MinerBean.java 数据库表 miner bean

MinerBean.java 数据库表 miner bean

package com.iteye.injavawetrust.miner;

import java.util.Date;

/**
 * 数据库表 miner bean
 * @author InJavaWeTrust
 *
 */
public class MinerBean {
	/**
	 * uuid
	 */
	private String id;
	/**
	 * 爬取批次号
	 */
	private String batchNo;
	/**
	 * 文件路径
	 */
	private String filePath;
	/**
	 * 文件名
	 */
	private String fileName;
	/**
	 * 爬取日期
	 */
	private Date getDate;
	
	public String getId() {
		return id;
	}
	public void setId(String id) {
		this.id = id;
	}
	public String getBatchNo() {
		return batchNo;
	}
	public void setBatchNo(String batchNo) {
		this.batchNo = batchNo;
	}
	public String getFilePath() {
		return filePath;
	}
	public void setFilePath(String filePath) {
		this.filePath = filePath;
	}
	public String getFileName() {
		return fileName;
	}
	public void setFileName(String fileName) {
		this.fileName = fileName;
	}
	public Date getGetDate() {
		return getDate;
	}
	public void setGetDate(Date getDate) {
		this.getDate = getDate;
	}
	

}

 

返回列表

 

基于51单片机的多功能智能路灯控制系统(仿真+代码+原理图) 智能路灯控制系统主要由51单片机最小系统+1602显示模块+DS1302时钟模块+光强检测模块+按键输入模块+声光报警模块+LED照明模块+状态检测电路+人体车辆红外检测模块组成。能实现以下功能: 1.能够按键设定系统时间、工作时间、光照阈值 2.工作时间内路灯点亮,凌晨12点后路灯关闭,有人或车通过是,路灯点亮10s 3.非工作时间光照强度低于阈值,路灯点亮 4.能够检测系统工作状态,路灯应亮未亮,声光报警 5.具备手动控制,能够切换自动/手动控制功能 阅读详情

相关推荐

DenseNet训练实践

在Cifar10上训练DenseNet,复现DenseNet论文的结果是后面两篇分析工作的基础,因此本系列的第一篇,将首先讨论深度学习的训练过程中的几个方面: 1. 数据集 1.1 Cifar10数据集 Cifar10数据集有6万张32X32的图片,是一个小型的数据集,但数据的复杂度足够,下图来自DenseNet论文,可以看出,不用数据增强的话,ResNet的正确率也很难超过90%,因而足以用来验...

一蓑烟雨任平生 1万+

java jsoup 多线程爬虫Miner

java jsoup多线程爬虫Miner 需要配置项: 1、URL包含关键字。 2、存储方式:DB-数据库存储;FILE-文件存储。 3、爬取页面最大深度。 4、下载页面线程数。 5、分析页面线程数。 6、存储线程数。 ------------------------------------------- 程序中用到的表: CREATE TABLE `miner` ( ...

18万+

最新版IAR9.40和注册工具

IAR最新版的IDE在编译速度和代码提示等各个方面上都有很大提升,软件这个东西果然还是越新越好。之前的时候,IAR v9系列网上很多人都说能用,但是又没有把 IAR v9系列的注册方法写的很清楚,导致我一直都用注册老版本v8的方法去注册v9。结果注册后都会提示。

xue_nuo的博客 4万+

MiseringThread.java 解析页面线程

MiseringThread.java 解析页面线程 http://injavawetrust.iteye.com package com.iteye.injavawetrust.miner; import java.util.Set; import org.apache.commons.logging.Log; import org.apache.commons.logging.Log...

16万+

Apache Flink集成HDFS报错Failed to create checkpoint storage at checkpoint coordinator side.

