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CN119368442A - Automatic classification and recycling system of electronic waste based on artificial intelligence recognition - Google Patents

Automatic classification and recycling system of electronic waste based on artificial intelligence recognition Download PDF

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Publication number
CN119368442A
CN119368442A CN202411442648.4A CN202411442648A CN119368442A CN 119368442 A CN119368442 A CN 119368442A CN 202411442648 A CN202411442648 A CN 202411442648A CN 119368442 A CN119368442 A CN 119368442A
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China
Prior art keywords
sorting
module
recognition
electronic waste
waste
Prior art date
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Pending
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CN202411442648.4A
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Chinese (zh)
Inventor
曹海宙
周芳
崔淑芬
林乐峰
张丰
叶超
辛晓桐
齐川
彭学兵
张志�
翁建武
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Zhejiang Huijin Environmental Protection Technology Co ltd
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Zhejiang Huijin Environmental Protection Technology Co ltd
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Priority to CN202411442648.4A priority Critical patent/CN119368442A/en
Publication of CN119368442A publication Critical patent/CN119368442A/en
Pending legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
    • B03C—MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
    • B03C1/00—Magnetic separation
    • B03C1/02—Magnetic separation acting directly on the substance being separated
    • B03C1/30—Combinations with other devices, not otherwise provided for
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/02—Measures preceding sorting, e.g. arranging articles in a stream orientating
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36—Sorting apparatus characterised by the means used for distribution
    • B07C5/361—Processing or control devices therefor, e.g. escort memory
    • B07C5/362—Separating or distributor mechanisms
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B09—DISPOSAL OF SOLID WASTE; RECLAMATION OF CONTAMINATED SOIL
    • B09B—DISPOSAL OF SOLID WASTE NOT OTHERWISE PROVIDED FOR
    • B09B3/00—Destroying solid waste or transforming solid waste into something useful or harmless
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B09—DISPOSAL OF SOLID WASTE; RECLAMATION OF CONTAMINATED SOIL
    • B09B—DISPOSAL OF SOLID WASTE NOT OTHERWISE PROVIDED FOR
    • B09B3/00—Destroying solid waste or transforming solid waste into something useful or harmless
    • B09B3/30—Destroying solid waste or transforming solid waste into something useful or harmless involving mechanical treatment
    • B09B3/35—Shredding, crushing or cutting
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B09—DISPOSAL OF SOLID WASTE; RECLAMATION OF CONTAMINATED SOIL
    • B09B—DISPOSAL OF SOLID WASTE NOT OTHERWISE PROVIDED FOR
    • B09B3/00—Destroying solid waste or transforming solid waste into something useful or harmless
    • B09B3/70—Chemical treatment, e.g. pH adjustment or oxidation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B09—DISPOSAL OF SOLID WASTE; RECLAMATION OF CONTAMINATED SOIL
    • B09B—DISPOSAL OF SOLID WASTE NOT OTHERWISE PROVIDED FOR
    • B09B5/00—Operations not covered by a single other subclass or by a single other group in this subclass
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/05—Programmable logic controllers, e.g. simulating logic interconnections of signals according to ladder diagrams or function charts
    • G05B19/054—Input/output
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
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    • G06F18/24—Classification techniques
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    • G06F18/25—Fusion techniques
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    • G06F18/25—Fusion techniques
    • G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
    • G06F18/256—Fusion techniques of classification results, e.g. of results related to same input data of results relating to different input data, e.g. multimodal recognition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
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    • G06Q10/00—Administration; Management
    • G06Q10/30—Administration of product recycling or disposal
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0054—Sorting of waste or refuse
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0063—Using robots
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B09—DISPOSAL OF SOLID WASTE; RECLAMATION OF CONTAMINATED SOIL
    • B09B—DISPOSAL OF SOLID WASTE NOT OTHERWISE PROVIDED FOR
    • B09B2101/00—Type of solid waste
    • B09B2101/15—Electronic waste

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Abstract

The invention relates to an electronic waste automatic classification and recovery system based on artificial intelligent recognition, which comprises a waste conveying module, an artificial intelligent recognition module, an intelligent sorting module, a dynamic sorting control system, a recovery processing module and a data analysis and feedback module. The waste conveying module sequentially conveys the electronic waste to the identification area, and the identification module carries out multidimensional feature identification on the waste through vision, spectrum and 3D morphological analysis technology. The intelligent sorting module sorts the materials to the appointed sorting box according to the identification result. The dynamic sorting control system can adjust sorting paths in real time according to task requirements. The recovery processing module is used for further processing the sorted materials through crushing, magnetic separation and rare noble metal extraction devices. The data analysis and feedback module analyzes and optimizes the data of the identification and sorting operation, and improves the processing efficiency and the recovery rate.

