Python 代码实现高性能异构分布式并行智能商铺系统
数据采集模块
采集商铺数据,包括库存、销售、客户行为等信息。
import requests
import json
from kafka import KafkaProducer
class DataCollector:
def __init__(self, api_endpoints, kafka_topic):
self.api_endpoints = api_endpoints
self.producer = KafkaProducer(bootstrap_servers='localhost:9092')
self.kafka_topic = kafka_topic
def collect_data(self):
for endpoint in self.api_endpoints:
response = requests.get(endpoint)
data = response.json()
self.producer.send(self.kafka_topic, json.dumps(data).encode('utf-8'))
if __name__ == "__main__":
endpoints = ["http://shop.example.com/inventory", "http://shop.example.com/sales"]
collector = DataCollector(endpoints, 'shop_data')
collector.collect_data()
数据处理模块
对采集的数据进行预处理和分析,以便后续使用。
from pyspark.sql import SparkSession
class DataProcessor:
def __init__(self):
self.spark = SparkSession.builder.appName("DataProcessing").getOrCreate()
def preprocess(self, input_path, output_path):
df = self.spark.read.json(input_path)
cleaned_df = df.dropna().filter(df['quantity'] > 0)
cleaned_df.write.parquet(output_path)
if __name__ == "__main__":
processor = DataProcessor()
processor.preprocess("/data/raw/shop_data.json", "/data/processed/shop_data.parquet")
分布式计算模块
利用异构计算资源(如CPU和GPU)并行处理数据,执行复杂的计算任务。
from pyspark.sql import SparkSession
class DistributedComputation:
def __init__(self):
self.spark = SparkSession.builder.appName("DistributedComputation").getOrCreate()
def compute_sales_trends(self, input_path):
df = self.spark.read.parquet(input_path)
sales_trends = df.groupBy("product_id").sum("sales_amount")
return sales_trends
if __name__ == "__main__":
computation = DistributedComputation()
trends = computation.compute_sales_trends("/data/processed/shop_data.parquet")
trends.show()
智能决策模块
基于数据分析的结果,利用机器学习或其他算法进行智能决策,如库存管理、定价优化等。
from sklearn.linear_model import LinearRegression
import numpy as np
class SmartDecision:
def __init__(self):
self.model = LinearRegression()
def train_model(self, X_train, y_train):
self.model.fit(X_train, y_train)
def predict_sales(self, X_test):
return self.model.predict(X_test)
if __name__ == "__main__":
# 示例数据
X_train = np.array([[1, 2], [2, 3], [3, 4]])
y_train = np.array([10, 15, 20])
X_test = np.array([[4, 5]])
decision = SmartDecision()
decision.train_model(X_train, y_train)
predictions = decision.predict_sales(X_test)
print(f"Predicted sales: {predictions}")
网络通信模块
实现各模块之间的数据传输和任务协调,确保分布式系统的高效运行。
import socket
class NetworkCommunicator:
def __init__(self, host, port):
self.server_address = (host, port)
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
def start_server(self):
self.sock.bind(self.server_address)
self.sock.listen(1)
print(f"Listening on {self.server_address}")
while True:
connection, client_address = self.sock.accept()
try:
data = connection.recv(1024)
if data:
print(f"Received data: {data}")
connection.sendall(b"Data received")
finally:
connection.close()
if __name__ == "__main__":
communicator = NetworkCommunicator('localhost', 8080)
communicator.start_server()
用户接口模块
提供用户交互界面,用于展示系统的分析结果和接受用户输入。
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/analyze', methods=['POST'])
def analyze():
data = request.form['data']
# 进行分析
result = f"分析结果: {data}"
return result
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5000)
C++ 代码实现高性能异构分布式并行智能商铺系统
数据采集模块
采集商铺数据,包括库存、销售、客户行为等信息。
#include <iostream>
#include <curl/curl.h>
#include <kafka/Producer.h>
class DataCollector {
public:
DataCollector(const std::string& kafka_topic) : topic(kafka_topic) {}
void collect_data(const std::vector<std::string>& api_endpoints) {
for (const auto& endpoint : api_endpoints) {
std::string data = fetch_data(endpoint);
if (!data.empty()) {
send_to_kafka(data);
}
}
}
private:
std::string fetch_data(const std::string& url) {
CURL* curl;
CURLcode res;
std::string response;
curl = curl_easy_init();
if (curl) {
curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_callback);
curl_easy_setopt(curl, CURLOPT_WRITEDATA, &response);
res = curl_easy_perform(curl);
curl_easy_cleanup(curl);
}
return response;
}
static size_t write_callback(void* contents, size_t size, size_t nmemb, void* userp) {
((std::string*)userp)->append((char*)contents, size * nmemb);
return size * nmemb;
}
void send_to_kafka(const std::string& data) {
// Kafka生产者逻辑
kafka::clients::producer::Producer producer({"localhost:9092"});
producer.send(kafka::clients::producer::Record(topic, data));
}
std::string topic;
};
int main() {
std::vector<std::string> endpoints = {
"http://shop.example.com/inventory",
"http://shop.example.com/sales"
};
DataCollector collector("shop_data");
collector.collect_data(endpoints);
return 0;
}
数据处理模块
对采集的数据进行预处理和分析,以便后续使用。
#include <iostream>
