diff --git a/404.html b/404.html index d72c8ca7..409fd495 100644 --- a/404.html +++ b/404.html @@ -6,12 +6,12 @@ 找不到页面 | waley - +
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找不到页面

我们找不到您要找的页面。

请联系原始链接来源网站的所有者，并告知他们链接已损坏。

- + \ No newline at end of file diff --git a/Coding.html b/Coding.html index 9cb511b1..25c8f5a0 100644 --- a/Coding.html +++ b/Coding.html @@ -6,12 +6,12 @@ Coding | waley - +
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Coding

即将上线

最后由 waleyGithub 于 更新
- + \ No newline at end of file diff --git a/Coding/category/evolutionary-computation.html b/Coding/category/evolutionary-computation.html index 4a512d3e..e11749c9 100644 --- a/Coding/category/evolutionary-computation.html +++ b/Coding/category/evolutionary-computation.html @@ -6,12 +6,12 @@ Evolutionary Computation | waley - +
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Evolutionary Computation

- + \ No newline at end of file diff --git a/Coding/evolutionary computation/FlappyBird.html b/Coding/evolutionary computation/FlappyBird.html index 27a192cb..6ae67252 100644 --- a/Coding/evolutionary computation/FlappyBird.html +++ b/Coding/evolutionary computation/FlappyBird.html @@ -6,12 +6,12 @@ FlappyBird | waley - +
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FlappyBird

使用MLP与遗传算法玩FlappyBird GitHub链接​

flappyBird

所需环境​

代码 Code​

MLP神经网络 neural network controller​

import numpy as np
import math

class MLP(object):
    def __init__(self, numInput, numHidden1, numHidden2, numOutput):
        self.fitness = 0
        self.numInput = numInput + 1 # Add bias node from input to hidden layer␣ 􏰀→1 only
        self.numHidden1 = numHidden1 # Feel free to adapt the code to add more␣ 􏰀→biases if you wish
        self.numHidden2 = numHidden2
        self.numOutput = numOutput
        self.w_i_h1 = np.random.randn(self.numHidden1, self.numInput)
        self.w_h1_h2 = np.random.randn(self.numHidden2, self.numHidden1)
        self.w_h2_o = np.random.randn(self.numOutput, self.numHidden2)
        self.ReLU = lambda x : max(0,x)
    def sigmoid(self, x):
        try:
            ans = (1 / (1 + math.exp(-x)))
        except OverflowError:
            ans = float('inf')
        return ans
class MLP(MLP):
     def feedForward(self, inputs):
        inputsBias = inputs[:]
        inputsBias.insert(len(inputs), 1)
        h1 = np.dot(self.w_i_h1, inputsBias)
        h1 = [self.ReLU(x) for x in h1]
        h2 = np.dot(self.w_h1_h2, h1)
        h2 = [self.ReLU(x) for x in h2]
        output = np.dot(self.w_h2_o, h2)
        output = [self.sigmoid(x) for x in output]
        return output
class MLP(MLP):
    def getWeightsLinear(self):
        flat_w_i_h1 = list(self.w_i_h1.flatten())
        flat_w_h1_h2 = list(self.w_h1_h2.flatten())
        flat_w_h2_o = list(self.w_h2_o.flatten())
        return (flat_w_i_h1 + flat_w_h1_h2 + flat_w_h2_o)
    def setWeightsLinear(self, Wgenome):
        numWeights_I_H1 = self.numHidden1 * self.numInput
        numWeights_H1_H2 = self.numHidden2 * self.numHidden1
        numWeights_H2_O = self.numOutput * self.numHidden2
            
        self.w_i_h1 = np.array(Wgenome[:numWeights_I_H1])
        self.w_i_h1 = self.w_i_h1.reshape((self.numHidden1, self.numInput))
            
        self.w_h1_h2 = np.array(Wgenome[numWeights_I_H1:(numWeights_H1_H2+numWeights_I_H1)])
        self.w_h1_h2 = self.w_h1_h2.reshape((self.numHidden2, self.numHidden1))
            
        self.w_h2_o = np.array(Wgenome[(numWeights_H1_H2 + numWeights_I_H1):])
        self.w_h2_o = self.w_h2_o.reshape((self.numOutput, self.numHidden2))

