Laser vision coding machine coding quality control method and system

Abstract

The present invention discloses a coding quality control method and system for a laser vision coding machine, which relates to the technical field of coding quality control, in order to solve the problem that the laser vision coding machine cannot optimize the coding according to the coding process. The present invention can accurately identify specific problems such as laser parameter abnormalities, equipment failures and hardware problems by performing abnormal identification on unqualified quality indicators in coding monitoring data. This accuracy enables subsequent improvement measures to solve the problem more targetedly. By formulating clear coding quality inspection standards and goals, the standardization and consistency of quality inspection work are ensured, the division standards of quality assessment are clarified, and the quality assessment results are more objective and accurate. By designing reasonable path types, starting points, end points and path sequences, the coding efficiency is improved. The path simulation step ensures the accuracy of the coding path and the absence of intersections or overlaps, avoiding repeated coding and waste.

Description

Technical Field

[0001] The present invention relates to the technical field of coding quality control, in particular to a coding quality control method and system for a laser vision coding machine. Background Art

[0002] Laser vision coding machine coding quality control refers to a series of measures and methods used to ensure that the coding machine can stably and accurately mark the product during the coding process and ensure that the coding quality meets specific standards and requirements.

[0003] The patent application with the announcement number CN111805092B discloses a method for quality control of laser coding of cigarette strips. The method mainly performs comprehensive judgment by real-time detection of whether the communication between the coding machine and the repeater in the laser coding system is normal, whether the laser light signal is normal, and whether the laser galvanometer deflection signal is normal. Once a coding failure occurs, the laser coding system is immediately stopped and processed. At the same time, the problem of laser coding at the moment when the conveyor line stops is optimized, and the fault is displayed, stored, and analyzed through the human-machine interface to ensure the quality of laser coding and prevent uncoded cigarettes from entering the distribution link or flowing into the market for sale. Although the above patent solves the problem of quality control, the following problems still exist in actual operation:

[0004] 1. Before and during the coding process of the object with the laser vision coding machine, the coding equipment detection and coding process monitoring were not performed, resulting in inaccurate coding data.

[0005] 2. Failure to conduct effective data quality analysis and abnormal situation identification on the coded data results in the inability to better carry out targeted abnormal situation handling.

[0006] 3. Failure to conduct faster equipment adjustment monitoring based on quality assessment data, and failure to conduct effective coding monitoring again during the adjustment process, resulting in poor control results. Summary of the invention

[0007] The purpose of the present invention is to provide a coding quality control method and system for a laser vision coding machine. By identifying abnormalities of unqualified quality indicators in coding monitoring data, specific problems such as laser parameter abnormalities, equipment failures and hardware problems can be accurately identified. This accuracy enables subsequent improvement measures to solve the problems more targeted. By formulating clear coding quality inspection standards and goals, the standardization and consistency of quality inspection work are ensured, the division standards of quality assessment are clarified, and the quality assessment results are more objective and accurate. By designing reasonable path types, starting points, end points and path sequences, the coding efficiency is improved. The path simulation step ensures the accuracy of the coding path and the absence of intersections or overlaps, avoids repeated coding and waste, and can solve the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The coding quality control method of the laser vision coding machine includes the following steps:

[0010] S1: Coding equipment management: Perform equipment detection and position confirmation on the coding equipment. After the equipment detection and position confirmation are completed, the standard coding equipment will be obtained;

[0011] S2: Parameter setting and path planning: Set the equipment parameters of the standard coding equipment according to the coding object, and plan the coding path according to the equipment parameters and the coding object, and mark the set equipment parameters and planned coding path as coding implementation data;

[0012] S3: Coding process monitoring: According to the coding implementation data, the standard coding equipment collects coding data of the coding process of the coding object, and obtains the target coding data after the coding data collection is completed;

[0013] S4: Monitoring data preprocessing: preprocess the target coding data, and obtain standard coding data after the data preprocessing is completed;

[0014] S5: Monitoring data quality assessment: Use visual inspection technology to perform quality inspection on standard coding data, and conduct quality assessment based on the quality inspection results. After the quality assessment is completed, the coding monitoring data is obtained;

[0015] S6: Control scheme generation: the coding monitoring data is identified for abnormalities according to the evaluation results, the coding parameters are improved according to the identified abnormalities, and an improvement scheme is generated for the improved coding parameters. After the improvement scheme is generated, the coding quality control data is obtained.

[0016] Preferably, the device detection and location confirmation of the coding device in S1 include:

[0017] The coding equipment is a laser vision coding machine. Before the laser vision coding machine starts coding, the laser vision coding machine must be tested first;

[0018] Equipment testing includes self-test, laser test, lens and galvanometer system test, visual system test, work platform test, control test, cooling system test, electrical test and functional test;

[0019] After the equipment inspection is completed and qualified, the coding equipment is inspected for alignment;

[0020] The alignment detection is as follows: first, the marking position of the coding object is obtained from the database. The marking position is the preset coding area. The visual system detects and simulates the position capture of the coding area. According to the position capture simulation result, it is determined whether the position captured by the visual system is the marking position.

[0021] If it is not the marked position, the position capture simulation is performed again on the visual system detection until the simulation result is the marked position;

[0022] If it is a marked position, the visual system detection module is calibrated, and after the calibration is completed, the alignment detection is completed;

[0023] Finally, the coding equipment that has passed the equipment inspection and completed the alignment inspection will be marked as the standard coding equipment.

[0024] Preferably, in S2, the device parameters of the standard coding device are set according to the coding object, and the coding path planning is performed according to the device parameters and the coding object, including:

[0025] Retrieving material property data of the coding object from the database, the material property data including the material, color, surface finish and object contour of the coding object;

[0026] Confirm the coding mode according to the material property data. The coding mode includes vector coding or bitmap coding.

[0027] After the coding mode is confirmed, the laser parameters are set, including power, frequency and speed.

