A method for conveying and sorting electronic products

Abstract

The present invention discloses a method for conveying and sorting electronic products, which is characterized by comprising: a multi-stage conveying device, a visual inspection module, a sorting robot arm and a central control module, wherein the multi-stage conveying device comprises a first-stage conveying line and a second-stage conveying line, and the first-stage conveying line is provided with a dynamic weighing sensor array; the visual inspection module comprises a high-frame rate industrial camera and an infrared profile scanner, and the visual inspection module is installed above the second-stage conveying line; the sorting robot arm comprises an electromagnetic-vacuum dual-mode gripper and an end-effector integrated pressure feedback sensor; the central control module is connected to each module via industrial Ethernet, uses a built-in path optimization algorithm to dynamically analyze the sorting robot arm, and feeds back the obtained analysis results to the visual inspection module and the central control module, and the central control module is connected to a product database.

Description

Technical Field

[0001] The present invention relates to the field of sorting technology, and in particular to a system and method for conveying and sorting electronic products. Background Art

[0002] Electronic products refer to various types of equipment and devices that use electronic technology for information processing, data storage, signal transmission and energy conversion. They are widely used in communications, entertainment, education, medical care, military and industrial control and other fields, and constitute an important infrastructure of the modern information society. During the processing of electronic products, different electronic products need to be sorted.

[0003] The existing electronic product conveying and sorting device is not convenient for automatically sorting the electronic products to be sorted when conveying and sorting the electronic products, so that personnel are required to continuously place the electronic products to be sorted, which increases the labor intensity of the staff and reduces the sorting efficiency of the electronic products.

[0004] Therefore, it is necessary to provide a system and method for conveying and sorting electronic products to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a system for conveying and sorting electronic products, characterized by comprising: a multi-stage conveying device, a visual inspection module, a sorting robot arm and a central control module, including a first-stage conveying line and a second-stage conveying line, wherein the first-stage conveying line is provided with a dynamic weighing sensor array;

[0008] The visual inspection module includes a high frame rate industrial camera and an infrared profile scanner, and the visual inspection module is installed above the second-level conveyor line;

[0009] The sorting robot arm includes an electromagnetic-vacuum dual-mode gripper and an end-effector integrated pressure feedback sensor;

[0010] The central control module is connected to each module via industrial Ethernet, uses a built-in path optimization algorithm to dynamically analyze the sorting robot arm, and feeds back the analysis results to the visual inspection module and the central control module. The central control module is connected to the product database.

[0011] As a preferred solution for the electronic product conveying and sorting system described in the present invention, the dynamic weighing sensor array uses piezoelectric ceramic sensors to collect weight data in real time with a sampling period of 5ms, and establishes a data verification mechanism with the visual detection module.

[0012] As a preferred solution for the electronic product conveying and sorting system described in the present invention, the sorting robot arm is a six-axis collaborative robot arm configuration, the gripping surface of the end effector is provided with an adaptive deformable silicone layer, and the contact pressure threshold is dynamically adjusted through a machine learning model.

[0013] As a preferred solution for the electronic product conveying and sorting system described in the present invention, the path optimization algorithm is implemented based on an improved genetic algorithm, and the real-time conveyor line load parameters are introduced as fitness function constraints. The sorting path planning response time is ≤50ms.

[0014] As a preferred solution for the electronic product conveying and sorting system described in the present invention, it also includes an exception handling module, which includes an audible and visual alarm unit and an emergency braking unit. When it is detected that the product parameters deviate from the database threshold by ±15%, the third-level emergency response mechanism is triggered.

[0015] As a preferred solution for the electronic product conveying and sorting system described in the present invention, the product database stores multi-dimensional feature parameters, including dimensional tolerances, weight ranges, and surface finish indicators, and achieves two-way data synchronization with the MES system through the OPC UA protocol.

[0016] As a preferred solution for the electronic product conveying and sorting system described in the present invention, the visual inspection module adopts multispectral imaging technology and integrates a composite light source array of the visible light band (400-700nm) and the short-wave infrared band (900-1700nm).

[0017] As a preferred solution of the method for conveying and sorting electronic products of the present invention, the method includes: S1, obtaining multi-dimensional features of the product by a dynamic weighing sensor and a visual inspection module;

[0018] S2, feature fusion and classification based on fuzzy logic algorithm;

[0019] S3. Dynamically adjust sorting priority based on real-time conveyor line status;

[0020] S4. Use predictive maintenance models to assess the health of key components.

