A coding device and method for adhesive label production

Abstract

The application is suitable for the technical field of laser coding, and provides a coding device and method for adhesive label production, wherein the coding device for adhesive label production comprises a belt conveyor and a coding machine main body, and a controller is arranged in the coding machine main body, which comprises: an auxiliary gas pressure regulation and control module, which is used for constructing an auxiliary gas pressure regulation and control model based on a rated auxiliary gas pressure, a processing precision confidence index and a material characteristic evaluation index, outputting an auxiliary gas pressure regulation amount and controlling an auxiliary gas system to perform pressure regulation; and a laser pulse frequency regulation and control module, which is used for outputting a laser pulse frequency regulation amount and controlling a laser pulse generator to perform frequency adjustment. According to the processing precision confidence index and the material characteristic index, the auxiliary gas pressure is increased, and the laser pulse frequency is adjusted in the application scheme.

Description

Technical Field

[0001] This invention belongs to the field of laser marking technology, and particularly relates to a marking device and method for producing self-adhesive labels. Background Technology

[0002] While traditional laser marking technology has advantages such as non-contact operation, permanent marking, and process flexibility, it faces multiple technical bottlenecks when dealing with multi-layer composite materials such as self-adhesive labels.

[0003] The base material and surface coating of self-adhesive labels differ significantly, and their laser absorption rate fluctuates due to the influence of material composition and structure. Darker coatings typically have higher absorption rates, while lighter coatings have lower rates. The depth of the coating color directly affects the laser energy conversion efficiency; the darker the color, the more concentrated the energy absorption. Surface cleanliness (such as dust, oil, or particle adhesion) interferes with the uniform action of the laser beam, leading to uneven energy distribution. When these material characteristics combine, they can easily cause uneven color depth in the marked area, blurred edges, and even localized burn-through or missing markings, severely damaging the visual effect and functional integrity of the label.

[0004] Existing coding devices generally employ fixed parameters or semi-closed-loop control modes. Parameter adjustments rely on operator experience and trial-and-error, lacking a comprehensive quantitative assessment capability of material properties, real-time equipment status, historical processing data, and dynamic process conditions, thus failing to achieve adaptive dynamic optimization of parameters. This results in a high dependence on manual experience in the production process, lengthy changeover and machine setup times, and difficulty in maintaining stable, high-precision coding quality when material batches fluctuate or equipment performance drifts, thus restricting production efficiency and product consistency. Therefore, existing technologies urgently need improvement to address these issues. Summary of the Invention

[0005] The purpose of this invention is to provide a coding device and method for producing self-adhesive labels, in order to solve the above-mentioned problems.

[0006] This invention is implemented as follows: a coding device for self-adhesive label production includes a belt conveyor and a coding machine body. The coding machine body is equipped with a controller, which includes: The material property evaluation module is used to construct a material property evaluation model based on the material's laser absorption rate, coating color depth, and cleanliness, and output the material property evaluation index. The laser condition assessment module is used to construct a laser condition assessment model based on the laser output power deviation, beam quality factor, galvanometer positioning error, and laser temperature, and output the laser condition index. The machining accuracy confidence assessment module is used to construct a machining accuracy confidence assessment model based on the previous machining accuracy feedback value, the complexity of the target marking content, the material property assessment index, and the laser state index, and output the machining accuracy confidence. The auxiliary gas pressure control module is used to construct an auxiliary gas pressure control model based on the rated auxiliary gas pressure, processing accuracy confidence level and material property evaluation index, output the auxiliary gas pressure adjustment amount and control the auxiliary gas system to perform pressure regulation. The laser pulse frequency control module is used to output the laser pulse frequency adjustment amount and control the laser pulser to adjust the frequency.

[0007] A further technical solution, in the material property evaluation module: the acquired laser absorptivity, coating color depth and cleanliness data of the material are successively substituted into the maximum-minimum normalization formula for processing, and the laser absorptivity index, coating color depth index and cleanliness index are generated in sequence.

[0008] Further technical solutions, in the material property evaluation model: The material property evaluation index is obtained by multiplying the laser absorption rate index, coating color depth index, and cleanliness index by their respective preset weighting coefficients, summing the results, and then multiplying by a penalty compensation term. The penalty compensation item is 1 minus (the product of the standard deviations of the laser absorption rate index, coating color depth index, and cleanliness index and the preset penalty coefficient); the sum of each preset weight coefficient is 1, and all are greater than zero; the penalty coefficient in the penalty compensation item is greater than zero.

