| The rapid economic development is increasingly constrained by resources and environmental pollution.From the perspective of material flow,it can be found that the generation of waste is continuous,and the management of renewable resources has become increasingly important.Taking Hefei city,a key city for the construction of the first batch of waste material recycling system,as an example,the study conducted a relatively comprehensive investigation and research on the material recycling system of renewable resources for urban residents in the main urban area of Hefei city.According to the impact factors of recycling,scientific methods are used to derive different degrees of impact factors.At the same time,intelligent material classification and identification are proposed for traditional manual sorting methods.By optimizing the target detection YOLOv5 algorithm,materials are classified,detected,and identified,helping to improve the recovery rate of renewable resource materials at the life end and improve the construction of the waste material recycling system.The main studies are as follows:(1)Investigation and analysis of the historical situation of renewable resources recovery among urban residents in Hefei.Using the methods of literature survey,interview survey,website inquiry,and field visit,the current situation of recycling of renewable resources among urban residents in Hefei was investigated,and data on the recycling volume,recycling price,sales price,and total recycling amount of waste nonferrous metals,waste paper,and waste plastic in Hefei from 2015 to 2021 were obtained,It is concluded that the total recovery amount of the three main types of renewable resource materials in Hefei at the residential end has increased from 1.2million tons in 2015 to 1.7 million tons in 2021,a total increase of 500000 tons.From2014 to 2021,the number of enterprises engaged in renewable resources in Hefei showed a growing trend,from 460 in 2014 to 5,376 in 2021,with an average annual growth rate of 40.47%,maintaining a continuous growth trend.(2)Investigation and analysis on the current situation of renewable resources recovery among urban residents in Hefei city.Based on geographic information systems,a field survey and sampling of communities in the main urban areas of Hefei city were conducted to investigate and analyze the current situation of renewable resources recovery.In the residential area with commercial housing,the total amount of renewable resources and materials in the garbage content was the highest,21.86%,followed by centralized resettlement housing(12.36%),apartment buildings(11.92%),unit housing(11.61%),and low-cost rental housing(4.63%).After removing low value materials,the total amount of renewable resource materials generated by residential units accounted for the highest proportion among the sources,at 8.41%;The second is apartment buildings(8.16%),centralized resettlement housing(6.11%),and commercial housing(5.52%).The lowest is low-rent housing,accounting for only2.41% of the total.Plastic materials account for the largest amount of garbage generated by residents,accounting for 43.30% on average;Secondly,the average proportion of glass materials is 28.60%,and that of paper materials are 13.99%;Metal materials are the least,accounting for 0.91% on average.According to the survey results,Hefei still has high resource and economic recovery value in the terminal garbage of residents.(3)Study on the factors affecting the quality of recycled resource materials for urban residents in Hefei.Using fault tree analysis,questionnaire survey,and field visits,combined with stakeholder analysis,significance analysis,binary logistic regression model,and semi structured interview methods,the impact factors on the recycling quality of renewable resource materials for urban residents in Hefei were obtained.The four types of influencing factors(family population(co-living),social demonstration,regulatory impact,and benefit perception)at the residential end are positively correlated with the willingness to recover.The regression coefficients for the number of family populations with 5 or more as a reference group are-2.811,-1.953,-0.387,and-0.106 for 1,2,3,and 4 persons,respectively;The regression coefficients corresponding to social demonstration,benefit perception,and regulatory impact are 1.020,1.309,and 1.330,respectively;Other stakeholders’ influencing factors: practitioners’ regulatory control,sorting technology,waste material recycling front-end and processing back-end information disconnection.(4)Classification detection and identification of renewable resource materials based on deep learning.YOLOv5 algorithm was selected to construct YOLOv5l’s renewable resource material classification detection and identification model,Mobile Netv3 was used as the material identification algorithm to lightweight the original model,and the YOLOv5l-Mobile Netv3 model was constructed.The number of parameters(MB)was reduced from 93.85 MB to 10.49 MB,the model was reduced to 0.11 times,and the recognition speed was increased by 1.35 times.Through the analysis and research on the factors affecting the recycling quality of renewable resource materials for urban residents in Hefei city and the optimization of the classification,detection,and identification model for renewable resource materials based on in-depth learning,it provides scientific basis for improving the recycling rate of renewable resource materials,constructing a complete recycling system for waste materials,and constructing a "waste free city" in Hefei city. |
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