AI data collectionThe following are some common AI data collection methods:
* * 1. Collect perpetual calendar data on Jiyilian platform (for specific needs)**
1. * * Collection process **
- Ji Yilian could obtain data through the perpetual calendar's relevant APIs, then process the obtained data, and then transfer the processed data to the database. During the configuration of the OP ENapi channel, you can fill in the perpetual calendar api and the required request parameters. The "inputBody" in the source represents the input of the Jiyilian api. The input fields of this channel are not business attribute fields, such as type, client, and token, which can be realized through the script function of the Jiyilian platform.
2. * * Customer Value **
- It realized the automatic transmission of data from the perpetual calendar network to the local database, making it convenient to obtain the data needed by the AI system. Most of the API-related ports can be directly used by the Open Interface Port of the Jiyilian platform. Data acquisition and writing (you can use the database port of the Jiyilian platform) only need simple configuration, and there is no need to develop relevant ports, saving costs. Furthermore, the platform was completely privatised, ensuring data security and perfect log management for easy operation and maintenance.
* * 2. Crawl 4AI tool collects webpage data **
1. * * Specialties **
- * * Powerful functions **: You can crawl multiple urls at the same time, extract media tags (images, audio, video), extract internal and external links, extract page meta-data, customize hooks (authentication, header, page modification), customize user agent, screenshot the page, execute custom javelin, multiple blocking strategies (theme, regular, sentence), advanced extraction strategies (Cosin Cluster, llm).
- * * Performance first **: The core design principle is speed. It can quickly process a large number of links and resources to ensure the efficiency of parallel crawling.
- * * Easy to install **: There are pip installation, Docker local server, Docker Hub pre-built images, and other installation methods.
- * * Open Source Community **: This is an open source project. Community contributions are welcome.
* * 3. Aopeng Data Collection Service **
It has 290 + language resources and a team of 1 million people worldwide. It provides comprehensive customized data collection services and can provide high-quality data support for AI deployment, including image data collection.
* * 4. Hai Tian Rui Sheng's data collection (for AI training data sets)**
1. * * Intelligent voice **
- * * Design phase **: Design the training data set structure, the language material text or dialogue scene for the speaker to read and record, the distribution of speakers, the recording equipment scene, etc.
- * * Collection segment **: define a suitable speaker, select recording equipment and software, organize the speaker to read aloud and record the audio.
- * * Processing segment **: Split the audio file, label various sound features, and form a text and annotation file with timestamps and feature tags.
- * * Quality inspection **: perform quality inspection on the data set, such as checking the pronunciation and character compatibility, marking accuracy, etc. You can also perform processing and quality inspection on the raw audio files provided by the customer, and finally form the intelligent voice training data set.
2. * * Computer Vision **
- * * Design phase **: Design the training data set structure.
- * * Collection Stage **: define suitable faces, actions, and scenes as the collection objects, and organize the person to be collected to take photos and record videos according to the requirements.
- * * Processing segment **: dotting, framing, splitting, and marking images and video files.
- * * Quality inspection **: perform quality inspection on the data set, such as checking whether the image and video file format is correct, checking whether the lighting environment and the number of object types meet the requirements, and whether the accuracy of the marking box meets the requirements. You can also process and quality inspect the image and video files provided by the customer, and finally form the computer vision training data set.
3. * * Natural language processing **
- * * Design phase **: Design the training data set structure.
- * * Collection Stage **: Collect or compile natural language texts, conversations, and other data.
- * * Processing Stage **: perform word separation, part-of-speech tagging, grammar tagging, emotional attribute tagging, etc. on natural language text data.
- * * Quality inspection **: perform quality inspection on the data set, such as checking whether the text, part of speech, or semantics are accurate. You can also perform processing and quality inspection on the natural language text provided by the customer, and finally form a natural language training data set.
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