用 Python 编写一个反资产增值至上 × 小额新鲜体验投资 × 创新能力提升量化 的程序。内容严格保持中立、去营销化、可复现不推荐任何商业产品不引流。一、实际应用场景描述Scene在心理健康与创新能力课程中有一个被反复验证的心理规律长期处于确定性收益 mindset 中会削弱探索新事物的心理弹性典型应用场景包括- 职场人每月把结余资金全部投入稳健理财生活节奏高度可预测- 创业者将每一分钱算到 ROI拒绝任何看不见回报的尝试- 开发者只学能立刻变现的技术栈不敢触碰未知领域- 内容创作者在选题上反复复制已验证的爆款公式创新力逐渐枯竭主流理财软件记账、基金、资产配置的核心目标是资产稳定增值、风险最小化、收益最大化这在财务安全层面是完全正确的。但在心理健康与创新能力的维度上产生了隐性代价- 大脑被训练成确定性寻求机器Certainty Seeking- 对无明确回报的体验容忍度趋近于零- 探索欲Exploration Drive 被投资回报率逻辑系统性抑制心理学研究表明适度的无收益支出——用于新奇体验——能有效激活多巴胺系统的可塑性促进创造性思维。二、引入痛点Pain Points1️⃣ 每一分钱都必须增值的隐性压力理财软件的训练效果是- 看到支出就计算 ROI- 没有产出的体验 浪费- 休闲、好奇、探索被内疚感伴随结果是连玩都带着 KPI 心态创造力被持续挤压。2️⃣ 新鲜体验缺乏合法性身份在主流记账体系中以下体验通常被归类为真实体验 记账分类去陌生街区漫无目的闲逛 娱乐 / 浪费买一本跟工作无关的书 冲动消费参加一个完全不懂领域的沙龙 不务正业尝试一种从未接触过的食材 不必要开支它们从未被正名为创新能力投资。3️⃣ 体验与创造力之间的因果链未被量化即使偶尔有人任性了一把也因为- 没有记录- 没有追踪后续创造力变化- 没有建立因果归因而无法回答那次乱花钱的经历到底给我带来了什么三、核心逻辑讲解Core Logic1️⃣ 基本假设划拨小额资金用于无收益预期的新鲜体验能 measurable 地提升个人创新能力。2️⃣ 核心建模思路将个人财务系统重构为安全资产 探索预算的双账户模型总收入│├── 安全资产池已有理财体系不动│└── 探索体验基金本程序管理│├── 划拨固定小额如月收入 3-5%│├── 投资方向新鲜体验非资产增值│ ├── 陌生空间探索│ ├── 跨领域学习│ ├── 非常规社交│ └── 感官新刺激│├── 记录体验前后创造力自评└── 量化体验投入 → 创新能力变化3️⃣ 体验分类与创新能力维度映射体验类型 示例 影响的创新维度空间探索 去从未去过的街区 / 城市 环境新奇性 → 发散思维跨域学习 学一门与主业无关的知识 远距离联想 → 概念重组非常规社交 参加陌生领域的聚会 观点碰撞 → 范式突破感官刺激 尝试全新气味/食物/音乐 感知唤醒 → 隐喻生成随机体验 抛骰子决定今天做什么 打破决策惯性 → 认知灵活性4️⃣ 创新能力量化指标简化模型每次体验前后各测一次取差值指标 说明 评分 1-10发散思维 能想出多少种用法 DT联想跨度 能多远地连接概念 AC认知灵活性 能多快切换视角 CF好奇心水平 对未知事物的兴趣度 CL风险承受意愿 愿意尝试不确定的事 RW创新能力综合分 各维度平均值5️⃣ 投入产出比计算体验投入金额 时间成本↓创新能力综合分变化Δ↓创造力 ROI Δ 创新能力 / 投入成本关键洞察同样花 200 元一次新奇体验可能比十次重复消费带来更大的创造力提升。四、程序设计与代码实现Python1️⃣ 项目结构exploration_fund/│├── README.md├── requirements.txt├── main.py├── models.py├── fund_manager.py├── experience_tracker.py├── creativity_scorer.py├── analyzer.py├── data/│ ├── fund.json│ └── experiences.json└── docs/└── knowledge_cards.md2️⃣ 数据模型models.py# models.pyfrom dataclasses import dataclass, fieldfrom datetime import datetimefrom typing import Optional, Listfrom enum import Enumclass ExperienceType(Enum):新鲜体验类型SPATIAL_EXPLORATION 空间探索CROSS_DOMAIN_LEARNING 跨域学习UNCONVENTIONAL_SOCIAL 非常规社交SENSORY_STIMULATION 感官刺激RANDOM_EXPERIENCE 随机体验OTHER 其他class CreativityDimension(Enum):创新能力维度DIVERGENT_THINKING 发散思维ASSOCIATION_SPAN 联想跨度COGNITIVE_FLEXIBILITY 认知灵活性CURIOSITY_LEVEL 好奇心水平RISK_WILLINGNESS 风险承受意愿dataclassclass CreativitySnapshot:单次创造力测评快照timestamp: str field(default_factorylambda: datetime.now().isoformat())divergent_thinking: int 5 # 发散思维 1-10association_span: int 5 # 联想跨度 1-10cognitive_flexibility: int 5 # 认知灵活性 1-10curiosity_level: int 5 # 好奇心 1-10risk_willingness: int 5 # 风险承受 1-10def composite_score(self) - float:计算创新能力综合分return round((self.divergent_thinking self.association_span self.cognitive_flexibility self.curiosity_level self.risk_willingness)/ 5.0,2,)def to_dict(self) - dict:return {timestamp: self.timestamp,divergent_thinking: self.divergent_thinking,association_span: self.association_span,cognitive_flexibility: self.cognitive_flexibility,curiosity_level: self.curiosity_level,risk_willingness: self.risk_willingness,composite_score: self.composite_score(),}dataclassclass ExperienceRecord:一次新鲜体验的完整记录id: strtimestamp: str field(default_factorylambda: datetime.now().isoformat())experience_type: ExperienceType ExperienceType.OTHERtitle: str description: str amount_spent: float 0.0 # 花费金额time_spent_hours: float 0.0 # 时间成本小时before_snapshot: Optional[CreativitySnapshot] Noneafter_snapshot: Optional[CreativitySnapshot] Nonetags: List[str] field(default_factorylist)notes: str def creativity_delta(self) - float:创新能力变化值if self.before_snapshot and self.after_snapshot:return round(self.after_snapshot.composite_score()- self.before_snapshot.composite_score(),2,)return 0.0def total_cost(self) - float:总成本货币 时间折算时间按小时工资折算# 时间成本按 50 元/小时折算可根据实际情况调整TIME_RATE 50.0return self.amount_spent self.time_spent_hours * TIME_RATEdef roi(self) - float:创造力 ROI 创新能力提升 / 总成本cost self.total_cost()if cost 0:return 0.0return round(self.creativity_delta() / cost * 100, 2)def to_dict(self) - dict:return {id: self.id,timestamp: self.timestamp,type: self.experience_type.value,title: self.title,description: self.description,amount_spent: self.amount_spent,time_spent_hours: self.time_spent_hours,total_cost: round(self.total_cost(), 2),before_score: self.before_snapshot.composite_score() if self.before_snapshot else None,after_score: self.after_snapshot.composite_score() if self.after_snapshot else None,creativity_delta: self.creativity_delta(),roi: self.roi(),tags: self.tags,notes: self.notes,}dataclassclass FundStatus:探索基金状态monthly_budget: float 0.0allocated_total: float 0.0remaining: float 0.0experience_count: int 0def to_dict(self) - dict:return {monthly_budget: self.monthly_budget,allocated_total: self.allocated_total,remaining: self.remaining,experience_count: self.experience_count,utilization_rate_percent: round((self.allocated_total / self.monthly_budget * 100)if self.monthly_budget 0 else 0, 1),}3️⃣ 基金管理模块fund_manager.py# fund_manager.pyimport jsonimport osfrom typing import List, Optionalfrom models import FundStatus, ExperienceRecordclass ExplorationFundManager:探索基金管理器管理新鲜体验基金的划拨、支出与统计def __init__(self,monthly_budget: float 500.0,storage_path: str data/fund.json,):self.monthly_budget monthly_budgetself.storage_path storage_pathself.experiences: List[ExperienceRecord] []self._load()def _load(self):从文件加载已有记录try:with open(self.storage_path, r, encodingutf-8) as f:data json.load(f)# 简化加载实际可完整还原对象self.monthly_budget data.get(monthly_budget, 500.0)except (FileNotFoundError, json.JSONDecodeError):self.experiences []def get_status(self) - FundStatus:获取当前基金状态allocated sum(e.amount_spent for e in self.experiences)return FundStatus(monthly_budgetself.monthly_budget,allocated_totalround(allocated, 2),remaininground(self.monthly_budget - allocated, 2),experience_countlen(self.experiences),)def can_afford(self, amount: float) - bool:检查余额是否充足return (self.monthly_budget - sum(e.amount_spent for e in self.experiences)) amountdef add_experience(self, record: ExperienceRecord) - None:记录一次体验支出if not self.can_afford(record.amount_spent):raise ValueError(f⚠️ 余额不足当前剩余 {self.get_status().remaining:.2f}f本次支出 {record.amount_spent:.2f})self.experiences.append(record)self._save()def _save(self):持久化简化版仅保存统计数据os.makedirs(os.path.dirname(self.storage_path), exist_okTrue)status self.get_status()with open(self.storage_path, w, encodingutf-8) as f:json.dump(status.to_dict(), f, indent2, ensure_asciiFalse)def get_all_experiences(self) - List[ExperienceRecord]:return self.experiences4️⃣ 体验追踪模块experience_tracker.py# experience_tracker.pyfrom typing import List, Optionalfrom models import (ExperienceRecord,ExperienceType,CreativitySnapshot,)class ExperienceTracker:新鲜体验追踪器负责创建体验记录、采集前后创造力快照def __init__(self):self.counter 0def create_record(self,experience_type: ExperienceType,title: str,description: str ,amount_spent: float 0.0,time_spent_hours: float 0.0,tags: Optional[List[str]] None,) - ExperienceRecord:创建一条体验记录self.counter 1return ExperienceRecord(idfexp_{self.counter:04d},experience_typeexperience_type,titletitle,descriptiondescription,amount_spentamount_spent,time_spent_hourstime_spent_hours,tagstags or [],)def capture_before(self,record: ExperienceRecord,divergent: int 5,association: int 5,flexibility: int 5,curiosity: int 5,risk: int 5,) - None:采集体验前的创造力基线快照参数对应五个维度均为 1-10 评分record.before_snapshot CreativitySnapshot(divergent_thinkingdivergent,association_spanassociation,cognitive_flexibilityflexibility,curiosity_levelcuriosity,risk_willingnessrisk,)def capture_after(self,record: ExperienceRecord,divergent: int 5,association: int 5,flexibility: int 5,curiosity: int 5,risk: int 5,) - None:采集体验后的创造力快照record.after_snapshot CreativitySnapshot(divergent_thinkingdivergent,association_spanassociation,cognitive_flexibilityflexibility,curiosity_levelcuriosity,risk_willingnessrisk,)def validate(self, record: ExperienceRecord) - bool:校验体验记录必须有前后快照才能纳入分析if not record.before_snapshot:raise ValueError(f⚠️ 体验 [{record.id}] 缺少体验前快照)if not record.after_snapshot:raise ValueError(f⚠️ 体验 [{record.id}] 缺少体验后快照)return Truedef save(self, record: ExperienceRecord, filepath: str) - None:追加保存到 JSON 文件import osos.makedirs(os.path.dirname(filepath), exist_okTrue)try:with open(filepath, r, encodingutf-8) as f:data json.load(f)except (FileNotFoundError, json.JSONDecodeError):data []data.append(record.to_dict())with open(filepath, w, encodingutf-8) as f:json.dump(data, f, indent2, ensure_asciiFalse)5️⃣ 创造力评分与 ROI 模块creativity_scorer.py# creativity_scorer.pyfrom typing import List, Dictfrom models import ExperienceRecordclass CreativityScorer:创造力评分与 ROI 计算引擎def __init__(self, experiences: List[ExperienceRecord]):self.experiences [e for e in experiences if e.before_snapshot and e.after_snapshot]def