An implementation method and system for automatic production line production traceability

Abstract

This invention relates to the field of production management traceability technology, and in particular to an automated production line production traceability method and system. Based on the production line's flow logic of "feeding at the beginning of the line - intermediate inspection - finished product at the end of the line," it achieves full-process traceability through three core steps. First, it collects feeding data from the automatic feeding port and performs profile detection to establish a strong correlation between material consumption and the number of qualified products. After production is completed, it performs three-step traceability, calibrating to ensure that the total deduction is consistent with the total amount of materials required for finished product production, forming a closed-loop traceability data system. This avoids the inefficiencies and errors of traditional manual recording and paper document management, as well as the lack of systematicity and completeness of some electronic methods, thus meeting the needs of enterprises for refined management and quality traceability of the production process.

Description

Technical Field

[0001] This invention relates to the field of production management traceability technology, and in particular to a method and system for implementing production traceability on automated production lines. Background Technology

[0002] In modern manufacturing, automated production lines are increasingly widely used, significantly improving production efficiency and product quality. As market demands for product quality and transparency in the production process continue to rise, production traceability systems have become crucial for ensuring product quality and optimizing production management. These systems record various information about the product manufacturing process, such as material sources, production time, and manufacturing techniques, helping companies to promptly identify and resolve problems and improve the precision of their production management.

[0003] In terms of production traceability on automated production lines, traditional methods typically involve manual recording and paper-based document management. Workers need to manually record information such as material loading data and product inspection results during production, and then compile this information into paper documents for safekeeping. When production traceability is required, workers need to spend a significant amount of time and effort searching and organizing these paper documents, which is inefficient and prone to errors. Additionally, some companies have adopted partially electronic methods, such as using simple databases to record material loading quantities and product flow information, but this approach lacks systematicity and completeness, and cannot achieve comprehensive traceability of the production process.

[0004] Traditional production traceability methods have significant drawbacks. Manual recording and paper-based document management are not only inefficient but also prone to errors and data loss, failing to guarantee the accuracy and completeness of production traceability information. While partially electronic methods improve recording efficiency to some extent, their lack of systematicity and completeness prevents them from establishing a strong correlation between material consumption and the quantity of qualified products, and from forming a closed-loop traceability data system throughout the entire process. Consequently, they struggle to meet enterprises' needs for refined production process management and quality traceability. Summary of the Invention

[0005] This invention primarily addresses the technical problem that existing solutions cannot form a closed-loop traceability data system for the entire production process. It provides a method and system for achieving traceability in automated production lines. Based on the production line's flow logic of "feeding at the beginning of the line - intermediate inspection - finished product at the end of the line," it achieves full-process traceability through three core steps: collecting feeding data from the automatic feeding port and performing profile detection to establish a strong correlation between material consumption and the number of qualified products; and performing three-step traceability after production is completed, calibrating to ensure that the total amount deducted is consistent with the total amount of materials required for finished product production, thus forming a closed-loop traceability data system for the entire process and meeting the needs of enterprises for refined management and quality traceability of the production process.

[0006] The above-mentioned technical problems of the present invention are mainly solved by the following technical solutions:

[0007] An automated production line traceability implementation method includes the following steps: S1. Initiating the production line material feeding process and collecting feeding data from several automatic feeding ports; S2. Completing equipment product assembly and gradually transferring the product on the production line conveyor mechanism, and acquiring image data for product inspection when the product reaches the image detection equipment station corresponding to the automatic feeding port. Specifically, based on the production line flow logic, and with the product BOM (Bill of Materials) as the core, the method first checks the BOM... Obtain the feeding port number and unit usage corresponding to the current workstation, and then perform real-time deduction based on the test results. If the product is qualified, the deduction is accurate based on the unit usage; if it is defective, the deduction is accurate based on the unit usage and the robotic arm is triggered to pick up the part, thus achieving a strong correlation between material consumption and the number of qualified products. S3. After production is completed, generate and report the unique SN code of the finished product. Based on the production line flow logic, perform three-step traceability, check the feeding data and deduction status of each feeding port in the early stage, and combine the actual output quantity of the finished product at the workstation reported by the final finished product SN at the end of the line to ensure that the total deduction is consistent with the total amount of materials required for the production of the finished product. This forms a traceability record containing "SN code - material code - material batch - deduction quantity - production time", completing the three-step traceability full-process traceability data closed loop.

[0008] By adopting the above technical solutions, the material feeding process on the production line is initiated to collect material feeding data, which can provide basic data for subsequent production traceability; by using the product BOM list as the core for profile detection and material deduction, a strong correlation can be achieved between material consumption and the number of qualified products; after production is completed, a unique SN code for the finished product is generated and reported, and three-step traceability and calibration are performed to ensure that the total amount deducted is consistent with the total amount of materials required for the production of the finished product, forming a complete traceability record and completing the closed loop of traceability data throughout the entire process.

[0009] Preferably, step S1 specifically includes starting the feeding process from the beginning of the production line, adding materials to n automatic feeding ports on the line respectively, and the collection device at each feeding port working in real time.

[0010] By adopting the above technical solution, the feeding process is started from the beginning of the production line, and multiple automatic feeding ports on the line add materials respectively, which can achieve efficient material addition; the data collection device at each feeding port works in real time, which can collect feeding data in a timely and accurate manner, providing basic data support for subsequent production traceability.

[0011] Preferably, the feeding data includes a barcode recognition sensor reading the material code on the material packaging, an RFID reading sensor obtaining material batch information, and a counting sensor counting the quantity of each feeding.

[0012] By adopting the above technical solution, barcode recognition sensors read material codes, RFID reading sensors obtain material batch information, and counting sensors count the quantity of each feeding, the feeding data of the automatic feeding port can be accurately collected, providing a detailed and accurate data foundation for subsequent material consumption and qualified product quantity correlation, production traceability, etc.

[0013] Preferably, the three-step traceability specifically includes associating and algorithmically processing data in the order of "material loading data - profile data - SN code" to bind the finished product SN code to each batch of materials at each loading port.

[0014] By adopting the above technical solution, and performing association and algorithmic data processing in the order of "material loading data - profile data - SN code", the finished product SN code is bound one-to-one with the material batch of each loading port, which facilitates accurate traceability of the material batch information corresponding to the finished product and improves the data association of the production traceability process.

[0015] Preferably, if the result of the image inspection equipment station's inspection of the product is defective in step S2, the robotic arm unit is immediately activated to remove the defective product from the production line and transfer it to the defective product temporary storage area, and the defective product quantity is accurately deducted according to the unit usage. The quantity of defective products at the current image inspection equipment station = the deducted unit usage at the current image inspection equipment station - the deducted unit usage at the next image inspection equipment station.