在网上查询相关资料后,在flink的lib目录添加flink-shaded-hadoop-3-3.1.1.7.0.3.0-79-7.0.jar、commons-cli-1.5.0.jar两个包,但报错依旧存在,添加了export HADOOP_CLASSPATH=后面发现是hadoop与flink的兼容性问题,hadoop原先使用的是3.3.4,与多个版本的flink集成都会出现报错,最后将Hadoop的版本改成3.1.3,使用flink run运行jar包就没有这个错误了,在hdfs上成功保存了检查点。

zywenmag的博客 625

从零搭建基于OpenSpec的现代化UI自动化测试框架实战指南

UI自动化测试是软件质量保障的关键环节,其核心在于通过脚本模拟用户操作,对Web应用界面进行功能验证。其原理在于利用浏览器驱动工具(如Selenium、Playwright)提供的API,实现对页面元素的定位、交互与断言。一个设计良好的自动化框架能显著提升测试脚本的可维护性、可读性和执行稳定性,从而在持续集成、回归测试等场景中发挥巨大价值。本文聚焦于如何构建一个现代化的UI测试框架,通过整合OpenSpec架构规范、Pytest测试引擎与Playwright浏览器自动化工具,形成清晰的页面对象模型(POM)

weixin_28836989的博客 286

StoreType.java 存储方式

StoreType.java 存储方式 package com.iteye.injavawetrust.miner; /** * 存储方式 * @author InJavaWeTrust * */ public enum StoreType { /** * DB-数据库存储 */ DB("DB"), /** * FILE-文件存储 */ FILE("FILE...

15万+

学习海康VisionMaster之顶点检测

2:应用场景:海康VisionMaster的顶点检测功能在工业视觉检测中具有广泛的应用场景,尤其在精密制造、电子元件检测和机械加工等领域,其通过高精度定位物体的关键点(如角点、交点或边缘顶点)来实现自动化测量与质量控制。基本参数这里就是画ROI,设定跟踪参数和之前的工具是一样的方法,这里画的方向需要注意,如下图所示,边缘的扫描方向正好对应需要查找的边缘点。一个不是很规则的物体需要检测一个凸台的顶角坐标,其实靠边缘与边缘交点是不太稳定的,如下图所示,边缘很短,很容易产生误抓边缘。1:什么是顶点检测?

yuhouxiyang的专栏 1526

MinerMonitorThread.java 监控线程

MinerMonitorThread.java 监控线程 package com.iteye.injavawetrust.miner; import org.apache.commons.logging.Log; import org.apache.commons.logging.LogFactory; /** * 监控线程 * @author Administrator * */...

14万+

在工作台 LS-DYNA 中检索和变换反作用力分量

在结构仿真中,通常需要由施加的位移或速度边界条件产生的反作用力。此外,通常需要根据用户定义的坐标系解析反作用力分量。在本文中,详细介绍了在Workbench (WB) LS-DYNA 中,相对于用户定义的坐标系检索和变换反作用力分量的过程。

jkh920184196的博客 886

【Unity程序技巧】Unity中单例模式管理器

单例模式(Singleton Pattern)是 最简单的设计模式之一

秩沅 1659

MinerHtmlThread.java 爬取页面线程

MinerHtmlThread.java 爬取页面线程 package com.iteye.injavawetrust.miner; import org.apache.commons.logging.Log; import org.apache.commons.logging.LogFactory; import org.jsoup.Connection; import org.jsoup...

14万+

Linux 设置双网卡通信,外网网卡和内网网卡

背景,Linux 主机已经安装了内网、外网 2 张网卡,目前外网网络已通,需要设置内网网卡转发通内网 内网需要通信的设备的网段,如果 10.99.0.0 代表 10.99 网段的所有 IP 表示 10.xx 网段走 10.xx.xx.xx 这个网关转发 :内网网卡设备示例 2、设置启动自动生效 打开 文件,如果没有则创建该文件,然后在文件内写入如下的内容修改上面的语句 为 即可 示例...

简简单单Onlinezuozuo 6606

MinerStoreThread.java 存储线程

MinerStoreThread.java 存储线程 package com.iteye.injavawetrust.miner; import java.util.HashMap; import java.util.Map; import org.apache.commons.logging.Log; import org.apache.commons.logging.LogFactor...