游戏 Game​

import pygame

class FlappyBird:
    def __init__(self):
        self.screen = pygame.display.set_mode((400, 708))
        self.bird = pygame.Rect(65, 50, 50, 50)
        self.background = pygame.image.load("assets/background.png").convert()
        self.birdSprites = [pygame.image.load("assets/1.png").convert_alpha(),
                            pygame.image.load("assets/2.png").convert_alpha(),
                            pygame.image.load("assets/dead.png")]
        self.wallUp = pygame.image.load("assets/bottom.png").convert_alpha()
        self.wallDown = pygame.image.load("assets/top.png").convert_alpha()
        self.gap = 130
        self.gravity = 5
        self.delay = False
        self.restart()

    def updateWalls(self):
        self.wallx -= 5
        self.distanceMoved += 5
        if self.wallx < -80:
            self.wallx = 400
            self.counter += 1
            self.offset = np.random.randint(-180, 200)

    def birdUpdate(self):
        if self.jump:
            self.jumpSpeed -= 1
            self.birdY -= self.jumpSpeed
            self.jump -= 1
        else:
            self.birdY += self.gravity
            self.gravity += 0.2
        self.bird[1] = self.birdY
        upRect = pygame.Rect(self.wallx,
                             360 + self.gap - self.offset + 10,
                             self.wallUp.get_width() - 10,
                             self.wallUp.get_height())
        downRect = pygame.Rect(self.wallx,
                               0 - self.gap - self.offset - 10,
                               self.wallDown.get_width() - 10,
                               self.wallDown.get_height())

        if upRect.colliderect(self.bird):
            self.dead = True
        if downRect.colliderect(self.bird):
            self.dead = True

        if not 0 < self.bird[1] < 720:
            self.dead = True

    def updateScreen(self):
        font = pygame.font.SysFont("Arial", 50)
        self.screen.fill((255, 255, 255))
        self.screen.blit(self.background, (0, 0))
        self.wallUpY = 360 + self.gap - self.offset
        self.wallDownY = 0 - self.gap - self.offset
        self.screen.blit(self.wallUp, (self.wallx, self.wallUpY))
        self.screen.blit(self.wallDown, (self.wallx, self.wallDownY))
        self.screen.blit(font.render(str(self.counter), -1, (255, 255, 255)),(200, 50))
        self.screen.blit(self.birdSprites[self.sprite], (70, self.birdY))

    def makeJump(self):
        self.jump = 17
        self.gravity = 5
        self.jumpSpeed = 10

    def restart(self):
        self.wallx = 400
        self.wallUpY = 0
        self.wallDownY = 0
        self.birdY = 400
        self.jump = 0 # A timer for the jump
        self.jumpSpeed = 10
        self.dead = False
        self.sprite = 1
        self.distanceMoved = 0
        self.counter = 0
        self.stepsSinceLastJump = 0
        self.offset = np.random.randint(-180, 300)

    def run(self, network):
        pygame.font.init()

        while self.dead == False:
            for event in pygame.event.get():
                if event.type == pygame.QUIT:
                    sys.exit()
                if (event.type == pygame.KEYDOWN) and not self.dead:
                    #self.makeJump()
                    if self.delay:
                        self.delay = False
                    else:
                        self.delay = True

            if self.delay: time.sleep(0.01)
                    
            # We don't want it going on forever, so set an upper limit
            if self.counter == 10:
                self.dead = True

            self.xdiff = 70 - self.wallx
            ydiffUp = self.birdY - self.wallUpY
            ydiffDown = self.birdY - self.wallDownY
            
            decision = network.feedForward([self.xdiff, ydiffUp, ydiffDown])

            if decision[0] > 0.5 and not self.dead:
                self.makeJump()

            self.updateScreen()
            self.updateWalls()
            self.birdUpdate()
            
            pygame.display.update()

        return self.distanceMoved

遗传算法 The Genetic Algorithm​

from deap import base
from deap import creator
from deap import tools

import random
import time
numInputNodes = 3
numHiddenNodes1 = 3
numHiddenNodes2 = 2
numOutputNodes = 1

IND_SIZE = ((numInputNodes+1) * numHiddenNodes1) + (numHiddenNodes1 * numHiddenNodes2) + (numHiddenNodes2 * numOutputNodes)

Create a single neural network controller that we will use. We will evolve weights and pass them to this network when we need to evaluate their fitness.

myNet = MLP(numInputNodes, numHiddenNodes1, numHiddenNodes2, numOutputNodes)

creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, -1.0, 1.0)
toolbox.register("individual", tools.initRepeat, creator.Individual,toolbox.attr_float, n=IND_SIZE)
def evaluate(indiv, myNet, game):
    myNet.setWeightsLinear(indiv)   # Load the individual's weights into the neural network
    game.restart()
    fitness = game.run(myNet) # Evaluate the individual by running the game (discuss)
    return fitness,
toolbox.register("evaluate", evaluate)
toolbox.register("select", tools.selTournament, tournsize=3)

toolbox.register("mutate", tools.mutGaussian, mu=0.0, sigma=0.5, indpb=0.1)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
stats = tools.Statistics(key=lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max)
logbook = tools.Logbook()

pop = toolbox.population(n=100)

