Product barcode correction method and system combined with time sequence analysis

Abstract

The invention discloses a product bar code correction method and system combined with time sequence analysis, and belongs to the technical field of automatic identification, and the method comprises the steps: configuring a scanner at a station 0 to carry out the scanning of a product bar code; performing health scoring on the real-time scanning image by using a health scoring network, establishing a product barcode score, and obtaining a time sequence order of a corresponding product; performing score judgment on the product barcode score, if a score judgment result is a result lower than a preset score threshold value, identifying the corresponding product as a weak barcode, and triggering a station jump correction instruction; carrying out station complement adaptation identification according to the station jump correction instruction; and selecting a complement station based on an adaptive optimization result, carrying out product information calling based on a time sequence by using the complement station, and carrying out corresponding product complement processing according to a product information calling result. The technical problems of high bar code error rate and low correction efficiency caused by unstable product bar code identification quality and incapability of dynamically adjusting a correction strategy in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic identification, and particularly to a product barcode correction method and system combined with time series analysis. Background Art

[0002] As an important information identification means in the manufacturing and retail industries, the reliability and accuracy of barcode technology have a crucial impact on product traceability, inventory management, and production efficiency. In practical applications, barcodes are read for data through scanning devices. However, limited by barcode printing quality, scanning device performance, and the complexity of the production environment, barcode information is prone to identification errors or information loss during the reading process, affecting the integrity of data collection. To address this issue, researching automatic correction technology for barcode information has become an important direction for ensuring data consistency throughout the product process.

[0003] Existing barcode correction methods usually rely on fixed rules or simple algorithms, with limited ability to remedy barcode information. At the initial stage of product barcode scanning, scanners often fail to identify barcodes or misjudge due to barcode damage, unclear printing, etc. The current solutions cannot dynamically adjust the correction strategy, nor fully combine the time series information of the production line, resulting in missing barcode data for some products, seriously affecting the accuracy and timeliness of subsequent product data processing. Therefore, there is an urgent need for a solution that combines time series analysis and an intelligent correction mechanism to reduce the error rate of barcode identification and improve the efficiency of the production process. Summary of the Invention

[0004] This application provides a product barcode correction method and system combined with time series analysis, aiming to solve the technical problems in the prior art of unstable product barcode recognition quality, inability to dynamically adjust the correction strategy, resulting in a high barcode error rate and low correction efficiency.

[0005] In view of the above problems, this application provides a product barcode correction method and system combined with time series analysis.

[0006] In the first aspect disclosed in this application, a product barcode correction method combined with time series analysis is provided. The method includes configuring a scanner at station 0 to scan the product barcode and establish a real-time scanned image; configuring a health scoring network, using the health scoring network to perform a health score on the real-time scanned image, establish a product barcode score value, and obtain the time series position of the corresponding product; perform a score discrimination on the product barcode score value. If the score discrimination result is lower than a preset score threshold, mark the corresponding product as a weak barcode and trigger a skip station correction instruction; perform a station complement code adaptation recognition according to the skip station correction instruction and establish an adaptation optimization result; select a complement code station based on the adaptation optimization result, use the complement code station to call product information based on the time series position, and perform corresponding product complement code processing according to the product information call result.

[0007] Another aspect disclosed in this application provides a product barcode correction system combined with timing analysis. The system includes a real-time scanning image establishment module for configuring a scanner at station 0 to scan the product barcode and establish a real-time scanning image; a scoring value establishment module for configuring a health scoring network, using the health scoring network to perform a health score on the real-time scanning image, establishing a product barcode scoring value, and obtaining the timing ranking of the corresponding product; a score discrimination module for performing a score discrimination on the product barcode scoring value. If the score discrimination result is lower than the preset score threshold, the corresponding product is marked as a weak barcode and a skip station correction instruction is triggered; an adaptation optimization result establishment module for performing a station complement code adaptation recognition according to the skip station correction instruction and establishing an adaptation optimization result; a complement code processing module for selecting a complement code station based on the adaptation optimization result, using the complement code station to call product information based on the timing ranking, and performing complement code processing on the corresponding product according to the product information call result.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Due to adopting a product barcode correction method combined with timing analysis, including technical solutions such as configuring a scanner to establish a real-time scanning image, using a health scoring network for scoring, discriminating barcode quality and triggering a skip station correction instruction, selecting a complement code station based on the adaptation optimization result and performing complement code processing, it solves the technical problems in the prior art that the recognition quality of product barcodes is unstable and the correction strategy cannot be dynamically adjusted, resulting in a high barcode error rate and low correction efficiency, and achieves the technical effects of improving barcode recognition accuracy, optimizing the correction process, and increasing production efficiency.