[0021] As a preferred solution of the method for conveying and sorting electronic products described in the present invention, a fuzzy logic algorithm is used to fuse the data collected by the weighing sensor and the visual detection module in S1.

[0022] As a preferred solution for the electronic product conveying and sorting method described in the present invention, S2 uses a fuzzy logic algorithm to obtain a fuzzy logic rule base, improves the quality of cluster head selection while achieving cluster energy balance, and combines the fuzzy control model with the optimization algorithm. S3 adjusts the sorting priority by classifying the electronic products based on S2.

[0023] The beneficial effects of the present invention are as follows: parallel processing is achieved through a multi-level conveying architecture, path optimization algorithms are used to reduce the idle stroke of the robotic arm, multi-spectral detection is enhanced by multi-sensor data fusion (weight + vision + pressure feedback), and the sorting robotic arm is designed as a dual-mode gripper, and the gripping surface is set as an adaptive deformable silicone layer; a multi-dimensional feature database can be used to perform a health assessment of the equipment using a predictive maintenance model, and a three-level emergency response mechanism is established according to the real-time status of the conveyor line, thereby realizing the priority of intelligent sorting. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0025] Figure 1 A schematic diagram of the system structure of a system and method for conveying and sorting electronic products according to an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of the method steps of a system and method for conveying and sorting electronic products according to an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of a pure fuzzy logic system for a system and method for conveying and sorting electronic products according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0029] Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without making any creative work should fall within the scope of protection of the present invention.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Example 1

[0032] Reference Figure 1 According to an embodiment of the present invention, a conveying and sorting system for electronic products comprises a multi-stage conveyor device 100, a visual inspection module 200, a sorting robot 300, and a central control module 400. The multi-stage conveyor device 100 includes a first-stage conveyor line 101 and a second-stage conveyor line 102. The first-stage conveyor line 101 is equipped with a dynamic weighing sensor array 103. The weighing sensor array 103 is precisely mounted inside the roller support bearing of the first-stage conveyor line 101. Each roller (50 mm diameter) has a sensor unit integrated at each end. The sensors are arranged in a matrix with equal spacing of 200 mm along the conveyor belt's running direction (X-axis), covering the entire effective weighing area (typically the 1.5-2 m range in the middle of the conveyor line). A staggered layout is used across the conveyor belt's width (Y-axis), ensuring that products of different sizes are covered by at least three sensor units. The direct force transmission path: product weight → conveyor belt → roller → sensor → base, forming the shortest force transmission chain and reducing energy loss in intermediate links.

[0033] The visual inspection module 200 includes a high frame rate industrial camera and an infrared profile scanner, and the visual inspection module is installed above the second-level conveyor line 102;

[0034] The sorting robot arm 300 includes an electromagnetic-vacuum dual-mode gripper and an end-effector integrated pressure feedback sensor 302;

[0035] The central control module 400 is connected to each module via industrial Ethernet, uses a built-in path optimization algorithm to dynamically analyze the sorting robot arm, and feeds back the analysis results to the visual inspection module and the central control module. The central control module is connected to the product database.

[0036] Specifically, the multi-stage conveyor device is a conveyor belt, divided into a first-stage conveyor line and a second-stage conveyor line. The first-stage conveyor line differs from the second-stage conveyor line in that a dynamic weighing sensor array 103 is installed on the surface of the first-stage conveyor line. The dynamic weighing sensor array 103 uses piezoelectric ceramic sensors, which collect weight data in real time with a 5ms sampling period and establish a data verification mechanism with the visual inspection module 200. The sensor array 103 is composed of multiple piezoelectric ceramic weighing cells, evenly spaced along the conveying direction of the first-stage conveyor line 101. Each weighing cell is embedded in the conveyor line roller support structure and directly contacts the bottom surface of the conveyor belt.