14万+

温度PID控制PLC程序

温度PID稳定控制程序,适用于欧姆龙PLC,对其他PLC也具有很大参考价值

Devexpress16.2.6破解工具

Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具

MinerUtil.java 爬虫工具类

MinerUtil.java 爬虫工具类 package com.iteye.injavawetrust.miner; import java.io.File; import java.io.FileNotFoundException; import java.io.FileOutputStream; import java.io.IOException; import java.io.Ou...

14万+

精雕软件(JDpaint) 5.5 绿色版

精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版

MinerThreadPool.java 线程池

MinerThreadPool.java 线程池 package com.iteye.injavawetrust.miner; import java.util.concurrent.BlockingQueue; import java.util.concurrent.LinkedBlockingQueue; import java.util.concurrent.ThreadPoolExe...

14万+

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

MinerQueue.java 访问队列

MinerQueue.java 访问队列 package com.iteye.injavawetrust.miner; import java.util.HashSet; import java.util.LinkedList; import java.util.Queue; import java.util.Set; import org.apache.commons.logging.L...

14万+

MinerUrl.java 解析页面后存储URL类

MinerUrl.java 解析页面后存储URL类 package com.iteye.injavawetrust.miner; /** * 解析页面后存储URL类 * @author InJavaWeTrust * */ public class MinerUrl { /** * 当前爬取URL */ private String url; /** * 当前爬取UR...

14万+

Html.java 存储页面信息类

Html.java 存储页面信息类 package com.iteye.injavawetrust.miner; /** * 存储页面信息类 * @author InJavaWeTrust * */ public class Html { /** * 页面URL */ private String url; /** * 页面信息 */ private St...

14万+

Miner.java 爬虫启动类

Miner.java 爬虫启动类 package com.iteye.injavawetrust.miner; import java.util.concurrent.ThreadPoolExecutor; import org.apache.commons.logging.Log; import org.apache.commons.logging.LogFactory; /** *...

14万+

MinerConstanits.java 常量类

MinerConstanits.java 常量类 package com.iteye.injavawetrust.miner; /** * 常量类 * @author InJavaWeTrust * */ public class MinerConstanits { /** * 存储目录 */ public static String HTMLPATH = "E:\\I...

14万+

MinerConfig.java 爬取配置类

MinerConfig.java 爬取配置类 package com.iteye.injavawetrust.miner; import java.util.List; /** * 爬取配置类 * @author InJavaWeTrust * */ public class MinerConfig { /** * URL包含关键字 */ public List<...

14万+

InnoDB 内存结构总览:Change Buffer、AHI 与 Log Buffer

本文系统梳理InnoDB内存结构中的ChangeBuffer、Adaptive Hash Index(AHI)与LogBuffer。ChangeBuffer缓存二级索引页不在内存时的变更,减少随机IO,仅在页加载时合并;聚簇索引修改立即执行,因数据本身不可延后。AHI基于高频等值查询自动构建哈希索引,加速定位,仅支持等值操作。LogBuffer为redo log内存缓冲,配合磁盘日志实现崩溃恢复。三者均服务于性能优化,其中ChangeBuffer与AHI共享BufferPool空间,而LogBuffer独立

bksczm的博客 301

V8 机制、手写核心函数与 TypeScript 类型体操

或固定初始化(如数组先定长、对象先给全字段)保持类型与形状稳定;这些都比"换框架"更基础。只能处理纯数据(丢函数、Date、RegExp、循环引用报错)。生产级深拷贝要处理:原始值短路、循环引用、V8(Chrome / Node 的 JS 引擎)不是"解释一行跑一行"那么简单,它用。图:V8 执行流程——解释执行 + 热点优化,优化失败会 Deopt 回退。JS 内存自动管理,但"自动"不等于"不会漏"。是"固定间隔最多执行一次"。手写 Promise 是"懂异步"的试金石。是"停下后才执行最后一次",

qq_38468358的博客 353

第三篇 第 6 课

前置:第三篇第 1 课(stdx 引入),第二篇第 1 课(std.regex)。环境:仓颉 SDK 1.2.0。本文所有代码与输出均逐行实测。

weixin_41618057的博客 524
上一篇: Html.java 存储页面信息类
下一篇: MinerConfig.java 爬取配置类
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值