Electronic waste automatic classification and recovery system based on artificial intelligence recognition

Abstract

The invention relates to an electronic waste automatic classification and recovery system based on artificial intelligence recognition. The electronic waste automatic classification and recovery system comprises a waste conveying module, an artificial intelligence 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 conveys the electronic waste to a recognition area in order, and the recognition module conducts multi-dimensional feature recognition on the waste through vision, spectrum and 3D morphological analysis technologies. And the intelligent sorting module sorts the materials to a specified sorting box according to the recognition result. The dynamic sorting control system can adjust a sorting path in real time according to task requirements. And the recovery processing module is used for further processing the sorted materials through a crushing device, a magnetic separation device and a rare and noble metal extraction device. And the data analysis and feedback module analyzes and optimizes data of identification and sorting operation, so that the processing efficiency and the recovery rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of waste treatment, and more specifically to an automatic classification and recycling system for electronic waste based on artificial intelligence recognition. Background Art

[0002] With the rapid development of science and technology and the popularization of electronic products, a large amount of electronic waste (such as old mobile phones, household appliances and hybrid circuit boards) is generated every year around the world. These electronic wastes contain a large amount of harmful substances, such as heavy metals and difficult-to-degrade plastics. Improper disposal will cause serious harm to the environment and human health. However, electronic waste also contains high-value rare and precious metals (such as gold, silver, platinum, etc.) and renewable materials (such as copper, plastics, etc.), which have high resource recovery potential.

[0003] The current electronic waste recycling method is still mainly manual sorting or semi-automatic classification, with low sorting efficiency and accuracy, and the following problems exist:

[0004] Low identification efficiency: Faced with electronic waste of various forms and complex components, traditional methods cannot effectively distinguish the different components.

[0005] Insufficient recycling accuracy: Traditional sorting methods cannot accurately separate high-value components in waste, resulting in waste of resources.

[0006] Low degree of automation: Most recycling systems rely on manual operation, which is prone to misjudgment, misclassification and other problems.

[0007] Therefore, there is an urgent need for an automatic classification and recycling system based on artificial intelligence technology that can effectively improve recognition efficiency, sorting accuracy and system automation level, and realize intelligent recycling of electronic waste. Summary of the invention

[0008] In order to solve the above problems, the present invention provides an automatic classification and recycling system for electronic waste based on artificial intelligence recognition. By integrating visual recognition, spectral analysis and deep learning models, it can identify and classify complex and diverse electronic waste in real time, and recycle it efficiently through intelligent sorting devices to maximize resource reuse.

[0009] To achieve the above object, the present invention provides the following technical solutions, which mainly include:

[0010] Waste transport module

[0011] This module is used to transport mixed electronic waste to the identification area in sequence and make preliminary distribution to ensure that each waste can be accurately detected by the identification module. The waste conveying module includes a conveyor belt, a distributor, a push device and a speed adjustment system. The system can dynamically adjust the conveying speed and distribution order according to the shape, size and identification processing capacity of the material.

[0012] Artificial intelligence recognition module

[0013] This module is the core component of the system, integrating multi-modal sensors (such as industrial cameras, spectrometers, 3D laser scanners) and a central processor, and is capable of multi-dimensional feature recognition of electronic waste. The recognition module includes:

[0014] Visual recognition unit: collects image data through industrial cameras and uses deep learning algorithms (such as CNN, ResNet, etc.) to identify the shape, color and texture of materials.

[0015] Spectral analysis unit: Detect the elemental composition and molecular characteristics of materials through a spectrometer to distinguish between metals and non-metals, and precious metals and common metals.

[0016] 3D scanning unit: Use a laser scanner to perform three-dimensional imaging and morphological reconstruction of materials, and identify the internal structure and surface features of the materials.

[0017] Intelligent sorting module

[0018] After the recognition module completes the material recognition, the system will generate sorting instructions, which will be executed by the intelligent sorting module. The intelligent sorting module includes multiple robotic arms, sorting push rods, mobile sorting units, and a dynamic sorting path planning system. The module can accurately sort different types of materials into designated sorting boxes based on the recognition results, and adjust the sorting path and strategy in real time.

