CN209777324U - assembled garbage sorting device - Google Patents
assembled garbage sorting device Download PDFInfo
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- CN209777324U CN209777324U CN201822180951.8U CN201822180951U CN209777324U CN 209777324 U CN209777324 U CN 209777324U CN 201822180951 U CN201822180951 U CN 201822180951U CN 209777324 U CN209777324 U CN 209777324U
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- 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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Abstract
The utility model discloses an assembled sortable rubbish device, include: the box body is arranged on the base, and a plurality of parallel first sliding rails are arranged on the base; support plates installed at both sides of the base; a top plate mounted on the support plate; the box body is arranged on the base and arranged between the supporting plates, and a plurality of supporting ribs are symmetrically arranged on the inner side wall of the box body; the bottom parts of the garbage cans are provided with second sliding rails which can slide in a matching way with the first sliding rails, and the garbage cans are arranged in the box body in a rotating way; the screening table is arranged in the box body, the supporting ribs are used for supporting the screening table, and a plurality of garbage classification ports and a controller are arranged on the screening table; the garbage throwing platform is arranged on the box body, a first garbage detection port, a plurality of second garbage detection ports, an illumination sensor and an image sensor are arranged on the throwing platform, and a mechanical arm is arranged on the lower portion of the garbage throwing platform.
Description
Technical Field
The utility model relates to a communal facility technical field, concretely relates to assembled sortable rubbish device.
Background
the existing garbage can is mostly provided with two cavities, one is a recyclable garbage collection can, the other is a non-recyclable garbage collection can, and in actual putting, people are relatively weak in consciousness of garbage classification, so that waste of a large amount of resources is caused. Therefore, if the garbage can be reasonably classified from the source, namely the personal garbage, and is discarded through the specifically arranged garbage classification device, the garbage classification treatment efficiency in China can be greatly improved.
The prior art published application No. 201810220590.7 intelligent classification garbage bin, the user discards the garbage that needs to be discarded in the detection barrel in the corresponding compartment according to the different classification of each compartment in the garbage bin, otherwise the user can not discard the garbage. Such a setting is clearly not reasonable for people who are not aware of the classification of the waste.
SUMMERY OF THE UTILITY MODEL
The utility model relates to a development an assembled sortable rubbish device, the utility model discloses a carry out classification to single rubbish and mixed rubbish through setting up different waste classification mouth and rubbish detection mouth and put in.
The utility model provides a technical scheme does:
An assembled waste sorting device comprising:
The box body is arranged on the base, and a plurality of parallel first sliding rails are arranged on the base;
Support plates installed at both sides of the base;
A top plate mounted on the support plate;
The box body is arranged on the base and arranged between the supporting plates, and a plurality of supporting ribs are symmetrically arranged on the inner side wall of the box body;
The bottom of each garbage can is provided with a second sliding rail which can be matched with the first sliding rail to slide, and the garbage cans are arranged inside the box body in a rotating mode;
A screening deck disposed inside the bin, the support ribs for supporting the screening deck, and a plurality of waste sorting ports and a controller disposed on the screening deck;
The garbage throwing platform is arranged on the box body, a first garbage detection port, a plurality of second garbage detection ports, an illumination sensor and an image sensor are arranged on the throwing platform, and a mechanical arm is arranged at the lower part of the garbage throwing platform;
The garbage classification openings correspond to the garbage cans one to one, and the second garbage detection openings correspond to the garbage classification openings one to one; and
the controller is connected with the illumination sensor, the image sensor and the second garbage detection port at the same time, and the controller controls the second garbage detection port to be opened and closed according to the illumination sensor and the image sensor.
Preferably, a garbage conveying belt is arranged on the screening table, and is arranged corresponding to the first garbage detection port, and the garbage conveying belt is connected with the controller.
Preferably, the method further comprises the following steps:
The position sensor is arranged on the throwing table, connected with the controller and used for monitoring the garbage capacity in the garbage can;
And the volume sensor is arranged on the throwing table, is connected with the controller and is used for monitoring the volume of the garbage to be classified.
