xsl
2eb0d8e807
产品方案 PPT 是交付物，之前只存在于本机，换开发机后会丢，所以入库。 - 3 份 pptx：智能测肤方案（正版 1.3MB / 深色版 1.3MB）+ 购物小程序方案模版（0.1MB）； 模版是 build_taobao_proposal_from_template.py 的输入，必须一起入库 - 3 个生成脚本：把硬编码的 D:\juzi\... 改为相对仓库根（Path(__file__).parents[1]）， 渲染用的截图素材 temp/ppt_assets 属临时产物，加进 .gitignore - build_taobao_proposal_pptx.py 是更早的一版（产物 AI智能测肤...pptx 当前不在仓库）， 保留用于追溯版式 - 新增 .gitignore：/sdk-java-35479221/（淘宝开放平台自动生成的 Java SDK， 对应已废弃的 AppKey 35479221，当前架构走 HTTP 透传，不引 SDK）
329 lines
17 KiB
Python
329 lines
17 KiB
Python
# -*- coding: utf-8 -*-
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"""AI智能测肤淘宝小程序产品方案 —— 深色版（内容与模板版一致，全新视觉）。
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设计系统：
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- 画布 26.67x15in（与模板一致，沿用已验证的版式坐标）
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- BG 深墨蓝 0E1E29 / 卡片 162C3A / 强调 青绿 38CDB8（水润科技感）
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- 文字 F2F6F9 / 次级 9FB1BF / 弱化 5E7482
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- 字体：标题正文 微软雅黑，大数字 Bahnschrift（Windows 自带 DIN 系）
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- 浅色界面截图直接置于深底，形成强对比
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"""
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from pathlib import Path
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from pptx import Presentation
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from pptx.util import Inches, Pt
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from pptx.dml.color import RGBColor
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from pptx.enum.shapes import MSO_SHAPE
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from pptx.enum.text import PP_ALIGN, MSO_ANCHOR
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from pptx.oxml.ns import qn
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from lxml import etree
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# 路径相对仓库根，换机器无需改（截图素材在 temp/ppt_assets，属于本地临时产物，不入库）
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_ROOT = Path(__file__).resolve().parents[1]
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OUT = str(_ROOT / "智能测肤-淘宝小程序产品方案-深色版.pptx")
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AST = str(_ROOT / "temp" / "ppt_assets")
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BG = RGBColor(0x0E, 0x1E, 0x29)
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CARD = RGBColor(0x16, 0x2C, 0x3A)
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PANEL = RGBColor(0x13, 0x25, 0x33)
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ACC = RGBColor(0x38, 0xCD, 0xB8)
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TXT = RGBColor(0xF2, 0xF6, 0xF9)
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MUT = RGBColor(0x9F, 0xB1, 0xBF)
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FAINT = RGBColor(0x6E, 0x84, 0x93)
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LINE = RGBColor(0x27, 0x42, 0x55)
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NUM_F = "Bahnschrift"
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def set_font(run, size, bold=False, color=TXT, latin="Segoe UI", ea="微软雅黑"):
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f = run.font
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f.size = Pt(size)
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f.bold = bold
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f.color.rgb = color
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f.name = latin
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rPr = run._r.get_or_add_rPr()
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for tag, face in ((qn("a:latin"), latin), (qn("a:ea"), ea)):
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e = rPr.find(tag)
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if e is None:
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e = etree.SubElement(rPr, tag)
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e.set("typeface", face)
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def tb(slide, x, y, w, h, paras, anchor=MSO_ANCHOR.TOP):
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box = slide.shapes.add_textbox(Inches(x), Inches(y), Inches(w), Inches(h))