Description

Electronic waste automatic classification and recovery system based on artificial intelligent recognition
Technical Field
The invention relates to the technical field of waste treatment, in particular to an electronic waste automatic classification and recovery system based on artificial intelligent recognition.
Background
With the rapid development of technology and the popularization of electronic products, a large amount of electronic waste (such as waste mobile phones, household appliances and hybrid circuit boards) is generated annually in the world. These electronic wastes contain a large amount of harmful substances such as heavy metals and plastics which are difficult to degrade, and the improper treatment can cause serious harm to the environment and human health. However, the electronic waste also contains high-value rare noble metals (such as gold, silver, platinum and the like) and renewable materials (such as copper, plastics and the like), and has higher resource recovery potential.
The current electronic waste recycling mode still takes manual sorting or semi-automatic sorting as the main mode, has lower sorting efficiency and precision, and has the following problems:
The identification efficiency is low, namely, the traditional method can not effectively distinguish different components in electronic wastes with various forms and complex components.
The recovery precision is insufficient, and the traditional sorting method can not accurately separate high-value components in the waste, so that the resource waste is caused.
The automation degree is low, most recovery systems rely on manual operation, and the problems of misjudgment, mistakes and the like are easy to generate.
Therefore, an automatic classification and recovery system based on artificial intelligence technology is needed, which can effectively improve the recognition efficiency, the sorting precision and the system automation level, and realize the intelligent recovery of electronic wastes.
Disclosure of Invention
In order to solve the problems, the invention provides an automatic electronic waste classification and recovery system based on artificial intelligent recognition, which can recognize and classify complex and diverse electronic wastes in real time through integrating visual recognition, spectral analysis and deep learning models, and efficiently recover the electronic wastes through an intelligent sorting device so as to maximize resource recycling.
In order to achieve the above object, the present invention provides the following technical solution, which mainly includes:
Waste conveying module
The module is used for sequentially conveying the mixed electronic wastes to the identification area and carrying out preliminary distribution so as to ensure that each waste can be accurately detected by the identification module. The waste transport module includes a conveyor belt, a dispenser, a pushing device, and a speed adjustment system. The system can dynamically adjust the conveying speed and the distribution sequence according to the shape, the size and the recognition processing capacity of the material.
Artificial intelligent recognition module
The module is a core component of the system, integrates a multi-mode sensor (such as an industrial camera, a spectrum analyzer and a 3D laser scanner) and a central processing unit, and can carry out multi-dimensional feature recognition on electronic wastes. The identification module comprises:
And the visual recognition unit is used for acquiring image data through an industrial camera and recognizing the shape, color and texture of the material by utilizing a deep learning algorithm (such as CNN, resNet and the like).
And the spectrum analysis unit is used for detecting the element components and the molecular characteristics of the material by a spectrum analyzer and distinguishing metal from nonmetal, noble metal and common metal.
And 3D scanning unit, which uses laser scanner to three-dimensionally image and reconstruct the shape of the material, to identify the internal structure and surface characteristics of the material.
Intelligent sorting module
After the identification module completes material identification, the system generates sorting instructions and the sorting instructions are executed by the intelligent sorting module. The intelligent sorting module comprises a plurality of mechanical arms, sorting push rods, a movable sorting unit and a dynamic sorting path planning system. The module can accurately sort different types of materials into the designated sorting boxes according to the identification result, and adjust sorting paths and strategies in real time.
Dynamic sorting control system
And a PLC (programmable logic controller) and a human-machine interface (HMI) control system are adopted to realize dynamic control and parameter adjustment of each sorting module and each transmission module. The control system can automatically adjust the sorting path according to the identification result, the sorting task and the sorting box state, and perform secondary sorting operation when the identification result is uncertain.
Recovery processing module
The module is used for further physical and chemical treatment of the primarily sorted electronic waste. The processing module comprises a crushing device, a magnetic separation device and a rare noble metal extraction device. The crushing device crushes the large electronic components into small particles, the magnetic separation device separates iron-containing metal, and the rare noble metal extraction device extracts rare noble metals such as gold, silver, palladium and the like from the electronic components through a chemical method.
