CN110723431A - Garbage classification method based on BP neural network recognition system - Google Patents

Garbage classification method based on BP neural network recognition system Download PDF

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Publication number
CN110723431A
CN110723431A CN201910884257.0A CN201910884257A CN110723431A CN 110723431 A CN110723431 A CN 110723431A CN 201910884257 A CN201910884257 A CN 201910884257A CN 110723431 A CN110723431 A CN 110723431A
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China
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garbage
rotary
neural network
turnover
rotating shaft
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CN201910884257.0A
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Chinese (zh)
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韩晓霞
张岩
褚俊文
秦晓微
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Taiyuan University of Technology
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Taiyuan University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F1/00—Refuse receptacles; Accessories therefor
    • B65F1/0033—Refuse receptacles; Accessories therefor specially adapted for segregated refuse collecting, e.g. receptacles with several compartments; Combination of receptacles
    • B65F1/004—Refuse receptacles; Accessories therefor specially adapted for segregated refuse collecting, e.g. receptacles with several compartments; Combination of receptacles the receptacles being divided in compartments by partitions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F1/00—Refuse receptacles; Accessories therefor
    • B65F1/14—Other constructional features; Accessories
    • B65F1/16—Lids or covers
    • B65F1/1623—Lids or covers with means for assisting the opening or closing thereof, e.g. springs
    • B65F1/1638—Electromechanically operated lids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F1/00—Refuse receptacles; Accessories therefor
    • B65F1/0033—Refuse receptacles; Accessories therefor specially adapted for segregated refuse collecting, e.g. receptacles with several compartments; Combination of receptacles
    • B65F2001/008—Means for automatically selecting the receptacle in which refuse should be placed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F2210/00—Equipment of refuse receptacles
    • B65F2210/168—Sensing means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F2210/00—Equipment of refuse receptacles
    • B65F2210/176—Sorting means
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
    • Y02W30/00—Technologies for solid waste management
    • Y02W30/10—Waste collection, transportation, transfer or storage, e.g. segregated refuse collecting, electric or hybrid propulsion

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
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  • Biomedical Technology (AREA)
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  • Data Mining & Analysis (AREA)
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  • Life Sciences & Earth Sciences (AREA)
  • Molecular Biology (AREA)
  • Artificial Intelligence (AREA)
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  • General Physics & Mathematics (AREA)
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  • Health & Medical Sciences (AREA)
  • Mechanical Engineering (AREA)
  • Processing Of Solid Wastes (AREA)

Abstract

The invention discloses a method for intelligently classifying garbage based on a BP (Back propagation) neural network, which comprises a barrel body, a rotary platform, a stepping motor, an ARM (advanced RISC machine) chip processor, a sensor, a machine vision detection system and a BP neural network processing system. The barrel body is divided into four loading areas which correspond to four kinds of garbage respectively, the rotating platform can rotate 360 degrees, and the garbage is conveyed into the loading areas after reaching the upper part of the corresponding classification area and turning downwards. And the ARM chip processor receives the electric signal and controls the corresponding module to act. The sensor and the image processing module are used for acquiring data information of garbage and transmitting the data into a trained BP neural network system to realize classification. The invention realizes the classification of recoverable garbage, dry garbage, wet garbage and harmful garbage through the BP neural network classification system, and has higher classification accuracy. The classification process is more intelligent and effective. The problems of difficult garbage classification and poor effect in the past are solved.

