Development of data acquisition module for intelligent balance

Wind tunnel force measurement experiment is the most basic and major experimental project in wind tunnel experiments, and the wind tunnel strain balance is the most important and indispensable measurement device in wind tunnel force measurement experiments. In this paper, for the polynomial balance calibration formula fitting method based on the least squares method can not eliminate the error caused by the nonlinearity of the balance to develop a neural network equipped with intelligent balance data acquisition module, by comparing with the traditional least squares method, the neural network can effectively improve the accuracy of the acquisition, and provide a new technical means for the comprehensive correction of the static error. In this paper, an intelligent balance data acquisition module equipped with neural network is developed, which can eliminate the nonlinear error brought by the insufficient rigidity of the balance in the calibration process. The hardware design and software design of the collector are given in this paper. Finally, the data accuracy is analysed according to the technical specification requirements.