[0016] Preferably, in step S2, after the product passes all the portrait inspection equipment stations, it is transferred to the last station for final assembly and production, and the quantity of finished products is the minimum value of the unit usage of all portrait inspection equipment stations minus the total quantity.

[0017] By adopting the above technical solution, after collecting material feeding data during the production line material feeding process, the products flow through the production line conveyor mechanism. After passing the inspection of each image detection equipment station, they are finally assembled and produced. This ensures that all products entering the final assembly stage are qualified products, and achieves a strong correlation between material consumption and the number of qualified products. At the same time, after production is completed, traceability records can be formed through three-step traceability, completing the closed loop of traceability data throughout the entire process.

[0018] Preferably, in step S3, verifying that the total amount deducted across the entire production line is consistent with the total amount of materials required for finished product production specifically includes: after receiving the SN code, first checking the material deduction status of each automatic feeding port in the previous stage; if there is any data that has not been deducted due to system delays, etc., deducting it in sequence according to the order of automatic feeding ports 1 to n; then, combining the actual output quantity of finished products reported by the final finished product SN at the end of the line, calibrating the deduction data of each automatic feeding port to ensure that the total amount deducted is consistent with the total amount of materials required for finished product production.

[0019] By adopting the above technical solution, after receiving the SN code, the deduction of material feeding data at each automatic feeding port in the early stage can be checked. Data that was not deducted due to system delays can be deducted. Combined with the actual output quantity of finished products at the end of the line, the deduction data of each automatic feeding port is calibrated to ensure that the total deduction is consistent with the total amount of materials required for finished product production, thus ensuring the accuracy of material consumption data and improving the data closed loop for production traceability.

[0020] A working system for an automated production line traceability implementation method includes n automatic feeding ports configured with barcode recognition, RFID reading, and counting sensors; n image detection stations configured with industrial cameras and pneumatic robots; and one end-of-line finished product SN reporting station configured with SN generator and barcode scanner. The automatic feeding ports correspond one-to-one with the detection stations, and the image detection station and the end-of-line finished product SN reporting station are connected by a conveyor track.

[0021] By adopting the above technical solutions, the system can complete the entire process of production traceability on automated production lines. The automatic feeding port can collect material codes, batch information and feeding quantity. The image detection station can perform product detection and process the results accordingly. The finished product SN reporting station at the end of the line can generate and report the unique SN code of the finished product, realize a strong correlation between material consumption and the number of qualified products, form a closed loop of traceability data, and complete production traceability.

[0022] Preferably, the automatic feeding port includes a feeding data acquisition module, which collects core material information, integrates the information into feeding data, and transmits it to the data storage module; the profile detection station includes a profile detection and processing module, which connects to other modules of the system via an industrial Ethernet, and transmits detection results and deduction instructions in real time to achieve an automated closed loop of "detection-judgment-processing"; the finished product SN reporting station at the end of the line includes a finished product SN reporting module, which automatically generates a unique SN code according to preset rules, and the scanning and uploading component scans and uploads the SN code to the finished product binding and traceability record module.

[0023] By adopting the above technical solutions, the material feeding data acquisition module can collect core material information and integrate it into feeding data, which is then transmitted to the data storage module for easy data collection and storage. The image detection and processing module connects with other modules via industrial Ethernet to transmit detection results and deduction instructions in real time, enabling an automated closed loop of "detection-judgment-processing" and improving detection and processing efficiency. The finished product SN reporting module automatically generates a unique SN code according to preset rules, and the barcode scanning and uploading component scans and uploads the SN code to the finished product binding and traceability record module, enabling automatic generation and uploading of the finished product SN code for subsequent traceability.

[0024] Preferably, it also includes a data storage module, which uses a timed backup mechanism to store data, including: feeding data transmitted by the feeding data acquisition module, including feeding port number, material code, material batch, quantity, and feeding time; portrait detection data generated by the portrait detection and processing module, including workstation number, detection result, and detection time; a finished product binding and traceability record module, which connects to other modules through a communication interface to complete data deduction, SN code binding, traceability record generation and storage; and a traceability query module, including an interactive terminal and query software, the query software supporting SN code input, traceability record display, and historical data statistics.

[0025] By adopting the above technical solutions, the data storage module uses a timed backup mechanism to store feeding data and profile detection data, which can ensure the security and integrity of the data; the finished product binding and traceability record module completes data deduction, SN code binding, traceability record generation and storage, which can realize effective management and traceability of production data; the traceability query module supports SN code input, traceability record display and historical data statistics, making it convenient for users to query production information and statistically analyze historical data.

[0026] The beneficial effects of this invention are:

[0027] 1. Collect material feeding data from the automatic feeding port and perform profiling to establish a strong correlation between material consumption and the number of qualified products. 2. Perform three-step traceability after production is completed, and calibration ensures that the total deduction matches the total amount of materials required for finished product production, forming a closed-loop traceability data system. 3. Avoid the inefficiencies and errors of traditional manual recording and paper document management, as well as the lack of systematicity and completeness of some electronic methods, meeting the needs of enterprises for refined management and quality traceability of the production process. Attached Figure Description

[0028] Figure 1 This is a flowchart of the present invention.

[0029] Figure 2 This is a production line architecture diagram of the present invention.

[0030] Figure 3 This is a production flow diagram of a single product according to the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this application will be further described in detail below through embodiments and in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0032] For ease of understanding, some terms used in this application are explained as follows: "Jig" refers to a special fixture used for positioning and clamping workpieces for assembly or inspection; "Motor Stage 1" refers to the first major station or stage in the motor assembly process; similarly, "Motor Stage 2" and "Motor Stage 3" represent the second and third major stations or stages, respectively; "Electrical Inspection" refers to a station for automated testing of the electrical performance of a product; "FPC" refers to a flexible circuit board, a bendable circuit board used for internal electrical connections in electronic products; "SN" refers to the product serial number, a unique identification code assigned to a final manufactured product. The explanations of the above terms do not affect the technical solution itself, but are only for clarifying the accompanying drawings and descriptions.

[0033] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0034] Example 1

[0035] This embodiment provides a method for implementing production traceability on an automated production line, such as... Figure 1 As shown, it includes the following steps:

[0036] S1. Initiate the material feeding process on the production line and collect feeding data from several automatic feeding ports. Specifically, this includes starting the feeding process from the beginning of the production line, with materials being added to each of the n automatic feeding ports on the line, and the data collection device at each feeding port operating in real time. Starting the feeding process from the beginning of the production line and having multiple automatic feeding ports on the line add materials efficiently; the real-time operation of the data collection device at each feeding port ensures timely and accurate collection of feeding data, providing fundamental data support for subsequent production traceability. Feeding data includes barcode recognition sensors reading material codes on material packaging, RFID sensors obtaining material batch information, and counting sensors counting the quantity of each feeding. Utilizing barcode recognition sensors to read material codes, RFID sensors to obtain material batch information, and counting sensors to count the quantity of each feeding accurately collects feeding data from the automatic feeding ports, providing a detailed and accurate data foundation for linking material consumption with the quantity of qualified products and for production traceability.

