selfrelease
fad458b2a7
- UIUX 文档：填充 19 个缺口（多主体画像/健康度/AI+看板/增长域/洞察域/创始人端/OODA/助推/商密） - UIUX 文档：插入 6 个新章节（十四~十九），旧章节重编号为二十~三十一，更新目录和交叉引用 - 作业指导书 x5：导航改为 6 域分组，新增 Context Bar/工作模式/Insight Rail/决策线程/多工作区等 UI 概念 - 新建 docs/2-task-uiux.md：50 个代码落地开发任务，按 P0-P6 分优先级 + 8 Sprint 规划 - 后端/前端：大量新增模型、路由、组件（来自之前 Phase 开发）
58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
"""趋势预测 + 异常检测。"""
|
|
|
|
import statistics
|
|
from datetime import datetime, timezone
|
|
|
|
|
|
def predict_trend(historical_scores: list[float], months_ahead: int = 3) -> dict:
|
|
"""基于历史评分预测未来健康度。"""
|
|
if len(historical_scores) < 2:
|
|
return {"predicted": [], "confidence": 0.0}
|
|
|
|
# 简单线性回归
|
|
n = len(historical_scores)
|
|
x_mean = sum(range(n)) / n
|
|
y_mean = sum(historical_scores) / n
|
|
|
|
numerator = sum((i - x_mean) * (y - y_mean) for i, y in enumerate(historical_scores))
|
|
denominator = sum((i - x_mean) ** 2 for i in range(n))
|
|
|
|
if denominator == 0:
|
|
slope = 0
|
|
else:
|
|
slope = numerator / denominator
|
|
|
|
intercept = y_mean - slope * x_mean
|
|
|
|
predicted = [slope * (n + i) + intercept for i in range(months_ahead)]
|
|
predicted = [max(0, min(100, p)) for p in predicted]
|
|
|
|
# 置信度基于历史数据量
|
|
confidence = min(0.9, n / 12)
|
|
|
|
return {
|
|
"predicted": [round(p, 1) for p in predicted],
|
|
"slope": round(slope, 2),
|
|
"confidence": round(confidence, 2),
|
|
}
|
|
|
|
|
|
def detect_anomalies(values: list[float], threshold: float = 2.0) -> list[int]:
|
|
"""统计方法识别指标突变。"""
|
|
if len(values) < 3:
|
|
return []
|
|
|
|
mean = statistics.mean(values)
|
|
stdev = statistics.stdev(values)
|
|
|
|
if stdev == 0:
|
|
return []
|
|
|
|
anomalies: list[int] = []
|
|
for i, v in enumerate(values):
|
|
z_score = abs(v - mean) / stdev
|
|
if z_score > threshold:
|
|
anomalies.append(i)
|
|
|
|
return anomalies
|