chore: 同步本地更改
- 新增 aio-mcp 项目框架 - 新增 .trae/skills/ OCR/SKU 设计工具 - 更新 auto-check 配置和 stress 包 - 删除 fnhelp 文档目录 - 更新 Docker 压力测试脚本
This commit is contained in:
张威33321
366
.trae/skills/ocr-extract/parse_sku_prices.py
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366
.trae/skills/ocr-extract/parse_sku_prices.py
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import json
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import re
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import os
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# Load OCR results
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with open(r"D:\workspace\membank\售价\ocr_results.json", "r", encoding="utf-8") as f:
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all_results = json.load(f)
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# Extract structured SKU prices from each image
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# Pattern: SKU name patterns like "蜗牛Nas-xxx-4g+64g", "J1900-4g+32g", etc.
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# Prices appear as decimal numbers typically followed by a discount of 5.00
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def extract_sku_prices(text_lines):
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"""Extract SKU name and price pairs from OCR text lines"""
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entries = []
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i = 0
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while i < len(text_lines):
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text, conf = text_lines[i]
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text = text.strip()
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# Look for SKU names containing known patterns
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sku_patterns = [
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r'(?:蜗牛Nas|蜗牛|Nas|cNas)[\s\w\-\.\+\(\)一-鿿]+?\d+g[+一-鿿]*\d*g',
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r'(?:J1900|j1900|J3160|j3160|I3|i3|I5|i5|1037U|1037u|N5105|N5100|N5095|J3355|J3060)[\-\.\+\(\)\s\w一-鿿]*?\d+g[\+一-鿿]*\d*g',
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r'(?:J1900|j1900|J3160|j3160|I3|i3|I5|i5|1037U|1037u|N5105|N5100|N5095|J3355|J3060)[\-\.\+\(\)\s\w一-鿿]*?\d+g',
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r'(?:16G[BW])[\-\s]*(?:windows|蜗牛|群晖)',
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r'(?:DDR|D3|D4)[\-\.\s]*\d+\s*[Gg]',
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]
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is_sku = False
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for pat in sku_patterns:
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if re.search(pat, text, re.IGNORECASE):
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is_sku = True
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break
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if is_sku:
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sku_name = text
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# Look for price pattern nearby: two numbers where one is ~5.00 off from the other
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# Pattern: price1 (like 247.50) ... 5.00 ... price2 (like 242.50)
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prices_found = []
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for j in range(i, min(i + 10, len(text_lines))):
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t2 = text_lines[j][0].strip()
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# Match decimal prices
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price_match = re.findall(r'\d{2,4}\.\d{2}', t2)
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for p in price_match:
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prices_found.append(float(p))
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# Also match integer prices
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price_match2 = re.findall(r'(?<!\d\.)(?:\d{2,4})(?:\.\d{2})?(?!\d|\.\d{3,})', t2)
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for p in price_match2:
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try:
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prices_found.append(float(p))
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except:
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pass
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# Deduplicate and find likely actual price (the larger number, minus 5 = smaller number)
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prices_found = sorted(set(prices_found))
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actual_price = None
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for p in prices_found:
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if p > 10 and (p - 5) in prices_found:
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actual_price = p # The larger one is the original price
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break
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if actual_price:
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discounted = actual_price - 5
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entries.append({
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'sku': sku_name,
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'price': discounted,
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'original_price': actual_price,
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'confidence': conf
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})
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i += 1
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return entries
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def extract_prices_v2(text_lines):
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"""
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Better approach: parse each image by looking for patterns:
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SKU name -> (price, discount 5.00, discounted_price)
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"""
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results = []
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# Flatten texts
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all_text = [t[0].strip() for t in text_lines]
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all_with_conf = list(text_lines)
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# Known CPU/memory patterns
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pattern = re.compile(
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r'.*?(?:蜗牛|Nas|NAS|nas|cNas|黑裙|黑群晖|客服|咨询).*?(?:'
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r'J1900|j1900|J3160|j3160|'
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r'i3|I3|i5|I5|'
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r'1037[Uu]|1037u|'
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r'N5105|N5100|N5095|'
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r'J3355|J3060|'
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r'16G[Bb]'
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r').*',
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re.IGNORECASE
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)
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# Scan through all texts looking for price patterns
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for i, (text, conf) in enumerate(all_with_conf):
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text = text.strip()
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# Skip non-SKU lines
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if len(text) < 3:
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continue
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# Check if this looks like a SKU description
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has_cpu = bool(re.search(r'(J1900|j1900|J3160|j3160|i3|I3|i5|I5|1037[Uu]|N5105|N5100|N5095|J3355|J3060|J4125)', text))
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has_nas = bool(re.search(r'(蜗牛|Nas|NAS|cNas|黑裙)', text))
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has_mem = bool(re.search(r'\d+g[\+一-鿿]*\d+g|\d+g\b|16G[Bb]', text))
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if has_cpu or (has_nas and has_mem) or ('16G' in text.upper() and ('windows' in text.lower() or '蜗牛' in text or '群晖' in text)):
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# This is a SKU name - look for nearby prices
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sku_name = text
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# Look at next 8 lines for prices
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prices = []
