C++网页特定数据采集库推荐：降雨预测项目需求

Hey there! Since you’ve already built a solid rainfall prediction neural network with your multi-text dataset, let’s tackle the extension you need—scheduling 30-minute web data collection, feeding it into your model, and saving results to text files. Here’s a breakdown of the best libraries for each part of the workflow:

1. Web Data Collection #

You’ll need tools to fetch web content and extract specific meteorological data:

• libcurl: The go-to for HTTP/HTTPS requests in C++. It’s lightweight, cross-platform, and handles all common request types. You can easily fetch raw webpage content, then parse it to pull out temperature, pressure, dew point, etc.
• Gumbo Parser: If the webpage has structured HTML, use this lightweight HTML parser to traverse the DOM and extract target data cleanly (no messy regex hacks needed).
• cpprestsdk (Casablanca): Great if the webpage returns JSON/XML data. It combines HTTP request capabilities with built-in data parsing, so you can fetch and parse structured data in one go.

2. Scheduled Tasks (30-Minute Interval) #

Choose based on whether you want a standalone program or a system-level schedule:

• System Cron (Linux/Unix) / Task Scheduler (Windows): The simplest option if you don’t need your program to run continuously. Set up a system cron job (or Windows Task Scheduler) to execute your C++ program every 30 minutes—no extra libraries required.
• Boost.Asio: If you want your program to run as a persistent service with internal scheduling, use steady_timer from Boost.Asio. It’s cross-platform, reliable, and lets you define a callback that triggers every 30 minutes to run your collection/prediction logic.
• Qt QTimer: If you’re already using Qt for your project, this is a no-brainer. It’s easy to integrate and handles timing with minimal code.

3. Neural Network Integration #

Depending on how your original model was built:

• LibTorch (PyTorch C++ Frontend): If your model was trained in PyTorch, load it directly in C++ with LibTorch. It lets you preprocess the collected data, run inference, and get predictions seamlessly.
• TensorFlow C++ API: Similar to LibTorch, but for TensorFlow-trained models. Perfect if your original pipeline uses TensorFlow.
• MLPACK: If you want a pure C++ machine learning library for future model tweaks or extensions, MLPACK has great support for neural networks and data preprocessing.

4. File Storage #

You don’t need fancy libraries here, but these can simplify things:

• C++ Standard Library: Use std::ofstream to write collected data and predictions directly to text files—simple and efficient for your colon-separated format.
• nlohmann/json: If you want to store data in a more structured format (e.g., JSON) alongside your colon-separated text, this header-only library makes JSON serialization/deserialization a breeze.

Quick Workflow Example #

1. Use libcurl to fetch the target webpage, parse it with Gumbo to extract temperature, pressure, dew point, and timestamp.
2. Use Boost.Asio’s steady_timer to schedule this collection step every 30 minutes.
3. Convert the extracted data into the tensor format your LibTorch/TensorFlow model expects, run inference to get the rainfall prediction.
4. Write the raw collected data + prediction to a text file using std::ofstream, following your existing colon-separated format.

Pro Tips #

• Add error handling for network failures (e.g., retry logic in libcurl) to avoid missing data points.
• Double-check that the collected data matches the preprocessing steps you used for your original dataset (e.g., missing value handling) to keep prediction accuracy consistent.
• For system-level scheduling, test the cron/task scheduler setup to ensure it triggers exactly every 30 minutes (account for any system clock drift if needed).

内容的提问来源于stack exchange，提问作者JavaApprentis