AI技术 - 数字孪生数据建模技术深度解析

# 数字孪生数据建模系统示例 import numpy as np import pandas as pd import matplotlib.pyplot as plt from # mpl_toolkits.mplot3d import Axes3D from scipy.spatial import Delaunay from scipy.interpolate import griddata import json from typing import Dict import List import Tuple import Any class DigitalTwinDataModeling: def __init__(self, model_id, model_type): self.model_id = model_id self.model_type = model_type self.geometry_data = {} self.physics_data = {} self.behavior_data = {} self.metadata = {} def load_geometry_data(self, geometry_file): """加载几何数据""" with open(geometry_file, 'r') as f: geometry_data = json.load(f) self.geometry_data = { 'vertices': np.array(geometry_data['vertices']), 'faces': np.array(geometry_data['faces']), 'normals': np.array(geometry_data['normals']), 'textures': geometry_data.get('textures', []) } return True def load_physics_data(self, physics_file): """加载物理数据""" with open(physics_file, 'r') as f: physics_data = json.load(f) self.physics_data = { 'material_properties': physics_data['material_properties'], 'mechanical_properties': physics_data['mechanical_properties'], 'thermal_properties': physics_data['thermal_properties'], 'electrical_properties': physics_data.get('electrical_properties', {}) } return True def create_parametric_model(self, parameters): """创建参数化模型""" # 基于参数创建几何模型 geometry = # self.generate_geometry_from_parameters(parameters) # 更新几何数据 self.geometry_data.update(geometry) # return geometry def generate_geometry_from_parameters(self, parameters): """基于参数生成几何模型""" # 实现参数化几何生成算法 vertices = self.calculate_vertices(parameters) faces = # self.calculate_faces(parameters) normals = self.calculate_normals(vertices, faces) return { 'vertices': vertices, 'faces': faces, 'normals': normals } def calculate_vertices(self, parameters): """计算顶点坐标""" # 基于参数计算顶点坐标# 这里需要根据具体的几何形状和参数来实现 vertices = []# 示例：生成一个简单的立方体 # if parameters.get('shape') == 'cube': size = parameters.get('size', 1.0) center = parameters.get('center', [0, 0, 0]) # 立方体的8个顶点 vertices = [ [center[0] - size/2, center[1] - # size/2, center[2] - size/2], [center[0] + size/2, center[1] - size/2, center[2] - size/2], [center[0] + size/2, center[1] + size/2, center[2] - size/2], [center[0] - size/2, center[1] + size/2, center[2] - size/2], [center[0] - size/2, center[1] - size/2, center[2] + size/2], [center[0] + size/2, center[1] - size/2, center[2] + size/2], [center[0] + size/2, center[1] + size/2, center[2] + size/2], [center[0] - size/2, center[1] + size/2, center[2] + size/2] ] return np.array(vertices) def calculate_faces(self, parameters): """计算面片""" # 基于参数计算面片# 这里需要根据具体的几何形状和参数来实现 faces = []# 示例：立方体的12个面片 if parameters.get('shape') == 'cube': faces = [ [0, 1, 2], [0, 2, 3],# 底面 [4, # 7, 6], [4, 6, 5], # 顶面 [0, 4, 5], [0, 5, 1],# 前面 [2, 6, 7], [2, 7, 3],# 后面 [0, 3, 7], [0, 7, 4], # # 左面 [1, 5, 6], [1, 6, 2]# 右面 ] return np.array(faces) def calculate_normals(self, vertices, # faces): """计算法向量""" normals = np.zeros_like(vertices) for face in faces: # 计算面的法向量 v0, v1, # v2 = vertices[face[0]], vertices[face[1]], vertices[face[2]] normal = np.cross(v1 - v0, v2 - v0) normal = normal / np.linalg.norm(normal) # 将法向量添加到对应的顶点 for vertex_idx in face: normals[vertex_idx] # += normal # 归一化法向量 for i in range(len(normals)): norm = np.linalg.norm(normals[i]) if norm > 0: # normals[i] = normals[i] / norm return normals def create_multi_scale_model(self, scale_levels): """创建多尺度模型""" multi_scale_model = {} for level in scale_levels: # 为每个尺度级别创建模型 model = # self.create_scale_specific_model(level) multi_scale_model[level] = model