新时代建筑设计企业大数据实践：从CAD到智能体的范式跃迁

# BIM模型时空数据融合算法
import ifcopenshell
import pandas as pd
from scipy.spatial import KDTree

def integrate_sensor_data(bim_file, sensor_csv):
    model = ifcopenshell.open(bim_file)
    sensors = pd.read_csv(sensor_csv)
    
    # 构建幕墙单元空间索引
    curtain_walls = [e for e in model.by_type('IfcCurtainWall')]
    positions = [e.ObjectPlacement.RelativePlacement.Location.Coordinates 
                for e in curtain_walls]
    kdtree = KDTree(positions)
    
    # 传感器数据时空匹配
    for _, sensor in sensors.iterrows():
        dist, idx = kdtree.query((sensor['x'], sensor['y'], sensor['z']))
        if dist < 0.1:  # 阈值设定为10cm
            cw = curtain_walls[idx]
            cw.add_property('WindPressure', sensor['pressure'])
            cw.add_property('Temperature', sensor['temp'])
    
    return model

// 基于PCL的哥特式拱顶重建算法
#include <pcl/features/normal_3d_omp.h>
#include <pcl/surface/gp3.h>

void reconstructGothicVault(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud) {
    // 法线估计
    pcl::NormalEstimationOMP<pcl::PointXYZ, pcl::Normal> ne;
    ne.setInputCloud(cloud);
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());
    ne.setSearchMethod(tree);
    pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
    ne.setRadiusSearch(0.1);
    ne.compute(*normals);

    // 曲面重建
    pcl::GreedyProjectionTriangulation<pcl::PointNormal> gp3;
    pcl::PointCloud<pcl::PointNormal>::Ptr cloud_with_normals(new pcl::PointCloud<pcl::PointNormal>);
    pcl::concatenateFields(*cloud, *normals, *cloud_with_normals);
    
    gp3.setSearchRadius(0.2);
    gp3.setMu(2.5);
    gp3.setMaximumNearestNeighbors(100);
    gp3.setMaximumSurfaceAngle(M_PI/4);
    gp3.setMinimumAngle(M_PI/18);
    gp3.setMaximumAngle(2*M_PI/3);
    
    gp3.setInputCloud(cloud_with_normals);
    pcl::PolygonMesh triangles;
    gp3.reconstruct(triangles);
}

# 基于Optuna的钢结构优化框架
import optuna
from ansys.mapdl.core import launch_mapdl

def objective(trial):
    # 超参数空间定义
    truss_depth = trial.suggest_float('truss_depth', 2.5, 5.0)
    member_dia = trial.suggest_float('member_dia', 0.3, 0.8)
    node_spacing = trial.suggest_int('node_spacing', 8, 15)
    
    # MAPDL有限元分析
    mapdl = launch_mapdl()
    mapdl.prep7()
    mapdl.units('SI')
    # ... 构建参数化模型
    mapdl.solve()
    
    # 提取结果
    max_stress = mapdl.post_processing.nodal_stress_component('S', 'MAX')
    total_weight = mapdl.get_value('ACTIVE', 0, 'MASS')
    
    # 目标函数：应力权重比
    return max_stress * 0.7 + total_weight * 0.3

study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=200)

print(f"最佳参数组合：{study.best_params}")
print(f"最优指标值：{study.best_value:.2f}")

# 基于Prophet的能耗预测模型
from prophet import Prophet
import numpy as np

def energy_forecast(train_data, future_regressors):
    model = Prophet(
        changepoint_prior_scale=0.15,
        seasonality_prior_scale=12.0,
        holidays_prior_scale=8.0
    )
    
    # 添加特殊事件影响因子
    model.add_regressor('event_level')
    model.add_regressor('occupancy_rate')
    
    # 拟合模型
    model.fit(train_data)
    
    # 构建未来数据框
    future = model.make_future_dataframe(periods=365)
    future = future.merge(future_regressors, on='ds')
    
    # 生成预测
    forecast = model.predict(future)
    return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

// 建筑数据湖仓一体化处理流程
val bimStream = spark.readStream
  .format("delta")
  .option("maxFilesPerTrigger", 10)
  .load("/delta/bim_versions")

val iotData = spark.read
  .format("parquet")
  .load("/data/iot_sensors")

val unifiedView = bimStream.join(
  iotData,
  expr("""ST_Within(
    ST_Point(sensor_lon, sensor_lat),
    ST_GeometryFromWKT(bim_boundary)
  )"""),
  "leftOuter"
)

unifiedView.writeStream
  .format("delta")
  .outputMode("append")
  .option("checkpointLocation", "/checkpoints/unified")
  .start("/delta/unified_model")

# 基于Neo4j的规范条款推理
from py2neo import Graph, NodeMatcher

class CodeChecker:
    def __init__(self):
        self.graph = Graph("bolt://localhost:7687", auth=("neo4j", "password"))
        self.matcher = NodeMatcher(self.graph)
    
    def check_fire_exit(self, design_params):
        # 查询相关规范条款
        fire_node = self.matcher.match("规范", name="防火规范").first()
        paths = self.graph.run(
            f"MATCH p=(n)-[:RELATED*]->(m) WHERE n.name='安全出口' RETURN p"
        ).to_series()
        
        # 逻辑推理
        required_width = None
        for path in paths:
            if "人员密度" in path.nodes and design_params['occupancy'] > path.nodes[1]['max_density']:
                required_width = path.nodes[-1]['min_width']
                break
        
        # 与设计值比对
        if design_params['exit_width'] < required_width:
            return f"安全出口宽度不足，要求≥{required_width}m"
        return "符合规范"

// 基于Three.js的城市空间分析
import * as THREE from 'three';
import { GPUComputationRenderer } from 'three/addons/misc/GPUComputationRenderer.js';

class WindFlowSimulator {
    constructor(scene) {
        this.windTexture = new THREE.DataTexture(windData, 512, 512);
        this.computeRenderer = new GPUComputationRenderer(512, 512, scene.renderer);
        
        this.velocityVariable = this.computeRenderer.addVariable(
            "uVelocity",
            document.getElementById('windSimulationShader').textContent,
            this.windTexture
        );
    }

    simulate(timeDelta) {
        this.computeRenderer.compute();
        const newVelocity = this.computeRenderer.getCurrentRenderTarget(
            this.velocityVariable
        ).texture;
        
        scene.overrideMaterial = new THREE.ShaderMaterial({
            uniforms: {
                uVelocity: { value: newVelocity },
                uTime: { value: performance.now() / 1000 }
            },
            vertexShader: document.getElementById('buildingVertexShader').textContent,
            fragmentShader: document.getElementById('buildingFragmentShader').textContent
        });
    }
}