[0037] S2. The equipment and products are assembled and gradually transferred on the production line conveyor mechanism. When the products are transferred to the image detection equipment station corresponding to the automatic feeding port, the image data is obtained for product detection. Specifically, based on the production line flow logic, with the product BOM list as the core, the feeding port number and unit usage corresponding to the current station are obtained through the BOM. Then, in combination with the detection results, real-time deduction is performed. If the product is qualified, the unit usage is accurately deducted. If the product is defective, the unit usage is accurately deducted while triggering the robot arm to pick up the part, so as to realize a strong correlation between material consumption and qualified products and quantity.

[0038] If the image inspection equipment station detects a defective product, the robotic arm unit immediately starts, removes the defective product from the production line and transfers it to the defective product temporary storage area, and deducts the defective product quantity precisely according to the unit usage. The quantity of defective products at the current image inspection equipment station = the deducted unit usage at the current image inspection equipment station - the deducted unit usage at the next image inspection equipment station.

[0039] After passing all portrait inspection equipment stations, the products are transferred to the final station for final assembly and production. The quantity of finished products equals the minimum value of all portrait inspection equipment stations minus the unit consumption. After collecting material loading data at the start of the production line material handling process, the products flow through the production line conveyor mechanism. Only after passing inspection at each portrait inspection equipment station are they finally assembled and produced. This ensures that all products entering the final assembly stage are qualified, and establishes a strong correlation between material consumption and the quantity of qualified products. Furthermore, after production is completed, traceability records can be generated through a three-step traceability system, completing a closed loop of traceability data throughout the entire process.

[0040] S3. After production is completed, a unique serial number (SN) code for the finished product is generated and reported. Based on the production line flow logic, a three-step traceability process is performed. This involves verifying the material feeding data and deductions at each feeding port in the early stages, and combining this with the actual output quantity of the finished product at the final reporting station. Calibration is then performed to ensure that the total deduction amount matches the total material required for the finished product production. This forms a traceability record containing "SN code - material code - material batch - deduction quantity - production time," completing the three-step traceability process and creating a closed-loop traceability data loop. Specifically, the three-step traceability process involves associating and algorithmically processing data in the order of "material feeding data - profile data - SN code" to bind the finished product SN code to each material batch at each feeding port. This facilitates accurate traceability of the material batch information corresponding to the finished product and improves the data association in the production traceability process.

[0041] Initiating the material feeding process on the production line and collecting material feeding data can provide basic data for subsequent production traceability; using the product BOM list as the core for profile detection and material deduction can achieve a strong correlation between material consumption and the number of qualified products; after production is completed, a unique SN code for the finished product is generated and reported, and three-step traceability and calibration are performed to ensure that the total deduction is consistent with the total amount of materials required for the production of the finished product, forming a complete traceability record and completing the closed loop of traceability data throughout the entire process.

[0042] The calibration process specifically includes: after receiving the SN code, first checking the material feeding data deduction status of each automatic feeding port in the early stage; if there is any undeducted data due to system delays, etc., deducting it in the order of automatic feeding ports 1 to n; then, combining the actual output quantity of finished products reported by the final finished product SN at the end of the line, calibrating the deduction data of each automatic feeding port to ensure that the total deduction is consistent with the total amount of materials required for finished product production.

[0043] A working system for an automated production line traceability method includes n automatic feeding ports equipped with barcode recognition, RFID reading, and counting sensors; n image detection stations equipped with industrial cameras and pneumatic robots; and one end-of-line finished product SN reporting station equipped with an SN generator and barcode scanner. The automatic feeding ports correspond one-to-one with the detection stations, and the image detection station and the end-of-line finished product SN reporting station are connected by a conveyor track. The system can complete the entire automated production line traceability process. The automatic feeding ports can collect material codes, batch information, and feeding quantities. The image detection stations can perform product detection and process the results accordingly. The end-of-line finished product SN reporting station can generate and report a unique SN code for the finished product, achieving a strong correlation between material consumption and the number of qualified products, forming a closed loop of traceability data, and completing production traceability.

[0044] A working system for an automated production line production traceability implementation method.

[0045] The automatic feeding port includes a feeding data acquisition module, which collects core material information, integrates the information into feeding data, and then transmits it to the data storage module.

[0046] The portrait detection station includes a portrait detection and processing module, which is connected to other modules of the system via industrial Ethernet to transmit detection results and deduction instructions in real time, realizing an automated closed loop of "detection-judgment-processing".

[0047] The finished product SN reporting station at the end of the production line includes a finished product SN reporting module, which automatically generates a unique SN code according to preset rules. The scanning and uploading component scans the SN code and uploads it to the finished product binding and traceability record module.

[0048] The data storage module uses a timed backup mechanism to store data, including: feeding data transmitted by the feeding data acquisition module, including feeding port number, material code, material batch, quantity, and feeding time; and image detection data generated by the image detection and processing module, including workstation number, detection result, and detection time.

[0049] The finished product binding and traceability recording module connects with other modules through a communication interface to complete data deduction, SN code binding, traceability record generation and storage.

[0050] The traceability query module includes an interactive terminal and query software. The query software supports SN code input, traceability record display, and historical data statistics.

[0051] The material feeding data acquisition module collects core material information and integrates it into feeding data, which is then transmitted to the data storage module for easy data collection and storage. The image detection and processing module connects to other modules via industrial Ethernet, transmitting detection results and deduction instructions in real time, achieving an automated closed loop of "detection-judgment-processing" and improving detection and processing efficiency. The finished product SN reporting module automatically generates a unique SN code according to preset rules, and the barcode scanning and uploading component scans and uploads the SN code to the finished product binding and traceability record module, enabling automatic generation and binding of finished product SN codes for subsequent traceability. The data storage module uses a timed backup mechanism to store feeding data and image detection data, ensuring data security and integrity. The finished product binding and traceability record module completes data deduction, SN code binding, and traceability record generation and storage, enabling effective management and traceability of production data. The traceability query module supports SN code input, traceability record display, and historical data statistics, facilitating user queries of production information and historical data analysis.

[0052] Example 2

[0053] Based on the production line's flow logic of "feeding at the beginning of the line - intermediate inspection - finished product at the end of the line," full-process traceability is achieved through three core steps, as follows:

[0054] Material feeding data acquisition steps: The material feeding process begins at the start of the production line, with n automatic feeding ports on the line adding materials one by one. The data acquisition device at each feeding port operates in real time. A barcode recognition sensor reads the material code on the packaging, an RFID reader obtains batch information, and a counting sensor counts the quantity fed each time. These three sensors are integrated to form the feeding data. The data acquisition device transmits the feeding data to a data storage module (such as a MySQL database) via Ethernet (wired) or WiFi (wireless), ensuring that each piece of feeding data is stored completely and in real time, laying the data foundation for subsequent traceability.

