Automatic classification and recycling system for electronic waste based on artificial intelligence recognition

Abstract

An automatic classification and recycling system for electronic waste based on artificial intelligence recognition, comprising a waste conveying module (1), an artificial intelligence recognition module (4), an intelligent sorting module (2), a dynamic sorting control system (5), a recycling processing module (3), and a data analysis and feedback module (6). The waste conveying module (1) orderly conveys electronic waste to a recognition area; the artificial intelligence recognition module (4) performs multi-dimensional feature recognition on the waste by means of visual, spectral, and 3D morphological analysis techniques; the intelligent sorting module (2) sorts materials to designated sorting boxes (203) on the basis of recognition results; the dynamic sorting control system (5) can adjust a sorting path in real time on the basis of a task requirement; the recycling processing module (3) further processes the sorted materials by means of crushing devices (301), magnetic separation devices (302), and rare and precious metal extraction devices (303); and the data analysis and feedback module (6) analyzes and optimizes data of recognition and sorting operations. Thus, the processing efficiency is improved, and the recycling rate is increased.

Description

AI-based automated sorting and recycling system for electronic waste Technical Field

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

[0002] With the rapid development of technology and the widespread use of electronic products, a large amount of electronic waste (such as discarded mobile phones, household appliances, and mixed circuit boards) is generated globally every year. This electronic waste contains a large number of hazardous substances, such as heavy metals and non-biodegradable plastics, and improper disposal can cause serious harm to the environment and human health. However, electronic waste also contains high-value rare and precious metals (such as gold, silver, and platinum) and renewable materials (such as copper and plastics), and has high resource recycling potential.

[0003] Current electronic waste recycling methods still rely mainly on manual sorting or semi-automated classification, resulting in low sorting efficiency and accuracy, and the following problems:

[0004] Low identification efficiency: Traditional methods cannot effectively distinguish the different components in electronic waste with its various forms and complex composition.

[0005] Insufficient recycling precision: Traditional sorting methods cannot accurately separate high-value components from waste, leading to resource waste.

[0006] Low level of automation: Most recycling systems rely on manual operation, which can easily lead to problems such as misjudgment and missorting.

[0007] Therefore, there is an urgent need for an automated sorting and recycling system based on artificial intelligence technology, which can effectively improve identification efficiency, sorting accuracy and system automation level, and realize intelligent recycling of electronic waste. Summary of the Invention

[0008] To address the aforementioned issues, this invention provides an automated electronic waste sorting and recycling system 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 efficiently recycle it through intelligent sorting devices, maximizing resource reuse.

[0009] To achieve the above objectives, the present invention provides the following technical solution, which mainly includes:

[0010] Waste Conveying Module

[0011] This module is used to sequentially transport mixed electronic waste to the identification area and perform initial distribution, ensuring that each piece of waste can be accurately detected by the identification module. The waste conveying module includes a conveyor belt, distributor, pushing device, and speed regulation system. The system can dynamically adjust the conveying speed and distribution sequence according to the shape, size, and identification processing capacity of the materials.

[0012] Artificial intelligence recognition module

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

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

[0015] Spectroscopic analysis unit: Detects the elemental composition and molecular properties of materials using a spectrometer, distinguishing between metals and non-metals, and between precious metals and common metals.

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

[0017] Intelligent sorting module

[0018] After the identification module completes material identification, the system generates sorting instructions, which are then executed by the intelligent sorting module. The intelligent sorting module includes multiple robotic arms, sorting pushers, moving sorting units, and a dynamic sorting path planning system. This module can accurately sort different categories of materials into designated sorting bins based on the identification results and adjust the sorting path and strategy in real time.

[0019] Dynamic sorting control system

[0020] The system employs a PLC (Programmable Logic Controller) and a Human-Machine Interface (HMI) to achieve dynamic control and parameter adjustment of each sorting and transmission module. The control system can automatically adjust the sorting path based on the identification results, sorting tasks, and sorting box status, and perform secondary sorting operations when the identification results are uncertain.

[0021] Recycling module

[0022] This module is used for further physical and chemical processing of the initially sorted electronic waste. The processing module includes a crushing unit, a magnetic separator, and a rare and precious metal extraction unit. The crushing unit pulverizes large electronic components into smaller particles, the magnetic separator separates ferrous metals, and the rare and precious metal extraction unit extracts rare and precious metals such as gold, silver, and palladium from the electronic components using chemical methods.

