// paradigm_comparison.go
// 模拟检索式 vs 生成式计算范式的吞吐量对比

package main

import (
	"fmt"
	"math"
	"sync"
	"time"
)

// 检索式系统：从缓存/数据库中读取已存在的数据
type RetrievalSystem struct {
	cacheSize   int
	latencyNS   float64 // 每次检索的纳秒级延迟
}

// 生成式系统：每次推理都产生新的Token
type GenerativeSystem struct {
	paramsCount  float64 // 模型参数量（十亿）
	computePerToken float64 // 每个Token需要的FLOPs
}

func (r *RetrievalSystem) Throughput(concurrency int) float64 {
	// 检索式吞吐 = 并发数 / 延迟（秒）
	return float64(concurrency) / (r.latencyNS * 1e-9)
}

func (g *GenerativeSystem) Throughput(concurrency int, batchSize int, outputTokens int) float64 {
	// 生成式吞吐估算：基于计算量和并发
	// 每个Token的计算量 ≈ 2 * 参数量（FLOPs）
	flopsPerToken := 2.0 * g.paramsCount * 1e9
	totalFlops := flopsPerToken * float64(outputTokens) * float64(batchSize)
	
	// H100 FP8峰值：约1979 TFLOPS
	h100Peak := 1979.0 * 1e12
	utilization := 0.45 // 实际利用率约45%
	
	effectiveFlops := h100Peak * utilization
	latencyMs := (totalFlops / effectiveFlops) * 1000
	
	// 并发场景下的吞吐（tokens/s）
	return float64(concurrency) * (float64(outputTokens) / (latencyMs / 1000.0))
}

func main() {
	fmt.Println("=== 计算范式吞吐量对比模拟 ===")
	fmt.Println()
	
	// 检索式系统参数
	retrieval := &RetrievalSystem{
		cacheSize:   100_000_000,
		latencyNS:   500_000, // 0.5ms 检索延迟
	}
	
	// 生成式系统参数（模拟70B模型推理）
	generative := &GenerativeSystem{
		paramsCount:     70.0,
		computePerToken: 140.0e9, // 140G FLOPs per token
	}
	
	fmt.Printf("检索式系统吞吐（10并发）: %.2f queries/s\n", retrieval.Throughput(10))
	fmt.Printf("检索式系统吞吐（100并发）: %.2f queries/s\n", retrieval.Throughput(100))
	fmt.Printf("检索式系统吞吐（1000并发）: %.2f queries/s\n", retrieval.Throughput(1000))
	fmt.Println()
	
	for _, batch := range []int{1, 4, 16} {
		tput := generative.Throughput(1, batch, 1024)
		fmt.Printf("生成式系统吞吐（70B模型, batch=%d, 1024 tokens）: %.2f tokens/s\n", batch, tput)
	}
	
	fmt.Println()
	fmt.Println("=== 关键洞察 ===")
	fmt.Println("检索式系统：延迟确定，可水平扩展，但不产生新知识")
	fmt.Println("生成式系统：计算密集，延迟随输出长度增长，但能创造新内容")
	fmt.Println("范式转变的本质：从'查找已知'到'生成未知'")
	
	// 计算1万亿token/天的算力需求
	dailyTokens := 1e12
	flopsPerToken := 2.0 * generative.paramsCount * 1e9
	totalDailyFlops := dailyTokens * flopsPerToken
	h100Count := totalDailyFlops / (1979e12 * 3600 * 24 * 0.45)
	
	fmt.Println()
	fmt.Printf("每日1万亿Token推理所需的H100 GPU数: %.0f\n", math.Ceil(h100Count))
	fmt.Printf("等效Blackwell GPU数（2x性能）: %.0f\n", math.Ceil(h100Count/2))
}

