Dry garbage deep sorting method and device based on spectrum recognition technology
A spectrum recognition and garbage technology, applied in character and pattern recognition, sorting, solid waste removal and other directions, can solve the problems of deep sorting technology of dry garbage that have not been publicly reported, and achieve the improvement of resource yield and effective utilization. and processing, to achieve the effect of regular updates
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0042] Embodiment 1: as figure 1 Shown, a kind of dry rubbish depth sorting method based on spectral recognition technology, described method comprises:
[0043] S1: Establish deep sorting categories for dry waste after source separation. The specific process is: according to the composition characteristics of dry waste and the direction of high-value utilization, the dry waste after source classification is divided into: cellulose, vinyl polymer, and low-value.
[0044] S2: Obtain a large amount of infrared spectrum data of different depth sorting categories of dry waste, and establish a dry waste infrared spectrum database. The specific process is: collect a large amount of dry waste after source classification, and use infrared spectrometer to collect dry waste samples at 650-4000cm -1 The spectral data of the band, get the infrared spectral data matrix X m×n , where m=72 represents the total amount of dry garbage, and n=3351 represents the infrared spectrum data of the ...
Embodiment 2
[0047] Example 2: Using the preprocessed dry garbage infrared spectrum data in Example 1, a random forest classification model containing 500 decision trees was constructed, each decision tree including 5 randomly extracted variables in the dry garbage infrared spectrum data. According to the formula Obtain the discriminant results of the random forest, and then use the leave-one-out cross-validation formula Calculate the final accuracy of the model, and the calculation shows that the accuracy of classification and identification of the model is 81.6%.
Embodiment 3
[0048] Example 3: Using the preprocessed dry garbage infrared spectrum data in Example 1, a random forest classification model containing 500 decision trees was constructed, each decision tree including 15 randomly selected variables in the dry garbage infrared spectrum data. According to the formula Obtain the discriminant results of the random forest, and then use the leave-one-out cross-validation formula Calculate the final accuracy of the model, and the calculation shows that the accuracy of classification and identification of the model is 83.1%.
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com