Data analysis and feedback module
The module is used for recording and analyzing the operation data and sorting results of the identification module, and optimizing the model through machine learning and data mining technologies. The data analysis module can dynamically update the recognition model according to the historical data and the new electronic waste types, and accuracy and adaptability of system recognition are improved.
Compared with the prior art, the invention has the following beneficial effects:
(1) The recognition precision is high, and the precise classification of the electronic wastes and the recognition of the complex materials are realized by integrating multi-mode recognition technologies (such as visual recognition, spectrum analysis and 3D scanning).
(2) The sorting efficiency is improved, namely the dynamic sorting control system can automatically adjust the sorting path according to the real-time task and the state of the sorting box, and the overall efficiency of sorting operation is improved.
(3) The resource recovery rate is high, and the recovery processing module adopts various processing modes such as crushing, magnetic separation, rare noble metal extraction and the like, so that the rare noble metal and other renewable materials in the electronic waste can be effectively recovered.
(4) The system is intelligent, and the data analysis and feedback module has the functions of online learning and model self-adaptive optimization, and can continuously optimize the identification and sorting strategy according to historical data, so that the long-term operation effect of the system is improved.
(5) The environment-friendly benefit is remarkable, the system can effectively reduce the environmental pollution in the electronic waste treatment process, improve the recycling rate of resources, and has good environment-friendly benefit and economic benefit.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system frame diagram of the overall invention.
Fig. 2 is a system frame diagram of the waste transfer module of the present invention.
Fig. 3 is a system frame diagram of the intelligent sorting module and the recycling processing module according to the present invention.
The reference numerals indicate that the system comprises a 1-waste conveying module, a 101-conveying belt, a 102-distributor, a 103-pushing device, a 104-speed adjusting system, a 2-intelligent sorting module, a 201-mechanical arm, a 202-sorting push rod, a 203-sorting box, a 3-recycling processing module, a 301-crushing device, a 302-magnetic separation device, a 303-rare noble metal extraction device, a 4-artificial intelligent identification module, a 5-dynamic sorting control system and a 6-data analysis and feedback module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Electronic waste automatic classification and recovery system based on artificial intelligence discernment, as shown in fig. 1 to 3, including:
Waste conveying module 1
The waste transport module 1 comprises a conveyor belt 101, a dispenser 102, a pushing device 103 and a speed adjustment system 104 for orderly transporting the mixed electronic waste to the identification zone.
Conveyor 101 for carrying and transporting the mixed electronic waste. Depending on the shape and number of electronic waste, the width and speed of the conveyor belt 101 can be adjusted to avoid overlapping or dumping of different types of waste.
A distributor 102, located at the beginning of the conveyor 101, is used to initially distribute large pieces and complex-shaped waste to different conveyor areas. The dispenser 102 controls the order of dispensing the different waste materials by a robotic arm or push rod.
Pushing device 103 the pushing device 103 is arranged at the end of the conveyor belt 101 for pushing the identified electronic waste to the intelligent sorting module 2. The pushing device 103 can adjust pushing force according to the sizes and the weights of different materials, so that the electronic waste can be ensured to stably move to the next link.
A speed adjustment system 104 for dynamically adjusting the running speed of the conveyor belt 101. When the processing speed of the identification module 4 is low, the speed adjustment system 104 can automatically reduce the conveying speed to avoid the accumulation of waste.
Artificial Intelligence recognition module 4
The artificial intelligence recognition module 4 is composed of multi-modal sensors such as an industrial camera, a spectrum analyzer, a 3D laser scanner, and a central processor 303. The module realizes accurate identification of electronic wastes by integrating various identification technologies.
The industrial camera 301 is used for capturing a surface image of the electronic waste, and recognizing its shape, color and surface texture features through a deep learning algorithm such as convolutional neural network CNN. The image data photographed by the camera 301 is transmitted to the central processor 303 for preliminary image analysis and shape classification.
A spectrum analyzer 302 for detecting the elemental composition of the electronic waste. By analyzing the absorption and reflection characteristics of different wavelength spectrums, the system can identify the elemental composition of the material and distinguish between metals and non-metals, rare noble metals and common metals.
And the 3D laser scanner is used for acquiring three-dimensional form data of the electronic waste. By means of laser scanning technology, the system can reconstruct three-dimensional model of electronic waste and identify its complex structure and internal elements.
