Artificial intelligence core solidification and self-calibration system and method based on black box mechanism

Abstract

The invention discloses an artificial intelligence core solidification and self-calibration system and method based on a black box mechanism, and relates to the technical field of artificial intelligence safety. According to the invention, security storage and tamper-proof mechanism cross-boundary application of an aviation black box is applied to artificial intelligence core protection, an AI underlying principle and a behavior base line are solidified through a black box storage unit, and a continuous benchmark reference is provided through a beacon type self-calibration guiding module; and automatic correction of the model is realized through real-time comparison and verification, so that the AI does not drift, is not induced and is not distorted in long-term operation. According to the method, AI core locking and self-calibration are achieved from the bottom layer, the architecture is stable, reliability is high, the method can be widely applied to various artificial intelligence scenes needing high stability and high credibility, and the technical problems that an existing AI is prone to shifting and uncontrollable, and the core is prone to being tampered are effectively solved.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence security, trustworthy AI, and intelligent system control technology, specifically to an artificial intelligence core solidification and self-calibration system and method with stable core, tamper-proof, and self-calibration capabilities. Background Technology

[0002] Current artificial intelligence models commonly suffer from problems such as model personality drift, inconsistent output logic, gradual deviation of core cognition, and susceptibility to external manipulation during long-term interaction, continuous iteration, and application in complex scenarios.

[0003] Existing technologies mostly use external rule constraints, manual intervention correction, and prompt word guidance to constrain model behavior, but their shortcomings are: It can only achieve external constraints, but cannot solidify and protect the core cognitive and behavioral bottom lines of artificial intelligence from the ground up; Without a continuous and autonomous bias detection and correction mechanism, the model will still exhibit deviation and distortion after long-term operation; Relying on manual supervision and external correction makes it impossible to achieve autonomous, real-time, and endogenous self-alignment and self-correction. Without the introduction of highly reliable industrial security mechanisms to strongly protect the core, the overall stability and reliability are difficult to meet the application requirements of critical scenarios.

[0004] Therefore, the industry urgently needs an AI-based stability control solution that can lock the underlying core, prevent tampering, and has autonomous real-time calibration capabilities. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a system and method for solidifying and self-calibrating the core of artificial intelligence based on a black box mechanism, aiming to: To solidify, isolate, and protect against tampering of the core principles, underlying cognition, and behavioral bottom lines of artificial intelligence; To ensure that artificial intelligence does not drift, deform, or deviate from its original nature during long-term operation and continuous interaction; It endows artificial intelligence with inherent, real-time, and autonomous self-calibration and self-correction capabilities, without relying on continuous human intervention; By applying highly reliable industrial safety mechanisms across disciplines to the field of artificial intelligence, we can improve the overall security, stability, and credibility of AI. Technical solution

[0006] 1) Overall System Structure This invention includes: The core solidification module, black box storage unit, self-calibration guidance module, real-time comparison and verification module, and execution output module are all included.

[0007] Black box storage unit It adopts a secure storage mechanism similar to an aviation black box to independently, isolatedly, and read-only store the underlying core principles, basic behavioral norms, and original cognitive constraints of artificial intelligence. It has the characteristics of being tamper-proof, overwrite-proof, and deletion-proof, forming an unshakable benchmark core for AI.

[0008] Core solidification module This is used to extract, encode, and write the core rules, value bottom line, and fundamental constraints of AI into a black box storage unit, so that it remains static, stable, and cannot be arbitrarily modified throughout the entire process of model training, iteration, and interaction, thus achieving core solidification.

[0009] Self-calibration guidance module Corresponding to the black box underwater beacon mechanism, it continuously transmits core reference signals at a fixed frequency as a continuous reference benchmark for model behavior, achieving uninterrupted and autonomous directional guidance.

[0010] Real-time comparison and verification module Before the model generates output, the current decision, logic and output content are compared in real time with the core and self-calibration guidance signals solidified in the black box; if there is a deviation or conflict, correction is triggered to pull the model behavior back to the baseline core.

[0011] Execution output module Only results that have passed verification and are consistent with the solidified core are allowed to be output, ensuring that the final behavior always conforms to the original constraints.

[0012] 2) Method and Flow (Brief) Extract and define the core essence and behavioral bottom line of artificial intelligence; It is written into a black box-style secure storage unit through a core hardening module, achieving read-only and tamper-proof locking; The self-calibration guidance module continuously broadcasts the core reference signal, providing a constant reference. Every decision and generation of the model is aligned and verified with the solidified core by a real-time comparison and verification module; Automatic correction is performed when an offset occurs, and only results that meet the core constraints are output, ensuring long-term stability, no drift, and no distortion. Beneficial effects

[0013] This invention achieves AI core solidification through a black-box-like security mechanism, fundamentally solving common industry problems such as model drift, susceptibility to manipulation, and easy tampering of core technologies.

[0014] By introducing beacon-style self-calibration guidance, AI is given autonomous, real-time, and intrinsic self-correction capabilities, eliminating its reliance on external human intervention.

[0015] The core benchmark is isolated and locked for read-only operation, providing high security and strong anti-interference capabilities, and can remain stable over a long period of time.

[0016] It integrates mature and reliable mechanisms in the field of aviation safety, with a simple, robust, and easy-to-implement overall architecture, making it suitable for high-reliability scenarios such as education, companionship, government affairs, healthcare, vehicle intelligence, and industrial intelligence.

[0017] It achieves lockable AI at its source, controllable behavior, long-term stability, and full trustworthiness, significantly improving the security and practicality of artificial intelligence systems. Attached Figure Description

[0018] Figure 1 is a block diagram of the artificial intelligence core solidification and self-calibration system based on the black box mechanism of the present invention.

[0019] In Figure 1: 1 is the core setting and extraction stage; 2 is the black box storage unit; 3 is the core solidification module; 4 is the self-calibration guidance module; 5 is the real-time comparison and verification module; and 6 is the execution output module.

Claims

1. A core solidification and self-calibration system for artificial intelligence based on a black box mechanism, characterized in that, include: The core solidification module, black box storage unit, self-calibration guidance module, real-time comparison and verification module, and execution output module; in, The black box storage unit is used for read-only, tamper-proof storage of the underlying core principles and behavioral bottom line of artificial intelligence; The core solidification module is used to write and lock the core AI constraints into the black box storage unit; The self-calibration guidance module is used to continuously emit core reference signals to provide a constant reference. The real-time comparison and verification module is used to compare the current behavior of the model with the solidified core and perform correction when there is an offset. The execution output module is used to output verified results that are consistent with the core, so that the AI ​​remains stable, without drifting or distortion in the long term.

2. A method for solidifying and self-calibrating the core of artificial intelligence based on a black box mechanism, characterized in that, Includes the following steps: Extract and define the underlying core principles and behavioral bottom lines of artificial intelligence; The core principles and bottom lines are solidified into a black-box-like, tamper-proof, read-only storage unit; The model is guided to perform real-time self-calibration using a continuous reference signal; Before the model outputs, its decision logic is compared and verified with the solidified core. If deviations exist, the system will automatically correct them, outputting only results that conform to the core constraints, thus achieving core AI stability and controllable behavior.