Create a single game object. We will use this single object evaluate each of our solutions.

game = FlappyBird()
fitnesses = [toolbox.evaluate(indiv, myNet, game) for indiv in pop]
for ind, fit in zip(pop, fitnesses):
    ind.fitness.values = fit
NGEN = 10

for g in range(NGEN):
    print("-- Generation %i --" % g)
      
    offspring = toolbox.select(pop, len(pop))
    offspring = list(map(toolbox.clone, offspring))

    for mutant in offspring:
        toolbox.mutate(mutant)
        del mutant.fitness.values
                         
    invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
    fitnesses = [toolbox.evaluate(indiv, myNet, game) for indiv in invalid_ind]
    for ind, fit in zip(invalid_ind, fitnesses):
         ind.fitness.values = fit
    
    pop[:] = offspring
    record = stats.compile(pop)
    logbook.record(gen=g, **record)

检验算法 Examination of the algorithm​

logbook.header = "gen", "avg", "evals", "std", "min", "max"
import matplotlib.pyplot as plt
%matplotlib inline
gen = logbook.select("gen")
avgs = logbook.select("avg")
stds = logbook.select("std")
plt.rc('axes', labelsize=14)
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14) 
plt.rc('legend', fontsize=14)

fig, ax1 = plt.subplots()

line1 = ax1.errorbar(gen, avgs, yerr=stds, errorevery=2)
ax1.set_xlabel("Generation")
ax1.set_ylabel("Mean Fitness")

FPresults

检验最优解 Examine the best solution​

indiv1 = tools.selBest(pop, 1)[0]
toolbox.evaluate(indiv1, myNet, game)
最后由 waleyGithub 于 更新
- + \ No newline at end of file diff --git a/Coding/evolutionary computation/Introduction.html b/Coding/evolutionary computation/Introduction.html index d07a3070..6a7e5860 100644 --- a/Coding/evolutionary computation/Introduction.html +++ b/Coding/evolutionary computation/Introduction.html @@ -6,7 +6,7 @@ An Introduction to Genetic Algorithms | waley - +
@@ -14,6 +14,6 @@ gene 前面的图片显示了一个这样的二进制编码染色体的例子，代表一个特定的个体。

- + \ No newline at end of file diff --git a/Coding/evolutionary computation/max-one problem.html b/Coding/evolutionary computation/max-one problem.html index 93cbc358..b702a327 100644 --- a/Coding/evolutionary computation/max-one problem.html +++ b/Coding/evolutionary computation/max-one problem.html @@ -6,13 +6,13 @@ OneMax Problem | waley - +
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OneMax Problem

OneMax问题是遗传算法的入门问题：如何使一段长度固定的二进制字符串所有位置上数字之和最大。 下面以长度10为例。

所需环境

代码

import random
from deap import algorithms, base, creator, tools

定义个体​

creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)

定义遗传操作​

def evalOneMax(individual): 
    return (sum(individual),)
toolbox.register("evaluate", evalOneMax)
toolbox.register("select", tools.selTournament, tournsize=3)

toolbox.register("mate", tools.cxUniform, indpb=0.1)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.01)

logbook = tools.Logbook()

主体部分​

pop = toolbox.population(n=300)

fitnesses = list(map(toolbox.evaluate, pop)) 
for ind, fit in zip(pop, fitnesses):
    ind.fitness.values = fit

NGEN = 50

for g in range(NGEN):
   print("-- Generation %i --" % g)
   
   offspring = toolbox.select(pop, len(pop))
   offspring = list(map(toolbox.clone, offspring))
   
   for child1, child2 in zip(offspring[::2], offspring[1::2]): 
       toolbox.mate(child1, child2)
       del child1.fitness.values
       del child2.fitness.values
       
   for mutant in offspring: 
       toolbox.mutate(mutant) 
       del mutant.fitness.values
       
   invalid_ind = [ind for ind in offspring if not ind.fitness.valid] 
   fitnesses = map(toolbox.evaluate, invalid_ind)
   for ind, fit in zip(invalid_ind, fitnesses):
       ind.fitness.values = fit
       
   pop[:] = offspring

最优个体​

best_ind = tools.selBest(pop, 1)[0]
print("Best individual is %s" % (best_ind)) 
print("With fitness %s" % (best_ind.fitness.values))