[0019] Dynamic sorting control system

[0020] The PLC (Programmable Logic Controller) and Human Machine Interface (HMI) control system is used to realize dynamic control and parameter adjustment of each sorting module and transmission module. The control system can automatically adjust the sorting path according to the recognition results, sorting tasks and sorting box status, and perform secondary sorting operations when the recognition results are uncertain.

[0021] Recycling module

[0022] This module is used to further physically and chemically process the electronic waste that has been initially sorted. The processing module includes a crushing device, a magnetic separation device, and a rare metal extraction device. The crushing device crushes large electronic components into small particles, the magnetic separation device separates iron-containing metals, and the rare metal extraction device extracts rare metals such as gold, silver, and palladium from electronic components through chemical methods.

[0023] Data analysis and feedback module

[0024] This module is used to record and analyze the operation data and sorting results of the recognition module, and optimize the model through machine learning and data mining technology. The data analysis module can dynamically update the recognition model based on historical data and newly emerging types of electronic waste, thereby improving the accuracy and adaptability of system recognition.

[0025] It can be seen from the above technical solution that compared with the prior art, the present invention has the following beneficial effects:

[0026] (1) High recognition accuracy: By integrating multimodal recognition technologies (such as visual recognition, spectral analysis, and 3D scanning), accurate classification of electronic waste and identification of complex materials are achieved.

[0027] (2) Improved sorting efficiency: The dynamic sorting control system can automatically adjust the sorting path according to the real-time tasks and sorting box status, thereby improving the overall efficiency of the sorting operation.

[0028] (3) High resource recovery rate: The recycling and processing module adopts multiple processing methods such as crushing, magnetic separation and rare metal extraction, which can effectively recover rare metals and other renewable materials in electronic waste.

[0029] (4) System intelligence: The data analysis and feedback module has online learning and model adaptive optimization functions, which can continuously optimize the recognition and sorting strategies based on historical data and improve the long-term operation effect of the system.

[0030] (5) Significant environmental benefits: The system can effectively reduce environmental pollution during the treatment of electronic waste and improve the recycling rate of resources, with good environmental and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0032] Figure 1 It is a system framework diagram of the whole invention.

[0033] Figure 2 This is a system framework diagram of the waste conveying module of the present invention.

[0034] Figure 3 It is a system framework diagram of the intelligent sorting module and the recycling processing module of the present invention.

[0035] Explanation of the reference numerals: 1-waste conveying module, 101-conveyor belt, 102-distributor, 103-pushing device, 104-speed adjustment system, 2-intelligent sorting module, 201-mechanical arm, 202-sorting push rod, 203-sorting box, 3-recycling and processing module, 301-crushing device, 302-magnetic separation device, 303-rare and precious metal extraction device, 4-artificial intelligence recognition module, 5-dynamic sorting control system, 6-data analysis and feedback module. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Example 1

[0038] Automatic classification and recycling system of electronic waste based on artificial intelligence recognition, such as Figures 1 to 3 As shown, including:

[0039] Waste conveyor module 1

[0040] 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 the mixed electronic waste to the identification area in an orderly manner.

[0041] Conveyor belt 101: used for carrying and conveying mixed electronic waste. According to the shape and quantity of the electronic waste, the width and conveying speed of the conveyor belt 101 can be adjusted to avoid overlapping or dumping of different types of waste.

[0042] Distributor 102: Located at the beginning of the conveyor belt 101, it is used to initially distribute large and complex-shaped waste to different conveyor belt areas. Distributor 102 controls the distribution order of different wastes through a mechanical arm or a push rod.

[0043] Pushing device 103: The pushing device 103 is disposed at the end of the conveyor belt 101 and is used to push the identified electronic waste to the intelligent sorting module 2. The pushing device 103 can adjust the pushing force according to the size and weight of different materials to ensure that the electronic waste can be stably moved to the next link.

[0044] Speed ​​adjustment system 104: used to dynamically adjust 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 waste accumulation.

