The invention relates to an LSTM-based equipment evaluation method and system, and relates to the field of intelligent transportation. The method comprises the following steps: constructing an input feature vector of an LSTM model, wherein the input feature vector comprises state data of the equipment; constructing a training set, a verification set and a test set based on historical data; constructing an initial LSTM (Long Short Term Memory) model; training, verifying and testing the initial LSTM model, and generating a final LSTM model; and based on equipment state data acquired in real time, evaluating the equipment state and the abnormality type through the LSTM model. According to the equipment evaluation method and system based on the LSTM, the running state of the future equipment is predicted based on the historical state sequence data of the equipment, such as the current, the voltage, the chip temperature, the idle memory size, the fault time, the fault type and the network packet loss rate, so that the real-time performance and the accuracy of equipment state monitoring are improved.
本申请涉及一种基于LSTM的设备评估方法及系统,涉及智能交通领域。所述方法包括构造LSTM模型的输入特征向量,所述输入特征向量包括所述设备的状态数据;基于历史数据,构建训练集、验证集、测试集;构建初始LSTM模型;训练、验证、测试所述初始LSTM模型,生成最终的LSTM模型;基于实时采集的设备状态数据,通过所述LSTM模型评估所述设备状态及异常类型。本申请的基于LSTM的设备评估方法及系统,基于设备的历史状态序列数据,如电流、电压、芯片温度、空闲内存大小、故障时间、故障类型、网络丢包率等特征用以预测未来设备的运行状态,从而提升设备状态监测的实时性和准确性。
Equipment evaluation method and system based on LSTM (Long Short Term Memory)
一种基于LSTM的设备评估方法及系统
2022-05-06
Patent
Elektronische Ressource
Chinesisch
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