[0055] Product Inspection and Data Deduction Steps: Products are assembled and gradually transferred on the production line conveyor. When a product reaches the image inspection station corresponding to an automatic feeding port, the image inspection unit at that station starts working: a high-definition industrial camera captures an image of the product's appearance, and the image processor performs edge detection, defect identification, and other processing on the image to determine if the product has scratches, assembly deviations, or other issues. If the inspection is qualified, the system calls the preset product BOM list to determine the unit quantity of material required at the corresponding feeding port (e.g., one part of a certain model per product) and sends a deduction instruction to the data storage module, subtracting the unit quantity from the material feeding data at the corresponding feeding port. If the inspection is unsatisfactory, the robotic arm unit immediately starts, removing the defective product from the production line and transferring it to the defective product temporary storage area, with precise deduction based on the unit quantity. The quantity of defective products at the current image inspection station = the deducted unit quantity at the current image inspection station - the deducted unit quantity at the next image inspection station.

[0056] Finished Product Binding and Traceability Record Storage Steps: After passing all image inspection equipment stations, the product is transferred to the last station at the end of the production line for final assembly and production. Upon completion of production, the finished product SN reporting module (e.g., a barcode scanner) automatically generates and reports a unique SN code for the finished product (format can be customized, such as "SN + Date + Serial Number"). After receiving the SN code, the finished product binding and traceability record module first checks the material deduction data at each feeding port. If there is any undeducted data due to system delays, it deducts the data sequentially from feeding port 1 to n. Then, combined with the actual output quantity of the finished product at the last station, it calibrates the deduction data at each feeding port to ensure that the total deduction is consistent with the total amount of materials required for finished product production. After calibration, the module binds the finished product SN code to the corresponding material code and batch number at each feeding port, forming a traceability record containing "SN code - material code - material batch - deducted quantity - production time," and stores it in the traceability record unit, completing the closed loop of traceability data throughout the entire process.

[0057] In addition, to meet actual traceability needs, the method also includes a traceability query step: the user enters the finished product's SN code in the interactive interface of the traceability query module, and the system quickly retrieves and displays all material traceability information corresponding to the finished product by using the SN code as an index in the traceability record unit, thus achieving "one-click traceability".

[0058] To implement the above method, this application also provides a supporting system, which consists of six core modules. The modules work together and exchange data, as detailed below:

[0059] The material feeding data acquisition module consists of n sets of data acquisition components (one set for each feeding port). Each set includes a barcode recognition sensor, an RFID reader sensor, a counting sensor, and a data transmission module. The sensors are responsible for collecting core material information, integrating the information into feeding data, and then transmitting it to the data storage module to ensure the accuracy and real-time performance of the data acquisition.

[0060] Data storage module: Utilizes a MySQL relational database, primarily storing two types of data: first, material feeding data transmitted by the material feeding data acquisition module (including feeding port number, material code, material batch, quantity, and feeding time); second, profile detection data generated by the profile detection and processing module (including workstation number, detection result, and detection time). The database employs a scheduled backup mechanism to prevent data loss.

[0061] Image Detection and Processing Module: Each image detection equipment station corresponds to a sub-unit of this module. The sub-unit includes a high-definition industrial camera (resolution no less than 20 megapixels), an image processor (supporting real-time image analysis), and a pneumatic robotic arm. The sub-unit connects to other modules of the system via industrial Ethernet, transmitting detection results and deduction instructions in real time to achieve an automated closed loop of "detection-judgment-processing".

[0062] Finished Product SN Reporting Module: Installed next to the control panel at the last workstation at the end of the production line, it consists of an SN code generator and a barcode scanning and uploading component. The SN code generator automatically generates a unique SN code according to preset rules, and the barcode scanning and uploading component scans and uploads the SN code to the finished product binding and traceability record module. The entire process requires no manual intervention, avoiding human error.

[0063] Finished Product Binding and Traceability Recording Module: As the core control module of the system, it is implemented using an industrial control computer and has built-in data processing algorithms. This module connects to other modules via a communication interface to complete core operations such as data deduction, serial number binding, traceability record generation and storage, and is crucial for realizing the traceability logic.

[0064] Traceability query module: Consists of an interactive terminal (such as an industrial tablet or computer) and query software. The query software supports functions such as SN code input, traceability record display (which can be exported to Excel format), and historical data statistics. Users can complete traceability queries with simple operations, reducing the operational threshold.

[0065] The core of this application lies in achieving end-to-end automated processing of "data collection - detection and deduction - finished product binding" through standardized algorithms, ensuring the accuracy and logic of traceability data. Specifically, it includes three core algorithms: material loading data collection algorithm, product detection and data deduction algorithm, and finished product binding and traceability record generation algorithm. The inputs, outputs, and process logic of each algorithm are as follows:

[0066] 1. Material loading data acquisition algorithm

[0067] Algorithm function: Real-time and accurate collection of feeding data from n automatic feeding ports, completion of data verification and storage, and provision of basic data support for subsequent traceability.

[0068] Input parameters: Port No. (1~n), material code reading signal (Code_Sig), material batch reading signal (Batch_Sig), material quantity counting signal (Qty_Sig), and acquisition trigger signal (Trig_Sig, material loading action trigger).

[0069] Output parameters: structured feeding data (Data_Feed = {Port_No, Mat_Code, Mat_Batch, Mat_Qty, Feed_Time}), data storage status (Save_Stat, success / failure), and abnormal alarm signals (Alarm_Sig, such as data missing alarm).

[0070] Algorithm flow:

[0071] Initialize algorithm parameters: Set data validation rules (e.g., Mat_Code is 10 characters, Mat_Qty is a positive integer) and data transmission timeout threshold (default 5s);

[0072] Listen for the Trig_Sig signal to trigger data acquisition: When Trig_Sig is detected to be "high" (feeding action started), data acquisition is initiated.

[0073] Synchronous reading of sensor signals: Obtain Mat_Code by parsing Code_Sig through barcode recognition sensor, obtain Mat_Batch by parsing Batch_Sig through RFID reading sensor, obtain Mat_Qty by accumulating Qty_Sig through counting sensor, and record the current time as Feed_Time;

[0074] Data validation: Check the Mat_Code format, Mat_Batch validity (e.g., conforming to the "year + batch number" format), and Mat_Qty rationality (e.g., not exceeding the maximum capacity of the feeding port) against the preset validation rules.