[0037] Layout parameters: 3-5 sensor units per meter of conveyor line; spacing between adjacent sensors ≤ 200mm; effective sensor range: 0.1-10kg (covering the weight of typical electronic products)

[0038] The sensors are designed to resist interference, with the sensor units featuring a double-layer vibration isolation design. The upper layer is an elastic silicone cushioning layer, isolating conveyor belt vibration and noise; the lower layer is a rigid aluminum alloy base bolted to the conveyor frame. Each sensor is connected in parallel via an RS-485 bus and communicates with the central control module 400 using the Modbus-RTU protocol. The dynamic weighing trigger mechanism uses a photoelectric trigger signal. A through-beam photoelectric sensor is installed at the conveyor entrance. When a product enters the weighing area, a sampling operation is triggered. This includes outputting a trigger pulse signal to the sensor array and simultaneously starting a timer (which calculates product speed).

[0039] A visual inspection module is installed on the multi-stage conveyor, connected to a sorting robot and a central control module. Sorting robots are installed at intervals on either side of the multi-stage conveyor. The sorting robot 300 is a six-axis collaborative robot. The gripping surface of the end effector 301 is equipped with an adaptive deformable silicone layer, and the contact pressure threshold is dynamically adjusted using a machine learning model. The sorting robot 300 spans the first-stage conveyor line 101 and the second-stage conveyor line 102. The end effector 301's operating radius covers both conveyor lines and the sorting buffer. Conveyor line encoders acquire real-time conveyor belt position data, and the robot's motion coordinate system is dynamically bound to the conveyor line speed.

[0040] The event-driven mechanism is shown in Table 1:

[0041] Table 1

[0042] Trigger Event Response Action First level weighing completed Launch the product profile prediction model Second level visual inspection completed Generate grasping path planning Sorting target box is in place Activate the robot kinematic chain

[0043] The action path of the sorting robot arm is realized by the built-in chip based on the improved genetic algorithm, and the real-time conveyor line load parameters are introduced as the fitness function constraint. The sorting path planning response time is ≤50ms. The path planning mathematical model is:

[0044] Given a starting point A ,end B and a series of target points T 1, T 2,..., Tn , where each target point must be covered. The grasping speed of the sorting robot is z The present application is to design a path so that the sorting robot arm starts from the starting point, passes through all target points in sequence at multiple angles, and finally reaches the end point, while satisfying the following constraints and objective functions. The use of a sorting robot arm to grasp electronic products is already a mature existing technology and will not be elaborated here.

[0045] 1) Start and end constraints: The sorting robot must start from the starting point A Start and end at the destination B .

[0046] 2) Coverage constraint: All target points must be covered.

[0047] 3) Sorting area constraints: The sorting and grabbing path of the sorting robot must always be kept

[0048] Hold in the region D Inside.

[0049] 4) Collision avoidance: The path of the robot arm must not intersect with any known obstacles.

[0050] In summary, the basic model establishes a mathematical model for path planning of the sorting robot arm.

[0051] Through the improved genetic algorithm: the robot chromosome is encoded, and the robot joint angle, conveyor line speed, and material box position are encoded into a gene sequence.

[0052] The fitness function expression is: Fitness = 1 / (t_total + 0.5×E_energy + 10×Collision_risk)

[0053] The evolutionary strategy is to generate 200 solutions per batch and converge to the optimal path within 5 generations. The control chip of the sorting robot arm has been updated to achieve zero-wait sorting and grabbing.

[0054] Generate a preliminary path based on historical data in the product database before visual inspection is completed

[0055] When the actual detection data arrives, the path correction time is ≤15ms.

[0056] The system also includes an exception handling module 500, which includes an audible and visual alarm unit and an emergency braking unit. This module triggers a three-level emergency response mechanism when a product parameter deviates by ±15% from a database threshold. The product database stores multi-dimensional feature parameters, including dimensional tolerances, weight ranges, and surface finish indicators, and synchronizes bidirectional data with the MES system via the OPC UA protocol.

[0057] The multi-objective collaborative scheduling task priority matrix is ​​shown in Table 2:

[0058] Table 2

[0059] Priority Judgment conditions Response Delay Emergency Fragile / High-risk Goods ≤50ms Ordinary Standard electronic products ≤100ms Buffer Level Repeatable sorting items ≤500ms

[0060] The robotic arm has undergone a technical upgrade, featuring a three-dimensional dynamic collision avoidance system, including static protection, dynamic protection, and emergency response strategies. Static protection: A pre-set safety boundary between the robotic arm and the conveyor line (distance ≥ 50mm) is established. Dynamic protection: A Time of Flight (ToF) sensor is used to monitor moving objects within a distance of 10-500mm in real time. The sorting robotic arm interacts deeply with the multi-stage conveyor system, including blockage warning, rhythm synchronization, and process quality traceability. The blockage warning uses a pressure sensor array to monitor the conveyor line load. When the load per unit length exceeds 5kg / m, the upstream feed speed is reduced. The rhythm synchronization increases the conveyor line speed to 1.5 times the nominal value during the robotic arm's sorting interval (t > 200ms). When the grasping action is initiated, the conveyor line speed is reduced to 0.8 times the nominal value. The process quality traceability records the grasping parameters (force, angle, and time) for each product, allowing for reverse optimization of sorting parameters when defects are discovered in subsequent processes.

