摘要
用户体验设计是产品成功的关键因素，传统的UX设计往往依赖设计师的直觉和经验。本文将探讨如何利用AI技术辅助用户体验设计与优化，从用户行为分析到界面生成，从可用性测试到个性化推荐，全面提升设计效率和用户满意度。
目录
AI在UX设计中的应用概述
智能UX设计系统架构
核心UX设计框架
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple, Union
from dataclasses import dataclass, asdict, field
from abc import ABC, abstractmethod
from enum import Enum
import json
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import warnings
warnings.filterwarnings('ignore')
class UserActionType(Enum):
"""用户行为类型枚举"""
CLICK = "click"
SCROLL = "scroll"
HOVER = "hover"
INPUT = "input"
NAVIGATION = "navigation"
SEARCH = "search"
PURCHASE = "purchase"
EXIT = "exit"
class DeviceType(Enum):
"""设备类型枚举"""
DESKTOP = "desktop"
MOBILE = "mobile"
TABLET = "tablet"
class DesignElement(Enum):
"""设计元素类型枚举"""
BUTTON = "button"
INPUT_FIELD = "input_field"
IMAGE = "image"
TEXT = "text"
NAVIGATION = "navigation"
CARD = "card"
MODAL = "modal"
FORM = "form"
@dataclass
class UserAction:
"""用户行为数据结构"""
user_id: str
session_id: str
timestamp: datetime
action_type: UserActionType
element_id: str
element_type: DesignElement
coordinates: Tuple[int, int]
device_type: DeviceType
page_url: str
duration: float
success: bool
context: Dict[str, Any]
@dataclass
class UserProfile:
"""用户画像数据结构"""
user_id: str
demographics: Dict[str, Any]
preferences: Dict[str, Any]
behavior_patterns: Dict[str, Any]
device_usage: Dict[DeviceType, float]
skill_level: str # beginner, intermediate, advanced
goals: List[str]
pain_points: List[str]
satisfaction_score: float
@dataclass
class DesignComponent:
"""设计组件数据结构"""
id: str
name: str
type: DesignElement
properties: Dict[str, Any]
position: Tuple[int, int]
size: Tuple[int, int]
style: Dict[str, Any]
interactions: List[str]
accessibility_score: float
performance_metrics: Dict[str, float]
@dataclass
class UIDesign:
"""UI设计数据结构"""
id: str
name: str
page_type: str
components: List[DesignComponent]
layout_grid: Dict[str, Any]
color_scheme: Dict[str, str]
typography: Dict[str, Any]
responsive_breakpoints: Dict[str, int]
accessibility_compliance: float
performance_score: float
class UserBehaviorAnalyzer:
"""用户行为分析器"""
def __init__(self):
self.clustering_model = KMeans(n_clusters=5, random_state=42)
self.scaler = StandardScaler()
self.pca = PCA(n_components=2)
def analyze_user_behavior(self, actions: List[UserAction]) -> Dict[str, Any]:
"""分析用户行为"""
# 基础统计分析
basic_stats = self._calculate_basic_statistics(actions)
# 行为模式识别
behavior_patterns = self._identify_behavior_patterns(actions)
# 用户聚类分析
user_clusters = self._perform_user_clustering(actions)
# 痛点识别
pain_points = self._identify_pain_points(actions)
# 转化漏斗分析
funnel_analysis = self._analyze_conversion_funnel(actions)
return {
'basic_statistics': basic_stats,
'behavior_patterns': behavior_patterns,
'user_clusters': user_clusters,
'pain_points': pain_points,
'funnel_analysis': funnel_analysis,
'recommendations': self._generate_behavior_recommendations(actions)
}
def _calculate_basic_statistics(self, actions: List[UserAction]) -> Dict[str, Any]:
"""计算基础统计信息"""
if not actions:
return {}
# 转换为DataFrame便于分析
df = pd.DataFrame([{
'user_id': action.user_id,
'action_type': action.action_type.value,
'element_type': action.element_type.value,
'device_type': action.device_type.value,
'duration': action.duration,
'success': action.success,
'timestamp': action.timestamp
} for action in actions])
stats = {
'total_actions': len(actions),
'unique_users': df['user_id'].nunique(),
'avg_session_duration': df.groupby(['user_id', 'timestamp'])['duration'].sum().mean(),
'success_rate': df['success'].mean(),
'device_distribution': df['device_type'].value_counts().to_dict(),
'action_distribution': df['action_type'].value_counts().to_dict(),
'element_interaction_frequency': df['element_type'].value_counts().to_dict()
}
return stats
def _identify_behavior_patterns(self, actions: List[UserAction]) -> Dict[str, Any]:
"""识别行为模式"""
patterns = {}
# 按用户分组分析
user_actions = {}
for action in actions:
if action.user_id not in user_actions:
user_actions[action.user_id] = []
user_actions[action.user_id].append(action)
# 分析常见的行为序列
common_sequences = self._find_common_sequences(user_actions)
patterns['common_sequences'] = common_sequences
# 分析时间模式
time_patterns = self._analyze_time_patterns(actions)
patterns['time_patterns'] = time_patterns
# 分析设备使用模式
device_patterns = self._analyze_device_patterns(actions)
patterns['device_patterns'] = device_patterns
return patterns
def _perform_user_clustering(self, actions: List[UserAction]) -> Dict[str, Any]:
"""执行用户聚类分析"""
# 构建用户特征矩阵
user_features = self._build_user_feature_matrix(actions)
if len(user_features) < 5: # 数据不足时返回空结果
return {'clusters': [], 'cluster_descriptions': []}
# 标准化特征
features_scaled = self.scaler.fit_transform(user_features.values)
# 执行聚类
cluster_labels = self.clustering_model.fit_predict(features_scaled)
# 分析聚类结果
clusters = {}
for i, user_id in enumerate(user_features.index):
cluster_id = cluster_labels[i]
if cluster_id not in clusters:
clusters[cluster_id] = []
clusters[cluster_id].append(user_id)
# 生成聚类描述
cluster_descriptions = self._describe_clusters(user_features, cluster_labels)
return {
'clusters': clusters,
'cluster_descriptions': cluster_descriptions,
'cluster_centers': self.clustering_model.cluster_centers_.tolist()
}
def _identify_pain_points(self, actions: List[UserAction]) -> List[Dict[str, Any]]:
"""识别用户痛点"""
pain_points = []
# 分析失败率高的元素
element_failures = {}
element_total = {}
for action in actions:
element_key = f"{action.element_type.value}_{action.element_id}"
if element_key not in element_total:
element_total[element_key] = 0
element_failures[element_key] = 0
element_total[element_key] += 1
if not action.success:
element_failures[element_key] += 1
# 找出失败率高的元素
for element, total in element_total.items():
if total >= 10: # 至少有10次交互
failure_rate = element_failures[element] / total
if failure_rate > 0.3: # 失败率超过30%
pain_points.append({
'type': 'high_failure_rate',
'element': element,
'failure_rate': failure_rate,
'total_interactions': total,
'description': f'元素 {element} 失败率过高 ({failure_rate:.1%})'
})
# 分析异常长的操作时间
long_duration_threshold = np.percentile([a.duration for a in actions], 95)
long_duration_actions = [a for a in actions if a.duration > long_duration_threshold]
if long_duration_actions:
element_durations = {}
for action in long_duration_actions:
element_key = f"{action.element_type.value}_{action.element_id}"
if element_key not in element_durations:
element_durations[element_key] = []
element_durations[element_key].append(action.duration)
for element, durations in element_durations.items():
if len(durations) >= 5: # 至少5次长时间操作
avg_duration = np.mean(durations)
pain_points.append({
'type': 'long_interaction_time',
'element': element,
'avg_duration': avg_duration,
'occurrences': len(durations),