Description

Garbage classification method based on BP neural network recognition system
Technical Field
The invention relates to a classification method, in particular to a classification method applied to an intelligent garbage can, and specifically relates to a garbage classification method based on a BP neural network identification system.
Background
With the continuous deepening of the urbanization degree of China, the garbage can becomes an indispensable daily necessity for a residential quarter of our lives, and has great influence on the sanitation and the body health of our living environment. Every time the garbage passes by the garbage bin, a peculiar smell exists, which is the reason why the garbage is not classified. In life, we generate various kinds of garbage, and garbage classification is a thing which we do but never do well all the time. The traditional intelligent garbage classification system classifies garbage through some sensor detection modules, does not achieve real garbage classification, and can cause the situation of misclassification. Therefore, the system needs to obtain a model after learning the classification of the garbage by means of artificial intelligence, so that the system can automatically classify the garbage, and the problem of difficult garbage classification is solved.
Disclosure of Invention
In view of this, the present invention provides a garbage classification method based on a BP neural network recognition system, and aims to solve the problem of low garbage classification efficiency in the prior art.
The technical problem to be solved by the invention is realized by the following technical scheme: a garbage classification method based on a BP neural network recognition system comprises a garbage can, a humidity sensor, a gas sensor and a camera,
the garbage can comprises a base, a can body, a can cover and a rotating platform, wherein the base is arranged at the bottom of the can body, the inner space of the can body is divided into four charging areas, the charging areas are separated by baffles, and a hollow cylinder is arranged in the middle of the can body;
the rotary platform comprises a 360-degree rotating shaft, a rotary motor, a turnover shaft, a turnover motor and a flat plate, the rotary motor is vertically fixed in the hollow column, a rotary gear is connected to an output shaft of the rotary motor, the 360-degree rotating shaft comprises a vertical rotary gear column capable of being meshed with the rotary gear and a horizontal rotating shaft connected to one side of the rotary gear column, the rotary gear column is located in the hollow column and meshed with the rotary gear, and the rotating shaft is located outside the hollow column; a barrel cover ejector rod is further connected to the top of the rotary gear column, the barrel cover is fixed on the barrel cover ejector rod, a processor is arranged in the barrel cover, and a machine vision detection system and a BP neural network are written in the processor;
the overturning motor is horizontally fixed in a rotating shaft, an overturning gear is connected to an output shaft of the overturning motor, the overturning shaft comprises a horizontal overturning gear column capable of being meshed with the overturning gear and a rotating shaft connected to one side of the overturning gear column, the overturning gear column is located in the rotating shaft and meshed with the overturning gear, the rotating shaft is located outside the rotating shaft and perpendicular to the rotating shaft, an annular groove is formed in the position of the rotating shaft corresponding to the joint of the overturning gear column and the rotating shaft and used for overturning the overturning shaft, a flat plate is connected to one side of the rotating shaft, the rotating shaft and the flat plate form a garbage containing structure, and the platform is located;
humidity transducer, gas sensor set up on rotary platform, the camera sets up on the bung ejector pin and links to each other with the treater, and the data input treater that the sensor acquireed.
After the garbage is placed on the rotary platform, a camera takes a picture of the garbage, a machine vision detection system processes information of the picture, obtains structural parameters, volume parameters and color parameters of the garbage, and transmits the obtained data to a BP (back propagation) neural network; the humidity sensor is used for acquiring the dry and wet conditions of the garbage and converting the dry and wet conditions of the garbage into data to be transmitted to the BP neural network; the gas sensor is used for acquiring odor information of the garbage, and converting the odor information of the garbage into data to be transmitted to the BP neural network. The BP neural network identifies the type of garbage according to the input data; after the kind of discernment rubbish, 360 degrees rotation axis rotations of treater control rotating electrical machines drive, with the platform rotate the corresponding charging area top, then the upset motor drive trip shaft of treater control is overturn downwards and is lasted a period, rubbish on the platform is poured into in the corresponding charging area, then the upset motor drive trip shaft of treater control is upwards overturned, horizontal position is got back to the platform, 360 degrees rotation axis rotations of treater control rotating electrical machines drive again, change back to initial position with the platform.
Further, an infrared sensor is arranged in the barrel cover and connected with the processor. When people get close to the garbage can, the infrared sensor wakes up the processor, and when garbage is poured into the loading area, the processor is dormant.
Furthermore, a pressure sensor is arranged on the rotating platform, data of the pressure sensor is input into the processor, and weight data can also be used as reference data for judging the garbage types.
Further, the processor is an ARM chip processor.
Further, the overturning angle of the overturning shaft is 90 degrees.
Compared with the prior art, the invention has the beneficial effects that:
compared with the traditional automatic garbage classification, the automatic garbage classification method is more intelligent and has higher classification accuracy. The invention is based on the model parameters obtained by training the acquired garbage parameters (input) and corresponding garbage categories (output) through a self-learning mechanism of a BP neural network. And writing the obtained BP neural network model into a microprocessor of the garbage can to realize intelligent classification of the garbage can.
When the garbage is classified, the garbage classification method can deal with garbage classification under different conditions by learning a large amount of data in a garbage yard, and improve the efficiency of real-time garbage classification.
