Chaoxing Document Downloader (超星文档下载)

Download PDFs from Chaoxing WPS viewer URLs using the getYunFiles API.

Core Principle

Every Chaoxing viewer URL contains an objectid (32-char hex). Call the getYunFiles API to get the direct PDF link — no cookies or auth tokens needed.

Arguments

$ARGUMENTS contains the user's download request — typically one or more entries with page count, name, and viewer URL. Parse them to extract the data.

Download Method

Step 1: Extract objectid from each URL

Find the objectid=([a-f0-9]{32}) parameter in each viewer URL.

Step 2: Call getYunFiles API

For each objectid, call:

https://contestyd.chaoxing.com/app/files/{objectid}/getYunFiles?key=allData

Response JSON contains:

• data.pdf — direct PDF URL on s3.cldisk.com or s3.ananas.chaoxing.com (preferred)
• data.download — alternative download URL with auth tokens (fallback)
• data.filename — original filename
• data.pagenum — page count

Step 3: Download the PDF

Use the data.pdf URL to download directly. No authentication headers needed.

Save to: ~/Downloads/chaoxing_pdfs/{用户给的名称}.pdf

Step 4: Validate page count

Compare data.pagenum with the user's expected page count. Report any mismatch.

Step 5: Convert PDF to TXT (with OCR fallback)

After downloading each PDF, automatically extract text to a plain text file. Use a two-stage approach: native text extraction first, then OCR fallback for image-based pages.

Prerequisites:

pip install pymupdf rapidocr-onnxruntime

Conversion method (Python):

import sys, os, fitz
from rapidocr_onnxruntime import RapidOCR

if sys.platform == "win32":
    sys.stdout.reconfigure(encoding="utf-8")

ocr = RapidOCR()
pdf_path = "~/Downloads/chaoxing_pdfs/{name}.pdf"
doc = fitz.open(pdf_path)
all_text = []

for i, page in enumerate(doc):
    # Stage 1: Try native text extraction
    native = page.get_text().strip()
    if len(native) > 50:
        all_text.append(f"--- 第{i+1}页 ---\n{native}")
        continue
    # Stage 2: OCR fallback for image-based pages
    pix = page.get_pixmap(dpi=200)
    img_bytes = pix.tobytes("png")
    result, _ = ocr(img_bytes)
    ocr_text = "\n".join([item[1] for item in result]) if result else ""
    label = "OCR" if len(ocr_text) > 0 else "(empty)"
    all_text.append(f"--- 第{i+1}页 [{label}] ---\n{ocr_text}")

doc.close()
full_text = "\n".join(all_text)

with open(pdf_path.replace(".pdf", ".txt"), "w", encoding="utf-8") as f:
    f.write(full_text)

# Summary
native_pages = sum(1 for p in all_text if "[OCR]" not in p and "[empty]" not in p)
ocr_pages = sum(1 for p in all_text if "[OCR]" in p)
print(f"Native: {native_pages}p, OCR: {ocr_pages}p, Total: {len(full_text)} chars")

Output files per download:

• {name}.pdf — original PDF
• {name}.txt — plain text extraction (native + OCR pages marked with [OCR])