Recently, when building a multimodal model dataset, we encountered IP risk control problems for cross-border collection (especially for sites protected by Cloudflare), and tried three strategies:
Tor network: too high latency (average > 3.2 seconds), unable to meet real-time requirements
Cloud server rotation: AWS/GCP IP segment marking rate increased by 37% month-on-month
Dynamic residential proxy: finally adopted intelligent routing solution, key configuration parameters:
Protocol layer fingerprint obfuscation (simulating Chrome 120+ behavioral characteristics)
Millisecond IP geo-switching (supporting precise positioning of 195+ countries)
Adaptive QPS control (dynamic adjustment based on the anti-climbing strength of the target site)
Measured results (30 days/5 million requests):
Traditional proxy group: success rate 68.7%, triggering verification code rate 19.3%
Optimization scheme group: success rate increased to 98.1%, verification code rate reduced to 0.6%
Note: It is recommended to use the following strategies to enhance robustness:
Add ±15% random offset to request timestamp
Use headless browser to render key pages
Deploy distributed verification code cracking module
Technical discussion:
How do you handle IP reputation maintenance during large-scale data collection? Is there a mature IP health assessment framework recommended?
(Attached with a sample of technical logs during the test → [non-commercial link] for technical verification reference only)