Compared with typical close-range photogrammetry scenarios, port machinery is larger in size and requires higher measurement accuracy. Traditional measurement methods necessitate photographing from a farther distance to ensure the entire object fits within the frame. However, this approach leads to excessive measurement size per pixel due to the distance, significantly degrading measurement accuracy and failing to meet high-precision requirements. To address this issue, this paper proposes a distributed vision measurement system based on pre-calibration. This system can achieve coordinate system alignment across multiple measurement stations using total station pre-calibration point sets, enabling the integration of data from multiple binocular vision measurement systems to conduct close-range measurements of large components, thereby significantly improving measurement accuracy. To optimize measurement results, the paper introduces redundant observational data within the pre-calibration point set and proposes a selection method based on an optical distortion model that quantifies and weights the confidence of each point. By assigning different weights to various observational data, this method enhances the reliability and accuracy of the measurement data. Measurement experiments simulating real port conditions demonstrate that the proposed method can significantly improve the accuracy of vision measurement in large component measurement scenarios, providing a new theoretical tool for the application of this method in ports.