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tf = box.text_frame
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tf.word_wrap = True
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tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0
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tf.vertical_anchor = anchor
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for i, pd in enumerate(paras):
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p = tf.paragraphs[0] if i == 0 else tf.add_paragraph()
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p.alignment = pd.get("align", PP_ALIGN.LEFT)
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if pd.get("sa"):
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p.space_after = Pt(pd["sa"])
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r = p.add_run()
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r.text = pd["t"]
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set_font(r, pd.get("size", 26), pd.get("bold", False), pd.get("color", TXT),
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pd.get("latin", "Segoe UI"))
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return box
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def rect(slide, x, y, w, h, fill, line=None, shape=MSO_SHAPE.RECTANGLE, radius=None):
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sp = slide.shapes.add_shape(shape, Inches(x), Inches(y), Inches(w), Inches(h))
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if radius is not None:
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sp.adjustments[0] = radius
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sp.fill.solid()
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sp.fill.fore_color.rgb = fill
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if line:
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sp.line.color.rgb = line
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sp.line.width = Pt(1)
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else:
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sp.line.fill.background()
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sp.shadow.inherit = False
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return sp
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def card(slide, x, y, w, h, fill=CARD, line=None):
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return rect(slide, x, y, w, h, fill, line, MSO_SHAPE.ROUNDED_RECTANGLE, 0.055)
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def glow(slide, x, y, d, hexv="38CDB8", alpha=13):
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"""低透明度圆形光晕（XML alpha 注入）"""
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sp = slide.shapes.add_shape(MSO_SHAPE.OVAL, Inches(x), Inches(y), Inches(d), Inches(d))
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sp.fill.solid()
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sp.fill.fore_color.rgb = RGBColor.from_string(hexv)
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sp.line.fill.background()
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sp.shadow.inherit = False
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sf = sp.fill._xPr.find(qn("a:solidFill"))
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clr = sf.find(qn("a:srgbClr"))
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a = etree.SubElement(clr, qn("a:alpha"))
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a.set("val", str(alpha * 1000))
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return sp
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def circle(slide, x, y, d, label, fill=ACC, color=BG, size=26, latin=NUM_F):
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sp = slide.shapes.add_shape(MSO_SHAPE.OVAL, Inches(x), Inches(y), Inches(d), Inches(d))
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sp.fill.solid()
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sp.fill.fore_color.rgb = fill
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sp.line.fill.background()
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sp.shadow.inherit = False
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tf = sp.text_frame
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tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0