[0009] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specific embodiments of this application are specifically given. Description of the Drawings

[0010] Figure 1 It is a flowchart showing the product barcode correction method combined with timing analysis provided by an embodiment of this application.

[0011] Figure 2 It is a structural diagram showing the product barcode correction system combined with timing analysis provided by an embodiment of this application.

[0012] Description of the reference numerals: real-time scanning image establishment module 11, scoring value establishment module 12, score discrimination module 13, adaptation optimization result establishment module 14, complement code processing module 15. Detailed Embodiments

[0013] The general idea of the technical solution provided by this application is as follows: The embodiments of this application provide a product barcode correction method and system combined with timing analysis. First, at station 0, the barcode is scanned and a real-time image is established. A health scoring network is used to evaluate the barcode quality, generate a scoring value and make a judgment; for barcodes below the preset threshold, they are marked as weak barcodes and a skip station correction instruction is triggered. Subsequently, the optimal supplementary code station is selected through the load and delay analysis of the supplementary code station, the product information is called, and the barcode correction operation is completed.

[0014] After introducing the basic principle of this application, the various non-limiting implementation manners of this application will be specifically introduced below in conjunction with the accompanying drawings of the specification.

[0015] Embodiment 1, as Figure 1 shown, the embodiments of this application provide a product barcode correction method combined with timing analysis, and the method includes: Step S100: Configure a scanner at station 0 to scan the product barcode and establish a real-time scanned image.

[0016] Specifically, station 0 refers to the first station on the production line, which is usually used for initialization processing, such as barcode scanning or product information entry, and is the starting point of the production process. A scanner is a barcode recognition device that uses optical technology to scan the barcode and parse it into a digital signal, and then transmits it to the data processing system. Common types of scanners include laser barcode scanners, linear imaging scanners, and two-dimensional imaging scanners.

[0017] Configure a high-precision barcode scanner at station 0 of the production line. By configuring parameters such as the light source, focal length, and photosensitive element, it is possible to quickly capture the barcode image on each product passing through the production line. Specifically, the scanner is fixedly installed above the conveyor belt at station 0, and the distance, scanning angle, and light source intensity between the scanner and the product surface are adjusted to make it cover the best range of the product barcode area. When the product on the conveyor belt passes through station 0, the sensor detects that the product enters the scanning area, and the scanner immediately triggers the shooting operation, collects the barcode image and transmits it to the background system in real time. The barcode image transmitted by the scanner is processed through built-in algorithms, including operations such as denoising, enhancing contrast, cropping the barcode area, and correcting the image tilt angle, so as to perform health scoring and recognition later.

[0018] By configuring a high-precision barcode scanner at station 0 and establishing a barcode scanned image in real time, it is possible to efficiently and accurately capture the real-time status image of the barcode, providing a reliable data basis for subsequent health scoring and correction operations.

[0019] Step S200: Configure a health scoring network, use the health scoring network to perform a health score on the real-time scanned image, establish a product barcode score value, and obtain the sequential position of the corresponding product.

[0020] Specifically, the health scoring network is a deep learning-based artificial neural network used to analyze barcode image quality, evaluate the health status of barcodes (such as clarity, integrity, and contrast), and give a numerical scoring result. The product barcode score value is a numerical index obtained based on the analysis result of the health scoring network, used to quantify the quality of the barcode image. The higher the score, the better the health status of the barcode. The sequential position refers to the sequential arrangement position of the product on the production line, recording the order in which the product passes through the scanning station.

[0021] Use a health scoring model based on a convolutional neural network (CNN), such as a deep learning model built with TensorFlow or PyTorch. After inputting the real-time scanned image, it automatically extracts barcode features (such as edge clarity, contrast, and continuity) and outputs a score value. Input the real-time scanned image into the health scoring network, and use the convolutional layer to extract the key features of the image (such as the smoothness, linearity, and continuity of barcode lines). The network will label the blurred area of the barcode and calculate the influence weight. The health scoring network calculates the comprehensive score value according to the extracted features using the weight model and saves the scoring result to the product database. At the same time, the system records the production sequential position of each product and corresponds it one by one with the barcode score value. The sequential position of the product is updated in real-time on the production line for subsequent skip station correction operations.