[0009] A further technical solution, in the laser status evaluation module: the difference between the actual laser output power and the rated output power of the laser is taken as the absolute value and then divided by the rated output power to obtain the laser output power deviation index; Divide the actual galvanometer positioning error value by the maximum allowable positioning error upper limit to obtain the galvanometer positioning error index; The actual beam quality factor and laser temperature value are successively substituted into the maximum-minimum normalization formula for processing, and the beam quality factor index and laser temperature index are generated in sequence.

[0010] Further technical solutions, in the laser condition evaluation model: The laser state index is calculated by taking an exponential function with the natural constant e as the base. The exponent of this exponential function is negative and is the sum of the squares of the laser output power deviation index, the squares of the beam quality factor index, the squares of the galvanometer positioning error index, and the squares of the laser temperature index, each multiplied by a corresponding preset influence coefficient. All influence coefficients are greater than zero.

[0011] Further technical solutions, in the confidence evaluation model for machining accuracy: The processing accuracy confidence level is obtained by multiplying the previous processing accuracy feedback value, the laser state index, and an enhancement factor related to the material property index, and then dividing by a factor that includes a penalty term for insufficient material property index and a penalty term for the complexity of the coding content; the previous processing accuracy feedback value and the coding content complexity are both dimensionless values ​​between 0 and 1; the material penalty coefficient and the content complexity penalty coefficient are both constants greater than zero.

[0012] Further technical solutions, in the auxiliary gas pressure control model: The auxiliary gas pressure adjustment amount is obtained by multiplying the rated auxiliary gas pressure by an adjustment factor; The adjustment factor is composed of the degree of insufficient confidence in processing accuracy (1 minus the confidence in processing accuracy) multiplied by a preset confidence weight coefficient, and the degree of insufficient material property index (1 minus the material property index) multiplied by a preset material property weight coefficient, and the sum of the two. The confidence level weighting coefficient and the material property weighting coefficient are both values ​​between 0 and 0.5.

[0013] A further technical solution, specifically in the laser pulse frequency control module, involves constructing a laser pulse frequency control model based on the rated pulse frequency, laser scanning speed, preset dot spacing, processing accuracy confidence level, and actual auxiliary gas pressure. This model outputs the laser pulse frequency adjustment amount and controls the laser pulser to adjust the frequency.

[0014] A further technical solution, in the laser pulse frequency modulation model: The laser pulse frequency adjustment amount is calculated by multiplying the rated pulse frequency by the adjustment scale, which is the difference between the product of the two terms and 1. The first product term is the ratio of the laser scanning speed exponent to (the sum of the dot pitch exponent and the minimum constant); The second product term is: 1 plus the machining accuracy confidence factor, and then minus the gas pressure factor. Among them, the confidence factor of processing accuracy is itself multiplied by the preset confidence factor, and the gas pressure factor is the difference between 1 and the actual auxiliary gas pressure index, and then multiplied by the preset gas pressure factor. The confidence level influence coefficient and the gas pressure influence coefficient are both values ​​between 0 and 0.5; the laser scanning speed index, the dot spacing index, and the actual auxiliary gas pressure index are obtained by normalizing the corresponding physical quantities through maximum-minimum value processing.

[0015] A coding method for producing self-adhesive labels is applied to the aforementioned coding device for producing self-adhesive labels. During the laser coding process, the auxiliary gas pressure and the laser pulse frequency are adjusted through the aforementioned device.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: The coding device for self-adhesive label production in this invention achieves real-time, quantitative evaluation of key factors affecting coding quality by introducing a material property evaluation module and a laser status evaluation module. As shown in the example, even if there are color differences or surface dust in the material, or fluctuations in laser performance, this information can be captured and converted into quantitative indices. This evaluation mechanism enables the device to understand the current working environment and equipment status, rather than relying solely on static preset values.

[0017] The introduction of a processing accuracy confidence assessment module enables this device to have predictive capabilities. In the example, this module can comprehensively consider historical processing data, the complexity of the target content, and the real-time material and laser status to predict the success probability of the coding task. This assessment allows the device to identify potential quality risks before the actual coding begins, thus providing a basis for decision-making in subsequent parameter adjustments and avoiding the drawbacks of traditional solutions that only make adjustments after problems are discovered.

[0018] The auxiliary gas pressure control module and the laser pulse frequency control module can adaptively adjust based on these evaluation and prediction results. In this invention, the auxiliary gas pressure is increased and the laser pulse frequency is adjusted according to the processing accuracy confidence level and material property index. This parameter optimization ensures that even under undesirable conditions, the deposition of laser energy and the removal of slag can reach the desired state, thereby guaranteeing the clarity and integrity of the QR code. Attached Figure Description

[0019] Figure 1 A schematic diagram of a coding device used in the production of self-adhesive labels; Figure 2 This is a schematic diagram of the controller module in this invention.