#include <fstream>
#include <nlohmann/json.hpp>
class DataProcessor {
public:
void preprocess(const std::string& input_path, const std::string& output_path) {
std::ifstream input_file(input_path);
nlohmann::json data;
input_file >> data;
// 数据清洗逻辑
for (auto it = data.begin(); it != data.end();) {
if (it->contains("quantity") && (*it)["quantity"] > 0) {
++it;
} else {
it = data.erase(it);
}
}
std::ofstream output_file(output_path);
output_file << data.dump(4);
}
};
int main() {
DataProcessor processor;
processor.preprocess("shop_data.json", "processed_shop_data.json");
return 0;
}
分布式计算模块
利用异构计算资源(如CPU和GPU)并行处理数据,执行复杂的计算任务。
#include <mpi.h>
#include <iostream>
#include <vector>
class DistributedComputation {
public:
void compute_sales_trends(const std::vector<int>& sales_data) {
int world_size, world_rank;
MPI_Comm_size(MPI_COMM_WORLD, &world_size);
MPI_Comm_rank(MPI_COMM_WORLD, &world_rank);
// 分发计算任务
int local_sum = 0;
for (size_t i = world_rank; i < sales_data.size(); i += world_size) {
local_sum += sales_data[i];
}
int global_sum;
MPI_Reduce(&local_sum, &global_sum, 1, MPI_INT, MPI_SUM, 0, MPI_COMM_WORLD);
if (world_rank == 0) {
std::cout << "Global Sales Trends: " << global_sum << std::endl;
}
}
};
int main(int argc, char** argv) {
MPI_Init(&argc, &argv);
DistributedComputation computation;
std::vector<int> sales_data = {100, 200, 300, 400, 500};
computation.compute_sales_trends(sales_data);
MPI_Finalize();
return 0;
}
智能决策模块
基于数据分析的结果,利用机器学习或其他算法进行智能决策,如库存管理、定价优化等。
#include <iostream>
#include <dlib/matrix.h>
#include <dlib/svm.h>
class SmartDecision {
public:
void train_model(const dlib::matrix<double>& X_train, const dlib::matrix<double>& y_train) {
dlib::svm_c_linear_trainer<kernel_type> trainer;
model = trainer.train(X_train, y_train);
}
double predict_sales(const dlib::matrix<double>& X_test) {
return model(X_test);
}
private:
typedef dlib::linear_kernel<dlib::matrix<double>> kernel_type;
dlib::decision_function<kernel_type> model;
};
int main() {
dlib::matrix<double> X_train(3, 2);
X_train = 1, 2,
2, 3,
3, 4;
dlib::matrix<double> y_train(3, 1);
y_train = 10, 15, 20;
dlib::matrix<double> X_test(1, 2);
X_test = 4, 5;
SmartDecision decision;
decision.train_model(X_train, y_train);
double prediction = decision.predict_sales(X_test);
std::cout << "Predicted sales: " << prediction << std::endl;
return 0;
}
网络通信模块
实现各模块之间的数据传输和任务协调,确保分布式系统的高效运行。
#include <iostream>
#include <boost/asio.hpp>
class NetworkCommunicator {
public:
NetworkCommunicator(boost::asio::io_context& io_context, short port)
: acceptor_(io_context, boost::asio::ip::tcp::endpoint(boost::asio::ip::tcp::v4(), port)) {
start_accept();
}
private:
void start_accept() {
auto socket = std::make_shared<boost::asio::ip::tcp::socket>(acceptor_.get_executor().context());
acceptor_.async_accept(*socket, [this, socket](boost::system::error_code ec) {
if (!ec) {
std::cout << "Received connection" << std::endl;
start_read(socket);
}
start_accept();
});
}
void start_read(std::shared_ptr<boost::asio::ip::tcp::socket> socket) {
auto buffer = std::make_shared<boost::asio::streambuf>();
boost::asio::async_read_until(*socket, *buffer, "\n",
[this, socket, buffer](boost::system::error_code ec, std::size_t bytes_transferred) {
if (!ec) {
std::istream is(buffer.get());
std::string data;
std::getline(is, data);
std::cout << "Received data: " << data << std::endl;
}
});
}
boost::asio::ip::tcp::acceptor acceptor_;
};
int main() {
try {
boost::asio::io_context io_context;
NetworkCommunicator server(io_context, 8080);
io_context.run();
} catch (std::exception& e) {
std::cerr << "Exception: " << e.what() << std::endl;
}
return 0;
}
用户接口模块
提供用户交互界面,用于展示系统的分析结果和接受用户输入。
#include <Wt/WApplication.h>
#include <Wt/WBreak.h>
#include <Wt/WContainerWidget.h>
#include <Wt/WLineEdit.h>
#include <Wt/WPushButton.h>
#include <Wt/WText.h>
class ShopManagementApp : public Wt::WApplication {
public:
ShopManagementApp(const Wt::WEnvironment& env) : Wt::WApplication(env) {
setTitle("Shop Management System");
auto container = root()->addWidget(std::make_unique<Wt::WContainerWidget>());
auto edit = container->addWidget(std::make_unique<Wt::WLineEdit>());
edit->setPlaceholderText("Enter data...");
auto button = container->addWidget(std::make_unique<Wt::WPushButton>("Analyze"));
container->addWidget(std::make_unique<Wt::WBreak>());
auto result = container->addWidget(std::make_unique<Wt::WText>("Result will be displayed here"));
button->clicked().connect([=] {
result->setText("Analysis result: " + edit->text());
});
}
};
int main(int argc, char** argv) {
return Wt::WRun(argc, argv, [](const Wt::WEnvironment& env) {
return std::make_unique<ShopManagementApp>(env);
});
}