[0022] By introducing the health scoring network, the system can accurately identify the quality of barcode images and generate a numerical scoring result for each product. Combining with the sequential position of the product, the system can quickly mark weak barcodes and plan correction paths. Generally speaking, this step greatly improves the automation level of barcode scanning and the reliability of data processing, laying a foundation for subsequent process optimization.

[0023] Step S300: Perform a score discrimination on the product barcode score value. If the score discrimination result is lower than the preset score threshold, mark the corresponding product as a weak barcode and trigger a skip station correction instruction.

[0024] Specifically, a weak barcode refers to a barcode with a score lower than the preset score threshold, usually caused by reasons such as blurred edges, breaks, discontinuity, or insufficient contrast, resulting in substandard barcode quality. The skip station correction instruction refers to the instruction generated by the system when the barcode is marked as a weak barcode, requiring the production line to skip the current station and go to the designated barcode correction station to perform barcode correction operations.

[0025] After the system obtains the product barcode scoring value, it compares it with the preset scoring threshold. If the value is lower than the threshold, it is determined as a weak barcode; if the value is higher than or equal to the threshold, it is determined as a normal barcode and no correction is required. For products determined to be weak barcodes, a skip-station correction instruction is generated for the products marked as weak barcodes, instructing the production line to skip the current station of the product and go to the designated barcode replenishment station.

[0026] Through the score discrimination, weak barcode identification, and triggering of the skip-station correction instruction, the system can quickly screen out the barcodes that need to be corrected and accurately guide them to the appropriate barcode replenishment station, thereby improving the automation level and barcode correction efficiency of the production line.

[0027] Step S400: Perform station barcode replenishment adaptation identification according to the skip-station correction instruction to establish an adaptation optimization result.

[0028] Specifically, station barcode replenishment adaptation identification refers to dynamically evaluating and screening the most suitable station for barcode replenishment correction according to the actual status of each station in the production line (such as workload, delay time). The adaptation optimization result refers to calculating and selecting an optimal station by combining the load analysis and delay constraints of the station to ensure the best balance between production efficiency and accuracy in the barcode replenishment operation. The delay time refers to the additional time required for the product to complete the barcode replenishment correction, including the transfer delay time and queue delay time caused by queuing or station transfer. The constraint condition refers to the maximum allowable range of the set load and delay time, which is used to screen available barcode replenishment stations.

[0029] The system receives the skip-station correction instruction, identifies the barcode products that need barcode replenishment and their current stations, and calls the barcode replenishment station information database. Analyze all potential barcode replenishment stations in the production line in turn, including: load analysis, delay time calculation. According to the maximum load limit and delay time limit of the production line, screen out the stations that meet the constraint conditions. Among the eligible stations, select the station with the lightest load and the shortest delay time as the barcode replenishment station. Output the optimization result, including the optimal station number, estimated delay time, and barcode replenishment operation time.

[0030] By combining the skip-station correction instruction with the station barcode replenishment adaptation identification, the system can dynamically adjust the barcode correction strategy to ensure the efficiency and stability of the production line. This method makes full use of the production line resources, avoids resource waste and production delays caused by randomly selecting stations, realizes the high-efficiency and intelligentization of barcode replenishment correction, and improves the overall production quality management level.

[0031] Step S500: Select the barcode replenishment station based on the adaptation optimization result, use the barcode replenishment station to call the product information based on the time sequence order, and perform the corresponding product barcode replenishment processing according to the product information call result.

[0032] Specifically, the time sequence position refers to the arrangement order of products on the production line, which is used to track the production status and transfer information of each product to ensure the correspondence between product information retrieval and complement code processing. Product information retrieval means that the system extracts product-related information from the database through the time sequence position, including product numbers, barcode images, and their health score values, which is used to guide complement code correction. Complement code processing refers to the operation of correcting or regenerating the barcode at the complement code position according to the result of product information retrieval. For example, barcode reprinting, error correction, or information completion.

