摘要：【中文】本发明公开了一种利用紫外光谱扫描快速鉴别真假蜂蜜的方法，用紫外可见分光光度计对待检蜂蜜样品进行扫描，对比待检蜂蜜样品与糖浆、纯蜂蜜在210nm～400nm波长段的光谱图及光谱图中波峰波谷位置特征，对待检蜂蜜进行鉴别。本发明所需设备简单，方法易学，实用性强，只要具有紫外可见分光光度计和简单耗材就可完成掺假检验工作，适合所有基层食品快检机构，方便企业用于蜂蜜原料掺假检验，满足了消费者快速鉴别假蜂蜜，特别是以糖浆冒充的假蜂蜜的需求，该方法极易推广使用。 【EN】The invention discloses a method for rapidly identifying true and false honey by ultraviolet spectrum scanning, which comprises the steps of scanning a honey sample to be detected by an ultraviolet visible spectrophotometer, comparing a spectrogram of the honey sample to be detected with syrup and pure honey in a wavelength range of 210 nm-400 nm and position characteristics of peaks and troughs in the spectrogram, and identifying the honey to be detected. The method has the advantages of simple required equipment, easy learning of the method and strong practicability, can finish adulteration inspection work as long as the ultraviolet-visible spectrophotometer and simple consumables are provided, is suitable for all basic food quick inspection mechanisms, is convenient for enterprises to use for adulteration inspection of honey raw materials, meets the requirement of consumers for quickly identifying fake honey, particularly fake honey faked with syrup, and is extremely easy to popularize and use.

摘要：【中文】本发明公开了一种基于LSTM的智能电网配电终端单元入侵检测方法，属于智能电网信息安全技术领域。本发明通过对正常程序的CPU功耗进行采集，特征提取并训练出一个预测模型；在攻击检测阶段，采集实时运行的DTU功耗数据得到当前t时刻的实际功耗特征；设定样本窗口，采集t时刻之前的n个样本，得到当前t时刻的理论预测功耗特征，计算实际功耗特征与理论预测功耗特征之间的欧氏距离，判断是否遭到攻击。本发明解决了配电终端单元无法安装入侵检测软件的问题；采用LSTM神经网络提高了检测精度，能够更好的捕捉系统功耗的正常变化；解决了电网环境电磁噪声大，传统的边信道异常检测方法面临着精度低的问题。 【EN】The invention discloses an LSTM-based intrusion detection method for a power distribution terminal unit of a smart power grid, and belongs to the technical field of information security of the smart power grid. The method comprises the steps of collecting the CPU power consumption of a normal program, extracting characteristics and training a prediction model; in an attack detection stage, collecting DTU power consumption data running in real time to obtain the actual power consumption characteristics at the current t moment; setting a sample window, collecting n samples before t time to obtain theoretical prediction power consumption characteristics of the current t time, calculating Euclidean distance between actual power consumption characteristics and the theoretical prediction power consumption characteristics, and judging whether the attack is suffered. The invention solves the problem that the power distribution terminal unit can not install the intrusion detection software; the LSTM neural network is adopted to improve the detection precision, and the normal change of the system power consumption can be better captured; the method solves the problems that the electromagnetic noise of the power grid environment is large, and the traditional side channel abnormity detection method faces low precision.