[0023] Data Analysis and Feedback Module

[0024] This module records and analyzes the operational data and sorting results of the identification module, optimizing the model through machine learning and data mining techniques. The data analysis module dynamically updates the identification model based on historical data and newly emerging types of electronic waste, improving the system's accuracy and adaptability.

[0025] As can be seen from the above technical solution, compared with the prior art, the beneficial effects of the present invention are as follows:

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

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

[0028] (3) High resource recovery rate: The recycling module adopts a variety of processing methods such as crushing, magnetic separation and rare and precious metal extraction, which can effectively recover rare and precious metals and other renewable materials from 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 identification and sorting strategy 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 electronic waste treatment process and improve the resource reuse rate, thus having good environmental and economic benefits. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

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

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

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

[0035] Explanation of reference numerals in the attached drawings: 1-Waste conveying module, 101-Conveyor belt, 102-Distributor, 103-Pushing device, 104-Speed ​​regulation system, 2-Intelligent sorting module, 201-Robotic 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 Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1

[0038] An automated electronic waste sorting and recycling system based on artificial intelligence recognition, as shown in Figures 1 to 3, includes:

[0039] Waste Conveying Module 1

[0040] The waste conveying module 1 includes a conveyor belt 101, a distributor 102, a pushing device 103, and a speed regulation system 104, which are used to orderly transport mixed electronic waste to the identification area.

[0041] Conveyor belt 101: Used to carry and transport mixed electronic waste. The width and conveying speed of conveyor belt 101 are adjustable according to the shape and quantity of electronic waste to avoid overlapping or dumping of different types of waste.

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

[0043] Pushing device 103: The pushing device 103 is located 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 move stably to the next stage.

[0044] Speed ​​regulation system 104: Used to dynamically adjust the running speed of conveyor belt 101. When the processing speed of identification module 4 is low, speed regulation 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 consists of multimodal sensors such as industrial cameras, spectral analyzers, 3D laser scanners, and a central processing unit 303. This module achieves accurate identification of electronic waste by integrating multiple recognition technologies.

[0047] Industrial camera 301: Used to capture surface images of electronic waste and identify its shape, color, and surface texture features using deep learning algorithms such as convolutional neural networks (CNNs). The image data captured by camera 301 is transmitted to central processing unit 303 for preliminary image analysis and shape classification.

[0048] Spectrometer 302: Used to detect the elemental composition of electronic waste. By analyzing the absorption and reflection characteristics of different wavelength spectra, the system can identify the elemental composition of materials and distinguish between metals and non-metals, and rare and precious metals and common metals.

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

[0050] Central Processing Unit 303: Integrates deep learning algorithms and data analysis modules to analyze and fuse data acquired by multimodal sensors and generate the final recognition result. Central Processing Unit 303 can simultaneously process image features, spectral features, and 3D features, and generate specific sorting instructions based on the recognition result.

[0051] Intelligent sorting module 2

[0052] The intelligent sorting module 2 consists of multiple robotic arms 201, sorting push rods 202, and sorting boxes 203, and can perform corresponding sorting operations based on 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 optimal sorting path through a control algorithm and precisely moves the specified materials to the corresponding sorting bin 203. The robotic arms 201 have six or more degrees of freedom, enabling them to flexibly handle the gripping and handling of complex materials.

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

[0055] Sorting bin 203: The system is equipped with multiple sorting bins 203, each used to collect materials of a specified category, such as plastics, copper, aluminum, rare and precious metals, etc. The sorting bin 203 is equipped with a weight sensor and a full load detection device. When the capacity of the sorting bin 203 reaches the set value, the system can automatically issue an alarm and switch to the standby sorting bin 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 control the operation of each module in real time.

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

[0059] Human-Machine Interface 602: Operators 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 backup sorting module 2 as needed.

[0060] Recycling processing module 3

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

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

[0063] Magnetic separator 302: The magnetic separator 302 separates iron-containing materials from electronic waste and automatically discharges them into the corresponding recycling container.