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for j in range(i+1, min(i+12, len(all_with_conf))):
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t = all_with_conf[j][0].strip()
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# Find decimal numbers
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nums = re.findall(r'(\d{2,4}\.\d{2})', t)
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prices.extend([float(n) for n in nums])
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# Also look at previous 2 lines
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for j in range(max(0, i-2), i):
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t = all_with_conf[j][0].strip()
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nums = re.findall(r'(\d{2,4}\.\d{2})', t)
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prices.extend([float(n) for n in nums])
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if prices:
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# Find pairs where diff = 5.00
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prices = sorted(set(prices))
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found_price = None
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for p in prices:
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if p > 20 and (p - 5.00) in [round(x, 2) for x in prices]:
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found_price = p
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break
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# If no pair found, take the most common price range
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if not found_price:
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valid_prices = [p for p in prices if 100 < p < 500]
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if valid_prices:
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found_price = max(valid_prices)
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if found_price and found_price > 20:
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discounted = round(found_price - 5.0, 2)
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results.append({
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'sku': sku_name,
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'price': discounted,
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'original_price': round(found_price, 2),
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'confidence': float(conf) if conf else 0
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})
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# Deduplicate - keep highest confidence for each SKU
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seen = {}
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for r in results:
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key = r['sku']
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if key not in seen or r['confidence'] > seen[key]['confidence']:
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seen[key] = r
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return sorted(seen.values(), key=lambda x: x['price'])
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def extract_prices_v3(text_lines):
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"""
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Most robust: look for every <SKU name> ... <price_a> ... 5.00 ... <price_b> pattern
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where price_a = price_b - 5 (or vice versa)
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"""
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results = []
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all_with_conf = list(text_lines)
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for i, (text, conf) in enumerate(all_with_conf):
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text = text.strip()
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# Must contain key SKU indicators
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has_indicator = (
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bool(re.search(r'(J1900|j1900|J3160|j3160|i3|I3|i5|I5|1037[Uu]|N5105|N5100|N5095|J3355|J3060|J4125|5005)', text)) or
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bool(re.search(r'(蜗牛|Nas|NAS|黑裙|黑群晖|客服|咨询)', text)) or
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bool(re.search(r'16G[Bb].*(?:windows|蜗牛|群晖|系统)', text)) or
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bool(re.search(r'(?:预装系统|系统).*(?:双盘位|单盘位)', text))
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)
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if not has_indicator or len(text) < 4:
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continue
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# But also filter out pure UI text
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skip_patterns = ['商品价格', '关注券', '折扣', '券后价', '商品ID', '活动', '修改', '查看', '营销', '官方', '拼多多', '商家']
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if any(s in text for s in skip_patterns):
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continue
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sku_name = text
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# Search window around this line for prices
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window_texts = []
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for j in range(max(0, i-3), min(i+15, len(all_with_conf))):
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window_texts.append(all_with_conf[j][0].strip())
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# Extract all decimal prices from the window
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prices = []
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for wt in window_texts:
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nums = re.findall(r'(\d{2,4}\.\d{2})', wt)
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prices.extend([float(n) for n in nums])
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if not prices:
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continue
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prices = sorted(set(prices))
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# Filter noise prices (too small)
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prices = [p for p in prices if p > 15]
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# Find the discount pattern: original_price and (original_price - 5)
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found_price = None
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price_set = set(round(p, 2) for p in prices)
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for p in prices:
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if round(p - 5.0, 2) in price_set:
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# p is the higher (original), p-5 is the discounted
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found_price = p
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break
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if round(p + 5.0, 2) in price_set:
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# p is the discounted, p+5 is the original
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found_price = p + 5.0
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break
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if not found_price:
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# Fallback: take the highest price that looks like a product price
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valid = [p for p in prices if 100 < p < 600]
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if valid:
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found_price = max(valid)
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if found_price and found_price > 20:
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results.append({
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'sku': sku_name,
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'price': round(found_price - 5.0, 2),
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'original_price': round(found_price, 2),
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'confidence': float(conf) if conf else 0
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})
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# Deduplicate by SKU name similarity
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def sku_key(s):
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return re.sub(r'[\s\-\.\+]+', '', s.lower())
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deduped = {}
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for r in results:
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key = sku_key(r['sku'])
