Industrial flexible load state space model parameter prediction method and system

By segmenting and storing load states and using an adaptive feature enhancement mode, and by employing a dual-exponential dynamic evaluation model and deep learning, the dynamic complexity and feature extraction limitations in the state-space model parameter prediction of industrial flexible load data are addressed. This enables efficient and accurate parameter analysis, thereby improving the operating efficiency and stability of industrial systems.

CN120893302APending Publication Date: 2025-11-04NARI TECH CO LTD
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Patent Information

Application Number
CN202511008379.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-22
Publication Date
2025-11-04

AI Technical Summary

Technical Problem

Existing technologies cannot effectively address the issues of dynamic complexity, limitations in feature extraction, and blind pattern selection in state-space model parameter prediction of industrial flexible load data, resulting in insufficient accuracy in parameter analysis.

Method used

By segmenting and storing load states, employing a dual-exponential dynamic evaluation model and an adaptive feature enhancement mode, and utilizing Lyapunov exponent, Shannon entropy, time-frequency analysis, and deep learning models, efficient data organization, feature extraction, and pattern selection are achieved.

Benefits of technology

It significantly improves the accuracy and reliability of parameter analysis, and optimizes the operating efficiency and stability of industrial systems.

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Abstract

The invention discloses an industrial flexible load state space model parameter prediction method and system, and the method comprises the steps: constructing a time sequence segmentation index storage library based on load state segmentation, calculating a heterogeneous dynamic index through phase-space reconstruction, constructing a time-frequency recurrence spectrum through time-frequency analysis, calculating a frequency domain recurrence index, and carrying out the prediction of the parameters of an industrial flexible load state space model. And adaptively selecting a feature enhancement mode of an iterative feature screening and adversarial generation network or a dynamic time-frequency decomposition and variational coding network based on the comprehensive feature coefficient, and finally training a deep learning model to realize parameter prediction. Through adaptive feature processing driven by dynamic complexity, the accuracy and reliability of industrial load parameter analysis are improved, and the operation efficiency and stability of industrial equipment are optimized.
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