Research On The Visualization System Of Monitoring And Early Warning Linked With Turbidity Data Collection

In the new era,General Secretary Xi put forward the new development concept of "green water and green mountains" in order to further improve the quality of people’s lives and enhance people’s well-being,and put environmental protection work in a pro-minent position,in which water pollution is a key link in governance.In response to the problem of water pollution,our country has promulgated some laws one after anot-her.With the successful implementation of the pilot work,the "river chief system" has also begun to be vigorously implemented nationwide.Turbidity is an important water quality indicator,and its effective measurement is of great significance for the discharge of industrial waste water,the qualified detectio-n of industrial water and domestic water.Turbidity sensors from the first generation of chemical sensors to the second generation of photoelectric sensors,still have not got rid of the shackles of electricity;currently developed to the third generation of opt-ical fiber sensors,measurement and transmission do not require electricity,and the transmission distance is not limited.In the past,turbidity testing mostly adopted manu-al sampling testing,which was not only inefficient,but also the credibility of the data could not be guaranteed.Therefore,it is also necessary to avoid human intervention as much as possible in the whole process of collection,transmission,maintenance,and judgment.Therefore,this paper proposes a solution of "optical fiber turbidity sensor & data monitoring and early warning visualization system" to fully ensure the stability and reliability of turbidity data.This design is based on the research of the research group’s collection terminal,and conducts the research work of the data monitoring and early warning visualization system.A basic measurement platform is constructed.At the same time,in order to adapt to changes in the test environment and ecological environment,machine learning is introduced to realize the water turbidity warning function,which effectively increases the stability and reliability of the measurement system.This design analyzes the monitoring and management needs of the river chief for the turbidity of the waters under its jurisdiction for the field river scene without the network environment,and carries out the overall plan design.The first is a detailed introduction to the acquisition of data by the collection terminal,data preprocessing,and data storage.According to the characteristics of the data and system requirements,the combination of TXT file storage and SQLite3 database storage,and the AES encry-ption algorithm are used to ensure data security while also making the execution process better.The second is to explain the functional realization of the data visualizat-ion part.The login system is developed based on the Python language,using the Django framework,the front-end page is designed with CSS and Bootstrap,and the visual chart display uses the Py Echarts visualization tool.By configuring different "_opt" configuration items,the chart style and auxiliary functions required by the system are realized.The data interface calls the preprocessed data file to realize data filling and present the final effect.The third is to introduce the design and realization process of the early warning function.First label the preprocessed data,and then divide the data set.The training set is used for the training of the neural network model to effectively distinguish between noisy data and valid data.With the increase of data,a new data set is formed,and dynamic training is carried out to realize intelligent turbidity warning.At the same time,the system can summarize and count the noise data,display the proportion of the noise data in the water area,and help the river chief to have a more comprehensive grasp of the water area.The experimental results show that different concentrations of formazin standard solutions are displayed by the collection terminal linkage visualization system.When the turbidity of the standard solution is 50 NTU,the system error is 3.80%;when the turbidity of the standard solution is 1000 NTU,the error is only 0.27%.As the turbidit-y increases,this solution exhibits higher accuracy.Moreover,the data displayed on the collection terminal and the visualization page are equal,and the data request speed reaches millisecond level.Some noise data was detected during the experiment.The noise data accounted for 0.42%,and the model test accuracy was about 82%,which can basically achieve effective early warning.The system runs stably during the test,with strong adaptability,real-time,stability,reliability,easy maintenance,and low cost.It meets the requirements for online turbidity monitoring in the wild river scene without internet.