RFID electronic tag intelligent production method and device, computer equipment and medium

Abstract

The invention relates to an RFID electronic tag intelligent production method and device, computer equipment and a medium. The method comprises the steps that raw material state information is acquired, and preliminary composite process parameters are calculated; acquiring real-time environment data and material characteristic data, performing parameter calculation on the real-time environment data and the material characteristic data through a self-adaptive algorithm, and adjusting preliminary composite process parameters according to a parameter calculation result; the abrasion degree of the die-cutting tool is detected through the machine vision technology, and the tool pressure is automatically adjusted based on the abrasion degree; the writing accuracy is monitored in real time, then writing parameters are adjusted, and whether the UV jet printing content meets the standard or not is detected through a machine vision algorithm; consistency detection is carried out on the written data and the jet printing content, and the uniqueness and the read-write capability of the TID are judged; and identifying the basic production label, automatically packaging the qualified label, and adjusting the packaging mode according to the label quality information to obtain a final production label. The method has the effect of improving the production efficiency of the electronic tag.

Description

Technical Field

[0001] The present application relates to the technical field of RFID electronic tag production, and in particular to an intelligent production method, device, computer equipment and medium for RFID electronic tags. Background Art

[0002] At present, RFID electronic tags need to go through multiple processes in the production process, including raw material lamination, die-cutting, data writing, UV printing, etc. The requirements for label quality in each production link are getting higher and higher, and the stability and accuracy of the production process directly affect the quality and pass rate of the final product.

[0003] Currently, the production of RFID electronic tags usually relies on semi-automatic operations, lacking a mechanism for real-time monitoring and adjustment of various parameters in the production process. For example, in the material compounding stage, it is usually difficult to dynamically adjust the process parameters according to environmental changes and material characteristics; the accuracy detection of data writing and printing content is not comprehensive enough, resulting in common data errors or inconsistencies. The lack of intelligent and automated feedback and adjustment mechanisms in each link of the production process leads to low production efficiency of RFID electronic tags.

[0004] The above-mentioned existing technical solutions have the following defects: the traditional production process is slow to respond to environmental and material changes, and lacks intelligent feedback and adjustment mechanisms for each link in the production process, resulting in low production efficiency, so there is room for improvement. Summary of the invention

[0005] In order to improve the production efficiency of RFID electronic tags, the present application provides an RFID electronic tag intelligent production method, device, computer equipment and medium.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions: An intelligent production method for RFID electronic tags, the intelligent production method for RFID electronic tags comprising: Acquiring raw material status information, and calculating preliminary composite process parameters according to the raw material status information; Acquiring real-time environmental data and material characteristic data, performing parameter calculation on the real-time environmental data and the material characteristic data through an adaptive algorithm, adjusting the preliminary composite process parameters according to the parameter calculation results, and then composite the materials according to the adjusted parameters; After the material is compounded, the wear degree of the die-cutting tool is detected by using machine vision technology, and the tool pressure is automatically adjusted based on the wear degree to perform material die-cutting according to the tool pressure; After the die-cutting of the material is completed, a data writing instruction is generated, the writing accuracy is monitored in real time, and then the corresponding writing parameters are adjusted, UV printing is performed after the data writing is completed, and the content of the UV printing is detected by a machine vision algorithm to see whether it meets the standards; Perform consistency check on written data and printed content, determine the uniqueness and read / write capability of the corresponding TID, and compare them one by one with the preset data content to ensure the accuracy of the content, and then obtain the basic production label; The basic production labels are identified and unqualified labels are eliminated through an intelligent label rejection mechanism, and then qualified labels are automatically packaged, and the packaging method is adjusted according to the label quality information to obtain the final production labels.

[0007] By adopting the above technical solution, by obtaining the raw material status information and calculating the preliminary composite process parameters, the quality and status of the raw materials can be accurately grasped, avoiding quality fluctuations in the production process due to unqualified raw materials or unstable status, thereby providing accurate basic data for subsequent production links, which is helpful to improve product consistency and stability, reduce defective rates, and improve the overall reliability of the production line; by standardizing real-time environmental data and material property data and calculating and adjusting the preliminary composite process parameters through adaptive algorithms, the process parameters can be automatically optimized according to real-time changes in environmental conditions (such as temperature, humidity, etc.) and changes in material properties, ensuring that the process always adapts to the current production environment, thereby improving the flexibility and response capabilities of the production process, reducing production instability or non-standard products caused by external factors, improving resource utilization, and reducing energy consumption; by using machine vision technology to detect the degree of wear of the die-cutting tool, and automatically adjusting the tool based on the degree of wear Pressure can realize real-time monitoring of tool wear, avoid excessive wear of the tool resulting in reduced cutting quality or frequent shutdowns, improve production efficiency, extend the service life of the equipment, and reduce maintenance costs; by performing consistency detection on the written data and printed content, it can ensure that each data and printed content in the label writing process meets the preset standards, avoid product qualification problems caused by writing errors or irregular printing, further improve product quality and traceability, and reduce the risk of product recalls and customer complaints; through the intelligent label rejection mechanism, unqualified labels are identified and rejected, and qualified labels are automatically packaged, and the packaging method is adjusted according to the label quality information, which can realize efficient quality control in the production process, ensure product consistency and quality after rejecting unqualified labels, and adjust the automatic packaging method to improve the operation efficiency of the production line, reduce manual intervention, reduce labor costs, and improve the automation and intelligence level of label production.

