Artificial Intelligence Applications in the Open Source Intelligence Cycle(1) - Data Collection Stage

Open source intelligence data is hidden in the vast virtual space raw data. Ordinary automated retrieval is difficult to meet the processing and analysis requirements of massive data. The application of artificial intelligence-based data collection models to open source intelligence extraction has significantly improved the adaptability of open source intelligence in the cyberspace era where data scale is exploding.

1. Independent and accurate collection and screening

In the open source intelligence data collection stage, autonomous sensors and web crawler programs driven by artificial intelligence are used to continuously and automatically capture a large amount of data, forming autonomous and semi-autonomous data collection for targets. The efficiency and coverage of crawlers obtaining data have been significantly improved. At the same time, the analysis platform based on machine learning is used to screen data sources such as social media, and a knowledge base-based active thematic search engine system model is constructed to realize automatic detection. It improves the efficiency of crawling and filtering data, and automatically generates normalized materials that include elements such as task, location and time.

2. Personalized preprocessing

Use artificial intelligence tools to perform pre-processing such as preliminary screening and classification during the collection stage, making the collected data easier for subsequent processing and analysis. Relying on the deep learning model, it conducts multi-dimensional learning on massive open source intelligence to form an efficient and usable intelligence classifier. Use the intelligence classifier for preprocessing, and at the same time as the collection is completed, it will carry out classified distribution, storage, and management to provide structured data support for subsequent processing and analysis links.

3. Information prioritization

Open source intelligence uses artificial intelligence technology to determine the importance level and prioritization of data through functions such as pre-labeling. For example, use the reinforcement learning model to motivate the model according to multi-dimensional credible features, and strengthen the credibility and relevance of crawler intelligence. At the same time, using the intelligence data type library, the data is globally integrated, judged and marked, and the information with the highest priority is extracted.