548 lines
21 KiB
Plaintext
Executable File
548 lines
21 KiB
Plaintext
Executable File
"""
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高考志愿填报规范检查器 V2.0
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支持多省份自动识别
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"""
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import re
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import json
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import sys
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from datetime import datetime
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# 各省规则库（Phase 1.5: 委托 truth loader，保留 legacy 接口）
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from pathlib import Path
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import os
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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from data.rules.loader import RuleLoader
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TRUTH_ROOT = Path(os.environ.get("GAOKAO_RULES_TRUTH_ROOT", str(PROJECT_ROOT / "rules" / "_truth")))
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LEGACY_RULE_KEYS = (
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"mode",
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"batch",
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"max_volunteers",
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"max_majors_per_group",
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"has_adjustment",
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"adjustment_scope",
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"retrieval_rule",
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"collection_count",
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"subject_mode",
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"official_url",
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"exam_subject_total",
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)
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def _scalar_from_loaded_value(value_dict, key):
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return value_dict.get(key)
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def _build_legacy_rule_map(truth_root):
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loader = RuleLoader.from_truth_root(truth_root)
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province_rules = {}
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for province in loader.active_provinces():
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loaded_rules = {
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rule.rule_id.split(".", 1)[1]: rule.value
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for rule in loader.list_province_rules(province)
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}
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province_rules[province] = {
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key: _scalar_from_loaded_value(loaded_rules[key], key)
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for key in LEGACY_RULE_KEYS
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if key in loaded_rules
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}
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return province_rules
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PROVINCE_RULES = _build_legacy_rule_map(TRUTH_ROOT)
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# 省份别名
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PROVINCE_ALIASES = {
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"湘": "湖南",
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"粤": "广东",
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"鄂": "湖北",
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"苏": "江苏",
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"闽": "福建",
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"皖": "安徽",
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"赣": "江西",
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"甘": "甘肃",
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"陇": "甘肃",
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"黑": "黑龙江",
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"桂": "广西",
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"京": "北京",
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"沪": "上海",
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"津": "天津",
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"琼": "海南",
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"浙": "浙江",
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"鲁": "山东",
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"冀": "河北",
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"渝": "重庆",
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"辽": "辽宁",
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"黔": "贵州",
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"青": "青海",
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"吉": "吉林",
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"豫": "河南",
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"川": "四川",
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"蜀": "四川",
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"新": "新疆",
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"滇": "云南",
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"云": "云南",
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"藏": "西藏",
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}
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def detect_province(text):
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"""
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从文本中自动检测省份。
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优先返回文本中最早出现的省份线索，避免被后面的院校名误导。
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"""
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matches = []
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# 1. 支持规则库中的省份全称
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for prov in PROVINCE_RULES.keys():
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idx = text.find(prov)
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if idx != -1:
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matches.append((idx, prov))
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# 2. 匹配简称
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for alias, prov in PROVINCE_ALIASES.items():
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pattern = f"({alias}[省市区]?)|(省{alias})"
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found = re.search(pattern, text)
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if found:
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matches.append((found.start(), prov))
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# 3. 匹配更广义的省份全称，但仅在 truth 规则库支持时返回
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prov_full_names = {
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"北京": "北京",
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"天津": "天津",
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"河北": "河北",
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"山西": "山西",
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"内蒙古": "内蒙古",
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"辽宁": "辽宁",
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"吉林": "吉林",
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"黑龙江": "黑龙江",
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"上海": "上海",
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"江苏": "江苏",
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"浙江": "浙江",
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"安徽": "安徽",
