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门户网站开发投标文件.doc,宿州网站建设公司哪家好,wordpress 万能 主题,盐城营销网站建设我将为您开发一个学生学习效率分析系统#xff0c;能够分析作业完成时间模式#xff0c;识别效率低谷期#xff0c;并提供个性化的时间管理建议。项目结构study_efficiency_analyzer/├── main.py # 主程序入口├── data_collector.py # 数据收集模块├── analyzer.py…我将为您开发一个学生学习效率分析系统能够分析作业完成时间模式识别效率低谷期并提供个性化的时间管理建议。项目结构study_efficiency_analyzer/├── main.py # 主程序入口├── data_collector.py # 数据收集模块├── analyzer.py # 数据分析模块├── visualizer.py # 数据可视化模块├── advisor.py # 建议生成模块├── models.py # 数据模型├── utils.py # 工具函数├── config.py # 配置文件├── student_data.csv # 学生数据文件├── requirements.txt # 依赖包├── README.md # 项目说明└── knowledge_cards.md # 核心知识点卡片1. requirements.txtpandas1.3.0numpy1.21.0matplotlib3.5.0seaborn0.11.0plotly5.0.0scikit-learn1.1.0datetimecollectionsstatistics2. config.py - 配置文件学习效率分析系统配置文件# 时间段定义TIME_PERIODS {early_morning: {start: 5, end: 9, name: 清晨时段},morning: {start: 9, end: 12, name: 上午时段},afternoon: {start: 12, end: 18, name: 下午时段},evening: {start: 18, end: 22, name: 晚上时段},night: {start: 22, end: 5, name: 深夜时段}}# 效率等级定义EFFICIENCY_LEVELS {high: {threshold: 0.8, name: 高效率, color: #28a745},medium: {threshold: 0.6, name: 中等效率, color: #ffc107},low: {threshold: 0.4, name: 低效率, color: #fd7e14},very_low: {threshold: 0.0, name: 极低效率, color: #dc3545}}# 学科分类SUBJECTS {math: 数学,chinese: 语文,english: 英语,science: 科学,social: 社会,other: 其他}# 分析参数ANALYSIS_CONFIG {min_data_days: 7, # 最少需要的数据天数efficiency_threshold: 0.6, # 效率阈值fatigue_detection_hours: 3, # 疲劳检测时长(小时)recommendation_count: 5 # 建议数量}# 可视化配置VISUALIZATION_CONFIG {figure_size: (12, 8),dpi: 300,style: seaborn-v0_8,color_palette: [#1f77b4, #ff7f0e, #2ca02c, #d62728, #9467bd]}3. models.py - 数据模型数据模型定义使用dataclass定义数据结构from dataclasses import dataclass, fieldfrom typing import List, Dict, Optionalfrom datetime import datetime, timefrom enum import Enumclass SubjectType(Enum):学科类型枚举MATH mathCHINESE chineseENGLISH englishSCIENCE scienceSOCIAL socialOTHER otherclass EfficiencyLevel(Enum):效率等级枚举VERY_LOW very_lowLOW lowMEDIUM mediumHIGH highdataclassclass StudyRecord:学习记录数据类date: str # 日期 YYYY-MM-DDstart_time: str # 开始时间 HH:MMend_time: str # 结束时间 HH:MMsubject: str # 学科duration_minutes: int # 持续时间(分钟)efficiency_score: Optional[float] None # 效率评分(0-1)notes: str # 备注def __post_init__(self):初始化后处理if self.efficiency_score is None:self.efficiency_score self._calculate_default_efficiency()def _calculate_default_efficiency(self) - float:计算默认效率分数# 基于时间段和持续时间的简单效率估算start_hour int(self.start_time.split(:)[0])duration_hours self.duration_minutes / 60# 基础效率base_efficiency 0.7# 时间段调整if 9 start_hour 11: # 上午高效时段base_efficiency 0.15elif 14 start_hour 16: # 下午中等时段base_efficiency 0.05elif 19 start_hour 21: # 晚上良好时段base_efficiency 0.1elif start_hour 7 or start_hour 22: # 深夜低效时段base_efficiency - 0.2# 持续时间调整 (过长会降低效率)if duration_hours 3:base_efficiency - (duration_hours - 3) * 0.1return max(0.1, min(1.0, base_efficiency))dataclassclass DailyAnalysis:每日分析结果date: strtotal_study_time: int # 总学习时间(分钟)average_efficiency: float # 平均效率subject_times: Dict[str, int] # 各学科时间分布time_periods: Dict[str, int] # 各时段时间分布low_efficiency_periods: List[Dict] # 低效率时段recommendations: List[str] # 建议列表dataclassclass StudentProfile:学生档案name: strgrade: strstudy_records: List[StudyRecord] field(default_factorylist)analysis_results: List[DailyAnalysis] field(default_factorylist)def add_record(self, record: