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With the advent of the "Industry 4.0" era, the requirements for measurement efficiency, size and accuracy in the assembly process of large-scale equipment have been continuously improved. In the assembly process of large-scale equipment, due to the limitations and occlusions of components and assembly tooling, a single laser tracker cannot complete the measurement of all target points. Therefore, it is necessary to use multiple trackers to work together to build a measurement network covering the entire assembly space. In this paper, through the forensic-based investigation optimization algorithm, the three-dimensional coordinates of the stations of multiple laser trackers are used as the high-dimensional input parameters, the side of the bounding box of the large-scale measurement field is used as the feasible region of the high dimensional parameters, the ray accessibility of the laser tracker is used as the statistical result, the number of public transit points of adjacent stations of the laser tracker and the number of visible digital-analog key points are used as constraints, the ratio of the number of visible points of multiple laser trackers to the total number of points is used as the objective function, the optimization is carried out in the form of greedy iterative fitness function, and finally the maximum coverage rate under the measurement network is obtained in complex environments such as tooling occlusion. In a word, this method shortens the station layout time and the overall assembly period through autonomous calculation, and has certain practical significance for improving the assembly efficiency of large-scale equipment.
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