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tf.vertical_anchor = MSO_ANCHOR.MIDDLE
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p = tf.paragraphs[0]
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p.alignment = PP_ALIGN.CENTER
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r = p.add_run()
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r.text = label
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set_font(r, size, True, color, latin)
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return sp
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def pic(slide, path, x, y, w, h):
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p = slide.shapes.add_picture(path, Inches(x), Inches(y), Inches(w), Inches(h))
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p.line.color.rgb = LINE
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p.line.width = Pt(1.25)
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return p
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def header(slide, num, title):
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tb(slide, 0.55, 0.22, 2.2, 1.5, [dict(t=num, size=96, color=ACC, latin=NUM_F)])
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tb(slide, 2.75, 0.88, 18, 0.8, [dict(t=title, size=44, bold=True)])
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def build():
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prs = Presentation()
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prs.slide_width = Inches(26.67)
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prs.slide_height = Inches(15)
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blank = prs.slide_layouts[6]
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def page():
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s = prs.slides.add_slide(blank)
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rect(s, 0, 0, 26.67, 15, BG)
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return s
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# ---- P1 封面 ----
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s = page()
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glow(s, 18.5, 8.2, 14, alpha=12)
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glow(s, -3.5, -3.5, 11, alpha=9)
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tb(s, 2.42, 4.35, 22, 0.6, [dict(t="淘宝开放平台 · 小程序产品方案（自研类型）", size=28, color=ACC)])
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tb(s, 2.42, 5.25, 22.5, 1.7, [dict(t="AI 智能测肤小程序方案", size=96, bold=True)])
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tb(s, 2.42, 7.9, 20, 0.9, [dict(t="西安巨子生物基因技术股份有限公司", size=44, color=MUT)])
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tb(s, 2.42, 12.9, 20, 0.5, [dict(t="在线肌肤检测 · 个性化护理建议 · 精准商品推荐", size=24, color=FAINT)])
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# ---- P2 公司背景与产品定位 ----
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s = page()
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tb(s, 2.75, 0.88, 18, 0.8, [dict(t="公司背景与产品定位", size=44, bold=True)])
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glow(s, 20.5, -4, 10, alpha=8)
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pic(s, f"{AST}\\report-full.png", 2.7, 2.45, 3.34, 10.29)
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tb(s, 1.4, 12.95, 6.0, 0.5, [dict(t="AI 肌肤检测报告（界面示意）", size=24, color=FAINT, align=PP_ALIGN.CENTER)])
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tb(s, 7.6, 2.75, 18, 0.5, [dict(t="西安巨子生物基因技术股份有限公司 · 港交所上市 · 重组胶原蛋白", size=24, color=ACC)])
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tb(s, 7.6, 4.6, 18.5, 2.1, [dict(t="让每位消费者三分钟获得", size=40, bold=True, sa=6),
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dict(t="一份可信赖的肌肤报告与护理方案", size=40, bold=True)])
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rows = [
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("1", "测得准", "AI 图像分析输出综合评分、肌龄、七项分项与三维指标，可解释、可回看"),
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("2", "推得对", "按展示肌龄分档推荐适配精华，报告页直达商品，缩短决策路径"),
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("3", "留得住", "历史报告沉淀与复访提醒，支撑会员运营与复购"),
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]