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"福建": "福建",
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"江西": "江西",
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"山东": "山东",
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"河南": "河南",
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"湖北": "湖北",
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"湖南": "湖南",
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"广东": "广东",
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"广西": "广西",
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"海南": "海南",
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"重庆": "重庆",
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"四川": "四川",
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"贵州": "贵州",
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"云南": "云南",
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"西藏": "西藏",
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"陕西": "陕西",
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"甘肃": "甘肃",
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"青海": "青海",
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"宁夏": "宁夏",
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"新疆": "新疆",
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}
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for full_name in prov_full_names.keys():
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idx = text.find(full_name)
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if idx != -1 and full_name in PROVINCE_RULES:
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matches.append((idx, full_name))
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if not matches:
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return None
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matches.sort(key=lambda item: item[0])
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return matches[0][1]
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class GaokaoSpecCheckerV2:
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"""
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高考志愿填报规范检查器 V2.0
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支持多省份自动识别
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"""
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def __init__(self, province=None):
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self.province = province
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self.province_rule = None
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self.errors = {
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"fatal": [],
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"serious": [],
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"warning": [],
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}
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def auto_detect_and_check(self, text):
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"""
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自动检测省份并检查
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"""
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# 自动检测省份
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if not self.province:
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self.province = detect_province(text)
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if not self.province:
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return self._report_no_province()
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if self.province not in PROVINCE_RULES:
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return self._report_unsupported_province()
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self.province_rule = PROVINCE_RULES[self.province]
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# 执行检查
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self._check_volunteer_unit(text)
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self._check_volunteer_count(text)
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self._check_majors_per_group(text)
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self._check_adjustment_rule(text)
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self._check_data_accuracy(text)
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self._check_subject_requirements(text)
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self._check_risk_disclosure(text)
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return self._generate_report()
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def _check_volunteer_unit(self, text):
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"""检查志愿单位"""
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max_v = self.province_rule["max_volunteers"]
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mode = self.province_rule["mode"]
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if mode == "院校专业组":
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# 检查1：是否说"学校"或"院校"
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wrong_patterns = [
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f"{max_v}个学校",
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f"{max_v}所学校",
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f"{max_v}个院校",
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]
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for pattern in wrong_patterns:
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if pattern in text:
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self.errors["fatal"].append({
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"rule": f"志愿单位错误（{self.province}）",
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"description": f"{self.province}是{mode}模式，应该是{self.province_rule['max_volunteers']}个'{mode}'，不是{max_v}个'学校'或'院校'",
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"fix": f"改为'{max_v}个院校专业组'"
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})
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break
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# 检查2：模式本身
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if "院校专业组" not in text and "专业组" not in text:
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self.errors["serious"].append({
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"rule": f"未提及'{mode}'概念（{self.province}）",
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"description": f"{self.province}采用{mode}模式，应在方案中明确",
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"fix": "明确使用'院校专业组'概念"
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})
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elif mode == "专业+学校":
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# 浙江、山东等模式
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if "专业组" in text and "组内" in text:
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self.errors["fatal"].append({
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"rule": f"模式错误（{self.province}）",
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"description": f"{self.province}是'专业+学校'模式，不是'院校专业组'模式，无调剂选项",
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"fix": "改为'专业+学校'，删除'组内服从'等概念"
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})
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if "调剂" in text and "无" not in text.split("调剂")[0][-10:]:
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# 简单检测：如果提到调剂但没说"无"
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if "不服从" not in text and "无需" not in text and "没有" not in text:
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self.errors["serious"].append({
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"rule": f"调剂概念错误（{self.province}）",
