代码实现高性能异构智慧城市车辆管理系统

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Python 代码实现高性能异构智慧城市车辆管理系统

数据收集模块

用于从各种传感器（如摄像头、GPS设备等）收集数据。

# data_collection.py

import cv2
import random
import time

def collect_camera_data(camera_id):
    # 模拟从摄像头获取数据
    cap = cv2.VideoCapture(camera_id)
    ret, frame = cap.read()
    cap.release()
    if ret:
        return frame
    else:
        return None

def collect_gps_data():
    # 模拟从GPS设备获取数据
    gps_data = {
        'latitude': random.uniform(-90, 90),
        'longitude': random.uniform(-180, 180),
        'timestamp': time.time()
    }
    return gps_data

数据处理模块

对收集到的数据进行预处理，如去噪、格式化等。

# data_processing.py

import cv2

def preprocess_camera_data(frame):
    # 将图像转换为灰度图并调整大小
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    resized_frame = cv2.resize(gray_frame, (640, 480))
    return resized_frame

def preprocess_gps_data(gps_data):
    # 处理GPS数据，例如将时间戳转换为可读格式
    gps_data['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(gps_data['timestamp']))
    return gps_data

数据存储模块

将处理后的数据存储在数据库中。

# data_storage.py

import sqlite3

def create_tables():
    conn = sqlite3.connect('vehicle_management.db')
    cursor = conn.cursor()
    cursor.execute('''CREATE TABLE IF NOT EXISTS camera_data (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        frame BLOB,
                        timestamp DATETIME DEFAULT CURRENT_TIMESTAMP)''')
    cursor.execute('''CREATE TABLE IF NOT EXISTS gps_data (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        latitude REAL,
                        longitude REAL,
                        timestamp TEXT)''')
    conn.commit()
    conn.close()

def store_camera_data(frame):
    conn = sqlite3.connect('vehicle_management.db')
    cursor = conn.cursor()
    cursor.execute('INSERT INTO camera_data (frame) VALUES (?)', (frame,))
    conn.commit()
    conn.close()

def store_gps_data(gps_data):
    conn = sqlite3.connect('vehicle_management.db')
    cursor = conn.cursor()
    cursor.execute('INSERT INTO gps_data (latitude, longitude, timestamp) VALUES (?, ?, ?)',
                   (gps_data['latitude'], gps_data['longitude'], gps_data['timestamp']))
    conn.commit()
    conn.close()

数据分析模块

对存储的数据进行分析，如车辆识别、路径预测等。

# data_analysis.py

import cv2
import numpy as np

def detect_vehicles(frame):
    # 使用简单的颜色过滤器检测车辆
    lower_bound = np.array([0, 0, 100])
    upper_bound = np.array([50, 50, 255])
    mask = cv2.inRange(frame, lower_bound, upper_bound)
    return mask

def predict_vehicle_path(gps_data):
    # 简单的路径预测算法，例如线性回归
    from sklearn.linear_model import LinearRegression

    latitudes = [data['latitude'] for data in gps_data]
    longitudes = [data['longitude'] for data in gps_data]
    timestamps = [[data['timestamp']] for data in gps_data]

    model = LinearRegression()
    model.fit(timestamps, latitudes)
    pred_lat = model.predict(timestamps)
    model.fit(timestamps, longitudes)
    pred_lon = model.predict(timestamps)

    predicted_path = list(zip(pred_lat, pred_lon))
    return predicted_path

用户接口模块

提供用户访问和管理系统的接口。

# user_interface.py

from flask import Flask, request, jsonify
import data_collection
import data_processing
import data_storage
import data_analysis

app = Flask(__name__)

@app.route('/collect_data', methods=['GET'])
def collect_data():
    camera_frame = data_collection.collect_camera_data(0)
    gps_data = data_collection.collect_gps_data()
    
    if camera_frame is not None:
        processed_frame = data_processing.preprocess_camera_data(camera_frame)
        data_storage.store_camera_data(processed_frame)
    
    processed_gps_data = data_processing.preprocess_gps_data(gps_data)
    data_storage.store_gps_data(processed_gps_data)
    
    return jsonify({'status': 'success'})

@app.route('/analyze_data', methods=['GET'])
def analyze_data():
    # 示例：获取并分析最近存储的数据
    # 这里省略了从数据库读取数据的代码
    # 假设我们有一个函数get_latest_data()来获取数据
    
    latest_camera_frame = get_latest_camera_data()
    latest_gps_data = get_latest_gps_data()
    
    detected_vehicles = data_analysis.detect_vehicles(latest_camera_frame)
    predicted_path = data_analysis.predict_vehicle_path(latest_gps_data)
    
    return jsonify({'detected_vehicles': detected_vehicles.tolist(), 'predicted_path': predicted_path})

def get_latest_camera_data():
    # 从数据库获取最新的摄像头数据
    # 这里省略了具体实现
    pass

def get_latest_gps_data():
    # 从数据库获取最新的GPS数据
    # 这里省略了具体实现
    pass

if __name__ == '__main__':
    data_storage.create_tables()
    app.run(host='0.0.0.0', port=5000)