StudyRecord):添加学习记录self.study_records.append(record)def get_recent_records(self, days: int 7) - List[StudyRecord]:获取最近N天的记录# 这里简化处理实际应该按日期筛选return self.study_records[-days:] if len(self.study_records) days else self.study_records4. data_collector.py - 数据收集模块数据收集模块负责收集和管理学生的学习数据import pandas as pdimport jsonfrom datetime import datetime, timedeltafrom typing import List, Dict, Optionalfrom .models import StudyRecord, StudentProfilefrom .utils import TimeUtils, ValidationUtilsclass DataCollector:数据收集器类def __init__(self, data_file: str student_data.csv):self.data_file data_fileself.student_profiles: Dict[str, StudentProfile] {}self.load_data()def load_data(self):从文件加载数据try:df pd.read_csv(self.data_file)self._process_dataframe(df)print(f成功加载 {len(self.student_profiles)} 个学生的数据)except FileNotFoundError:print(f数据文件 {self.data_file} 不存在将创建新文件)self._create_sample_data()except Exception as e:print(f加载数据失败: {e})self._create_sample_data()def _process_dataframe(self, df: pd.DataFrame):处理DataFrame数据for _, row in df.iterrows():student_name row.get(student_name, 未知学生)if student_name not in self.student_profiles:self.student_profiles[student_name] StudentProfile(namestudent_name,graderow.get(grade, 未知年级))# 创建学习记录record StudyRecord(daterow[date],start_timerow[start_time],end_timerow[end_time],subjectrow[subject],duration_minutesint(row[duration_minutes]),efficiency_scorefloat(row.get(efficiency_score, 0.5)),notesrow.get(notes, ))self.student_profiles[student_name].add_record(record)def _create_sample_data(self):创建示例数据sample_data [# 学生1小明的学习记录{student_name: 小明,grade: 初二,date: 2024-01-15, start_time: 09:00, end_time: 10:30,subject: math, duration_minutes: 90, efficiency_score: 0.8, notes: 状态不错},{student_name: 小明,grade: 初二,date: 2024-01-15, start_time: 14:00, end_time: 15:30,subject: english, duration_minutes: 90, efficiency_score: 0.6, notes: },{student_name: 小明,grade: 初二,date: 2024-01-15, start_time: 19:00, end_time: 21:00,subject: chinese, duration_minutes: 120, efficiency_score: 0.7, notes: 有点累},{student_name: 小明,grade: 初二,date: 2024-01-16, start_time: 08:30, end_time: 10:00,subject: science, duration_minutes: 90, efficiency_score: 0.85, notes: 早上效率高},{student_name: 小明,grade: 初二,date: 2024-01-16, start_time: 15:00, end_time: 17:00,subject: math, duration_minutes: 120, efficiency_score: 0.5, notes: 下午犯困},{student_name: 小明,grade: 初二,date: 2024-01-16, start_time: 22:00, end_time: 23:30,subject: english, duration_minutes: 90, efficiency_score: 0.4, notes: 太晚了效率低},# 学生2小红的学习记录{student_name: 小红,grade: 初三,date: 2024-01-15, start_time: 07:00, end_time: 08:30,subject: english, duration_minutes: 90, efficiency_score: 0.9, notes: 晨读效果好},{student_name: 小红,grade: 初三,date: 2024-01-15, start_time: 10:00, end_time: 12:00,subject: math, duration_minutes: 120, efficiency_score: 0.75, notes: },{student_name: 小红,grade: 初三,date: 2024-01-15, start_time: 20:00, end_time: 21:30,subject: science, duration_minutes: 90, efficiency_score: 0.8, notes: 晚上专注}]df pd.DataFrame(sample_data)df.to_csv(self.data_file, indexFalse, encodingutf-8-sig)self._process_dataframe(df)print(已创建示例数据)def add_study_record(self, student_name: str, record: StudyRecord) - bool:添加学习记录try:if student_name not in self.student_profiles:self.student_profiles[student_name] StudentProfile(namestudent_name,grade未知年级)self.student_profiles[student_name].add_record(record)self._save_data()return Trueexcept