[0008] In one example, the present application may be further configured as follows: calculating preliminary composite process parameters according to the raw material status information specifically includes: According to the raw material state information, characterize the raw material state information using a calculation method based on a physical model to obtain state characterization information; The preliminary composite process parameters are obtained by calculating by combining historical data and the state characterization information through a multivariate analysis algorithm.

[0009] By adopting the above technical solution and calculating the preliminary composite process parameters according to the raw material status information, it is possible to accurately evaluate the quality of the raw materials and promptly discover deviations or unqualified raw material status, thereby ensuring that parameter adjustments in subsequent process steps can be optimized according to the actual status of the raw materials, avoiding quality fluctuations caused by substandard material quality or unstable status during production, thereby improving the overall quality and efficiency of the production process, reducing the scrap rate and rework costs caused by unqualified materials, and improving the qualification rate of the production line and the consistency of products.

[0010] In one example, the present application may be further configured as follows: performing parameter calculation on the real-time environment data and the material characteristic data by using an adaptive algorithm, and adjusting the preliminary composite process parameters according to the parameter calculation results, specifically including: Standardizing the real-time environmental data and material characteristic data and converting them into unified quantitative features; Input the quantitative features into the preset formula In the method, different variables are adaptively adjusted by weighting coefficients to obtain the parameter calculation result, where P is the parameter calculation result, ω i is the weighting coefficient, x i is the quantitative feature, n is the number of features; The parameter calculation result is compared and calculated with the preliminary composite process parameters to obtain parameter difference information, and the preliminary process parameters are adjusted according to the parameter difference information and preset parameter adjustment rules.

[0011] By adopting the above technical solution, by standardizing the real-time environmental data and material property data and adjusting the preliminary composite process parameters through adaptive algorithm calculation, it is possible to achieve automatic adaptation to the dynamically changing external environment and material properties in the production process, adjust the process parameters according to the real-time changing environmental conditions, ensure that the entire production process is always in the optimal working state, avoid production process abnormalities caused by environmental fluctuations or unstable material properties, improve the flexibility and stability of the production line, and minimize resource waste, save energy and production costs.

[0012] In one example, the present application may be further configured as follows: the use of machine vision technology to detect the degree of wear of the die-cutting tool and automatically adjusting the tool pressure based on the degree of wear specifically includes: Real-time acquisition of die-cutting tool images, and pre-processing of the die-cutting tool images using an image processing algorithm to obtain standard image data; Extracting tool contour features from the standard image data using an edge detection-based image analysis method, and comparing the tool contour features with preset complete tool features to obtain corresponding wear area features; The wear degree of the wear area feature is judged according to a preset wear threshold, and the tool pressure is adjusted according to the wear degree.

[0013] By adopting the above technical solution and using machine vision technology to detect the degree of wear of the die-cutting tool, it is possible to monitor the use status of the die-cutting tool in real time and detect the wear of the tool in time, thereby avoiding continuing production when the tool is excessively worn and reducing production problems or downtime caused by deterioration in tool quality; by automatically adjusting the tool pressure based on the degree of wear, it is possible to adjust the pressure in time according to the wear of the tool to ensure that the tool is always in the best working condition, avoid affecting the cutting quality or production efficiency due to improper pressure, improve the continuity and stability of production, reduce downtime, and thus improve the overall efficiency and economic benefits of the production line.

[0014] In one example, the present application can be further configured as follows: the consistency detection of the written data and the printed content is performed to determine the uniqueness and read / write capability of the corresponding TID, and a one-to-one comparison is performed with the preset data content to ensure the accuracy of the content, specifically including: Obtain the TID value of the tag, and compare the TID value with the records in the preset TID library one by one through the hash algorithm. If the comparison results are inconsistent, mark the tag as TID inconsistent abnormality; Read the stored data in the tag and compare it bit by bit with the preset data content, and then calculate the data matching degree. If the data matching degree is lower than the set threshold, mark the tag as a data mismatch exception; Perform read and write operations on the tag, detect the response time and data read and write correctness of the tag, if timeout and / or error occurs, execute a repair program on the tag, if the repair fails, mark the tag as abnormal in read and write capability.

[0015] By adopting the above technical solution and performing consistency detection on the written data and printed content, it is possible to ensure that the written information and printed content of each label meet the preset standard requirements, avoid quality problems caused by incorrect label data writing or unqualified printed content, ensure product qualification and traceability, and enhance consumer trust and brand value; at the same time, through this detection, real-time monitoring of product quality can be achieved, abnormalities can be discovered and repaired in time, the risk of unqualified products entering the market can be reduced, the quality defect rate and the cost of product recall can be reduced, and the accuracy and efficiency of the production process can be improved.

[0016] In one example, the present application may be further configured as follows: the automatic packaging of qualified labels and adjusting the packaging method according to the label quality information specifically include: Acquire the quality data of each qualified label at different production links, and comprehensively generate the label quality information based on the quality data; Through preset quality standard rules, the preset quality standard rules are dynamically adjusted according to different production targets, the label quality information is graded, and different packaging methods are selected according to the quality grades.

[0017] By adopting the above technical solution, unqualified labels are identified and removed through the intelligent label removal mechanism, which can ensure that all labels in the final packaging are qualified labels, ensure that every label circulating in the market meets the standards, and avoid customer complaints or brand damage caused by the circulation of unqualified labels; at the same time, through automatic packaging and adjusting the packaging method according to label quality information, the automation level of the production line can be improved, manual intervention can be reduced, packaging efficiency can be improved, and labor costs can be reduced. The packaging method can be adjusted according to the label quality information to ensure the appropriateness and accuracy of the label packaging, improve the packaging quality, avoid product damage or returns due to improper packaging methods, and enhance the flexibility and response speed of the production line.