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"description": f"{self.province}采用'专业+学校'模式，**没有调剂选项**",
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"fix": "删除所有'服从调剂'相关描述"
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})
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def _check_volunteer_count(self, text):
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"""检查志愿数量"""
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max_v = self.province_rule["max_volunteers"]
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# 提取方案中提到的志愿数
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count_patterns = [
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r'共(\d+)个',
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r'填报(\d+)个',
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r'填了(\d+)个',
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]
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for pattern in count_patterns:
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matches = re.findall(pattern, text)
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for match in matches:
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count = int(match)
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if count > max_v:
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self.errors["fatal"].append({
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"rule": f"志愿数量超标（{self.province}）",
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"description": f"方案提到{count}个志愿，超过{self.province}本批次的{max_v}个上限",
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"fix": f"志愿数不超过{max_v}个"
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})
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elif count < max_v * 0.5 and "少" not in text:
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self.warnings = getattr(self, 'warnings', [])
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self.warnings.append({
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"rule": f"志愿数量较少（{self.province}）",
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"description": f"方案只填了{count}个，建议填满{max_v}个（除非明确不需要）",
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"fix": f"建议填满{max_v}个志愿"
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})
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def _check_majors_per_group(self, text):
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"""检查每组专业数"""
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max_m = self.province_rule["max_majors_per_group"]
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mode = self.province_rule["mode"]
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if mode == "院校专业组" and max_m > 1:
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# 院校专业组模式：每组最多6个专业
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if "6个专业" not in text and "六个专业" not in text:
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self.errors["warning"].append({
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"rule": f"专业数说明缺失（{self.province}）",
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"description": f"未说明每个专业组最多{max_m}个专业",
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"fix": f"明确说明每组最多{max_m}个专业"
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})
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elif mode == "专业+学校":
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# 专业+学校模式：每志愿1个专业
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if "1个专业" not in text and "1所学校" not in text:
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self.errors["warning"].append({
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"rule": f"专业数说明缺失（{self.province}）",
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"description": f"未说明{self.province}是'专业+学校'模式，每志愿只填1个专业",
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"fix": "明确'每个志愿1个专业'"
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})
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def _check_adjustment_rule(self, text):
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"""检查调剂规则"""
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if not self.province_rule["has_adjustment"]:
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# 无调剂模式
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if "服从调剂" in text and "无需" not in text and "无调剂" not in text:
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self.errors["fatal"].append({
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"rule": f"调剂规则错误（{self.province}）",
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"description": f"{self.province}采用'专业+学校'模式，**没有调剂选项**",
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"fix": "删除所有'服从调剂'相关描述"
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})
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else:
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# 有调剂模式
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adjustment_scope = self.province_rule["adjustment_scope"]
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if "服从调剂" in text and "全部专业" in text:
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if adjustment_scope == "组内专业":
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self.errors["fatal"].append({
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"rule": f"调剂范围错误（{self.province}）",
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"description": f"{self.province}的调剂范围是'组内专业'，不是'全部专业'",
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"fix": "改为'组内专业调剂'"
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})
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def _check_data_accuracy(self, text):
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"""检查数据准确性"""
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# 主观概率
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prob_pattern = r'(\d{2,3})\s*%\s*[\u4e00-\u9fa5]*(?:录取|概率|机会|把握)'
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matches = re.findall(prob_pattern, text)
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if matches:
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self.errors["serious"].append({
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"rule": "主观概率估算",
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"description": f"方案中含主观概率{set(matches)}，未基于真实数据",
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"fix": "删除主观概率，改用2025年位次作为参考"
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})
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# 2026年位次未说明
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if "位次" in text and "2026" in text and "待官方" not in text and "以官方为准" not in text:
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self.errors["serious"].append({
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"rule": "2026年位次",
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"description": "2026年位次待官方公布（6月25日出分后），不应假设",
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"fix": "明确'2026年位次待官方公布'"
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})
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def _check_subject_requirements(self, text):
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"""检查选科要求"""
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if self.province_rule["subject_mode"] == "3+1+2":
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# 检查是否有"物+化+生"一刀切
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if re.search(r'会计.{0,20}物.{0,5}化.{0,5}生', text):
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self.errors["serious"].append({
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"rule": "选科要求一刀切",