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for i, (n, lead, desc) in enumerate(rows):
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y = 7.5 + i * 2.0
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circle(s, 7.6, y + 0.12, 0.72, n, color=BG, size=28)
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tb(s, 8.8, y, 15.3, 1.8, [dict(t=lead, size=30, bold=True, sa=6), dict(t=desc, size=24, color=MUT)])
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# ---- P3 目录 ----
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s = page()
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glow(s, 20, 9.5, 11, alpha=8)
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tb(s, 2.42, 2.6, 8, 1.3, [dict(t="目录", size=72, bold=True)])
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rect(s, 8.3, 2.6, 0.028, 10.2, LINE)
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toc = [("01", "市场调研分析"), ("02", "小程序方案及价值"), ("03", "商业化运营规划及目标")]
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for i, (n, t) in enumerate(toc):
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y = 3.1 + i * 3.2
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circle(s, 9.6, y, 1.35, n, color=BG, size=40)
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tb(s, 11.8, y + 0.22, 14, 0.9, [dict(t=t, size=44, bold=True)])
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# ---- P4 01 市场调研分析 ----
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s = page()
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header(s, "01", "市场调研分析")
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cols = [
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("商家痛点", "选品讲解靠经验，「适不适合我」难讲清；直播与详情页流量难沉淀为可复访的服务。"),
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("消费者诉求", "想先弄清肤质与肌龄再决策；怕踩雷、要依据，希望报告能看懂、直接告诉我买什么。"),
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]
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xs = [1.5, 9.72, 17.94]
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for i, (t, body) in enumerate(cols):
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card(s, xs[i] - 0.35, 2.35, 7.9, 6.6)
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tb(s, xs[i], 2.75, 7.2, 0.6, [dict(t=t, size=32, bold=True, color=ACC)])
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tb(s, xs[i], 3.6, 7.1, 5.0, [dict(t=body, size=26, color=MUT)])
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card(s, xs[2] - 0.35, 2.35, 7.9, 6.6, fill=PANEL, line=LINE)
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tb(s, xs[2], 2.75, 7.2, 0.6, [dict(t="市场与商业化空间", size=32, bold=True, color=ACC)])
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tb(s, xs[2], 3.6, 7.2, 1.0, [dict(t="4357亿", size=54, bold=True, color=ACC, latin=NUM_F)])
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tb(s, xs[2], 4.65, 7.2, 0.5, [dict(t="2024 年全国化妆品类零售额", size=22, color=MUT)])
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tb(s, xs[2], 5.5, 7.2, 1.0, [dict(t="1069亿", size=54, bold=True, color=TXT, latin=NUM_F)])
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tb(s, xs[2], 6.55, 7.2, 0.5, [dict(t="抗衰护肤品细分市场规模（2024）", size=22, color=MUT)])
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tb(s, xs[2], 7.3, 7.1, 1.5, [dict(t="高客单、高复购；线上「可解释的决策工具」仍是缺口", size=24, color=MUT)])
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tb(s, 1.5, 9.55, 10, 0.5, [dict(t="需求原声", size=24, bold=True, color=FAINT)])
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rect(s, 0, 10.25, 26.67, 2.25, PANEL)
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for i, q in enumerate(["「不知道自己适不适合这款抗老精华」", "「想先测一下再买，别乱试」", "「报告要能看懂，最好直接告诉我买什么」"]):
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tb(s, 1.5 + i * 8.6, 10.25, 8.2, 2.25, [dict(t=q, size=26, bold=True)], anchor=MSO_ANCHOR.MIDDLE)
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tb(s, 1.5, 12.9, 24, 0.5, [dict(t="数据来源：国家统计局社会消费品零售总额（化妆品类，2024）；智研咨询《2024 年中国抗衰老行业研究》", size=22, color=FAINT)])
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# ---- P5 02 方案与价值 ----
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s = page()
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header(s, "02", "小程序方案及价值")
