Intelligent arrangement method and system based on variable frequency integrated machine

By using digital twin modeling, rhythmic neural networks, and energy efficiency trust models, combined with an environment-driven module, intelligent layout of the variable frequency integrated machine system is achieved, solving the problems of energy efficiency optimization and environmental perception in multi-machine collaborative operation, and improving the system's energy efficiency and stability.

CN122419285APending Publication Date: 2026-07-17ORDOS HAOHUA CLEAN COAL CO LTD
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Patent Information

Application Number
CN202610570993.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-28
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing variable frequency integrated machine systems lack a real-time rhythm coordination mechanism in multi-machine collaborative operation, making it difficult to achieve overall energy efficiency optimization of the cluster. Furthermore, they lack real-time perception and dynamic compensation capabilities for external environmental factors, resulting in decreased operating efficiency and insufficient robustness.

Method used

By using digital twin modeling, rhythm neural networks, energy efficiency trust models, and environment-driven modules, we can achieve multi-machine rhythm adaptive control, time-period difference optimization, and environmental response, thereby building an intelligent deployment system and improving the system's dynamic response efficiency, stability, and robustness.

Benefits of technology

It significantly improves the energy efficiency matching and operational stability of multi-machine collaboration, enhances the overall energy efficiency of the system and its robustness under complex operating conditions, and ensures stable operation under external environmental disturbances.

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Abstract

The application discloses an intelligent arrangement method and system based on a variable frequency integrated machine, and relates to the technical field of variable frequency integrated machine arrangement. The method comprises the following steps: acquiring real-time operation data of each variable frequency integrated machine, establishing a virtual power image model, and performing power trend prediction; acquiring relevant operation rhythm sequence of each variable frequency integrated machine and inputting the sequence into a rhythm neural network model to generate a multi-machine rhythm coordination curve and perform rhythm adaptive control between the variable frequency integrated machines; collecting historical energy efficiency performance data of each variable frequency integrated machine, establishing an energy efficiency trust model, and generating a power distribution decision; dividing the operation cycle of each variable frequency integrated machine into time periods, calculating energy efficiency integral values of each time period, and performing time period priority adjustment; collecting external environment parameters and performing cross comparison with a power mapping matrix to calculate an environment disturbance coefficient and trigger a power mapping correction mechanism. The application realizes efficient, stable and robust intelligent arrangement and energy efficiency optimization of a variable frequency integrated machine cluster.
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