'description': f'元素 {element} 交互时间过长 (平均 {avg_duration:.1f}秒)'
})
return pain_points
def _analyze_conversion_funnel(self, actions: List[UserAction]) -> Dict[str, Any]:
"""分析转化漏斗"""
# 定义典型的转化步骤
funnel_steps = [
UserActionType.NAVIGATION,
UserActionType.CLICK,
UserActionType.INPUT,
UserActionType.PURCHASE
]
# 按用户分析转化路径
user_funnels = {}
for action in actions:
if action.user_id not in user_funnels:
user_funnels[action.user_id] = []
user_funnels[action.user_id].append(action.action_type)
# 计算每步的转化率
step_conversions = {}
total_users = len(user_funnels)
for i, step in enumerate(funnel_steps):
users_at_step = sum(1 for user_actions in user_funnels.values()
if step in user_actions)
conversion_rate = users_at_step / total_users if total_users > 0 else 0
step_conversions[step.value] = {
'users': users_at_step,
'conversion_rate': conversion_rate
}
return {
'funnel_steps': [step.value for step in funnel_steps],
'step_conversions': step_conversions,
'overall_conversion_rate': step_conversions.get('purchase', {}).get('conversion_rate', 0)
}
def _find_common_sequences(self, user_actions: Dict[str, List[UserAction]]) -> List[Dict[str, Any]]:
"""找出常见的行为序列"""
sequences = {}
for user_id, actions in user_actions.items():
if len(actions) < 2:
continue
# 生成行为序列
for i in range(len(actions) - 1):
seq = (actions[i].action_type.value, actions[i+1].action_type.value)
if seq not in sequences:
sequences[seq] = 0
sequences[seq] += 1
# 排序并返回最常见的序列
common_sequences = sorted(sequences.items(), key=lambda x: x[1], reverse=True)[:10]
return [{'sequence': seq, 'frequency': freq} for seq, freq in common_sequences]
def _analyze_time_patterns(self, actions: List[UserAction]) -> Dict[str, Any]:
"""分析时间模式"""
if not actions:
return {}
# 按小时分析活动分布
hourly_activity = {}
for action in actions:
hour = action.timestamp.hour
if hour not in hourly_activity:
hourly_activity[hour] = 0
hourly_activity[hour] += 1
# 按星期几分析活动分布
daily_activity = {}
for action in actions:
day = action.timestamp.weekday() # 0=Monday, 6=Sunday
if day not in daily_activity:
daily_activity[day] = 0
daily_activity[day] += 1
return {
'hourly_distribution': hourly_activity,
'daily_distribution': daily_activity,
'peak_hour': max(hourly_activity.items(), key=lambda x: x[1])[0] if hourly_activity else None,
'peak_day': max(daily_activity.items(), key=lambda x: x[1])[0] if daily_activity else None
}
def _analyze_device_patterns(self, actions: List[UserAction]) -> Dict[str, Any]:
"""分析设备使用模式"""
device_stats = {}
for device_type in DeviceType:
device_actions = [a for a in actions if a.device_type == device_type]
if device_actions:
device_stats[device_type.value] = {
'total_actions': len(device_actions),
'avg_duration': np.mean([a.duration for a in device_actions]),
'success_rate': np.mean([a.success for a in device_actions]),
'most_common_action': max(set([a.action_type.value for a in device_actions]),
key=[a.action_type.value for a in device_actions].count)
}
return device_stats
def _build_user_feature_matrix(self, actions: List[UserAction]) -> pd.DataFrame:
"""构建用户特征矩阵"""
user_features = {}
# 按用户分组
user_actions = {}
for action in actions:
if action.user_id not in user_actions:
user_actions[action.user_id] = []
user_actions[action.user_id].append(action)
# 为每个用户计算特征
for user_id, user_action_list in user_actions.items():
features = {
'total_actions': len(user_action_list),
'avg_duration': np.mean([a.duration for a in user_action_list]),
'success_rate': np.mean([a.success for a in user_action_list]),
'unique_elements': len(set([a.element_id for a in user_action_list])),
'mobile_usage': sum(1 for a in user_action_list if a.device_type == DeviceType.MOBILE) / len(user_action_list)
}
# 添加行为类型分布
for action_type in UserActionType:
type_count = sum(1 for a in user_action_list if a.action_type == action_type)
features[f'{action_type.value}_ratio'] = type_count / len(user_action_list)
user_features[user_id] = features
return pd.DataFrame.from_dict(user_features, orient='index')
def _describe_clusters(self, user_features: pd.DataFrame, cluster_labels: np.ndarray) -> List[Dict[str, Any]]:
"""描述聚类结果"""
descriptions = []
for cluster_id in set(cluster_labels):
cluster_mask = cluster_labels == cluster_id
cluster_data = user_features[cluster_mask]
description = {
'cluster_id': cluster_id,
'size': len(cluster_data),
'characteristics': {}
}
# 计算每个特征的平均值
for feature in user_features.columns:
avg_value = cluster_data[feature].mean()
description['characteristics'][feature] = avg_value
# 生成文字描述
if description['characteristics'].get('mobile_usage', 0) > 0.7:
description['label'] = '移动端重度用户'
elif description['characteristics'].get('success_rate', 0) < 0.5:
description['label'] = '困难用户群体'
elif description['characteristics'].get('total_actions', 0) > user_features['total_actions'].mean():
description['label'] = '活跃用户'
else:
description['label'] = '普通用户'
descriptions.append(description)
return descriptions
def _generate_behavior_recommendations(self, actions: List[UserAction]) -> List[str]:
"""生成行为分析建议"""
recommendations = []
if not actions:
return recommendations
# 分析成功率
success_rate = np.mean([a.success for a in actions])
if success_rate < 0.7:
recommendations.append("整体成功率较低，建议优化用户界面和交互流程")
# 分析设备使用
mobile_actions = [a for a in actions if a.device_type == DeviceType.MOBILE]
if len(mobile_actions) > len(actions) * 0.6:
recommendations.append("移动端使用较多，建议优先优化移动端体验")
# 分析操作时长
avg_duration = np.mean([a.duration for a in actions])
if avg_duration > 10: # 假设10秒为阈值
recommendations.append("用户操作时间较长，建议简化交互流程")
return recommendations
class IntelligentUIGenerator:
"""智能UI生成器"""
def __init__(self):
self.design_patterns = self._load_design_patterns()
self.component_library = self._load_component_library()
self.style_guide = self._load_style_guide()
def generate_ui_design(self, requirements: Dict[str, Any],
user_profiles: List[UserProfile]) -> UIDesign:
"""生成UI设计"""
# 分析需求
design_context = self._analyze_requirements(requirements)
# 基于用户画像调整设计
user_preferences = self._analyze_user_preferences(user_profiles)
# 选择合适的设计模式
selected_pattern = self._select_design_pattern(design_context, user_preferences)
# 生成组件布局
layout = self._generate_layout(selected_pattern, requirements)
# 选择和配置组件
components = self._select_and_configure_components(layout, user_preferences)
# 应用样式和主题
styled_components = self._apply_styling(components, user_preferences)
# 生成响应式设计
responsive_design = self._make_responsive(styled_components, requirements)
return UIDesign(
id=f"design_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
name=requirements.get('name', 'Generated Design'),
page_type=requirements.get('page_type', 'general'),
components=responsive_design,
layout_grid=layout,
color_scheme=user_preferences.get('colors', self.style_guide['default_colors']),