整合代码

你可以将这些模块整合到一个主程序中，定期收集和处理数据，并通过Web接口提供服务。这样，一个高性能异构智慧城市车辆管理系统就基本实现了。

C++ 代码实现高性能异构智慧城市车辆管理系统

数据收集模块

用于从各种传感器（如摄像头、GPS设备等）收集数据。

// data_collection.h

#ifndef DATA_COLLECTION_H
#define DATA_COLLECTION_H

#include <opencv2/opencv.hpp>
#include <random>
#include <ctime>

cv::Mat collectCameraData(int camera_id);
std::tuple<double, double, std::time_t> collectGPSData();

#endif // DATA_COLLECTION_H
// data_collection.cpp

#include "data_collection.h"

cv::Mat collectCameraData(int camera_id) {
    cv::VideoCapture cap(camera_id);
    cv::Mat frame;
    if (cap.isOpened()) {
        cap >> frame;
    }
    cap.release();
    return frame;
}

std::tuple<double, double, std::time_t> collectGPSData() {
    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<> lat_dist(-90, 90);
    std::uniform_real_distribution<> lon_dist(-180, 180);
    
    double latitude = lat_dist(gen);
    double longitude = lon_dist(gen);
    std::time_t timestamp = std::time(nullptr);
    
    return std::make_tuple(latitude, longitude, timestamp);
}

数据处理模块

对收集到的数据进行预处理，如去噪、格式化等。

// data_processing.h

#ifndef DATA_PROCESSING_H
#define DATA_PROCESSING_H

#include <opencv2/opencv.hpp>
#include <tuple>
#include <string>

cv::Mat preprocessCameraData(const cv::Mat& frame);
std::tuple<double, double, std::string> preprocessGPSData(double latitude, double longitude, std::time_t timestamp);

#endif // DATA_PROCESSING_H
// data_processing.cpp

#include "data_processing.h"

cv::Mat preprocessCameraData(const cv::Mat& frame) {
    cv::Mat gray_frame, resized_frame;
    cv::cvtColor(frame, gray_frame, cv::COLOR_BGR2GRAY);
    cv::resize(gray_frame, resized_frame, cv::Size(640, 480));
    return resized_frame;
}

std::tuple<double, double, std::string> preprocessGPSData(double latitude, double longitude, std::time_t timestamp) {
    char buffer[80];
    std::strftime(buffer, sizeof(buffer), "%Y-%m-%d %H:%M:%S", std::localtime(&timestamp));
    std::string formatted_time(buffer);
    return std::make_tuple(latitude, longitude, formatted_time);
}

数据存储模块

将处理后的数据存储在数据库中。

// data_storage.h

#ifndef DATA_STORAGE_H
#define DATA_STORAGE_H

#include <sqlite3.h>
#include <opencv2/opencv.hpp>
#include <tuple>
#include <string>

class DataStorage {
public:
    DataStorage(const std::string& db_name);
    ~DataStorage();
    void createTables();
    void storeCameraData(const cv::Mat& frame);
    void storeGPSData(double latitude, double longitude, const std::string& timestamp);

private:
    sqlite3* db;
};

#endif // DATA_STORAGE_H
// data_storage.cpp

#include "data_storage.h"

DataStorage::DataStorage(const std::string& db_name) {
    if (sqlite3_open(db_name.c_str(), &db) != SQLITE_OK) {
        throw std::runtime_error("Cannot open database");
    }
}

DataStorage::~DataStorage() {
    sqlite3_close(db);
}

void DataStorage::createTables() {
    const char* create_camera_table = "CREATE TABLE IF NOT EXISTS camera_data ("
                                      "id INTEGER PRIMARY KEY AUTOINCREMENT, "
                                      "frame BLOB, "
                                      "timestamp DATETIME DEFAULT CURRENT_TIMESTAMP)";
    const char* create_gps_table = "CREATE TABLE IF NOT EXISTS gps_data ("
                                   "id INTEGER PRIMARY KEY AUTOINCREMENT, "
                                   "latitude REAL, "
                                   "longitude REAL, "
                                   "timestamp TEXT)";
    
