吊索作为桥梁的关键承重部件，在运营过程中易受到外部环境作用而产生锈蚀，其中点蚀形成的锈蚀坑处因锈蚀坑的几何形态影响产生应力集中，直接影响在役桥梁的安全运营；提出精确的钢丝锈蚀坑形态拟合方法，是开展吊索疲劳寿命精细化分析的前提。锈蚀坑的几何形态具有复杂且不规则的特点，传统采用规则几何体拟合的方法会忽略局部几何不规则区域的应力集中；直接使用锈蚀坑原始点云数据进行存储和计算，虽然能够完整保留锈蚀坑几何信息，但数据量过大。为高效且精确地评估锈蚀坑几何形态，该文利用超景深显微镜采集蚀坑点云数据，引入球谐函数表征锈蚀坑的3维形态并简化锈蚀坑数据集，实现锈蚀坑的几何重构；建立ABAQUS有限元模型，将其与真实蚀坑、传统椭球拟合结果进行对比分析，以验证该简化与重构方法的有效性。结果表明：采用20阶球谐函数可实现锈蚀坑的有效简化，蚀坑数据集存储空间平均减少99.47%，且高度保留锈蚀坑的几何信息，疲劳寿命的平均误差为0.80%。该研究可为蚀坑的细观模型构建和数据集简化提供理论基础。

As a critical load-bearing component of bridges, suspension cables are prone to corrosion under environmental exposure during service. The geometric morphology of corrosion pits, particularly those caused by pitting corrosion, induces stress concentration that directly impacts the safe operation of in-service bridges. Therefore, developing an accurate method for fitting the morphology of corrosion pits on steel wires is a prerequisite for conduc-ting refined fatigue life analysis of suspension cables. The geometry of corrosion pits is complex and irregular. Traditional methods that approximate pits with regular geometric shapes fail to account for stress concentrations in locally irregular areas, while directly using the original point cloud data, though preserving complete geometric information, entails excessively large data volumes. To efficiently and accurately assess the geometric morphology of corrosion pits, this study employed a super-depth microscope to collect point cloud data of corrosion pits. The spherical harmonic function was introduced to characterize their three-dimensional morphology of corrosion pits, simplify the dataset, and achieve geometric reconstruction. An ABAQUS finite element model was established and compared with models based on real pit data and traditional ellipsoid fitting to validate the effectiveness of the simplification and reconstruction method. The results show that using a 20 th-order spherical harmonics function can effectively simplify the corrosion pits, reducing the storage space of the corrosion pit dataset by an average of 99.47% while retaining the geometric information with high fidelity. With fatigue life as the evaluation metric, the average error is 0.80%. This study provides a theoretical foundation for the construction of mesoscale models of corrosion pits and the simplification of corrosion pit datasets.