typography=user_preferences.get('typography', self.style_guide['default_typography']),
responsive_breakpoints=self.style_guide['breakpoints'],
accessibility_compliance=self._calculate_accessibility_score(responsive_design),
performance_score=self._estimate_performance_score(responsive_design)
)
def _analyze_requirements(self, requirements: Dict[str, Any]) -> Dict[str, Any]:
"""分析设计需求"""
context = {
'page_type': requirements.get('page_type', 'general'),
'target_audience': requirements.get('target_audience', 'general'),
'primary_goals': requirements.get('goals', []),
'content_types': requirements.get('content_types', []),
'interaction_complexity': requirements.get('complexity', 'medium'),
'device_priorities': requirements.get('device_priorities', ['desktop', 'mobile'])
}
return context
def _analyze_user_preferences(self, user_profiles: List[UserProfile]) -> Dict[str, Any]:
"""分析用户偏好"""
if not user_profiles:
return self.style_guide['default_preferences']
preferences = {
'colors': {},
'typography': {},
'layout_density': 'medium',
'interaction_style': 'standard',
'accessibility_needs': []
}
# 分析设备使用偏好
mobile_users = sum(1 for profile in user_profiles
if profile.device_usage.get(DeviceType.MOBILE, 0) > 0.5)
if mobile_users > len(user_profiles) * 0.6:
preferences['layout_density'] = 'compact'
# 分析技能水平
beginner_users = sum(1 for profile in user_profiles
if profile.skill_level == 'beginner')
if beginner_users > len(user_profiles) * 0.5:
preferences['interaction_style'] = 'simple'
preferences['layout_density'] = 'spacious'
# 分析年龄分布（如果有数据）
ages = [profile.demographics.get('age') for profile in user_profiles
if profile.demographics.get('age')]
if ages:
avg_age = np.mean(ages)
if avg_age > 50:
preferences['typography']['font_size'] = 'large'
preferences['colors']['contrast'] = 'high'
return preferences
def _select_design_pattern(self, context: Dict[str, Any],
preferences: Dict[str, Any]) -> Dict[str, Any]:
"""选择设计模式"""
page_type = context['page_type']
# 基于页面类型选择基础模式
if page_type == 'landing':
base_pattern = self.design_patterns['hero_section']
elif page_type == 'dashboard':
base_pattern = self.design_patterns['grid_layout']
elif page_type == 'form':
base_pattern = self.design_patterns['single_column']
elif page_type == 'ecommerce':
base_pattern = self.design_patterns['product_grid']
else:
base_pattern = self.design_patterns['standard_layout']
# 基于用户偏好调整
if preferences.get('layout_density') == 'compact':
base_pattern['spacing'] = 'tight'
elif preferences.get('layout_density') == 'spacious':
base_pattern['spacing'] = 'loose'
return base_pattern
def _generate_layout(self, pattern: Dict[str, Any],
requirements: Dict[str, Any]) -> Dict[str, Any]:
"""生成布局"""
layout = {
'type': pattern['type'],
'grid': pattern['grid'],
'spacing': pattern.get('spacing', 'medium'),
'sections': []
}
# 基于需求生成区域
content_types = requirements.get('content_types', [])
if 'header' in content_types or pattern['type'] != 'single_column':
layout['sections'].append({
'name': 'header',
'position': 'top',
'height': 'auto',
'components': ['navigation', 'logo']
})
if 'hero' in content_types:
layout['sections'].append({
'name': 'hero',
'position': 'main',
'height': '60vh',
'components': ['headline', 'cta_button', 'hero_image']
})
if 'content' in content_types:
layout['sections'].append({
'name': 'content',
'position': 'main',
'height': 'auto',
'components': ['text_blocks', 'images']
})
if 'sidebar' in content_types:
layout['sections'].append({
'name': 'sidebar',
'position': 'side',
'width': '25%',
'components': ['filters', 'related_content']
})
if 'footer' in content_types or pattern['type'] != 'single_column':
layout['sections'].append({
'name': 'footer',
'position': 'bottom',
'height': 'auto',
'components': ['links', 'contact_info']
})
return layout
def _select_and_configure_components(self, layout: Dict[str, Any],
preferences: Dict[str, Any]) -> List[DesignComponent]:
"""选择和配置组件"""
components = []
component_id = 1
for section in layout['sections']:
section_components = section.get('components', [])
for comp_type in section_components:
if comp_type in self.component_library:
comp_config = self.component_library[comp_type].copy()
# 基于偏好调整组件配置
if preferences.get('interaction_style') == 'simple':
comp_config['complexity'] = 'low'
component = DesignComponent(
id=f"comp_{component_id}",
name=comp_config['name'],
type=DesignElement(comp_config['type']),
properties=comp_config['properties'],
position=(0, 0), # 将在布局阶段确定
size=comp_config['default_size'],
style=comp_config['default_style'],
interactions=comp_config['interactions'],
accessibility_score=comp_config['accessibility_score'],
performance_metrics=comp_config['performance_metrics']
)
components.append(component)
component_id += 1
return components
def _apply_styling(self, components: List[DesignComponent],
preferences: Dict[str, Any]) -> List[DesignComponent]:
"""应用样式"""
styled_components = []
# 获取颜色方案
color_scheme = preferences.get('colors', self.style_guide['default_colors'])
typography = preferences.get('typography', self.style_guide['default_typography'])
for component in components:
styled_component = component
# 应用颜色
if component.type in [DesignElement.BUTTON]:
styled_component.style['background_color'] = color_scheme.get('primary', '#007bff')
styled_component.style['text_color'] = color_scheme.get('on_primary', '#ffffff')
elif component.type in [DesignElement.TEXT]:
styled_component.style['text_color'] = color_scheme.get('text', '#333333')
# 应用字体
styled_component.style['font_family'] = typography.get('font_family', 'Arial, sans-serif')
styled_component.style['font_size'] = typography.get('font_size', '16px')
# 应用间距
spacing = preferences.get('layout_density', 'medium')
if spacing == 'compact':
styled_component.style['padding'] = '8px'
styled_component.style['margin'] = '4px'
elif spacing == 'spacious':
styled_component.style['padding'] = '24px'
styled_component.style['margin'] = '16px'
else:
styled_component.style['padding'] = '16px'
styled_component.style['margin'] = '8px'
styled_components.append(styled_component)
return styled_components
def _make_responsive(self, components: List[DesignComponent],
requirements: Dict[str, Any]) -> List[DesignComponent]:
"""生成响应式设计"""
responsive_components = []
breakpoints = self.style_guide['breakpoints']
for component in components:
responsive_component = component
# 为不同断点设置样式
responsive_styles = {}
# 移动端适配
if 'mobile' in requirements.get('device_priorities', []):
responsive_styles['mobile'] = {
'width': '100%',
'font_size': '14px',
'padding': '12px'
}
# 平板适配
responsive_styles['tablet'] = {
'width': '100%' if component.type in [DesignElement.FORM, DesignElement.CARD] else 'auto',
'font_size': '15px'
}
# 桌面端适配
responsive_styles['desktop'] = {
'width': 'auto',
'font_size': '16px'
}
responsive_component.style['responsive'] = responsive_styles