[0018] The second object of the invention is achieved by the following technical solutions: An intelligent production device for RFID electronic tags, the intelligent production device for RFID electronic tags comprising: A raw material status acquisition module is used to obtain raw material status information and calculate preliminary composite process parameters based on the raw material status information; A real-time data calculation module, used to obtain real-time environmental data and material characteristic data, perform parameter calculation on the real-time environmental data and the material characteristic data through an adaptive algorithm, and adjust the preliminary composite process parameters according to the parameter calculation results, and then perform material composite according to the adjusted parameters; A process parameter adjustment module, used to detect the degree of wear of the die-cutting tool using machine vision technology after the material is compounded, and automatically adjust the tool pressure based on the degree of wear, so as to perform material die-cutting according to the tool pressure; The tool wear detection module is used to generate a data writing instruction after the die-cutting of the material is completed, monitor the writing accuracy in real time, and then adjust the corresponding writing parameters, perform UV printing after the data writing is completed, and detect whether the content of the UV printing meets the standards through a machine vision algorithm; Data consistency detection module, used for data writing and UV printing detection module, is used to detect the consistency of written data and printed content, determine the uniqueness and read-write capability of the corresponding TID, and compare it one by one with the preset data content to ensure the accuracy of the content, and then obtain the basic production label; The label quality control module is used to identify the basic production labels and remove unqualified labels through an intelligent label removal mechanism, and then automatically pack qualified labels and adjust the packaging method according to the label quality information to obtain the final production labels.

[0019] By adopting the above technical solution, by obtaining the raw material status information and calculating the preliminary composite process parameters, the quality and status of the raw materials can be accurately grasped, avoiding quality fluctuations in the production process due to unqualified raw materials or unstable status, thereby providing accurate basic data for subsequent production links, which is helpful to improve product consistency and stability, reduce defective rates, and improve the overall reliability of the production line; by standardizing real-time environmental data and material property data and calculating and adjusting the preliminary composite process parameters through adaptive algorithms, the process parameters can be automatically optimized according to real-time changes in environmental conditions (such as temperature, humidity, etc.) and changes in material properties, ensuring that the process always adapts to the current production environment, thereby improving the flexibility and response capabilities of the production process, reducing production instability or non-standard products caused by external factors, improving resource utilization, and reducing energy consumption; by using machine vision technology to detect the degree of wear of the die-cutting tool, and automatically adjusting the tool based on the degree of wear Pressure can realize real-time monitoring of tool wear, avoid excessive wear of the tool resulting in reduced cutting quality or frequent shutdowns, improve production efficiency, extend the service life of the equipment, and reduce maintenance costs; by performing consistency detection on the written data and printed content, it can ensure that each data and printed content in the label writing process meets the preset standards, avoid product qualification problems caused by writing errors or irregular printing, further improve product quality and traceability, and reduce the risk of product recalls and customer complaints; through the intelligent label rejection mechanism, unqualified labels are identified and rejected, and qualified labels are automatically packaged, and the packaging method is adjusted according to the label quality information, which can realize efficient quality control in the production process, ensure product consistency and quality after rejecting unqualified labels, and adjust the automatic packaging method to improve the operation efficiency of the production line, reduce manual intervention, reduce labor costs, and improve the automation and intelligence level of label production.

[0020] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned RFID electronic tag intelligent production method are implemented.

[0021] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned RFID electronic tag intelligent production method.

[0022] In summary, this application includes the following beneficial technical effects: 1. By calculating the preliminary composite process parameters based on the raw material status information, the quality of the raw materials can be accurately evaluated, and the deviation or unqualified raw material status can be discovered in time, so as to ensure that the parameter adjustment in the subsequent process steps can be optimized according to the actual status of the raw materials, avoiding quality fluctuations caused by substandard material quality or unstable status during production, thereby improving the overall quality and efficiency of the production process, reducing the scrap rate and rework costs caused by unqualified materials, and improving the qualified rate of the production line and the consistency of products; 2. By standardizing the real-time environmental data and material property data and adjusting the preliminary composite process parameters through adaptive algorithm calculation, it is possible to automatically adapt to the dynamically changing external environment and material properties during the production process, adjust the process parameters according to the real-time changing environmental conditions, ensure that the entire production process is always in the best working state, avoid production process abnormalities caused by environmental fluctuations or unstable material properties, improve the flexibility and stability of the production line, and minimize resource waste, save energy and production costs; 3. By using machine vision technology to detect the degree of wear of the die-cutting tool, it is possible to monitor the use status of the die-cutting tool in real time and detect the wear of the tool in time, thereby avoiding continued production when the tool is excessively worn and reducing production problems or downtime caused by the decline in tool quality; by automatically adjusting the tool pressure based on the degree of wear, it is possible to adjust the pressure in time according to the tool wear to ensure that the tool is always in the best working condition, avoid affecting the cutting quality or production efficiency due to improper pressure, improve the continuity and stability of production, reduce downtime, and thus improve the overall efficiency and economic benefits of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an intelligent production method of RFID electronic tags in one embodiment of the present application; Figure 2 This is a flowchart for implementing step S10 in the intelligent production method of RFID electronic tags in one embodiment of the present application; Figure 3 It is a flowchart for implementing step S20 in the intelligent production method of RFID electronic tags in one embodiment of the present application; Figure 4 This is a flowchart for implementing step S30 in the RFID electronic tag intelligent production method in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S50 in the intelligent production method of RFID electronic tags in one embodiment of the present application; Figure 6 This is a flowchart for implementing step S60 in the RFID electronic tag intelligent production method in one embodiment of the present application; Figure 7This is a principle block diagram of an intelligent production device for RFID electronic tags in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below in conjunction with the accompanying drawings.