And the central processor 303 integrates a deep learning algorithm and a data analysis module and is used for analyzing and fusing the data acquired by the multi-mode sensor and generating a final recognition result. The central processor 303 is capable of processing the image features, the spectral features, and the three-dimensional features simultaneously, and generating specific sorting instructions based on the recognition results.
Intelligent sorting module 2
The intelligent sorting module 2 is composed of a plurality of mechanical arms 201, sorting pushers 202 and sorting bins 203, and can perform corresponding sorting operations according to the results of the recognition module 4.
Robot 201 the system is equipped with a plurality of robots 201, each robot 201 determining the optimal sorting path by a control algorithm and precisely moving the designated material into the corresponding sorting bin 203. The robot arm 201 has 6 or more degrees of freedom, and can flexibly cope with gripping and carrying of complex materials.
Sorting pushers 202, located at the end of conveyor 101, are capable of pushing large objects to a designated sorting area. The push rod 202 is driven by a servo motor, and can adjust the thrust according to the sizes and weights of different objects, so that the objects can be ensured to stably enter the sorting area.
Sorting bins 203 the system is configured with a plurality of sorting bins 203, each sorting bin 203 for collecting a specified class of materials such as plastics, copper, aluminum, rare precious metals, etc. The sorting bin 203 is provided with a weight sensor and a full load detection device, and when the capacity of the sorting bin 203 reaches a set value, the system can automatically give an alarm and switch to the standby sorting bin 203.
Dynamic sorting control system 5
The dynamic sorting control system 5 adopts a PLC controller 601 and a man-machine interface 602 to control the operation of each module in real time.
The PLC controller 601 is connected with the mechanical component conveyor belt 101, the mechanical arm 201, the pushing device 103 and the like, and realizes the dynamic control of the sorting module 2 by receiving the sorting instruction of the identification module 4. The PLC controller 601 can dynamically adjust parameters of each module according to real-time feedback data such as recognition accuracy, transfer speed.
Human-machine interface 602 an operator can monitor the system status in real time via human-machine interface 602 and adjust sorting paths, modify sorting priorities or enable alternate sorting modules 2 as needed.
Recovery processing module 3
The recovery processing module 3 is used for further physical and chemical processing of the sorted electronic waste so as to realize efficient extraction of rare noble metals and other recyclable materials.
Crushing device 301. The crushing device 301 adopts a plurality of layers of crushing blades, so that large electronic wastes can be crushed into fine particles, and the subsequent magnetic separation and chemical treatment are facilitated. The crushing device 301 is equipped with anti-clogging means, and when material clogging is detected, the system can automatically adjust the blade turning and activate the anti-clogging function for cleaning.
And the magnetic separation device 302 is used for separating iron-containing materials in the electronic waste through the magnetic separation device 302 and automatically discharging the iron-containing materials into corresponding recovery containers.
And the rare noble metal extracting device 303 is used for extracting rare noble metals such as gold, silver, palladium and the like from the sorted fine particles by adopting a hydrometallurgy or chemical separation technology and generating a high-purity metal product.
Data analysis and feedback module 6
The data analysis and feedback module 6 is used for recording the working states of the identification module 4 and the sorting module 2 and optimizing the identification model based on the data analysis result.
Real-time data recording the data analysis and feedback module 6 is able to record the status information of each identification and sorting operation and store it in a central database.
And the recognition model optimization, namely when the recognition precision or the sorting accuracy is lower than a preset threshold value, the data analysis and feedback module 6 can automatically generate a new model optimization scheme and update recognition model parameters. The module is based on a machine learning algorithm, can continuously learn from historical data and dynamically optimize the identification strategy.
The specific working procedure is as follows:
1. waste input and initial transfer process
1.1 Waste input and initial delivery
The electronic waste is placed on the conveyor belt 101 of the waste transfer module 1, and after the system is started, the conveyor belt 101 delivers the mixed electronic waste to the initial distribution area at a set speed.
At the same time as the conveyor belt 101 starts to run, the dispenser 102 is started, ensuring that the different types of electronic waste are distributed uniformly, avoiding accumulation or overlapping, and ensuring the accuracy of subsequent identification.
A pushing device 103 is located at the end of the conveyor belt 101 for pushing material of a particular shape or size to the identification zone in order to optimize the subsequent identification efficiency.
1.2 Speed Regulation
The speed adjustment system 104 is capable of dynamically adjusting the speed of the conveyor belt 101 based on the processing capabilities of the artificial intelligence recognition module 4.
If the processing capacity of the identification module 4 reaches an upper limit or the waste identification queue length exceeds a predetermined value, the system automatically slows down the transfer rate, ensuring that each object can be identified without omission or accumulation.