[0020] In the attached diagram, 1 is a belt conveyor; 2 is the main body of the coding machine. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0023] like Figure 1 As shown, a coding device for producing self-adhesive labels is provided in an embodiment of the present invention, including a belt conveyor 1 and a coding machine body 2. The coding machine body is provided with a controller, which includes: a material property evaluation module, used to construct a material property evaluation model based on the laser absorption rate, coating color depth and cleanliness of the material, and output the material property evaluation index; The laser condition assessment module is used to construct a laser condition assessment model based on the laser output power deviation, beam quality factor, galvanometer positioning error, and laser temperature, and output the laser condition index. The machining accuracy confidence assessment module is used to construct a machining accuracy confidence assessment model based on the previous machining accuracy feedback value (obtained through online monitoring), the complexity of the target marking content, the material property assessment index, and the laser state index, and output the machining accuracy confidence. The auxiliary gas pressure control module is used to construct an auxiliary gas pressure control model based on the rated auxiliary gas pressure, processing accuracy confidence level and material property evaluation index, output the auxiliary gas pressure adjustment amount and control the auxiliary gas system to perform pressure regulation. The laser pulse frequency control module is used to output the laser pulse frequency adjustment amount and control the laser pulser to adjust the frequency.

[0024] In this embodiment, the coding device for self-adhesive label production refers to a device used for laser marking on self-adhesive labels. This device aims to optimize the coding process through evaluation and control, ensuring marking quality.

[0025] The material property evaluation module is the functional unit within the device responsible for acquiring and analyzing the physical and chemical properties of the self-adhesive label materials. This module processes parameters such as the material's laser absorption rate, coating color depth, and cleanliness to quantify the material's response to laser processing. The material property evaluation model is the mathematical or algorithmic framework within the module used to transform raw material data into evaluation indices. This model comprehensively considers the impact of material parameters on the laser marking effect. The material property evaluation index is the numerical value output by the material property evaluation module, used to characterize the absorption and conversion efficiency of the self-adhesive label material to laser energy. This index provides a quantitative basis for adjusting subsequent process parameters.

[0026] The laser condition assessment module is a functional unit within the device responsible for monitoring and evaluating the laser's operating status and performance. This module aims to understand the laser's health status and ensure the stability and quality of its output. The laser condition assessment model is the mathematical or algorithmic framework within the laser condition assessment module used to convert various laser performance parameters into assessment indices. This model reflects key indicators such as laser output power, beam quality, galvanometer positioning error, and temperature. The laser condition index is a numerical value output by the laser condition assessment module, used to characterize the laser's current operational stability and output quality. This index provides a basis for determining whether the laser is in the desired operating state.

[0027] The machining accuracy confidence assessment module is a functional unit within the device responsible for predicting the expected accuracy and reliability of the laser marking task. This module comprehensively analyzes various factors to predict the quality of the marking results. The machining accuracy confidence assessment model is the mathematical or algorithmic framework within the module used to comprehensively evaluate and predict the confidence level of machining accuracy. This model incorporates historical machining data, target content complexity, and information such as material and laser conditions. The machining accuracy confidence level is a numerical value output by the machining accuracy confidence assessment module, representing the probability that the marking task will achieve the preset accuracy requirements. This value is typically between 0 and 1, with a higher value indicating a higher probability of achieving the desired accuracy.

[0028] The auxiliary gas pressure control module is a functional unit within the equipment responsible for adjusting the auxiliary gas pressure according to process requirements. Auxiliary gas is typically used to remove slag generated during processing and to protect optical lenses. The auxiliary gas pressure control model refers to the mathematical or algorithmic framework within the auxiliary gas pressure control module used to calculate the auxiliary gas pressure adjustment amount. This model determines the desired gas pressure based on factors such as rated pressure, processing confidence level, and material properties. The auxiliary gas pressure adjustment amount refers to the value calculated and output by the auxiliary gas pressure control module, used to guide the auxiliary gas system in pressure adjustment.

[0029] In a preferred embodiment of the present invention, the material property evaluation module sequentially substitutes the obtained laser absorptivity, coating color depth, and cleanliness data of the material into the maximum-minimum normalization formula for processing, and sequentially generates the laser absorptivity index, coating color depth index, and cleanliness index.

