从0到1的AI创业路径：Pitch Deck黄金结构

import numpy as npfrom typing import List, Dict class PitchDeckAnalyzer:    GOLDEN_PAGES = [        '封面', '问题', '解决方案', '市场规模',        '产品展示', '牵引力', '团队', '竞品分析',        '商业模式', '融资需求'    ]        def __init__(self):        self.pages = {}        self.scores = {}        def add_page(self, page_name, content):        self.pages[page_name] = {            'word_count': content.get('word_count', 0),            'data_points': content.get('data_points', 0),            'has_customer_quote': content.get('has_customer_quote', False),            'has_growth_metric': content.get('has_growth_metric', False),            'clarity_score': content.get('clarity_score', 0.5),            'relevance_score': content.get('relevance_score', 0.5)        }        def evaluate_page(self, page_name):        if page_name not in self.pages:            return {'error': 'page not found'}        page = self.pages[page_name]        scores = {}        wc = page['word_count']        if 50 <= wc <= 100:            scores['density'] = 100        elif wc < 50:            scores['density'] = max(40, wc / 50 * 100)        else:            scores['density'] = max(30, 100 - (wc - 100) * 0.5)        dp = page['data_points']        scores['data_support'] = min(100, dp * 25)        credibility = 50        if page['has_customer_quote']:            credibility += 25        if page['has_growth_metric']:            credibility += 25        scores['credibility'] = credibility        scores['clarity'] = page['clarity_score'] * 100        scores['relevance'] = page['relevance_score'] * 100        weights = {'density': 0.20, 'data_support': 0.25, 'credibility': 0.20, 'clarity': 0.20, 'relevance': 0.15}        total = sum(scores[k] * weights[k] for k in weights)        scores['total'] = round(total, 1)        self.scores[page_name] = scores        return scores        def evaluate_deck(self):        for pn in self.pages:            self.evaluate_page(pn)        coverage = min(1.0, len(self.pages) / len(self.GOLDEN_PAGES))        critical_front = ['问题', '解决方案', '封面']        fp_bonus = sum(self.scores[fp]['total'] for fp in critical_front if fp in self.scores)        fp_avg = fp_bonus / max(len(critical_front), 1)        all_totals = [self.scores[p]['total'] for p in self.scores]        avg_score = np.mean(all_totals) if all_totals else 0        final = 0.30 * coverage * 100 + 0.50 * avg_score + 0.20 * fp_avg        return {            'final_score': round(final, 1),            'structure_coverage': round(coverage * 100, 1),            'avg_page_score': round(avg_score, 1),            'front_page_score': round(fp_avg, 1),            'page_count': len(self.pages)        }        def get_suggestions(self):        suggestions = []        for pn, sc in self.scores.items():            if sc['density'] < 60:                suggestions.append(f'[{pn}] 信息密度过高，建议精简到50-100字')            if sc['data_support'] < 50:                suggestions.append(f'[{pn}] 数据支撑不足，建议添加具体数据')            if sc['credibility'] < 60:                suggestions.append(f'[{pn}] 可信度偏低，建议添加客户证言')        return suggestions analyzer = PitchDeckAnalyzer()pages_data = {    '封面': {'word_count': 30, 'data_points': 1, 'has_customer_quote': False, 'has_growth_metric': False, 'clarity_score': 0.9, 'relevance_score': 0.9},    '问题': {'word_count': 80, 'data_points': 3, 'has_customer_quote': True, 'has_growth_metric': False, 'clarity_score': 0.85, 'relevance_score': 0.95},    '解决方案': {'word_count': 70, 'data_points': 2, 'has_customer_quote': False, 'has_growth_metric': False, 'clarity_score': 0.8, 'relevance_score': 0.85},    '市场规模': {'word_count': 90, 'data_points': 4, 'has_customer_quote': False, 'has_growth_metric': False, 'clarity_score': 0.75, 'relevance_score': 0.8},    '牵引力': {'word_count': 75, 'data_points': 4, 'has_customer_quote': True, 'has_growth_metric': True, 'clarity_score': 0.85, 'relevance_score': 0.9},    '团队': {'word_count': 85, 'data_points': 2, 'has_customer_quote': False, 'has_growth_metric': False, 'clarity_score': 0.7, 'relevance_score': 0.85},    '融资需求': {'word_count': 70, 'data_points': 3, 'has_customer_quote': False, 'has_growth_metric': False, 'clarity_score': 0.85, 'relevance_score': 0.9}}for name, data in pages_data.items():    analyzer.add_page(name, data) print('=' * 50)print('Pitch Deck 黄金结构评分分析')print('=' * 50)result = analyzer.evaluate_deck()print(f'最终评分: {result["final_score"]}分')print(f'结构完整度: {result["structure_coverage"]}%')print(f'页面平均分: {result["avg_page_score"]}分')print(f'前3页评分: {result["front_page_score"]}分')print(f'\n各页面评分:')for page, scores in analyzer.scores.items():    print(f'  {page}: {scores["total"]}分')print(f'\n优化建议:')for s in analyzer.get_suggestions():    print(f'  * {s}')