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tb(s, 1.5, 2.55, 11.5, 0.6, [dict(t="产品价值与商业价值", size=32, bold=True, color=ACC)])
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vals = [
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("检测可信", "AI 图像分析替代主观猜测，输出可解释的检测报告"),
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("决策提效", "报告直达适配商品，缩短「浏览—下单」路径，提升转化与客单匹配"),
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("服务资产", "历史报告沉淀，支撑复访召回、换季关怀与复购运营"),
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]
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for i, (lead, desc) in enumerate(vals):
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y = 3.45 + i * 1.95
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rect(s, 1.5, y + 0.16, 0.26, 0.26, ACC)
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tb(s, 2.1, y, 10.6, 1.7, [dict(t=lead, size=30, bold=True, sa=6), dict(t=desc, size=24, color=MUT)])
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tb(s, 14.2, 2.55, 11, 0.6, [dict(t="适用行业与运用场景", size=32, bold=True, color=ACC)])
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for t, cx, cy in [("护肤", 14.2, 3.5), ("功效型美妆", 19.3, 3.5), ("抗老精华", 14.2, 4.55), ("品牌会员中心", 19.3, 4.55)]:
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sp = card(s, cx, cy, 4.7, 0.85, fill=BG, line=ACC)
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tf = sp.text_frame
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tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0
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tf.vertical_anchor = MSO_ANCHOR.MIDDLE
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p = tf.paragraphs[0]
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p.alignment = PP_ALIGN.CENTER
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r = p.add_run()
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r.text = t
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set_font(r, 26, False, ACC)
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tb(s, 14.2, 5.95, 11, 3.4, [dict(t=t, size=26, sa=14) for t in
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["· 店铺首页服务入口", "· 商品详情页挂载", "· 直播间互动测肤", "· 会员日与换季活动"]])
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rect(s, 0, 10.1, 26.67, 2.8, PANEL)
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tb(s, 1.5, 10.4, 10, 0.5, [dict(t="商业价值", size=24, bold=True, color=FAINT)])
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for i, (t, d) in enumerate([("提升转化", "把不确定变成可解释的购买理由"),
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("优化客单", "按肌龄匹配商品，减少试错退货"),
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("沉淀会员", "报告资产驱动复访与复购")]):
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x = 1.5 + i * 8.6
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tb(s, x, 11.1, 7.8, 1.5, [dict(t=t, size=28, bold=True, sa=6), dict(t=d, size=22, color=MUT)])
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# ---- P6 02 产品核心功能与链路 ----
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s = page()
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header(s, "02", "产品核心功能与链路")
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feats = [
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("01", "授权登录", "淘宝账号授权，协议确认后进入服务"),
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("02", "基础档案", "出生年份、地区、肤质，一次填写本地缓存"),
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("03", "三步拍摄", "正脸 / 左脸 / 右脸拍摄或相册选图"),
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("04", "AI 检测报告", "综合评分、肌龄、七项分项与三维指标"),
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("05", "护理与推荐", "护理关注选三，按肌龄推荐适配精华"),
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("06", "历史报告", "最近多份完整报告，可回看对比"),
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]
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for i, (n, t, d) in enumerate(feats):
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y = 2.35 + i * 1.12
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circle(s, 1.5, y + 0.06, 0.6, n, color=BG, size=22)
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tb(s, 2.4, y, 9.9, 1.05, [dict(t=t, size=28, bold=True, sa=3), dict(t=d, size=22, color=MUT)])
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for i, (f, cap) in enumerate([("phone-profile.png", "基础档案"), ("phone-camera.png", "正面拍摄引导"), ("phone-report.png", "AI 检测报告")]):
