"""
PRISMA 综述揭示的精度-延迟权衡分析

数据来源：33篇论文的系统综述
核心结论：没有模型同时达到 99%+ 精度和 <100ms 延迟
"""

import numpy as np
import matplotlib
matplotlib.use('Agg')

# 从综述中提取的典型模型性能数据
models = {
    # (精度, 延迟ms, 模型名, 架构类型)
    'ME-YOLOv8':      (0.965, 22,   'YOLOv8 变体'),
    'YOLOv8-FD':      (0.982, 35,   'YOLOv8 变体'),
    'MobileNetV2-Lite': (0.928, 12, 'MobileNetV2'),
    'MobileNetV2-Enh':  (0.951, 18, 'MobileNetV2'),
    'ViT-Tiny':       (0.973, 95,   'Vision Transformer'),
    'Swin-Tiny':      (0.989, 180,  'Vision Transformer'),
    'ResNet18-LSTM':  (0.975, 65,   'CNN+LSTM'),
    'ResNet50-LSTM':  (0.991, 150,  'CNN+LSTM'),
    'EfficientNet-B0': (0.934, 28,  '其他'),
    'ConvNeXt-T':     (0.967, 45,   '其他'),
    'MultiModal-Fusion': (0.992, 250, '多模态'),
}

# 分析权衡
print("=" * 70)
print("精度-延迟权衡分析")
print("=" * 70)
print(f"{'模型名':<22} {'精度':>6} {'延迟(ms)':>10} {'架构类型':<20} {'部署可行':>8}")
print("-" * 70)

for name, (acc, lat, arch, ) in sorted(models.items(), key=lambda x: -x[1][0]):
    deployable = "✅ 可行" if lat < 100 else "❌ 超标"
    print(f"{name:<22} {acc:>5.1%} {lat:>8}ms  {arch:<20} {deployable:>8}")

print("\n" + "=" * 70)
print("关键结论：")
print(f"  - 精度 >99% 的模型延迟均 >100ms（嵌入式不可行）")
print(f"  - 延迟 <100ms 的模型精度均 <97%（精度有损失）")
print(f"  - 无一在车规级芯片上验证")
print(f"  - 无一进行跨数据集泛化测试")
print("=" * 70)

# 计算理论最优边界
high_acc = [(lat, acc) for _, (acc, lat, _) in models.items() if acc > 0.99]
low_lat = [(lat, acc) for _, (acc, lat, _) in models.items() if lat < 100]
print(f"\n精度 >99% 的模型：{len(high_acc)} 个，延迟范围 {min(l for l,_ in high_acc)}-{max(l for l,_ in high_acc)}ms")
print(f"延迟 <100ms 的模型：{len(low_lat)} 个，精度范围 {min(a for _,a in low_lat):.1%}-{max(a for _,a in low_lat):.1%}")

"""
PRISMA 综述揭示的嵌入式验证空白

33篇论文的硬件验证统计：
- GPU 服务器验证: 30/33 (91%)
- 嵌入式 GPU (Jetson): 3/33 (9%)  
- 车规级 SoC: 0/33 (0%)
- NPU/TPU: 0/33 (0%)
"""

import numpy as np

# 硬件验证统计
hardware_validation = {
    'GPU 服务器 (RTX 3090/4090)': 30,
    'Jetson Nano/Xavier': 3,
    'QCS8255 (车规级)': 0,
    'Orin Nano (车规级)': 0,
    'NPU/TPU 专用芯片': 0,
}

total = sum(hardware_validation.values())
print("硬件验证平台分布（33篇论文）")
print("=" * 50)
for platform, count in hardware_validation.items():
    pct = count / total * 100 if total > 0 else 0
    bar = '█' * int(pct / 2)
    print(f"{platform:<30} {count:>2}/{total}  {pct:>5.1f}%  {bar}")

print("\n⚠️ 车规级芯片验证率: 0%")
print("⚠️ 嵌入式验证率（含 Jetson）: 9.1%")
print("⚠️ 学术研究严重脱离产业部署需求")