[0061] The product database stores multi-dimensional feature parameters, including dimensional tolerances, weight ranges, and surface finish indicators, and achieves bidirectional data synchronization with the MES system via the OPC UA protocol. The visual inspection module 200 uses multispectral imaging technology and integrates a composite light source array for the visible light band (400-700nm) and the short-wave infrared band (900-1700nm). The visual inspection module detects electronic products that need to be sorted and sends them to the central control module via Ethernet. The central control module sends sorting instructions to the sorting robot controller, which executes sorting and grasping through the end effector. The visual inspection module synchronizes the clock with the controller of the sorting robot using the IEEE 1588 Precision Time Protocol (PTP). The deviation between the visual module and the robot clock is less than 1μs, and the entire process from image acquisition to robot response is ≤20ms.

[0062] The visual inspection module performs photo preprocessing using the WhiteBalance and ColorToGreyscaleFilter functions to perform white balance and grayscale preprocessing, removing irrelevant information. The high-frame-rate industrial camera coordinate extraction and sorting module uses the ExtractBlobs function to obtain the camera coordinates of six electronic products. The SortBlobs function then sorts the six electronic products by their X coordinates. The 9-point calibration module uses the CalibrateAdvanced function to establish a coordinate transformation relationship between the pixel coordinates of the nine points and the robot arm coordinates. The electronic product color extraction module first uses the TrainExtractColor function to train a color library containing four colors: black, yellow, red, and blue. The ExtractColor function then retrieves the color number of the electronic product: 1 represents black, 2 represents yellow, 3 represents red, and 4 represents blue. During the coordinate transformation phase, the transformation matrix obtained from the 9-point calibration is used to convert the sorted camera coordinates into the robot arm coordinates. The TCP communication module uses the TCPDevice function to establish a socket connection with the robot arm controller. After receiving a capture command from the robotic arm controller, the system sends color and position information to the robotic arm controller. System debugging is required to ensure coordinated operation between the robotic arm controller, smart camera, and PLC. This system debugging primarily involves setting parameters for conveyor chain tracking and debugging communications between the smart camera and robotic arm controller. To ensure the encoder outputs pulses between 1250 and 2500 pulses per meter of conveyor chain travel, the robotic arm controller simultaneously collects the rising and falling edges of phases A and B, collecting four signals per cycle. The calculated signals collected by the controller range from 5000 to 10000. A count of less than 5000 will affect the robotic arm's tracking accuracy, while a count of more than 10000 will not improve it. The minimum conveyor chain speed is 4 mm / s, and the maximum is 2000 mm / s. The conveyor chain tracking parameter settings are shown in Table 3:

[0063] Table 3

[0064] Parameter name Setting value Parameter Annotations CountsPerMeter 10000 The number of calculation signals actually collected by the controller for every 1m of conveyor chain running SyncSeparation 0 Synchronous separation size QueueTrckDist 0 Queue length StartWinWidth 0.2 Starting window size, by running the WaitWobj instruction will connect to the first artifact in the window Maximum distance 500 Maximum distance Minimum distance 0 Minimum distance Adjustment Speed 250 The speed of the robot arm catching up with the conveyor chain must be greater than the running speed of the conveyor chain

[0065] The IP addresses of the smart camera and robotic arm controller should be on the same network segment. The smart camera serves as the socket communication server, and the robotic arm serves as the client. Both the smart camera and the industrial robotic arm controller are started simultaneously. First, the smart camera establishes a socket communication server using the TCPDevice function. The robotic arm controller creates a socket client using the SocketCreate function and establishes a connection to the smart camera server using the SocketConnect function. When the robotic arm controller detects that the conveyor chain is connected to a workpiece using the WaitWObi function, it immediately sends a capture command to the smart camera using the SocketSend function. After receiving the command using the Read function, the smart camera compares it with the string "OK." If the comparison successfully confirms it as a capture command, it sends the color coordinate information as a string to the robotic arm using the Write function. The robotic arm controller stores the received string into String2 using the SocketReceive function and then performs data analysis to parse the string into robotic arm coordinate values ​​and color information.