Preferably, the method further comprises the following steps:
a display panel mounted on one side of the support plate;
A solar panel mounted on the top plate.
preferably, the number of the garbage boxes is 4, the number of the garbage classification ports is 4, and the number of the second garbage detection ports is 4.
Drawings
fig. 1 is the utility model provides an assembled intelligent waste classification structure schematic diagram.
Fig. 2 is the utility model provides an assembled intelligent waste classification structure exploded view.
Fig. 3 is a bottom plate structure diagram provided by the present invention.
fig. 4 is a box structure diagram provided by the present invention.
Fig. 5 is a structural view of the garbage can provided by the present invention.
Fig. 6 is the structure diagram of the screening table provided by the utility model.
fig. 7 is a structural view of the putting table provided by the utility model.
Fig. 8 is a bottom view of the putting table provided by the present invention.
Fig. 9 is a structural view of the mechanical arm provided by the present invention.
Detailed Description
The present invention is further described in detail below with reference to the drawings so that those skilled in the art can implement the invention with reference to the description.
as shown in fig. 1-9, the utility model provides an assembled intelligent classification garbage bin, include: a base 100 placed on the ground; the base 100 is used for mounting the garbage can 700, the box body 600 and the bearing support plate 300, the upper part of the support plate 300 is used for mounting the top plate 400, and the top plate 400 is provided with the solar panel 500; the case 600 is installed on the base 100 to seal the trash can 700, and 6 support ribs 610 (symmetrically arranged) are provided in the case 600 to support the sieving table 800; the garbage cans 700 are four in total and are used for containing different types of garbage; the screening table 800 is provided with a garbage conveying belt 830, 4 garbage sorting ports 820 and two controllers 810 and 840; the throwing table 900 comprises 1 first garbage detection port 910, 4 second garbage detection ports 920, an image sensor 930, an illumination sensor 950, a position sensor 960, a volume sensor 980 and two mechanical arm mounting seats 940, 970, and is used for detecting whether garbage is thrown correctly and throwing the garbage into a correct garbage can 700; in this embodiment, specifically, a box 600 is mounted on a base 100, a plurality of parallel first sliding rails 110 are mounted on the base 100, supporting plates 300 are mounted on two sides of the base 100, the box 600 is disposed between the two supporting plates 300, and 6 supporting ribs 610 are symmetrically disposed on the inner side wall of the box 600, a second sliding rail 710 is mounted at the bottom of a trash can 700, and can slide in cooperation with the first sliding rails 110, so that a cleaner can take out the trash can 700 respectively for trash cleaning, the trash can 700 is mounted inside the box 600, a screening table 800 is disposed inside the box 600, the supporting ribs 610 are used for supporting the screening table 800, a plurality of trash classification ports 820 and controllers 810 and 840 are disposed on the screening table 800, and the trash depositing table 900 is mounted on the box 600; the garbage sorting device comprises 4 garbage sorting ports 820, 4 second garbage detection ports 920, 4 garbage sorting ports 820, a controller 810, an illumination sensor 950, an image sensor 930 and the second garbage detection ports 920, wherein the 4 garbage sorting ports 820 are in one-to-one correspondence with the 4 garbage cans 700, the 4 second garbage detection ports 920 are in one-to-one correspondence with the 4 garbage sorting ports 820, the controller 810 is simultaneously connected with the illumination sensor 950 and the image sensor 930 and respectively controls the second garbage detection ports 920 to be opened and closed, the controller 810 is simultaneously connected with the illumination sensor 950, the image sensor 930 and the mechanical arm 200, and the controller 810 controls the mechanical arm 200 according to the illumination sensor 950 and the image sensor 930.
In another embodiment, as shown in fig. 1, a display panel 200 is installed at one side of a support plate 300, and the display panel 200 is used to display the working condition of the garbage sorting apparatus, so that the maintenance of workers is facilitated.
In another embodiment, as shown in fig. 1, a solar panel 500 is mounted on the top plate 400, and the solar panel 500 provides electric energy for the sorting device, thereby reasonably utilizing resources.