responsive_components.append(responsive_component)
return responsive_components
def _calculate_accessibility_score(self, components: List[DesignComponent]) -> float:
"""计算可访问性评分"""
if not components:
return 0.0
total_score = sum(comp.accessibility_score for comp in components)
return total_score / len(components)
def _estimate_performance_score(self, components: List[DesignComponent]) -> float:
"""估算性能评分"""
if not components:
return 1.0
# 基于组件数量和复杂度估算
component_count = len(components)
complexity_penalty = component_count * 0.01 # 每个组件减少1%性能
# 基于组件类型调整
heavy_components = [comp for comp in components
if comp.type in [DesignElement.IMAGE, DesignElement.MODAL]]
heavy_penalty = len(heavy_components) * 0.05
performance_score = 1.0 - complexity_penalty - heavy_penalty
return max(0.1, performance_score) # 最低10%
def _load_design_patterns(self) -> Dict[str, Any]:
"""加载设计模式"""
return {
'hero_section': {
'type': 'hero_layout',
'grid': '12_column',
'spacing': 'medium',
'sections': ['header', 'hero', 'content', 'footer']
},
'grid_layout': {
'type': 'grid_layout',
'grid': '12_column',
'spacing': 'tight',
'sections': ['header', 'sidebar', 'main', 'footer']
},
'single_column': {
'type': 'single_column',
'grid': '8_column_centered',
'spacing': 'medium',
'sections': ['header', 'content', 'footer']
},
'product_grid': {
'type': 'product_grid',
'grid': '12_column',
'spacing': 'medium',
'sections': ['header', 'filters', 'products', 'footer']
},
'standard_layout': {
'type': 'standard',
'grid': '12_column',
'spacing': 'medium',
'sections': ['header', 'content', 'footer']
}
}
def _load_component_library(self) -> Dict[str, Any]:
"""加载组件库"""
return {
'navigation': {
'name': 'Navigation Bar',
'type': 'navigation',
'properties': {'items': [], 'style': 'horizontal'},
'default_size': (1200, 60),
'default_style': {'background': '#ffffff', 'border': '1px solid #e0e0e0'},
'interactions': ['click', 'hover'],
'accessibility_score': 0.9,
'performance_metrics': {'load_time': 0.1, 'size_kb': 5}
},
'logo': {
'name': 'Logo',
'type': 'image',
'properties': {'src': '', 'alt': 'Logo'},
'default_size': (120, 40),
'default_style': {},
'interactions': ['click'],
'accessibility_score': 0.8,
'performance_metrics': {'load_time': 0.2, 'size_kb': 10}
},
'headline': {
'name': 'Headline',
'type': 'text',
'properties': {'text': '', 'level': 'h1'},
'default_size': (800, 80),
'default_style': {'font_size': '48px', 'font_weight': 'bold'},
'interactions': [],
'accessibility_score': 1.0,
'performance_metrics': {'load_time': 0.05, 'size_kb': 1}
},
'cta_button': {
'name': 'Call to Action Button',
'type': 'button',
'properties': {'text': 'Click Here', 'action': ''},
'default_size': (200, 50),
'default_style': {'border_radius': '4px', 'padding': '12px 24px'},
'interactions': ['click', 'hover', 'focus'],
'accessibility_score': 0.95,
'performance_metrics': {'load_time': 0.05, 'size_kb': 2}
},
'hero_image': {
'name': 'Hero Image',
'type': 'image',
'properties': {'src': '', 'alt': 'Hero Image'},
'default_size': (1200, 600),
'default_style': {'object_fit': 'cover'},
'interactions': [],
'accessibility_score': 0.7,
'performance_metrics': {'load_time': 1.0, 'size_kb': 200}
},
'text_blocks': {
'name': 'Text Content',
'type': 'text',
'properties': {'text': '', 'level': 'p'},
'default_size': (800, 200),
'default_style': {'line_height': '1.6'},
'interactions': [],
'accessibility_score': 1.0,
'performance_metrics': {'load_time': 0.05, 'size_kb': 2}
},
'images': {
'name': 'Content Images',
'type': 'image',
'properties': {'src': '', 'alt': ''},
'default_size': (400, 300),
'default_style': {},
'interactions': ['click'],
'accessibility_score': 0.8,
'performance_metrics': {'load_time': 0.5, 'size_kb': 100}
},
'filters': {
'name': 'Filter Controls',
'type': 'form',
'properties': {'fields': []},
'default_size': (300, 400),
'default_style': {},
'interactions': ['input', 'click'],
'accessibility_score': 0.85,
'performance_metrics': {'load_time': 0.2, 'size_kb': 15}
},
'related_content': {
'name': 'Related Content',
'type': 'card',
'properties': {'items': []},
'default_size': (300, 200),
'default_style': {'border': '1px solid #e0e0e0'},
'interactions': ['click'],
'accessibility_score': 0.8,
'performance_metrics': {'load_time': 0.3, 'size_kb': 20}
},
'links': {
'name': 'Footer Links',
'type': 'navigation',
'properties': {'items': [], 'style': 'vertical'},
'default_size': (200, 150),
'default_style': {},
'interactions': ['click'],
'accessibility_score': 0.9,
'performance_metrics': {'load_time': 0.1, 'size_kb': 3}
},
'contact_info': {
'name': 'Contact Information',
'type': 'text',
'properties': {'text': '', 'level': 'p'},
'default_size': (300, 100),
'default_style': {'font_size': '14px'},
'interactions': [],
'accessibility_score': 1.0,
'performance_metrics': {'load_time': 0.05, 'size_kb': 1}
}
}
def _load_style_guide(self) -> Dict[str, Any]:
"""加载样式指南"""
return {
'default_colors': {
'primary': '#007bff',
'secondary': '#6c757d',
'success': '#28a745',
'warning': '#ffc107',
'danger': '#dc3545',
'text': '#333333',
'background': '#ffffff',
'on_primary': '#ffffff',
'on_secondary': '#ffffff'
},
'default_typography': {
'font_family': 'Arial, sans-serif',
'font_size': '16px',
'line_height': '1.5',
'font_weight': 'normal'
},
'breakpoints': {
'mobile': 576,
'tablet': 768,
'desktop': 992,
'large_desktop': 1200
},
'default_preferences': {
'colors': {},
'typography': {},
'layout_density': 'medium',
'interaction_style': 'standard',
'accessibility_needs': []
}
}
class UsabilityTester:
"""可用性测试器"""
def __init__(self):
self.test_scenarios = self._load_test_scenarios()
self.accessibility_checker = AccessibilityChecker()
self.performance_analyzer = PerformanceAnalyzer()
def run_automated_usability_test(self, design: UIDesign,
test_scenarios: List[Dict[str, Any]] = None) -> Dict[str, Any]:
"""运行自动化可用性测试"""
if test_scenarios is None:
test_scenarios = self.test_scenarios
test_results = {
'overall_score': 0.0,
'accessibility_results': {},
'performance_results': {},
'usability_issues': [],
'recommendations': [],
'scenario_results': []
}
# 可访问性测试
accessibility_results = self.accessibility_checker.check_accessibility(design)
test_results['accessibility_results'] = accessibility_results
# 性能测试
performance_results = self.performance_analyzer.analyze_performance(design)
test_results['performance_results'] = performance_results
# 场景测试
for scenario in test_scenarios:
scenario_result = self._test_scenario(design, scenario)
test_results['scenario_results'].append(scenario_result)
# 识别可用性问题
usability_issues = self._identify_usability_issues(design, test_results)
test_results['usability_issues'] = usability_issues
# 生成改进建议
recommendations = self._generate_usability_recommendations(test_results)
test_results['recommendations'] = recommendations
