Research On Key Technologies Of Aquaculture Environmental Monitoring System

Due to the rapid development of China’s market economy and the people’s living needs for high-quality products,aquatic products have been favored by more and more people.my country is a big country of aquaculture,and the area of aquaculture is still expanding,and it has become an important part of my country’s agricultural economy.For offshore aquaculture,there are still many farmers adopting extensive and empirical methods,which not only cannot meet a large number of market demands,is not conducive to the economic interests of farmers,but also hinders the development and management of the industry.At the same time,there are many types of offshore aquaculture,and the breeding environment is complex.Compared with freshwater aquaculture and factory aquaculture,it is more difficult to manage.During the research on the status quo of offshore aquaculture,we found many problems that need to be solved urgently.For example,some farmers still adopt the inherent water quality testing method of manually collecting water quality data samples and using chemical methods to analyze data parameters.This method not only wastes a lot of time,but also is difficult to guarantee in terms of accuracy,and is not conducive to the production of economic benefits for farmers.At present,the Internet of Things technology is developing rapidly,and the trend of agricultural modernization is becoming more and more obvious.In order to keep up with the development trend of intelligent fishery in my country,this paper studies the key technologies of aquaculture environmental monitoring system.The research in this paper is mainly in the following aspects:(1)This paper realizes the data transmission of the system based on Wi Fi wireless communication.The STM 32 embedded module and the Wi Fi wireless communication module are used to connect the data collected by the water quality and meteorological sensors to the Internet,and the Wi Fi wireless communication module is used to connect with the router.Realize the transmission of data to the cloud server.At the same time,in order to improve the utilization rate of chip resources and reduce the cost of software development,the RT-Thread embedded real-time operating system is built on the STM32L475 hardware platform to complete the Wi Fi wireless communication thread.Finally,the sensor data(dissolved oxygen concentration,temperature,PH value,and meteorological indicators such as wind speed,wind direction,rainfall,and so on)are transmitted from the local collection terminal to the cloud server,and the data information can be viewed through web pages,mobile devices.(2)Research on data preprocessing technology of main control module in aquaculture water environment monitoring system.Use box plots to intuitively perform single-dimensional abnormality detection on the collected abnormal data,use K-means clustering method to perform multi-dimensional abnormality detection,eliminate outliers,and use linear interpolation to fill in the excluded data and missing data;use wavelet drop The noise reduction method is used to denoise the data,and different denoising functions are used to compare the dissolved oxygen data.Finally,the self-adaptive weighting algorithm is used to fuse the data of multiple groups of homogeneous sensors in the same area,and the five groups of dissolved oxygen data collected at the same time are used to illustrate.The significance of dimensionality reduction.(3)Research on Dissolved Oxygen Prediction Model Based on Neural Network.In order to improve the early warning and regulation ability in aquaculture,the factors affecting the dissolved oxygen in the water body were firstly studied,and then a dissolved oxygen prediction model based on the encoder-decoder structure was proposed.The model takes a variety of water temperature,PH value,dissolved oxygen and meteorological factors as input parameters,and uses the preprocessed parameters as the input data of the prediction model.Sequence prediction models are compared and compared using multiple evaluation metrics.The experimental results show that the model has good predictive ability for dissolved oxygen time series.