AI 工业质检真正难在哪：从缺陷模型到 RT-Thread 产线闭环

相机/光源
    │ 触发与图像
    ▼
边缘计算单元（预处理、模型推理、置信度计算）
    │ request_id + verdict + model_version + latency
    ▼
RT-Thread 控制节点（状态机、超时、队列、看门狗、I/O）
    │
    ├── PLC/机械执行机构：放行、剔除、停线
    └── 本地缓存/工厂系统：图片索引、批次、结果、告警

#!/usr/bin/env python3
import argparse
import csv
import math
import sys


def ratio(numerator, denominator):
    return numerator / denominator if denominator else 0.0


def percentile(values, p):
    if not values:
        return 0.0
    values = sorted(values)
    index = max(0, math.ceil(len(values) * p) - 1)
    return values[index]


parser = argparse.ArgumentParser()
parser.add_argument('csv_file')
parser.add_argument('--max-fn-rate', type=float, default=0.02)
parser.add_argument('--max-fp-rate', type=float, default=0.03)
parser.add_argument('--max-p95-ms', type=float, default=120.0)
args = parser.parse_args()

with open(args.csv_file, newline='', encoding='utf-8') as file:
    rows = list(csv.DictReader(file))

required = {'ground_truth', 'predicted', 'latency_ms', 'model_version'}
if not rows or not required.issubset(rows[0]):
    sys.exit(f'CSV 缺少字段，必须包含: {sorted(required)}')

valid_labels = {'OK', 'DEFECT'}
for line, row in enumerate(rows, start=2):
    if row['ground_truth'] not in valid_labels or row['predicted'] not in valid_labels:
        sys.exit(f'第 {line} 行标签非法')

tp = sum(r['ground_truth'] == 'DEFECT' and r['predicted'] == 'DEFECT' for r in rows)
fn = sum(r['ground_truth'] == 'DEFECT' and r['predicted'] == 'OK' for r in rows)
fp = sum(r['ground_truth'] == 'OK' and r['predicted'] == 'DEFECT' for r in rows)
tn = sum(r['ground_truth'] == 'OK' and r['predicted'] == 'OK' for r in rows)
latencies = [float(r['latency_ms']) for r in rows]
versions = {r['model_version'] for r in rows}

fn_rate = ratio(fn, tp + fn)
fp_rate = ratio(fp, fp + tn)
p95_ms = percentile(latencies, 0.95)

print(f'samples={len(rows)} model_versions={sorted(versions)}')
print(f'TP={tp} FN={fn} FP={fp} TN={tn}')
print(f'false_negative_rate={fn_rate:.4%}')
print(f'false_positive_rate={fp_rate:.4%}')
print(f'p95_latency_ms={p95_ms:.2f}')

passed = (
    len(versions) == 1
    and fn_rate <= args.max_fn_rate
    and fp_rate <= args.max_fp_rate
    and p95_ms <= args.max_p95_ms
)

if not passed:
    sys.exit('QUALITY GATE: FAILED')

print('QUALITY GATE: PASSED')

cat > inspection_results.csv <<'CSV'
ground_truth,predicted,latency_ms,model_version
OK,OK,71.2,scratch-detector-1.4.0
DEFECT,DEFECT,84.5,scratch-detector-1.4.0
OK,OK,69.8,scratch-detector-1.4.0
DEFECT,DEFECT,91.3,scratch-detector-1.4.0
OK,DEFECT,78.6,scratch-detector-1.4.0
CSV

python3 quality_gate.py inspection_results.csv \
  --max-fn-rate 0.02 \
  --max-fp-rate 0.40 \
  --max-p95-ms 120