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x = 13.3 + i * 3.85
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pic(s, f"{AST}\\{f}", x, 2.3, 3.3, 6.48)
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tb(s, x, 8.95, 3.3, 0.5, [dict(t=cap, size=24, color=MUT, align=PP_ALIGN.CENTER)])
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for i, st in enumerate(["启动授权", "档案填写", "三步拍摄", "生成报告", "护理推荐", "商品转化"]):
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x = 2.1 + i * 3.9
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circle(s, x + 1.1, 10.3, 0.8, str(i + 1), color=BG, size=24)
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tb(s, x, 11.3, 3.0, 0.5, [dict(t=st, size=24, bold=True, align=PP_ALIGN.CENTER)])
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if i < 5:
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tb(s, x + 2.95, 10.42, 0.9, 0.6, [dict(t="→", size=30, bold=True, color=FAINT, align=PP_ALIGN.CENTER)])
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tb(s, 1.5, 12.35, 24.5, 1.6,
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[dict(t="七项分析：轮廓紧致 · 皱纹细纹 · 肤色色素 · 平滑细腻 · 水分水润 · 敏感泛红 · 痘痘皮损", size=22, color=MUT, sa=6),
|
||
dict(t="三维指标：紧塑力 / 活力感 / 稳定力，支撑综合评分与展示肌龄", size=22, color=MUT, sa=6),
|
||
dict(t="推荐逻辑：按展示肌龄分档匹配精华组别，报告页可跳转商品详情", size=22, color=MUT)])
|
||
|
||
# ---- P7 02 差异与互补 ----
|
||
s = page()
|
||
header(s, "02", "与平台现有能力的差异与互补")
|
||
for t, x, w in [("对比维度", 1.4, 4.6), ("平台现有货架与内容", 6.2, 9.8), ("本小程序互补价值", 16.2, 9.8)]:
|
||
tb(s, x, 2.7, w, 0.6, [dict(t=t, size=28, bold=True, color=ACC)])
|
||
for i, (lab, a, b) in enumerate([
|
||
("决策依据", "以图文、评价与直播讲解为主", "基于本人面部数据生成可解释报告"),
|
||
("选品方式", "用户自行对比商品与参数", "按肌龄与肤质状态主动匹配商品"),
|
||
("服务形态", "偏一次性浏览与成交", "报告沉淀可回看，支撑复访复购"),
|
||
("商家价值", "流量与成交工具丰富", "新增「专业测肤」信任入口，提升转化")]):
|
||
y = 3.85 + i * 2.1
|
||
tb(s, 1.4, y + 0.55, 4.6, 0.7, [dict(t=lab, size=28, bold=True)])
|
||
card(s, 6.2, y, 9.8, 1.9)
|
||
tb(s, 6.65, y + 0.35, 8.9, 1.3, [dict(t=a, size=26, color=MUT)], anchor=MSO_ANCHOR.MIDDLE)
|
||
card(s, 16.2, y, 9.8, 1.9, fill=PANEL, line=ACC)
|
||
tb(s, 16.65, y + 0.35, 8.9, 1.3, [dict(t=b, size=26)], anchor=MSO_ANCHOR.MIDDLE)
|
||
tb(s, 1.4, 12.65, 24, 0.8, [dict(t="「不做货架的替代，补足『专业解释 → 信任 → 决策』的服务缺口」", size=30, bold=True, color=ACC)])
|
||
|
||
# ---- P8 03 商业化运营规划及目标 ----
|
||
s = page()
|
||
header(s, "03", "商业化运营规划及目标")
|
||
tb(s, 2.75, 1.78, 14, 0.5, [dict(t="自研类型申请 · 西安巨子生物 · 首期服务自有旗舰店", size=22, color=FAINT)])
|
||
tb(s, 1.5, 2.6, 10, 0.6, [dict(t="目标覆盖", size=32, bold=True, color=ACC)])
|
||
for i, (t, d) in enumerate([("首期（0–6 个月）", "服务自有淘宝旗舰店与核心精华线，验证「测肤—推荐—购买」闭环"),
|
||
("扩展（6 个月以上）", "复制至品牌矩阵店铺，沉淀为会员中心的标准服务能力")]):
|
||
x = 1.5 + i * 12.22
|
||
card(s, x, 3.4, 11.45, 1.85)
|
||
tb(s, x + 0.45, 3.7, 10.6, 1.35, [dict(t=t, size=28, bold=True, sa=5), dict(t=d, size=24, color=MUT)])
|
||
tb(s, 1.5, 5.65, 10, 0.6, [dict(t="上线后的运营节奏", size=32, bold=True, color=ACC)])
|
||
for i, (t, d) in enumerate([("上线期（0–1 月）", "店铺首页与详情页入口曝光；引导完成首测；紧盯拍摄成功率与报告完成率。"),
|
||
("成长期（1–3 月）", "大促与直播挂载测肤互动；会员专属复测提醒；持续优化推荐与话术转化。"),
|
||
("沉淀期（3 月以上）", "历史报告召回与换季护理活动；A/B 优化推荐规则；提升复测率与连带购买。")]):
|
||
x = 1.5 + i * 8.13
|
||
card(s, x, 6.45, 7.4, 3.2)
|
||
tb(s, x + 0.45, 6.8, 6.5, 2.5, [dict(t=t, size=28, bold=True, sa=8), dict(t=d, size=24, color=MUT)])
|
||
tb(s, 1.5, 10.1, 10, 0.6, [dict(t="关键指标", size=32, bold=True, color=ACC)])
|
||
for i, (t, d) in enumerate([("体验", "拍摄完成率 · 报告完成率 · 报告回访时长"),
|
||
("转化", "推荐点击率 · 详情跳转率 · 测肤后下单转化"),
|
||
("运营", "复测率 · 连带购买 · 会员渗透"),
|
||
("合规", "数据按账号隔离 · 权限按需申请 · 不作医疗诊断承诺")]):
|
||
x = 1.5 + i * 5.99
|
||
card(s, x, 10.9, 5.71, 2.35)
|
||
tb(s, x + 0.4, 11.2, 4.95, 1.8, [dict(t=t, size=28, bold=True, sa=6), dict(t=d, size=22, color=MUT)])
|
||
|
||
# ---- P9 致谢 ----
|
||
s = page()
|
||
glow(s, 19, 8.5, 13, alpha=11)
|
||
glow(s, -3, -3, 10, alpha=8)
|
||
tb(s, 4.85, 5.6, 18, 1.6, [dict(t="THANK YOU", size=96, bold=True)])
|
||
tb(s, 4.9, 8.35, 16, 0.6, [dict(t="西安巨子生物基因技术股份有限公司", size=30)])
|
||
tb(s, 4.9, 9.25, 16, 0.5, [dict(t="AI 智能测肤 · 淘宝小程序产品方案", size=24, color=MUT)])
|
||
|
||
prs.save(OUT)
|
||
print("saved:", OUT, "slides:", len(prs.slides))
|
||
|
||
|
||
if __name__ == "__main__":
|
||
build()
|