[0028] After the laser parameters are set, adjust the lens focal length according to the material characteristic data;

[0029] After adjusting the lens focal length, complete the equipment parameter setting, and after the equipment parameter setting is completed, perform coding path planning;

[0030] Coding path planning is to design the coding path according to the coding area and coding mode. The designed coding path includes path type, starting point, end point and path sequence;

[0031] Perform path simulation on the designed coding path, and determine whether there are intersecting or overlapping paths based on the simulation results. If so, redesign the coding path;

[0032] The data of completed equipment parameter settings and the coding path data with qualified path simulation are marked as coding implementation data.

[0033] Preferably, the step of collecting coding data of the coding process of the coding object by the standard coding device according to the coding implementation data in S3 includes:

[0034] Read the coding implementation data, and start the standard coding equipment according to the read coding implementation data;

[0035] The standard coding equipment moves the coding object along the planned path using the laser beam of the standard coding equipment, and uses sensors and visual systems to collect coding data in real time during the path movement.

[0036] The coding data includes laser parameters, coding time, coding position and coding image data;

[0037] After the coding data collection is completed, data identification is performed;

[0038] Data identification is to integrate the coded data, and to timestamp and uniquely code the coded data after data integration;

[0039] After the timestamp and unique coding mark are completed, the target coding data is obtained.

[0040] Preferably, the target coding data is preprocessed in S4, including:

[0041] Preprocess the target coding data according to the data type;

[0042] Among them, the data preprocessing of laser parameters includes normalization processing, outlier processing and smoothing processing;

[0043] Data preprocessing for coding time includes time synchronization, timestamp formatting, and time series analysis;

[0044] Data preprocessing of the coding position includes coordinate conversion, position calibration and position accuracy assessment;

[0045] Data preprocessing of coded image data includes image enhancement, image denoising, image segmentation and feature extraction;

[0046] Laser parameters, coding time, coding position and coding image data are all pre-processed to obtain standard coding data.

[0047] Preferably, performing image enhancement processing on the coded image data includes:

[0048] Extract the gray value of each pixel contained in the coding image data;

[0049] Extracting pixel points adjacent to each pixel point, taking each pixel point as a main pixel point, and taking pixel points adjacent to each pixel point as observation pixel points;

[0050] Extracting the grayscale value corresponding to the observed pixel point;

[0051] The grayscale value of the observed pixel is combined with the grayscale value of the corresponding main pixel to obtain a grayscale comprehensive coefficient;

[0052] The grayscale comprehensive coefficient is obtained by the following formula:

[0053]

[0054] Among them, J represents the grayscale comprehensive coefficient; n represents the number of main pixels; H i H represents the gray value of the i-th observed pixel corresponding to each main pixel; z Indicates the gray value corresponding to each main pixel; H b Represents the standard deviation of the grayscale values ​​of n observed pixels corresponding to each main pixel;

[0055] The grayscale comprehensive coefficient is compared with a preset grayscale coefficient threshold, and it is determined whether to adjust the grayscale value of the main pixel point according to the comparison result.

[0056] Preferably, comparing the grayscale comprehensive coefficient with a preset grayscale coefficient threshold, and determining whether to adjust the grayscale value of the main pixel point according to the comparison result, includes:

[0057] Compare the grayscale comprehensive coefficient with a preset grayscale coefficient threshold to obtain a difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold;

[0058] comparing the difference with a preset difference threshold;

[0059] When the difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold exceeds the preset difference threshold, it is determined that the grayscale value of the main pixel point corresponding to the difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold exceeds the preset difference threshold needs to be adjusted;

[0060] The main pixel point that needs to be adjusted in grayscale value is taken as the target main pixel point;

[0061] Extracting the gray value of the target main pixel;

[0062] Extracting the grayscale value of the observation pixel corresponding to the target main pixel;

[0063] The grayscale value of the target main pixel is adjusted by combining the grayscale value of the target main pixel with the grayscale value of the observed pixel, and the grayscale value of the adjusted target main pixel is obtained by the following formula:

[0064]

[0065] Among them, H mrepresents the gray value of the target main pixel after adjustment; H represents the gray value of the target main pixel before adjustment; m represents the number of observed pixels corresponding to the target main pixel; H mi H represents the gray value of the i-th observed pixel corresponding to the target main pixel; mz Indicates the gray value corresponding to the target main pixel; J c represents the difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold; J cy Indicates the preset difference threshold.

[0066] Preferably, in S5, the standard coding data is quality inspected by using visual inspection technology, and quality assessment is performed according to the quality inspection result, including:

[0067] Establish the inspection standards and targets for coding quality, including clarity, contrast, position accuracy and image integrity;

[0068] Use image processing algorithms and visual systems to analyze standard coding data;

[0069] Data analysis is to first read the image data in the standard coding data, and confirm the feature data in the read image data, the feature data includes texture, shape, color and size;

[0070] Generate feature vectors from feature data, and obtain image feature data after the feature vectors are generated;

[0071] Retrieving standard feature data from a database, and calculating quality indicators of the standard feature data and image feature data;

[0072] After the quality index calculation is completed, the key index difference data between the standard feature data and the image feature data is obtained;

[0073] The key indicator difference data include position deviation difference data, clarity difference data, contrast difference data and integrity difference data;

[0074] Each difference in the key indicator difference data is subjected to quality assessment respectively, and the quality assessment is divided into qualified quality indicators and unqualified quality indicators;

[0075] When the difference range of the key indicator difference data exceeds the preset range, it is marked as an unqualified quality indicator; when the difference range of the key indicator difference data does not exceed the preset range, it is marked as a qualified quality indicator;

[0076] The qualified quality indicators and unqualified quality indicators are uniformly marked as coded monitoring data.

[0077] Preferably, in S6, the coding monitoring data is identified as abnormal according to the evaluation result, the coding parameters are improved according to the identified abnormality, and an improvement plan is generated for the improved coding parameters, including:

[0078] The unqualified quality indicators in the coding monitoring data are identified as abnormal. The abnormal identification is to classify the abnormal conditions according to the attributes of the abnormal values. The abnormal conditions include abnormal laser parameters, equipment failures and hardware problems.