[0075] If the verification fails: generate an Alarm_Sig (e.g., a buzzer alarm + a screen message "Data format error"), and return to step 3 to collect data again;

[0076] If the verification is successful: Generate structured feed data Data_Feed;

[0077] Data transmission and storage: The Data_Feed is sent to the data storage module through the data transmission component, and an SQL insert statement is executed (INSERT INTO feed_data (Port_No, Mat_Code, Mat_Batch, Mat_Qty,Feed_Time) VALUES (...)).

[0078] Storage status judgment: If a "storage successful" feedback is received from the database within the timeout threshold, Save_Stat = "success" is set, and the algorithm enters the next round of listening; if no feedback is received within the timeout or a "storage failed" feedback is received, the transmission is retried (up to 3 times). If it still fails, Save_Stat = "failure" is set and Alarm_Sig ("data storage abnormality") is triggered.

[0079] 2. Product Inspection and Data Deduction Algorithm

[0080] Algorithm function: Link product inspection results with corresponding feed port data to achieve precise data linkage of "deducting when qualified and not consuming when defective", ensuring that data deduction is synchronized with production progress.

[0081] Input parameters: current station number (Station_No, which corresponds one-to-one with Port_No, with values ​​from 1 to n), product inspection image (Img_Det), product BOM list (BOM_Table, which includes the mapping relationship between Station_No and Port_No and the unit usage Mat_Unit), and current feeding data in the data storage module (Data_Feed_Cur = {Port_No,Remain_Qty}, where Remain_Qty is the remaining quantity).

[0082] Output parameters: detection result (Det_Res, qualified / unqualified), data deduction result (Deduct_Res, successful / failed), updated remaining quantity (Remain_Qty_New), robot arm action command (Arm_Cmd, start / not start).

[0083] Algorithm flow:

[0084] 1. Image preprocessing: Denoise the input Img_Det (Gaussian filtering) and convert it to grayscale to enhance the contrast of image features;

[0085] 2. Conformity Inspection: Product features (such as dimensions, assembly gaps, and surface flatness) are extracted using the image analysis module and compared with the standard feature values ​​in the BOM_Table.

[0086] If the characteristic deviation exceeds the allowable range (e.g., gap > 0.1mm): set Det_Res = "Defective", Arm_Cmd = "Start" (robot takes out defective product), Deduct_Res = "Do not execute", and the algorithm ends;

[0087] If the characteristic deviation is within the allowable range: set Det_Res="qualified" and Arm_Cmd="do not start";

[0088] 3. Station-Loading Port Mapping: Based on the correspondence between Station_No and Port_No in BOM_Table, determine the loading port number that matches the current station (e.g., Station_No=2 corresponds to Port_No=2).

[0089] 4. Remaining Quantity Check: Retrieve the Data_Feed_Cur corresponding to the Port_No from the data storage module, obtain the current Remain_Qty, and determine whether Remain_Qty is ≥ Mat_Unit (unit usage):

[0090] If Remain_Qty < Mat_Unit: Set Deduct_Res = "Failed", trigger Alarm_Sig ("Insufficient material, cannot deduct", product flow is paused, and the algorithm ends;

[0091] If Remain_Qty ≥ Mat_Unit: Calculate Remain_Qty_New = Remain_Qty - Mat_Unit, and generate a data deduction instruction;

[0092] Data update: Send an UPDATE command to the data storage module (UPDATE feed_data SET Remain_Qty=Remain_Qty_New WHERE Port_No = current Port_No) to update the remaining quantity at the corresponding feed port;

[0093] Deduction result confirmation: If the database receives a "successful update" response, set Deduct_Res = "success", generate a product transfer instruction (triggering the conveyor to send the product to the next workstation); if the update fails, retry the update (maximum 2 times), and if it still fails, set Deduct_Res = "failure" and trigger Alarm_Sig ("data deduction error"), and the algorithm ends.

[0094] 3. Finished Product Binding and Traceability Record Generation Algorithm

[0095] Algorithm Function: Integrates data from the entire process, completes the final calibration and deduction of material feeding data and uniquely binds it to the finished product SN code - material batch, generates and stores structured traceability records, and realizes a closed loop of traceability data.

[0096] Input parameters: unique SN code of finished product (SN_Code), detection and deduction logs of each station (Log_DetDed ={Station_No, Det_Res, Deduct_Res, Port_No, Mat_Unit}), final remaining quantity of each feeding port in the data storage module (Data_Feed_Final = {Port_No, Remain_Qty_Final, Mat_Code, Mat_Batch}), and finished product production quantity (Prod_Qty, total number of finished products produced in a single batch).

[0097] Output parameters: Structured traceability record (Data_Trace = {SN_Code, Mat_List, Prod_Time,Deduct_Total}, where Mat_List is {Port_No: {Mat_Code, Mat_Batch, Deduct_Qty}}, and Deduct_Total is the total deduction quantity), record storage status (Trace_Save_Stat, success / failure), calibration alarm signal (Calib_Alarm, triggered / not triggered).

[0098] Algorithm flow:

[0099] Deduction log verification: Iterate through Log_DetDed and filter out workstation records where Det_Res="qualified" but Deduct_Res="failed" (e.g., deduction failure due to network latency):

[0100] If such a record exists: For the corresponding Port_No, query Remain_Qty_Final in Data_Feed_Final. If Remain_Qty_Final ≥ Mat_Unit, perform a deduction correction operation (Remain_Qty_Final_New = Remain_Qty_Final - Mat_Unit, update the database); if Remain_Qty_Final < Mat_Unit, trigger Calib_Alarm ("There is a workstation with uncorrected deductions, manual verification is required").

[0101] If no such record exists: Proceed to the data calibration stage;

[0102] Data calibration calculation:

[0103] Calculate the total material usage for a single finished product (Total_Unit = ΣMat_Unit (1~n), which is the sum of the unit usage for all workstations);

[0104] Calculate the total deduction quantity for a single finished product (Total_Deduct_Single=Σ(initialMat_Qty - Remain_Qty_Final) / Prod_Qty, i.e., the average of the total deduction quantities at each feeding port);

[0105] Determine if Total_Deduct_Single - Total_Unit is ≤ 0.1% (within acceptable error range):

[0106] If the error exceeds the range: Calib_Alarm ("Data calibration error, the total amount deducted deviates too much from the theoretical amount") is triggered, record generation is paused, and manual calibration is awaited;

[0107] If the error is within the acceptable range: calibration passed, proceed to the binding stage;

[0108] Material batch binding: Based on the mapping relationship between Port_No and Mat_Code and Mat_Batch in Data_Feed_Final, and combined with the deduction quantity of each finished product at each feeding port (Deduct_Qty=Mat_Unit), generate Mat_List (e.g., Port_No=1: {Mat_Code=A001, Mat_Batch=B202501, Deduct_Qty=1}).