[0064] Rare and precious metal extraction device 303: It uses hydrometallurgical or chemical separation technology to extract rare and precious metals such as gold, silver, and palladium from 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 identification module 4 and the sorting module 2, and to optimize the identification 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] Recognition Model Optimization: When the recognition accuracy or sorting accuracy falls below a preset threshold, the data analysis and feedback module 6 can automatically generate a new model optimization scheme and update the recognition model parameters. This module is based on machine learning algorithms 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 conveying process

[0071] 1.1 Waste input and initial transport

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

[0073] As the conveyor belt 101 starts running, the distributor 102 is activated to ensure that different types of electronic waste are evenly distributed, avoiding accumulation or overlap, and 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 recognition area in order to optimize the subsequent recognition efficiency.

[0075] 1.2 Speed ​​Adjustment

[0076] The speed regulation 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 its limit or the waste identification queue length exceeds the predetermined value, the system will automatically slow down the transmission speed to ensure that each object can be identified without being missed or piling up.

[0078] 2. Electronic waste identification process

[0079] 2.1 Visual Recognition

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

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

[0082] 2.2 Spectral Analysis

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

[0084] 2.3 3D Scanning and Internal Structure Identification

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

[0086] The recognition results are processed by the three-dimensional reconstruction algorithm in the central processing unit 303 and fused with surface feature and spectral feature data to form a complete multimodal 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 acquired 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 Start-up of the intelligent sorting module

[0092] After receiving the recognition result, the intelligent sorting module 2 controls the robotic arm 201 to perform sorting operations according to the type and size of the objects.

[0093] Based on the path planning generated by the recognition module 4, the robotic arm 201 automatically selects the best sorting path and grabs specific types of waste into the corresponding sorting bins 203.

[0094] 3.2 Sorting pusher-assisted sorting

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

[0096] Each sorting box 203 is equipped with a full load detection device, which 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 if the object recognition result has a high degree of 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] After initial sorting, the electronic waste is sent to the crushing device 301 in the recycling module 3.

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

[0103] 4.2 Magnetic Separation

[0104] The magnetic separator 302 separates the iron-containing materials from the crushed material and automatically discharges them into the designated recycling bin.

[0105] 4.3 Extraction of rare and precious metals

[0106] The sorted fine particulate material is chemically separated by the rare and precious metal extraction device 303.

[0107] The extracted waste liquid will be treated to render it harmless through a treatment device and then recycled to extract chemicals.

[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, and recovery rate.

[0111] 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 the parameters based on the results of each operation.

[0114] 5.3 Sorting Path and Strategy Optimization

[0115] The data analysis module 6 uses reinforcement learning algorithms to automatically adjust the sorting path based on real-time data and changes in sorting efficiency.

[0116] To further improve the efficiency and accuracy of the system in the treatment of complex electronic waste, this invention achieves accurate classification and efficient recycling of electronic waste with high mixing and complex internal structures 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 implementation scheme.

[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 employs advanced path planning algorithms, such as Dijkstra's algorithm, A algorithm, and reinforcement learning strategies, to achieve intelligent path planning and resource optimization allocation for the robotic arm 201 in sorting tasks.

[0120] Implementation of the path planning algorithm: Based on the waste type, size, and location data provided by the identification module 4, the path planning algorithm can automatically generate the optimal sorting path. 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 possible 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 and precious metals such as gold and palladium are detected, the system will prioritize the robotic arm 201 for sorting and skip other low-priority materials.

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

[0123] Introduction of multi-level sorting strategy

[0124] For electronic waste with complex internal structures or a mixture of multiple materials, the system introduces a multi-level sorting strategy. The multi-level sorting module can further subdivide the processing based on the initial classification results of the materials.

[0125] Primary sorting: After the identification module 4 completes the identification, the sorting module 2 divides the waste into major categories such as metal, plastic and mixed electronic components, and guides them to each primary sorting box 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 and separate them using a magnetic separator 302 and a rare and precious metal extraction device 303, separating rare and precious metals such as gold, silver, and platinum from common metals such as iron and aluminum.

[0127] Three-stage sorting and detailed classification of complex materials: For multi-layer composite materials, the system employs a layer-by-layer separation strategy. The robotic arm 201 uses the crushing device 301 to break down the composite material into individual material layers, and performs precise identification and classification on each separated layer.

[0128] Extended design of sorting module 2

[0129] To accommodate different types and sizes of electronic waste, this invention introduces replaceable grippers, suction cups, and cutting devices in sorting module 2. Each sorting unit can automatically switch tools according to the type and shape of the waste, thereby improving sorting accuracy and efficiency.