Exception as e:print(f添加记录失败: {e})return Falsedef interactive_add_record(self):交互式添加学习记录print(\n 添加学习记录 )student_name input(学生姓名: ).strip()if not student_name:print(学生姓名不能为空)return# 获取日期date_input input(日期 (YYYY-MM-DD回车使用今天): ).strip()if not date_input:date_input datetime.now().strftime(%Y-%m-%d)# 验证日期格式if not ValidationUtils.validate_date(date_input):print(日期格式错误请使用 YYYY-MM-DD 格式)return# 获取时间start_time input(开始时间 (HH:MM): ).strip()end_time input(结束时间 (HH:MM): ).strip()if not ValidationUtils.validate_time(start_time) or not ValidationUtils.validate_time(end_time):print(时间格式错误请使用 HH:MM 格式)return# 获取学科print(可选学科:, , .join([math, chinese, english, science, social, other]))subject input(学科: ).strip()# 获取备注notes input(备注 (可选): ).strip()# 计算持续时间try:start_dt datetime.strptime(f{date_input} {start_time}, %Y-%m-%d %H:%M)end_dt datetime.strptime(f{date_input} {end_time}, %Y-%m-%d %H:%M)if end_dt start_dt:print(结束时间必须晚于开始时间)returnduration_minutes int((end_dt - start_dt).total_seconds() / 60)# 创建记录record StudyRecord(datedate_input,start_timestart_time,end_timeend_time,subjectsubject,duration_minutesduration_minutes,notesnotes)if self.add_study_record(student_name, record):print(✅ 记录添加成功)else:print(❌ 记录添加失败)except ValueError as e:print(f时间计算错误: {e})def _save_data(self):保存数据到文件try:records []for profile in self.student_profiles.values():for record in profile.study_records:records.append({student_name: profile.name,grade: profile.grade,date: record.date,start_time: record.start_time,end_time: record.end_time,subject: record.subject,duration_minutes: record.duration_minutes,efficiency_score: record.efficiency_score,notes: record.notes})df pd.DataFrame(records)df.to_csv(self.data_file, indexFalse, encodingutf-8-sig)except Exception as e:print(f保存数据失败: {e})def get_student_names(self) - List[str]:获取所有学生姓名return list(self.student_profiles.keys())def get_student_profile(self, student_name: str) - Optional[StudentProfile]:获取学生档案return self.student_profiles.get(student_name)def get_all_profiles(self) - Dict[str, StudentProfile]:获取所有学生档案return self.student_profiles5. analyzer.py - 数据分析模块数据分析模块负责分析学习数据的模式和效率import pandas as pdimport numpy as npfrom datetime import datetime, timefrom typing import List, Dict, Tuplefrom collections import defaultdict, Counterfrom .models import StudyRecord, DailyAnalysis, EfficiencyLevelfrom .config import TIME_PERIODS, EFFICIENCY_LEVELS, ANALYSIS_CONFIGfrom .utils import TimeUtils, StatisticsUtilsclass EfficiencyAnalyzer:效率分析器类def __init__(self):self.time_period_mapping self._create_time_period_mapping()def _create_time_period_mapping(self) - Dict[int, str]:创建小时到时间段的映射mapping {}for period_name, period_info in TIME_PERIODS.items():start period_info[start]end period_info[end]if start end: # 正常时段for hour in range(start, end):mapping[hour] period_nameelse: # 跨日时段如深夜for hour in range(start, 24):mapping[hour] period_namefor hour in range(0, end):mapping[hour] period_namereturn mappingdef analyze_student_efficiency(self, student_profile) - List[DailyAnalysis]:分析学生学习效率analyses []# 按日期分组记录daily_records self._group_by_date(student_profile.study_records)for date, records in daily_records.items():analysis self._analyze_daily_data(date, records)analyses.append(analysis)# 按日期排序analyses.sort(keylambda x: x.date)return analysesdef _group_by_date(self, records: List[StudyRecord]) - Dict[str, List[StudyRecord]]:按日期分组记录daily_groups