2. Electronic waste identification process
2.1 Visual identification
After the electronic waste enters the artificial intelligence recognition module 4, the industrial camera 301 collects surface images.
The image data is transmitted to the central processor 303 and analyzed by the deep learning model to primarily identify the material type such as plastic, metal, ceramic, etc.
2.2 Spectroscopic analysis
After visual recognition, the spectrum analyzer 302 is activated to further detect elemental constituents of the material by analyzing spectral reflectance characteristics at different wavelengths.
2.33D scanning and internal Structure identification
For electronic waste with complex morphology, 3D laser scanners are used to acquire three-dimensional morphology data of the material.
The recognition result is processed by a three-dimensional reconstruction algorithm in the central processing unit 303 and is fused with the surface feature and the spectrum feature data to form a complete multi-modal feature vector.
2.4 Identification results and sort instruction Generation
The central processing unit 303 fuses the images, spectrums and three-dimensional features acquired by the sensors, and inputs the fused images, spectrums and three-dimensional features into a pre-trained deep learning model to generate recognition results.
The system generates specific sorting instructions according to the identification result and sends the instructions to the intelligent sorting module 2.
3. Intelligent sorting flow
3.1 Intelligent sorting Module Start
After receiving the identification result, the intelligent sorting module 2 controls the mechanical arm 201 to perform sorting operation according to the type and the size of the object.
The robot 201 automatically selects an optimal sorting path according to the path plan generated by the recognition module 4, and grabs the wastes of the specific category into the corresponding sorting bin 203.
3.2 Sorting push rod assisted sorting
For objects of complex shape or larger size, the system pushes them into the designated sorting area by sorting pushers 202.
Each sorting bin 203 is provided with a full load detection device and triggers an alarm signal when the capacity reaches a set value.
3.3 Dynamic sort control and Path adjustment
If the sorting bin 203 is fully loaded, or if the object identification results have a high uncertainty, the dynamic sort control system 5 will automatically adjust the sort path and transfer the objects to the alternate sorting bin 203 for further processing.
4. Recovery processing flow
4.1 Material crushing
The primarily sorted electronic waste is sent to the crushing device 301 in the recycling module 3.
The crushing device 301 crushes the large-sized electronic components into small particles by the multi-layered crushing blade.
4.2 Magnetic separation and separation
The magnetic separator 302 separates the crushed material into iron-containing material and automatically discharges the iron-containing material into a designated recovery tank.
4.3 Rare noble Metal extraction
The sorted fine particulate material is chemically separated by a rare noble metal extraction device 303.
The extracted waste liquid is subjected to innocent treatment through a treatment device, and the extracted chemicals are recycled.
5. Data analysis and feedback process
5.1 Real-time data recording and analysis
The data analysis and feedback module 6 is capable of recording data related to the identification and sorting operations such as identification accuracy, sorting accuracy, recovery rate, etc.
The data analysis module 6 continuously optimizes the recognition model by accumulation of historical data.
5.2 Model self-learning and optimization
The system can automatically retrain the model and update parameters according to the result of each operation.
5.3 Sorting Path and policy optimization
The data analysis module 6 adopts a reinforcement learning algorithm to automatically adjust the sorting path according to the real-time data and the change of the sorting efficiency.
In order to further improve the efficiency and the precision of the system in the complex electronic waste treatment, the invention realizes the accurate classification and the efficient recovery of the electronic waste with high mixing degree and complex internal structure through the optimization of the dynamic sorting control system 5, the lifting of the material identification model 4 and the introduction of a multi-stage sorting strategy. The following are further extended embodiments.
Example 2 optimization strategy for dynamic sorting control System
Dynamic path planning and sorting policy management
The dynamic sorting control system 5 adopts advanced path planning algorithms, such as Dijkstra algorithm, A algorithm and reinforcement learning strategy, so as to realize intelligent path planning and resource optimization allocation of the mechanical arm 201 in sorting tasks.
Implementation of the path planning algorithm is able to automatically generate an optimal sorting path based on the type, size and position data of the waste provided by the identification module 4. The system will avoid obstacles and other sorting robots 201, depending on the priority of the current task, ensuring that the sorting operation is completed in the shortest time.
Task priority management the dynamic sorting control system 5 is able to assign different sorting priorities to different types of waste. For example, when a rare precious metal such as gold, palladium element is detected, the system may prioritize the robot 201 for sorting operations and skip other low priority materials.