[0109] Trace record construction: Generate the current time as Prod_Time, calculate Deduct_Total=Total_Deduct_Single, and construct the structured trace record Data_Trace;

[0110] Record storage: Transfer Data_Trace to the trace record unit and execute the SQL INSERT INTO trace_data (SN_Code, Mat_List, Prod_Time, Deduct_Total) VALUES (...));

[0111] Storage status confirmation: If a "storage successful" feedback is received within 3 seconds, set Trace_Save_Stat="success" and generate a trace completion log; if storage fails, retry the transmission (up to 2 times). If it still fails, set Trace_Save_Stat="failure" and trigger Alarm_Sig ("trace record storage failed"), and the algorithm ends.

[0112] Example 3

[0113] The automated production line traceability implementation method provided in this application includes a material loading data collection step, a product inspection and material deduction step, and a traceability and calibration step after production is completed. By sequentially executing these steps, a strong correlation is established between material consumption and the number of qualified products, forming a closed loop of traceability data throughout the entire process, thereby improving the accuracy and efficiency of production traceability.

[0114] Specifically, the material feeding data acquisition process includes initiating the feeding process from the beginning of the production line, with n automatic feeding ports on the line adding materials one by one, and the data acquisition device at each feeding port operating in real time. The acquisition devices include barcode recognition sensors, RFID reading sensors, and counting sensors. The barcode recognition sensor can be a laser barcode scanner, which reads the material code on the packaging by emitting a laser beam, featuring fast scanning speed and high accuracy; or a CCD barcode scanner, which recognizes barcodes through an image sensor and has good adaptability to different types of barcodes. The RFID reading sensor can be a high-frequency RFID reader / writer, which can quickly and accurately obtain material batch information; or an ultra-high-frequency RFID reader / writer, which has a longer reading distance. The counting sensor can be a photoelectric counter, which counts the quantity of each feeding by detecting the obstruction of light by objects; or an electromagnetic counter, suitable for some special environments. These sensors work together to integrate the collected material feeding data and transmit it to the subsequent processing module.

[0115] The product inspection and material deduction process involves assembling products on the production line conveyor and gradually transferring them to the image inspection station corresponding to the automatic feeding port. Image data is acquired for product inspection at this station. The image inspection station is equipped with an industrial camera. One option is an area scan camera, which can acquire a two-dimensional image of the object in one go, suitable for comprehensive inspection of product appearance; another is a line scan camera, which provides higher resolution for products requiring high-precision inspection, such as electronic components. Based on the production line flow logic and using the product BOM (Bill of Materials) as the core, the feeding port number and unit quantity corresponding to the current station are first obtained from the BOM, and then real-time deduction is performed based on the inspection results. If the inspection result is qualified, the deduction is precise based on the unit quantity; if the inspection result is defective, the deduction is precise based on the unit quantity and a robotic arm is triggered to pick up the part. The robotic arm can be a multi-joint robotic arm, which has flexible movement capabilities and can accurately grasp defective products; a Cartesian coordinate robotic arm can also be used, suitable for scenarios with high motion accuracy requirements. The robotic arm removes defective products from the production line and transfers them to a temporary storage area for defective products, while precisely deducting the amount used per unit. After the products pass all the image inspection equipment stations, they are transferred to the last station for final assembly and production. The quantity of finished products equals the minimum deduction per unit from all the image inspection equipment stations.

[0116] The traceability and calibration steps after production completion involve generating and reporting a unique serial number (SN) for the finished product. The three-step traceability process specifically includes associating and algorithmically processing data in the order of "material loading data - profile data - SN code" to bind the finished product SN code to each batch of materials at each loading port. The calibration process involves receiving the SN code, first verifying the material deduction status of each automatic loading port in the previous stages. If there is any undeducted data due to system delays, it is deducted sequentially from automatic loading ports 1 to n. Then, combined with the actual output quantity of the finished product SN reported by the end-of-line station, the deduction data of each automatic loading port is calibrated to ensure that the total deduction is consistent with the total amount of materials required for finished product production. This forms a traceability record containing "SN code - material code - material batch - deducted quantity - production time," completing the closed loop of traceability data throughout the three-step traceability process.

[0117] The implementation principle of this embodiment is as follows: by automating material loading data collection, product inspection and material deduction, and post-production traceability and calibration, it overcomes the problems of low efficiency, inaccurate and incomplete information in traditional production traceability methods. Utilizing advanced sensors and industrial cameras, real-time data acquisition and processing are achieved, ensuring a strong correlation between material consumption and the number of qualified products. Through a three-step traceability and calibration mechanism, a closed-loop traceability data system is formed, providing enterprises with accurate and complete production traceability information. This helps enterprises to conduct refined management and quality traceability, improving the level of production management and product quality.

[0118] The working system of the automated production line traceability implementation method provided in this application includes n automatic feeding ports configured with barcode recognition, RFID reading, and counting sensors, n image detection stations configured with industrial cameras and pneumatic robots, and one end-of-line finished product SN reporting station configured with SN generator and barcode scanner. The automatic feeding ports correspond one-to-one with the detection stations, and the image detection station and the end-of-line finished product SN reporting station are connected by a conveyor track.

[0119] Specifically, the automatic feeding port includes a feeding data acquisition module, which collects core material information, integrates the information into feeding data, and transmits it to the data storage module. The working principles and alternative solutions of the barcode recognition sensor, RFID reading sensor, and counting sensor in the feeding data acquisition module are the same as in Example 1. The automatic feeding port can also be equipped with a feeding control module to control the feeding speed and quantity. The feeding control module can use a PLC controller, which has the characteristics of high reliability and simple programming; or it can use a microcontroller controller, which is suitable for some scenarios with high cost requirements.

[0120] The image detection station includes an image detection and processing module, which connects to other modules of the system via an industrial Ethernet network to transmit detection results and deduction instructions in real time, achieving an automated closed loop of "detection-judgment-processing". The industrial camera in the image detection and processing module can be of different types, such as an area scan camera or a line scan camera; its working principle and alternative solutions are the same as in Example 1. The image detection and processing module can also be equipped with an image processing algorithm optimization module to improve the accuracy and efficiency of image detection. This module can employ deep learning algorithms, training on a large amount of image data to improve detection accuracy; alternatively, it can use traditional image processing algorithms, such as edge detection and feature extraction.

[0121] The finished product serial number (SN) reporting station at the end of the production line includes a finished product SN reporting module, which automatically generates a unique SN code according to preset rules. The scanning and uploading component scans and uploads the SN code to the finished product binding and traceability record module. The SN generator can use a random number generation algorithm to generate a unique SN code, or it can combine product production information, such as production time and batch number, to generate SN codes with specific rules. The barcode scanner can be a laser scanner, which features high scanning speed; or a CCD scanner, which has good adaptability to different types of barcodes.