defaultdict(list)for record in records:daily_groups[record.date].append(record)return dict(daily_groups)def _analyze_daily_data(self, date: str, records: List[StudyRecord]) - DailyAnalysis:分析单日数据# 计算总学习时间total_time sum(record.duration_minutes for record in records)# 计算平均效率avg_efficiency np.mean([record.efficiency_score for record in records])# 学科时间分布subject_times defaultdict(int)for record in records:subject_times[record.subject] record.duration_minutes# 时段时间分布time_periods defaultdict(int)low_efficiency_periods []for record in records:# 获取主要时段main_period self._get_main_time_period(record.start_time)time_periods[main_period] record.duration_minutes# 检查是否为低效率时段if record.efficiency_score ANALYSIS_CONFIG[efficiency_threshold]:low_efficiency_periods.append({time: f{record.start_time}-{record.end_time},subject: record.subject,efficiency: record.efficiency_score,duration: record.duration_minutes})# 生成建议recommendations self._generate_recommendations(records, total_time, avg_efficiency, low_efficiency_periods)return DailyAnalysis(datedate,total_study_timetotal_time,average_efficiencyavg_efficiency,subject_timesdict(subject_times),time_periodsdict(time_periods),low_efficiency_periodslow_efficiency_periods,recommendationsrecommendations)def _get_main_time_period(self, start_time: str) - str:获取开始时间所在的主要时段hour int(start_time.split(:)[0])return self.time_period_mapping.get(hour, unknown)def _generate_recommendations(self, records: List[StudyRecord],total_time: int, avg_efficiency: float,low_efficiency_periods: List[Dict]) - List[str]:生成个性化建议recommendations []# 基于总学习时间的建议if total_time 120: # 少于2小时recommendations.append( 建议增加每日学习时间至少保证2-3小时的有效学习)elif total_time 480: # 超过8小时recommendations.append(⏰ 学习时间过长可能导致疲劳建议适当休息提高效率)# 基于平均效率的建议if avg_efficiency 0.5:recommendations.append( 整体学习效率偏低建议优化学习方法保持专注)elif avg_efficiency 0.8:recommendations.append( 学习效率很高继续保持这种良好的学习状态)# 基于低效率时段的建议if low_efficiency_periods:late_night_sessions [p for p in low_efficiency_periodsif self._get_main_time_period(p[time].split(-)[0]) night]if late_night_sessions:recommendations.append( 避免在深夜学习这会严重影响效率和健康)long_sessions [p for p in low_efficiency_periods if p[duration] 180]if long_sessions:recommendations.append(⏳ 单次学习时间过长容易疲劳建议采用番茄工作法25-45分钟为一个学习单元)# 基于学科分布的建讱subject_times defaultdict(int)for record in records:subject_times[record.subject] record.duration_minutesif subject_times:max_subject max(subject_times.items(), keylambda x: x[1])if max_subject[1] total_time * 0.6: # 某一学科占比超过60%recommendations.append(f⚖️ {max_subject[0]}学科投入时间较多注意平衡各科学习)# 通用建议recommendations.extend([ 建议制定详细的学习计划合理分配各时段任务, 学习间隙适当运动有助于保持大脑活力, 及时复习巩固避免临时抱佛脚, 保证充足睡眠早睡早起有利于学习效率提升])return recommendations[:ANALYSIS_CONFIG[recommendation_count]]def identify_efficiency_patterns(self, student_profile) - Dict:识别学习效率模式if len(student_profile.study_records) ANALYSIS_CONFIG[min_data_days]:return {error: f需要至少{ANALYSIS_CONFIG[min_data_days]}天的数据才能进行有效分析}records student_profile.study_records# 时段效率分析period_efficiency self._analyze_period_efficiency(records)# 学科效率分析subject_efficiency self._analyze_subject_efficiency(records)# 时间长度效率分析duration_efficiency self._analyze_duration_efficiency(records)# 疲劳模式分析fatigue_patterns self._analyze_fatigue_patterns(records)return {period_efficiency: period_efficiency,subject_efficiency: subject_efficiency,duration_efficiency: duration_efficiency,关注我有更多实用程序等着你