[0079] Perform abnormal trend analysis based on the classified abnormal situations. The abnormal trend analysis is to judge whether it is an occasional event or a long-term event based on the abnormal frequency and degree of the abnormal situation.

[0080] According to the abnormal trend analysis results and abnormal identification data, the coding parameter improvement plan is formulated, and the improvement plan is the adjustment of equipment parameters;

[0081] Convert the developed improvement plan into visual data, where the visual data includes text data and image data;

[0082] The improvement plan after the visual data conversion is marked as coding quality control data.

[0083] The coding quality control system based on the laser vision coding machine includes:

[0084] Improved transmission control unit, used for:

[0085] Transmit the coding quality control data to the work terminal for solution display;

[0086] The staff adjusts the corresponding equipment parameters according to the displayed coding quality control data. The equipment parameter adjustment includes manual adjustment or automatic adjustment;

[0087] After the equipment parameters are adjusted, the coding monitoring is carried out again, and the effectiveness of the coding quality control data is evaluated based on the coding monitoring results;

[0088] Among them, when the coding monitoring results are valid, the parameter standards in the coding quality control data are included in the scope of the standard operating procedures;

[0089] When the coding monitoring effect is ineffective, re-perform abnormal identification and abnormal cause analysis, and readjust the improvement plan.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] 1. The coding quality control method and system of the laser vision coding machine provided by the present invention improve the coding efficiency by designing reasonable path type, starting point, end point and path sequence. The path simulation step ensures the accuracy and non-crossing or overlapping of the coding path, avoids repeated coding and waste, and further improves work efficiency. The laser vision coding machine is suitable for a variety of materials and surfaces, such as metal, plastic, glass, etc., and has a wide range of applicability. Through real-time monitoring of the coding process and data collection, potential quality problems can be discovered in time.

[0092] 2. The coding quality control method and system of the laser vision coding machine provided by the present invention can accurately identify specific problems such as laser parameter abnormalities, equipment failures and hardware problems by identifying abnormalities of unqualified quality indicators in the coding monitoring data and classifying them based on the attributes of the abnormal values. This accuracy enables subsequent improvement measures to solve the problems more targetedly. By formulating clear coding quality inspection standards and goals, the standardization and consistency of quality inspection work are ensured, the classification standards of quality assessment are clarified, and the quality assessment results are more objective and accurate.

[0093] 3. The coding quality control method and system of the laser vision coding machine provided by the present invention, when the monitoring results are valid, the parameter standards are incorporated into the standard operating procedures, which is helpful to stabilize and improve the coding quality. When the monitoring effect is invalid, the abnormality identification and cause analysis are re-performed, and the improvement plan is adjusted. This iterative process helps to continuously optimize the coding quality control method, and incorporates the parameter standards in the effective coding quality control data into the scope of the standard operating procedures, which helps to achieve standardization and normalization of the coding process. This can not only improve production efficiency, but also ensure the stability and consistency of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 It is a schematic diagram of the coding quality control steps of the laser visual coding machine of the present invention;

[0095] Figure 2 The figure is a schematic diagram of the coding quality control process of the laser vision coding machine of the present invention. DETAILED DESCRIPTION

[0096] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0097] In order to solve the problem in the prior art that the laser vision coding machine is used to code objects without coding equipment detection and coding process monitoring before and during coding, resulting in inaccurate coding data, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0098] The coding quality control method of the laser vision coding machine includes the following steps:

[0099] S1: Coding equipment management: Perform equipment detection and position confirmation on the coding equipment. After the equipment detection and position confirmation are completed, the standard coding equipment will be obtained;

[0100] Among them, the stability and reliability of the laser vision coder are effectively improved through comprehensive equipment detection and dynamic calibration mechanism;

[0101] S2: Parameter setting and path planning: Set the equipment parameters of the standard coding equipment according to the coding object, and plan the coding path according to the equipment parameters and the coding object, and mark the set equipment parameters and planned coding path as coding implementation data;

[0102] Among them, automated path planning and simulation reduce manual intervention, improve the automation level of the coding process, and reduce operational difficulty and error rate;

[0103] S3: Coding process monitoring: According to the coding implementation data, the standard coding equipment collects coding data of the coding process of the coding object, and obtains the target coding data after the coding data collection is completed;

[0104] Among them, through real-time monitoring of the coding process and data collection, potential quality problems can be discovered in time;

[0105] S4: Monitoring data preprocessing: preprocess the target coding data, and obtain standard coding data after the data preprocessing is completed;

[0106] Among them, by pre-processing the target coding data in a comprehensive and detailed manner, the quality control method of the laser vision coding machine not only improves the coding quality, but also enhances the stability and consistency of the system;

[0107] S5: Monitoring data quality assessment: Use visual inspection technology to perform quality inspection on standard coding data, and conduct quality assessment based on the quality inspection results. After the quality assessment is completed, the coding monitoring data is obtained;

[0108] Among them, timely detection and treatment of quality problems can avoid the outflow of unqualified products and reduce waste and losses;

[0109] S6: Control scheme generation: the coding monitoring data is identified for abnormalities according to the evaluation results, the coding parameters are improved according to the identified abnormalities, and an improvement scheme is generated for the improved coding parameters. After the improvement scheme is generated, the coding quality control data is obtained;

[0110] Among them, by identifying abnormalities of unqualified quality indicators in the coding monitoring data and classifying them based on the attributes of the abnormal values, specific problems such as laser parameter abnormalities, equipment failures and hardware problems can be accurately identified.

[0111] Perform device detection and location confirmation for the coding device in S1, including:

[0112] The coding equipment is a laser vision coding machine. Before the laser vision coding machine starts coding, the laser vision coding machine must be tested first;

[0113] Equipment testing includes self-test, laser test, lens and galvanometer system test, visual system test, work platform test, control test, cooling system test, electrical test and functional test;

[0114] After the equipment inspection is completed and qualified, the coding equipment is inspected for alignment;

[0115] The alignment detection is as follows: first, the marking position of the coding object is obtained from the database. The marking position is the preset coding area. The visual system detects and simulates the position capture of the coding area. According to the position capture simulation result, it is determined whether the position captured by the visual system is the marking position.

