- Products and Solutions
- Why Callisto?
- Join us
- News and Blogs
Advanced analysis AI agent
Continuous monitoring and analysis of vehicle data identify potential risks, optimize abnormal alarm strategies, and reduce false positives and negatives.
In vehicle cybersecurity operations, abnormal alarm analysis primarily uses traffic log data from vehicles, including sensor and system data. However, with millions of vehicles generating vast amounts of log data, current data analysis technologies and rule-based strategies are limited. These limitations make it difficult to accurately identify all anomalies, resulting in false positives and negatives.
The Callisto advanced analysis AI agent harnesses the powerful data integration and analysis capabilities of the Butterfly Large Model. It integrates over 3,000 vehicle signals and more than 30 types of vehicle threat correlation analysis, utilizing traffic logs and cloud data from fleet vehicles. It also takes into account external factors like weather and road conditions that could impact vehicle safety. Through long-term monitoring and in-depth analysis, the agent accurately identifies potential unknown risks and provides effective mitigation measures, offering strong technical support and ensuring safe vehicle operations.
Powerful technology and rich automotive experience support
Relying on the data integration technology and excellent analysis capabilities of the Butterfly Large Model, combined with more than 3,000 vehicle signals and more than 30 vehicle threat analysis and judgment experiences mastered by Callisto, it provides a solid technical foundation and professional knowledge guarantee for the agent.
Multi-source data and external environment considerations
Based on the networked fleet vehicle traffic log data and cloud data, and comprehensively considering the impact of external environmental factors such as weather and road conditions on vehicle safety, we comprehensively collect and analyze vehicle safety-related information.
Long-term monitoring analysis and accurate output
Through long-term continuous monitoring and in-depth analysis, we can accurately discover potential unknown risks and provide effective mitigation measures to provide strong technical support and guarantee for safe vehicle operations.
'%3e%3ccircle%20cx='19.0016'%20cy='19.0001'%20r='2.4'%20fill='white'/%3e%3c/g%3e%3ccircle%20cx='19'%20cy='19'%20r='5.5'%20stroke='white'/%3e%3cdefs%3e%3cfilter%20id='filter0_f_1906_673'%20x='0.601562'%20y='0.600098'%20width='36.8008'%20height='36.8'%20filterUnits='userSpaceOnUse'%20color-interpolation-filters='sRGB'%3e%3cfeFlood%20flood-opacity='0'%20result='BackgroundImageFix'/%3e%3cfeBlend%20mode='normal'%20in='SourceGraphic'%20in2='BackgroundImageFix'%20result='shape'/%3e%3cfeGaussianBlur%20stdDeviation='8'%20result='effect1_foregroundBlur_1906_673'/%3e%3c/filter%3e%3c/defs%3e%3c/svg%3e)
Intelligent Insights
Advanced analysis AI agent can comprehensively analyze vehicle traffic logs, cloud data and external environmental factors to accurately identify potential unknown risks and improve the accuracy and reliability of anomaly detection.
Continuous optimization
Through long-term continuous monitoring and in-depth analysis, advanced analysis agents can reduce abnormal false positives and false negatives, optimize abnormal alarm strategies, and ensure comprehensive coverage of automotive safety operations.
Powered by Butterfly AI
With the powerful data integration technology and analysis capabilities of the Butterfly Large Model , the advanced analysis AI agent provides targeted mitigation measures and suggestions, providing effective technical support for vehicle safety operations and improving the overall safety assurance level.
Use Case
In the process of car safety operation, the Callisto advanced analysis AI agent discovered from the abnormal traffic logs of the past three months that the abnormal engine alarm of a certain vehicle may have false alarms. The agent called all the log data on the day when the vehicle had an abnormal alarm and queried the weather data on that day through the Internet. After in-depth analysis, it was found that the period when the engine abnormal alarm was generated was the highest temperature of the day. There were no abnormal engine alarms in other periods, and the engine abnormal alarm was concluded. Related to extreme weather and providing recommendations on corresponding mitigation measures.