# ai_factory_economics.py
# AI工厂经济模型：从芯片成本到Token定价

def ai_factory_unit_economics(
    gpu_count: int,
    gpu_unit_cost: float,      # 单GPU成本（美元）
    power_per_gpu_kw: float,   # 单GPU功耗（kW）
    electricity_cost: float,   # 电价（美元/kWh）
    cluster_life_years: int,   # 集群使用寿命（年）
    utilization: float,        # 平均利用率
    tokens_per_gpu_per_sec: float,  # 单GPU每秒Token数
    cooling_overhead: float = 1.3,  # 冷却+配电开销
    facility_cost_factor: float = 0.3,  # 设施成本占芯片成本比例
    opex_pct_capex: float = 0.08,  # 运维占Capex比例
) -> dict:
    """计算AI工厂的Token生产成本"""
    
    # --- 资本开支 ---
    gpu_capex = gpu_count * gpu_unit_cost
    network_capex = gpu_capex * 0.15  # InfiniBand + NVLink
    facility_capex = gpu_capex * facility_cost_factor
    total_capex = gpu_capex + network_capex + facility_capex
    
    # 年化资本成本（线性折旧）
    annual_depreciation = total_capex / cluster_life_years
    
    # --- 运营开支 ---
    # 总功耗 = GPU功耗 * 冷却和配电开销
    total_power_mw = gpu_count * power_per_gpu_kw * cooling_overhead / 1000
    annual_power_cost = total_power_mw * 1000 * 24 * 365 * electricity_cost
    
    # 运维成本
    annual_opex = total_capex * opex_pct_capex
    
    total_annual_cost = annual_depreciation + annual_power_cost + annual_opex
    
    # --- Token产出 ---
    annual_tokens = (
        gpu_count * tokens_per_gpu_per_sec * 
        3600 * 24 * 365 * utilization
    )
    
    # --- 单位成本 ---
    cost_per_million_tokens = (total_annual_cost / annual_tokens) * 1_000_000
    
    return {
        "total_capex": total_capex,
        "annual_cost": total_annual_cost,
        "annual_power_cost": annual_power_cost,
        "annual_tokens": annual_tokens,
        "cost_per_million_tokens": cost_per_million_tokens,
        "total_power_mw": total_power_mw,
        "cost_breakdown": {
            "depreciation_pct": annual_depreciation / total_annual_cost * 100,
            "power_pct": annual_power_cost / total_annual_cost * 100,
            "opex_pct": annual_opex / total_annual_cost * 100,
        }
    }

# 方案A：100K H100集群（传统）
result_a = ai_factory_unit_economics(
    gpu_count=100_000,
    gpu_unit_cost=30_000,
    power_per_gpu_kw=0.7,
    electricity_cost=0.08,
    cluster_life_years=5,
    utilization=0.65,
    tokens_per_gpu_per_sec=100,  # H100推理吞吐
)

# 方案B：50K Blackwell集群
result_b = ai_factory_unit_economics(
    gpu_count=50_000,
    gpu_unit_cost=35_000,
    power_per_gpu_kw=1.0,
    electricity_cost=0.08,
    cluster_life_years=5,
    utilization=0.70,
    tokens_per_gpu_per_sec=300,  # Blackwell推理吞吐（预估3x H100）
)

# 方案C：1GW超大规模集群（类似黄仁勋所述的AI工厂规模）
result_c = ai_factory_unit_economics(
    gpu_count=1_000_000,
    gpu_unit_cost=25_000,    # Vera Rubin预估单价
    power_per_gpu_kw=0.8,    # Vera Rubin功耗
    electricity_cost=0.06,   # 大规模采购电价优惠
    cluster_life_years=6,
    utilization=0.75,
    tokens_per_gpu_per_sec=500,  # Vera Rubin预估推理吞吐
)

for name, r in [("100K H100", result_a), ("50K Blackwell", result_b), ("1M Rubin (1GW)", result_c)]:
    print(f"\n=== {name} ===")
    print(f"  总Capex: ${r['total_capex']/1e9:.2f}B")
    print(f"  年均总成本: ${r['annual_cost']/1e9:.2f}B")
    print(f"  年电费: ${r['annual_power_cost']/1e6:.0f}M")
    print(f"  总功耗: {r['total_power_mw']:.0f} MW")
    print(f"  年产出Token: {r['annual_tokens']/1e15:.2f} quadrillion")
    print(f"  Cost/M tokens: ${r['cost_per_million_tokens']:.2f}")
    print(f"  成本结构: 折旧{r['cost_breakdown']['depreciation_pct']:.1f}% / "
          f"电力{r['cost_breakdown']['power_pct']:.1f}% / "
          f"运维{r['cost_breakdown']['opex_pct']:.1f}%")

