java邮件附件变成ATT00001.bin

java 邮件发送附件在本地和测试服务器都没问题
在华为云的服务器出现了附件 变成ATT00001.bin
由于生产环境不方便测试
1.直接加了静态代码块:
static {
System.setProperty(“mail.mime.splitlongparameters”, “false”);
System.setProperty(“mail.mime.charset”, “UTF-8”);
}
2.附件名加了
part.setFileName(MimeUtility.encodeText(fds.getName())) ;
(原来附件名是MimeUtility.encodeWord(fds.getName()) 生产环境不支持)



import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import javax.activation.DataHandler;
import javax.activation.FileDataSource;
import javax.mail.Message;
import javax.mail.Multipart;
import javax.mail.Session;
import javax.mail.Transport;
import javax.mail.internet.*;
import java.util.Properties;

public class MailUtil {


	// 代理配置信息
	private  int proxyPort;

	private  String proxyHost;

	private  String smtpHost;

	private  String smtpPort;

	private  String user;

	private  String password;

	private  String mailName;
	static {
		System.setProperty("mail.mime.splitlongparameters", "false");
		System.setProperty("mail.mime.charset", "UTF-8");
	}

	public LangFangMailUtil(int proxyPort,String proxyHost,String smtpHost,String smtpPort,String user,String password,String mailName){
		this.proxyPort =proxyPort;
		this.proxyHost =proxyHost;
		this.smtpHost =smtpHost;
		this.smtpPort =smtpPort;
		this.user =user;
		this.password =password;
		this.mailName =mailName;
	}

	public  void sendMail(String addressee, String body, String data,String title) throws Exception {
		Properties properties = System.getProperties();
		properties.setProperty("proxySet", "true");
		properties.put("mail.smtp.proxy.host", proxyHost);
		properties.put("mail.smtp.proxy.port", proxyPort);
		properties.put("mail.transport.protocol", "smtp");// 连接协议
		properties.put("mail.smtp.host", smtpHost);// 主机名
		properties.put("mail.smtp.port", smtpPort);// 端口号
		properties.put("mail.smtp.auth", "true");
//		properties.put("mail.smtp.ssl.enable", "true");// 设置是否使用ssl安全连接 ---一般都使用
		properties.put("mail.debug", "false");// 设置是否显示debug信息 true 会在控制台显示相关信息
//		properties.put("mail.smtp.socketFactory.class", "javax.net.ssl.SSLSocketFactory");// 设置是否使用ssl安全连接

		// 得到回话对象
		Session session = Session.getInstance(properties);
		// 获取邮件对象
		Message message = new MimeMessage(session);
		// 设置发件人邮箱地址
		message.setFrom(new InternetAddress(user, mailName, "UTF-8"));
		// 设置收件人邮箱地址
		String[] addressees = addressee.split(",");
		InternetAddress[] internetAddress = new InternetAddress[addressees.length];
		for (int i = 0; i < addressees.length; i++) {
			internetAddress[i] = new InternetAddress(addressees[i]);
		}
		message.setRecipients(Message.RecipientType.TO, internetAddress);//多个收件人
		// 设置邮件标题
		message.setSubject(title);
		// 设置邮件内容
		MimeBodyPart part2 = new MimeBodyPart();
		part2.setText(body, "UTF-8");
		//单个文件上传
		Multipart multipart = new MimeMultipart();
		MimeBodyPart part = new MimeBodyPart();
		FileDataSource fds = new FileDataSource(data);
		System.err.println(fds.getName());
		part.setFileName(MimeUtility.encodeText(fds.getName()));
		
		part.setDataHandler(new DataHandler(fds));
		multipart.addBodyPart(part);

		multipart.addBodyPart(part2);
		message.setContent(multipart);

		// 得到邮差对象
		Transport transport = session.getTransport();
		// 连接自己的邮箱账户
		transport.connect(user, password);// 密码为QQ邮箱开通的stmp服务后得到的客户端授权码
		// 发送邮件
		transport.sendMessage(message, message.getAllRecipients());
		transport.close();
	}

}
TSP(Traveling Salesman Problem,旅行商问题)是一个经典的组合优化问题,目标是找到一条经过所有城市的最短路径。att48.tsp 是一个包含48个城市的 TSP 问题,我们可以使用模拟退火算法来求解该问题。 首先,我们需要读取 att48.tsp 文件并计算出任意两个城市之间的距离。可以使用以下代码实现: ``` python import math # 读取 TSP 文件并计算城市之间的距离 def read_tsp_file(filename): with open(filename, 'r') as f: lines = f.readlines() # 读取城市坐标 cities = [] for line in lines: if line[0].isdigit(): city_info = line.split() city_id = int(city_info[0]) x = float(city_info[1]) y = float(city_info[2]) cities.append((city_id, x, y)) # 计算城市之间的距离 distances = {} for i in range(len(cities)): for j in range(i+1, len(cities)): city1 = cities[i] city2 = cities[j] distance = math.sqrt((city1[1]-city2[1])**2 + (city1[2]-city2[2])**2) distances[(city1[0], city2[0])] = distance distances[(city2[0], city1[0])] = distance return cities, distances # 测试 cities, distances = read_tsp_file('att48.tsp') print("城市坐标:", cities) print("城市之间的距离:", distances) ``` 接下来,我们可以实现一个 TSP 的目标函数,即计算给定路径的总距离: ``` python # 计算路径的总距离 def total_distance(path, distances): distance = 0 for i in range(len(path)-1): distance += distances[(path[i], path[i+1])] distance += distances[(path[-1], path[0])] return distance ``` 然后,我们可以使用模拟退火算法来求解 TSP 问题。以下是一个求解 att48.tsp 的模拟退火算法的代码实现: ``` python import random import math # 模拟退火算法 def simulated_annealing_tsp(distances, n_iterations, initial_temp, temp_reduction): # 随机生成一个初始解 current_path = list(distances.keys()) random.shuffle(current_path) current_fitness = total_distance(current_path, distances) # 记录温度 current_temp = initial_temp # 迭代优化 for i in range(n_iterations): # 随机生成一个新解 candidate_path = list(current_path) index1, index2 = sorted(random.sample(range(len(candidate_path)), 2)) candidate_path[index1:index2+1] = reversed(candidate_path[index1:index2+1]) # 计算新解的适应度 candidate_fitness = total_distance(candidate_path, distances) # 计算适应度提高的程度 delta_fitness = candidate_fitness - current_fitness # 判断是否接受新解 if delta_fitness < 0 or math.exp(-delta_fitness / current_temp) > random.random(): current_path = candidate_path current_fitness = candidate_fitness # 降低温度 current_temp *= temp_reduction return current_path, current_fitness # 测试 cities, distances = read_tsp_file('att48.tsp') best_path, best_fitness = simulated_annealing_tsp(distances, 10000, 100, 0.95) print("最优路径:", best_path) print("最优路径的总距离:", best_fitness) ``` 在该代码中,我们随机生成一个初始解,然后在每次迭代中随机生成一个新解,并根据一定的概率接受该解或者继续保留当前解。随着迭代次数的增加和温度的降低,算法最终收敛到一个局部最优解。我们最终得到的最优路径和总距离都可以通过调用 `simulated_annealing_tsp` 函数来获得。
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