[0066] In summary, by utilizing multi-stage conveying devices, through multimodal perception fusion and cross-domain collaborative control, the visual inspection module and the sorting robot arm achieve full-chain optimization from "seeing" to "grasping", solving the efficiency bottleneck problem caused by the vision-execution separation in traditional sorting systems.

[0067] Example 2

[0068] Reference Figure 2-Figure 3 This embodiment differs from the previous embodiment in that it provides an electronic product sorting method, including: S1, obtaining multi-dimensional features of products through dynamic weighing sensors and visual inspection modules; S2, fusing and classifying product features based on a fuzzy logic algorithm; S3, dynamically adjusting sorting priorities based on real-time multi-level conveyor line status; and S4, using a predictive maintenance model to assess the health of key components.

[0069] Specifically, step 1 analyzes the multi-dimensional features of electronic products using a multi-stage conveyor system and a visual inspection module. Step 2 integrates and classifies the multi-dimensional features of the products in step 1 using a fuzzy logic algorithm based on the analysis in step 1. Step 3 adjusts the sorting priority based on the product classification in step 2. Step 4 monitors the operating components involved in the above steps.

[0070] Fuzzy reasoning systems can be divided into two types: pure fuzzy logic systems, the Sugeno type, and the Mamadani type. Pure fuzzy logic systems are the core of other types of fuzzy logic systems and are generalized models that utilize this type of linguistic information under the principles of general fuzzy logic. A pure fuzzy logic system can be viewed as a mapping relationship. The input fuzzy set A is obtained through the fuzzy rule base and fuzzy reasoning in the fuzzy logic system to obtain the output fuzzy set B, as shown in the following example: Figure 3 As shown. A node will participate in the cluster head election only when its residual energy is at least equal to the average residual energy of its neighboring nodes. Since the selection of CH is based on the principle of high score priority, the appearance of one CH often affects the appearance of other CHs. This will lead to the formation of CH vacancy areas, making it difficult for nodes in this area to establish communication connections. To address this problem, this application adopts a CH coexistence mechanism, in which, after a CH is generated, other nodes still have the opportunity to become CHs. Using the density of neighboring nodes and manual experience, competing nodes can calculate the number of CHs allowed to coexist in the current neighborhood:

[0071] Where: N is the number of CHs allowed to coexist, is the number of neighbor nodes, N is the total number of nodes, and is the optimal ratio of CHs under ideal circumstances. Once the score ranking of a competing node is less than , the node will automatically become a CH.

[0072] Choosing the right optimization algorithm is crucial for optimizing the trajectory planning of industrial robotic arms. Genetic algorithms, which mimic the principles of natural selection and genetics, offer advantages such as strong global search capabilities and rapid convergence, making them suitable for solving complex, nonlinear, and multi-modal optimization problems.

[0073] Path optimization algorithm is a path behavior optimization algorithm with the advantages of good parallelism and easy implementation. It is suitable for solving path planning, scheduling optimization and other problems.

[0074] Stochastic gradient descent is an optimization algorithm based on the principle of gradient descent, suitable for solving convex optimization problems. In robotic arm trajectory planning, it can be used to adjust fuzzy control parameters.

[0075] The simulated annealing algorithm is an optimization algorithm based on the physical annealing process and is suitable for solving local optimal solutions. In robotic arm trajectory planning, it can be used to escape local optimal solutions. Combining fuzzy control models with optimization algorithms can leverage the strengths of both and improve the accuracy and stability of robotic arm trajectory planning. Optimization algorithms are used to optimize parameters in fuzzy control models to improve control effectiveness. For example, genetic algorithms can be used to optimize the membership function parameters in fuzzy controllers. Optimization algorithms can be used to optimize fuzzy control rules to improve control accuracy. For example, ant colony algorithms can be used to optimize the rule strength within fuzzy control rules. Optimization algorithms can be used to optimize fuzzy control structures to improve control performance. For example, simulated annealing algorithms can be used to optimize the structure of fuzzy controllers, such as by adding or removing fuzzy control rules.