Best individual is [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] With fitness 100.0

记录数据​

stats = tools.Statistics(key=lambda ind: ind.fitness.values)
import numpy 
stats.register("avg", numpy.mean) 
stats.register("std", numpy.std) 
stats.register("min", numpy.min) 
stats.register("max", numpy.max)

{'avg': 98.19, 'std': 1.2438783434618248, 'min': 93.0, 'max': 100.0}

record = stats.compile(pop)
print(record)
logbook = tools.Logbook()
logbook.record(gen=0, evals=30, **record)
logbook.header = "gen", "avg", "evals", "std", "min", "max"
print(logbook)
genavgevalsstdminmax
098.19301.2438893100

数据可视化​

import matplotlib.pyplot as plt 
%matplotlib inline
gen = logbook.select("gen")
avgs = logbook.select("avg")
stds = logbook.select("std")
plt.rc('axes', labelsize=14)
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14)
plt.rc('legend', fontsize=14)
fig, ax1 = plt.subplots()
line1 = ax1.errorbar(gen, avgs, yerr=stds, errorevery=2) 
ax1.set_xlabel("Generation")
ax1.set_ylabel("Mean Fitness")

Text(0, 0.5, 'Mean Fitness')

oneMax

最后由 waleyGithub 于 更新
- + \ No newline at end of file diff --git a/Product Manager.html b/Product Manager.html index e233d706..dd091388 100644 --- a/Product Manager.html +++ b/Product Manager.html @@ -6,12 +6,12 @@ 简介 | waley - +
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简介

There should be an introduction.

最后由 waleyGithub 于 更新
- + \ No newline at end of file diff --git "a/Product Manager/category/\346\226\271\346\263\225\350\256\272.html" "b/Product Manager/category/\346\226\271\346\263\225\350\256\272.html" index 52e46952..8cd7a630 100644 --- "a/Product Manager/category/\346\226\271\346\263\225\350\256\272.html" +++ "b/Product Manager/category/\346\226\271\346\263\225\350\256\272.html" @@ -6,12 +6,12 @@ 方法论 | waley - +
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方法论

- + \ No newline at end of file diff --git "a/Product Manager/category/\351\241\271\347\233\256.html" "b/Product Manager/category/\351\241\271\347\233\256.html" index 91713473..3c0bf0f8 100644 --- "a/Product Manager/category/\351\241\271\347\233\256.html" +++ "b/Product Manager/category/\351\241\271\347\233\256.html" @@ -6,12 +6,12 @@ 项目 | waley - +
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项目

- + \ No newline at end of file diff --git "a/Product Manager/\346\226\271\346\263\225\350\256\272/Nielsen\342\200\230s 10 usability heuristics.html" "b/Product Manager/\346\226\271\346\263\225\350\256\272/Nielsen\342\200\230s 10 usability heuristics.html" index 7937a006..d12b219a 100644 --- "a/Product Manager/\346\226\271\346\263\225\350\256\272/Nielsen\342\200\230s 10 usability heuristics.html" +++ "b/Product Manager/\346\226\271\346\263\225\350\256\272/Nielsen\342\200\230s 10 usability heuristics.html" @@ -6,12 +6,12 @@ 尼尔森十大可用性原则 | waley - +
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尼尔森十大可用性原则

1. 反馈原则 Visibility of system status​

系统应该在合理的时间、用正确的方式，向用户提示或反馈目前系统正在发生什么。

The system should always keep users informed about what is going on, through appropriate feedback within reasonable time.

2. 隐喻原则 Match between system and the real world​

系统应该使用用户的语言，使用用户熟悉的单词、短语和概念，而不是面向系统的术语。遵循现实世界的惯例，使信息以自然和逻辑的顺序出现。

The system should speak the users' language, with words, phrases and concepts familiar to the user, rather than system-oriented terms. Follow real-world conventions, making information appear in a natural and logical order.

3. 回退原则 User control and freedom​

用户经常错误地选择系统功能，他们将需要一个明确标记的 "紧急出口 "来离开不需要的状态，而不需要通过一个扩展的对话。支持撤销和重做。

Users often choose system functions by mistake and will need a clearly marked "emergency exit" to leave the unwanted state without having to go through an extended dialogue. Support undo and redo.

4. 一致原则 Consistency and standards​

用户不应该怀疑不同的词语、情况或行动是否意味着同样的事情。遵循平台惯例。

Users should not have to wonder whether different words, situations, or actions mean the same thing. Follow platform conventions.