[0116] If it is not the marked position, the position capture simulation is performed again on the visual system detection until the simulation result is the marked position;

[0117] If it is a marked position, the visual system detection module is calibrated, and after the calibration is completed, the alignment detection is completed;

[0118] Finally, the coding equipment that has passed the equipment inspection and completed the alignment inspection will be marked as the standard coding equipment.

[0119] Specifically, it covers all key components of the laser vision coding machine, including laser, lens and galvanometer system, visual system, work platform, control system, cooling system, electrical system and functional detection. This comprehensive detection process ensures that the equipment is in the best condition before formal operation, thereby effectively preventing coding quality problems caused by equipment failure or poor performance. By accurately capturing and simulating the marking position of the coding object, the solution achieves high-precision control of the coding position. This mechanism ensures that the coding content can accurately fall within the preset coding area, improving the aesthetics and readability of the coding. If the position captured by the visual system does not match the preset marking position, the solution will require re-position capture simulation until the accurate position is reached. This dynamic calibration mechanism ensures the continuous accuracy and reliability of the visual system, thereby further improving the coding accuracy, and streamlines the equipment detection and alignment detection to ensure that operations can be performed in accordance with unified standards before each coding. This standardized operating process not only improves work efficiency, but also reduces the risk of human operating errors. Through comprehensive equipment detection and dynamic calibration mechanisms, the stability and reliability of the laser vision coding machine are effectively improved. This means that the equipment can maintain stable performance during long-term operation and reduce the possibility of failure.

[0120] In S2, the device parameters of the standard coding device are set according to the coding object, and the coding path is planned according to the device parameters and the coding object, including:

[0121] Retrieving material property data of the coding object from the database, the material property data including the material, color, surface finish and object contour of the coding object;

[0122] Confirm the coding mode according to the material property data. The coding mode includes vector coding or bitmap coding.

[0123] After the coding mode is confirmed, the laser parameters are set, including power, frequency and speed.

[0124] After the laser parameters are set, adjust the lens focal length according to the material characteristic data;

[0125] After adjusting the lens focal length, complete the equipment parameter setting, and after the equipment parameter setting is completed, perform coding path planning;

[0126] Coding path planning is to design the coding path according to the coding area and coding mode. The designed coding path includes path type, starting point, end point and path sequence;

[0127] Perform path simulation on the designed coding path, and determine whether there are intersecting or overlapping paths based on the simulation results. If so, redesign the coding path;

[0128] The data of completed equipment parameter settings and the coding path data with qualified path simulation are marked as coding implementation data.

[0129] Specifically, the precise setting of equipment parameters according to the specific material characteristics of the coded object (such as material type, color, surface finish and contour) ensures a high degree of customization of the coding process. This precision helps to reduce errors, improve coding quality, and make the printed marks clearer and more accurate. It supports two modes, vector coding and bitmap coding, which can be flexibly selected according to different needs, improving the versatility and adaptability of the equipment. By adjusting the laser parameters (power, frequency, speed) and lens focal length, it can adapt to different material characteristics and coding requirements, further enhancing the flexibility of the equipment. The coding path planning stage takes into account the coding area and coding mode, and improves the coding efficiency by designing reasonable path types, starting points, end points and path sequences. The path simulation step ensures the accuracy and non-crossing or overlapping of the coding path, avoids repeated coding and waste, and further improves work efficiency. The data of equipment parameter settings and path simulation are marked as coding implementation data, which is convenient for subsequent quality control and traceability. This data recording method helps ensure the consistency and stability of the coding process, facilitates problem troubleshooting and improvement, and automated path planning and simulation reduce manual intervention, improves the automation level of the coding process, and reduces operational difficulty and error rate.

[0130] According to the coding implementation data in S3, the coding data of the coding object is collected by the standard coding equipment, including:

[0131] Read the coding implementation data, and start the standard coding equipment according to the read coding implementation data;

[0132] The standard coding equipment moves the coding object along the planned path using the laser beam of the standard coding equipment, and uses sensors and visual systems to collect coding data in real time during the path movement.

[0133] The coding data includes laser parameters, coding time, coding position and coding image data;

[0134] After the coding data collection is completed, data identification is performed;

[0135] Data identification is to integrate the coded data, and to timestamp and uniquely code the coded data after data integration;

[0136] After the timestamp and unique coding mark are completed, the target coding data is obtained.

[0137] Specifically, by real-time acquisition of laser parameters (such as power, frequency, spot size, etc.), the accuracy and consistency of the laser beam during the coding process can be ensured, thereby improving the coding quality. Accurate recording of coding position and coding time helps to analyze any deviation or error in the coding process, so as to make timely adjustments and optimizations. From reading coding implementation data to starting equipment, to real-time data acquisition and identification, the entire process is highly automated, reducing manual intervention and improving production efficiency. Through real-time monitoring and data analysis, problems in the coding process can be discovered and corrected in a timely manner to ensure the continuity and stability of the production line. According to different materials, shapes and coding requirements, laser parameters and coding paths can be flexibly adjusted to achieve personalized coding effects. Laser vision coding machines are suitable for a variety of materials and surfaces, such as metal, plastic, glass, etc., and have a wide range of applicability. Through real-time monitoring of the coding process and data acquisition, potential quality problems can be discovered in a timely manner, providing real-time feedback for quality control and improvement.

