Research On Aquaculture Systems Based On The Internet Of Things

China’s total fishery production has repeatedly become the world’s first,but the current aquaculture industry is still facing many problems,such as the level of automation and efficiency as a whole is not high,too dependent on labor,can not accurately obtain information on the aquaculture environment,resulting in high labor costs,waste of resources,water pollution,unscientific management and other issues.With the rapid development of key technologies,aquaculture model has gradually changed from extensive economy to fine intelligence,in which the intelligent farming system is an important stage of fishery modernization.In this paper,an Internet of Things-based aquaculture system is designed and developed.This article focuses on the following:(1)Through embedded technology,water quality detection and meteorological testing are integrated and an embedded aquaculture testing subsystem is designed.In this paper,embedded detection system is used as the link of environmental detection,which has played an effect on the improvement and optimization of base system.This system enables real-time monitoring of water quality and meteorological parameters in aquaculture environments and sends data packaging to servers,which parse and process this data to provide a data base for subsequent aquaculture big data mining.(2)In the aquaculture process,oxygenation machine is needed for the regulation of dissolved oxygen in the water environment,and a set of subsystis based on the intelligent control of the aquaculture environment based on fuzzy control is designed with PLC,which has the advantages of stable and reliable compatibility in equipment control and regulation.This subsysysty can not only receive real-time information on aquaculture water quality and meteorological environment through wireless communication module,but also realize the function of effectively regulating dissolved oxygen quantity in view of the nonlinearity,inertia and time lag in the process of dissolved oxygen control.The system mainly receives the sensor data sent by the server through the wireless module,after data processing by the PLC,and displays the environment information in real time in the upper computer,processing the information by using fuzzy algorithm,the result of processing is transmitted to the drive as the output of the PLC,and the drive controls the oxygen booster to regulate the amount of dissolved oxygen in the water.(3)In order to realize the function of water quality classification in aquaculture environment,combined with the current popular Hadoop big data framework,the BP neural network algorithm is applied to Hadoop’s Map Reduce distributed programming model,and the function of water quality evaluation classification can be achieved by using parameter information in water environment,using big data and data mining technology.Finally,it is verified that the system can transmit and display the above parameter information in real time,and provide historical data and environmental abnormal alarm function.Fuzzy control in the process of adjusting dissolved oxygen over-adjustment small,high precision,dissolved oxygen deviation ±0.4 mg/L,can reduce the number of oxygen booster start and stop,extend the life of the equipment.Trained by a large amount of data in had a large amount of data in the system of BP neural network culture water quality classification based on Hadoop,the correct rate and recall rate of this water quality classification model were 94.7% and 96.26%,respectively,which can guarantee the accuracy of water quality classification,and the acceleration effect of large data sets is obvious,and can be applied to large data applications in aquaculture.