5. 防错原则 Error prevention​

比好的错误信息更好的是精心的设计，它可以从一开始就防止问题的发生。要么消除容易出错的条件，要么检查这些条件，并在用户承诺行动之前向他们提供一个确认选项。

Even better than good error messages is a careful design which prevents a problem from occurring in the first place. Either eliminate error-prone conditions or check for them and present users with a confirmation option before they commit to the action.

6. 记忆原则 Recognition rather than recall​

通过使对象、行动和选项可见，尽量减少用户的记忆负荷。用户不应该记住从对话的一个部分到另一个部分的信息。在适当的时候，系统的使用说明应该是可见的或容易检索的。

Minimize the user's memory load by making objects, actions, and options visible. The user should not have to remember information from one part of the dialogue to another. Instructions for use of the system should be visible or easily retrievable whenever appropriate.

7. 灵活易用原则 Flexibility and efficiency of use​

加速器--新手用户看不到--往往可以加快专家用户的互动，这样系统就可以同时满足没有经验和有经验的用户。允许用户定制频繁的操作。

Accelerators -- unseen by the novice user -- may often speed up the interaction for the expert user such that the system can cater to both inexperienced and experienced users. Allow users to tailor frequent actions.

8. 简约设计原则 Aesthetic and minimalist design​

对话不应包含不相关或很少需要的信息。对话中每一个额外的信息单元都会与相关的信息单元竞争，并降低它们的相对可见度。

Dialogues should not contain information which is irrelevant or rarely needed. Every extra unit of information in a dialogue competes with the relevant units of information and diminishes their relative visibility.

9. 容错原则 Help users recognize, diagnose, and recover from errors​

错误信息应该用简单的语言表达（没有代码），精确地指出问题，并建设性地提出解决方案。

Error messages should be expressed in plain language (no codes), precisely indicate the problem, and constructively suggest a solution.

10. 帮助原则 Help and documentation​

尽管如果系统不需要文件就能使用会更好，但可能还是有必要提供帮助和文件。任何这样的信息都应该易于搜索，集中在用户的任务上，列出要执行的具体步骤，而且不要太大。

Even though it is better if the system can be used without documentation, it may be necessary to provide help and documentation. Any such information should be easy to search, focused on the user's task, list concrete steps to be carried out, and not be too large.

最后由 waleyGithub 于 更新
- + \ No newline at end of file diff --git "a/Product Manager/\346\226\271\346\263\225\350\256\272/Product Manager Persona.html" "b/Product Manager/\346\226\271\346\263\225\350\256\272/Product Manager Persona.html" index 55a96bb7..325c89c2 100644 --- "a/Product Manager/\346\226\271\346\263\225\350\256\272/Product Manager Persona.html" +++ "b/Product Manager/\346\226\271\346\263\225\350\256\272/Product Manager Persona.html" @@ -6,12 +6,12 @@ 完美的产品经理画像 | waley - +
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完美的产品经理画像

  • 结合市场和行业变量
  • 发现问题井提出解决方案
  • 用数据指标量化业务场景
  • 用产品思维指导需求落地
最后由 waleyGithub 于 更新
- + \ No newline at end of file diff --git "a/Product Manager/\346\226\271\346\263\225\350\256\272/\344\272\247\345\223\201\344\273\213\347\273\215.html" "b/Product Manager/\346\226\271\346\263\225\350\256\272/\344\272\247\345\223\201\344\273\213\347\273\215.html" index 11419e50..c8acfaa7 100644 --- "a/Product Manager/\346\226\271\346\263\225\350\256\272/\344\272\247\345\223\201\344\273\213\347\273\215.html" +++ "b/Product Manager/\346\226\271\346\263\225\350\256\272/\344\272\247\345\223\201\344\273\213\347\273\215.html" @@ -6,12 +6,12 @@ 产品介绍 | waley - +
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结构

1. 项目背景(消费者或者社会需求)​

2. 产品定位(核心USP)​

3. 产品功能介绍(非核心USP)​

4. 产品的社会价值(商业模式)​

5. 产品光环(研发人员背景、导师介绍)​

  • USP理论（unique selling proposition）独特销售主张包括以下四个方面：

    1. 强调产品具体的特殊功效和利益 —— 每一个广告都必须对消费者有一个销售的主张；
    2. 这种特殊性是竞争对手无法提出的 —— 这一项主张，必须是竞争对手无法也不能提出的，须是具有独特性的；
    3. 有强劲的销售力 —— 这一项主张必须很强，足以影响成百万的社会公众。
    4. 20世纪90年代，达彼斯将USP定义为：USP的创造力在于揭示一个品牌的精髓，并通过强有力的、有说服力的数据证实它的独特性，使之所向披靡，势不可挡。