# 计算总体评分
overall_score = self._calculate_overall_usability_score(test_results)
test_results['overall_score'] = overall_score
return test_results
def _test_scenario(self, design: UIDesign, scenario: Dict[str, Any]) -> Dict[str, Any]:
"""测试特定场景"""
scenario_result = {
'scenario_name': scenario['name'],
'success': False,
'completion_time': 0.0,
'error_count': 0,
'issues': []
}
# 模拟用户路径
required_components = scenario.get('required_components', [])
found_components = []
for comp_type in required_components:
matching_components = [comp for comp in design.components
if comp.type.value == comp_type]
if matching_components:
found_components.append(comp_type)
else:
scenario_result['issues'].append(f"缺少必需组件: {comp_type}")
scenario_result['error_count'] += 1
# 检查组件可访问性
for component in design.components:
if component.accessibility_score < 0.7:
scenario_result['issues'].append(f"组件 {component.name} 可访问性评分过低")
scenario_result['error_count'] += 1
# 判断场景是否成功
scenario_result['success'] = len(found_components) == len(required_components)
# 估算完成时间（基于组件复杂度）
estimated_time = len(design.components) * 2 + scenario_result['error_count'] * 5
scenario_result['completion_time'] = estimated_time
return scenario_result
def _identify_usability_issues(self, design: UIDesign,
test_results: Dict[str, Any]) -> List[Dict[str, Any]]:
"""识别可用性问题"""
issues = []
# 基于可访问性结果识别问题
accessibility_results = test_results['accessibility_results']
if accessibility_results.get('overall_score', 1.0) < 0.8:
issues.append({
'type': 'accessibility',
'severity': 'high',
'description': '整体可访问性评分过低',
'affected_components': [comp.id for comp in design.components
if comp.accessibility_score < 0.8]
})
# 基于性能结果识别问题
performance_results = test_results['performance_results']
if performance_results.get('load_time', 0) > 3.0:
issues.append({
'type': 'performance',
'severity': 'medium',
'description': '页面加载时间过长',
'affected_components': []
})
# 基于场景测试识别问题
failed_scenarios = [result for result in test_results['scenario_results']
if not result['success']]
if failed_scenarios:
issues.append({
'type': 'functionality',
'severity': 'high',
'description': f'{len(failed_scenarios)} 个关键场景测试失败',
'affected_components': []
})
# 检查组件布局问题
overlapping_components = self._check_component_overlap(design.components)
if overlapping_components:
issues.append({
'type': 'layout',
'severity': 'medium',
'description': '存在组件重叠问题',
'affected_components': overlapping_components
})
return issues
def _generate_usability_recommendations(self, test_results: Dict[str, Any]) -> List[str]:
"""生成可用性改进建议"""
recommendations = []
# 基于问题生成建议
for issue in test_results['usability_issues']:
if issue['type'] == 'accessibility':
recommendations.append("改进组件的可访问性，添加适当的ARIA标签和键盘导航支持")
elif issue['type'] == 'performance':
recommendations.append("优化图片大小和组件加载，考虑懒加载技术")
elif issue['type'] == 'functionality':
recommendations.append("检查关键用户流程，确保所有必需组件都存在且可用")
elif issue['type'] == 'layout':
recommendations.append("调整组件布局，避免重叠和视觉混乱")
# 基于评分生成通用建议
overall_score = test_results['overall_score']
if overall_score < 0.6:
recommendations.append("整体可用性需要大幅改进，建议重新审视设计方案")
elif overall_score < 0.8:
recommendations.append("可用性有改进空间，重点关注用户体验细节")
return recommendations
def _calculate_overall_usability_score(self, test_results: Dict[str, Any]) -> float:
"""计算总体可用性评分"""
scores = []
# 可访问性评分
accessibility_score = test_results['accessibility_results'].get('overall_score', 0.5)
scores.append(accessibility_score * 0.3)
# 性能评分
performance_results = test_results['performance_results']
load_time = performance_results.get('load_time', 5.0)
performance_score = max(0, 1 - (load_time - 1) / 4) # 1秒为满分，5秒为0分
scores.append(performance_score * 0.2)
# 场景测试评分
scenario_results = test_results['scenario_results']
if scenario_results:
successful_scenarios = sum(1 for result in scenario_results if result['success'])
scenario_score = successful_scenarios / len(scenario_results)
scores.append(scenario_score * 0.3)
# 问题严重程度评分
issues = test_results['usability_issues']
issue_penalty = 0
for issue in issues:
if issue['severity'] == 'high':
issue_penalty += 0.1
elif issue['severity'] == 'medium':
issue_penalty += 0.05
elif issue['severity'] == 'low':
issue_penalty += 0.02
issue_score = max(0, 1 - issue_penalty)
scores.append(issue_score * 0.2)
return sum(scores)
def _check_component_overlap(self, components: List[DesignComponent]) -> List[str]:
"""检查组件重叠"""
overlapping = []
for i, comp1 in enumerate(components):
for j, comp2 in enumerate(components[i+1:], i+1):
if self._components_overlap(comp1, comp2):
overlapping.extend([comp1.id, comp2.id])
return list(set(overlapping))
def _components_overlap(self, comp1: DesignComponent, comp2: DesignComponent) -> bool:
"""检查两个组件是否重叠"""
# 简化的重叠检测
x1, y1 = comp1.position
w1, h1 = comp1.size
x2, y2 = comp2.position
w2, h2 = comp2.size
return not (x1 + w1 <= x2 or x2 + w2 <= x1 or y1 + h1 <= y2 or y2 + h2 <= y1)
def _load_test_scenarios(self) -> List[Dict[str, Any]]:
"""加载测试场景"""
return [
{
'name': '用户注册流程',
'description': '新用户完成注册',
'required_components': ['form', 'input_field', 'button'],
'expected_completion_time': 120,
'critical': True
},
{
'name': '产品搜索',
'description': '用户搜索并找到产品',
'required_components': ['input_field', 'button', 'navigation'],
'expected_completion_time': 60,
'critical': True
},
{
'name': '内容浏览',
'description': '用户浏览页面内容',
'required_components': ['text', 'image', 'navigation'],
'expected_completion_time': 30,
'critical': False
},
{
'name': '联系我们',
'description': '用户查找联系信息',
'required_components': ['text', 'navigation'],
'expected_completion_time': 45,
'critical': False
}
]
class AccessibilityChecker:
"""可访问性检查器"""
def check_accessibility(self, design: UIDesign) -> Dict[str, Any]:
"""检查可访问性"""
results = {
'overall_score': 0.0,
'wcag_compliance': {},
'issues': [],
'recommendations': []
}
# 检查各个WCAG原则
perceivable_score = self._check_perceivable(design)
operable_score = self._check_operable(design)
understandable_score = self._check_understandable(design)
robust_score = self._check_robust(design)
results['wcag_compliance'] = {
'perceivable': perceivable_score,
'operable': operable_score,
'understandable': understandable_score,
'robust': robust_score
}
# 计算总体评分
results['overall_score'] = (perceivable_score + operable_score +
understandable_score + robust_score) / 4
# 生成问题和建议
if perceivable_score < 0.8:
results['issues'].append("感知性问题：颜色对比度或文本可读性不足")
results['recommendations'].append("提高颜色对比度，确保文本清晰可读")
if operable_score < 0.8:
results['issues'].append("可操作性问题：键盘导航或焦点管理不当")
results['recommendations'].append("改进键盘导航和焦点指示")
if understandable_score < 0.8:
results['issues'].append("可理解性问题：界面逻辑或标签不清晰")
results['recommendations'].append("简化界面逻辑，使用清晰的标签")
if robust_score < 0.8:
results['issues'].append("健壮性问题：缺少语义化标记")
results['recommendations'].append("使用语义化HTML和ARIA标签")
