127 lines
4.8 KiB
Python
127 lines
4.8 KiB
Python
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# coding=utf-8
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import re
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class OcrParser:
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@staticmethod
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def parse(ocr_lines):
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"""
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解析 OCR 文本行，提取场站结构化数据。
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不依赖 LLM，使用规则和正则匹配。
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"""
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result = {
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"station_name": "",
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"distance": "",
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"price": None,
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"piles": [],
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"parking": "",
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"tags": []
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}
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# 预处理：清洗无关字符
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clean_lines = []
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for line in ocr_lines:
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# 去除 "行 X: " 前缀
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text = re.sub(r"^行\s*\d+:\s*", "", line)
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# 去除 "(置信度: ...)" 后缀
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text = re.sub(r"\s*\(置信度:.*?\)\s*$", "", text)
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text = text.strip()
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if text:
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clean_lines.append(text)
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# 1. 场站名称 (Station Name)
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# 策略：通常是第一行，且不包含特定关键词（如km, /度, ￥等）
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for line in clean_lines:
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if len(line) < 2: continue
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if line.startswith("（") or line.startswith("(") or line == "…": continue
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# 排除明显属性行
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if any(k in line.lower() for k in ["km", "/度", "￥", "停车", "积分", "组团"]):
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continue
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# 排除纯数字
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if re.match(r"^\d+$", line): continue
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# 排除可能是充电枪信息的行 (例如 "闲2/4", "快", "慢")
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if re.search(r"\d+/\d+", line): continue
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if line in ["快", "慢", "超", "快充", "慢充", "超充"]: continue
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result["station_name"] = line
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break
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# 2. 距离 (Distance)
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for line in clean_lines:
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# 匹配 "7.4km", "17.4km"
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m = re.search(r"(\d+(\.\d+)?)\s*km", line, re.IGNORECASE)
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if m:
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result["distance"] = m.group(0)
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break
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# 3. 价格 (Price)
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for line in clean_lines:
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# 匹配 "0.7111/度", "1.2/度"
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m = re.search(r"(\d+\.\d+)(?=/度)", line)
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if m:
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try:
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result["price"] = float(m.group(1))
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break
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except: pass
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# 4. 充电枪信息 (Piles)
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# 规则：查找 "快", "慢", "超" 以及 "x/y" 格式
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# 示例： "快", "闲3/4" (跨行) 或 "快充 3/4" (同行)
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current_type = "未知"
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# 辅助：先尝试在单行内同时匹配类型和数量
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# 如果单行内有 "快" 和 "3/4"，则直接提取
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# 如果单行只有类型，更新 current_type
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# 如果单行只有 "3/4"，使用 current_type
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for line in clean_lines:
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# 更新当前类型上下文
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if "快" in line: current_type = "快"
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elif "慢" in line: current_type = "慢"
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elif "超" in line: current_type = "超"
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# 匹配 "3/4" 或 "闲3/4"
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# 排除价格行 (包含小数点，如 0.7111/度 这里的 / 前后也可能是数字，需小心)
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if "/度" in line: continue
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# 匹配分数结构：整数/整数
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# (?:闲)? 可选的前缀 "闲"
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m = re.search(r"(?:闲)?(\d+)/(\d+)", line)
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if m:
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# 再次确认不是价格（有时候 OCR 会把 . 漏掉，变成 07111/度，但通常会有 /度）
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# 简单校验：总数通常不会特别大，比如不会是 2024/12 (日期)
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try:
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free = int(m.group(1))
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total = int(m.group(2))
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# 过滤可能的日期或异常值
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if total > 500: continue
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result["piles"].append({
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"type": current_type,
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"free": free,
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"total": total
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})
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except: pass
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# 5. 停车信息 (Parking)
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for line in clean_lines:
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if "停车" in line:
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# 清理前缀符号，如 "·收费停车" -> "收费停车"
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clean_text = re.sub(r"^[·\.\sP]+", "", line)
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result["parking"] = clean_text
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break
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# 6. 标签 (Tags)
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# 收集除上述已解析字段外的简短关键词
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known_keywords = ["快", "慢", "超", "P", "组团", "积分"]
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for line in clean_lines:
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# 简单匹配一些常见 Tag
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if "积分" in line: result["tags"].append(line)
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elif "组团" in line: result["tags"].append("组团")
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elif line.strip() == "P": result["tags"].append("P")
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return result
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