    char* err_msg = nullptr;
    if (sqlite3_exec(db, create_camera_table, 0, 0, &err_msg) != SQLITE_OK) {
        std::string error = "Failed to create camera_data table: ";
        error += err_msg;
        sqlite3_free(err_msg);
        throw std::runtime_error(error);
    }
    if (sqlite3_exec(db, create_gps_table, 0, 0, &err_msg) != SQLITE_OK) {
        std::string error = "Failed to create gps_data table: ";
        error += err_msg;
        sqlite3_free(err_msg);
        throw std::runtime_error(error);
    }
}

void DataStorage::storeCameraData(const cv::Mat& frame) {
    sqlite3_stmt* stmt;
    const char* sql = "INSERT INTO camera_data (frame) VALUES (?)";
    
    if (sqlite3_prepare_v2(db, sql, -1, &stmt, 0) != SQLITE_OK) {
        throw std::runtime_error("Failed to prepare statement");
    }
    
    std::vector<uchar> buf;
    cv::imencode(".png", frame, buf);
    
    if (sqlite3_bind_blob(stmt, 1, buf.data(), buf.size(), SQLITE_STATIC) != SQLITE_OK) {
        throw std::runtime_error("Failed to bind blob");
    }
    
    if (sqlite3_step(stmt) != SQLITE_DONE) {
        throw std::runtime_error("Failed to execute statement");
    }
    
    sqlite3_finalize(stmt);
}

void DataStorage::storeGPSData(double latitude, double longitude, const std::string& timestamp) {
    sqlite3_stmt* stmt;
    const char* sql = "INSERT INTO gps_data (latitude, longitude, timestamp) VALUES (?, ?, ?)";
    
    if (sqlite3_prepare_v2(db, sql, -1, &stmt, 0) != SQLITE_OK) {
        throw std::runtime_error("Failed to prepare statement");
    }
    
    if (sqlite3_bind_double(stmt, 1, latitude) != SQLITE_OK ||
        sqlite3_bind_double(stmt, 2, longitude) != SQLITE_OK ||
        sqlite3_bind_text(stmt, 3, timestamp.c_str(), -1, SQLITE_STATIC) != SQLITE_OK) {
        throw std::runtime_error("Failed to bind values");
    }
    
    if (sqlite3_step(stmt) != SQLITE_DONE) {
        throw std::runtime_error("Failed to execute statement");
    }
    
    sqlite3_finalize(stmt);
}

数据分析模块

对存储的数据进行分析，如车辆识别、路径预测等。

// data_analysis.h

#ifndef DATA_ANALYSIS_H
#define DATA_ANALYSIS_H

#include <opencv2/opencv.hpp>
#include <vector>
#include <tuple>
#include <string>

cv::Mat detectVehicles(const cv::Mat& frame);
std::vector<std::tuple<double, double>> predictVehiclePath(const std::vector<std::tuple<double, double, std::string>>& gps_data);

#endif // DATA_ANALYSIS_H
// data_analysis.cpp

#include "data_analysis.h"

cv::Mat detectVehicles(const cv::Mat& frame) {
    cv::Mat mask;
    cv::inRange(frame, cv::Scalar(0, 0, 100), cv::Scalar(50, 50, 255), mask);
    return mask;
}

std::vector<std::tuple<double, double>> predictVehiclePath(const std::vector<std::tuple<double, double, std::string>>& gps_data) {
    // 简单的路径预测算法（线性回归）
    std::vector<double> latitudes, longitudes;
    std::vector<int> timestamps;
    
    for (const auto& data : gps_data) {
        latitudes.push_back(std::get<0>(data));
        longitudes.push_back(std::get<1>(data));
        struct std::tm tm;
        std::istringstream ss(std::get<2>(data));
        ss >> std::get_time(&tm, "%Y-%m-%d %H:%M:%S");
        timestamps.push_back(std::mktime(&tm));
    }
    
    cv::Mat latitudes_mat(latitudes), longitudes_mat(longitudes), timestamps_mat(timestamps);
    
    cv::Mat lat_coef, lon_coef;
    cv::solve(timestamps_mat, latitudes_mat, lat_coef, cv::DECOMP_SVD);
    cv::solve(timestamps_mat, longitudes_mat, lon_coef, cv::DECOMP_SVD);
    
    std::vector<std::tuple<double, double>> predicted_path;
    for (int timestamp : timestamps) {
        double pred_lat = lat_coef.at<double>(0) * timestamp + lat_coef.at<double>(1);
        double pred_lon = lon_coef.at<double>(0) * timestamp + lon_coef.at<double>(1);
        predicted_path.emplace_back(pred_lat, pred_lon);
    }
    
    return predicted_path;
}

用户接口模块

提供用户访问和管理系统的接口。

// user_interface.cpp

#include <cpprest/http_listener.h>
#include <cpprest/json.h>
#include "data_collection.h"
#include "data_processing.h"
#include "data_storage.h"
#include "data_analysis.h"

