Abstract: Measuring institutions generate substantial verification data during periodic inspections of measuring instruments, but the immense economic and social value hidden within this data remains largely untapped. Currently, the evaluation of measuring instrument performance is limited to assessing the quality or metrological performance of individual instruments. This study proposes a generalizable method for evaluating the performance and predicting the failure of measuring instruments, using the vast amount of data generated from total station verifications. The process begins with analyzing and processing the verification data of total stations. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is employed to comprehensively evaluate the performance of individual total stations. The Vague set decision theory is then utilized to assess the overall quality of total stations from various manufacturers. Early warning analysis of the total stations' validity is conducted using the maximum permissible error and slope methods. Finally, based on this methodology, a system for measuring instrument performance evaluation and failure early warning is constructed. Through experiments on actual total station verification data, the developed system effectively achieves performance evaluation and failure prediction for total stations.