Study On Multi-parameter Sensing Technology Of Temperature,Salinity And Depth

Seawater temperature,salinity and depth are three important parameters of ocean basic hydrological information.Detection of seawater temperature,salinity and depth is the key technology of marine information detection,and CTD sensor is the core device to detect seawater temperature,salinity and depth.In order to quickly detect the distribution of temperature and salinity of the ocean profile in the depth direction,the traditional CTD profile instrumentspast needs to be released at a certain speed.Too fast release speed will cause the superposition of the gravity of detection pressure.Too fast drag rate will cause superposition of negative detection pressure.These two stacking effects will lead to the increase of depth detection error.The research and production of sensors based on new structures,new materials and new technologies to meet the rapid detection requirements of temperature,salinity and depth are the technical problems that researchers need to overcome.In this paper,based on thick film platinum resistor has the advantages of stable performance,good linearity,high precision,a twisted thick film platinum resistance sensitive core which can save space and get the big ratio of length to width is designed.The conductivity sensor based on the four electrode structure can effectively avoid electrode polarization compared with the two electrode structure.Therefore,an open four electrode conductivity sensitive core with simple structure and center symmetry is designed.Based on the capacitive pressure sensor has the characteristics of good temperature characteristic,high detection precision and low power consumption,a new type of double-sided variable-pitch capacitive pressure sensor sensitive element based on thick film process is designed.The purpose of this study is to solve the problems of complex structure,large volume and great importance existing in traditional CTD sensors from aspects of material selection and structure design.And through the improvement of the assembly structure and packaging form of the sensitive core,in order to meet the needs of the ocean deep fast moving measurement.First of all,the detection mechanism of seawater temperature,salinity and depth was studied.The design principle of temperature,salinity and depth sensors were analyzed.Then the structure and size of the sensitive units were designed,and the 3D modeld were drawn by Solid Works software.And ANSYS software was used for the sensor stress field,temperature field and other multi-field finite element simulation.After that,the thick film screen printing process and high temperature sintering process were studied.The screen printing template was designed and the sensitive units were made.Then,the test systems and experiment platforms were set up,and the calibration experiments and performance tests were carried out for the produced sensors.The temperature sensor test system was built by using constant temperature water tank,signal conditioning circuit,data acquisition device and computer to complete the performance test of the sensor in temperature environment.The test of conductivity was achieved by the preparation of standard solutions of potassium chloride.The temperature of the solutions were controlled by thermostat.A pressure test system was built and tested with a piston pressure gauge.Then,a three datas acquisition system was designed,including data acquisition and data communication hardware and software design based on STM32,which realized the data transmission with the host computer.The hardware circuit design included temperature,pressure,conductivity,sensitive signal conversion circuit,STM32 main control circuit module,power supply control module,data communication module and so on.The software design included the system initialization program,the data acquisition driver and the design of the data sending program.The upper computer software was developed by C# programming language.Finally,many algorithms for sensor nonlinear compensation and multisensor information fusion were studied.The results of the best linear fit compensation,polynomial normalized fitting compensation and BP neural network compensation were summarized and analyzed.The quadric surface data fusion was used to realize the data fusion of pressure and temperature,and the influence of temperature on the pressure sensor test results were reduced.The gray relational theory was applied to RBF neural network.Compared with single RBF neural network data fusion,the gray RBF neural network effectively improved the fusion accuracy and reduced the influence of temperature and pressure interference on the accuracy of conductivity measurement.The test and analysis results show that these methods can reduce the measurement error,improve the detection precision and meet the practical application requirements of the sensors.