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if key not in deduped or r['confidence'] > deduped[key]['confidence']:
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deduped[key] = r
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return sorted(deduped.values(), key=lambda x: x['price'])
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print("=" * 80)
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print("Extracting SKU Price Summary")
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print("=" * 80)
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all_entries = []
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for img_name, lines in all_results.items():
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print(f"\nProcessing: {img_name}")
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entries = extract_prices_v3(lines)
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print(f" Found {len(entries)} SKUs")
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all_entries.extend(entries)
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# Global deduplication
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def normalize_sku(name):
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"""Normalize SKU name for deduplication"""
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name = name.strip()
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# Remove leading symbols
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name = re.sub(r'^[>\s\-\.]+', '', name)
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# Standardize separators
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name = name.replace('+', '+').replace('＋', '+')
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# Remove trailing garbage
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name = re.sub(r'[>\s\.\-]+$', '', name)
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return name
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# Categorize entries
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# Dedup keeping highest confidence
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global_dedup = {}
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for e in all_entries:
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norm = normalize_sku(e['sku'])
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# Further normalize for key
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key = re.sub(r'[\s\-\>\.\(\)]', '', norm.lower())
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# Extract CPU type for grouping
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if key not in global_dedup or e['confidence'] > global_dedup[key]['confidence']:
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global_dedup[key] = e
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final_entries = sorted(global_dedup.values(), key=lambda x: x['price'])
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# Group by CPU/platform
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groups = {}
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for e in final_entries:
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name = e['sku']
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# Extract group
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if '16G' in name.upper() or '16g' in name.lower():
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group = '系统U盘 (16GB)'
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elif re.search(r'(i3|I3|i5|I5)', name):
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if '双盘' in name or '雙盤' in name:
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group = 'NAS 双盘位 - i3/i5系列'
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else:
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group = 'NAS - i3/i5系列'
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elif re.search(r'1037[Uu]', name):
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if '单盘' in name or '單盤' in name or 'A4' in name:
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group = 'NAS 单盘位 - 1037U系列'
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elif '双盘' in name or '雙盤' in name:
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group = 'NAS 双盘位 - 1037U系列'
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else:
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group = 'NAS - 1037U系列'
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elif re.search(r'J1900|j1900', name):
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group = 'NAS 双盘位 - J1900系列'
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elif re.search(r'i3|I3', name):
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group = 'NAS - i3系列'
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elif re.search(r'i5|I5', name):
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group = 'NAS - i5系列'
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elif '黑裙' in name or '客服' in name or '咨询' in name:
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group = 'NAS - 黑裙晖系列'
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else:
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group = '其他'
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if group not in groups:
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groups[group] = []
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groups[group].append(e)
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# Generate markdown output
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md_lines = []
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md_lines.append("# SKU 售价汇总")
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md_lines.append("")
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md_lines.append(f"> 从拼多多商家后台截图 OCR 识别提取")
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md_lines.append(f"> 提取日期: 2026-06-16")
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md_lines.append(f"> 数据来源: `售价/` 目录下 {len(all_results)} 张截图")
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md_lines.append(f"> 共识别 {len(final_entries)} 个 SKU 售价（已去重）")
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md_lines.append("")
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md_lines.append("---")
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md_lines.append("")
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for group_name in sorted(groups.keys()):
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entries = groups[group_name]
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md_lines.append(f"## {group_name}")
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md_lines.append("")
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md_lines.append("| SKU 名称 | 券后售价 (¥) | 券前原价 (¥) | OCR 置信度 |")
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md_lines.append("|---------|------------:|------------:|----------:|")
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for e in entries:
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name = normalize_sku(e['sku'])
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# Clean up OCR artifacts in name
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name = name.replace('關注券5元优惠券', '').strip()
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name = re.sub(r'\s+', ' ', name)
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md_lines.append(f"| {name} | {e['price']:.2f} | {e['original_price']:.2f} | {e['confidence']:.2f} |")
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md_lines.append("")
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md_lines.append("---")
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md_lines.append("")
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md_lines.append("## 说明")
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md_lines.append("")
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md_lines.append("- **券后售价** = 券前原价 - 5.00元 (店铺关注券)")
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md_lines.append("- 价格数据通过 RapidOCR 从拼多多商家后台截图自动识别提取")
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md_lines.append("- 同一SKU在不同截图中出现时保留置信度最高的识别结果")
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md_lines.append("- OCR 识别可能存在误差，请以实际后台数据为准")
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output_path = r"D:\workspace\membank\售价\SKU售价汇总.md"
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with open(output_path, "w", encoding="utf-8") as f:
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f.write("\n".join(md_lines))
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print(f"\n\nSummary saved to: {output_path}")
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print(f"Total: {len(final_entries)} SKUs, {len(groups)} categories")
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