[0033] Determine the target complement code position according to the adaptation optimization result, and control the conveyor belt or manipulator to send the product to this position. During the transmission process, the system synchronously updates the time sequence position of the product to ensure the consistency between the complement code operation and the product information. After the product arrives at the complement code position, the system extracts specific product-related information from the database through the time sequence position, including: product number and barcode image. Health score value and the identified barcode defect types (such as blurring, discontinuity, etc.). According to the retrieved product information, the complement code position processes the barcode through a preset correction mechanism, including: Barcode reprinting: Use a barcode printer to regenerate a clear barcode and cover the old barcode. Optical error correction: Correct blurred or broken barcode lines through laser engraving. Rescanning: For barcodes that fail to be read, re-identify the barcode by optimizing the scanning angle or adjusting the light source. Specifically, the complement code position can be configured with high-precision barcode printers (such as Zebra series), laser engraving equipment, or high-resolution scanners, combined with image processing software (such as OpenCV) to achieve efficient complement code. After the complement code processing is completed, the system records the complement code operation results, including barcode status before and after correction, correction time consumption, and other information for subsequent traceability and process optimization.

[0034] By selecting the complement code position based on the adaptation optimization result and invoking product information for complement code processing, the system can ensure the accuracy and efficiency of barcode correction operations while reducing manual intervention. It can not only dynamically allocate complement code tasks but also perform personalized correction operations for specific problems, significantly improving the automation level and the reliability of barcode data.

[0035] Furthermore, the use of the health scoring network to perform health scoring on the real-time scanned image and establish a product barcode scoring value includes: using the positioning preprocessing layer of the health scoring network to perform focus positioning on the real-time scanned image to establish a focus positioning result; performing edge contour extraction on the focus positioning result, and performing edge smoothness scoring based on the edge contour extraction result to generate a first scoring result; performing edge linearity scoring based on the edge contour extraction result to establish a second scoring result; performing edge continuity scoring based on the edge contour extraction result to establish a third scoring result; performing contrast recognition between the focus positioning result and the background of the real-time scanned image to establish a fourth scoring result; and establishing a product barcode scoring value based on the first scoring result, the second scoring result, the third scoring result, and the fourth scoring result.

[0036] Specifically, the positioning preprocessing layer refers to the first layer in the health scoring network, which is used to identify the region of interest of the barcode image and remove the background parts that are not related to the barcode, thereby improving the accuracy and efficiency of subsequent feature analysis. The edge smoothness score refers to the calculation of the smoothness of the barcode based on the angle change of the barcode line. A high score means that the barcode line is smooth and has no mutations. The edge straightness score refers to the evaluation of the straight line characteristics of the barcode line, and the determination coefficient of the linear fit is used to quantify whether the barcode has good linearity. The edge continuity score is used to measure whether there are breakpoints or gaps in the barcode line. A high score means that the barcode line has no obvious interruption.

[0037] After the barcode image is input into the health scoring network, the positioning preprocessing layer first identifies the region of interest (ROI) of the barcode. This process is achieved by combining a sliding window with a convolution operation. For example, the image segmentation algorithm of OpenCV is used to separate the barcode area from the background. The edge lines of the barcode are extracted by the Canny edge detection method, and a contour image is generated. The contour extraction results provide the basis for subsequent scoring. Example: For a blurred barcode, contour extraction identifies most of the lines, but breakpoints and blurred areas still exist. The contour lines are analyzed, the angle changes between adjacent points on the lines are calculated, and a score is given based on the smoothness of the change. Barcodes with smaller angle changes have higher scores. Linear regression is used to fit the contour lines, and the coefficient of determination is calculated to quantify the linearity. The higher the fitting accuracy, the higher the score. The number of contour interruption points and the maximum gap length are detected, and the interruption situation is quantified as a score. Barcodes with fewer gaps and better continuity have higher scores.

[0038] By analyzing the gray value difference between the barcode area and the background, the contrast is quantified and given a score. For example, by calculating the gray mean difference between the barcode area and the background. The smoothness, linearity, continuity and contrast scores are combined to generate a barcode score in a weighted ratio. For example, smoothness accounts for 30%, linearity accounts for 25%, continuity accounts for 25%, and contrast accounts for 20%.

[0039] By leveraging the multi-dimensional analysis of real-time scanned images through a health scoring network, the system can accurately and comprehensively evaluate the barcode quality, generate scientific scoring values, and provide a basis for subsequent correction operations. Overall, this step significantly improves the reliability of barcode recognition and the automation level of the production line.