return results
def _check_perceivable(self, design: UIDesign) -> float:
"""检查感知性"""
score = 1.0
# 检查颜色对比度
if not self._has_sufficient_contrast(design.color_scheme):
score -= 0.3
# 检查文本大小
typography = design.typography
font_size = int(typography.get('font_size', '16px').replace('px', ''))
if font_size < 14:
score -= 0.2
# 检查图片alt文本
image_components = [comp for comp in design.components
if comp.type == DesignElement.IMAGE]
for img in image_components:
if not img.properties.get('alt'):
score -= 0.1
return max(0.0, score)
def _check_operable(self, design: UIDesign) -> float:
"""检查可操作性"""
score = 1.0
# 检查交互元素大小
interactive_components = [comp for comp in design.components
if comp.interactions]
for comp in interactive_components:
width, height = comp.size
if width < 44 or height < 44: # WCAG建议最小44px
score -= 0.2
# 检查键盘导航支持
focusable_components = [comp for comp in design.components
if 'focus' in comp.interactions]
if len(focusable_components) < len(interactive_components):
score -= 0.3
return max(0.0, score)
def _check_understandable(self, design: UIDesign) -> float:
"""检查可理解性"""
score = 1.0
# 检查导航一致性
nav_components = [comp for comp in design.components
if comp.type == DesignElement.NAVIGATION]
if len(nav_components) > 1:
# 简化检查：假设多个导航组件可能造成混乱
score -= 0.2
# 检查表单标签
form_components = [comp for comp in design.components
if comp.type == DesignElement.FORM]
for form in form_components:
if not form.properties.get('labels'):
score -= 0.3
return max(0.0, score)
def _check_robust(self, design: UIDesign) -> float:
"""检查健壮性"""
score = 1.0
# 检查语义化标记（简化检查）
text_components = [comp for comp in design.components
if comp.type == DesignElement.TEXT]
for text in text_components:
if not text.properties.get('level'): # 缺少语义级别
score -= 0.1
# 检查ARIA支持
components_with_aria = [comp for comp in design.components
if 'aria' in comp.properties]
if len(components_with_aria) < len(design.components) * 0.5:
score -= 0.3
return max(0.0, score)
def _has_sufficient_contrast(self, color_scheme: Dict[str, str]) -> bool:
"""检查颜色对比度是否足够"""
# 简化的对比度检查
# 实际应用中应该使用真实的颜色对比度计算
primary = color_scheme.get('primary', '#000000')
background = color_scheme.get('background', '#ffffff')
# 简单的明暗检查
primary_is_dark = primary.lower() in ['#000000', '#333333', '#666666']
background_is_light = background.lower() in ['#ffffff', '#f8f9fa', '#e9ecef']
return primary_is_dark and background_is_light
class PerformanceAnalyzer:
"""性能分析器"""
def analyze_performance(self, design: UIDesign) -> Dict[str, Any]:
"""分析设计性能"""
results = {
'load_time': 0.0,
'size_estimate': 0.0,
'optimization_opportunities': [],
'performance_score': 0.0
}
# 计算预估加载时间
total_load_time = 0.0
total_size = 0.0
for component in design.components:
comp_load_time = component.performance_metrics.get('load_time', 0.1)
comp_size = component.performance_metrics.get('size_kb', 5)
total_load_time += comp_load_time
total_size += comp_size
results['load_time'] = total_load_time
results['size_estimate'] = total_size
# 识别优化机会
optimization_opportunities = []
# 检查大型图片
large_images = [comp for comp in design.components
if (comp.type == DesignElement.IMAGE and
comp.performance_metrics.get('size_kb', 0) > 100)]
if large_images:
optimization_opportunities.append({
'type': 'image_optimization',
'description': f'{len(large_images)} 个图片文件过大',
'potential_savings': sum(comp.performance_metrics.get('size_kb', 0) * 0.5
for comp in large_images)
})
# 检查组件数量
if len(design.components) > 20:
optimization_opportunities.append({
'type': 'component_reduction',
'description': '组件数量过多，可能影响性能',
'potential_savings': (len(design.components) - 20) * 0.05
})
# 检查复杂交互
complex_components = [comp for comp in design.components
if len(comp.interactions) > 3]
if complex_components:
optimization_opportunities.append({
'type': 'interaction_simplification',
'description': f'{len(complex_components)} 个组件交互过于复杂',
'potential_savings': len(complex_components) * 0.1
})
results['optimization_opportunities'] = optimization_opportunities
# 计算性能评分
performance_score = self._calculate_performance_score(results)
results['performance_score'] = performance_score
return results
def _calculate_performance_score(self, results: Dict[str, Any]) -> float:
"""计算性能评分"""
load_time = results['load_time']
size_estimate = results['size_estimate']
# 基于加载时间评分（1秒为满分，5秒为0分）
time_score = max(0, 1 - (load_time - 1) / 4)
# 基于文件大小评分（100KB为满分，1MB为0分）
size_score = max(0, 1 - (size_estimate - 100) / 900)
# 综合评分
return (time_score + size_score) / 2
# 使用示例和演示
def demo_ux_design_system():
"""演示UX设计系统"""
print("🎨 AI辅助的用户体验设计与优化系统演示")
print("=" * 60)
# 1. 生成示例用户行为数据
print("\n1. 用户行为分析")
print("-" * 30)
# 创建示例用户行为数据
sample_actions = []
users = ['user1', 'user2', 'user3', 'user4', 'user5']
for i in range(100):
action = UserAction(
user_id=np.random.choice(users),
session_id=f"session_{i//10}",
timestamp=datetime.now() - timedelta(minutes=np.random.randint(0, 1440)),
action_type=np.random.choice(list(UserActionType)),
element_id=f"element_{np.random.randint(1, 10)}",
element_type=np.random.choice(list(DesignElement)),
coordinates=(np.random.randint(0, 1200), np.random.randint(0, 800)),
device_type=np.random.choice(list(DeviceType)),
page_url="/home",
duration=np.random.exponential(5),
success=np.random.choice([True, False], p=[0.8, 0.2]),
context={}
)
sample_actions.append(action)
# 分析用户行为
behavior_analyzer = UserBehaviorAnalyzer()
behavior_analysis = behavior_analyzer.analyze_user_behavior(sample_actions)
print("用户行为分析结果:")
stats = behavior_analysis['basic_statistics']
print(f" 总行为数: {stats['total_actions']}")
print(f" 独立用户: {stats['unique_users']}")
print(f" 成功率: {stats['success_rate']:.1%}")
print(f" 设备分布: {stats['device_distribution']}")
print(f"\n发现 {len(behavior_analysis['pain_points'])} 个痛点:")
for pain_point in behavior_analysis['pain_points'][:3]:
print(f" - {pain_point['description']}")
# 2. 创建用户画像
print("\n2. 用户画像生成")
print("-" * 30)
sample_profiles = [
UserProfile(
user_id="user1",
demographics={"age": 28, "gender": "female", "location": "urban"},
preferences={"color_scheme": "light", "layout": "compact"},
behavior_patterns={"session_duration": 15, "pages_per_session": 5},
device_usage={DeviceType.MOBILE: 0.7, DeviceType.DESKTOP: 0.3},
skill_level="intermediate",
goals=["quick_access", "efficiency"],
pain_points=["slow_loading", "complex_navigation"],
satisfaction_score=0.75
),
UserProfile(
user_id="user2",
demographics={"age": 45, "gender": "male", "location": "suburban"},
preferences={"color_scheme": "high_contrast", "layout": "spacious"},
behavior_patterns={"session_duration": 25, "pages_per_session": 8},
device_usage={DeviceType.DESKTOP: 0.8, DeviceType.MOBILE: 0.2},
skill_level="beginner",
goals=["clear_information", "simple_interface"],
pain_points=["small_text", "confusing_layout"],
satisfaction_score=0.65