// capex_comparison.go
// AI基础设施投资的历史对比与场景分析

package main

import (
	"fmt"
	"math"
)

type InfrastructureEra struct {
	name          string
	era           string
	peakAnnualCapex float64 // 峰值年投资（万亿美元，2026年美元调整）
	durationYears int
	primaryDriver string
}

func main() {
	eras := []InfrastructureEra{
		{"铁路建设", "1860-1890", 0.08, 30, "西进运动 & 工业革命"},
		{"电网铺设", "1910-1940", 0.12, 30, "电气化浪潮"},
		{"州际高速公路", "1956-1980", 0.06, 24, "汽车普及 & 国防"},
		{"互联网骨干", "1995-2005", 0.35, 10, ".com 泡沫"},
		{"移动通信 (4G/5G)", "2010-2025", 0.50, 15, "智能手机 & 移动互联网"},
		{"AI基础设施 (预测)", "2024-2030", 3.50, 7, "生成式AI & AGI"},
	}

	fmt.Println("=== 基础设施投资周期的历史对比 ===")
	fmt.Printf("%-25s %-15s %20s %10s %-25s\n", "周期", "年代", "峰值年投资(万亿美元)", "年数", "核心驱动")
	fmt.Println("------------------------------------------------------------------------------------------")
	
	var maxCapex float64
	var maxEra string
	totalAiCapex := 0.0
	
	for _, e := range eras {
		fmt.Printf("%-25s %-15s %20.2f %10d %-25s\n", 
			e.name, e.era, e.peakAnnualCapex, e.durationYears, e.primaryDriver)
		if e.peakAnnualCapex > maxCapex {
			maxCapex = e.peakAnnualCapex
			maxEra = e.name
		}
		if e.name == "AI基础设施 (预测)" {
			totalAiCapex = e.peakAnnualCapex * float64(e.durationYears)
		}
	}
	
	fmt.Println("------------------------------------------------------------------------------------------")
	fmt.Printf("\nAI基础设施峰值年投资是互联网时代的 %.0f 倍\n", 3.50/0.35)
	fmt.Printf("AI基础设施峰值年投资是移动通信时代的 %.0f 倍\n", 3.50/0.50)
	fmt.Printf("AI基础设施7年累计投资（按中位数3.5万亿/年）: $%.1f 万亿美元\n", totalAiCapex)
	fmt.Printf("这相当于: 重建全球电网两次的总成本\n")
	
	// 按比例拆解投资结构
	fmt.Println("\n=== 4万亿美元年投资的拆解 ===")
	categories := map[string]float64{
		"GPU/加速器芯片":    1.20,
		"HBM/DRAM内存":    0.50,
		"服务器/机柜系统":   0.60,
		"网络设备":         0.30,
		"数据中心建设":     0.60,
		"电力基础设施":     0.50,
		"冷却系统":         0.20,
		"软件/平台":        0.10,
	}
	
	var total float64
	for _, v := range categories {
		total += v
	}
	
	for name, value := range categories {
		pct := value / total * 100
		fmt.Printf("  %-20s: $%.2f 万亿 (%5.1f%%)\n", name, value, pct)
	}
	fmt.Printf("  %-20s: $%.2f 万亿 (100%%)\n", "合计", total)
	
	// 等效AI工厂数量
	factoryCost := 50.0 // 1GW AI工厂约500亿美元
	factoryCount := total / (factoryCost / 1000)
	fmt.Printf("\n等效1GW AI工厂数量: %.0f 座\n", math.Ceil(factoryCount))
	fmt.Printf("等效100K GPU集群数量: %.0f 个\n", math.Ceil(factoryCount*10))
}

