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图库网站cms,免费不良正能量网站链接,专业瓷砖美缝网站怎么做,免费网站后台模板下载题目：智能交通流量分析与预测系统 需求分析 一.功能需求 实时交通流量监控 交通拥堵预测 最优路径推荐 交通事故检测 交通数据可视化大屏 二.非功能需求 实时数据更新（5秒延迟） 支持百万级数据处理 预测准确率80% 724小时稳定运行 三.系统设计 架构设计 数据源：摄…题目：智能交通流量分析与预测系统需求分析一.功能需求实时交通流量监控交通拥堵预测最优路径推荐交通事故检测交通数据可视化大屏二.非功能需求实时数据更新（5秒延迟）支持百万级数据处理预测准确率80%7×24小时稳定运行三.系统设计架构设计数据源：摄像头、地磁传感器、GPS数据流处理：Apache Kafka + Spark Streaming批处理：Hadoop + Spark预测模型：LSTM神经网络可视化：D3.js + WebGL四.系统流程数据采集 → 数据清洗 → 实时处理 → 模型预测 → 结果展示 ↓ 数据存储 → 离线分析 → 模型训练 以下实现一个完整的"基于Python的智能交通流量分析与预测系统"。这个系统包含所有需求功能，并可以直接运行。import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from datetime import datetime, timedelta import time import threading import queue import warnings warnings.filterwarnings('ignore') # 设置中文显示 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False print("="*60) print("智能交通流量分析与预测系统 v1.0") print("="*60) # 1. 数据模拟生成模块 class TrafficDataGenerator: """模拟交通数据生成器""" def __init__(self, num_roads=10, num_cameras=50): self.num_roads = num_roads self.num_cameras = num_cameras self.roads = [f"道路{i+1}" for i in range(num_roads)] self.cameras = [f"摄像头{i+1}" for i in range(num_cameras)] self.sensors = [f"地磁传感器{i+1}" for i in range(30)] def generate_realtime_data(self, timestamp): """生成实时交通数据""" data = [] base_hour = timestamp.hour for road in self.roads: # 模拟早晚高峰 if 7 = base_hour = 9 or 17 = base_hour = 19: base_flow = np.random.randint(800, 1200) speed = np.random.uniform(20, 40) elif 10 = base_hour = 16: base_flow = np.random.randint(500, 800) speed = np.random.uniform(40, 60) else: base_flow = np.random.randint(100, 300) speed = np.random.uniform(60, 80) # 添加随机波动 flow = int(base_flow * np.random.uniform(0.8, 1.2)) # 计算拥堵指数 (0-1, 越高越拥堵) congestion = 1 - (speed / 80) data.append({ 'timestamp': timestamp, 'road': road, 'flow': flow, 'speed': round(speed, 1), 'congestion': round(congestion, 2), 'accident': np.random.random() 0.02 # 2%概率发生事故 }) return pd.DataFrame(data) def generate_historical_data(self, days=30): """生成历史数据""" all_data = [] end_time = datetime.now() start_time = end_time - timedelta(days=days) current = start_time while current = end_time: # 模拟每天的数据模式 for hour in range(24): timestamp = current.replace(hour=hour, minute=0, second=0) for road in self.roads: # 周末交通模式不同 if current.weekday() = 5: # 周末 if 10 = hour = 22: base_flow = np.random.randint(600, 1000) else: base_flow = np.random.randint(100, 300) else: # 工作日 if 7 = hour = 9: base_flow = np.random.randint(800, 1200) elif 17 = hour = 19: base_flow = np.random.randint(700, 1100) elif 10 = hour = 16: base_flow = np.random.randint(500, 800) else: base_flow = np.random.randint(100, 300) flow = int(base_flow * np.random.uniform(0.9, 1.1)) all_data.append({ 'timestamp': timestamp, 'road': road, 'flow': flow, 'day_of_week': current.weekday(), 'hour': hour, 'is_weekend': 1 if current.weekday() = 5 else 0 }) current += timedelta(days=1)