Command Pattern

本文介绍命令模式设计的基本原理,通过实例展示如何将请求封装为对象,实现参数化操作及支持撤销功能。文章还探讨了该模式如何解耦请求发起者与执行者,使代码更加灵活且易于维护。

Command Pattern:

Encapsulates a request as an object, thereby letting you parametrize other objects with different requests, queue or log requests, and support undo-able operations. 

Command Pattern is usually used in the scenario that we want to perform multiple operations on the same data. For example, in a image processor, we could choose to rotate, flip or invert colors of the photo or even to undo the operations. It also allows to decouple the requester from an action from the object that actually performs the action.

Example without using Command Pattern:

class imageProcess {
private:
  bool rotateImage;
  bool flipImage;
  bool deleteImage;
public:
  void inputHandler() {
    if(rotateImage) {rotate();}
    else if(flipImage) {flipImage();}
    else if(deleteImage) {deleteImage();}
  }
};
The drawback is quite clear: we will have to write a lot of condition code for each action. Also, if we have more commands, we will have to change the existing code very often. To solve this problem, there is a mechanism that all the actions can somehow be handled polymorphically which makes the code clean and less error prone. This isCommand Pattern.

1: Allows to store a lists of code that is executed at a later time or many times.

2: Client says that a specific Command to run when execute() is called on one of the encapsulated objects. (dynamic binding)

3: An object called the "Invoker" transfers this Command to another Object called a "Receiver" to execute the right code.

For example:

TurnTVOn --> Device Botton(invoker) --> TurnTVOn --> Television.TurnTVOn() (receiver)

#include "../header.h"
using namespace std;

class Command {
public:
  virtual void execute() = 0;
};

class ElectronicDevice {
public:
  virtual void on() = 0;
  virtual void off() = 0;
  virtual void volumnUp() = 0;
  virtual void volumnDown() = 0;
};

class TurnTvOn : public Command {
private:
  ElectronicDevice* newDevice;
public:
  TurnTvOn(ElectronicDevice* device) {
    newDevice = device;
  }
  void execute() {
    newDevice->on();
  }
};

class VolumnUp : public Command {
private:
  ElectronicDevice* newDevice;
public:
  VolumnUp(ElectronicDevice* device) {
    newDevice = device;
  }
  void execute() {
    newDevice->volumnUp();
  }
};

// this is the invoker.
class DeviceButton {
private:
  Command* theCommand;
public:
  DeviceButton(Command* newCommand) {
    theCommand = newCommand;
  }
  void press() {
    theCommand->execute();
  }
};

// this is the receiver
class Television : public ElectronicDevice {
private:
  int volumn;
public:
  Television(int v) : volumn(v) {}

  void on() {
    cout << "TV is ON" << endl;
  }
  void off() {
    cout << "TV is OFF" << endl;
  }
  void volumnUp() {
    volumn++;
    cout << "volumn is at: " << volumn << endl;
  }
  void volumnDown() {
    volumn--;
    cout << "volumn is at: " << volumn << endl;
  }
};

//test
int main(void) {
  int volumn = 0;
  ElectronicDevice* device = new Television(volumn);
  TurnTvOn* onCommand = new TurnTvOn(device);
  DeviceButton* onPressed = new DeviceButton(onCommand);
  onPressed->press();

  VolumnUp* volumnCommand = new VolumnUp(device);
  onPressed = new DeviceButton(volumnCommand);
  onPressed->press();
}

【轴承故障诊断】基于融合鱼鹰和柯西变异的麻雀优化算法OCSSA-VMD-CNN-BILSTM轴承诊断研究【西储大学数据】(Matlab代码实现)内容概要:本文提出了一种基于融合鱼鹰和柯西变异的麻雀优化算法(OCSSA)优化变分模态分解(VMD)参数,并结合卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)的轴承故障诊断模型。该方法利用西储大学公开的轴承数据集进行验证,通过OCSSA算法优化VMD的分解层数K和惩罚因子α,有效提升信号分解精度,抑制模态混叠;随后利用CNN提取故障特征的空间信息,BiLSTM捕捉时间序列的动态特征,最终实现高精度的轴承故障分类。整个诊断流程充分结合了信号预处理、智能优化与深度学习的优势,显著提升了复杂工况下轴承故障诊断的准确性与鲁棒性。; 适合人群:具备一定信号处理、机器学习及MATLAB编程基础的研究生、科研人员及从事工业设备故障诊断的工程技术人员。; 使用场景及目标:①应用于旋转机械设备的智能运维与故障预警系统;②为轴承等关键部件的早期故障识别提供高精度诊断方案;③推动智能优化算法与深度学习在工业信号处理领域的融合研究。; 阅读建议:建议读者结合MATLAB代码实现,深入理解OCSSA优化机制、VMD参数选择策略以及CNN-BiLSTM网络结构的设计逻辑,通过复现实验掌握完整诊断流程,并可进一步尝试迁移至其他设备的故障诊断任务中进行验证与优化。
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