[0045] Artificial Intelligence Recognition Module 4

[0046] The artificial intelligence recognition module 4 is composed of multimodal sensors such as industrial cameras, spectrometers, 3D laser scanners and a central processor 303. The module achieves accurate recognition of electronic waste by integrating multiple recognition technologies.

[0047] Industrial camera 301: used to capture the surface image of electronic waste and identify its shape, color and surface texture features through deep learning algorithms such as convolutional neural network (CNN). The image data captured by camera 301 is transmitted to central processor 303 for preliminary image analysis and shape classification.

[0048] Spectrum Analyzer 302: used to detect the elemental composition of electronic waste. By analyzing the absorption and reflection characteristics of spectra at different wavelengths, the system can identify the elemental composition of the material and distinguish between metals and non-metals, rare metals and common metals.

[0049] 3D laser scanner: used to obtain three-dimensional morphological data of electronic waste. Through laser scanning technology, the system can reconstruct the three-dimensional model of electronic waste and identify its complex structure and internal components.

[0050] Central Processing Unit 303: Integrates deep learning algorithms and data analysis modules to analyze and fuse data collected by multimodal sensors and generate final recognition results. Central Processing Unit 303 can simultaneously process image features, spectral features, and three-dimensional features, and generate specific sorting instructions based on the recognition results.

[0051] Intelligent sorting module 2

[0052] The intelligent sorting module 2 is composed of multiple mechanical arms 201 , a sorting push rod 202 and a sorting box 203 , and can perform corresponding sorting operations according to the results of the recognition module 4 .

[0053] Robotic arm 201: The system is equipped with multiple robotic arms 201. Each robotic arm 201 determines the best sorting path through a control algorithm and accurately moves the specified material to the corresponding sorting box 203. The robotic arm 201 has 6 or more axes of multi-degree of freedom and can flexibly handle the grasping and handling of complex materials.

[0054] Sorting push rod 202: Located at the end of the conveyor belt 101, it can push large objects to the designated sorting area. The push rod 202 is driven by a servo motor and can adjust the thrust according to the size and weight of different objects to ensure that the objects enter the sorting area stably.

[0055] Sorting box 203: The system is equipped with multiple sorting boxes 203, each of which is used to collect materials of a specified category, such as plastic, copper, aluminum, rare metals, etc. The sorting box 203 is equipped with a weight sensor and a full load detection device. When the capacity of the sorting box 203 reaches the set value, the system can automatically issue an alarm and switch to the spare sorting box 203.

[0056] Dynamic sorting control system 5

[0057] The dynamic sorting control system 5 uses a PLC controller 601 and a human-machine interface 602 to perform real-time control on the operation of each module.

[0058] PLC controller 601: connects various mechanical components such as conveyor belt 101, mechanical arm 201, pushing device 103, etc., and realizes dynamic control of sorting module 2 by receiving sorting instructions from recognition module 4. PLC controller 601 can dynamically adjust the parameters of each module according to real-time feedback data such as recognition accuracy and transmission speed.

[0059] Human-machine interface 602: The operator can monitor the system status in real time through the human-machine interface 602, and adjust the sorting path, modify the sorting priority or enable the spare sorting module 2 as needed.

[0060] Recycling module 3

[0061] The recycling processing module 3 is used to perform further physical and chemical processing on the sorted electronic waste to achieve efficient extraction of rare and precious metals and other recyclable materials.

[0062] Crushing device 301: Crushing device 301 uses multi-layer crushing blades to crush large electronic waste into fine particles, which is convenient for subsequent magnetic separation and chemical treatment. Crushing device 301 is equipped with an anti-blocking device. When material blockage is detected, the system can automatically adjust the blade direction and activate the anti-blocking function for cleaning.

[0063] Magnetic separation device 302: The iron-containing materials in the electronic waste are separated by the magnetic separation device 302 and automatically discharged into the corresponding recycling container.

[0064] Precious metal extraction device 303: adopts hydrometallurgical or chemical separation technology to extract precious metals such as gold, silver, palladium, etc. from the sorted fine particles and generate high-purity metal products.

[0065] Data Analysis and Feedback Module 6

[0066] The data analysis and feedback module 6 is used to record the working status of the recognition module 4 and the sorting module 2, and optimize the recognition model based on the data analysis results.