[0040] Furthermore, the first scoring result is calculated as follows: ; ; where, represents the edge smoothness score, represents the average angular change between edge points, represents the edge angular change threshold, represents the total number of points on the same straight line, represents any point on the current straight line, represents the th point to the th point direction angle, represents the

[0041] Specifically, the edge smoothness score is a score used to evaluate the smoothness of the barcode edge line. The higher the score, the smoother the barcode edge, the more uniform the line, and the better the quality. The average angular change refers to the average value of the angular changes between adjacent lines on the barcode edge, reflecting the smoothness of the lines. If the angular change is small, it indicates that the line connection is smooth. The edge angular change threshold is a reference value used to measure smoothness, usually a preset standard, indicating within what range the line can be considered smooth. The direction angle refers to the direction between a point and another point on the edge line, used to describe the trend of the barcode line. The total number of points refers to the number of all points on the edge line, indicating the length and complexity of the barcode line.

[0042] First, by extracting the coordinate of all points on the barcode edge line ( , ), calculate the direction angle of each pair of adjacent points, specifically: . If the line is smooth, the changes in these direction angles will be very small; if the line is not smooth, the direction angle will change significantly. Then, statistically analyze the changes in the direction angles of all adjacent points and calculate the average value of the angular changes on the barcode edge. The smaller this average value, the smoother the line. According to the statistically analyzed angular change value, compare it with the set smoothness standard to generate a scoring value. The scoring range is usually between 0 and 100, and the higher the score, the smoother the barcode line.

[0043] Through edge smoothness scoring, the system can quantify the quality of barcode lines. Especially when the barcode has breaks or unevenness due to blurred printing or damage, the score will decrease significantly. This scoring mechanism can effectively identify barcodes that need to be corrected, thereby improving the accuracy of barcode scanning.

[0044] Furthermore, the second scoring result is calculated as follows: ; ; where represents the edge linearity score, represents the coefficient of determination of the fitted line for the edge points, represents the minimum standard of the coefficient of determination, represents the predicted value on the fitted line, represents the average of all points value, represents the th point's true ordinate value.

[0045] Specifically, the edge linearity score is used to evaluate whether the barcode edge lines exhibit good linear characteristics. The higher the score, the closer the barcode lines are to a straight line and the better the quality. The coefficient of determination is a statistic used to measure the degree of fit between the fitted line and the actual edge points. The closer the value is to 1, the closer the distribution of the edge points is to a straight line and the better the fitting effect. The minimum standard of the coefficient of determination is a threshold used to measure whether the fitting result meets the minimum requirement of linearity.

[0046] Using the least squares method to perform linear fitting on the extracted edge points to generate the equation of the fitted line, based on the actual ordinate of the edge points and the predicted value of the fitted line, calculate . Compare the calculated with the set minimum standard to generate the edge linearity score. The score range is from 0 to 100, and the closer it is to 100, the better the linearity.

[0047] The edge linearity score can effectively identify line bending problems caused by low printing quality or damage by evaluating whether the overall trend of the barcode edge is close to a straight line. This scoring method is crucial for barcode recognition quality. The automated implementation of this method can also reduce manual intervention, improve production efficiency and the reliability of barcode recognition.

[0048] Furthermore, the third scoring result is calculated as follows: ; ; Among them, represents the edge continuity score, represents the maximum gap length in the edge contour, represents the threshold of the maximum allowable gap length, represents the true abscissa value of the point, true abscissa value of the point, true ordinate value of the

[0049] Specifically, the edge continuity score is used to measure whether the bar code edge lines are continuous without obvious breakpoints or gaps. The higher the score, the more continuous the edge lines are and the better the bar code quality. The maximum gap length refers to the maximum value of the distance between adjacent points in the bar code edge lines, indicating the severity of the edge breakpoints or gaps. The smaller the value, the smaller the gap and the more continuous the edge. The threshold of the maximum allowable gap length is a preset standard for determining whether there are unacceptable gaps at the edge. If exceeds this threshold, it indicates that the bar code quality is poor.

[0050] Calculate the distance between each pair of adjacent points in turn, and calculate the distance using the Pythagorean theorem according to the coordinates of the two points. The result records the spacing between all points and is used to find the maximum distance (i.e., the maximum gap length). Find the maximum gap length between all pairs of points and compare it with the threshold of the maximum allowable gap length . If exceeds the threshold, it indicates that the continuity of the bar code edge is poor and a correction process needs to be triggered. Calculate the score value according to the ratio of the gap length to the threshold. The score range is from 0 to 100, and the smaller the maximum gap, the higher the score.

[0051] Through the calculation of the edge continuity score, the system can accurately evaluate the integrity of the bar code edge and automatically detect breakpoints and gaps. Combining the score results, it can quickly determine whether the bar code needs to be corrected and avoid scanning failures caused by bar code defects.