As shown in fig. 4, in another embodiment, 4 trash bin taking doors 620 corresponding to trash bins 700 of different types are provided on the front and rear surfaces of the bin body 600, different trash classification marks are arranged on the surfaces of the 4 trash bin taking doors 620, so that people can conveniently throw trash, workers can clean the trash, and 6 support ribs 610 are installed on two sides inside the bin body 600 to support the trash screening table 800.
As shown in fig. 5, in another embodiment, the bottom of the 4 trash cans 700 is provided with a slide rail 710, one side of the trash can is provided with an armrest 720, and the 4 trash cans 700 are respectively recyclable trash (blue), kitchen garbage (green), harmful trash (red), and other trash (yellow), so that the cleaning personnel can classify and dispose the trash conveniently.
As shown in fig. 6, in another embodiment, the screening deck 800 includes controllers 810 and 840, 4 waste sorting ports 820, and a conveyor belt 830, wherein the controller 810 can be used to control the opening and closing of the 4 second waste detection ports 920, the controller 840 can also be used to control the robot 200 to pick up and sort the waste and put the waste into the corresponding waste sorting port 820, and the conveyor belt 830 is used to carry mixed waste to assist the robot in sorting the waste.
As shown in fig. 7 and 8, in another embodiment, 4 second trash detection ports 920 are used for throwing single trash, the first trash detection port 910 is used for throwing mixed trash, the image sensor 930 and the illumination sensor 950 are respectively used for detecting recyclable trash, harmful trash, kitchen waste and other trash, the position sensor 960 is used for monitoring the capacity of the 4 trash cans 700, the volume sensor 980 is used for monitoring the volume of the thrown trash, and when a certain capacity is reached in the trash can 700, the volume is fed back to the controllers 810 and 840 to be uploaded to the mobile phone client of the worker, and when the situation that the thrown trash cannot be accommodated in the trash can is detected, the volume is also fed back to the controllers and to be uploaded to the mobile phone client of the worker.
In another embodiment, as shown in fig. 9, the robotic arms 200 are hydraulically driven and controlled by the controller 840 to perform a corresponding garbage sorting operation.
In another embodiment, the controller operates to identify and classify garbage based on machine learning as the feature extractor and deep learning, which creates the classifier.
as shown in fig. 1 to 9, the working process of the present invention is as follows:
step one, when the garbage to be classified is single garbage, the garbage to be classified is placed on corresponding second garbage detection ports 920 on the garbage putting table 900, and the initial states of the 4 second garbage detection ports 920 are closed states;
When the garbage to be classified is mixed garbage, putting the garbage to be classified on a first garbage detection port 910 on the garbage putting table;
Step two, when the garbage to be classified is single garbage, the controller 810 judges that the placement is correct, the corresponding second garbage detection port 920 is opened, otherwise, the corresponding second garbage detection port 920 is still in a closed state;
When the garbage to be classified is mixed garbage, the type of the mixed garbage is judged, and the controller 840 controls the mechanical arm to convey the garbage to the corresponding garbage classification port 820 according to the judgment, and the garbage is put into the corresponding garbage can 700.
The assembled garbage sorting device and the working process thereof provided by the present invention will be further described with reference to specific embodiments.
Example 1
When the garbage to be classified is single garbage, the garbage placement correctness is judged by establishing a BP neural network model, and the method comprises the following steps:
Step 1, establishing a BP neural network model.
The utility model discloses a BP network architecture constitute by the three-layer, and the first layer is the input layer, totally n nodes, has corresponding n detected signal who represents equipment operating condition, and these signal parameters are given by data preprocessing module. The second layer is a hidden layer, and has m nodes, and is determined by the training process of the network in a self-adaptive mode. The third layer is an output layer, p nodes are provided in total, and the output is determined by the response actually needed by the system.
the mathematical model of the network is:
Inputting a vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a vector: o ═ O1,o2,...,op)T
the utility model discloses in, the input layer node is n ═ 4, and the output layer node is p ═ 2. The number m of hidden layer nodes is estimated by the following formula:
The input signal has 4 parameters expressed as: x is the number of1Is a contrast ratio coefficient, x2Is a transmittance coefficient, x3Is the garbage position coefficient, x4Is the volume coefficient of the garbage to be classified.
