## 3 Methods
### The Proposed Algorithm  
This subsection presents the core contribution of this manuscript, which is the integration of the Iterative Closest Point (ICP) algorithm with KD-Tree-based optimization for efficient and accurate case model matching. To improve clarity, we summarize the main steps of the proposed method as follows:  
Step 1: Hierarchical model division based on structure and function. Step 2: Construction of model parameter vector sets using geometric and physical attributes. Step 3: ICP-based point cloud registration using KD-Tree to accelerate nearest neighbor search. Step 4: Calculation of model similarity using a robust similarity function. Step 5: Node weight assignment using Analytic Hierarchy Process (AHP). Step 6: Retrieval and filtering of key components for dimensional reuse and annotation. A visual representation of the above steps is provided in Figure 3 to enhance the understanding of the method's flow and highlight the integration of various algorithms in each stage. The Iterative Closest Point (ICP) algorithm is a classic point cloud registration method, widely used in computer vision and 3D geometric processing fields [60; 61], particularly in applications such as object recognition, 3D reconstruction, and engineering drawing registration. The primary goal of ICP is to compute the optimal rigid transformation between two point clouds (source and target) through iterative optimization, minimizing  
Figure 3: Workflow of the proposed method. the distance between them so that the source point cloud matches the target point cloud as closely as possible [62; 63; 64]. To improve the performance of the ICP algorithm in large data scenarios, the KD-Tree technique [65] is introduced to optimize the efficiency of nearest point search. KD-Tree is a hierarchical binary tree data structure primarily used to store and quickly retrieve point sets in multi-dimensional space [66; 67]. By partitioning and organizing spatial data, KD-Tree significantly accelerates nearest neighbor searches, particularly in high-dimensional and large-scale point cloud data, demonstrating excellent performance [68; 69; 70]. In the ICP algorithm, the introduction of KD-Tree can drastically reduce the time complexity of nearest point searches during each iteration, thereby accelerating the overall registration process. By using the KD-Tree algorithm to speed up nearest neighbor searches, combined with the ICP algorithm, the model's node entities are randomly matched with the historical case nodes for computation, and different weights are assigned to obtain the final similar case result. The specific steps are as follows:  
Step 1: Hierarchical Model Division  
Based on the structure, functionality, and assembly levels of the refrigerated vehicle, the model is divided into five levels: Product Layer, Module Layer, First-level Configuration Layer, Second-level Configuration Layer, and Material Layer, as shown in Figure 4. The Product Layer is used to differentiate between different case models; the Module Layer builds the overall structure; the Configuration Layers are selected based on customer customization requirements; and the Material Layer provides the raw materials required for product manufacturing. Each level is treated as a node entity to improve retrieval efficiency and accuracy. Step 2: Establishing the Model Parameter Vector Set  
The model parameter vectors are constructed from geometric information and feature attribute parameters, which can represent the model's characteristics. In this study, the vector set is built by extracting parameters corresponding to the child class layers from the  
Figure 4: Van-type refrigerated truck model hierarchy division. shape parameters, volume, surface area, and moment of inertia data of the node models in each layer. This vector set is used to describe the features of the parent class layer. First, the shape parameters \(D_{a}\), \(D_{b}\), and \(D_{c}\) (length, width, and height) of each node entity model in the \(x\), \(y\), and \(z\) directions are extracted and normalized.