Drawings
Fig. 1 is a schematic structural diagram of a garbage can for intelligent garbage classification according to the present invention.
Fig. 2 is a schematic view of the rotating and flipping structure of the present invention.
Fig. 3 is a schematic view of the 360-degree rotation axis structure of the present invention.
Fig. 4 is a schematic view of the structure of the rotary gear of the present invention.
Fig. 5 is a schematic view of the construction of the tumble shaft of the present invention.
FIG. 6 is a schematic diagram of the input/output structure of the BP neural network model according to the present invention.
FIG. 7 is a flowchart of the garbage classification based on BP neural network according to the present invention.
In the figure: 1-barrel cover, 2-infrared sensor, 3-camera, 4-humidity sensor, 5-gas sensor, 6-pressure sensor, 7-rotary platform, 8-recoverable garbage loading area, 9-dry garbage loading area, 10-wet garbage loading area, 11-harmful garbage loading area, 12-barrel body, 13-hollow cylinder, 14-rotary motor, 15-rotary gear column, 16-rotary gear, 17-rotary shaft, 18-barrel cover top rod, 19-rotary motor, 20-rotary gear, 21-rotary gear column and 22-rotary shaft.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific embodiments:
fig. 1 is a schematic structural diagram of a trash can for intelligent trash classification, which includes: the garbage bin comprises a bin cover 1, a bin body 12, an infrared sensor 2, a camera 3, a humidity sensor 4, a gas sensor 5, a pressure sensor 6, a rotary platform 7, a recyclable garbage loading area 8, a dry garbage loading area 9, a wet garbage loading area 10 and a harmful garbage loading area 11.
An ARM chip processor is selected as a microprocessor in the barrel cover 1, a machine vision detection system and a BP neural network are written in the ARM chip processor, and the ARM chip processor controls a corresponding motor to rotate after obtaining signals of the sensor.
The BP neural network is a system that has been trained to obtain model parameters, and the parameters obtained by the sensor and the camera are input into the system to obtain corresponding outputs, so that classification of data and classification of garbage are achieved, and the input and output of the BP neural network are shown in fig. 6.
The camera 3 takes a picture of the garbage, the machine vision detection system processes the information of the picture, the structural parameters, the volume parameters and the color parameters of the garbage are obtained, and the obtained data are transmitted to the BP neural network.
The humidity sensor 4 is used for acquiring the dry and wet conditions of the garbage and converting the dry and wet conditions of the garbage into data to be transmitted to the BP neural network.
The gas sensor 5 is used for acquiring odor information of the garbage, and converting the odor information of the garbage into data to be transmitted to the BP neural network.
The pressure sensor 6 is used for acquiring the quality parameters of the garbage, and converting the quality parameters of the garbage into data to be transmitted to the BP neural network.
The rotary platform comprises a 360-degree rotating shaft, a rotating motor, a turning shaft, a turning motor and a flat plate. Step motors are selected as the rotating motor and the overturning motor, and the rotating motor drives the rotating shaft to rotate by 360 degrees, so that the platform is positioned in the corresponding loading area; the turnover motor drives the turnover shaft to rotate, so that the garbage is poured into the charging area. The rotating platform 7 is an executing mechanism for garbage classification, the BP neural network processes input data to obtain output data, if the output data is 1, the garbage is dry garbage, the BP neural network system transmits an electric signal to the ARM chip processor to control the rotating motor to drive the rotating shaft to rotate 90 degrees clockwise, then the overturning motor is controlled to drive the overturning shaft to overturn 90 degrees downwards, and the garbage is placed in the dry garbage loading area 9. Then the rotating platform is reset and rotated to the initial position, and the garbage 8 can be recovered above the area. And finishing a classification action. The other classification is the same, if the garbage is wet garbage, the rotating platform rotates 180 degrees, and if the garbage is harmful garbage, the rotating platform rotates 270 degrees.
The 360-degree rotating shaft is shown in fig. 3, the rotating motor is instructed to drive the gear to rotate, and the rotating gear column meshed with the gear drives the rotating shaft to rotate. The rotary gear column is a cylinder body with a gear groove arranged inside.
Turning shaft as shown in fig. 5, after the rotating shaft reaches a designated position, the turning motor is instructed to control the rotation of the gear, and the turning gear column engaged with the gear drives the rotating shaft to turn downwards. The turnover gear column is a column body with a gear groove arranged inside.
The BP neural network input and output are shown in fig. 6. As shown in the figure, six parameters obtained by the sensor and the machine vision detection system are used as input data of the BP neural network input layer, and the four types of garbage are respectively labeled as recoverable garbage 0, dry garbage 1, wet garbage 2 and harmful garbage 3. And inputting the parameters of the classified garbage and the label types into a BP neural network system for training to obtain model parameters. The training process of the BP neural network parameter weight is as follows: firstly, initializing parameter matrixes of an input layer and a hidden layer, the hidden layer and an output layer, wherein the activation functions of the hidden layer and the output layer adopt sigmoid functions, and the mathematical form is as follows:. However, the device is not suitable for use in a kitchenAnd inputting training data, training by using a back propagation method, and acquiring parameters. And finally, recording the acquired parameters, writing the parameters into a program, and then loading the program into an application system.
The working flow chart of the BP neural network-based garbage classification is shown in fig. 7.
The present invention has been described in connection with the accompanying drawings, and it is to be understood that the invention is not limited to the precise form set forth herein, and that various insubstantial modifications of the inventive concepts and solutions, or their direct application to other applications without such modifications, are intended to be covered by the scope of the invention.