[0025] In one embodiment, if Figure 1 As shown, the present application discloses an intelligent production method for RFID electronic tags, which specifically includes the following steps: S10: Obtaining raw material status information, and calculating preliminary composite process parameters according to the raw material status information.

[0026] Specifically, by collecting the state information of the raw materials, such as the temperature, humidity, density and other physical parameters of the raw materials, comprehensive raw material state information can be obtained. These state information are collected by sensors, and after data processing, the current state of the raw materials is converted into quantifiable values, and preliminary calculations are performed through preset formulas or algorithms to obtain preliminary composite process parameters, which include but are not limited to composite temperature, composite pressure, composite time, etc.

[0027] S20: acquiring real-time environmental data and material characteristic data, performing parameter calculation on the real-time environmental data and material characteristic data through an adaptive algorithm, adjusting preliminary composite process parameters according to the parameter calculation results, and then composite materials according to the adjusted parameters.

[0028] Specifically, real-time environmental data include factors such as temperature, humidity, air pressure, wind speed, etc. at the production site. These data can be obtained in real time through sensors. Material property data include information such as viscosity, fluidity, hygroscopicity, etc. of raw materials, which will affect the accuracy of the composite process. Through adaptive algorithms, such as those based on neural networks or regression analysis, these real-time data are converted into unified quantitative features. Through the analysis and weighted calculation of these features, updated composite process parameters are obtained, and then the composite conditions such as temperature and pressure are dynamically adjusted through the control equipment to ensure the efficiency and accuracy of the material composite process.

[0029] S30: After the material is compounded, the wear degree of the die-cutting tool is detected by machine vision technology, and the tool pressure is automatically adjusted based on the wear degree to perform material die-cutting according to the tool pressure.

[0030] Specifically, the high-resolution camera or industrial camera installed on the production line collects images of the die-cutting tool in real time, and the image is pre-processed by the image processing algorithm to remove noise and enhance image clarity. Then, the edge detection-based algorithm is used to extract the contour information of the tool from the image, and then these contour features are compared with the preset standard tool contour to obtain the degree of wear. If the wear area exceeds the preset threshold, the system automatically adjusts the pressure of the tool through the feedback control algorithm to ensure that the tool pressure is always in the best state during the die-cutting process, thereby improving the die-cutting quality.

[0031] S40: After the material die-cutting is completed, a data writing instruction is generated, and the writing accuracy is monitored in real time, and then the corresponding writing parameters are adjusted. After the data writing is completed, UV printing is performed, and the machine vision algorithm is used to detect whether the content of the UV printing meets the standards.

[0032] Specifically, after the die-cutting operation is completed, the corresponding data writing instructions are generated according to the characteristics of the die-cut label, and the data is written into the storage area of ​​the RFID tag through the control device. During the data writing process, the writing accuracy of each label is monitored in real time. If a writing error is detected, the writing parameters, such as writing frequency and power, are adjusted immediately to ensure that the data is written accurately. After the data is written, the label is printed on the label through the UV printing device, and the printed content is detected using a machine vision algorithm to ensure that the printed content is consistent with the preset content and meets the label clarity and specification requirements.

[0033] S50: Perform consistency check on the written data and the printed content, determine the uniqueness and read / write capability of the corresponding TID, and compare them one by one with the preset data content to ensure the accuracy of the content, thereby obtaining a basic production label.

[0034] Specifically, by reading the TID value of the RFID tag, it is compared with the preset TID library through a hash algorithm. If the comparison results are consistent, it proves that the TID uniqueness is qualified, otherwise it is marked as a TID inconsistency exception. At the same time, the data stored in the tag is read and compared bit by bit with the preset data content to calculate the data matching degree. If the matching degree is lower than the set threshold, it is marked as a data mismatch exception. For any abnormal situation during the read and write operation, such as read and write timeout or error, the repair program is immediately started. If the repair fails, the tag is marked as abnormal in read and write capability. Only tags with completely consistent data content and no abnormalities can be qualified tags.

[0035] S60: The basic production labels are identified and unqualified labels are eliminated through the intelligent label elimination mechanism, and then the qualified labels are automatically packaged, and the packaging method is adjusted according to the label quality information to obtain the final production labels.

[0036] Specifically, machine vision technology and image recognition algorithms are used to perform an all-round scan of basic production labels to identify and remove labels that do not meet the standards. For qualified labels, quality data from different links in the production process is collected, including die-cutting accuracy, data writing accuracy, printing quality and other information, and these data are combined to generate comprehensive quality information for the labels. Through preset quality standard rules, label quality information is analyzed and graded, and appropriate packaging methods are selected according to different quality levels. For example, high-quality labels choose moisture-proof and scratch-resistant packaging methods, while low-quality labels take additional quality assurance measures to ensure that the final packaging method of each label meets its quality requirements and ensure the quality and performance of the final production labels.

[0037] In one embodiment, if Figure 2 As shown, in step S10, the preliminary composite process parameters are calculated according to the raw material status information, specifically including: S11: According to the raw material state information, a calculation method based on a physical model is used to characterize the raw material state information to obtain state characterization information.