  • USP理论的三个特点：

    1. 必须包含特定的商品效用，即每个广告都要对消费者提出一个说辞，给予消费者一个明确的利益承诺
    2. 必须是唯一的、独特的，是其他同类竞争产品不具有或没有宣传过的说辞
    3. 必须有利于促进销售，即这一说辞一定要强有力，能招来数以百万计的大众
最后由 waleyGithub 于 更新
- + \ No newline at end of file diff --git "a/Product Manager/\346\226\271\346\263\225\350\256\272/\347\224\250\346\210\267\344\275\223\351\252\214\344\272\224\350\246\201\347\264\240.html" "b/Product Manager/\346\226\271\346\263\225\350\256\272/\347\224\250\346\210\267\344\275\223\351\252\214\344\272\224\350\246\201\347\264\240.html" index f00fed95..9e9ac6e0 100644 --- "a/Product Manager/\346\226\271\346\263\225\350\256\272/\347\224\250\346\210\267\344\275\223\351\252\214\344\272\224\350\246\201\347\264\240.html" +++ "b/Product Manager/\346\226\271\346\263\225\350\256\272/\347\224\250\346\210\267\344\275\223\351\252\214\344\272\224\350\246\201\347\264\240.html" @@ -6,12 +6,12 @@ 用户体验五要素 | waley - +
跳到主要内容

用户体验五要素

战略层​

就是用户和经营者分别想从这个产品中获得什么。

用户想要获得的，就是“用户需求”，是来自公司或者团队外部的需求，为了确定用户需求，首先要确定产品的target user。确定产品的目标用户，有以下三个步骤：

  • 用户细分：将用户分成较小的有共同需求的组（人口统计学、消费心态档案等）；
  • 用户研究：知道用户是谁（问卷调查、访谈、实地考察、焦点小组、卡片分类等）；
  • 人物角色：从用户研究中提取出可以成为样例的用户画像。

通过使用以上几个手法，能确定出用户的需求，确定出产品在战略层的内容。

经营者想要获得的，就是“商业目标”，这是来自公司内部的需求，可以是用户数量目标、品牌宣传目的或者盈利目的。总之，老板或者团队想要获得的，也属于战略层的内容。

范围层​

范围层，就是这个产品有哪些功能，这个产品都可以干些什么。

当你把用户需求和网站目标转变成网站应该提供给用户什么样的内容和功能时，战略就变成了范围。根据范围层的具体内容，可以将产品分为两大类：工具型和内容型，或者有的产品两者兼有。

工具型产品要考虑的是功能规格，对产品的功能组合进行详细描述。功能规格的描述语言应该：乐观、具体、客观。

  • 乐观：防止做什么，而不是不能做什么。例如：网站不允许未登录用户发言 ➡️ 如果用户发言时未登录，系统应该引导用户登录并返回
  • 具体：尽可能解释清楚。例如：论坛发言需要过滤敏感词语 ➡️ 发言中包含附件中词语的需要替换为*号，词库可更新
  • 客观：避免主观语气。例如：界面的风格应该是时尚的 ➡️ 产品外观应该符合企业品牌形象

内容型产品要考虑的是内容需求，对各种内容元素的要求进行详细描述。

内容可以包括文本、图像、音频、视频等。在确定产品要提供的内容后，要根据用户需求、产品目标（战略层的内容）和可行性评定内容的优先级。在产品运转过程中，要安排日常的运维工作，而内容的更新频率要符合用户的期望。因此，内容的维护工作要根据战略目标来安排。如：你期望用户多久访问一次？从用户需求来看，他们希望多久更新一次。此外，还要根据不同用户准备不同特性的内容。

结构层​

在收集完用户需求并将其排列好优先级后，需要将这些分散的片段组成一个整体，这就是结构层：创建产品功能和内容之间的关系。

结构层分为交互设计和信息架构两个大的部分。

  • 交互设计：描述“可能的用户行为”，定义“系统如何配合与响应”这些用户行为。不要让用户去适应产品，而要让产品与用户互相适应，预测对方的下一步。

  • 信息架构：关注如何将信息表达给用户，着重于设计组织分类和导航结构，让用户容易找到。

交互设计应该至少包括概念模型和错误处理。

信息架构有两种分类体系：从上到下和从下到上。其结构可以有层级结构、矩阵结构、自然结构和线性结构。一般来说，网站都是以上多种结构的综合，一种结构为主，其他结构为辅。