[0076] Based on the requirements of the robot's trajectory planning, a fuzzy control model is established, including input, output, membership function, and fuzzy control rules. Based on the actual situation, an appropriate optimization algorithm is selected and the corresponding optimization algorithm parameters are designed. The optimization algorithm is used to optimize the parameters in the fuzzy control model to improve control effectiveness.

[0077] Optimize the fuzzy control rules using an optimization algorithm to improve control accuracy. Optimize the fuzzy control structure using an optimization algorithm to improve control performance. Verify the effectiveness of the optimized trajectory planning through simulation experiments or actual applications to ensure the smoothness and accuracy of the robot's motion trajectory. Based on the verification results, adjust the optimization strategy to further improve the optimization effect of the robot's trajectory planning.

[0078] In summary, WT2F-CRA establishes a fitness function based on FND and utilizes a fuzzy logic algorithm to obtain an optimal fuzzy logic rule base, improving cluster head selection quality while achieving cluster energy balance. Furthermore, it uses T2FL to infer the communication radius of cluster heads, restricting their range, and adopts a clustering mechanism with coexisting hubs, further extending network lifetime.

[0079] Further, the method may be implemented in any type of computing platform operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device.

[0080] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for conveying and sorting electronic products, characterized in that: include: A multi-stage conveying device (100), a visual inspection module (200), a sorting robot arm (300) and a central control module (400), wherein the multi-stage conveying device (100) comprises a first-stage conveying line (101) and a second-stage conveying line (102), and a dynamic weighing sensor array (103) is provided on the first-stage conveying line (101); The visual inspection module (200) comprises a high-frame-rate industrial camera and an infrared profile scanner, and the visual inspection module (200) is installed above the second-level conveyor line (102); The sorting robot arm (300) includes an electromagnetic-vacuum dual-mode gripper and an end-effector integrated pressure feedback sensor (302); The central control module (400) is connected to the visual inspection module via industrial Ethernet, uses a built-in path optimization algorithm to perform dynamic analysis on the sorting robot arm, and feeds back the obtained analysis results to the visual inspection module and the central control module, and the central control module is connected to the product database; The path optimization algorithm is based on an improved genetic algorithm, which introduces real-time conveyor line load parameters as fitness function constraints, and the sorting path planning response time is ≤50ms; The method comprises the following steps: S1. Obtain multi-dimensional features of the product through dynamic weighing sensors and visual inspection modules; S2, feature fusion and classification based on fuzzy logic algorithm; S3. Dynamically adjust sorting priority based on real-time conveyor line status; S4. Use predictive maintenance models to assess the health of key components; The S2 uses a fuzzy logic algorithm to obtain a fuzzy logic rule base, improves the quality of cluster head selection and achieves cluster energy balance, and combines the fuzzy control model with the optimization algorithm. The S3 adjusts the sorting priority by classifying electronic products based on the S2.

2. The method for conveying and sorting electronic products according to claim 1, characterized in that: The dynamic weighing sensor array (103) uses piezoelectric ceramic sensors to collect weight data in real time with a sampling period of 5 ms, and establishes a data verification mechanism with the visual detection module (200).

3. The method for conveying and sorting electronic products according to claim 1, characterized in that: The sorting robot arm (300) is a six-axis collaborative robot arm configuration, the gripping surface of the end effector (301) is provided with an adaptive deformable silicone layer, and the contact pressure threshold is dynamically adjusted through a machine learning model.

4. The method for conveying and sorting electronic products according to claim 1, characterized in that: It also includes an abnormality handling module (500), which includes an audible and visual alarm unit and an emergency braking unit, and triggers a three-level emergency response mechanism when it is detected that a product parameter deviates from a database threshold by ±15%.

5. The method for conveying and sorting electronic products according to claim 1, characterized in that: The product database stores multi-dimensional feature parameters, including dimensional tolerances, weight ranges, and surface finish indicators, and achieves bidirectional data synchronization with the MES system through the OPC UA protocol.

6. The method for conveying and sorting electronic products according to claim 1, characterized in that: The visual detection module (200) adopts multispectral imaging technology and integrates a composite light source array of visible light band and short-wave infrared band.

7. The method for conveying and sorting electronic products according to claim 1, characterized in that: The fuzzy logic algorithm is used to fuse the data collected by the weighing sensor and visual detection module in S1.

Images