[0038] Specifically, physical models are used to characterize various state information of raw materials in detail. For example, thermodynamic models are used to dynamically model environmental parameters such as temperature and humidity of raw materials, and state characterization information is obtained by simulating the performance changes of raw materials under different conditions. This information can accurately reflect the performance of raw materials in the composite process and provide basic data support for subsequent process parameter calculations.

[0039] S12: Preliminary composite process parameters are obtained by calculating the multivariate analysis algorithm in combination with historical data and state characterization information.

[0040] Specifically, multivariate analysis methods, such as principal component analysis or multiple regression analysis, are used to combine historical production data with state characterization information, input into the calculation model for analysis, and obtain preliminary composite process parameters. These parameters are corrected according to the trend of historical data, and process conditions that meet current production requirements can be pre-set.

[0041] In one embodiment, if Figure 3 As shown, in step S20, the real-time environmental data and material characteristic data are calculated by an adaptive algorithm, and the preliminary composite process parameters are adjusted according to the parameter calculation results, specifically including: S21: Standardize the real-time environmental data and material property data and convert them into unified quantitative features.

[0042] Specifically, the collected real-time environmental data and material property data are first standardized to have the same dimensions and quantitative standards. For example, the temperature, humidity, density and other data collected by different sensors are converted into values ​​of a uniform range using normalization or Z-score standardization methods to facilitate subsequent calculations and comparisons.

[0043] S22: Input the quantitative features into the preset formula In the above example, different variables are adaptively adjusted by weighting coefficients to obtain parameter calculation results, where P is the parameter calculation result, ω i is the weighting coefficient, x i is the quantitative feature, and n is the number of features.

[0044] Specifically, the standardized quantitative features are input into the preset weighted formula, and each characteristic value xi is assigned a different weighting coefficient ωi according to its importance in actual production. The calculation result P of the formula is the final adjustment parameter. In this way, dynamic adjustments can be made according to the impact of different variables on the process results to obtain the composite process parameters that best suit the current environment and material characteristics.

[0045] S23: Compare and calculate the parameter calculation results with the preliminary composite process parameters to obtain parameter difference information, and adjust the preliminary process parameters according to the parameter difference information and preset parameter adjustment rules.

[0046] Specifically, the calculated new process parameters are compared with the preliminary process parameters, and the parameter difference information is obtained through difference analysis. If the difference is large, the preliminary process parameters are slightly or significantly adjusted according to the preset adjustment rules, such as setting the threshold range or gradient adjustment strategy, to ensure that the final composite process conditions match the current environment and material characteristics.

[0047] In one embodiment, if Figure 4 As shown, in step S30, the wear degree of the die-cutting tool is detected by using machine vision technology, and the tool pressure is automatically adjusted based on the wear degree, specifically including: S31: collecting die-cutting tool images in real time, and pre-processing the die-cutting tool images using an image processing algorithm to obtain standard image data.

[0048] Specifically, the image of the die-cutting tool is collected in real time by a high-resolution camera or industrial camera, and the image data is pre-processed by an image processing algorithm, including denoising, image enhancement, and contrast adjustment, to improve the image quality. Through these processes, the state of the tool surface and blade can be clearly displayed, and standard image data can be obtained, which is the basis for subsequent tool wear analysis.

[0049] S32: Using an edge detection-based image analysis method, tool contour features are extracted from standard image data, and the tool contour features are compared with preset complete tool features to obtain corresponding wear area features.

[0050] Specifically, edge detection algorithms such as Canny edge detection or Sobel operator are used to analyze the standard image data and extract the contour features of the tool. By comparing with the preset complete tool features, contour changes, such as wear or damage areas of the blade, can be found. Based on these feature comparison results, the features of the wear area, such as the length, depth or area of ​​wear, are further extracted to evaluate the degree of wear of the tool.

[0051] S33: Determine the degree of wear of the wear area characteristics according to a preset wear threshold, and adjust the tool pressure according to the degree of wear.

[0052] Specifically, the extracted wear area features are compared with the preset wear threshold. If the wear area exceeds the preset threshold, it indicates that the tool has reached a certain degree of wear and needs to adjust the operating conditions. According to the degree of wear, the pressure of the tool is automatically adjusted to ensure that the tool can continue to cut effectively during the die-cutting operation, while avoiding material damage or further damage to the tool due to insufficient or excessive pressure.

[0053] In one embodiment, if Figure 5 As shown, in step S50, the consistency of the written data and the printed content is checked to determine the uniqueness and read / write capability of the corresponding TID, and a one-to-one comparison is performed with the preset data content to ensure the accuracy of the content, specifically including: S51: Obtain the TID value of the tag, and compare the TID value with the records in the preset TID library one by one through a hash algorithm. If the comparison results are inconsistent, mark the tag as TID inconsistent exception.

[0054] Specifically, the TID value of each tag is first obtained through the radio frequency identification reading device. The TID value is the unique identification of the tag. After obtaining the TID value, the hash algorithm is used to process the read TID value and compare it with the records in the preset TID library one by one. The function of the hash algorithm is to convert the TID value into a hash code of fixed length, which can reduce the amount of calculation in the comparison process and improve the comparison efficiency. If the comparison result finds that the TID value is inconsistent with the record in the preset TID library, it means that the TID of the tag is abnormal, and the system marks the tag as a TID inconsistent abnormality to prevent it from entering the next production link.