Real-time adjustment of sorting strategy the system can automatically adjust the sorting path as the capacity of the sorting bin 203 approaches saturation, transferring the same type of material to the standby sorting bin 203.
Introduction of multistage sorting strategy
For electronic waste with complex internal structures or multiple material mixtures, the system introduces a multistage sorting strategy. The multi-stage sorting module can conduct subdivision processing according to the preliminary sorting result of the materials.
Primary sorting-after recognition by the recognition module 4, the sorting module 2 sorts the waste into major categories such as metal, plastic, mixed electronic components and directs it into the respective primary sorting bins 203 through different sorting paths.
Secondary sorting, wherein the system is further subdivided according to the primary sorting result. For example, for mixed metal materials, the system may sort the mixed metal materials by a magnetic separation device 302 and a rare noble metal extraction device 303 to separate rare noble metals such as gold, silver, and platinum from common metals such as iron and aluminum.
Three sorts and subdivision of complex materials for multilayer composites, the system employs a layer-by-layer separation strategy. The robotic arm 201 breaks down the composite material into individual material layers by the breaking device 301 and performs precise identification and classification on each separate layer.
Expanded design of sorting modules 2
In order to accommodate different kinds and sizes of electronic waste, the invention introduces replaceable grippers, suction cups and cutting devices in the sorting module 2. Each sorting unit can automatically switch tools according to the type and the form of the waste, so that sorting precision and efficiency are improved.
Replaceable holders the system is equipped with various types of holders such as fine element holders, large material holders and rotatable suction cups, and quick replacement is achieved by an automatic tool change system.
And the sucking disc and the vacuum adsorption system are used for stably grabbing the light materials with irregular shapes.
The system is provided with a precise laser cutting device and a precise laser decomposing tool, and is used for separating the multi-layer composite materials.
Example 3 precision optimization of identification Module
In order to improve the accuracy of the system in the identification of complex mixed materials, the invention introduces the fusion of various identification technologies and the optimization of a deep learning model into the identification module 4.
Fusion of multimodal recognition techniques
The system integrates visual recognition, spectrum analysis 302 and 3D morphological recognition technology, and multi-dimensional feature analysis of complex electronic wastes is realized through a feature fusion model.
Feature fusion model a multi-modal feature fusion model is built in the central processor 303 to fuse the image features, the spectral features and the three-dimensional morphological features.
And the feature selection and dimension reduction is that the system adopts principal component analysis PCA, LDA and other dimension reduction algorithms to remove redundant information, thereby improving the recognition speed.
Multistage feature extraction of complex materials the recognition module 4 extracts and recognizes the features of each layer independently by progressively stripping the surface layers.
Training and optimization of deep learning model
The system employs a variety of deep learning models such as convolutional neural networks, residual networks, and transducer-based models in the central processor 303 for classification of complex materials.
Trade-off between accuracy and speed of material identification
When complex materials are identified, the system realizes the balance of precision and speed through the combination of a multi-stage model and a lightweight model.
Example 4 data analysis and feedback mechanism
Data recording and storage
The system is capable of data logging each identification, sorting and processing operation and storing and analyzing historical data by means of the data analysis module 6.
Real-time feedback and model optimization
The system can feed back the identification and sorting results in real time through the data analysis module 6.
Sorting path and policy optimization
The data analysis module 6 is able to optimize the sorting strategy based on the data of the historic sorting paths.
