CN-121972379-A - Omnibearing plastic spraying optimization method and system for folding sofa bed frame

CN121972379ACN 121972379 ACN121972379 ACN 121972379ACN-121972379-A

Abstract

The invention relates to the technical field of data processing, in particular to an omnibearing plastic spraying optimization method and system for a folding sofa bed frame. The method comprises the steps of acquiring geometric data of the surface of a folding sofa bed frame in real time through a sensor array, obtaining shape characteristics based on the data, dividing a surface area, determining geometric subareas of a plane and a corner area, generating a preliminary electric field configuration scheme according to electric field strength requirement characteristics of each geometric subarea, analyzing the scheme through a neural network model, optimizing electric field strength distribution, further generating a control signal based on refined electric field strength distribution, obtaining a dynamic response sequence, optimizing control parameters through extracting deposition effect indexes, and monitoring geometric changes in real time according to updated control parameters to obtain a final electric field parameter set. The invention solves the problem of uneven electric field distribution caused by geometric changes in different states in the plastic spraying process of the folding sofa bed frame, and realizes dynamic optimization of electric field intensity and uniformity of plastic spraying effect.

Inventors

XU HUIPING
Xu Yintian
XU ZHONGLIANG

Assignees

嘉兴安泰环保科技有限公司

Dates

Publication Date: 20260505
Application Date: 20251209

Claims (10)

1. An all-round plastic spraying optimization method for a folding sofa bed frame, which is characterized by comprising the following steps: Step S1, acquiring real-time geometric data of the surface of a folding sofa bed frame through a sensor array, and obtaining shape characteristics according to the real-time geometric data; S2, extracting corresponding electric field intensity demand characteristics from the geometric partition, and obtaining a preliminary electric field configuration scheme based on the electric field intensity demand characteristics; Step S3, generating a control signal according to the refined electric field intensity distribution to obtain a dynamic response sequence, extracting a deposition effect index from the dynamic response sequence, optimizing dynamic response sequence parameters according to the deposition effect index, generating a self-adaptive adjustment path according to the optimized dynamic response sequence parameters, and acquiring updated control parameters according to the self-adaptive adjustment path; and S4, monitoring the geometric change of the folding sofa bed frame according to the updated control parameters, and adjusting the electric field intensity distribution through rapid electric field redistribution according to the geometric change to obtain a final electric field parameter set.
2. The method of claim 1, wherein in step S1, the shape characteristics of the sofa bed frame are obtained, comprising: the method comprises the steps of collecting real-time geometric data of the surface of a folding sofa frame through a sensor array, preprocessing the real-time geometric data, extracting characteristic points of a plane area and a corner area, generating shape characteristic data representing distribution of the plane area and the corner area according to the characteristic points, normalizing the shape characteristic data to obtain standardized shape characteristics, filtering and denoising the standardized shape characteristics, and determining the final sofa frame shape characteristics.
3. The method of claim 2, wherein in step S1, determining geometric partitions characterizing the planes and corner areas comprises: The method comprises the steps of processing geometric data of a folding sofa bed frame through a region segmentation algorithm according to the shape characteristics, dividing the shape characteristics into a plane region and a high curvature corner region, extracting boundary characteristics of each region according to the plane region and the high curvature corner region, determining types and boundary ranges of each region according to the boundary characteristics, verifying the boundary ranges, judging that if deviation of the boundary ranges exceeds a preset threshold value, readjusting segmentation parameters, and generating classified geometric partition data according to the adjusted segmentation parameters.
4. The method of claim 1, wherein in step S2, a preliminary electric field configuration scheme is obtained, comprising: Extracting geometric features of a plane area and a high-curvature corner area from the geometric partition, calculating uniform electric field intensity parameters for the plane area, calculating differential electric field intensity parameters for the high-curvature corner area, judging that if the ratio of the plane area is higher than a preset threshold value, preferentially distributing uniform electric field intensity, generating a preliminary electric field configuration scheme according to the uniform electric field intensity and the differential electric field intensity, and carrying out normalization processing on the preliminary electric field configuration scheme to obtain standardized configuration data.
5. The method of claim 1, wherein in step S2, a refined electric field intensity distribution is obtained, comprising: the method comprises the steps of inputting a preliminary electric field configuration scheme into a pre-trained neural network model, analyzing the matching degree of the preliminary electric field configuration scheme and historical deposition data through the neural network model, generating an optimized adjustment vector according to the matching degree, determining pulse mode parameters of a high curvature corner area according to the optimized adjustment vector, adjusting the preliminary electric field configuration scheme according to the pulse mode parameters, and generating refined electric field intensity distribution data.
6. The method of claim 1, wherein in step S3, a dynamic response sequence is obtained, comprising: Generating a voltage and current control signal according to the refined electric field intensity distribution, regulating the output acceleration of each node, judging whether the field intensity deviation of a high curvature corner area exceeds a preset threshold value, if so, correcting the control signal through a feedback loop, and generating a dynamic response sequence according to the corrected control signal.
7. The method of claim 6, wherein in step S3, obtaining updated control parameters comprises: Extracting a real-time deposition effect index from the dynamic response sequence, optimizing dynamic response sequence parameters by adopting a gradient descent algorithm according to the real-time deposition effect index, calculating an updated voltage current value according to the optimized dynamic response sequence parameters, generating a self-adaptive adjustment path aiming at the updated voltage current value, verifying the stability of the self-adaptive adjustment path, judging that if the path deviation exceeds a preset threshold value, re-optimizing parameters, and generating updated control parameters according to a verification result.
8. The method of claim 1, wherein in step S4, a final electric field parameter set is obtained, comprising: The method comprises the steps of monitoring geometric change events of a folding sofa bed frame in real time according to updated control parameters, judging that if the folding sofa bed frame is switched to a folding state from a plane state, triggering rapid electric field reconfiguration, adjusting electric field intensity distribution according to the rapid electric field reconfiguration, generating dynamic electric field parameters aiming at geometric change, verifying the dynamic electric field parameters, judging whether parameter deviation exceeds a preset threshold value, if so, readjusting, and generating a final electric field parameter set according to an adjustment result.
9. An all-round spray optimization system for folding sofa bed frames for implementing the method according to any one of claims 1-8, characterized in that it comprises: The geometric partitioning unit is used for acquiring real-time geometric data of the surface of the folding sofa bed frame through the sensor array, and obtaining shape characteristics according to the real-time geometric data; The electric field distribution acquisition unit is used for extracting corresponding electric field intensity demand characteristics from the geometric partition, and obtaining a preliminary electric field configuration scheme based on the electric field intensity demand characteristics; The control signal generation unit is used for generating a control signal according to the refined electric field intensity distribution to obtain a dynamic response sequence, extracting a deposition effect index from the dynamic response sequence, optimizing dynamic response sequence parameters according to the deposition effect index, generating a self-adaptive adjustment path according to the optimized dynamic response sequence parameters, and acquiring updated control parameters according to the self-adaptive adjustment path; The geometric change monitoring unit is used for monitoring the geometric change of the folding sofa bed frame according to the updated control parameters; and the electric field redistribution unit is used for obtaining a final electric field parameter set by rapidly redistributing and adjusting electric field intensity distribution according to the geometric change.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the method of any of claims 1-8.