[0055] S52: Read the stored data in the tag and compare it bit by bit with the preset data content, and then calculate the data matching degree. If the data matching degree is lower than the set threshold, mark the tag as a data mismatch exception.

[0056] Specifically, after reading the data of the tag, the system will extract all the stored data contents such as product information, production batch, etc. from the memory of the tag, and then compare these contents with the preset data contents bit by bit. The bit-by-bit comparison is performed by comparing the correctness of each bit of data one by one, and the matching degree is usually calculated by a comparison algorithm such as XOR operation. If the comparison result shows that the data matching degree is lower than a predetermined threshold (for example, 90), it indicates that the data stored in the tag may be erroneous or damaged. The system marks it as a data mismatch exception and prevents it from continuing to flow into subsequent production links to ensure that unqualified tags are eliminated.

[0057] S53: Perform read and write operations on the tag, detect the tag's response time and data read and write correctness, and if a timeout and / or error occurs, execute a repair program on the tag. If the repair fails, mark the tag as abnormal in read and write capability.

[0058] Specifically, during the read and write operations, the system will attempt to perform read and write operations on the tag, and determine whether it is normal by detecting the tag's response time during the read and write process. If the tag's response time exceeds the set threshold, or an error such as timeout, data inconsistency, etc. occurs during the read and write process, it means that the tag may have a hardware failure or communication problem. At this time, the system will start an automatic repair program, such as rewriting data, adjusting communication parameters, or replacing read and write devices, to repair the tag's read and write problems. If the tag still cannot return to normal after the repair program is executed, the system will mark the tag as abnormal in read and write capabilities and remove it to prevent it from affecting the quality of the final product.

[0059] In one embodiment, if Figure 6 As shown, in step S60, the qualified labels are automatically packaged, and the packaging method is adjusted according to the label quality information, which specifically includes: S61: Obtain quality data of each qualified label at different production links, and comprehensively generate label quality information based on the quality data.

[0060] Specifically, during the entire production process, the system will track the quality of each qualified label and obtain the quality data of the label in each production link such as die-cutting, data writing, printing, packaging, etc. These quality data may include the test results of each link, actual operating parameters such as pressure, speed, etc., and the qualified status of visual inspection. By comprehensively analyzing these data, the system generates comprehensive quality information of the label, reflecting the quality status of the label in the entire production process, thereby providing a reliable basis for subsequent packaging and distribution.

[0061] S62: Through preset quality standard rules, the preset quality standard rules are dynamically adjusted according to different production goals, the label quality information is graded, and different packaging methods are selected according to the quality grades.

[0062] Specifically, the system dynamically adjusts the quality information of qualified labels according to the preset quality standard rules. The quality standard rules will be flexibly adjusted according to different production goals. For example, if the target of a batch of labels is mass production, the quality requirements may be relatively loose; if the production target is high-end products, higher quality standards are required. The system will select the appropriate packaging method based on the quality classification of the labels, such as Grade A, Grade B, Grade C, etc. For high-quality labels, high-end protective materials may be selected for packaging, such as anti-static, moisture-proof, and pressure-proof packaging; for medium and low-quality labels, standard packaging methods are used to minimize production costs while ensuring transportation safety.

[0063] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0064] In one embodiment, a device for intelligently producing RFID electronic tags is provided, and the device for intelligently producing RFID electronic tags corresponds one-to-one to the method for intelligently producing RFID electronic tags in the above embodiment. Figure 7 As shown, the RFID electronic tag intelligent production device includes a raw material status acquisition module, a real-time data calculation module, a process parameter adjustment module, a tool wear detection module, a data consistency detection module, and a label quality control module. The detailed description of each functional module is as follows: the raw material status acquisition module is used to obtain the raw material status information and calculate the preliminary composite process parameters according to the raw material status information; A real-time data calculation module is used to obtain real-time environmental data and material characteristic data, perform parameter calculation on the real-time environmental data and material characteristic data through an adaptive algorithm, and adjust preliminary composite process parameters according to the parameter calculation results, and then composite the materials according to the adjusted parameters; A process parameter adjustment module is used to detect the degree of wear of the die-cutting tool using machine vision technology after the material is compounded, and automatically adjust the tool pressure based on the degree of wear to perform material die-cutting according to the tool pressure; The tool wear detection module is used to generate data writing instructions after the material die-cutting is completed, monitor the writing accuracy in real time, and then adjust the corresponding writing parameters. After the data writing is completed, UV printing is performed, and the machine vision algorithm is used to detect whether the content of the UV printing meets the standards; Data consistency detection module, used for data writing and UV printing detection module, is used to detect the consistency of written data and printed content, determine the uniqueness and read-write capability of the corresponding TID, and compare it one by one with the preset data content to ensure the accuracy of the content, and then obtain the basic production label; The label quality control module is used to identify the basic production labels and remove unqualified labels through the intelligent label removal mechanism, and then automatically pack the qualified labels and adjust the packaging method according to the label quality information to obtain the final production labels.

[0065] Optionally, the raw material status collection module specifically includes: The state characterization calculation submodule is used to characterize the raw material state information according to the raw material state information by using a calculation method based on a physical model to obtain state characterization information; The multivariate analysis submodule is used to calculate the preliminary composite process parameters by combining historical data and state characterization information through a multivariate analysis algorithm.