In summary, the invention can realize highly automatic, intelligent and accurate sorting and recycling in complex electronic waste treatment by means of fusion of multi-mode recognition technology, introduction of multi-stage sorting strategy and design of data analysis and dynamic feedback mechanism, and provides innovative solution for environmental protection treatment and resource recycling of electronic waste.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1.基于人工智能识别的电子废弃物自动分类与回收系统，包括废弃物传送模块(1)、智能分拣模块(2)、回收处理模块(3)、人工智能识别模块(4)、动态分拣控制系统(5)和数据分析与反馈模块(6)，其特征在于：1. An automatic classification and recycling system for electronic waste based on artificial intelligence recognition, comprising a waste transmission module (1), an intelligent sorting module (2), a recycling processing module (3), an artificial intelligence recognition module (4), a dynamic sorting control system (5) and a data analysis and feedback module (6), characterized in that: 所述废弃物传送模块(1)包括传送带(101)、分配器(102)、推送装置(103)和速度调节系统(104)，用于将混合电子废弃物传送至识别区；The waste conveying module (1) comprises a conveyor belt (101), a distributor (102), a pushing device (103) and a speed regulating system (104), and is used to convey mixed electronic waste to an identification area; 所述人工智能识别模块(4)包括工业摄像头、光谱分析仪、3D激光扫描仪和中央处理器，通过图像识别、光谱分析和三维形态识别对电子废弃物进行多维度特征识别；The artificial intelligence recognition module (4) comprises an industrial camera, a spectrum analyzer, a 3D laser scanner and a central processing unit, and performs multi-dimensional feature recognition of electronic waste through image recognition, spectrum analysis and three-dimensional morphology recognition; 所述智能分拣模块(2)包括机械臂(201)、分拣推杆(202)和多个分拣箱(203)，能够根据识别模块(4)生成的分拣指令将不同种类的电子废弃物分拣至对应的分拣箱(203)；The intelligent sorting module (2) comprises a mechanical arm (201), a sorting push rod (202) and a plurality of sorting boxes (203), and is capable of sorting different types of electronic waste into corresponding sorting boxes (203) according to the sorting instructions generated by the recognition module (4); 所述动态分拣控制系统(5)包括PLC控制器和人机界面，用于对识别和分拣操作进行动态控制和参数调节；The dynamic sorting control system (5) includes a PLC controller and a human-machine interface, which are used to dynamically control and adjust parameters of the identification and sorting operations; 所述回收处理模块(3)包括破碎装置(301)、磁选装置(302)和稀贵金属提取装置(303)，用于对分拣后的电子废弃物进行物理和化学处理；The recycling module (3) comprises a crushing device (301), a magnetic separation device (302) and a rare metal extraction device (303), and is used to perform physical and chemical treatment on the sorted electronic waste; 所述数据分析与反馈模块(6)用于记录和分析识别结果及分拣操作的相关数据，并基于数据分析结果优化识别模型和分拣策略。The data analysis and feedback module (6) is used to record and analyze the recognition results and relevant data of the sorting operation, and optimize the recognition model and sorting strategy based on the data analysis results. 2.根据权利要求1所述的基于人工智能识别的电子废弃物自动分类与回收系统，其特征在于：所述工业摄像头用于拍摄电子废弃物的外观图像，生成的图像数据通过中央处理器中的深度学习模型进行分析，识别电子废弃物的外观形状、颜色和纹理特征。2. According to the electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1, it is characterized in that: the industrial camera is used to capture the appearance image of the electronic waste, and the generated image data is analyzed by the deep learning model in the central processing unit to identify the appearance shape, color and texture characteristics of the electronic waste. 3.根据权利要求1或2所述的基于人工智能识别的电子废弃物自动分类与回收系统，其特征在于：所述光谱分析仪通过光谱分析技术识别电子废弃物的元素成分，生成材料成分谱图，并结合中央处理器中的成分识别模型，区分出贵金属、普通金属及非金属材料。3. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 or 2 is characterized in that: the spectrum analyzer identifies the elemental composition of electronic waste through spectrum analysis technology, generates a material composition spectrum, and combines with the component recognition model in the central processing unit to distinguish between precious metals, ordinary metals and non-metallic materials. 4.根据权利要求1所述的基于人工智能识别的电子废弃物自动分类与回收系统，其特征在于：所述3D激光扫描仪通过激光扫描获取电子废弃物的三维形态数据，并在中央处理器中进行三维模型重建，识别复杂形状或内部结构特征，从而提高系统对电子废弃物内部元件的识别能力。4. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized in that: the 3D laser scanner obtains the three-dimensional morphological data of the electronic waste through laser scanning, and reconstructs the three-dimensional model in the central processing unit to identify complex shapes or internal structure features, thereby improving the system's ability to identify internal components of the electronic waste. 5.根据权利要求1所述的基于人工智能识别的电子废弃物自动分类与回收系统，其特征在于：所述智能分拣模块(2)包括多个机械臂(201)，每个机械臂(201)通过预设的分拣路径将识别后的废弃物移动至对应的分拣箱(203)；所述分拣推杆(202)用于将大件或形状复杂的物品推送至指定的分拣区域。5. