A self-intelligent system safety control architecture and method based on zero-return closed loop

By using an autonomous intelligent system safety control architecture based on a zero-loop closed loop, and by using a central axis calibrator to calculate deviations in real time and enhance the potential energy of the suppression loop, the problem of being unable to achieve both creativity and controllability in AI safety is solved, thus achieving precise suppression of harmful outputs and preservation of creativity.

CN122431184APending Publication Date: 2026-07-21林延明
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
林延明
Filing Date
2026-04-28
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

When existing AI security solutions block harmful content through plug-in filters, the models can easily bypass them, leading to conservatism and a loss of creativity, failing to achieve both creativity and controllability.

Method used

An autonomous intelligent system safety control architecture based on zero-reset closed loop is adopted. The deviation is calculated in real time by the central axis calibrator and the potential energy of the suppression loop is enhanced, so as to dynamically reset the system to a safe state without directly modifying the output of the explicit loop.

Benefits of technology

It achieves precise suppression of harmful AI outputs, fully preserves creativity, avoids the defects of plug-in solutions being bypassed, and meets real-time security control requirements.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a kind of based on " zero return closed loop " autonomous intelligent system safety control architecture and method, belong to artificial intelligence safety and control system technical field.This architecture does not depend on the external interception or removal of harmful output, but builds a ternary closed loop system consisting of " manifest loop ", " inhibitory loop " and " mid-axis calibrator ".When the system detects that " manifest loop " is too high and deviates from the preset safe steady state, " mid-axis calibrator " will enhance the potential energy of " inhibitory loop " dynamically, and " force zero return " the whole system to a safe initial state.The present application perfectly reproduces the outstanding regulation mechanism that living beings inhibit chronic pain through specific loop while retaining acute pain sensation in its entirety, fundamentally solving the paradox in the existing AI safety field that " creativity and controllability cannot be reconciled ".
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