[0052] Furthermore, the score value discrimination of the product bar code score includes: if the score discrimination result meets the preset score threshold result, a normal transfer instruction is generated; and the transfer process of the corresponding product is executed according to the normal transfer instruction.

[0053] Specifically, after receiving the barcode score value, it is compared with a preset score threshold. If the score value is greater than or equal to the threshold, the barcode is considered qualified; if it is lower than the threshold, it is marked as a weak barcode that needs to be corrected. For example: The set score threshold of the system is 80. The score of barcode A is 85 and it is judged as qualified; the score of barcode B is 75 and it is marked as a weak barcode.

[0054] For the barcodes judged to be qualified, the system automatically generates a normal transfer instruction and sends it to the control module of the conveyor belt or production equipment through an industrial communication protocol. This instruction may include product number, barcode score value, and target station information. After receiving the normal transfer instruction, the production equipment (such as a conveyor belt or a manipulator) starts the corresponding operation to smoothly transfer the product to the next station. The operating status of the equipment is monitored in real time by sensors to ensure that the transfer process is error-free. While performing the transfer process, the system records the score value and transfer instruction into the database for subsequent analysis and quality traceability.

[0055] Through the discrimination of the score value and the generation and execution of the normal transfer instruction, the system can achieve rapid screening and efficient processing of barcode quality, greatly improving the automation level of the production line and the accuracy of barcode transfer.

[0056] Furthermore, the step of performing station complement code adaptation recognition according to the skip station correction instruction and establishing an adaptation optimization result includes: performing a complement code load analysis of the execution station to establish a load constraint, where the complement code load analysis includes continuous load analysis and temporary window load analysis; obtaining the delay time of the station complement code to establish a delay constraint, where the delay time includes transfer delay time and complement code queue delay time; performing station complement code adaptation optimization according to the load constraint and the delay constraint to generate an adaptation optimization result.

[0057] Specifically, continuous load analysis refers to evaluating the average workload of a station in a stable operating state. Temporary window load analysis refers to analyzing the additional load on a station due to sudden task increases during a specific time period. Transfer delay time refers to the time it takes for a product to be transferred from the current station to the complement code station, which is determined by the conveyor belt speed and the transfer distance. Complement code queue delay time refers to the delay time generated when a product waits in line for tasks at the complement code station. Load constraint refers to the load upper limit set by the system according to the production line operation capacity. If the current load of a station exceeds this threshold, it is considered unqualified. Delay constraint refers to the upper limit of the delay time set by the system. If the transfer delay time or queue delay time of a station exceeds this limit, that station is excluded.

[0058] The system receives the barcode correction instruction, analyzes the product information that needs to skip stations for supplementary coding and its corresponding station positions, and initiates the station status assessment process. The current workload of all candidate supplementary coding stations is evaluated through the real-time data acquisition module: calculate the average task volume currently processed at the station. For example, the current continuous load at Station 1 is 70%. Statistically analyze the load fluctuations caused by short-term task backlogs during a certain period. For example, the temporary load at Station 2 reaches 85%.

[0059] The transfer delay time is calculated by the sensor collecting the conveyor belt speed and transmission distance. The supplementary coding queue delay time is calculated based on the number of queued products and the processing speed. The station transfer information is collected in real time through the PLC controller and sensors. Compare the loads and delay times of all candidate stations with the preset constraints, and exclude the stations that exceed the limits. Among the eligible stations, the system calculates the optimal supplementary coding station based on the principles of minimizing load and shortest delay time, and generates a specific supplementary coding plan. The adapted optimization results are sent to the production equipment to control the conveyor belt or robotic arm to send the product to the optimal supplementary coding station and monitor the supplementary coding operation in real time.

[0060] Through the combination of the skip station correction instruction and the station supplementary coding adaptation recognition, the system realizes the intelligent scheduling and optimization of production line resources. This method can dynamically select the optimal station to execute barcode correction, significantly reducing the delay time and resource waste of the production line. It not only improves the barcode correction efficiency but also provides technical support for the intelligent management of the entire production process.

[0061] In summary, the product barcode correction method provided by the embodiments of this application in combination with timing analysis has the following technical effects: 1. The barcode correction method in combination with timing analysis significantly improves the accuracy of barcode recognition and the integrity of production data by dynamically adjusting the scanning, scoring, and correction strategies. By monitoring the barcode health status in real time, it ensures that quality problems can be detected and processed in a timely manner, reduces the barcode loss rate, optimizes production efficiency, and is especially suitable for automated production lines with high-precision requirements.

