ADD file via upload

班长后端/data_processing.py

@@ -1,12 +0,0 @@
-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

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

班长后端/utils/nlp_utils.py

@@ -1,31 +0,0 @@
-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