Claims (5)

1. A garbage classification method based on a BP neural network recognition system is characterized by comprising a garbage can, a humidity sensor, a gas sensor and a camera; the garbage can comprises a base, a can body (12), a can cover (1) and a rotary platform (7), wherein the base is arranged at the bottom of the can body, the inner space of the can body is divided into four charging areas, the charging areas are separated by baffles, and a hollow cylinder (13) is arranged in the middle of the can body; the rotary platform comprises a 360-degree rotary shaft, a rotary motor (14), a turnover shaft, a turnover motor (19) and a flat plate, the rotary motor (14) is vertically fixed in the hollow column body (13), an output shaft of the rotary motor (14) is connected with a rotary gear (16), the 360-degree rotary shaft comprises a vertical rotary gear column (15) capable of being meshed with the rotary gear and a horizontal rotary shaft (17) connected to one side of the rotary gear column, the rotary gear column (15) is meshed with the rotary gear (16), and the rotary shaft is positioned outside the hollow column body; a barrel cover ejector rod (18) is further connected to the top of the rotary gear column, the barrel cover (1) is fixed on the barrel cover ejector rod (18), a processor is arranged in the barrel cover (1), and a machine vision detection system and a BP neural network are written in the processor; the turnover motor (19) is horizontally fixed in the rotating shaft (17), a turnover gear (20) is connected to an output shaft of the turnover motor (19), the turnover shaft comprises a horizontal turnover gear column (21) capable of being meshed with the turnover gear and a rotating shaft (22) connected to one side of the turnover gear column, the turnover gear column (21) is located in the rotating shaft and meshed with the turnover gear, the rotating shaft (22) is located outside the rotating shaft and perpendicular to the rotating shaft (17), an annular groove is formed in a position, corresponding to the joint of the turnover gear column and the rotating shaft, of the rotating shaft and used for turnover of the turnover shaft, a flat plate is connected to one side of the rotating shaft (22), the rotating shaft (17), the rotating shaft (22) and the flat plate form a platform for placing garbage, and the platform is; humidity transducer (4), gas sensor (5) set up on rotary platform (7), camera (3) set up on bung ejector pin (18) and link to each other with the treater, the data input treater that the sensor acquireed.
2. The garbage classification method based on the BP neural network identification system as claimed in claim 1, wherein the barrel cover is further provided with an infrared sensor (2), and the infrared sensor (2) is connected with the processor.
3. The garbage classification method based on the BP neural network recognition system as claimed in claim 1 or 2, wherein the rotating platform is further provided with a pressure sensor (6), and the data of the pressure sensor is input into the processor.
4. The garbage classification method based on the BP neural network identification system as claimed in claim 1 or 2, wherein the processor is an ARM chip processor.
5. The garbage classification method based on the BP neural network recognition system as claimed in claim 1 or 2, wherein the flip angle of the flip axis is 90 degrees.
CN201910884257.0A 2019-09-19 2019-09-19 Garbage classification method based on BP neural network recognition system Pending CN110723431A (en)

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Cited By (5)

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Publication number Priority date Publication date Assignee Title
CN111301886A (en) * 2020-02-25 2020-06-19 天津工业大学 Garbage classification and recovery system based on RBF neural network and control method
CN111498333A (en) * 2020-05-11 2020-08-07 桂林电子科技大学 Intelligent classification garbage can
CN112320135A (en) * 2020-11-25 2021-02-05 北京轩昂环保科技股份有限公司 Intelligent classification dustbin
CN112793954A (en) * 2021-01-29 2021-05-14 西安科技大学 Intelligent paid garbage recycling bin and method
CN113060444A (en) * 2020-10-26 2021-07-02 三江学院 Garbage classification device

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CN113060444A (en) * 2020-10-26 2021-07-02 三江学院 Garbage classification device
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CN112320135B (en) * 2020-11-25 2021-11-19 轩昂环保科技股份有限公司 Intelligent classification dustbin
CN112793954A (en) * 2021-01-29 2021-05-14 西安科技大学 Intelligent paid garbage recycling bin and method

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