摘要
为降低噪声与干扰等因素的影响,及时精准发现异常数据,帮助决策者制定更加合理科学的洪水、干旱等自然灾害应对策略,设计基于嵌入式AI技术的水位流速在线监测系统。传感模块利用位移传感器与力传感器等采集与水流流速相关的模拟信号;采集模块通过模数转换器转换采集的模拟信号得到与水位流速相关的数字信号;经由无线通信模块,传输数字信号至嵌入式处理器内;嵌入式处理器利用AI技术中的径向基函数神经网络,处理接收的数字信号,输出水位流速在线监测结果,经由无线通信模块传输至显示模块内,为用户呈现水位流速在线监测结果。若监测结果中,水位上升过快或流速变化过快,则通过预警模块发出警报。实验结果表明,该系统可有效采集水位流速相关的模拟信号,并得到水位流速在线监测结果;当监测结果中存在异常情况时,该系统可立即发出警报。
In order to reduce the impact of noise,interference and other factors,timely and accurate detection of abnormal data,help decision makers to develop more reasonable and scientific flood,drought and other natural disaster response strategies,design an online monitoring system based on embedded AI technology.The sensor module uses the displacement sensor and the force sensor to collect the analog signal related to the flow velocity.The acquisition module converts the collected analog signal through the analog-to-digital converter to obtain the digital signal related to the water level velocity.Transmit digital signal to embedded processor via wireless communication module;The embedded processor uses the radial basis function neural network in AI technology to process the received digital signal,output the online monitoring results of water level and velocity,and transmit them to the display module through the wireless communication module to present the online monitoring results of water level and velocity to the user.When the monitoring results show that the water level rises too fast or the velocity changes too fast,an alarm is issued through the early warning module.The experimental results indicate that the system can effectively collect the analog signals related to water level velocity,and get the on-line monitoring results of water level velocity.When there is an abnormal situation in the monitoring results,the system can effectively issue an alarm.
作者
王宏志
WANG Hongzhi(Xinjiang Herun Technology Co.,Ltd.,Urumqi 830000,China)
出处
《电子设计工程》
2025年第16期60-63,69,共5页
Electronic Design Engineering
关键词
嵌入式技术
AI技术
水位流速
在线监测
传感器
径向基函数
embedded technology
AI technology
water level velocity
online monitoring
sensor
radial basis function