# supply_chain_bottleneck.py
# AI供应链瓶颈模型

def compute_bottleneck_impact(
    hbm_supply_gb: float,        # HBM年供应量（GB）
    hbm_per_gpu_gb: float,       # 每个GPU需要HBM（GB）
    gpu_demand: int,             # GPU年需求
    coWos_capacity: int,         # CoWoS年产能（片）
    gpu_per_wafer: float,        # 每片晶圆GPU数
    power_available_mw: int,     # 可用电力（MW）
    power_per_cluster_mw: float, # 每个集群电力需求（MW）
    site_land_available: int,    # 可用站点数
) -> dict:
    """计算供应链瓶颈下的实际可交付AI算力"""
    
    # HBM约束
    hbm_required = gpu_demand * hbm_per_gpu_gb
    hbm_constrained_gpus = int(hbm_supply_gb / hbm_per_gpu_gb)
    hbm_utilization = min(1.0, hbm_supply_gb / hbm_required)
    
    # CoWoS约束
    cowos_gpus = coWos_capacity * gpu_per_wafer
    cowos_utilization = min(1.0, cowos_gpus / gpu_demand)
    
    # 电力约束
    power_clusters = power_available_mw / power_per_cluster_mw
    # 每个集群假设100K GPU
    power_gpus = power_clusters * 100_000
    power_utilization = min(1.0, power_gpus / gpu_demand)
    
    # 土地/选址约束
    site_gpus = site_land_available * 100_000
    site_utilization = min(1.0, site_gpus / gpu_demand)
    
    # 综合约束（最短板）
    effective_gpus = min(
        hbm_constrained_gpus, 
        int(cowos_gpus), 
        int(power_gpus), 
        int(site_gpus)
    )
    
    unmet_demand_pct = (1 - effective_gpus / gpu_demand) * 100
    
    return {
        "gpu_demand": gpu_demand,
        "effective_gpus": effective_gpus,
        "unmet_demand_pct": unmet_demand_pct,
        "bottleneck_analysis": {
            "HBM_utilization": hbm_utilization,
            "CoWoS_utilization": cowos_utilization,
            "Power_utilization": power_utilization,
            "Site_utilization": site_utilization,
        },
        "primary_bottleneck": min(
            ("HBM", hbm_utilization),
            ("CoWoS", cowos_utilization),
            ("Power", power_utilization),
            ("Site", site_utilization),
            key=lambda x: x[1]
        )[0]
    }

# 2026年场景
result_2026 = compute_bottleneck_impact(
    hbm_supply_gb=45_000_000,      # ~45万TB HBM年供应
    hbm_per_gpu_gb=144,            # H100/H200级别
    gpu_demand=4_000_000,          # 400万GPU年需求    
    coWos_capacity=450_000,        # CoWoS年产能
    gpu_per_wafer=6,               # 每片6颗GPU
    power_available_mw=20_000,     # 可用20GW
    power_per_cluster_mw=100,      # 100MW/集群
    site_land_available=80,        # 80个可用站点
)

# 2028年场景（假设瓶颈缓解）
result_2028 = compute_bottleneck_impact(
    hbm_supply_gb=120_000_000,     # HBM产能扩张
    hbm_per_gpu_gb=288,            # Vera Rubin时代
    gpu_demand=10_000_000,         # 1000万GPU年需求
    coWos_capacity=900_000,        # CoWoS产能翻倍
    gpu_per_wafer=4,               # 更大芯片，每片更少
    power_available_mw=50_000,     # 50GW
    power_per_cluster_mw=200,      # 更高密度集群
    site_land_available=200,       # 更多站点
)

for year, r in [("2026", result_2026), ("2028", result_2028)]:
    print(f"\n=== {year}年供应约束分析 ===")
    print(f"GPU需求: {r['gpu_demand']:,}")
    print(f"有效供应: {r['effective_gpus']:,}")
    print(f"未满足需求: {r['unmet_demand_pct']:.1f}%")
    print(f"主要瓶颈: {r['primary_bottleneck']}")
    print("约束利用率:")
    for k, v in r['bottleneck_analysis'].items():
        print(f"  {k}: {v*100:.1f}%")