[0067] Real-time data recording: The data analysis and feedback module 6 can record the status information of each identification and sorting operation and store it in the central database.

[0068] Identification model optimization: When the recognition accuracy or sorting accuracy is lower than the preset threshold, the data analysis and feedback module 6 can automatically generate a new model optimization solution and update the recognition model parameters. This module is based on a machine learning algorithm and can continuously learn from historical data and dynamically optimize the recognition strategy.

[0069] The specific workflow is as follows:

[0070] 1. Waste input and initial transfer process

[0071] 1.1 Waste input and initial transport

[0072] The electronic waste is placed on the conveyor belt 101 of the waste conveying module 1. After the system is started, the conveyor belt 101 conveys the mixed electronic waste into the initial distribution area at a set speed.

[0073] When the conveyor belt 101 starts running, the distributor 102 is started to ensure that different types of electronic waste are evenly distributed to avoid accumulation or overlap, thereby ensuring the accuracy of subsequent identification.

[0074] The pushing device 103 is located at the end of the conveyor belt 101 and is used to push materials of a specific shape or size to the identification area so as to optimize the subsequent identification efficiency.

[0075] 1.2 Speed ​​regulation

[0076] The speed adjustment system 104 can dynamically adjust the speed of the conveyor belt 101 according to the processing capability of the artificial intelligence recognition module 4 .

[0077] If the processing capacity of the identification module 4 reaches the upper limit or the length of the waste identification queue exceeds a predetermined value, the system will automatically slow down the transmission speed to ensure that each object can be identified without being missed or accumulated.

[0078] 2. E-waste identification process

[0079] 2.1 Visual Recognition

[0080] After the electronic waste enters the artificial intelligence recognition module 4, the industrial camera 301 first collects the surface image.

[0081] The image data is transmitted to the central processor 303 and analyzed by a deep learning model to preliminarily identify the material type such as plastic, metal, ceramic, etc.

[0082] 2.2 Spectral analysis

[0083] After visual identification, the spectrum analyzer 302 is activated to further detect the elemental composition of the material by analyzing the spectral reflectance characteristics at different wavelengths.

[0084] 2.33D scanning and internal structure recognition

[0085] For electronic waste with complex shapes, 3D laser scanners are used to obtain three-dimensional morphological data of the material.

[0086] The recognition result is processed by a three-dimensional reconstruction algorithm in the central processor 303 and fused with the surface feature and spectral feature data to form a complete multi-modal feature vector.

[0087] 2.4 Recognition results and sorting instruction generation

[0088] The central processing unit 303 fuses the images, spectra and three-dimensional features collected by each sensor, and inputs them into a pre-trained deep learning model to generate recognition results.

[0089] The system generates specific sorting instructions based on the recognition results and sends the instructions to the intelligent sorting module 2.

[0090] 3. Intelligent sorting process

[0091] 3.1 Intelligent sorting module starts

[0092] After the intelligent sorting module 2 receives the recognition result, the system controls the robot arm 201 to perform sorting operations according to the type and size of the object.

[0093] The robot arm 201 automatically selects the best sorting path according to the path planning generated by the recognition module 4 , and grabs the waste of a specific category into the corresponding sorting box 203 .

[0094] 3.2 Sorting push rod assisted sorting

[0095] For objects with complex shapes or large sizes, the system pushes them into the designated sorting area through the sorting push rod 202 .

[0096] Each sorting box 203 is provided with a full load detection device, and triggers an alarm signal when the capacity reaches a set value.

[0097] 3.3 Dynamic sorting control and path adjustment

[0098] If the sorting box 203 is full, or the object recognition result has a high uncertainty, the dynamic sorting control system 5 will automatically adjust the sorting path and transfer the object to the spare sorting box 203 for further processing.

[0099] 4. Recycling Process

[0100] 4.1 Material crushing

[0101] The electronic waste that has been preliminarily sorted is sent to the crushing device 301 in the recycling processing module 3 .

[0102] The crushing device 301 crushes large electronic components into small particles by using multiple layers of crushing blades.

