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Garbage sorting and recycling system based on RBF neural network and control method

A garbage sorting and recycling system technology, applied in the direction of garbage collection, garbage bins, waste collection and transfer, etc., can solve the problems of high overall cost, lack of regulation and management, and insufficient docking, so as to improve recycling efficiency and avoid secondary waste. Secondary pollution, the effect of improving work efficiency

Active Publication Date: 2020-06-19
TIANJIN POLYTECHNIC UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] 1. The compartments of the dustbin can only be placed horizontally, the structure is relatively cumbersome, and the space cannot be used well
[0009] 2. The degree of intelligence is not high. It can only realize the automatic opening and closing of the door of the garbage bin, but cannot realize the intelligent classification of garbage, and cannot supervise and guide the user's garbage sorting behavior. It does not conform to the general trend of mandatory garbage sorting
[0010] 3. Due to the addition of the main control on the basis of the traditional dustbin, the overall cost in actual delivery is higher than that of the traditional dustbin
The risk of damage to electronic equipment is high, and the investment and maintenance costs are relatively high
[0011] 4. Unable to achieve data interaction with the Internet, the function is relatively single, and the actual application value is not great
Only software APP, no matching smart hardware
[0014] 2. The function is mainly of a public welfare nature, and cannot well realize profits based on garbage classification and recycling
[0015] 3. The main factors that make it difficult to implement mandatory garbage sorting measures in cities are not only the poor awareness of users on sorting, but also the heavy burden of sanitation work and the high cost of human guidance and supervision
[0019] 1. There are currently about 6 million waste pickers operating without a license in my country, lacking effective regulations and management, resulting in secondary pollution and disease transmission during the processing of waste
[0020] 2. Due to the uncertainty of the amount of garbage discarded and the heavy environmental sanitation burden, garbage cans in streets and residential areas often overflow due to failure to clean them in time
[0021] 3. Garbage classification standards and related knowledge are not popular among citizens. The mandatory classification policy makes garbage classification a psychological burden and cannot be effectively implemented
The existing garbage sorting assistant APP cannot effectively restrain citizens' garbage sorting behavior
[0022] 4. In areas where compulsory garbage classification has been implemented in our country, full-time personnel are required to guard most of the garbage bins at present, and provide garbage classification explanations and guidance for users at any time, which increases the burden of urban sanitation work and increases the cost of manpower and material resources
[0023] 5. Most of the existing intelligent waste classification products only have a single hardware or a single software, and their target groups are relatively single, unable to form a system and form a solid profit chain, which often increases the development cost of the development company and makes it difficult to achieve profitability
[0024] 6. In the traditional urban sanitation work, often due to the insufficient connection between the sanitation department and the garbage recycling enterprise, it is impossible to carry out targeted recycling of garbage in a timely and efficient manner, and many garbage can only be landfilled and incinerated
[0028] 3. Solve the problems of low intelligence, lack of automatic garbage sorting ability, and lack of real-time network data feedback in the existing smart garbage bins on the market, and solve the overflow problem of traditional garbage bins when they cannot be cleaned in time

Method used

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  • Garbage sorting and recycling system based on RBF neural network and control method
  • Garbage sorting and recycling system based on RBF neural network and control method
  • Garbage sorting and recycling system based on RBF neural network and control method

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Embodiment Construction

[0074] The RBF neural network-based garbage sorting and recycling system and control method of the present invention will be described in detail below in conjunction with the embodiments and drawings.

[0075] Such as Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, the garbage sorting and recycling system based on RBF neural network of the present invention includes a base plate 8 and a support frame 15 arranged on the base plate 8, and the support frame 15 is respectively provided with:

[0076] The primary sorting layer is arranged on the top of the supporting frame 15 . Such as Figure 4 , Figure 7 As shown, by forming the garbage sorting cover plate 12 that is used to drop into four garbage inlets 16 of four different types of garbage respectively, and being respectively arranged at the four garbage inlets 16 entrances for obtaining the garbage inlet 16 where it is located Garbage input signals are sent to the control unit 10 to form four first ...

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Abstract

The invention discloses a garbage sorting and recycling system based on an RBF neural network. The garbage sorting and recycling system comprises a primary sorting layer arranged at the top of a supporting frame, a garbage recognition layer arranged on the middle part of the support frame, a garbage collection layer arranged on the lower part of the supporting frame, four second photoelectric sensors arranged on the lower part of the garbage recognition layer and on the upper part of the garbage collection layer and a control unit. The primary sorting layer is provided with a garbage sorting cover plate with four garbage inlets, and each garbage inlet is provided with a photoelectric sensor. The garbage recognition layer is provided with a skip bucket mechanism which receives garbage and is controlled by the control unit to turn over to dump the garbage into the next layer and a garbage recognition camera. The garbage collection layer is provided with a garbage collection mechanism receiving different types of garbage dumped by the skip bucket mechanism in a sorting mode, and the garbage collection mechanism is installed on a bottom plate and can be rotated at a set angle under thecontrol of the control unit. The control unit is arranged on the bottom plate. According to the garbage sorting and recycling system based on the RBF neural network, traditional human labor is replaced with intelligent hardware, meanwhile, the accuracy rate of garbage sorting is higher than the accuracy rate of manual sorting, and the workload of sanitation workers is greatly reduced.

Description

technical field [0001] The invention relates to a garbage sorting and recycling system. In particular, it relates to a garbage sorting and recycling system and control method based on RBF neural network. Background technique [0002] Everyone throws out a lot of garbage every day. In some areas with better garbage management, most of the garbage will be treated harmlessly, such as sanitary landfill, incineration, composting, etc., while the garbage in more places is often simply piled up or landfilled. , leading to the spread of odor and polluting soil and groundwater. The cost of harmless disposal of garbage is very high. Depending on the treatment method, the cost of disposing of a ton of garbage ranges from about one hundred yuan to several hundred yuan. People consume a lot of resources, produce on a large scale, consume a lot, and produce a lot of garbage. The consequences will be disastrous. [0003] The purpose of classification is to divert waste, utilize existin...

Claims

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Application Information

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IPC IPC(8): B65F1/00B65F1/10B65F1/16B65F1/14
CPCB65F1/0033B65F1/10B65F1/16B65F1/1623B65F1/1484B65F1/14B65F2210/138B65F2210/172B65F2210/196Y02W30/10
Inventor 邹佰翰苑晓兵朱梓王茜毕君郁王瑞昆
Owner TIANJIN POLYTECHNIC UNIV
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