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"description": "财经类专业选科要求因校而异，不能假设都要求'物+化+生'",
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"fix": "逐校核实选科要求"
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})
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def _check_risk_disclosure(self, text):
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"""检查风险提示"""
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risk_keywords = ["退档", "风险", "调剂", "体检", "单科"]
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has_risk = any(kw in text for kw in risk_keywords)
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if not has_risk:
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self.errors["serious"].append({
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"rule": "风险提示缺失",
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"description": "方案未明确说明退档风险（体检/单科/不服从调剂）",
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"fix": "增加风险提示章节"
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})
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def _report_no_province(self):
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"""未检测到省份的报告"""
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return """
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╔══════════════════════════════════════════════════════════════════╗
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║ ⚠️ 未检测到省份信息 ║
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╚══════════════════════════════════════════════════════════════════╝
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【问题】
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方案中未明确省份信息，无法进行针对性检查。
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【支持检测的省份】
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北京、天津、河北、山西、内蒙古、辽宁、吉林、黑龙江
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上海、江苏、浙江、安徽、福建、江西、山东、河南
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湖北、湖南、广东、广西、海南、重庆、四川、贵州
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云南、西藏、陕西、甘肃、青海、宁夏、新疆
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【解决方式】
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请在方案中明确省份信息，例如：
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"湖南考生，578分..."
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"浙江省，630分..."
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"""
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def _report_unsupported_province(self):
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"""省份不支持的报告"""
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return f"""
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╔══════════════════════════════════════════════════════════════════╗
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║ ⚠️ 暂不支持 {self.province} ║
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╚══════════════════════════════════════════════════════════════════╝
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【问题】
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当前检查器暂不支持{self.province}的具体规则检查。
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【已支持的省份】
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{', '.join(sorted(PROVINCE_RULES.keys()))}
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【后续计划】
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将持续添加更多省份支持。
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"""
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def _generate_report(self):
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"""生成检查报告"""
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report = f"""
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╔══════════════════════════════════════════════════════════════════╗
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║ ✅ 志愿方案规范检查报告 ║
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╠══════════════════════════════════════════════════════════════════╣
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║ 检测省份：{self.province} ║
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║ 志愿模式：{self.province_rule['mode']} ║
|
||
║ 志愿数量：{self.province_rule['max_volunteers']}个（{self.province_rule['batch']}） ║
|
||
║ 每组专业：{self.province_rule['max_majors_per_group']}个 ║
|
||
║ 调剂选项：{'有' if self.province_rule['has_adjustment'] else '无'} ║
|
||
║ 调剂范围：{self.province_rule['adjustment_scope']} ║
|
||
║ 选科模式：{self.province_rule['subject_mode']} ║
|
||
║ 检查时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ║
|
||
╚══════════════════════════════════════════════════════════════════╝
|
||
"""
|
||
|
||
if self.errors["fatal"]:
|
||
report += "\n🔴 【致命错误】\n" + "─" * 70 + "\n"
|
||
for i, err in enumerate(self.errors["fatal"], 1):
|
||
report += f"""
|
||
{i}. {err['rule']}
|
||
❌ 问题：{err['description']}
|
||
✅ 修正：{err['fix']}
|
||
"""
|
||
|
||
if self.errors["serious"]:
|
||
report += "\n🟡 【严重错误】\n" + "─" * 70 + "\n"
|
||
for i, err in enumerate(self.errors["serious"], 1):
|
||
report += f"""
|
||
{i}. {err['rule']}
|
||
⚠️ 问题：{err['description']}
|
||
🔧 修正：{err['fix']}
|
||
"""
|
||
|
||
if self.errors["warning"]:
|
||
report += "\n🟢 【一般警告】\n" + "─" * 70 + "\n"
|
||
for i, warn in enumerate(self.errors["warning"], 1):
|
||
report += f"""
|
||
{i}. {warn['rule']}
|
||
💡 建议：{warn['description']}
|
||
📌 做法：{warn['fix']}
|
||
"""
|
||
|
||
total = sum(len(v) for v in self.errors.values())
|
||
report += f"""
|
||
═══════════════════════════════════════════════════════════════════
|
||
📊 【检查统计】
|
||
═══════════════════════════════════════════════════════════════════
|
||
🔴 致命错误：{len(self.errors['fatal'])} 个
|
||
🟡 严重错误：{len(self.errors['serious'])} 个
|
||
🟢 一般警告：{len(self.errors['warning'])} 个
|
||
📊 问题总数：{total} 个
|
||
"""
|
||
|
||
if total == 0:
|
||
report += "\n 🎉 方案基本合规！\n"
|
||
elif len(self.errors["fatal"]) > 0:
|
||
report += "\n ❌ 必须修改致命错误后才能使用\n"
|
||
else:
|
||
report += "\n ⚠️ 建议补充完善后使用\n"
|
||
|
||
report += f"""
|
||
═══════════════════════════════════════════════════════════════════
|
||
📌 【重要提醒】
|
||
═══════════════════════════════════════════════════════════════════
|
||
• 最终以{self.province}省教育考试院官方信息为准
|
||
• 官方网址：{self.province_rule['official_url']}
|
||
• 2026年招生计划6月15-20日公布
|
||
• 2026年实际位次6月25日出分后确定
|
||
═══════════════════════════════════════════════════════════════════
|
||
"""
|
||
|
||
return report
|
||
|
||
|
||
# 主函数
|
||
if __name__ == "__main__":
|
||
if len(sys.argv) < 2:
|
||
# 默认测试
|
||
print("用法: python spec_checker_v2.py <方案文件> [省份]")
|
||
print("或: python spec_checker_v2.py (无参数时显示测试)")
|
||
print()
|
||
|
||
# 测试：湖南方案
|
||
print("=" * 70)
|
||
print("测试1：湖南方案（错误版）")
|
||
print("=" * 70)
|
||
|
||
bad_plan = """
|
||
湖南578分考生志愿方案
|
||
本次共填报45个学校志愿：
|
||
志愿01：江西财经大学，会计学
|
||
录取概率35%
|
||
"""
|
||
|
||
checker = GaokaoSpecCheckerV2()
|
||
print(checker.auto_detect_and_check(bad_plan))
|
||
|
||
# 测试：浙江方案
|
||
print("\n\n")
|
||
print("=" * 70)
|
||
print("测试2：浙江方案（专业+学校模式）")
|
||
print("=" * 70)
|
||
|
||
zj_plan = """
|
||
浙江省，630分，选科物化生
|
||
本次共填报80个专业+学校志愿：
|
||
志愿01：浙江大学，计算机科学与技术
|
||
志愿02：浙江工业大学，软件工程
|
||
每个志愿填1个专业。
|
||
"""
|
||
|
||
checker = GaokaoSpecCheckerV2()
|
||
print(checker.auto_detect_and_check(zj_plan))
|
||
|
||
# 测试：山东方案
|
||
print("\n\n")
|
||
print("=" * 70)
|
||
print("测试3：山东方案")
|
||
print("=" * 70)
|
||
|
||
sd_plan = """
|
||
山东高考，620分
|
||
填报96个志愿：
|
||
01-山东大学-会计学
|
||
02-中国海洋大学-金融学
|
||
"""
|
||
|
||
checker = GaokaoSpecCheckerV2()
|
||
print(checker.auto_detect_and_check(sd_plan))
|
||
else:
|
||
# 从文件读取方案
|
||
filename = sys.argv[1]
|
||
province = sys.argv[2] if len(sys.argv) > 2 else None
|
||
|
||
with open(filename, 'r', encoding='utf-8') as f:
|
||
plan = f.read()
|
||
|
||
checker = GaokaoSpecCheckerV2(province)
|
||
print(checker.auto_detect_and_check(plan))
|