[0138] In order to solve the problem in the prior art that the coded data is not effectively analyzed for data quality and abnormal situation identification, which leads to the inability to better handle the abnormal situation according to the abnormal situation, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0139] Preprocess the target coding data in S4, including:

[0140] Preprocess the target coding data according to the data type;

[0141] Among them, the data preprocessing of laser parameters includes normalization processing, outlier processing and smoothing processing;

[0142] Data preprocessing for coding time includes time synchronization, timestamp formatting, and time series analysis;

[0143] Data preprocessing of the coding position includes coordinate conversion, position calibration and position accuracy assessment;

[0144] Data preprocessing of coded image data includes image enhancement, image denoising, image segmentation and feature extraction;

[0145] Laser parameters, coding time, coding position and coding image data are all pre-processed to obtain standard coding data.

[0146] Specifically, by preprocessing the target coding data in a comprehensive and detailed manner, the quality control method of the laser vision coding machine not only improves the coding quality, but also enhances the stability and consistency of the system, and ultimately achieves accurate monitoring and continuous improvement of the coding process. Data preprocessing removes outliers and noise to make the final data higher in quality, thereby ensuring the accuracy and reliability of subsequent analysis. By standardizing and normalizing different types of data (laser parameters, coding time, coding position, and image data), it helps to compare data on the same scale and enhance data consistency. The preprocessed standard coding data has clearer features, making subsequent data Analysis and quality assessment are more efficient, saving time and computing resources. By processing the coding time and position data, real-time monitoring can be achieved, timely feedback can be given to problems in the coding process, and the responsiveness of the coding system can be improved. Coordinate conversion and position calibration of the coding position data can improve the accuracy and precision of coding and ensure that laser coding is completed at the predetermined position. Through the automated data preprocessing process, the need for manual intervention is reduced, human errors are reduced, and work efficiency is also improved. After standardization, outlier processing and data integration, the standard coding data obtained provides accurate decision-making basis for management and helps optimize production management processes.

[0147] Specifically, performing image enhancement processing on the coded image data includes:

[0148] Extract the gray value of each pixel contained in the coding image data;

[0149] Extracting pixel points adjacent to each pixel point, taking each pixel point as a main pixel point, and taking pixel points adjacent to each pixel point as observation pixel points;

[0150] Extracting the grayscale value corresponding to the observed pixel point;

[0151] The grayscale value of the observed pixel is combined with the grayscale value of the corresponding main pixel to obtain a grayscale comprehensive coefficient;

[0152] The grayscale comprehensive coefficient is obtained by the following formula:

[0153]

[0154] Among them, J represents the grayscale comprehensive coefficient; n represents the number of main pixels; H i H represents the gray value of the i-th observed pixel corresponding to each main pixel; z Indicates the gray value corresponding to each main pixel; H b Represents the standard deviation of the grayscale values ​​of n observed pixels corresponding to each main pixel;

[0155] The grayscale comprehensive coefficient is compared with a preset grayscale coefficient threshold, and it is determined whether to adjust the grayscale value of the main pixel point according to the comparison result.

[0156] The technical effect of the above technical solution is: by extracting the grayscale value of each pixel and its adjacent pixels and using these grayscale values ​​to calculate the grayscale comprehensive coefficient, the solution can more accurately evaluate the grayscale characteristics of each pixel. According to the comparison result of the grayscale comprehensive coefficient and the preset threshold, the grayscale value of the main pixel is adjusted, which helps to remove noise in the image, enhance the details of the image, and thus improve the clarity of the image. The calculation of the grayscale comprehensive coefficient takes into account the difference between the grayscale value of the observed pixel and the grayscale value of the main pixel, which helps to enhance the contrast between different areas in the image. By adjusting the grayscale value, the bright and dark areas in the image can be made clearer, improving the visual effect of the image. In the scheme, the grayscale comprehensive coefficient is obtained by combining the grayscale value of the observed pixel with the grayscale value of the main pixel. This process is actually a smoothing process for the image. By adjusting the grayscale value, some small fluctuations and noise in the image can be smoothed out, making the image smoother and more natural. The scheme uses a preset grayscale coefficient threshold to determine whether to adjust the grayscale value of the main pixel, which provides users with a certain degree of flexibility. Users can adjust the threshold according to actual needs to achieve the best image enhancement effect. This technical solution is not only applicable to grayscale images obtained by laser vision coding machines, but may also be applicable to other types of grayscale image processing scenarios. By adjusting parameters and algorithm details, this solution can adapt to different image processing needs and scenarios.

[0157] In summary, this technical solution improves the clarity, contrast and smoothness of the image by performing fine image enhancement processing on the coded image data, while providing a flexible image processing method and wide applicability. These technical effects make this solution have potential application value and practical significance in the field of image processing.

[0158] Specifically, comparing the grayscale comprehensive coefficient with a preset grayscale coefficient threshold, and determining whether to adjust the grayscale value of the main pixel point according to the comparison result, includes:

[0159] Compare the grayscale comprehensive coefficient with a preset grayscale coefficient threshold to obtain a difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold;

[0160] comparing the difference with a preset difference threshold;

[0161] When the difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold exceeds the preset difference threshold, it is determined that the grayscale value of the main pixel point corresponding to the difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold exceeds the preset difference threshold needs to be adjusted;

[0162] The main pixel point that needs to be adjusted in grayscale value is taken as the target main pixel point;

[0163] Extracting the gray value of the target main pixel;

[0164] Extracting the grayscale value of the observation pixel corresponding to the target main pixel;

[0165] The grayscale value of the target main pixel is adjusted by combining the grayscale value of the target main pixel with the grayscale value of the observed pixel, and the grayscale value of the adjusted target main pixel is obtained by the following formula:

[0166]

[0167] Among them, H m represents the gray value of the target main pixel after adjustment; H represents the gray value of the target main pixel before adjustment; m represents the number of observed pixels corresponding to the target main pixel; H mi H represents the gray value of the i-th observed pixel corresponding to the target main pixel; mz Indicates the gray value corresponding to the target main pixel; J c represents the difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold; J cy Indicates the preset difference threshold.