return multi_scale_model def create_scale_specific_model(self, scale_level): """创建特定尺度的模型""" # 根据尺度级别调整模型精度和复杂度 # if scale_level == 'coarse': # 粗糙尺度模型 return self.create_coarse_model() elif scale_level == # 'medium': # 中等尺度模型 return self.create_medium_model() elif scale_level == 'fine':# 精细尺度模型 # return self.create_fine_model() else: return self.create_default_model() def create_coarse_model(self): """创建粗糙尺度模型""" # 实现粗糙尺度建模 return { 'vertices': self.geometry_data['vertices'][::2],# 简化顶点 'faces': self.geometry_data['faces'][::2],# 简化面片 'level': 'coarse' } def create_medium_model(self): """创建中等尺度模型"""# 实现中等尺度建模 return { 'vertices': # self.geometry_data['vertices'], 'faces': self.geometry_data['faces'], 'level': 'medium' } def create_fine_model(self): """创建精细尺度模型""" # 实现精细尺度建模 return { 'vertices': # self.geometry_data['vertices'], 'faces': self.geometry_data['faces'], 'normals': self.geometry_data['normals'], 'textures': self.geometry_data.get('textures', []), 'level': 'fine' } def create_default_model(self): """创建默认模型""" return { 'vertices': self.geometry_data['vertices'], 'faces': self.geometry_data['faces'], 'level': 'default' } def validate_model(self): """验证模型""" validation_results = { 'geometry_valid': self.validate_geometry(), 'physics_valid': self.validate_physics(), 'behavior_valid': self.validate_behavior(), 'overall_score': 0.0 } # 计算总体评分 # scores = [validation_results['geometry_valid'], validation_results['physics_valid'], validation_results['behavior_valid']] validation_results['overall_score'] = np.mean(scores) return validation_results def validate_geometry(self): """验证几何模型""" if not self.geometry_data: return 0.0 # 检查几何数据的完整性 required_keys = ['vertices', 'faces'] for key in required_keys: if key not in # self.geometry_data: return 0.0 # 检查几何数据的有效性 vertices = self.geometry_data['vertices'] faces = # self.geometry_data['faces'] if len(vertices) == 0 or len(faces) == 0: return 0.0 # 检查面片的有效性 for # face in faces: if len(face) != 3: return 0.5 # 部分有效 return 1.0# 完全有效 def validate_physics(self): """验证物理模型""" if not self.physics_data: return 0.0# 检查物理数据的完整性 # required_keys = ['material_properties', 'mechanical_properties'] for key in required_keys: if key not in self.physics_data: return 0.0 return 1.0 # 完全有效 def validate_behavior(self): """验证行为模型""" if # not self.behavior_data: return 0.0 # 检查行为数据的完整性 return 1.0# 完全有效 def optimize_model(self, # optimization_parameters): """优化模型""" # 基于优化参数优化模型 optimized_model = # self.run_optimization_algorithm(optimization_parameters) return optimized_model def run_optimization_algorithm(self, parameters): """运行优化算法""" # 实现模型优化算法# 这里可以使用遗传算法、粒子群算法等优化算法 return self.geometry_data# 简化实现 def export_model(self, # format_type='obj'): """导出模型""" if format_type == 'obj': return self.export_to_obj() elif format_type == 'stl': return self.export_to_stl() elif format_type == 'json': return self.export_to_json() else: return None def export_to_obj(self): """导出为OBJ格式""" obj_content = [] # 添加顶点 for vertex in # self.geometry_data['vertices']: obj_content.append(f"v {vertex[0]} {vertex[1]} {vertex[2]}") # 添加面片 # for face in self.geometry_data['faces']: obj_content.append(f"f {face[0]+1} {face[1]+1} {face[2]+1}") return '\n'.join(obj_content) def export_to_stl(self): """导出为STL格式""" # 实现STL格式导出 pass def export_to_json(self): """导出为JSON格式""" return json.dumps({ 'model_id': self.model_id, 'model_type': # self.model_type, 'geometry_data': self.geometry_data, 'physics_data': self.physics_data, 'behavior_data': self.behavior_data, 'metadata': self.metadata }, indent=2)