[0103] 4.2 Magnetic separation and separation

[0104] The magnetic separation device 302 separates the ferrous material from the crushed material and automatically discharges it into a designated recycling bin.

[0105] 4.3 Extraction of rare and precious metals

[0106] The sorted fine particle materials are chemically separated by the rare metal extraction device 303 .

[0107] The waste liquid after extraction will be treated harmlessly through a treatment device, and the extraction chemicals will be recycled.

[0108] 5. Data analysis and feedback process

[0109] 5.1 Real-time data recording and analysis

[0110] The data analysis and feedback module 6 can record relevant data of the identification and sorting operations such as identification accuracy, sorting accuracy, recovery rate, etc.

[0111] The data analysis module 6 continuously optimizes the recognition model by accumulating historical data.

[0112] 5.2 Model Self-Learning and Optimization

[0113] The system can automatically retrain the model and update parameters based on the results of each operation.

[0114] 5.3 Sorting Path and Strategy Optimization

[0115] The data analysis module 6 adopts a reinforcement learning algorithm to automatically adjust the sorting path according to the real-time data and changes in sorting efficiency.

[0116] In order to further improve the efficiency and accuracy of the system in the treatment of complex electronic waste, the present invention achieves accurate classification and efficient recycling of electronic waste with high mixing degree and complex internal structure by optimizing the dynamic sorting control system 5, improving the material identification model 4 and introducing a multi-level sorting strategy. The following is a further expanded specific implementation plan.

[0117] Example 2: Optimization strategy for dynamic sorting control system

[0118] Dynamic path planning and sorting strategy management

[0119] The dynamic sorting control system 5 adopts advanced path planning algorithms, such as Dijkstra algorithm, A algorithm and reinforcement learning strategy, to realize intelligent path planning and optimal resource allocation of the robot arm 201 in the sorting task.

[0120] Implementation of the path planning algorithm: The path planning algorithm can automatically generate the optimal sorting path based on the waste type, size and location data provided by the recognition module 4. The system will avoid obstacles and other robotic arms 201 that are sorting according to the priority of the current task to ensure that the sorting operation is completed in the shortest time.

[0121] Task priority management: The dynamic sorting control system 5 can assign different sorting priorities to different types of waste. For example, when rare metals such as gold and palladium are detected, the system will prioritize the robot arm 201 for sorting operations and skip other low-priority materials.

[0122] Real-time adjustment of sorting strategy: When the capacity of the sorting box 203 is close to saturation, the system can automatically adjust the sorting path and transfer the same type of materials to the spare sorting box 203.

[0123] Introduction of multi-level sorting strategy

[0124] For electronic waste with complex internal structures or mixed materials, the system introduces a multi-level sorting strategy. The multi-level sorting module can perform subdivision processing based on the preliminary classification results of the materials.

[0125] Primary sorting: After the identification module 4 completes the identification, the sorting module 2 classifies the waste into major categories such as metal, plastic, and mixed electronic components, and guides them to various primary sorting boxes 203 through different sorting paths.

[0126] Secondary sorting: The system further subdivides the results of the primary sorting. For example, for mixed metal materials, the system will classify them through the magnetic separation device 302 and the rare metal extraction device 303 to separate rare metals such as gold, silver, and platinum from common metals such as iron and aluminum.

[0127] Tertiary sorting and subdivision of complex materials: For multi-layer composite materials, the system adopts a layer-by-layer separation strategy. The robot arm 201 decomposes the composite material into single material layers through the crushing device 301, and accurately identifies and classifies each separated layer.

[0128] Extended design of sorting module 2

[0129] In order to adapt to different types and sizes of electronic waste, the present 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 form of the waste, thereby improving sorting accuracy and efficiency.

[0130] Replaceable grippers: The system is equipped with various types of grippers such as small component grippers, large material grippers and rotatable suction cups, which can be quickly replaced through the automatic tool switching system.

[0131] Suction cup and vacuum adsorption system: For lightweight materials with irregular shapes, the system uses a vacuum adsorption system to achieve stable grasping.

