf:.2f}: SENT TO CLOUD for analysis")
                # 异步调用（模拟非阻塞）
                report = cloud.analyze_roi(roi, (x1,y1,x2,y2))
                print(f"    Report: {report['defect_type']} severity {report['severity']}")

                # 模拟人工复核（这里随机模拟修正）
                if random.random() < 0.1:  # 10% 的概率复核发现错误
                    corrected_label = "气泡" if report["defect_type"] != "气泡" else "划痕"
                    flywheel.collect_correction(
                        roi_image=roi,
                        predicted_label=report["defect_type"],
                        corrected_label=corrected_label,
                        context={"illumination": "high", "temperature": 25.5}
                    )

        # 模拟周期触发样本生成（每20帧生成一批）
        if frame_id % 20 == 0:
            gen_samples = generator.generate_defects(None, "划痕", 10)

        # 模拟边缘部署更新（shadow promote 检查）
        if flywheel.shadow_model and random.random() < 0.01:
            flywheel.promote_shadow()

        # 模拟产线节拍
        time.sleep(0.1)  # 10 fps

    print("Main loop finished.")


if __name__ == "__main__":
    main_loop()