The data acquired by the sensors belong to different physical quantities, and the dimensions of the data are different. Therefore, the data needs to be normalized to a number between 0-1 before it is input into the artificial neural network.
Specifically, a garbage image is collected through an image sensor, and is subjected to characteristic comparison with a prestored image to collect a contrast ratio delta, and after normalization, a contrast ratio coefficient x is obtained1：
Wherein, deltaminAnd deltamaxA minimum contrast ratio for the image feature and a maximum contrast ratio for the image feature, respectively.
similarly, the transmittance psi acquired by the illumination sensor is normalized by the following equation to obtain the transmittance coefficient x2：
wherein psiminand psimaxrespectively, the minimum and maximum transmittance of the illumination sensor.
Collecting the garbage position H in the garbage can by using a position sensor, and normalizing to obtain a position coefficient x3：
Wherein HminAnd HmaxRespectively the minimum position and the maximum position of the garbage in the garbage can.
The volume sensor collects the volume V of the garbage to be classified and feeds the garbage into the garbage classification deviceObtaining the volume coefficient x of the garbage to be classified after the row normalization4：
Wherein, VminAnd VmaxRespectively the maximum volume of the garbage to be classified and the maximum volume of the garbage to be classified.
The 2 parameters of the output signal are respectively expressed as: o1Opening state of second waste detection port for placing said single waste, o2Is an emergency shutdown signal.
Opening state signal o of second garbage detection port1the output value is 0 or 1, when the output value is 0, the current single garbage is placed wrongly, and at the moment, the second garbage detection port for placing the single garbage is in a closed state; when the output value is 1, the single garbage is placed correctly, and at the moment, the second garbage detection port for placing the single garbage is in an open state.
Emergency stop signal o2The operation state of the current garbage classification system is represented, the output value is 0 or 1, when the output value is 0, the current garbage classification system is in an abnormal state, and at the moment, emergency shutdown is required; when the output value is 1, the current garbage classification system is in a normal state and can continue to operate.
And 2, training the BP neural network.
after the BP neural network node model is established, the training of the BP neural network can be carried out. Obtaining a training sample according to historical experience data of the product, and giving a connection weight w between an input node i and a hidden layer node jijConnection weight w between hidden layer node j and output layer node kjkThreshold value theta of hidden layer node jjThreshold value theta of output layer node kk、wij、wjk、θj、θkAre all random numbers between-1 and 1.
in the training process, continuously amendingPositive wijAnd wjkUntil the system error is less than or equal to the expected error, the training process of the neural network is completed.
As shown in table 1, a set of training samples is given, along with the values of the nodes in the training process.
TABLE 1 training Process node values
And 3, collecting operation parameters and inputting the operation parameters into a neural network to obtain a regulation and control coefficient and an emergency shutdown signal.
And solidifying the trained artificial neural network in an FPGA chip to enable a hardware circuit to have the functions of prediction and intelligent decision making, thereby forming intelligent hardware. After the intelligent hardware is powered on and started, the initial state of the second garbage detection port is a closed state, namely o1＝0。
Simultaneously using an image sensor, an illumination sensor, a position sensor and a volume sensor, an initial contrast ratio delta is acquired0Initial transmittance psi0And the position H of the garbage in the garbage can0volume V of initial garbage to be sorted0Normalizing the parameters to obtain an initial input vector of the BP neural networkObtaining an initial output vector through operation of a BP neural network
And 4, controlling the opening state of a second garbage detection port for placing the garbage to be classified.
obtaining an initial output vectorThen, the opening state of the second garbage detection port can be regulated and controlled, and the contrast ratio delta of the ith sampling period is obtained through the sensoriTransmittance psiiAnd the garbage position H in the garbage caniTo be dividedVolume V of class rubbishiObtaining an input vector of the ith sampling period by formattingObtaining an output vector to the ith sampling period through the operation of a BP neural networkAnd then controlling the opening state of the second garbage detection port.
And 5, monitoring an emergency stop signal of a second garbage detection port for garbage to be classified so as to perform emergency stop.
according toThe value of (2) judges the set working state, whether the working state is in an abnormal working state, and when the garbage system is in a normal working state, the garbage system needs to be immediately stopped so as to carry out maintenance and avoid further damage of the equipment.