[0066] Optionally, the real-time data calculation module specifically includes: The data standardization submodule is used to standardize the real-time environmental data and material characteristic data and convert them into unified quantitative features; Parameter calculation submodule, used to input quantitative features into preset formulas In the above example, different variables are adaptively adjusted by weighting coefficients to obtain parameter calculation results, where P is the parameter calculation result, ω i is the weighting coefficient, x i is the quantitative feature, n is the number of features; The parameter adjustment submodule is used to compare and calculate the parameter calculation results with the preliminary composite process parameters to obtain parameter difference information, and adjust the preliminary process parameters according to the parameter difference information and preset parameter adjustment rules.

[0067] Optionally, the process parameter adjustment module specifically includes: The image acquisition submodule is used to acquire die-cutting tool images in real time and pre-process the die-cutting tool images using image processing algorithms to obtain standard image data; The wear feature extraction submodule is used to extract tool contour features from standard image data using an edge detection-based image analysis method, and compare the tool contour features with preset complete tool features to obtain corresponding wear area features; The wear judgment submodule is used to judge the wear degree of the wear area characteristics according to the preset wear threshold value and adjust the tool pressure according to the wear degree.

[0068] Optionally, the data consistency detection module specifically includes: The TID consistency detection submodule is used to obtain the TID value of the tag and compare the TID value with the records in the preset TID library one by one through the hash algorithm. If the comparison results are inconsistent, the tag is marked as TID inconsistent abnormal; The data matching detection submodule is used to read the stored data in the tag and compare it bit by bit with the preset data content, and then calculate the data matching degree. If the data matching degree is lower than the set threshold, the tag is marked as a data mismatch exception; The read / write capability detection submodule is used to perform read / write operations on the tag, detect the tag's response time and data read / write correctness, and if a timeout and / or error occurs, execute a repair program on the tag. If the repair fails, the tag is marked as having abnormal read / write capability.

[0069] Optionally, the label quality control module specifically includes: The label quality data acquisition submodule is used to obtain the quality data of each qualified label at different production links, and comprehensively generate label quality information based on the quality data; The quality grading submodule is used to grade the label quality information through preset quality standard rules, which are dynamically adjusted according to different production goals, and select different packaging methods according to the quality grade.

[0070] The specific definition of the RFID electronic tag intelligent production device can be found in the definition of the RFID electronic tag intelligent production method above, which will not be repeated here. Each module in the above-mentioned RFID electronic tag intelligent production device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0071] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an intelligent production method of RFID electronic tags is realized.

[0072] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtaining raw material status information, and calculating preliminary composite process parameters based on the raw material status information; Acquire real-time environmental data and material characteristic data, calculate parameters of the real-time environmental data and material characteristic data through an adaptive algorithm, adjust preliminary composite process parameters according to the parameter calculation results, and then composite materials according to the adjusted parameters; After the material is compounded, the wear degree of the die-cutting tool is detected by machine vision technology, and the tool pressure is automatically adjusted based on the wear degree to die-cut the material according to the tool pressure; After the material die-cutting is completed, the data writing instruction is generated, the writing accuracy is monitored in real time, and the corresponding writing parameters are adjusted. After the data writing is completed, UV printing is performed, and the machine vision algorithm is used to detect whether the content of the UV printing meets the standards; the consistency of the written data and the printing content is tested to determine the uniqueness and read-write ability of the corresponding TID, and a one-to-one comparison is performed with the preset data content to ensure the accuracy of the content, and then the basic production label is obtained; The basic production labels are identified and unqualified labels are removed through the intelligent label rejection mechanism, and then the qualified labels are automatically packaged, and the packaging method is adjusted according to the label quality information to obtain the final production labels.

[0073] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtaining raw material status information, and calculating preliminary composite process parameters based on the raw material status information; Acquire real-time environmental data and material characteristic data, calculate parameters of the real-time environmental data and material characteristic data through an adaptive algorithm, adjust preliminary composite process parameters according to the parameter calculation results, and then composite materials according to the adjusted parameters; After the material is compounded, the wear degree of the die-cutting tool is detected by machine vision technology, and the tool pressure is automatically adjusted based on the wear degree to die-cut the material according to the tool pressure; After the material die-cutting is completed, the data writing instruction is generated, the writing accuracy is monitored in real time, and the corresponding writing parameters are adjusted. After the data writing is completed, UV printing is performed, and the machine vision algorithm is used to detect whether the content of the UV printing meets the standards; the consistency of the written data and the printing content is tested to determine the uniqueness and read-write ability of the corresponding TID, and a one-to-one comparison is performed with the preset data content to ensure the accuracy of the content, and then the basic production label is obtained; The basic production labels are identified and unqualified labels are removed through the intelligent label rejection mechanism, and then the qualified labels are automatically packaged, and the packaging method is adjusted according to the label quality information to obtain the final production labels.

[0074] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0075] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0076] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for intelligent production of RFID electronic tags, characterized in that: The RFID electronic tag intelligent production method comprises: Acquiring raw material status information, and calculating preliminary composite process parameters according to the raw material status information; Acquiring real-time environmental data and material characteristic data, performing parameter calculation on the real-time environmental data and the material characteristic data through an adaptive algorithm, adjusting the preliminary composite process parameters according to the parameter calculation results, and then composite the materials according to the adjusted parameters; After the material is compounded, the wear degree of the die-cutting tool is detected by using machine vision technology, and the tool pressure is automatically adjusted based on the wear degree to perform material die-cutting according to the tool pressure; After the die-cutting of the material is completed, a data writing instruction is generated, the writing accuracy is monitored in real time, and then the corresponding writing parameters are adjusted, UV printing is performed after the data writing is completed, and the content of the UV printing is detected by a machine vision algorithm to see if it meets the standards; Perform consistency check on written data and printed content, determine the uniqueness and read / write capability of the corresponding TID, and compare them one by one with the preset data content to ensure the accuracy of the content, and then obtain the basic production label; The basic production labels are identified and unqualified labels are eliminated through an intelligent label rejection mechanism, and then qualified labels are automatically packaged, and the packaging method is adjusted according to the label quality information to obtain the final production labels.