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized in that: the intelligent sorting module (2) includes a plurality of mechanical arms (201), each mechanical arm (201) moves the identified waste to the corresponding sorting box (203) through a preset sorting path; the sorting push rod (202) is used to push large or complex-shaped items to a designated sorting area. 6.根据权利要求1所述的基于人工智能识别的电子废弃物自动分类与回收系统，其特征在于：所述动态分拣控制系统(5)能够根据识别结果和分拣任务的复杂度动态调整分拣路径和策略；当识别结果不确定或分拣箱(203)满载时，动态分拣控制系统(5)能够自动选择备用分拣箱，并重新生成分拣路径。6. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized in that: the dynamic sorting control system (5) can dynamically adjust the sorting path and strategy according to the recognition result and the complexity of the sorting task; when the recognition result is uncertain or the sorting box (203) is fully loaded, the dynamic sorting control system (5) can automatically select a spare sorting box and regenerate the sorting path. 7.根据权利要求1所述的基于人工智能识别的电子废弃物自动分类与回收系统，其特征在于：所述回收处理模块(3)中的破碎装置(301)用于将分拣后的大块电子废弃物粉碎成细小颗粒，并通过磁选装置(302)分离出含铁金属；所述稀贵金属提取装置(303)通过化学分离法提取金、银、钯等稀贵金属，生成高纯度金属产品。7. According to claim 1, the electronic waste automatic classification and recycling system based on artificial intelligence identification is characterized in that: the crushing device (301) in the recycling processing module (3) is used to crush the large pieces of electronic waste after sorting into fine particles, and separate the iron-containing metals through the magnetic separation device (302); the rare metal extraction device (303) extracts rare metals such as gold, silver, palladium, etc. through chemical separation method to generate high-purity metal products. 8.根据权利要求7所述的基于人工智能识别的电子废弃物自动分类与回收系统，其特征在于：所述破碎装置(301)包括多层破碎刀片、自动物料输送装置和防堵塞检测装置；当检测到破碎装置(301)中物料堵塞时，系统能够自动调整破碎刀片的旋转方向，并启动防堵塞装置清理堵塞物料。8. According to the electronic waste automatic classification and recycling system based on artificial intelligence recognition as described in claim 7, it is characterized in that: the crushing device (301) includes a multi-layer crushing blade, an automatic material conveying device and an anti-blocking detection device; when material blockage is detected in the crushing device (301), the system can automatically adjust the rotation direction of the crushing blade and start the anti-blocking device to clear the blocked material. 9.根据权利要求1所述的基于人工智能识别的电子废弃物自动分类与回收系统，其特征在于：所述数据分析与反馈模块(6)能够实时记录每次识别与分拣操作的状态信息，并根据记录的识别精度、分拣准确率和分拣时间进行数据分析；当识别精度或分拣准确率低于预设阈值时，数据分析与反馈模块(6)能够自动生成新的模型优化方案，并更新识别模型参数。9. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized in that: the data analysis and feedback module (6) can record the status information of each recognition and sorting operation in real time, and perform data analysis based on the recorded recognition accuracy, sorting accuracy and sorting time; when the recognition accuracy or sorting accuracy is lower than a preset threshold, the data analysis and feedback module (6) can automatically generate a new model optimization scheme and update the recognition model parameters. 10.根据权利要求1所述的基于人工智能识别的电子废弃物自动分类与回收系统，其特征在于：所述系统的动态分拣控制系统(5)能够通过人机界面实时显示分拣操作状态和回收统计信息，并允许操作人员通过人机界面调整分拣路径、修改分拣优先级或启用备用分拣模块，实现系统的动态控制与分拣策略的灵活调整。10. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized in that: the dynamic sorting control system (5) of the system can display the sorting operation status and recycling statistical information in real time through the human-machine interface, and allow the operator to adjust the sorting path, modify the sorting priority or enable the spare sorting module through the human-machine interface, so as to realize the dynamic control of the system and the flexible adjustment of the sorting strategy.
CN202411442648.4A 2024-10-16 2024-10-16 Automatic classification and recycling system of electronic waste based on artificial intelligence recognition Pending CN119368442A (en)

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CN120038122A (en) * 2025-04-24 2025-05-27 大连水泥集团有限公司 Intelligent material processing method and system based on image recognition
CN120346903A (en) * 2025-06-24 2025-07-22 河北远大中正生物科技有限公司 Multistage magnetic separation equipment for high-purity iron powder and magnetic separation method thereof
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CN120038122A (en) * 2025-04-24 2025-05-27 大连水泥集团有限公司 Intelligent material processing method and system based on image recognition
CN120462937A (en) * 2025-06-05 2025-08-12 和县隆盛精密机械有限公司 An AGV guided vehicle material intelligent conveying system and conveying method
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