# -*- coding: utf-8 -*- """ 螺栓拧紧力矩-转角时序数据生成脚本（自建模拟数据集） 生成两类数据： 1. benchmark/ 标准工况基准集（1200 条）—— 5 类质量状态的标准拧紧曲线 2. extended/ 扩展工况集（1800 条）—— 不同螺栓等级、工具磨损、环境温度下的工况扩展 质量标签编码： 0 = 合格, 1 = 欠拧, 2 = 过拧, 3 = 滑牙, 4 = 虚拧 物理模型说明： 拧紧过程力矩-转角曲线分为空行程（run-down）、贴合（seating）、弹性段（elastic）、 保持段（hold）四个阶段。不同质量状态对应不同的终态行为： - 合格：力矩升至目标扭矩后保持 - 欠拧：力矩提前停止，终值明显低于目标扭矩 - 过拧：力矩越过目标扭矩后停止，终值明显高于目标扭矩 - 滑牙：力矩升至峰值后急剧跌落（螺纹滑丝） - 虚拧：力矩始终维持低位（错扣/未正确啮合） 用法： python data/generate_data.py """ from __future__ import annotations import json import os from pathlib import Path import numpy as np import pandas as pd BASE_DIR = Path(__file__).resolve().parent RAW_DIR = BASE_DIR / "raw" # 采样参数 DT = 0.01 # 采样间隔 0.01s（100 Hz） SPEED_RANGE = (110.0, 190.0) # 电动扭矩扳手转速 deg/s # 螺栓规格基础参数（8.8 级通用紧固参考值，扭矩单位 N·m，角度单位 deg） # stiffness 为弹性段斜率（N·m/deg） BOLT_SPECS = { "M12": {"target_torque": 80.0, "stiffness": 1.70, "snug_angle": 35.0}, "M16": {"target_torque": 180.0, "stiffness": 2.20, "snug_angle": 40.0}, "M20": {"target_torque": 320.0, "stiffness": 2.80, "snug_angle": 45.0}, "M24": {"target_torque": 520.0, "stiffness": 3.40, "snug_angle": 50.0}, } # 扩展工况：螺栓性能等级、工具磨损状态、环境温度 GRADES = ["8.8", "10.9", "12.9"] # 性能等级 WEARS = [0, 1, 2] # 0=新 1=中度磨损 2=重度磨损 TEMPS = [-10, 20, 50] # 环境温度 ℃ LABELS = {0: "合格", 1: "欠拧", 2: "过拧", 3: "滑牙", 4: "虚拧"} def simulate_tightening( spec: str, class_id: int, grade: str, wear: int, temp: float, rng: np.random.Generator, ) -> dict: """生成单条拧紧力矩-转角时序，返回 {angle, torque, ...}。""" p = BOLT_SPECS[spec] target = p["target_torque"] snug_angle = p["snug_angle"] # 等级、磨损、温度对物理参数的扰动 grade_factor = 1.0 + (int(grade.split(".")[0]) - 8.8) * 0.03 # 等级越高刚度/强度略高 wear_noise = 1.0 + wear * 0.5 # 磨损加大噪声 temp_factor = 1.0 + (temp - 20) * 0.002 # 温度对摩擦的微弱影响 k = p["stiffness"] * grade_factor * temp_factor * rng.uniform(0.94, 1.06) snug_torque = (3.0 + rng.uniform(-0.5, 2.5)) * temp_factor speed = rng.uniform(*SPEED_RANGE) * rng.uniform(0.96, 1.04) # 依据质量状态确定各阶段角度 if class_id == 0: # 合格：升至目标扭矩并保持 elastic_end = snug_angle + (target - snug_torque) / k hold_angle = rng.uniform(10.0, 20.0) final_angle = elastic_end + hold_angle elif class_id == 1: # 欠拧：提前停止，终值约为目标 60%～82% ratio = rng.uniform(0.60, 0.82) elastic_end = snug_angle + (target * ratio - snug_torque) / k final_angle = elastic_end + rng.uniform(0.0, 8.0) elif class_id == 2: # 过拧：越过目标，终值约为目标 115%～135% ratio = rng.uniform(1.15, 1.35) elastic_end = snug_angle + (target * ratio - snug_torque) / k final_angle = elastic_end + rng.uniform(5.0, 15.0) elif class_id == 3: # 滑牙：升至峰值后跌落 peak_ratio = rng.uniform(0.75, 0.95) peak_angle = snug_angle + (target * peak_ratio - snug_torque) / k final_angle = peak_angle + rng.uniform(8.0, 15.0) else: # 虚拧：力矩始终低位，大量空转 final_angle = snug_angle + rng.uniform(60.0, 100.0) # 生成角度序列 n = max(int(final_angle / (speed * DT)) + 1, 20) angle = np.arange(n) * speed * DT angle[-1] = final_angle # 精确对齐终止角度 # 生成力矩序列 torque = np.zeros(n) # 传感器噪声采用「读数相关」模型：约 1.5% 读数 + 0.05 N·m 底噪， # 避免绝对噪声淹没低扭矩工况（如虚拧）的真实信号 for i, a in enumerate(angle): if class_id == 3: # 滑牙：上升段 + 峰值后跌落 if a <= peak_angle: if a <= snug_angle: tau = snug_torque * (0.25 + 0.75 * a / snug_angle) else: tau = snug_torque + k * (a - snug_angle) else: peak_torque = snug_torque + k * (peak_angle - snug_angle) drop_k = k * rng.uniform(4.0, 7.0) # 跌落斜率更陡 tau = max(peak_torque - drop_k * (a - peak_angle), 0.30 * peak_torque) elif class_id == 4: # 虚拧：全程低位，未正确啮合 tau = snug_torque * (0.30 + 0.15 * np.sin(a / 6.0)) + rng.uniform(0, 1.0) else: # 合格/欠拧/过拧：空行程 + 弹性上升 + 保持 if a <= snug_angle: tau = snug_torque * (0.25 + 0.75 * a / snug_angle) # 空行程摩擦 elif a <= elastic_end: tau = snug_torque + k * (a - snug_angle) # 弹性段 else: tau = snug_torque + k * (elastic_end - snug_angle) # 保持段（稳定） torque[i] = tau torque += rng.normal(0.0, 0.015 * np.abs(torque) * wear_noise + 0.05, size=n) # 叠加传感器噪声 return { "angle": np.round(angle, 3).tolist(), "torque": np.round(torque, 3).tolist(), "speed": round(speed, 2), } def build_dataset(n_per_class: dict, extended: bool, rng: np.random.Generator) -> list[dict]: """按类别数量生成数据集，返回记录列表。""" records: list[dict] = [] spec_list = list(BOLT_SPECS.keys()) seq = 0 for class_id, count in n_per_class.items(): for _ in range(count): spec = spec_list[seq % len(spec_list)] if extended: grade = GRADES[seq % len(GRADES)] wear = WEARS[seq % len(WEARS)] temp = TEMPS[seq % len(TEMPS)] else: grade = "8.8" wear = 0 temp = 20.0 seq += 1 curve = simulate_tightening(spec, class_id, grade, wear, temp, rng) prefix = "E" if extended else "B" rec_id = f"{prefix}{seq:04d}" records.append({ "id": rec_id, "spec": spec, "grade": grade, "wear": wear, "temp": temp, "target_torque": BOLT_SPECS[spec]["target_torque"], "speed": curve["speed"], "label": class_id, "label_name": LABELS[class_id], "angle": curve["angle"], "torque": curve["torque"], }) return records def write_records(records: list[dict], out_jsonl: Path, out_meta: Path) -> None: """写出 JSONL 时序文件与 metadata.csv 汇总文件。""" out_jsonl.parent.mkdir(parents=True, exist_ok=True) with open(out_jsonl, "w", encoding="utf-8") as f: for r in records: f.write(json.dumps(r, ensure_ascii=False) + "\n") meta_rows = [ { "id": r["id"], "spec": r["spec"], "grade": r["grade"], "wear": r["wear"], "temp": r["temp"], "target_torque": r["target_torque"], "speed": r["speed"], "label": r["label"], "label_name": r["label_name"], "length": len(r["angle"]), } for r in records ] pd.DataFrame(meta_rows).to_csv(out_meta, index=False, encoding="utf-8-sig") def main() -> None: rng = np.random.default_rng(42) # 基准集：合格 700 + 缺陷 500（4 类各 125） benchmark = build_dataset( {0: 700, 1: 125, 2: 125, 3: 125, 4: 125}, extended=False, rng=rng, ) # 扩展集：扩充稀有缺陷样本 extended = build_dataset( {0: 600, 1: 300, 2: 300, 3: 300, 4: 300}, extended=True, rng=rng, ) write_records(benchmark, RAW_DIR / "benchmark" / "records.jsonl", RAW_DIR / "benchmark" / "metadata.csv") write_records(extended, RAW_DIR / "extended" / "records.jsonl", RAW_DIR / "extended" / "metadata.csv") print(f"[OK] 基准集 {len(benchmark)} 条 -> {RAW_DIR / 'benchmark'}") print(f"[OK] 扩展集 {len(extended)} 条 -> {RAW_DIR / 'extended'}") print(f"[OK] 合计 {len(benchmark) + len(extended)} 条") if __name__ == "__main__": main()