using namespace web;
using namespace web::http;
using namespace web::http::experimental::listener;

void handleCollectData(http_request request) {
    cv::Mat camera_frame = collectCameraData(0);
    auto gps_data = collectGPSData();
    
    if (!camera_frame.empty()) {
        cv::Mat processed_frame = preprocessCameraData(camera_frame);
        DataStorage storage("vehicle_management.db");
        storage.storeCameraData(processed_frame);
    }
    
    auto processed_gps_data = preprocessGPSData(std::get<0>(gps_data), std::get<1>(gps_data), std::get<2>(gps_data));
    DataStorage storage("vehicle_management.db");
    storage.storeGPSData(std::get<0>(processed_gps_data), std::get<1>(processed_gps_data), std::get<2>(processed_gps_data));
    
    json::value response;
    response[U("status")] = json::value::string(U("success"));
    request.reply(status_codes::OK, response);
}

void handleAnalyzeData(http_request request) {
    // 示例：获取并分析最近存储的数据
    // 这里省略了从数据库读取数据的代码
    // 假设我们有一个函数getLatestData()来获取数据

    // 获取最新的摄像头数据
    cv::Mat latest_camera_frame = getLatestCameraData();
    // 获取最新的GPS数据
    std::vector<std::tuple<double, double, std::string>> latest_gps_data = getLatestGPSData();
    
    cv::Mat detected_vehicles = detectVehicles(latest_camera_frame);
    std::vector<std::tuple<double, double>> predicted_path = predictVehiclePath(latest_gps_data);
    
    json::value response;
    // 添加检测到的车辆数据
    std::vector<unsigned char> buf;
    cv::imencode(".png", detected_vehicles, buf);
    response[U("detected_vehicles")] = json::value::string(U("data:image/png;base64,") + utility::conversions::to_base64(buf));
    // 添加预测的路径数据
    json::value path_array = json::value::array();
    for (size_t i = 0; i < predicted_path.size(); ++i) {
        json::value point;
        point[U("latitude")] = json::value::number(std::get<0>(predicted_path[i]));
        point[U("longitude")] = json::value::number(std::get<1>(predicted_path[i]));
        path_array[i] = point;
    }
    response[U("predicted_path")] = path_array;
    
    request.reply(status_codes::OK, response);
}

cv::Mat getLatestCameraData() {
    // 从数据库获取最新的摄像头数据
    // 这里省略了具体实现
    // 假设返回一张灰度图像
    return cv::Mat();
}

std::vector<std::tuple<double, double, std::string>> getLatestGPSData() {
    // 从数据库获取最新的GPS数据
    // 这里省略了具体实现
    // 假设返回一个包含GPS数据的向量
    return {};
}

int main() {
    uri_builder uri(U("http://localhost:8080"));
    auto addr = uri.to_uri().to_string();
    http_listener listener(addr);

    listener.support(methods::GET, [](http_request request) {
        if (request.relative_uri().path() == U("/collect_data")) {
            handleCollectData(request);
        } else if (request.relative_uri().path() == U("/analyze_data")) {
            handleAnalyzeData(request);
        } else {
            request.reply(status_codes::NotFound, U("Not Found"));
        }
    });

    try {
        listener.open().wait();
        std::wcout << L"Listening for requests at: " << addr << std::endl;
        std::string line;
        std::getline(std::cin, line);
    } catch (const std::exception& e) {
        std::cerr << "Exception: " << e.what() << std::endl;
    }

    return 0;
}

整合代码

你可以将这些模块整合到一个主程序中，定期收集和处理数据，并通过Web接口提供服务。这样，一个高性能异构智慧城市车辆管理系统就基本实现了。

CMakeLists.txt

为了编译这个项目，可以使用CMake：

cmake_minimum_required(VERSION 3.10)
project(SmartCityVehicleManagement)

set(CMAKE_CXX_STANDARD 17)

find_package(OpenCV REQUIRED)
find_package(CPPRESTSDK REQUIRED)
find_package(SQLite3 REQUIRED)

include_directories(${OpenCV_INCLUDE_DIRS})
include_directories(${SQLite3_INCLUDE_DIRS})

add_executable(SmartCityVehicleManagement main.cpp data_collection.cpp data_processing.cpp data_storage.cpp data_analysis.cpp)

target_link_libraries(SmartCityVehicleManagement ${OpenCV_LIBS} cpprest ${SQLite3_LIBRARIES})

这样一个完整的高性能异构智慧城市车辆管理系统就基本实现了。请确保安装了OpenCV、SQLite和C++ REST SDK等库，并根据需要进行适当调整。

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