2. The intelligent production method of RFID electronic tags according to claim 1, characterized in that: The calculating of preliminary composite process parameters according to the raw material status information specifically includes: According to the raw material state information, characterize the raw material state information using a calculation method based on a physical model to obtain state characterization information; The preliminary composite process parameters are obtained by calculating by combining historical data and the state characterization information through a multivariate analysis algorithm.

3. The intelligent production method of RFID electronic tags according to claim 1 is characterized in that: The performing parameter calculation on the real-time environment data and the material characteristic data by using an adaptive algorithm, and adjusting the preliminary composite process parameters according to the parameter calculation results, specifically includes: Standardizing the real-time environmental data and material characteristic data and converting them into unified quantitative features; Input the quantitative features into the preset formula In the method, different variables are adaptively adjusted by weighting coefficients to obtain the parameter calculation result, where P is the parameter calculation result, ω i is the weighting coefficient, x i is the quantitative feature, n is the number of features; The parameter calculation result is compared and calculated with the preliminary composite process parameters to obtain parameter difference information, and the preliminary process parameters are adjusted according to the parameter difference information and preset parameter adjustment rules.

4. The intelligent production method of RFID electronic tags according to claim 1, characterized in that: The method of using machine vision technology to detect the degree of wear of the die-cutting tool and automatically adjusting the tool pressure based on the degree of wear specifically includes: Acquire die-cutting tool images in real time, and pre-process the die-cutting tool images using an image processing algorithm to obtain standard image data; Extracting tool contour features from the standard image data using an edge detection-based image analysis method, and comparing the tool contour features with preset complete tool features to obtain corresponding wear area features; The wear degree of the wear area feature is judged according to a preset wear threshold, and the tool pressure is adjusted according to the wear degree.

5. The intelligent production method of RFID electronic tags according to claim 1, characterized in that: The consistency test of the written data and the printed content is performed to determine the uniqueness and read / write capability of the corresponding TID, and a one-to-one comparison is performed with the preset data content to ensure the accuracy of the content, specifically including: Obtain the TID value of the tag, and compare the TID value with the records in the preset TID library one by one through the hash algorithm. If the comparison results are inconsistent, mark the tag as TID inconsistent abnormality; Read the stored data in the tag and compare it bit by bit with the preset data content, and then calculate the data matching degree. If the data matching degree is lower than the set threshold, mark the tag as a data mismatch exception; Perform read and write operations on the tag, detect the response time and data read and write correctness of the tag, if timeout and / or error occurs, execute a repair program on the tag, if the repair fails, mark the tag as abnormal in read and write capability.

6. The intelligent production method of RFID electronic tags according to claim 1, characterized in that: The automatic packaging of qualified labels and adjusting the packaging method according to label quality information specifically include: Acquire the quality data of each qualified label at different production links, and comprehensively generate the label quality information based on the quality data; Through preset quality standard rules, the preset quality standard rules are dynamically adjusted according to different production targets, the label quality information is graded, and different packaging methods are selected according to the quality grades.

7. An intelligent production device for RFID electronic tags, characterized in that: The RFID electronic tag intelligent production device comprises: A raw material status acquisition module is used to obtain raw material status information and calculate preliminary composite process parameters based on the raw material status information; A real-time data calculation module, used to obtain real-time environmental data and material characteristic data, perform parameter calculation on the real-time environmental data and the material characteristic data through an adaptive algorithm, and adjust the preliminary composite process parameters according to the parameter calculation results, and then perform material composite according to the adjusted parameters; A process parameter adjustment module, used to detect the degree of wear of the die-cutting tool using machine vision technology after the material is compounded, and automatically adjust the tool pressure based on the degree of wear, so as to perform material die-cutting according to the tool pressure; The tool wear detection module is used to generate a data writing instruction after the die-cutting of the material is completed, monitor the writing accuracy in real time, and then adjust the corresponding writing parameters, perform UV printing after the data writing is completed, and detect whether the content of the UV printing meets the standards through a machine vision algorithm; Data consistency detection module, used for data writing and UV printing detection module, is used to detect the consistency of written data and printed content, determine the uniqueness and read-write capability of the corresponding TID, and compare it one by one with the preset data content to ensure the accuracy of the content, and then obtain the basic production label; The label quality control module is used to identify the basic production labels and remove unqualified labels through an intelligent label removal mechanism, and then automatically pack qualified labels and adjust the packaging method according to the label quality information to obtain the final production labels.

8. The RFID electronic tag intelligent production device according to claim 7 is characterized in that: The raw material status acquisition module specifically includes: A state characterization calculation submodule, used to characterize the raw material state information according to the raw material state information using a calculation method based on a physical model to obtain state characterization information; The multivariate analysis submodule is used to calculate the preliminary composite process parameters by combining the historical data and the state characterization information through a multivariate analysis algorithm.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the RFID electronic tag intelligent production method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the intelligent production method of RFID electronic tags as claimed in any one of claims 1 to 6 are implemented.

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