[0062] 2. Through multi-dimensional analysis (such as smoothness, linearity, and continuity) of barcode images by the health scoring network, it provides a refined quality assessment method. This method can comprehensively identify barcode defect types, reduce misjudgments caused by traditional single scoring indicators, and improve the reliability and scientificity of barcode health scoring.

[0063] 3. Through score discrimination and normal transfer instruction generation, the system can achieve automatic screening and processing of barcode quality, preventing unqualified barcodes from entering subsequent processes. This mechanism improves the automation level of the production line, reduces manual intervention, ensures the smooth flow of high-quality products, and optimizes the overall production efficiency.

[0064] Embodiment 2. Based on the same inventive concept as the product barcode correction method combined with timing analysis in the foregoing embodiment, as Figure 2 shown, the embodiment of the present application provides a product barcode correction system combined with timing analysis. The system includes: A real-time scan image establishment module 11, configured to configure a scanner at station 0 to scan a product barcode and establish a real-time scan image; a scoring value establishment module 12, configured to configure a health scoring network, use the health scoring network to perform a health score on the real-time scan image, establish a product barcode scoring value, and obtain the timing ranking of the corresponding product; a score discrimination module 13, configured to perform a score discrimination on the product barcode scoring value. If the score discrimination result is a result lower than a preset score threshold, the corresponding product is marked as a weak barcode and a skip station correction instruction is triggered; an adaptation optimization result establishment module 14, configured to perform a station complement code adaptation recognition according to the skip station correction instruction and establish an adaptation optimization result; a complement code processing module 15, configured to select a complement code station based on the adaptation optimization result, use the complement code station to call product information based on the timing ranking, and perform corresponding product complement code processing according to the product information call result.

[0065] Further, the scoring value establishment module 12 is further configured to perform the following steps: use the positioning preprocessing layer of the health scoring network to perform focus positioning on the real-time scan image and establish a focus positioning result; perform edge contour extraction on the focus positioning result, perform an edge smoothness score based on the edge contour extraction result, and generate a first score result; perform an edge linearity score based on the edge contour extraction result and establish a second score result; perform an edge continuity score based on the edge contour extraction result and establish a third score result; perform a contrast recognition between the focus positioning result and the background on the real-time scan image and establish a fourth score result; establish a product barcode scoring value according to the first score result, the second score result, the third score result, and the fourth score result.

[0066] Further, the scoring value establishment module 12 is further configured to perform the following steps: ; ; Wherein, represents the edge smoothness score, represents the average angle change between edge points, represents the edge angle change threshold, represents the total number of points on the same straight line, represents any point on the current straight line, represents the th point to the Characterize the point to the point's direction angle.

[0067] Furthermore, the scoring value establishing module 12 is further configured to perform the following steps: ; ; wherein, characterizes the edge linearity score, characterizes the coefficient of determination of the fitting line of the edge points, characterizes the minimum standard of the coefficient of determination, characterizes the predicted value on the fitting line, characterizes the average of all points value, characterizes the point's true ordinate value.

[0068] Furthermore, the scoring value establishing module 12 is further configured to perform the following steps: ; ; wherein, characterizes the edge continuity score, characterizes the maximum notch length in the edge contour, characterizes the maximum notch length threshold allowed, characterizes the point's true abscissa value, characterizes the point's true abscissa value, characterizes the point's true ordinate value.

[0069] Furthermore, the score discrimination module 13 is further configured to perform the following steps: If the score discrimination result is a result that meets the preset scoring threshold, generate a normal transfer instruction; perform the transfer processing of the corresponding product according to the normal transfer instruction.

[0070] Furthermore, the adaptation optimization result establishing module 14 is further configured to perform the following steps: perform the complement load analysis of the station, establish the load constraint, and the complement load analysis includes continuous load analysis and temporary window load analysis; obtain the delay time of the station complement, establish the delay constraint, and the delay time includes transfer delay time and complement queue delay time; perform the adaptation optimization of the station complement according to the load constraint and the delay constraint, and generate the adaptation optimization result.

[0071] Any step of the method described above can be stored as computer instructions or programs in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor to implement any one of the methods in the embodiments of the present application, without further limitation here.