# robot_reasoning_stack.py
# 基于推理的机器人控制架构模拟

import random
import math
from typing import List, Tuple, Optional

class Environment:
    """机器人工作的非结构化环境"""
    
    def __init__(self, width: float, height: float):
        self.width = width
        self.height = height
        self.obstacles = []
        self.targets = []
        
    def add_obstacle(self, x: float, y: float, radius: float):
        self.obstacles.append((x, y, radius))
        
    def add_target(self, x: float, y: float, label: str):
        self.targets.append((x, y, label))

class PerceptionModule:
    """感知模块：视觉语言模型驱动"""
    
    def __init__(self, model_resolution: int = 512):
        self.resolution = model_resolution
        self.detection_threshold = 0.85
        
    def detect_objects(self, env: Environment, 
                       robot_pos: Tuple[float, float]) -> List[dict]:
        """模拟3D语义检测"""
        detections = []
        for ox, oy, r in env.obstacles:
            dist = math.sqrt((ox - robot_pos[0])**2 + (oy - robot_pos[1])**2)
            # 模拟检测置信度随距离衰减
            confidence = max(0.3, 1.0 - dist / 10.0)
            if confidence >= self.detection_threshold:
                detections.append({
                    "type": "obstacle",
                    "position": (ox, oy),
                    "radius": r,
                    "confidence": round(confidence, 3),
                    "semantic_label": "不可通行障碍物"
                })
        for tx, ty, label in env.targets:
            dist = math.sqrt((tx - robot_pos[0])**2 + (ty - robot_pos[1])**2)
            confidence = max(0.5, 1.0 - dist / 15.0)
            if confidence >= self.detection_threshold:
                detections.append({
                    "type": "target",
                    "position": (tx, ty),
                    "semantic_label": label,
                    "confidence": round(confidence, 3)
                })
        return detections

class ReasoningPlanner:
    """推理规划模块：基于LLM的任务规划"""
    
    def __init__(self, model: str = "Nemotron-340B"):
        self.model = model
        self.task_history = []
        
    def plan_path(self, robot_pos: Tuple[float, float], 
                  target: Tuple[float, float],
                  obstacles: List[Tuple[float, float, float]]) -> List[Tuple[float, float]]:
        """
        推理式路径规划：结合障碍物感知和目标导向
        模拟LLM的few-shot推理过程
        """
        # 第一步：空间推理——理解相对位置
        dx = target[0] - robot_pos[0]
        dy = target[1] - robot_pos[1]
        direct_distance = math.sqrt(dx**2 + dy**2)
        
        print(f"[推理规划] 当前坐标: {robot_pos}")
        print(f"[推理规划] 目标坐标: {target}")
        print(f"[推理规划] 直线距离: {direct_distance:.2f}m")
        print(f"[推理规划] 障碍物数量: {len(obstacles)}")
        
        # 第二步：检查直线路径是否被阻塞
        path_blocked = False
        for ox, oy, r in obstacles:
            # 简化的点到线段距离计算
            t = max(0, min(1, ((ox - robot_pos[0])*dx + (oy - robot_pos[1])*dy) / 
                          (dx**2 + dy**2 + 0.001)))
            closest_x = robot_pos[0] + t * dx
            closest_y = robot_pos[1] + t * dy
            dist_to_obstacle = math.sqrt((closest_x - ox)**2 + (closest_y - oy)**2)
            if dist_to_obstacle < r + 0.3:
                path_blocked = True
                print(f"[推理规划] ⚠️ 路径被障碍物阻断（位于({ox:.1f},{oy:.1f})）")
                break
        
        # 第三步：如果阻断，规划绕行路径
        if path_blocked:
            waypoints = self._compute_avoidance_path(robot_pos, target, obstacles)
        else:
            waypoints = [robot_pos, target]
        
        print(f"[推理规划] 规划路径点数: {len(waypoints)}")
        return waypoints
    
    def _compute_avoidance_path(self, start, end, obstacles):
        """计算绕行路径——模拟LLM的空间推理能力"""
        # 实际场景中，这会由LLM调用路径规划API完成
        # 这里用简单A*风格的绕行模拟
        waypoints = [start]
        