[0168] The technical effect of the above technical solution is: by comparing the grayscale comprehensive coefficient with the preset grayscale coefficient threshold and calculating the difference between them, the solution can identify those pixels (i.e., target main pixels) whose grayscale characteristics are greatly different from the overall image. Adjusting the grayscale values ​​of these key pixels can more effectively improve the local features of the image, such as enhancing edges, reducing noise, or adjusting contrast. Fine adjustment of the grayscale value of the target main pixel helps to retain and enhance the detail information in the image. This makes the image visually clearer and more delicate, and improves the overall quality of the image. By processing only the pixels that need to be adjusted, indiscriminate operations on the entire image are avoided. This not only reduces the amount of calculation, but also improves the efficiency and speed of image processing. The solution allows users to adjust the preset grayscale coefficient threshold and difference threshold according to actual needs. This enables the solution to adapt to different image processing scenarios and needs, and improves its flexibility and applicability. By setting the difference threshold, the solution can avoid excessive grayscale value adjustment of the image. This helps to maintain the naturalness and authenticity of the image and avoid unnatural processing effects.

[0169] This technical solution is expected to achieve significant technical results in improving image quality, retaining detail information, improving processing efficiency, and maintaining the naturalness of images by accurately identifying and adjusting the grayscale values ​​of key pixels.

[0170] In S5, the standard coding data is inspected by visual inspection technology, and quality assessment is performed based on the quality inspection results, including:

[0171] Establish the inspection standards and targets for coding quality, including clarity, contrast, position accuracy and image integrity;

[0172] Use image processing algorithms and visual systems to analyze standard coding data;

[0173] Data analysis is to first read the image data in the standard coding data, and confirm the feature data in the read image data, the feature data includes texture, shape, color and size;

[0174] Generate feature vectors from feature data, and obtain image feature data after the feature vectors are generated;

[0175] Retrieving standard feature data from a database, and calculating quality indicators of the standard feature data and image feature data;

[0176] After the quality index calculation is completed, the key index difference data between the standard feature data and the image feature data is obtained;

[0177] The key indicator difference data include position deviation difference data, clarity difference data, contrast difference data and integrity difference data;

[0178] Each difference in the key indicator difference data is subjected to quality assessment respectively, and the quality assessment is divided into qualified quality indicators and unqualified quality indicators;

[0179] When the difference range of the key indicator difference data exceeds the preset range, it is marked as an unqualified quality indicator; when the difference range of the key indicator difference data does not exceed the preset range, it is marked as a qualified quality indicator;

[0180] The qualified quality indicators and unqualified quality indicators are uniformly marked as coded monitoring data.

[0181] Specifically, by formulating clear coding quality inspection standards and goals (such as clarity standards, contrast standards, position accuracy and image integrity), the standardization and consistency of quality inspection work are ensured, and the classification standards for quality assessment (qualified quality indicators and unqualified quality indicators) are clarified, making the quality assessment results more objective and accurate. The use of image processing algorithms and visual systems to analyze standard coding data has greatly improved the inspection efficiency, reduced manual intervention and reduced costs. Through the generation of feature vectors and the calculation of key indicator difference data, a rapid and accurate assessment of coding quality is achieved. The inspection standards and goals cover multiple aspects of coding quality, including clarity, contrast, position accuracy and image integrity, ensuring the comprehensiveness of quality inspection. Detailed analysis and processing of feature data, such as texture, shape, color and size, further improves the accuracy and reliability of quality inspection. The preset range and quality assessment standards can be adjusted according to actual needs to meet the needs of different products and application scenarios. With the development of image processing technology and visual inspection technology, this solution can be further upgraded and optimized to improve the level of quality inspection. By uniformly marking qualified quality indicators and unqualified quality indicators as coding monitoring data, the traceability and recordability of quality inspection results are achieved. By timely discovering and handling quality problems, the outflow of unqualified products can be avoided and waste and losses can be reduced.

[0182] In S6, the coding monitoring data is identified for anomalies based on the evaluation results, coding parameters are improved based on the identified anomalies, and an improvement plan is generated for the improved coding parameters, including:

[0183] The unqualified quality indicators in the coding monitoring data are identified as abnormal. The abnormal identification is to classify the abnormal conditions according to the attributes of the abnormal values. The abnormal conditions include abnormal laser parameters, equipment failures and hardware problems.

[0184] Perform abnormal trend analysis based on the classified abnormal situations. The abnormal trend analysis is to judge whether it is an occasional event or a long-term event based on the abnormal frequency and degree of the abnormal situation.

[0185] According to the abnormal trend analysis results and abnormal identification data, the coding parameter improvement plan is formulated, and the improvement plan is the adjustment of equipment parameters;

[0186] Convert the developed improvement plan into visual data, where the visual data includes text data and image data;

[0187] The improvement plan after the visual data conversion is marked as coding quality control data.

[0188] Specifically, by identifying the abnormalities of unqualified quality indicators in the coding monitoring data and classifying them based on the attributes of the abnormal values, specific problems such as laser parameter abnormalities, equipment failures and hardware problems can be accurately identified. This accuracy enables subsequent improvement measures to solve the problem more targeted. The abnormal trend analysis can determine whether it is an occasional event or a long-term event based on the frequency and degree of the abnormal situation. This analysis capability helps to discover potential quality problems in advance, so that preventive measures can be taken to avoid the occurrence or deterioration of the problem. According to the abnormal trend analysis results and abnormal identification data, improvement plans for coding parameters can be quickly formulated, such as adjusting equipment parameters. This data-based decision-making method is not only efficient, but also can be flexibly adjusted according to actual conditions to adapt to different production needs. The improvement plan is converted into visual data, including text data and image data, making the improvement plan more intuitive and easy to understand. This helps relevant personnel better understand the problem and solution, improve communication efficiency and execution, and mark the improvement plan after the visual data conversion as coding quality control data, forming a data-driven quality control process. This process makes quality control more objective and quantifiable, which helps to improve the overall quality control level. By promptly discovering and solving problems, it can reduce downtime and rework costs caused by quality problems, thereby improving production efficiency and economic benefits.

