A data-driven product collaborative design management method and system

By constructing a dual-stream heterogeneous quantitative acquisition network and a recursive deep belief network, combined with a swarm intelligence optimization algorithm and marginal probability estimation, the data collection and optimization problems in existing product collaborative design management methods are solved, and efficient multi-objective collaborative optimization and adaptive design management are achieved.

CN120087187BActive Publication Date: 2025-09-12NANCHANG UNIV
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Patent Information

Application Number
CN202510083371.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-01-20
Publication Date
2025-09-12
Estimated Expiration
2045-01-20

AI Technical Summary

Technical Problem

Existing product collaborative design management methods have problems such as low efficiency, poor reuse rate, and local optimality of optimization results in data collection, knowledge expression and decision optimization. They lack in-depth analysis of the dynamic characteristics and coupling relationships in the design process, resulting in limited system practicality and scalability.

Method used

A dual-stream heterogeneous quantitative acquisition network is constructed, data collection and feature extraction are performed through multi-layer convolutional neural networks and long short-term memory networks, association matrices and dynamic coupling strengths are established, and collaborative decision-making units are generated using recursive deep belief networks and swarm intelligence optimization algorithms. Multi-objective optimization solutions are generated by combining marginal probability estimation and Pareto front screening, and convergence verification and online learning adjustments are performed through quantum annealing algorithms.

Benefits of technology

It has achieved all-round collection of product structure parameters and design process data, improved data integrity and adaptive learning capabilities, and significantly enhanced the quality of design solutions and the robustness and adaptability of the system.

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Abstract

The present invention discloses a data-driven product collaborative design management method and system, which relates to the technical field of product design management. The method comprises constructing a dual-stream heterogeneous quantitative acquisition network; establishing an association matrix and calculating the dynamic coupling strength between parameters; designing a recursive deep belief network based on the association matrix and the dynamic coupling strength, encoding product structure parameters and design process data into a probabilistic graphical model, and constructing a design knowledge base; using a swarm intelligence optimization algorithm to dynamically allocate design rules in the design knowledge base, generate collaborative decision-making units, and establish a constraint propagation link; generating a multi-objective collaborative optimization solution group, and screening the optimal solution group based on the Pareto front. The present invention achieves comprehensive collection of product structure parameters and design process data through the dual-stream heterogeneous quantitative acquisition network, thereby improving data integrity.
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