        # 找到最近的障碍物
        closest_obs = min(obstacles, 
                         key=lambda o: math.sqrt((o[0]-start[0])**2 + (o[1]-start[1])**2))
        
        # 生成绕行点
        angle = math.atan2(end[1] - start[1], end[0] - start[0])
        perp_angle = angle + math.pi / 2  # 垂直方向
        bypass_dist = closest_obs[2] + 1.0  # 绕行距离
        
        wp1 = (start[0] + math.cos(angle)*2 + math.cos(perp_angle)*bypass_dist,
               start[1] + math.sin(angle)*2 + math.sin(perp_angle)*bypass_dist)
        wp2 = (end[0] - math.cos(angle)*2 + math.cos(perp_angle)*bypass_dist,
               end[1] - math.sin(angle)*2 + math.sin(perp_angle)*bypass_dist)
        
        waypoints.extend([wp1, wp2, end])
        return waypoints

class MotionController:
    """执行控制模块：将规划转化为电机指令"""
    
    def __init__(self, max_speed: float = 1.0):
        self.max_speed = max_speed
        self.position = (0.0, 0.0)
        
    def execute_path(self, waypoints: List[Tuple[float, float]], 
                     dt: float = 0.1) -> List[Tuple[float, float]]:
        """执行导航路径，模拟物理运动"""
        trajectory = []
        current_pos = waypoints[0]
        trajectory.append(current_pos)
        
        for target in waypoints[1:]:
            dx = target[0] - current_pos[0]
            dy = target[1] - current_pos[1]
            distance = math.sqrt(dx**2 + dy**2)
            steps = int(distance / (self.max_speed * dt))
            
            for i in range(1, steps + 1):
                t = i / steps
                x = current_pos[0] + dx * t
                y = current_pos[1] + dy * t
                trajectory.append((round(x, 2), round(y, 2)))
            
            current_pos = target
        
        self.position = current_pos
        return trajectory

# 模拟一次完整的机器人推理操作
def run_robot_reasoning_demo():
    print("=" * 60)
    print("推理机器人操作模拟演示")
    print("=" * 60)
    
    # 初始化环境
    env = Environment(10.0, 10.0)
    env.add_obstacle(3.0, 3.0, 0.5)
    env.add_obstacle(5.0, 6.0, 0.8)
    env.add_target(8.0, 2.0, "待抓取零件箱")
    
    # 初始化模块
    perception = PerceptionModule()
    planner = ReasoningPlanner()
    controller = MotionController()
    
    # Step 1: 感知
    robot_start = (1.0, 1.0)
    print("\n[Step 1] 环境感知...")
    detections = perception.detect_objects(env, robot_start)
    for d in detections:
        print(f"  检测到: {d['semantic_label']} @ {d['position']} "
              f"(置信度: {d['confidence']})")
    
    # Step 2: 推理规划
    print("\n[Step 2] 任务推理与路径规划...")
    target_pos = (8.0, 2.0)
    obstacles = env.obstacles
    waypoints = planner.plan_path(robot_start, target_pos, obstacles)
    
    # Step 3: 执行
    print("\n[Step 3] 运动执行...")
    trajectory = controller.execute_path(waypoints)
    print(f"  执行完成，最终位置: {controller.position}")
    print(f"  路径点数: {len(trajectory)}")
    print(f"  任务达成: {'✅ 成功' if controller.position == target_pos else '❌ 失败'}")
    
    print("\n" + "=" * 60)
    print("关键结论：推理机器人的核心能力栈")
    print("1. 感知: VLM将视觉信号转化为结构化语义")
    print("2. 规划: LLM在非结构化环境中进行因果推理")
    print("3. 执行: 将高层决策转化为底层运动控制")
    print("4. 瓶颈: 实时性（端到端延迟需<100ms）")
    print("=" * 60)

if __name__ == "__main__":
    run_robot_reasoning_demo()