)
]
print("用户画像:")
for profile in sample_profiles:
print(f" {profile.user_id}: {profile.skill_level}用户, 主要使用{max(profile.device_usage, key=profile.device_usage.get).value}")
# 3. 智能UI设计生成
print("\n3. 智能UI设计生成")
print("-" * 30)
design_requirements = {
'name': '产品展示页面',
'page_type': 'ecommerce',
'target_audience': 'general',
'goals': ['product_discovery', 'purchase_conversion'],
'content_types': ['header', 'hero', 'content', 'sidebar', 'footer'],
'device_priorities': ['mobile', 'desktop'],
'complexity': 'medium'
}
ui_generator = IntelligentUIGenerator()
generated_design = ui_generator.generate_ui_design(design_requirements, sample_profiles)
print(f"生成设计: {generated_design.name}")
print(f"组件数量: {len(generated_design.components)}")
print(f"可访问性评分: {generated_design.accessibility_compliance:.2f}")
print(f"性能评分: {generated_design.performance_score:.2f}")
print("\n主要组件:")
for component in generated_design.components[:5]:
print(f" - {component.name} ({component.type.value})")
# 4. 自动化可用性测试
print("\n4. 自动化可用性测试")
print("-" * 30)
usability_tester = UsabilityTester()
test_results = usability_tester.run_automated_usability_test(generated_design)
print(f"整体可用性评分: {test_results['overall_score']:.2f}")
print("\nWCAG合规性:")
wcag_results = test_results['accessibility_results']['wcag_compliance']
for principle, score in wcag_results.items():
print(f" {principle}: {score:.2f}")
print(f"\n发现 {len(test_results['usability_issues'])} 个可用性问题:")
for issue in test_results['usability_issues']:
print(f" - {issue['description']} (严重程度: {issue['severity']})")
print(f"\n改进建议 ({len(test_results['recommendations'])}):")
for recommendation in test_results['recommendations'][:3]:
print(f" - {recommendation}")
# 5. 性能分析
print("\n5. 性能分析")
print("-" * 30)
performance_results = test_results['performance_results']
print(f"预估加载时间: {performance_results['load_time']:.2f} 秒")
print(f"预估文件大小: {performance_results['size_estimate']:.1f} KB")
print(f"性能评分: {performance_results['performance_score']:.2f}")
print(f"\n优化机会 ({len(performance_results['optimization_opportunities'])}):")
for opportunity in performance_results['optimization_opportunities']:
print(f" - {opportunity['description']}")
if 'potential_savings' in opportunity:
print(f" 潜在节省: {opportunity['potential_savings']:.1f} KB")
# 6. 生成设计报告
print("\n6. 生成设计报告")
print("-" * 30)
report_generator = UXReportGenerator()
design_report = report_generator.generate_design_report(
generated_design, behavior_analysis, test_results, sample_profiles
)
# 保存报告
with open('ux_design_report.md', 'w', encoding='utf-8') as f:
f.write(design_report)
print("UX设计报告已生成: ux_design_report.md")
print("\n🎯 UX设计系统演示完成！")
print("\n生成的文件:")
print("- ux_design_report.md (UX设计报告)")
class UXReportGenerator:
"""UX报告生成器"""
def generate_design_report(self, design: UIDesign,
behavior_analysis: Dict[str, Any],
test_results: Dict[str, Any],
user_profiles: List[UserProfile]) -> str:
"""生成设计报告"""
report = f"""# UX设计分析报告
## 项目概述
### 设计信息
- **设计名称**: {design.name}
- **页面类型**: {design.page_type}
- **组件数量**: {len(design.components)}
- **生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
### 设计目标
基于用户行为分析和用户画像，生成符合用户需求的界面设计。
## 用户行为分析
### 基础统计
"""
stats = behavior_analysis['basic_statistics']
report += f"""
- **总用户行为**: {stats['total_actions']} 次
- **独立用户数**: {stats['unique_users']} 人
- **整体成功率**: {stats['success_rate']:.1%}
- **平均会话时长**: {stats.get('avg_session_duration', 0):.1f} 分钟
### 设备使用分布
"""
for device, count in stats['device_distribution'].items():
percentage = count / stats['total_actions'] * 100
report += f"- **{device}**: {count} 次 ({percentage:.1f}%)\n"
report += f"""
### 用户痛点
发现 {len(behavior_analysis['pain_points'])} 个主要痛点:
"""
for pain_point in behavior_analysis['pain_points']:
report += f"- {pain_point['description']}\n"
report += f"""
## 用户画像分析
### 目标用户群体
分析了 {len(user_profiles)} 个用户画像:
"""
for profile in user_profiles:
primary_device = max(profile.device_usage, key=profile.device_usage.get)
report += f"""
#### {profile.user_id}
- **技能水平**: {profile.skill_level}
- **主要设备**: {primary_device.value}
- **满意度**: {profile.satisfaction_score:.1%}
- **主要目标**: {', '.join(profile.goals)}
"""
report += f"""
## 设计方案
### 组件构成
"""
component_types = {}
for component in design.components:
comp_type = component.type.value
if comp_type not in component_types:
component_types[comp_type] = 0
component_types[comp_type] += 1
for comp_type, count in component_types.items():
report += f"- **{comp_type}**: {count} 个\n"
report += f"""
### 设计特点
- **颜色方案**: {design.color_scheme.get('primary', 'N/A')} (主色调)
- **字体**: {design.typography.get('font_family', 'N/A')}
- **响应式设计**: 支持 {len(design.responsive_breakpoints)} 个断点
## 可用性测试结果
### 整体评分
- **可用性评分**: {test_results['overall_score']:.2f}/1.0
### WCAG可访问性合规
"""
wcag_results = test_results['accessibility_results']['wcag_compliance']
for principle, score in wcag_results.items():
status = "✅ 通过" if score >= 0.8 else "⚠️ 需改进" if score >= 0.6 else "❌ 不合规"
report += f"- **{principle}**: {score:.2f} {status}\n"
report += f"""
### 发现的问题
"""
for issue in test_results['usability_issues']:
severity_icon = "🔴" if issue['severity'] == 'high' else "🟡" if issue['severity'] == 'medium' else "🟢"
report += f"- {severity_icon} {issue['description']}\n"
report += f"""
## 性能分析
### 性能指标
"""
performance = test_results['performance_results']
report += f"""
- **预估加载时间**: {performance['load_time']:.2f} 秒
- **预估文件大小**: {performance['size_estimate']:.1f} KB
- **性能评分**: {performance['performance_score']:.2f}/1.0
### 优化建议
"""
for opportunity in performance['optimization_opportunities']:
report += f"- {opportunity['description']}\n"
if 'potential_savings' in opportunity:
report += f" *潜在节省: {opportunity['potential_savings']:.1f} KB*\n"
report += f"""
## 改进建议
### 优先级改进项
"""
for recommendation in test_results['recommendations']:
report += f"- {recommendation}\n"
report += f"""
### 下一步行动
1. **立即修复**: 解决高严重程度的可用性问题
2. **性能优化**: 实施性能优化建议
3. **用户测试**: 进行真实用户测试验证
4. **迭代改进**: 基于用户反馈持续优化
## 附录
### 设计规范
- **最小触摸目标**: 44px × 44px
- **颜色对比度**: 至少 4.5:1
- **字体大小**: 最小 14px
- **响应式断点**: {', '.join(f'{k}px' for k in design.responsive_breakpoints.values())}
---
**报告生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
**生成工具**: AI辅助UX设计系统
"""
return report
if __name__ == "__main__":
demo_ux_design_system()
个性化用户体验
智能推荐系统
class PersonalizationEngine:
"""个性化引擎"""
def __init__(self):
self.user_models = {}
self.content_features = {}
self.recommendation_models = self._load_recommendation_models()
def personalize_experience(self, user_id: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""个性化用户体验"""
# 获取用户模型
user_model = self._get_or_create_user_model(user_id)
# 生成个性化内容
personalized_content = self._generate_personalized_content(user_model, context)