Example 2
In the second step, when the garbage to be classified is mixed garbage, the garbage classification is judged by establishing a BP neural network model, and the method comprises the following steps:
Step 1, establishing a BP neural network model.
Fully interconnected connections are formed among neurons of each layer on the BP model, the neurons in each layer are not connected, and the output and the input of neurons in an input layer are the same, namely oi＝xi. The operating characteristics of the neurons of the intermediate hidden and output layers are
opj＝fj(netpj)
Where p represents the current input sample, ωjiis the connection weight from neuron i to neuron j, opiIs the current input of neuron j, opjis the output thereof; f. ofjis notlinear, slightly non-decreasing, functions, typically taking the form of an S-function, i.e. fj(x)＝1/(1+e-x)。
The utility model adopts a BP network system structure which comprises three layers, wherein the first layer is an input layer and comprises n nodes, which correspond to n detection signals representing the working state of the classified garbage system of the utility model, and the signal parameters are given by a data preprocessing module; the second layer is an intermediate layer, and the intermediate layer comprises m nodes which are determined by the training process of the network in a self-adaptive mode; the third layer is an output layer, p nodes are provided in total, and the output is determined by the response actually needed by the system.
The mathematical model of the network is:
Inputting a vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a vector: o ═ o (o)1,o2,...,op)T
The utility model discloses in, the input layer node is n for 4, and the output layer node is p for 5, hides layer node m for 5.
The input signal has 4 parameters expressed as: x is the number of1Is a contrast ratio coefficient, x2Is a transmittance coefficient, x3Is the garbage position coefficient, x4Is the volume coefficient of the garbage to be classified.
The output layer 5 parameters are respectively expressed as: o1Is a set 1 st waste classification opening o2To a set 2 nd waste classification opening, o3to a set 3 rd waste classification opening, o4To a set 4 th waste classification opening, o5for an emergency shutdown signal, the output layer neuron value isFor the output layer neuron sequence number, k is {1,2,3,4,5}, i is the set ith garbage classification port, i is {1,2,3,4,5}, and when o is the set numberkis 1, at this time, at ok1 st ~ 4 rubbish classification mouth and emergency shutdown state that correspond.
emergency stop signal o5The operation state of the current garbage classification system is represented, the output value is 0 or 1, when the output value is 0, the current garbage classification system is in an abnormal state, and at the moment, emergency shutdown is required; when the output value is 1, the current garbage classification system is in a normal state and can continue to operate.
and step two, training the BP neural network.
After the BP neural network node model is established, the training of the BP neural network can be carried out. And obtaining a training sample according to historical experience data of the product, and giving a connection weight between the input node i and the intermediate layer node j and a connection weight between the intermediate layer node j and the output layer node k.
(1) Training method
Each subnet adopts a separate training method; when training, firstly providing a group of training samples, wherein each sample consists of an input sample and an ideal output pair, and when all actual outputs of the network are consistent with the ideal outputs of the network, the training is finished; otherwise, the ideal output of the network is consistent with the actual output by correcting the weight; the output samples for each subnet training are shown in table 2.
TABLE 2 output samples for network training
(2) training algorithm
The BP network is trained by using a back Propagation (Backward Propagation) algorithm, and the steps can be summarized as follows:
The first step is as follows: and selecting a network with a reasonable structure, and setting initial values of all node thresholds and connection weights.
The second step is that: for each input sample, the following calculations are made:
(a) Forward calculation: for j unit of l layer
in the formula (I), the compound is shown in the specification,For the weighted sum of the j unit information of the l layer at the nth calculation,is the connection weight between the j cell of the l layer and the cell i of the previous layer (i.e. the l-1 layer),is the previous layer (i.e. l-1 layer, node number n)l-1) The operating signal sent by the unit i; when i is 0, order is the threshold of the j cell of the l layer.
If the activation function of the unit j is a sigmoid function, then
And is
If neuron j belongs to the first hidden layer (l ═ 1), then there are
If neuron j belongs to the output layer (L ═ L), then there are
And ej(n)＝xj(n)-oj(n)；
(b) and (3) calculating the error reversely:
for output unit
Pair hidden unit
(c) Correcting the weight value:
η is the learning rate.
