The last day of 2015

本文回顾了作者2015年的工作经历,包括业务库维护与优化、负责新猎头项目的开发、参与新招聘站点的开发、小型Web聊天项目的开发等,并表达了对于个人英语水平提升及未来发展的期待。

Today is the last day of 2015. Also I first wrote blog.  Because I writing is poor,  finally I decided to write with poor English.

This year is very fast.I remember this time to work overtime to 11:00 in company last year, then waited a long time for the car  and  I was waiting for the bus welcome New Year.

Fortunately, this is not too much overtime this year.First of all I say the content of work and acquisition.

1、Business library routine maintenance and optimization(Business library has many historical issues,I hope that more useful under my leadership)

2、Responsible for the development project of the 'new headhunter'

3、Involved in Development of the new recruiting station(It's appearance has improved a lot and adds the concept of talent currency.Later we will to improve it's funciton and structure.I believe it will bring more customers)

4、Development of small projects about webchat(I need to put in more effort to understand and learn more knowledge)

5、Other(Including merge code、assignments....)

We are using zentao to assign tasks and to carry out.I feel there are endless task this year, and did't go to learn new things.I have been doing repetitive things and fell heart tired.

I applied for English classes at Shenzhen University.I like English but it's poor.Occasionally listen to Japanese lesson.I hope that  I  went to Japan with students after graduation .so,I need earn more money.

Today,Manager talked to me about year end of matter. also talked me that I don't have the change to promotion just to label me with 'A' to rise salary.I know that manager is try his best for us.

Off work,Just to say this today,I hope I can make more progress in new year.To meet all difficulties with smile微笑.

 

 

 

【电动汽车充电站有序充电调度的分散式优化】基于蒙特卡诺和拉格朗日的电动汽车优化调度(分时电价调度)(Matlab代码实现)内容概要:本文介绍了基于蒙特卡洛和拉格朗日方法的电动汽车充电站有序充电调度优化方案,重点在于采用分散式优化策略应对分时电价机制下的充电需求管理。通过构建数学模型,结合不确定性因素如用户充电行为和电网负荷波动,利用蒙特卡洛模拟生成大量场景,并运用拉格朗日松弛法对复杂问题进行分解求解,从而实现全局最优或近似最优的充电调度计划。该方法有效降低了电网峰值负荷压力,提升了充电站运营效率与经济效益,同时兼顾用户充电便利性。 适合人群:具备一定电力系统、优化算法和Matlab编程基础的高校研究生、科研人员及从事智能电网、电动汽车相关领域的工程技术人员。 使用场景及目标:①应用于电动汽车充电站的日常运营管理,优化充电负荷分布;②服务于城市智能交通系统规划,提升电网与交通系统的协同水平;③作为学术研究案例,用于验证分散式优化算法在复杂能源系统中的有效性。 阅读建议:建议读者结合Matlab代码实现部分,深入理解蒙特卡洛模拟与拉格朗日松弛法的具体实施步骤,重点关注场景生成、约束处理与迭代收敛过程,以便在实际项目中灵活应用与改进。
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