# 调整界面布局
personalized_layout = self._adjust_layout_for_user(user_model, context)
# 推荐相关内容
content_recommendations = self._recommend_content(user_model, context)
# 优化交互方式
interaction_preferences = self._optimize_interactions(user_model)
return {
'personalized_content': personalized_content,
'layout_adjustments': personalized_layout,
'content_recommendations': content_recommendations,
'interaction_preferences': interaction_preferences,
'confidence_score': self._calculate_personalization_confidence(user_model)
}
def update_user_model(self, user_id: str, interaction_data: Dict[str, Any]):
"""更新用户模型"""
if user_id not in self.user_models:
self.user_models[user_id] = self._create_initial_user_model()
user_model = self.user_models[user_id]
# 更新偏好
self._update_preferences(user_model, interaction_data)
# 更新行为模式
self._update_behavior_patterns(user_model, interaction_data)
# 更新兴趣标签
self._update_interest_tags(user_model, interaction_data)
def _get_or_create_user_model(self, user_id: str) -> Dict[str, Any]:
"""获取或创建用户模型"""
if user_id not in self.user_models:
self.user_models[user_id] = self._create_initial_user_model()
return self.user_models[user_id]
def _create_initial_user_model(self) -> Dict[str, Any]:
"""创建初始用户模型"""
return {
'preferences': {
'color_scheme': 'auto',
'layout_density': 'medium',
'font_size': 'medium',
'animation_level': 'medium'
},
'behavior_patterns': {
'session_duration': 0,
'click_frequency': 0,
'scroll_speed': 0,
'preferred_content_types': []
},
'interest_tags': [],
'skill_level': 'intermediate',
'device_preferences': {},
'interaction_history': [],
'satisfaction_indicators': []
}
class ABTestOptimizer:
"""A/B测试优化器"""
def __init__(self):
self.active_tests = {}
self.test_results = {}
self.statistical_analyzer = StatisticalAnalyzer()
def create_ab_test(self, test_config: Dict[str, Any]) -> str:
"""创建A/B测试"""
test_id = f"test_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
self.active_tests[test_id] = {
'name': test_config['name'],
'variants': test_config['variants'],
'traffic_split': test_config.get('traffic_split', [0.5, 0.5]),
'success_metrics': test_config['success_metrics'],
'start_date': datetime.now(),
'duration_days': test_config.get('duration_days', 14),
'participants': {},
'results': {'variant_a': [], 'variant_b': []}
}
return test_id
def assign_user_to_variant(self, test_id: str, user_id: str) -> str:
"""为用户分配测试变体"""
if test_id not in self.active_tests:
return 'control'
test = self.active_tests[test_id]
# 如果用户已经分配过，返回之前的分配
if user_id in test['participants']:
return test['participants'][user_id]
# 基于用户ID的哈希值分配变体
import hashlib
hash_value = int(hashlib.md5(f"{test_id}_{user_id}".encode()).hexdigest(), 16)
split_point = test['traffic_split'][0]
if (hash_value % 100) / 100 < split_point:
variant = 'variant_a'
else:
variant = 'variant_b'
test['participants'][user_id] = variant
return variant
def record_test_result(self, test_id: str, user_id: str,
metric_name: str, value: float):
"""记录测试结果"""
if test_id not in self.active_tests:
return
test = self.active_tests[test_id]
variant = test['participants'].get(user_id)
if variant:
test['results'][variant].append({
'user_id': user_id,
'metric': metric_name,
'value': value,
'timestamp': datetime.now()
})
def analyze_test_results(self, test_id: str) -> Dict[str, Any]:
"""分析测试结果"""
if test_id not in self.active_tests:
return {}
test = self.active_tests[test_id]
results = test['results']
analysis = {
'test_name': test['name'],
'participants': {
'variant_a': len(set(r['user_id'] for r in results['variant_a'])),
'variant_b': len(set(r['user_id'] for r in results['variant_b']))
},
'metrics_analysis': {},
'statistical_significance': {},
'recommendations': []
}
# 分析每个指标
for metric in test['success_metrics']:
metric_analysis = self._analyze_metric(results, metric)
analysis['metrics_analysis'][metric] = metric_analysis
# 统计显著性检验
significance = self.statistical_analyzer.test_significance(
metric_analysis['variant_a_values'],
metric_analysis['variant_b_values']
)
analysis['statistical_significance'][metric] = significance
# 生成建议
analysis['recommendations'] = self._generate_test_recommendations(analysis)
return analysis
def _analyze_metric(self, results: Dict[str, List], metric_name: str) -> Dict[str, Any]:
"""分析单个指标"""
variant_a_values = [r['value'] for r in results['variant_a']
if r['metric'] == metric_name]
variant_b_values = [r['value'] for r in results['variant_b']
if r['metric'] == metric_name]
return {
'variant_a_values': variant_a_values,
'variant_b_values': variant_b_values,
'variant_a_mean': np.mean(variant_a_values) if variant_a_values else 0,
'variant_b_mean': np.mean(variant_b_values) if variant_b_values else 0,
'improvement': self._calculate_improvement(variant_a_values, variant_b_values)
}
def _calculate_improvement(self, variant_a: List[float], variant_b: List[float]) -> float:
"""计算改进幅度"""
if not variant_a or not variant_b:
return 0.0
mean_a = np.mean(variant_a)
mean_b = np.mean(variant_b)
if mean_a == 0:
return 0.0
return (mean_b - mean_a) / mean_a * 100
class StatisticalAnalyzer:
"""统计分析器"""
def test_significance(self, group_a: List[float], group_b: List[float]) -> Dict[str, Any]:
"""统计显著性检验"""
if len(group_a) < 2 or len(group_b) < 2:
return {'significant': False, 'p_value': 1.0, 'confidence': 0.0}
# 简化的t检验
from scipy import stats
try:
t_stat, p_value = stats.ttest_ind(group_a, group_b)
significant = p_value < 0.05
confidence = (1 - p_value) * 100
return {
'significant': significant,
'p_value': p_value,
'confidence': confidence,
't_statistic': t_stat
}
except:
return {'significant': False, 'p_value': 1.0, 'confidence': 0.0}
总结与最佳实践
UX设计最佳实践
class UXDesignBestPractices:
"""UX设计最佳实践"""
@staticmethod
def get_design_principles() -> Dict[str, List[str]]:
"""获取设计原则"""
return {
'用户中心设计': [
'深入了解用户需求',
'基于用户行为数据设计',
'持续收集用户反馈',
'迭代优化用户体验',
'考虑不同用户群体'
],
'可访问性设计': [
'遵循WCAG指南',
'确保键盘导航',
'提供替代文本',
'保证颜色对比度',
'支持屏幕阅读器'
],
'响应式设计': [
'移动优先设计',
'灵活的网格系统',
'适配多种设备',
'优化触摸交互',
'考虑网络条件'
],
'性能优化': [
'优化加载速度',
'减少HTTP请求',
'压缩图片资源',
'使用CDN加速',
'实施懒加载'
]
}
@staticmethod
def get_testing_guidelines() -> Dict[str, List[str]]:
"""获取测试指南"""
return {
'可用性测试': [
'定义测试目标',
'选择代表性用户',
'设计真实场景',
'观察用户行为',
'收集定量数据'
],
'A/B测试': [
'明确假设',
'控制变量',
'确保样本量',
'统计显著性',
'长期监控效果'
],
'自动化测试': [
'建立测试流程',
'定期执行检查',
'监控关键指标',
'及时发现问题',
'持续改进'
]
}
def main():
"""主函数"""
print("🎨 AI辅助的用户体验设计与优化系统")
print("=" * 50)
# 运行演示
demo_ux_design_system()
# 显示最佳实践
practices = UXDesignBestPractices()
design_principles = practices.get_design_principles()
print("\n📋 UX设计最佳实践:")
print("-" * 30)
for category, principle_list in design_principles.items():
print(f"\n{category}:")
for principle in principle_list[:3]:
print(f" ✓ {principle}")
print("\n🎯 UX设计优化完成！")
if __name__ == "__main__":
main()
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