当前物联网系统中数据格式不统一,对大量数据进行处理分析和存储较为困难,本研究基于人工脉冲神经网络建立了新型AI数据融合平台,对多种数据源进行整合处理。对传统的神经网络进行改进,提出改进型的脉冲神经网络,结合了BP神经网络的空间信息,将脉冲信息通过膜电压的变化转换为时间信息,输出层输出的数据结合了空间信息与时间信息。结合多层双向LSTM网络建立PALC模型,提取数据的形态特征和语义特征,对平台中非结构化数据的实体信息进行抽取和融合。实验结果显示,本研究数据融合平台的数据库每秒查询率更高,QPS最高可达到59 863,信息抽取的准确率最高为99%。

The current data formats in the Internet of Things system are not unified,which makes it difficult to process,analyze and store a large amount of data.In this study,a new AI data fusion platform is established based on artificial pulse neural network to integrate and process multiple data sources.An improved pulse neural network is proposed,which combines the spatial information of BP neural network and converts the pulse information into time information through the change of membrane voltage.The output data of the output layer combines the spatial information and time information.The PALC model is established based on the multi-layer bidirectional LSTM network to extract the morphological and semantic features of the data,and extract and merge the entity information of the unstructured data on the platform.The experiment results show that the database of the data fusion platform in this study has a higher query rate per second,the highest QPS can reach 59 863,and the highest accuracy rate of information extraction is 99%.