  • 层级结构：也称树状结构或中心辐射（hub-and spoke）结构，即节点与其他相关节点之间存在父级/子级的关系。这种结构最为常见。
  • 自然结构：不会遵循任何一致的模式，适合于探索一系列关系不明确或者一直在演变的主题，如鼓励用户自由探险的某些娱乐网站。但如果用户下次还需要依靠同样的路径去找到同样的内容，则不太适合。
  • 矩阵结构：通常帮助那些带着不同需求而来的用户，使他们能在相同内容中，寻找各自想要的东西，如：用户可以选择通过颜色或尺寸来浏览产品。
  • 线性结构：常常用于小规模的结构，如单篇文章或专题；大规模的结构则被用于限制那些需要呈现的内容顺序对于符合用户需求非常关键的应用程序。

框架层​

框架层决定某个板块或按钮等交互元素应该放在页面的什么地方。结构层中形成了大量的需求，框架层中，我们要更进一步地提炼这些需求，确定详细的界面外观、导航和信息设计，使晦涩的结构变得实在。

在设计框架层的内容时，要遵循两条原则：遵循用户日常使用习惯和恰当使用生活中的比喻。

框架层包含界面设计、导航设计和信息设计三个方面。

  • 界面设计为用户提供做某些事的能力。做界面设计时，要选择合适的元素，让用户一眼就能看到最重要的东西。
  • 导航设计给用户提供去某个地方的能力。做导航设计时，要提供网页之间的跳转方法，传达元素与内容之间的关系，传达内容与页面之间的关系。
  • 信息设计是将想法传达给用户。信息设计是将各种设计元素聚合到一起，反映用户的思路，支持他们的任务和目标。包括视觉、分组等各个方面。

表现层​

表现层是用户所能看见的一切，字体的大小，导航的颜色，整体给人的感觉。在这一层，内容、功能和美学汇集到一起来产生一个最终设计，从而满足其他层面的所有目标。

成功的界面设计有两个特点：

  • 他们遵循的是一条流畅的路径。用户在浏览过程中有流畅感，不会被阻碍。
  • 在不需要用太多细节来吓倒用户的前提下，为用户提供有效选择的、某种可能的“引导”。
最后由 waleyGithub 于 更新
- + \ No newline at end of file diff --git "a/Product Manager/\351\241\271\347\233\256/Eat-UoY.html" "b/Product Manager/\351\241\271\347\233\256/Eat-UoY.html" index f09d6e50..ae0b3fc7 100644 --- "a/Product Manager/\351\241\271\347\233\256/Eat-UoY.html" +++ "b/Product Manager/\351\241\271\347\233\256/Eat-UoY.html" @@ -6,7 +6,7 @@ Eat UoY | waley - +
@@ -17,6 +17,6 @@

  • One of the problems of our users' pain points are is that users desire to track the delivery details in time, so our solution is that users can check the delivery detail in real-time map, they can track delivery man's real-time location. They will know how long they will receive their food.

  • Also, we provide delivery man information in the page, they can contact to delivery man directly.

    • 交互系统原型和评估 Interactive System Prototype and Evaluation​

    关键功能 Key Freatures​

    keyFutures

    交互流程 UX Workflow​

    UX-Workflow

    ​线框图 Wireframes​

    原始草图 Original Drafts​

    wireframesDraft

    原形 Prototype​

    prototype

    原型的专家检查评估 Expert Inspection Evaluation of the Prototype​

    The Collaborative Heuristic Evaluation (CHE) of the food delivery app prototype was conducted by a team of 5 evaluators. One evaluator doubled as the driver and another evaluator as the notetaker. The session took place online using GoogleMeet.

    The set of tasks were as follows:

    During the CHE the driver shared her screen with the other evaluators and clicked through the prototype upon request. On the problem recording sheet, the notetaker wrote down the problems found, their location within the prototype and the heuristics, which were violated. On the same sheet, the evaluators then secretly rated the severity of those problems on a scale from 1 (very minor) to 5 (very major). 0 was rated if it wasn’t regarded as a problem. The heuristics, which the evaluators referred to in the CHE, was the set of Petrie and Power Heuristics (appendix C).

    Prototype Evaluation

    重新设计 Redesign​

    From the CHE, we see that the three most severe problems have an agreed rating of 4, which is the second-highest rating on the scale. Two of those problems (no. 1 and 3) are concerned with the first component of functionality. Based on the severity of the usability problems 1 and 3 as well as their importance of functionality, we have therefore chosen the first component for carrying out a redesign.