[0189] In order to solve the problem in the prior art that there is no faster equipment adjustment monitoring based on quality assessment data, and no effective coding monitoring of the adjustment process, which leads to poor control effect, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0190] The coding quality control system based on the laser vision coding machine includes:

[0191] Improved transmission control unit, used for:

[0192] Transmit the coding quality control data to the work terminal for solution display;

[0193] The staff adjusts the corresponding equipment parameters according to the displayed coding quality control data. The equipment parameter adjustment includes manual adjustment or automatic adjustment;

[0194] After the equipment parameters are adjusted, the coding monitoring is carried out again, and the effectiveness of the coding quality control data is evaluated based on the coding monitoring results;

[0195] Among them, when the coding monitoring results are valid, the parameter standards in the coding quality control data are included in the scope of the standard operating procedures;

[0196] When the coding monitoring effect is ineffective, re-perform abnormal identification and abnormal cause analysis, and readjust the improvement plan.

[0197] Specifically, by transmitting the coding quality control data to the work terminal for real-time display, the staff can quickly obtain the current status of the coding quality. This real-time feedback mechanism enables the staff to adjust the equipment parameters immediately, so as to quickly respond to quality problems and reduce delays and waste in production. It supports both manual and automatic equipment parameter adjustment methods to meet the needs of different scenarios. Automated adjustment can improve production efficiency and reduce human errors, while manual adjustment provides the possibility of detailed intervention in special circumstances. The effectiveness of coding quality control data is evaluated through coding monitoring effects, forming a closed loop of continuous improvement. When the monitoring results are valid, the parameter standards are incorporated into the standard operating procedures, which helps to stabilize and improve the coding quality. When the monitoring effect is invalid, the abnormality identification and cause analysis are re-performed, and the improvement plan is adjusted. This iterative process helps to continuously optimize the coding quality control method, incorporate the parameter standards in the effective coding quality control data into the scope of the standard operating procedures, and help to achieve standardization and normalization of the coding process. This can not only improve production efficiency, but also ensure the stability and consistency of product quality. It includes the process of abnormality identification and cause analysis, which helps to quickly locate the problem and take corresponding solutions. By readjusting the improvement plan, production can be resumed quickly and downtime caused by quality problems can be reduced. In the process of participating in coding quality control, staff can continuously learn and master new skills and knowledge.

[0198] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0199] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. The coding quality control method of the laser visual coding machine is characterized by: The steps include: S1: Coding equipment management: Perform equipment detection and position confirmation on the coding equipment. After the equipment detection and position confirmation are completed, the standard coding equipment is obtained; S2: Parameter setting and path planning: Set the equipment parameters of the standard coding equipment according to the coding object, and plan the coding path according to the equipment parameters and the coding object, and mark the set equipment parameters and planned coding path as coding implementation data; S3: Coding process monitoring: According to the coding implementation data, the standard coding equipment collects coding data of the coding process of the coding object, and obtains the target coding data after the coding data collection is completed; S4: Monitoring data preprocessing: preprocess the target coding data, and obtain standard coding data after the data preprocessing is completed; S5: Monitoring data quality assessment: Use visual inspection technology to perform quality inspection on standard coding data, and conduct quality assessment based on the quality inspection results. After the quality assessment is completed, the coding monitoring data is obtained; S6: Control scheme generation: the coding monitoring data is identified for abnormalities according to the evaluation results, the coding parameters are improved according to the identified abnormalities, and an improvement scheme is generated for the improved coding parameters. After the improvement scheme is generated, the coding quality control data is obtained; Preprocess the target coding data in S4, including: Preprocess the target coding data according to the data type; Among them, the data preprocessing of laser parameters includes normalization processing, outlier processing and smoothing processing; Data preprocessing for coding time includes time synchronization, timestamp formatting, and time series analysis; Data preprocessing of the coding position includes coordinate conversion, position calibration and position accuracy assessment; Data preprocessing of coded image data includes image enhancement, image denoising, image segmentation and feature extraction; Laser parameters, coding time, coding position and coding image data are all pre-processed to obtain standard coding data; Performing image enhancement processing on the coded image data includes: Extract the gray value of each pixel contained in the coding image data; Extracting pixel points adjacent to each pixel point, taking each pixel point as a main pixel point, and taking pixel points adjacent to each pixel point as observation pixel points; Extracting the grayscale value corresponding to the observed pixel point; The grayscale value of the observed pixel is combined with the grayscale value of the corresponding main pixel to obtain a grayscale comprehensive coefficient; The grayscale comprehensive coefficient is obtained by the following formula: Among them, J represents the grayscale comprehensive coefficient; n represents the number of main pixels; H i H represents the gray value of the i-th observed pixel corresponding to each main pixel; z Indicates the gray value corresponding to each main pixel; H b Represents the standard deviation of the grayscale values ​​of n observed pixels corresponding to each main pixel; The grayscale comprehensive coefficient is compared with a preset grayscale coefficient threshold, and it is determined whether to adjust the grayscale value of the main pixel point according to the comparison result.

2. The coding quality control method of the laser visual coding machine according to claim 1 is characterized in that: Perform device detection and location confirmation for the coding device in S1, including: The coding equipment is a laser vision coding machine. Before the laser vision coding machine starts coding, the laser vision coding machine must be tested first; Equipment testing includes self-test, laser test, lens and galvanometer system test, visual system test, work platform test, control test, cooling system test, electrical test and functional test; After the equipment inspection is completed and qualified, the coding equipment is inspected for alignment; The alignment detection is as follows: first, the marking position of the coding object is obtained from the database. The marking position is the preset coding area. The visual system detects and simulates the position capture of the coding area. According to the position capture simulation result, it is determined whether the position captured by the visual system is the marking position. If it is not the marked position, the position capture simulation is performed again on the visual system detection until the simulation result is the marked position; If it is a marked position, the visual system detection module is calibrated, and after the calibration is completed, the alignment detection is completed; Finally, the coding equipment that has passed the equipment inspection and completed the alignment inspection will be marked as the standard coding equipment.