[0122] The system also includes a data storage module, a finished product binding and traceability recording module, and a traceability query module. The data storage module uses a timed backup mechanism to store data, including feeding data transmitted by the feeding data acquisition module (containing feeding port number, material code, material batch, quantity, and feeding time); and image detection data generated by the image detection and processing module (containing workstation number, detection result, and detection time). The data storage module can use a hard disk array for data storage, featuring large capacity and high reliability; it can also use cloud storage for convenient remote access and management. The finished product binding and traceability recording module connects to other modules via a communication interface to complete data deduction, SN code binding, and traceability record generation and storage. The traceability query module includes an interactive terminal and query software. The query software supports SN code input, traceability record display, and historical data statistics. The interactive terminal can use a touch screen display for user convenience; it can also use a computer terminal for more powerful computing and display capabilities.

[0123] The implementation principle of this embodiment is as follows: This working system organically combines all aspects of automated production line traceability. Through the collaborative work of various modules, it achieves real-time acquisition, processing, storage, and traceability query of production data. Utilizing advanced sensors, industrial cameras, SN generators, and corresponding processing modules, the accuracy and efficiency of production traceability are ensured. The data storage module's timed backup mechanism guarantees data security and reliability, while the traceability query module provides enterprises with convenient traceability query methods, helping them to promptly identify and resolve problems in the production process and improve the level of precision in production management.

[0124] Example 4

[0125] An engine sensor assembly line in an automotive parts factory has two automatic loading ports (n=2), used for loading sensor chips (loading port 1) and metal casings (loading port 2), respectively. Each loading port corresponds to a sensor image detection station, and the last station at the end of the line is the sensor welding completion station. The method and system of this invention are used to achieve production traceability. The specific process is as follows:

[0126] Material loading data acquisition: When adding a sensor chip at loading port 1, the barcode recognition sensor (Newland NVH200) of the acquisition module reads the chip packaging barcode to obtain the material code "XC-2025-001"; the RFID reading sensor (UHF reader) reads the chip's built-in RFID tag to obtain the material batch "XC20250701"; and the counting sensor (Keyence IL-300) uses infrared sensing to count the number of materials passing through, determining the loading quantity "80". When adding a metal casing at loading port 2, the same process is used to acquire the material code "WK-2025-002", batch "WK20250702", and quantity "80". The acquisition module transmits the two sets of structured loading data (including the loading port number and timestamp "2025-07-10 08:30:00") to a MySQL database (deployed on a Dell PowerEdge R750 server) for storage via industrial Ethernet, and the database returns a "storage successful" message.

[0127] Product Inspection and Data Deduction: The sensor semi-finished product flows to the image inspection station corresponding to loading port 1 (9:00:15) via conveyor belt. The high-definition industrial camera (Hikvision 5-megapixel) at the station captures an image of the chip welding position. The image processor (Huawei Atlas 200 AI module) performs edge detection on the image and determines that the welding offset is 0.02mm (less than the allowable deviation of 0.05mm), outputting the inspection result "qualified". The system calls the product BOM list (pre-set in the data storage module), determines the material unit usage "1" for loading port 1 at this station, sends a deduction instruction to the database, updates the remaining quantity at loading port 1 from "80" to "79", and the database reports "deduction successful". The semi-finished product continues to flow to the station corresponding to loading port 2 (9:00:30). This station detects that the shell assembly gap is 0.03mm (qualified), deducts the quantity at loading port 2 from "1" according to the BOM unit usage "1" to "79", and the semi-finished product flows to the end of the line. At 9:01:00, another half-finished product was detected at station 1 of the loading port with a chip soldering offset of 0.1mm (defective). The robotic arm (SMC MGPM16-20Z) was started and grabbed the defective product to the temporary storage area within 1 second. The system performed a deduction operation and recorded "Defect detected, deducted" in the log.

[0128] Finished Product Binding and Traceability Record Storage: At 9:05:00, qualified semi-finished products are transferred to the welding station at the end of the line. After welding production is completed, the SN code generator (integrated in Advantech IPC-610L) of the finished product SN reporting module generates a unique SN code "SN20250710001" according to the rule of "SN + Date + Serial Number". The barcode scanner (Zebra DS2200) scans the SN code printed on the surface of the finished product and uploads it to the finished product binding module. The binding module retrieves the deduction logs of each station and finds that there is one "qualified but not deducted" record at feeding port 2 due to network delay (corresponding to the semi-finished product at 9:00:45). It immediately checks the current remaining quantity of feeding port 2, "79", confirms that it is ≥ the unit usage of "1", and performs a supplementary deduction operation to update the remaining quantity to "78". The module then calculates the total BOM usage for the two finished products (feed port 1:2, feed port 2:2) and compares it with the actual deducted total (feed port 1:2, feed port 2:2). The error is 0, and the calibration passes. The module binds the SN code "SN20250710001" with "XC-2025-001 (XC20250701), WK-2025-002 (WK20250702)" to generate a traceability record (including the production time "2025-07-10 09:05:00"), and stores it in the traceability record unit.

[0129] Traceability Inquiry: At 10:00, quality inspectors enter the SN code "SN20250710001" into the traceability inquiry terminal (industrial tablet). The system retrieves the traceability record within 1 second, displays the material code, batch number, and deducted quantity of the sensor, confirms that the batch of materials used is compliant, and completes the traceability verification.

[0130] Example 5

[0131] Application of an automated assembly line for electronic components

[0132] A smartphone motherboard assembly line in an electronics factory has three automatic loading ports (n=3) for loading motherboard substrates (loading port 1), capacitors (loading port 2), and resistors (loading port 3), respectively, corresponding to three image inspection stations. The end of the line is a motherboard functional testing and packaging station. This invention enables traceability, and the specific process is as follows:

[0133] Material loading data acquisition: At 8:00, material loading begins. Loading port 1 acquires the motherboard substrate code "ZB-2025-003", batch number "ZB20250705", and quantity "100" via the acquisition module. Loading port 2 acquires the capacitor code "DR-2025-008", batch number "DR20250706", and quantity "300" (3 capacitors are required per motherboard). Loading port 3 acquires the resistor code "DZ-2025-012", batch number "DZ20250707", and quantity "200" (2 resistors are required per motherboard). The acquired data is transmitted to the database for storage via WiFi.

[0134] Product Inspection and Data Deduction: 8:15, the motherboard semi-finished product is transferred to station 1 (corresponding to loading port 1). The image inspection equipment photographs the appearance of the substrate and determines that there are no scratches (qualified). The quantity at loading port 1 is deducted by "1" according to the BOM usage, bringing the total to "99". 8:18, it is transferred to station 2 (corresponding to loading port 2). The capacitor soldering is inspected and found to be qualified. The quantity at loading port 2 is deducted by "3" according to the usage, bringing the total to "297". 8:20, it is transferred to station 3 (corresponding to loading port 3). The resistor soldering is inspected and found to be qualified. The quantity at loading port 3 is deducted by "2" according to the usage, bringing the total to "198". The semi-finished product is transferred to the end of the line. 8:22, a semi-finished product at station 2 is found to have a capacitor with a missing solder joint (defective). The robot arm removes it, and the quantity at loading port 2 is deducted to maintain "294".