The third step: inputting a new sample or a new period sample until the network converges, and randomly re-ordering the input sequence of the samples in each period during training.
The BP algorithm adopts a gradient descent method to solve the extreme value of a nonlinear function, and has the problems of local minimum, low convergence speed and the like. A more effective algorithm is a Levenberg-Marquardt optimization algorithm, which enables the network learning time to be shorter and can effectively inhibit the network from being locally minimum. The weight adjustment rate is selected as
Δω＝(JTJ+μI)-1JTe
Wherein J is a Jacobian (Jacobian) matrix of the differential of the error to the weight, I is an input vector, e is an error vector, and the variable mu is a scalar quantity which is self-adaptive and adjusted and is used for determining whether the learning is finished according to a Newton method or a gradient method.
When the system is designed, the system model is a network which is only initialized, the weight needs to be learned and adjusted according to data samples obtained in the using process, and therefore the self-learning function of the system is designed. Under the condition of appointing learning samples and quantity, the system can carry out self-learning so as to continuously improve the network performance.
While the embodiments of the invention have been described above, it is not intended to be limited to the details shown, or described, but rather to cover all modifications, which would come within the scope of the appended claims, and all changes which come within the meaning and range of equivalency of the art are therefore intended to be embraced therein.
Claims (5)
1. An assembled sortable waste device, comprising:
The box body is arranged on the base, and a plurality of parallel first sliding rails are arranged on the base;
Support plates installed at both sides of the base;
A top plate mounted on the support plate;
The box body is arranged on the base and arranged between the supporting plates, and a plurality of supporting ribs are symmetrically arranged on the inner side wall of the box body;
The bottom of each garbage can is provided with a second sliding rail which can be matched with the first sliding rail to slide, and the garbage cans are arranged inside the box body in a rotating mode;
A screening deck disposed inside the bin, the support ribs for supporting the screening deck, and a plurality of waste sorting ports and a controller disposed on the screening deck;
The garbage throwing platform is arranged on the box body, a first garbage detection port, a plurality of second garbage detection ports, an illumination sensor and an image sensor are arranged on the throwing platform, and a mechanical arm is arranged at the lower part of the garbage throwing platform;
the garbage classification openings correspond to the garbage cans one to one, and the second garbage detection openings correspond to the garbage classification openings one to one; and
The controller is connected with the illumination sensor, the image sensor and the second garbage detection port at the same time, and the controller controls the second garbage detection port to be opened and closed according to the illumination sensor and the image sensor.
2. The assembled waste sorting device of claim 1, wherein a waste conveyor is provided on the screening deck corresponding to the first waste detection port, and wherein the waste conveyor is connected to the controller.
3. The assembled waste sorting device of claim 2, further comprising:
The position sensor is arranged on the throwing table, connected with the controller and used for monitoring the garbage capacity in the garbage can;
And the volume sensor is arranged on the throwing table, is connected with the controller and is used for monitoring the volume of the garbage to be classified.
4. The modular waste sorting apparatus of claim 3, further comprising:
A display panel mounted on one side of the support plate;
a solar panel mounted on the top plate.
5. the assembled waste sorting device of claim 4, wherein the number of the waste bin is 4, the number of the waste sorting ports is 4, and the number of the second waste detection ports is 4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109516032A (en) * | 2018-12-25 | 2019-03-26 | 吉林大学 | A kind of assembled intelligent sorting rubbish system and its control method |
| CN109516032B (en) * | 2018-12-25 | 2024-05-10 | 吉林大学 | Assembled intelligent garbage classification system and control method thereof |
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- 2018-12-25 CN CN201822180951.8U patent/CN209777324U/en active Active
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109516032A (en) * | 2018-12-25 | 2019-03-26 | 吉林大学 | A kind of assembled intelligent sorting rubbish system and its control method |
| CN109516032B (en) * | 2018-12-25 | 2024-05-10 | 吉林大学 | Assembled intelligent garbage classification system and control method thereof |
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