Log4j教程:配置与自定义布局解析

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"日志布局-T19.7_Log4j java 经典教程 经典教材" 这篇资源主要介绍了Log4j,一个广泛使用的Java日志框架,它提供了灵活的日志记录功能,使得开发者能够在应用程序运行过程中记录各种级别的信息。Log4j的主要目标是方便地记录项目运行状态,如错误信息、程序流程以及调试结果,避免使用传统的文件流读写方式。 Log4j的使用通常包括以下几个步骤: 1. 首先,在项目中引入Log4j的库文件。 2. 然后,创建一个配置文件(通常名为`log4j.properties`或`log4j.xml`),放置在类路径(classpath)下。 配置文件中,日志布局是非常关键的一部分,它决定了日志信息的显示格式。文中提到了三种常见的布局类型: 1. 简单布局(SimpleLayout):这是最基本的布局,仅包含日志信息的基本元素,如日期、级别和消息,例如: ```properties log4j.appender.FILE1=org.apache.log4j.FileAppender log4j.appender.FILE1.file=c:/file1.log log4j.appender.FILE1.layout=org.apache.log4j.SimpleLayout ``` 2. 网页布局(HTMLLayout):生成HTML格式的日志,方便在浏览器中查看,适合报告和分析: ```properties log4j.appender.FILE2=org.apache.log4j.FileAppender log4j.appender.FILE2.file=c:/file2.html log4j.appender.FILE2.layout=org.apache.log4j.HTMLLayout ``` 3. 自定义布局(PatternLayout):允许开发者通过指定模式字符串来自定义日志输出的格式,提供最大的灵活性: ```properties log4j.appender.FILE3=org.apache.log4j.FileAppender log4j.appender.FILE3.file=c:/file3.log log4j.appender.FILE3.layout=org.apache.log4j.PatternLayout log4j.appender.FILE3.layout.conversionPattern=... // 自定义布局格式 ``` 在`conversionPattern`中,你可以设置如`%d{HH:mm:ss,SSS} [%t] %-5p %c{1}:%L - %m%n`这样的模式,其中`%d`表示日期,`%t`是线程名,`%p`是日志级别,`%c`是类名,`%L`是行号,`%m`是日志消息,`%n`是换行符。 在Java代码中,使用Log4j进行日志记录通常涉及以下步骤: 1. 获取Logger实例,通常通过`Logger.getLogger(Class)`方法。 2. 使用Logger的方法(如`debug()`, `info()`, `warn()`, `error()`等)记录不同级别的日志信息。 Log4j的一个显著优点是它的可配置性,可以根据需要调整日志输出级别,控制日志的详细程度,从而在开发和生产环境中实现不同的日志管理策略。例如,开发阶段可能需要更详细的调试信息,而在生产环境中则可能只关注错误和警告。 总结来说,Log4j是Java开发中的一个强大工具,它简化了日志处理,提供了多种布局选择以适应不同的需求,并且可以灵活地通过配置文件进行定制。了解并熟练使用Log4j对于任何Java开发者来说都是至关重要的,因为它能帮助追踪和诊断问题,提高软件的可靠性和维护性。
2023-06-10 上传

请说明下面这段程序每一个量代表什么:%%%%机组组合%%%%; %%%%线性化MILP模型%%%; %%%考虑风电场景、不同电价场景、电动汽车充放电%%% %%%电动汽车数量按照10倍压缩%%%% clear clc %%%%%机组组合数据%%%%%%%%%%%; Ji=10;%机组数量; Time=24;%时间尺度; SS=20;%场景数量; Pmax=[455,455,130,130,162,80,85,55,55,55];%机组最大出力; Pmin=[150,150,20,20,25,20,25,10,10,10];%机组最小出力; a=[1000,970,700,680,450,370,480,660,665,670]; b=[16.19,17.26,16.60,16.50,19.7,22.26,27.74,25.92,27.27,27.79]; c=[0.00048,0.00031,0.002,0.0021,0.00398,0.00712,0.00079,0.00413,0.00222,0.00173]; Ton=[8,8,5,5,6,3,3,1,1,1];%最小开机时间; Toff=[8,8,5,5,6,3,3,1,1,1];%最小停机时间; Tcs=[5,5,4,4,4,2,2,0,0,0];%冷启动时间; Sh=[4500,5000,550,560,900,170,260,30,30,30];%热启动费用; Sc=[9000,10000,1100,1120,1800,340,520,60,60,60];%冷启动费用; T=[8,8,-5,-5,-6,-3,-3,-1,-1,-1];%初始运行状态; Xbefore=zeros(8,10); for t=1:8 for j=1:10 if T(j)+t<=0 Xbefore(t,j)=0; else Xbefore(t,j)=1; end end end Xf=Xbefore(1,:);%初始序列; Xbefore=[zeros(1,10);Xbefore]; PL=[700,750,850,950,1000,1100,1150,1200,1300,1400,1450,1500,1400,1300,1200,1050,1000,1100,1200,1400,1300,1100,900,800];%日负荷; delta_hot=[130,130,60,60,90,40,40,40,40,40];%爬坡速率 delta_cold=[150,150,20,20,25,20,25,10,10,10];%开停机爬坡速率 R=0.1*PL;%备用容量,这里取10%PL; Pf=[89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84,89.84;99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76,99.76;100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100;86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52,86.52;93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54,93.54;85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14,85.14;76.14,76.14,76.14,76.14,76.14,68.36,68.36,68.36,61.72,61.72,61.72,61.72,61.72,61.72,58.32,58.32,58.32,58.76,58.76,58.76;57.72,57.72,57.72,57.72,57.72,42.48,42

2023-06-02 上传

Collecting face_recognition Using cached face_recognition-1.3.0-py2.py3-none-any.whl (15 kB) Requirement already satisfied: Pillow in d:\anaconda\envs\pytorch\lib\site-packages (from face_recognition) (9.4.0) Requirement already satisfied: face-recognition-models>=0.3.0 in d:\anaconda\envs\pytorch\lib\site-packages (from face_recognition) (0.3.0) Collecting dlib>=19.7 Using cached dlib-19.24.1.tar.gz (3.2 MB) Preparing metadata (setup.py) ... done Requirement already satisfied: Click>=6.0 in d:\anaconda\envs\pytorch\lib\site-packages (from face_recognition) (8.1.3) Requirement already satisfied: numpy in d:\anaconda\envs\pytorch\lib\site-packages (from face_recognition) (1.24.2) Requirement already satisfied: colorama in d:\anaconda\envs\pytorch\lib\site-packages (from Click>=6.0->face_recognition) (0.4.6) Building wheels for collected packages: dlib Building wheel for dlib (setup.py) ... error error: subprocess-exited-with-error × python setup.py bdist_wheel did not run successfully. │ exit code: 1 ╰─> [7 lines of output] running bdist_wheel running build running build_py running build_ext ERROR: CMake must be installed to build dlib [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for dlib Running setup.py clean for dlib Failed to build dlib Installing collected packages: dlib, face_recognition Running setup.py install for dlib ... error error: subprocess-exited-with-error × Running setup.py install for dlib did not run successfully. │ exit code: 1 ╰─> [9 lines of output] running install D:\anaconda\envs\pytorch\lib\site-packages\setuptools\command\install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools. warnings.warn( running build running build_py running build_ext ERROR: CMake must be installed to build dlib [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: legacy-install-failure × Encountered error while trying to install package. ╰─> dlib note: This is an issue with the package mentioned above, not pip. hint: See above for output from the failure.

2023-06-02 上传