解决Request failed grizzly 2.4.0日志获取问题

本文探讨了Grizzly框架的日志配置问题,包括如何使用logging.properties文件来设置控制台和文件日志级别,以及如何在Eclipse中通过运行配置指定日志配置文件的位置。

 

The thing is that i can't get Grizzly to log something to the console and haven't found any logfiles yet. Is grizzly using log4j or logging-api? Could anyone provide me with a logging.properties or something similar?

I'm starting the Server through a run config in eclipse specifiy

-Djava.util.logging.config.file=${project_loc}\src\main\resources\logging.properties

as an argument. The references file exists and contains

.handlers= java.util.logging.ConsoleHandler
.level= ALL
java.util.logging.FileHandler.pattern = logs/java%u.log
java.util.logging.FileHandler.limit = 50000
java.util.logging.FileHandler.count = 1
java.util.logging.FileHandler.formatter = java.util.logging.XMLFormatter
java.util.logging.ConsoleHandler.level = ALL
java.util.logging.ConsoleHandler.formatter = java.util.logging.SimpleFormatter
org.glassfish.level = FINEST

转自:https://stackoverflow.com/questions/21329733/grizzly-standalone-logging 

[2025-08-20T18:12:52] [] [WARNING] [] [org.glassfish.grizzly.filterchain.DefaultFilterChain] [tid: _ThreadID=77 _ThreadName=ApusicLogManager] [timeMillis: 1755684772002] [levelValue: 900] [[ GRIZZLY0013: Exception during FilterChain execution java.lang.NoClassDefFoundError: Could not initialize class sun.security.ssl.SSLEngineImpl at sun.security.ssl.SSLContextImpl$AbstractTLSContext.createSSLEngineImpl(SSLContextImpl.java:610) at sun.security.ssl.SSLContextImpl.engineCreateSSLEngine(SSLContextImpl.java:202) at javax.net.ssl.SSLContext.createSSLEngine(SSLContext.java:361) at org.glassfish.grizzly.ssl.SSLEngineConfigurator.createSSLEngine(SSLEngineConfigurator.java:190) at org.glassfish.grizzly.ssl.SSLEngineConfigurator.createSSLEngine(SSLEngineConfigurator.java:162) at org.glassfish.grizzly.ssl.SSLBaseFilter.handleRead(SSLBaseFilter.java:296) at org.glassfish.grizzly.filterchain.ExecutorResolver$9.execute(ExecutorResolver.java:95) at org.glassfish.grizzly.filterchain.DefaultFilterChain.executeFilter(DefaultFilterChain.java:261) at org.glassfish.grizzly.filterchain.DefaultFilterChain.executeChainPart(DefaultFilterChain.java:178) at org.glassfish.grizzly.filterchain.DefaultFilterChain.execute(DefaultFilterChain.java:110) at org.glassfish.grizzly.filterchain.DefaultFilterChain.process(DefaultFilterChain.java:89) at org.glassfish.grizzly.ProcessorExecutor.execute(ProcessorExecutor.java:53) at org.glassfish.grizzly.portunif.PUFilter.handleRead(PUFilter.java:208) at org.glassfish.grizzly.filterchain.ExecutorResolver$9.execute(ExecutorResolver.java:95) at org.glassfish.grizzly.filterchain.DefaultFilterChain.executeFilter(DefaultFilterChain.java:261) at org.glassfish.grizzly.filterchain.DefaultFilterChain.executeChainPart(DefaultFilterChain.java:178) at org.glassfish.grizzly.filterchain.DefaultFilterChain.execute(DefaultFilterChain.java:110) at org.glassfish.grizzly.filterchain.DefaultFilterChain.process(DefaultFilterChain.java:89) at org.glassfish.grizzly.ProcessorExecutor.execute(ProcessorExecutor.java:53) at org.glassfish.grizzly.nio.transport.TCPNIOTransport.fireIOEvent(TCPNIOTransport.java:549) at org.glassfish.grizzly.strategies.AbstractIOStrategy.fireIOEvent(AbstractIOStrategy.java:89) at org.glassfish.grizzly.strategies.WorkerThreadIOStrategy.run0(WorkerThreadIOStrategy.java:94) at org.glassfish.grizzly.strategies.WorkerThreadIOStrategy.access$100(WorkerThreadIOStrategy.java:33) at org.glassfish.grizzly.strategies.WorkerThreadIOStrategy$WorkerThreadRunnable.run(WorkerThreadIOStrategy.java:114) at org.glassfish.grizzly.threadpool.AbstractThreadPool$Worker.doWork(AbstractThreadPool.java:569) at org.glassfish.grizzly.threadpool.AbstractThreadPool$Worker.run(AbstractThreadPool.java:549) at java.lang.Thread.run(Thread.java:750) ]]解决错误
08-21
内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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