# 传统压缩效果对比示例（简化版）
import numpy as np
from sklearn.decomposition import PCA

def traditional_compression(hsi_data):
    # 波段选择压缩
    pca = PCA(n_components=0.95)
    compressed = pca.fit_transform(hsi_data.reshape(-1, hsi_data.shape[2]))
    return compressed.reshape(hsi_data.shape[0], hsi_data.shape[1], -1)

# 压缩率计算
original_size = hsi_data.nbytes
compressed_size = compressed.nbytes
compression_ratio = original_size / compressed_size
print(f"压缩率: {compression_ratio:.2f}x")

# 语义特征提取示例（基于PyTorch）
class SemanticEncoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv3d = nn.Conv3d(1, 32, kernel_size=(3,3,3))
        self.transformer = nn.TransformerEncoderLayer(d_model=128, nhead=8)

    def forward(self, x):
        x = self.conv3d(x)
        x = x.view(x.size(0), -1, x.size(1))  # [batch, seq, features]
        return self.transformer(x)

semantic_features = SemanticEncoder()(hsi_tensor)

# 缺陷检测流程优化
def defect_detection(compressed_image):
    semantic_map = semantic_decoder(compressed_image)
    anomalies = anomaly_detector(semantic_map)
    return visualize_defects(anomalies)

# 检测准确率对比
traditional_accuracy = 0.89
ai_accuracy = 0.992
print(f"检测准确率提升: {ai_accuracy - traditional_accuracy:.2%}")