[0130] Replaceable grippers: The system is equipped with a variety of grippers such as grippers for small components, grippers for large materials, and rotatable suction cups, which can be quickly changed through an 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 gripping.

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

[0133] Example 3: Accuracy Optimization of the Recognition Module

[0134] To improve the accuracy of the system in identifying complex mixed materials, this invention introduces the fusion of multiple identification technologies and the optimization of a deep learning model in identification module 4.

[0135] Fusion of multimodal recognition technologies

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

[0137] Feature fusion model: A multimodal feature fusion model was built 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 dimensionality reduction algorithms such as principal component analysis (PCA) and LDA to remove redundant information and improve recognition speed.

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

[0140] Training and optimization of deep learning models

[0141] The system employs various deep learning models, such as convolutional neural networks, residual networks, and Transformer-based models, in the central processing unit 303 to classify complex materials.

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

[0143] When identifying complex materials, the system achieves a balance between accuracy and speed by combining multi-level 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 identification and sorting results through the data analysis module 6.

[0149] Sorting Path and Strategy Optimization

[0150] Data analysis module 6 can optimize sorting strategies based on historical sorting path data.

[0151] In summary, this 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, enables highly automated, intelligent, and precise sorting and recycling in complex electronic waste treatment, providing an innovative solution for the environmentally friendly treatment and resource recycling of electronic waste.

[0152] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0153] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those 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 invention. Therefore, the invention is not 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

An automated electronic waste sorting and recycling system based on artificial intelligence recognition includes a waste conveying module (1), an intelligent sorting module (2), a recycling and 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) includes a conveyor belt (101), a distributor (102), a pushing device (103), and a speed regulation system (104) for conveying mixed electronic waste to the identification area; The artificial intelligence recognition module (4) includes an industrial camera, a spectrum analyzer, a 3D laser scanner and a central processing unit, which performs multi-dimensional feature recognition of electronic waste through image recognition, spectrum analysis and three-dimensional morphology recognition; The intelligent sorting module (2) includes a robotic arm (201), a sorting push rod (202) and multiple sorting boxes (203), which can sort different types of electronic waste into the corresponding sorting boxes (203) according to the sorting instructions generated by the identification 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 for identification and sorting operations; The recycling module (3) includes a crushing device (301), a magnetic separation device (302), and a rare and precious metal extraction device (303), which are 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 identification results and related data of the sorting operation, and to optimize the identification model and sorting strategy based on the data analysis results. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1, characterized in that: 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 the central processing unit to identify the appearance, shape, color, and texture features of the electronic waste. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1 or 2, characterized in that: The spectrometer uses spectral analysis technology to identify the elemental composition of electronic waste, generates a material composition spectrum, and, combined with the component identification model in the central processing unit, distinguishes between precious metals, common metals, and non-metallic materials. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1, characterized in that: The 3D laser scanner acquires three-dimensional morphological data of electronic waste through laser scanning, and reconstructs a three-dimensional model in the central processing unit to identify complex shapes or internal structural features, thereby improving the system's ability to identify internal components of electronic waste. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1, characterized in that: The intelligent sorting module (2) includes multiple robotic arms (201), each robotic 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 the designated sorting area. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1, characterized in that: The dynamic sorting control system (5) can dynamically adjust the sorting path and strategy according to the identification result and the complexity of the sorting task; when the identification result is uncertain or the sorting box (203) is full, the dynamic sorting control system (5) can automatically select a spare sorting box and regenerate the sorting path. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1, characterized in that: The crushing device (301) in the recycling module (3) is used to crush the sorted large pieces of electronic waste 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, and palladium through chemical separation to generate high-purity metal products. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 7, characterized in that: The crushing device (301) includes multi-layer crushing blades, 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 blades and activate the anti-blocking device to clear the blocked material. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1, characterized in that: The data analysis and feedback module (6) can record the status information of each identification and sorting operation in real time, and perform data analysis based on the recorded identification accuracy, sorting accuracy and sorting time; when the identification accuracy or sorting accuracy is lower than the preset threshold, the data analysis and feedback module (6) can automatically generate a new model optimization scheme and update the identification model parameters. The electronic waste automatic classification and recycling system based on artificial intelligence recognition according to claim 1, characterized in that: The dynamic sorting control system (5) of the system can display the sorting operation status and recycling statistics in real time through the human-machine interface, and allows operators to adjust the sorting path, modify the sorting priority or enable the backup 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.

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