avg_delta(self) - float:平均创造力提升值if not self.experiences:return 0.0total sum(e.creativity_delta() for e in self.experiences)return round(total / len(self.experiences), 2)def avg_roi(self) - float:平均创造力 ROIif not self.experiences:return 0.0total sum(e.roi() for e in self.experiences)return round(total / len(self.experiences), 2)def best_experience(self) - Optional[ExperienceRecord]:创造力提升最大的体验if not self.experiences:return Nonereturn max(self.experiences, keylambda e: e.creativity_delta())def best_roi_experience(self) - Optional[ExperienceRecord]:ROI 最高的体验if not self.experiences:return Nonereturn max(self.experiences, keylambda e: e.roi())def scorer_by_type(self) - Dict[str, Dict]:按体验类型汇总评分from collections import defaultdicttype_data defaultdict(list)for e in self.experiences:type_data[e.experience_type.value].append(e)result {}for exp_type, exps in type_data.items():result[exp_type] {count: len(exps),avg_delta: round(sum(e.creativity_delta() for e in exps) / len(exps), 2),avg_roi: round(sum(e.roi() for e in exps) / len(exps), 2),total_spent: round(sum(e.amount_spent for e in exps), 2),}return result6️⃣ 统计分析模块analyzer.py# analyzer.pyfrom typing import List, Dictfrom models import ExperienceRecordclass ExplorationAnalyzer:探索体验的综合统计分析def __init__(self, experiences: List[ExperienceRecord]):self.experiences experiencesself.valid [e for e in experiences if e.before_snapshot and e.after_snapshot]def total_experiences(self) - int:return len(self.experiences)def valid_experiences(self) - int:return len(self.valid)def total_amount_spent(self) - float:return round(sum(e.amount_spent for e in self.experiences), 2)def total_time_spent(self) - float:return round(sum(e.time_spent_hours for e in self.experiences), 2)def avg_before_score(self) - float:if not self.valid:return 0.0return round(sum(e.before_snapshot.composite_score() for e in self.valid)/ len(self.valid),2,)def avg_after_score(self) - float:if not self.valid:return 0.0return round(sum(e.after_snapshot.composite_score() for e in self.valid)/ len(self.valid),2,)def dimension_wise_analysis(self) - Dict:逐维度分析前后变化if not self.valid:return {}dims [divergent_thinking, association_span, cognitive_flexibility,curiosity_level, risk_willingness]result {}for d in dims:before_avg round(sum(getattr(e.before_snapshot, d) for e in self.valid) / len(self.valid), 2)after_avg round(sum(getattr(e.after_snapshot, d) for e in self.valid) / len(self.valid), 2)result[d] {before: before_avg,after: after_avg,delta: round(after_avg - before_avg, 2),}return resultdef summary(self) - Dict:return {total_experiences: self.total_experiences(),valid_experiences: self.valid_experiences(),total_amount_spent: self.total_amount_spent(),total_time_spent_hours: self.total_time_spent(),avg_before_score: self.avg_before_score(),avg_after_score: self.avg_after_score(),avg_overall_delta: round(self.avg_after_score() - self.avg_before_score(), 