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Signed-off-by: pnxf6w84l <1378224436@qq.com>

班长后端/data_processing.py

@@ -0,0 +1,12 @@
+from utils.nlp_utils import CallsignExtractor
+
+# 测试样例
+extractor = CallsignExtractor()
+sample_texts = [
+    "Jade23 requesting descent to FL180",
+    "Tower, Snowbird inbound with info Q",
+    "Speedbird 123 cleared direct to WP"
+]
+
+for text in sample_texts:
+    print(f"文本: {text} → 呼号: {extractor.extract(text)}")

班长后端/data_tosql.py

@@ -1,3 +1,4 @@
+
 import pandas as pd
 from sqlalchemy import create_engine, text
 

班长后端/utils/nlp_utils.py

@@ -0,0 +1,31 @@
+from transformers import pipeline
+import re
+
+class CallsignExtractor:
+    def __init__(self):
+        # 初始化模型（懒加载减少内存占用）
+        self._ner_model = None
+        self._patterns = [
+            r"\b[A-Z]{2,}\d{2,4}\b",  # 如AA1234
+            r"\b[A-Z][a-z]+\d*\b",    # 如Jade23
+            r"\b[A-Z]+\s?\d{3,4}\b"   # 如SPEED 123
+        ]
+
+    @property
+    def ner_model(self):
+        if self._ner_model is None:
+            self._ner_model = pipeline("ner", model="dslim/bert-base-NER")
+        return self._ner_model
+
+    def extract(self, text: str) -> str:
+        """主提取函数"""
+        # 先尝试正则匹配（更快）
+        for pattern in self._patterns:
+            match = re.search(pattern, text)
+            if match:
+                return match.group()
+
+        # 正则失败再用BERT
+        entities = self.ner_model(text)
+        org_entities = [e["word"] for e in entities if e["entity"].startswith("ORG")]
+        return org_entities[0] if org_entities else None