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<p>To solve the problem of low efficiency of engineering archives management in the general environment, this paper researches vectorized engineering archives management mode. Firstly, the composition of the archive management system is introduced, the basic system is established according to the cyclic management process of engineering archives, and the vectorization technology and characteristics are explained. Subsequently, the traditional vectorization algorithm based on edge structure extraction is used as an example, and further vectorization calculation is carried out using the bilateral filtering model, weighted least squares method, Lo gradient minimization model, Snake parameter activity contour model, geometric activity contour model, and aggregation algorithm. The advantages of vectorization techniques in information extraction are exploited to provide necessary assistance to the actual archive management work. The results show that the images processed by vectorization have the lowest compression rate, with an average compression rate of 55.67%; among the three methods, the image storage space and compression time under vectorization technology are the smallest at about 324MB and 3.4s; and the method in this paper has the highest output signal-to-noise ratio and the lowest mean square error, which well preserves the shape of the signal time domain waveform and has better noise reduction performance.</p>
</abstract>

To solve the problem of low efficiency of engineering archives management in the general environment, this paper researches vectorized engineering archives management mode. Firstly, the composition of the archive management system is introduced, the basic system is established according to the cyclic management process of engineering archives, and the vectorization technology and characteristics are explained. Subsequently, the traditional vectorization algorithm based on edge structure extraction is used as an example, and further vectorization calculation is carried out using the bilateral filtering model, weighted least squares method, Lo gradient minimization model, Snake parameter activity contour model, geometric activity contour model, and aggregation algorithm. The advantages of vectorization techniques in information extraction are exploited to provide necessary assistance to the actual archive management work. The results show that the images processed by vectorization have the lowest compression rate, with an average compression rate of 55.67%; among the three methods, the image storage space and compression time under vectorization technology are the smallest at about 324MB and 3.4s; and the method in this paper has the highest output signal-to-noise ratio and the lowest mean square error, which well preserves the shape of the signal time domain waveform and has better noise reduction performance.

Research on vectorized engineering file management model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Research on vectorized engineering file management model"}

To solve the problem of low efficiency of engineering archives management in the general environment, this paper researches vectorized engineering archives...

Research on vectorized engineering file management model

Pubblicato online: 30 giu 2023

Pagine: -

Ricevuto: 30 set 2022

Accettato: 09 gen 2023

DOI: https://doi.org/10.2478/amns.2023.1.00475

Parole chiave
Vectorization algorithm, Engineering file management model, Circular process, Bilateral filtering model, Aggregation algorithm

© 2023 Siruo Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on vectorized engineering file management model

Pubblicato online: 30 giu 2023

Pagine: -

Ricevuto: 30 set 2022

Accettato: 09 gen 2023

DOI: https://doi.org/10.2478/amns.2023.1.00475

Parole chiaveVectorization algorithm, Engineering file management model, Circular process, Bilateral filtering model, Aggregation algorithm

© 2023 Siruo Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Vectorization algorithm, Engineering file management model, Circular process, Bilateral filtering model, Aggregation algorithm