Description

Omnibearing plastic spraying optimization method and system for folding sofa bed frame Technical Field The invention relates to the technical field of data processing, in particular to an omnibearing plastic spraying optimization method and system for a folding sofa bed frame. Background With the continuous development of modern furniture design, folding sofa bed frames are increasingly favored by consumers as a furniture product with both comfort and functionality. Folding sofa bed frames have complex geometries and exhibit dynamic changes in folding and unfolding during use. This characteristic makes conventional surface treatment techniques, particularly spray molding processes, a number of challenges in practical applications. Traditional plastic spraying technology relies on fixed electric field intensity distribution, and can guarantee even spraying effect under static state. However, due to the dynamics of the geometry of the surface of the folding sofa bed frame, particularly the shape of the surface area of the frame during folding and unfolding, conventional spray molding methods cannot accommodate these geometry changes in real time, resulting in instability of the coating quality, particularly in high curvature corner areas. Common problems include uneven coating thickness, insufficient adhesion, and unsatisfactory spray effect, especially in the corner areas in the folded state, which tends to be less uniform than in the planar areas. The root of the problems is that the traditional plastic spraying process can not acquire the geometric change data of the surface of the folding sofa bed frame in real time, and the electric field intensity of spraying can not be adjusted according to the characteristics of different areas. Therefore, how to solve the problem of uneven electric field distribution under dynamic geometric variation and realize the omnibearing plastic spraying optimization aiming at the folding sofa bed frame becomes a technical problem to be solved urgently in the industry. In order to cope with challenges brought by geometric shape changes of the surface of a folding sofa bed frame, the invention provides an omnibearing plastic spraying optimization method and system for the folding sofa bed frame. The method combines a sensor array, a neural network model and an electric field intensity optimization control technology, can sense the change of the surface geometric form of the sofa bed frame in real time, and dynamically adjusts the electric field intensity according to the requirements of different areas, thereby realizing precise plastic spraying coating distribution. Disclosure of Invention The invention provides an omnibearing plastic spraying optimization method and system for a folding sofa bed frame, which are used for solving the problem that the traditional plastic spraying process cannot effectively adapt to folding and unfolding states in the geometric change process of the sofa bed frame surface through an accurate electric field distribution optimization technology In a first aspect, an omnidirectional spray optimization method for a folding sofa bed frame, the method comprising: Step S1, acquiring real-time geometric data of the surface of a folding sofa bed frame through a sensor array, and obtaining shape characteristics according to the real-time geometric data; S2, extracting corresponding electric field intensity demand characteristics from the geometric partition, and obtaining a preliminary electric field configuration scheme based on the electric field intensity demand characteristics; Step S3, generating a control signal according to the refined electric field intensity distribution to obtain a dynamic response sequence, extracting a deposition effect index from the dynamic response sequence, optimizing dynamic response sequence parameters according to the deposition effect index, generating a self-adaptive adjustment path according to the optimized dynamic response sequence parameters, and acquiring updated control parameters according to the self-adaptive adjustment path; and S4, monitoring the geometric change of the folding sofa bed frame according to the updated control parameters, and adjusting the electric field intensity distribution through rapid electric field redistribution according to the geometric change to obtain a final electric field parameter set. As a preferred embodiment of the present invention, in step S1, the shape feature of the sofa bed frame is obtained, including: the method comprises the steps of collecting real-time geometric data of the surface of a folding sofa frame through a sensor array, preprocessing the real-time geometric data, extracting characteristic points of a plane area and a corner area, generating shape characteristic data representing distribution of the plane area and the corner area according to the characteristic points, normalizing the shape characteristic data to obtain standardized shape characteristics, fi