光缆安全系统信号识别特征数据集

本数据主要用于光纤振动入侵识别系统的信号分类与模式识别优化，典型应用场景包括： 1、信号源判别模型训练：基于数据集中的能量特征比值、可信度和动作能量等字段，对比自然扰动（如风吹、雨水、车辆震动）与人为扰动（如触碰、挖掘）信号，实现高置信度筛选； 2、误报率优化与算法精调：结合模式识别结果与定位计算值，调优模型误差阈值，有效降低系统误判与漏判概率； 3、系统性能评估与实验验证：通过多时段数据横向对比，验证识别算法在复杂信道下的鲁棒性，优化滤波器和识别逻辑； 4、自学习闭环构建：将实际环境中采集的数据反馈至模型迭代训练中，实现自适应优化机制。

This dataset is primarily utilized for signal classification and pattern recognition optimization of optical fiber vibration intrusion detection systems. Its typical application scenarios are as follows:
1. Training of signal source discrimination models: Based on fields including energy feature ratio, credibility, and action energy in the dataset, compare signals from natural disturbances (such as wind, rain, and vehicle vibration) and man-made disturbances (such as touching and excavation) to achieve high-confidence screening;
2. False positive rate optimization and algorithm fine-tuning: Combine pattern recognition results and positioning calculation values to adjust model error thresholds, effectively reducing the probability of system misjudgment and missed judgment;
3. System performance evaluation and experimental verification: Perform cross-temporal comparative analysis on multi-period data to verify the robustness of recognition algorithms in complex channels, and optimize filters and recognition logic;
4. Self-learning closed-loop construction: Feed back data collected in actual environments to model iterative training, thereby implementing an adaptive optimization mechanism.

搜集汇总

背景与挑战

背景概述

该数据集由广东复安科技发展有限公司提供，包含光缆安全系统的光纤振动信号关键特征数据，如定位计算结果、动作能量和可信度评分等，主要用于光纤振动入侵识别系统的信号分类模型训练和误报率优化。数据集支持复杂环境下异常事件识别模型的训练与性能提升。