A Neural Network-Based Anti-Disassembly Identification System and Method for Computer Room Equipment

CN121881216BActive Publication Date: 2026-05-26NANJING COENQI INFORMATION TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-20
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing anti-disassembly technologies for data center equipment struggle to accurately identify highly concealed disassembly behaviors and lack system modeling for the complexity and diversity of equipment structures, resulting in limited robustness and generalization ability of the identification results.

Method used

The neural network-based anti-disassembly identification method for computer room equipment generates a structural connection constraint matrix by constructing a set of structural nodes and connection relationships of detachable components, calculating the difference degree, and using a multi-layer feedforward neural network for disassembly risk assessment and early warning.

Benefits of technology

It enables precise characterization of structural changes in computer room equipment and intelligent identification of unauthorized dismantling activities, improving the accuracy and real-time performance of identification, generating visualized alarm results and linking emergency response measures, thereby enhancing the level of security protection.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses a neural network-based anti-disassembly identification system and method for data center equipment, belonging to the field of neural network technology. Based on the data center equipment management backend, it obtains equipment structural assembly information, constructs a set of detachable components and corresponding structural nodes and their connections, introduces connection attribute parameters, establishes a structural connection constraint matrix for detachable components, constructs a baseline state model of equipment structural connections, calculates structural differences, and generates component-level abnormal state identifiers. The structural differences and abnormal states of each detachable component are used as structural state feature vectors input to the disassembly behavior identification neural network model, outputting a disassembly risk score to determine whether illegal disassembly of the data center equipment exists. When illegal disassembly is detected, the system generates corresponding alarm information and triggers warning and handling operations, achieving intelligent identification and risk assessment of data center equipment disassembly behavior, improving the accuracy and real-time performance of equipment structural safety supervision.
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Citation Information

Patent Citations

  • Anti-disassembly detection method and device for waterproof intelligent equipment, electronic equipment and storage medium

    CN116449270A

  • Security and protection monitoring method with disassembly alarm function and device easy to overhaul

    CN118430150A