    Problem no. 1 sits between selecting and paying aspects of the component. The detected problem describes the missing function of deleting an item from the basket. This violates heuristic 17 of Petrie & Power, because users will expect to be able to edit the basket by adding or deleting as many items as they like or changing their mind on items they have previously added. It may also happen that a user adds an item to the basket by mistake, so it has to be easy to recover from this mistake. The proposal for redesign in this case is to add a bin icon next to each item of the basket . The interaction will be improved by preventing users from making unconscious slips. By decreasing the quantity of a basket item to zero it will not automatically disappear from the basket, which may accidentally happen by tapping the minus too many times.

    Problem no. 2 is concerned with the interaction flow from selecting to paying of the first component of functionality. Here we see heuristics 14 and 16 of Petrie & Power violated because the sequence of interaction is illogical and doesn’t follow established web conventions. The current process flow can be hardly confusing to the extent that it creates a gulf of execution when the user wants to view the basket but it is not clear how to get back to the basket from the order detail screen. Therefore, we suggest to improve the interaction flow between the restaurant menu screen and the payment screen.

    Based on CHE, we will replace the button currently labelled “pay” with a basket icon on the restaurant menu screen. To increase recognition it will be the same icon as used for the basket in the bottom menu bar. To go forward from the basket screen, the improved interaction flow will take the user to the order detail screen by tapping the “pay” button, from where they will be directed to the payment screen by tapping the “pay” button again. Through this redesign of the interaction flow, we can ensure a logical process following known web and logical conventions in the interaction which is expected by the user.

    In order to improve the user's experience, we consider that when users chose the product they might regret it or by mistake, so it's quite easy to deal with a mistake on the same page. Through this redesign of the fault tolerance, we consider that this will be more user friendly easy to make an order.

    用户评价 User Evaluation​

    In order to further improve the food delivery app, a remote usability testing was conducted with 10 participants using the prototype. The purpose of this user evaluation was to assess the usability of the app in terms of task flow, information architecture and interface design.

    The participants were recruited by email and were sent an information sheet about the testing session, as well as a consent sheet, which they were asked to fill out and send back prior to the session.

    The user evaluation took place online using GoogleMeet. The participants were sent a link to the prototyping software Figma in order to access the prototype.

    During the user evaluation, an evaluator was present, as well as an observer. Each session was scheduled for 30 minutes.

    At the beginning of each session the participant was briefed with an introductory explanation and expectation of the test. The evaluator then asked a few background questions about gender, age, occupation and prior experience with food delivery apps.

    For the main test, the user was asked to share their screen and open the prototype on Figma. The user was presented with a scenario and a set of tasks to perform while thinking out loud. Afterwards, the participant answered questions about their overall satisfaction.

    重新设计 Redesign​

    Addressing the first problem from user evaluation, the location icon will be removed from the home screen. The function behind this location icon is to show results based on availability on Campus West, East or both. But this could not be understood by half of the participants. To make it more clear, this type of filtering option will now be available on the search screen below the search bar.

    The second problem is fairly easy to fix. We will add a banner to the food advertisement photo in order to make its meaning clear. By doing this, we incorporate the Gestalt principle of a focal point and breaking similarity intentionally to make it stand out visually so it will attract the viewer’s attention first. Redesign

    - + \ No newline at end of file diff --git "a/Product Manager/\351\241\271\347\233\256/Mobile CRM.html" "b/Product Manager/\351\241\271\347\233\256/Mobile CRM.html" index 24d40517..3c46209a 100644 --- "a/Product Manager/\351\241\271\347\233\256/Mobile CRM.html" +++ "b/Product Manager/\351\241\271\347\233\256/Mobile CRM.html" @@ -6,12 +6,12 @@ Mobile CRM | waley - +
    跳到主要内容

    Mobile CRM

    项目概述​

    目前因为疫情原因很多客户会提出移动客服，在手机上接电话，下工单，以及营销相关业务。现有web系统已经可以在手机App上进行工单处理、以及监控。

    业务场景​

    • 在线客服

      • 本移动端的核心业务，包括有电话呼入呼出，网聊客服场景

    • 客户管理

      • 客户信息管理，并提供如工单，短信推送等业务

    • 工单处理

      • 支持创建工单，以及坐席权限范围内工单的持续跟进等

    • 代办事项

    • 排班管理

    web端功能分析​

    CRM

    移动端功能结构​

    structure_of_mobileCRM

    页面流程图​

    workflow

    最后由 waleyGithub 于 更新
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