[0132] Laser cutting device and decomposition tool: The system is equipped with a precision laser cutting device and decomposition tool for the separation of multi-layer composite materials.

[0133] Example 3: Accuracy optimization of recognition module

[0134] In order to improve the accuracy of the system in identifying complex mixed materials, the present invention introduces the fusion of multiple recognition technologies and the optimization of deep learning models in the recognition module 4.

[0135] Fusion of multimodal recognition technologies

[0136] The system integrates visual recognition, spectral analysis 302 and 3D morphological recognition technologies, and realizes multi-dimensional feature analysis of complex electronic waste through feature fusion models.

[0137] Feature fusion model: A multimodal feature fusion model is constructed in the central processing unit 303 to fuse image features, spectral features and three-dimensional morphological features.

[0138] Feature selection and dimensionality reduction: The system uses principal component analysis (PCA) and LDA and other dimensionality reduction algorithms to remove redundant information and improve recognition speed.

[0139] Multi-level feature extraction of complex materials: Recognition module 4 extracts and identifies the features of each layer independently by gradually peeling off the surface layers.

[0140] Training and Optimization of Deep Learning Models

[0141] The system uses a variety of deep learning models such as convolutional neural networks, residual networks and Transformer-based models in the central processor 303 to classify complex materials.

[0142] The trade-off between material identification accuracy and speed

[0143] When identifying complex materials, the system achieves a balance between accuracy and speed by combining multi-level models and lightweight models.

[0144] Example 4: Data analysis and feedback mechanism

[0145] Data recording and storage

[0146] The system can record data for each identification, sorting and processing operation, and store and analyze historical data through the data analysis module 6.

[0147] Real-time feedback and model optimization

[0148] The system can provide real-time feedback on the recognition and sorting results through the data analysis module 6.

[0149] Sorting path and strategy optimization

[0150] The data analysis module 6 can optimize the sorting strategy based on the data of the historical sorting paths.

[0151] In summary, the present invention, through the integration of multimodal recognition technology, the introduction of multi-level sorting strategies, and the design of data analysis and dynamic feedback mechanisms, can achieve highly automated, intelligent, and precise sorting and recycling in complex electronic waste treatment, providing an innovative solution for the environmentally friendly treatment and resource recovery of electronic waste.

[0152] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred 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 parts can be referred to the method part.

[0153] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

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: 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; 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; 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); 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; The recycling processing 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; 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. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized by: The industrial camera is used to capture images of the appearance of electronic waste. The generated image data is analyzed by a deep learning model in a central processing unit to identify the appearance shape, color and texture features of the electronic waste.

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 distinguishes between precious metals, ordinary metals and non-metallic materials in combination with a component recognition model in a central processor.

4. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized by: The 3D laser scanner acquires the three-dimensional morphological data of the electronic waste through laser scanning, and reconstructs the three-dimensional model in the central processor to identify complex shapes or internal structural features, thereby improving the system's ability to identify internal components of the electronic waste.

5. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized by: The intelligent sorting module (2) comprises a plurality of mechanical arms (201), each of which moves the identified waste to a corresponding sorting box (203) via a preset sorting path; the sorting push rod (202) is used to push large or complex-shaped objects to a designated sorting area.

6. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized by: 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. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized by: The crushing device (301) in the recycling 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 and precious metal extraction device (303) extracts rare and precious metals such as gold, silver, palladium, etc. through a chemical separation method to generate high-purity metal products.

8. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 7 is characterized by: The crushing device (301) comprises a multi-layer crushing blade, an automatic material conveying device and an anti-blocking detection device; when material blockage in the crushing device (301) is detected, the system can automatically adjust the rotation direction of the crushing blade and start the anti-blocking device to clear the blocked material.

9. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized by: 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 solution and update the recognition model parameters.

10. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 is characterized by: The dynamic sorting control system (5) of the system can display the sorting operation status and recycling statistical information in real time through a human-machine interface, and allows operators to adjust the sorting path, modify the sorting priority or enable a spare sorting module through the human-machine interface, thereby realizing dynamic control of the system and flexible adjustment of the sorting strategy.

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