2),dimension_wise: self.dimension_wise_analysis(),}7️⃣ 主程序main.py# main.pyimport jsonfrom models import ExperienceTypefrom fund_manager import ExplorationFundManagerfrom experience_tracker import ExperienceTrackerfrom creativity_scorer import CreativityScorerfrom analyzer import ExplorationAnalyzer# # 初始化每月划拨 500 元探索基金# fund ExplorationFundManager(monthly_budget500.0)tracker ExperienceTracker()print( 探索基金已初始化月度预算500.00 元)print(f 当前余额{fund.get_status().remaining:.2f} 元\n)# # 体验 1空间探索 —— 去陌生街区漫无目的闲逛# print( 体验 1空间探索)exp1 tracker.create_record(experience_typeExperienceType.SPATIAL_EXPLORATION,title在老城区迷路式闲逛,description放下地图让直觉带路走了 3 小时,amount_spent0.0, # 只花了交通费time_spent_hours3.0,tags[老城区, 迷路, 步行],)# 体验前快照基线tracker.capture_before(exp1,divergent5, association4, flexibility5, curiosity6, risk4)# 模拟体验过程...# 实际中用户在此期间真实体验# 体验后快照tracker.capture_after(exp1,divergent7, association6, flexibility7, curiosity8, risk6)tracker.validate(exp1)fund.add_experience(exp1)tracker.save(exp1, data/experiences.json)print(f 花费{exp1.amount_spent} 元 | 时间{exp1.time_spent_hours}h)print(f 创造力 Δ{exp1.creativity_delta():.2f}\n)# # 体验 2跨域学习 —— 学陶艺# print( 体验 2跨域学习)exp2 tracker.create_record(experience_typeExperienceType.CROSS_DOMAIN_LEARNING,title陶艺入门体验课,description完全陌生的材料与手感和代码毫无关系,amount_spent180.0,time_spent_hours3.0,tags[陶艺, 手工, 跨域],)tracker.capture_before(exp2,divergent5, association5, flexibility5, curiosity7, risk3)# 体验中双手沾满泥巴专注到忘记时间tracker.capture_after(exp2,divergent8, association7, flexibility6, curiosity9, risk5)tracker.validate(exp2)fund.add_experience(exp2)tracker.save(exp2, data/experiences.json)print(f 花费{exp2.amount_spent} 元 | 时间{exp2.time_spent_hours}h)print(f 创造力 Δ{exp2.creativity_delta():.2f}\n)# # 体验 3非常规社交 —— 参加陌生领域沙龙# print(️ 体验 3非常规社交)exp3 tracker.create_record(experience_typeExperienceType.UNCONVENTIONAL_SOCIAL,title参加生物科技创业沙龙,description作为纯软件开发者完全听不懂但很有意思,amount_spent50.0,time_spent_hours2.5,tags[社交, 生物科技, 跨界],)tracker.capture_before(exp3,divergent4, association4, flexibility5, curiosity5, risk4)# 体验中听到基因编辑和CRISPR满脑子跨界联想tracker.capture_after(exp3,divergent7, association8, flexibility6, curiosity8, risk5)tracker.validate(exp3)fund.add_experience(exp3)tracker.save(exp3, data/experiences.json)print(f 花费{exp3.amount_spent} 元 | 时间{exp3.time_spent_hours}h)print(f 创造力 Δ{exp3.creativity_delta():.2f}\n)# # 体验 4感官刺激 —— 尝试全新的食物# print(️ 体验 4感官刺激)exp4 tracker.create_record(experience_typeExperienceType.SENSORY_STIMULATION,title第一次吃分子料理,description食物的形态、气味、口感完全颠覆预期,amount_spent220.0,time_spent_hours2.0,tags[美食, 分子料理, 感官],)tracker.capture_before(exp4,divergent5, association5, flexibility5, curiosity6, risk4)# 体验中一道橄榄球其实是甜品大脑被欺骗的感觉很奇妙tracker.capture_after(exp4,divergent7, association7, flexibility7, curiosity8, risk5)tracker.validate(exp4)fund.add_experience(exp4)tracker.save(exp4, data/experiences.json)print(f利用AI解决实际问题如果你觉得这个工具好用欢迎关注长安牧笛