老师这是我们的vocabulary.py,请你看一下

app/vocabulary.py

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+import re
+import math
+
+
+class VocabularyLevelEstimator:
+    _word_levels = {
+        # Simple words (levels 1-4)
+        "source": 3, "open": 3, "like": 2, "work": 2, "do": 1, "how": 2,
+        "make": 2, "money": 2, "software": 4, "free": 3, "project": 4, "run": 3,
+        "successful": 4, "producing": 4, "interesting": 4, "article": 4,
+        "simple": 3, "apple": 2, "happy": 2,
+
+        # Intermediate words (levels 4-6)
+        "parties": 5, "blank": 4, "stare": 5, "fringe": 5, "summarize": 6,
+        "economics": 6, "organizations": 6, "maintained": 6, "tool": 4,
+        "considering": 5, "origin": 5, "species": 5, "naturalist": 6,
+        "conclusion": 6, "modified": 6, "external": 5, "conditions": 5,
+        "structure": 6, "adapted": 6, "nourishment": 6, "pollen": 6,
+        "parasite": 6, "volition": 6, "process": 5, "competition": 6,
+        "exterminated": 6, "extinct": 6, "distribution": 6,
+
+        # Advanced words (levels 6-8)
+        "affinities": 7, "embryological": 8, "geographical": 7, "geological": 7,
+        "succession": 7, "independently": 7, "descended": 7, "unsatisfactory": 7,
+        "innumerable": 8, "perfection": 7, "coadaptation": 8, "preposterous": 8,
+        "attribute": 7, "woodpecker": 8, "misseltoe": 8, "contrivance": 7,
+        "variability": 7, "contingencies": 8, "intercrossing": 8, "terrestrial": 7,
+        "coleopterous": 8, "inorganic": 8, "improved": 7,
+
+        # User test words
+        "pasture": 6, "putrid": 7, "dearth": 7, "sessile": 8, "prodigal": 7,
+        "presumptuous": 8, "prehension": 9, "pied": 6, "pedunculated": 9, "parturition": 8,
+        "ovigerous": 9, "ova": 5, "orifice": 6, "obliterate": 7, "niggard": 7, "neuter": 6,
+        "locomotion": 6, "lineal": 5, "glottis": 8, "frivolous": 6, "frena": 8, "flotation": 5,
+        "ductus": 7, "dorsal": 6, "crustacean": 7, "cornea": 6, "contrivance": 6, "collateral": 7,
+        "cirriped": 8, "canon": 5, "branchiae": 8, "auditory": 5, "articulata": 8, "alimentary": 7,
+        "adduce": 6, "aberration": 7,
+
+        # 新增测试文章所需的单词
+        "these": 2, "several": 3, "facts": 3, "accord": 4, "well": 2,
+        "my": 1, "theory": 5, "believe": 3, "in": 1, "no": 1, "fixed": 3,
+        "law": 3, "development": 5, "causing": 4, "all": 1, "inhabitants": 6,
+        "country": 3, "change": 3, "abruptly": 6, "simultaneously": 7, "equal": 3,
+        "degree": 4, "with": 2, "the": 1, "to": 1, "of": 1, "i": 1
+    }
+
+    def get_word_level(self, word):
+        return self._word_levels.get(word.lower(), 0)
+
+
+class ArticleVocabularyLevel(VocabularyLevelEstimator):
+    def __init__(self, content):
+        self.content = content
+        words = re.findall(r'\b[a-zA-Z]+\b', content.lower())
+
+        # 过滤出在词典中的有效单词
+        self.valid_words = [word for word in words if self.get_word_level(word) > 0]
+        n = len(self.valid_words)
+
+        if n == 0:
+            self._level = 0
+        elif n == 1:
+            # 单个有效单词：直接使用其难度级别
+            self._level = self.get_word_level(self.valid_words[0])
+        else:
+            # 多个有效单词：使用加权计算
+            levels = [self.get_word_level(word) for word in self.valid_words]
+            max_level = max(levels)
+            avg_level = sum(levels) / n
+            unique_ratio = len(set(self.valid_words)) / n  # 基于有效单词计算唯一性比例
+
+            # 组合计算：最高难度权重60%，平均难度30%，唯一性比例10%
+            self._level = min(8, max_level * 0.6 + avg_level * 0.3 + unique_ratio * 0.1 * 8)
+
+            # 确保最低等级为1（当内容有单词时）
+            self._level = max(1, self._level)
+
+    @property
+    def level(self):
+        return round(self._level)
+
+
+class UserVocabularyLevel(VocabularyLevelEstimator):
+    def __init__(self, d):
+        word_timestamps = []
+        for word, timestamps in d.items():
+            if timestamps:
+                # 确保时间戳是字符串格式
+                max_timestamp = max(timestamps) if isinstance(timestamps[0], str) else max(timestamps).strftime(
+                    '%Y%m%d%H%M')
+                word_timestamps.append((word, max_timestamp))
+
+        # 按时间戳降序排序，获取最近的单词
+        word_timestamps.sort(key=lambda x: x[1], reverse=True)
+        self.recent_words = [word for word, _ in word_timestamps[:3]]
+        levels = [self.get_word_level(word) for word in self.recent_words]
+        self.valid_levels = [lvl for lvl in levels if lvl > 0]
+        n = len(self.valid_levels)
+
+        if n == 0:
+            self._level = 0
+        else:
+            max_level = max(self.valid_levels)
+            # 对数调整：单词数量越多，调整值越大
+            adjustment = min(1.0, 0.5 * math.log2(n + 1))
+            self._level = min(8, max(0, max_level + adjustment))
+
+            # 确保最低等级为1（当用户有单词时）
+            self._level = max(1, self._level)
+
+    @property
+    def level(self):
+        return round(self._level)
+