3. The coding quality control method of the laser visual coding machine according to claim 2 is characterized in that: In S2, the device parameters of the standard coding device are set according to the coding object, and the coding path is planned according to the device parameters and the coding object, including: Retrieving material property data of the coding object from the database, the material property data including the material, color, surface finish and object contour of the coding object; Confirm the coding mode according to the material property data. The coding mode includes vector coding or bitmap coding. After the coding mode is confirmed, the laser parameters are set, including power, frequency and speed. After the laser parameters are set, adjust the lens focal length according to the material characteristic data; After adjusting the lens focal length, complete the equipment parameter setting, and after the equipment parameter setting is completed, perform coding path planning; Coding path planning is to design the coding path according to the coding area and coding mode. The designed coding path includes path type, starting point, end point and path sequence; Perform path simulation on the designed coding path, and determine whether there are intersecting or overlapping paths based on the simulation results. If so, redesign the coding path; The data of completed equipment parameter settings and the coding path data with qualified path simulation are marked as coding implementation data.

4. The coding quality control method of the laser visual coding machine according to claim 3 is characterized in that: According to the coding implementation data in S3, the coding data of the coding object is collected by the standard coding equipment, including: Read the coding implementation data, and start the standard coding equipment according to the read coding implementation data; The standard coding equipment moves the coding object along the planned path using the laser beam of the standard coding equipment, and uses sensors and visual systems to collect coding data in real time during the path movement. The coding data includes laser parameters, coding time, coding position and coding image data; After the coding data collection is completed, data identification is performed; Data identification is to integrate the coded data, and to timestamp and uniquely code the coded data after data integration; After the timestamp and unique coding mark are completed, the target coding data is obtained.

5. The coding quality control method of the laser visual coding machine according to claim 4 is characterized in that: Comparing the grayscale comprehensive coefficient with a preset grayscale coefficient threshold, and determining whether to adjust the grayscale value of the main pixel point according to the comparison result, including: Compare the grayscale comprehensive coefficient with a preset grayscale coefficient threshold to obtain a difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold; comparing the difference with a preset difference threshold; When the difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold exceeds the preset difference threshold, it is determined that the grayscale value of the main pixel point corresponding to the difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold exceeds the preset difference threshold needs to be adjusted; The main pixel point that needs to be adjusted in grayscale value is taken as the target main pixel point; Extracting the gray value of the target main pixel; Extracting the grayscale value of the observation pixel corresponding to the target main pixel; The grayscale value of the target main pixel is adjusted by combining the grayscale value of the target main pixel with the grayscale value of the observed pixel, and the grayscale value of the adjusted target main pixel is obtained by the following formula: Among them, H m represents the gray value of the target main pixel after adjustment; H represents the gray value of the target main pixel before adjustment; m represents the number of observed pixels corresponding to the target main pixel; H mi H represents the gray value of the i-th observed pixel corresponding to the target main pixel; mz Indicates the gray value corresponding to the target main pixel; J c represents the difference between the grayscale comprehensive coefficient and the grayscale coefficient threshold; J cy Indicates the preset difference threshold.

6. The coding quality control method of the laser visual coding machine according to claim 5 is characterized in that: In S5, the standard coding data is inspected by visual inspection technology, and quality assessment is performed based on the quality inspection results, including: Establish the inspection standards and targets for coding quality, including clarity, contrast, position accuracy and image integrity; Use image processing algorithms and visual systems to analyze standard coding data; Data analysis is to first read the image data in the standard coding data, and confirm the feature data in the read image data, the feature data includes texture, shape, color and size; Generate feature vectors from feature data, and obtain image feature data after the feature vectors are generated; Retrieving standard feature data from a database, and calculating quality indicators of the standard feature data and image feature data; After the quality index calculation is completed, the key index difference data between the standard feature data and the image feature data is obtained; The key indicator difference data include position deviation difference data, clarity difference data, contrast difference data and integrity difference data; Each difference in the key indicator difference data is subjected to quality assessment respectively, and the quality assessment is divided into qualified quality indicators and unqualified quality indicators; When the difference range of the key indicator difference data exceeds the preset range, it is marked as an unqualified quality indicator; when the difference range of the key indicator difference data does not exceed the preset range, it is marked as a qualified quality indicator; The qualified quality indicators and unqualified quality indicators are uniformly marked as coded monitoring data.

7. The coding quality control method of the laser visual coding machine according to claim 6 is characterized in that: In S6, the coding monitoring data is identified for anomalies based on the evaluation results, coding parameters are improved based on the identified anomalies, and an improvement plan is generated for the improved coding parameters, including: The unqualified quality indicators in the coding monitoring data are identified as abnormal. The abnormal identification is to classify the abnormal conditions according to the attributes of the abnormal values. The abnormal conditions include abnormal laser parameters, equipment failures and hardware problems. Perform abnormal trend analysis based on the classified abnormal situations. The abnormal trend analysis is to judge whether it is an occasional event or a long-term event based on the abnormal frequency and degree of the abnormal situation. According to the abnormal trend analysis results and abnormal identification data, the coding parameter improvement plan is formulated, and the improvement plan is the adjustment of equipment parameters; Convert the developed improvement plan into visual data, where the visual data includes text data and image data; The improvement plan after the visual data conversion is marked as coding quality control data.

8. A coding quality control system based on a laser vision coding machine, applied in the coding quality control method of a laser vision coding machine as claimed in claim 7, characterized in that: include: Improved transmission control unit, used for: Transmit the coding quality control data to the work terminal for solution display; The staff adjusts the corresponding equipment parameters according to the displayed coding quality control data. The equipment parameter adjustment includes manual adjustment or automatic adjustment; After the equipment parameters are adjusted, the coding monitoring is carried out again, and the effectiveness of the coding quality control data is evaluated based on the coding monitoring results; Among them, when the coding monitoring results are valid, the parameter standards in the coding quality control data are included in the scope of the standard operating procedures; When the coding monitoring effect is ineffective, re-perform abnormal identification and abnormal cause analysis, and readjust the improvement plan.

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