Research On The Collection And Processing Technology Of Water Quality Environmental Data In Pond Aquaculture

Raise water before raising fish.Water quality in breeding determined growth status of aquatic products as well as the economic interests of farmers.The Chinese aquaculture industry is characterized by a long growth cycle of aquatic products,a large number of workers required to operate in large-scale aquaculture and a low production capacity these features have long time limited the growth rate of Chinese aquaculture industry.In today’s society,With the rapid economic growth and changing health perceptions.Demand for seafood continues to grow.And today’s traditional aquaculture system cannot meet the demands of the market.After reviewing national and international literature,I found Io Trelated technologies in the aquaculture Industry can be used to improve the quality and efficiency of aquatic products.The workload of the breeding base can be reduced,thereby reducing the demand for staff in the breeding base,effectively saving breeding costs,improving the efficiency of aquatic product breeding,and farmers have huge economic benefits.According to the literature,many breeding bases are located in remote locations with inconvenient transportation,and most farmers prefer traditional breeding methods.The monitoring of various parameters in aquaculture waters mainly relies on manual sampling and chemical experiments to analyze data.The traditional solution to this situation is less productivity,long time and less accuracy.Wasting resources,maintenance equipment and meters is also difficult,in the end,the economic benefits of farmers are declining.In order to change the status of today’s aquaculture industry and promote the advancement of technology,this paper develops a monitoring system for real time aquaculture environmental parameters using related Io T devices.The main tasks of this paper are to fulfill the following tasks:Real-time monitoring of various water quality environmental parameters,such as nitrogen content,ammonia,dissolved oxygen content,p H,water temperature,air temperature and other parameters,by means of appropriate sensor devices.Develop a hardware terminal for water quality parameter acquisition,develop a matching embedded application program,use a microprocessor to automatically control related sensor equipments to complete data collection in a certain process,and collect the collected data.After integration,upload to the server port using 4G communication protocol.Design the frontend and backend of the data platform with Django architecture.Once server have received the data sent by the terminal,use the MQTT protocol to receive and store the data in a suitable data table to prepare for later data visualization and data prediction model development.Design and develop database management system.The data management system mainly includes three parts: data storage part,data security management part,and data visualization part.The data storage function is respectively realized,users with different levels of permissions are given different database permissions,and the data visualization part screens out the latest 15 groups of data in the data table records and draws them into a line chart,displaying the changing trend of each parameter in real time.Design an algorithm model and complete data prediction.Create relevant datasets based on the collected data.The GA-BP model is established,the data set collected by the platform is selected to train the GA-BP neural network model,and 50 sets of data are used for data testing.After predicting the result,it will be displayed on the data visualization module.Once the data change trend is abnormal,the user can find it in time and take preventive measures in advance to reduce the possible damage.After the system is configured,each module of the terminal data acquisition unit is checked to confirm the quality of the connection to the server,the visualization of the database and the prediction effect of the data model.This system is currently applied to the Dalian Aquaculture Base.After testing,the system data transmission is stable,the data transmission is correct,and other indicators are also satisfied.At present,the system is still in the operation stage,which can meet the needs of related aquaculture data monitoring.