[0072] Furthermore, the first or second as described above may not only represent an order relationship, but may also represent a specific concept, and / or refer to the selection of multiple elements either individually or in whole. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A product barcode correction method combined with timing analysis, characterized in that The method includes: Configuring a scanner at station 0 to scan the product barcode and establish a real-time scan image; Configuring a health scoring network, using the health scoring network to perform a health score on the real-time scan image, establish a product barcode score value, and obtain the chronological ranking of the corresponding product; Performing a score discrimination on the product barcode score value. If the score discrimination result is a result lower than a preset score threshold, then mark the corresponding product as a weak barcode and trigger a skip station correction instruction; Performing a station complement code adaptation recognition according to the skip station correction instruction and establishing an adaptation optimization result; Selecting a complement code station based on the adaptation optimization result, using the complement code station to call product information based on the chronological ranking, and performing a complement code process on the corresponding product according to the product information call result.

2. The product barcode correction method combined with timing analysis according to claim 1, wherein The using the health scoring network to perform a health score on the real-time scan image and establish a product barcode score value includes: Using the positioning preprocessing layer of the health scoring network to perform a focus positioning on the real-time scan image and establish a focus positioning result; Performing an edge contour extraction on the focus positioning result, and performing an edge smoothness score based on the edge contour extraction result to generate a first score result; Performing an edge linearity score based on the edge contour extraction result and establishing a second score result; Performing an edge continuity score based on the edge contour extraction result and establishing a third score result; Performing a contrast recognition between the focus positioning result and the background on the real-time scan image and establishing a fourth score result; Establishing a product barcode score value according to the first score result, the second score result, the third score result, and the fourth score result.

3. The product barcode correction method combined with timing analysis according to claim 2, wherein The first score result is calculated as follows: ; ; Among them, characterizes the edge smoothness score, characterizes the average angular change between edge points, characterizes the edge angle change threshold, characterizes the total number of points on the same straight line, characterizes any point on the current straight line, characterizes the angle from the point to the angle from the point to the point.

4. The product barcode correction method combined with timing analysis according to claim 2, characterized in that, The second score result is calculated as follows: ; ; Among them, characterizes the edge linearity score, characterizes the coefficient of determination of the fitted line of the edge points, characterizes the minimum standard of the coefficient of determination, characterizes the predicted value on the fitted line, characterizes the average of all points value, characterizes the true ordinate value of the point.

5. The product barcode correction method combined with timing analysis according to claim 2, characterized in that, The third score result is calculated as follows: ; ; in, Characterize the edge continuity score, Characterizes the maximum notch length in the edge profile, Characterizes the maximum gap length threshold allowed, Characterization The actual horizontal coordinate value of the point, Characterization The actual horizontal coordinate value of the point, Characterization The true vertical coordinate value of the point.

6. The product barcode correction method combined with timing analysis according to claim 1, wherein The performing a score discrimination on the product barcode score value includes: If the score discrimination result is a result that meets the preset score threshold, then generate a normal transfer instruction; Performing a transfer process on the corresponding product according to the normal transfer instruction.

7. The product barcode correction method combined with timing analysis according to claim 1, wherein The performing a station complement code adaptation recognition according to the skip station correction instruction and establishing an adaptation optimization result includes: Performing a complement code load analysis of the station, establishing a load constraint, and the complement code load analysis includes a continuous load analysis and a temporary window load analysis; Obtaining the delay time of the station complement code and establishing a delay constraint, and the delay time includes a transfer delay time and a complement code queue delay time; Performing a station complement code adaptation optimization according to the load constraint and the delay constraint to generate an adaptation optimization result.

8. The product barcode correction system combined with timing analysis is characterized in that, For implementing the method according to any one of claims 1 to 7, the system includes: A real-time scan image establishment module, configured to configure a scanner at station 0 to scan the product barcode and establish a real-time scan image; A score value establishment module, configured to configure a health scoring network, use the health scoring network to perform a health score on the real-time scan image, establish a product barcode score value, and obtain the chronological ranking of the corresponding product; A score discrimination module, configured to perform a score discrimination on the product barcode score value. If the score discrimination result is a result lower than a preset score threshold, then mark the corresponding product as a weak barcode and trigger a skip station correction instruction; An adaptation optimization result establishment module, configured to perform station complement code adaptation recognition according to the hopping station correction instruction and establish an adaptation optimization result; A complement code processing module, configured to select a complement code station based on the adaptation optimization result, use the complement code station to call product information based on the timing order, and perform corresponding product complement code processing according to the product information call result.

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