[0135] Finished Product Binding and Traceability Record Storage: At 8:30, the end-of-line station completes motherboard functional testing and packaging. The SN reporting module generates and uploads the SN code "SN20250710056". The binding module checks the logs and finds no undeducted records. The total deductions from the three feeding ports (feeding port 1:1, feeding port 2:3, feeding port 3:2) are calibrated to match the BOM usage of one motherboard. The SN code is then bound to the three material batches, generating and storing traceability records.

[0136] Traceability Application: When a batch of motherboards subsequently exhibited malfunctions, by entering the faulty motherboard's serial number "SN20250710056", the traceability to the resistor batch "DZ20250707" was quickly established. By checking the resistor test report for that batch, parameter deviations were discovered, allowing for the timely identification of 100 motherboards from the same batch, preventing the spread of batch quality issues. The traceability efficiency was improved by 90% compared to traditional manual methods.

[0137] As can be seen from the above two embodiments, the solution of the present invention can be flexibly adapted to automated production lines in different industries such as automotive parts and electronic components, realize accurate traceability of finished products and material batches, and effectively improve the production line's quality control capabilities and problem tracing efficiency.

[0138] According to such Figure 2 As can be seen from the production model, the generation of each image (regardless of whether it is a good or defective product) represents the consumption of one component. If the product generates image two, it means that image one must be a good product, and the material consumption at the time of image generation can be obtained.

[0139] Table 1 Material Consumption Table

[0140]

[0141] A finished product is only produced when all four images are of good quality; therefore, the production process of a single finished product can be simulated. Figure 3 As shown.

[0142] The specific embodiments described herein are merely illustrative examples illustrating the spirit of the invention. The above embodiments only express several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art to which this application pertains can make various modifications or additions to the described specific embodiments or use similar methods to replace them, but without departing from the spirit of this application or exceeding the scope defined by the appended claims. For those skilled in the art, multiple variations and improvements can be made without departing from the concept of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for implementing production traceability in an automated production line, characterized in that, Includes the following steps: S1. Start the material feeding process on the production line and collect feeding data from several automatic feeding ports; S2. The equipment and products are assembled on the production line conveyor and gradually transferred. When the products are transferred to the portrait detection equipment station corresponding to the automatic feeding port, portrait data is obtained for product detection. Specifically, based on the production line flow logic, with the product BOM list as the core, the feeding port number and unit usage corresponding to the current station are obtained through the BOM. Then, real-time deduction is performed in combination with the detection results. If the product is qualified, the unit usage is accurately deducted. If the product is defective, the unit usage is accurately deducted while triggering the robot arm to pick up the parts, so as to realize a strong correlation between material consumption and the number of qualified products. S3. After production is completed, a unique SN code for the finished product is generated and reported. Based on the production line flow logic, a three-step traceability is performed to verify the material feeding data and deduction status of each material feeding port in the early stage. Combined with the actual output quantity of the finished product at the end of the line and the SN reporting station, the total deduction of the entire production line is calibrated to ensure that the total amount of materials deducted is consistent with the total amount of materials required for the production of the finished product. A traceability record containing "SN code - material code - material batch - deduction quantity - production time" is formed, completing the three-step traceability full-process traceability data closed loop. Step S1 specifically includes starting the feeding process from the beginning of the production line, adding materials to the n automatic feeding ports on the line, and the collection device at each feeding port working in real time. The feeding data includes barcode recognition sensors reading material codes on material packaging, RFID reading sensors acquiring material batch information, and counting sensors counting the quantity of each feeding. The three-step traceability specifically includes associating and algorithmically processing data in the order of "material loading data - profile data - SN code" to bind the finished product SN code to each batch of materials at each loading port; In step S2, after the product passes all the portrait inspection equipment stations, it is transferred to the last station for final assembly and production. The quantity of finished products = the minimum value of the unit usage of all portrait inspection equipment stations minus the quantity of finished products. In step S3, the verification ensures that the total amount deducted across the entire production line is consistent with the total amount of materials required for finished product production. Specifically, this includes: after receiving the SN code, first checking the material deduction status of each automatic feeding port in the previous stage; if there is any data that has not been deducted due to system delays, etc., then deducting it in the order of automatic feeding ports 1 to n; then, combining the actual output quantity of finished products reported by the final finished product SN at the end of the line, calibrating the deduction data of each automatic feeding port to ensure that the total amount deducted is consistent with the total amount of materials required for finished product production.

2. The method for implementing production traceability on an automated production line according to claim 1, characterized in that, If the result of the image inspection equipment station's inspection of the product is defective in step S2, the robotic arm unit will immediately start, remove the defective product from the production line and transfer it to the defective product temporary storage area, and accurately deduct the amount based on the unit usage. The amount of defective products at the current image inspection equipment station = the deducted unit usage at the current image inspection equipment station - the deducted unit usage at the next image inspection equipment station.

3. A working system for an automated production line traceability implementation method, applicable to the automated production line traceability implementation method according to any one of claims 1-2, characterized in that, It includes n automatic feeding ports configured with barcode recognition, RFID reading, and counting sensors; n image detection stations configured with industrial cameras and pneumatic robots; and one end-of-line finished product SN reporting station configured with SN generator and barcode scanner. The automatic feeding ports correspond one-to-one with the detection stations, and the image detection station and the end-of-line finished product SN reporting station are connected by a conveyor track.

4. The working system of the automated production line production traceability implementation method according to claim 3, characterized in that, The automatic feeding port includes a feeding data acquisition module, which collects core material information, integrates the information into feeding data, and then transmits it to the data storage module. The portrait detection station includes a portrait detection and processing module, which is connected to other modules of the system via an industrial Ethernet to transmit detection results and deduction instructions in real time, thereby realizing an automated closed loop of "detection-judgment-processing". The finished product SN reporting station at the end of the production line includes a finished product SN reporting module, which automatically generates a unique SN code according to preset rules, and a code scanning and uploading component scans and uploads the SN code to the finished product binding and traceability record module.

5. The working system of the automated production line production traceability implementation method according to claim 4, characterized in that, It also includes, The data storage module uses a timed backup mechanism to store data, including: feeding data transmitted by the feeding data acquisition module, including feeding port number, material code, material batch, quantity, and feeding time; and image detection data generated by the image detection and processing module, including workstation number, detection result, and detection time. The finished product binding and traceability recording module connects with other modules through a communication interface to complete data deduction, SN code binding, traceability record generation and storage; The traceability query module includes an interactive terminal and query software. The query software supports SN code input, traceability record display, and historical data statistics.

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