The aircraft air data system (ADS) is a critical component for ensuring safe flight operations and plays an essential role in the overall aircraft testing process. Currently, testing of ADS requires ground depressurization equipment to simulate airflow and generate changes in air parameters. Subsequently, maintenance personnel use ground support equipment to review the parameters output by ADS after data acquisition and calculation, checking whether they meet specified standards. These data are then recorded in actual measurement logs. However, during manual inspection and documentation, there is a risk of misreading or misjudging values, which could compromise flight safety. To address this issue, the air data parameter health prediction system was developed to enable automatic extraction and intelligent validation of ADS test data. This system utilizes historical measurement data from different batches of aircraft to fit Gaussian distributions for various parameters, thereby enabling health prediction for new test data. Additionally, a simple inspection device has been designed for pre-flight checks of ADS. By analyzing flight parameter data, this device can quickly determine whether there is blockage in ADS, thus preventing potential flight safety hazards caused by system blockages.