[0030] In this embodiment, the material property evaluation module is a core component of the coding device, and its main function is to quantitatively analyze and evaluate the material properties of self-adhesive labels. By collecting and processing key material-related parameters, it aims to establish a model that accurately reflects the material's influence on the laser coding process, and ultimately output a comprehensive material property evaluation index, providing basic data support for subsequent processing parameter control. The acquired data on laser absorptivity, coating color depth, and cleanliness are raw physical quantities directly collected from the self-adhesive label material or obtained through detection. Laser absorptivity reflects the material's ability to absorb laser energy, directly affecting coding efficiency and effect; coating color depth characterizes the color saturation or thickness of the coating on the label surface, significantly influencing the interaction between the laser and the material; cleanliness indicates the degree of impurities or contamination on the material surface, which may interfere with laser action or affect coding quality. These raw data typically have different dimensions, units, and numerical ranges.

[0031] This application employs a maximum-minimum normalization formula, a commonly used data preprocessing method that linearly maps raw data to a specified range (usually [0,1] or [-1,1]). Its basic form is: X norm = (XX) min ) / (X max -X mi n), where X is the original data, X min and X max X represents the minimum and maximum values ​​of the dataset, respectively. norm The data is normalized. Besides maximum-minimum normalization, specifically, the original laser absorptivity, coating color depth, and cleanliness data are taken as input and converted to values ​​on a uniform scale by applying a pre-defined normalization formula. This process aims to eliminate differences in numerical range and units between the original data, ensuring the accuracy and stability of subsequent model calculations. Ultimately, laser absorptivity, coating color depth, and cleanliness indices are generated. These indices are normalized quantifications representing the standardized performance of the material in terms of laser absorption, coating color depth, and surface cleanliness. These indices are typically mapped to values ​​between 0 and 1, where 0 may represent the lowest impact or worst characteristic, and 1 represents the highest impact or best characteristic. These standardized indices, as uniform inputs, can be directly used in material property evaluation models, avoiding computational biases caused by the heterogeneity of the original data and improving the interpretability and robustness of the model.

[0032] In a preferred embodiment of the present invention, the material property evaluation model includes: ; in The laser absorption influence coefficient is... The coefficient representing the influence of coating color. This is the cleanliness impact coefficient. , It is the penalty coefficient, and , , as well as All are greater than 0. for , as well as standard deviation The laser absorptivity index, This is the coating color depth index. Cleanliness index This is a material property index.

[0033] In this embodiment, the material property index This is a comprehensive index that quantitatively evaluates the suitability of self-adhesive label materials for laser processing. This index reflects the material's response characteristics under laser irradiation, providing a crucial basis for adjusting subsequent processing parameters. Its value typically ranges from 0 to 1; a higher value indicates a more suitable material for laser marking, while a lower value indicates poorer adaptability.

[0034] Laser absorption influence coefficient Coating color influence coefficient and the influence coefficient of cleanliness These coefficients characterize the relative importance of three material properties—laser absorptivity, coating color depth, and cleanliness—to the final coding quality. These coefficients are set based on actual processing experience, experimental data analysis, or expert knowledge of different self-adhesive label materials to ensure the model accurately reflects the actual impact of each factor. For example, for some materials, laser absorptivity may be the most critical factor determining the coding effect. The value can be set relatively high.

[0035] Laser Absorption Index Coating color depth index and cleanliness index These indices are the results of standardizing the original material property data. They eliminate the dimensional differences in the original data, allowing the properties of different physical quantities to be compared and calculated on the same scale. For example, the laser absorptivity index... The coating color depth index can be obtained by measuring and normalizing the absorption ratio of a material to a specific wavelength of laser light; Color depth information can be obtained and normalized through optical density measurement or image analysis techniques; cleanliness index The cleanliness of a surface can be assessed and normalized using methods such as surface morphology detection or grayscale analysis.

[0036] Penalty coefficient Used to adjust the material property index when there are large differences between material property factors. The degree of negative impact. This coefficient can be adjusted according to the stringency of the requirements for coding quality consistency. For example, when the requirements for coding quality consistency are extremely high, it can be appropriately increased. The value of makes even slight inconsistencies in material properties lead to... Significant decrease.

[0037] Standard deviation Used to quantify laser absorptivity index Coating color depth index and cleanliness index The dispersion or inconsistency among these three indices. When the values ​​of these three indices are close to each other, the standard deviation is small, indicating that the material properties are relatively uniform; when there are large differences between them, the standard deviation is large, indicating that there is significant inconsistency in the material properties.

[0038] This application's solution addresses the inaccuracy of traditional material property assessment methods by constructing a comprehensive material property evaluation model. The core of this model lies in the normalized laser absorptivity index... Coating color depth index and cleanliness index A weighted summation is performed, and a penalty term based on the standard deviation of these exponents is introduced. Specifically, the weighted average part of the model... Able to determine the laser absorption influence coefficient based on the preset value. Coating color influence coefficient And cleanliness influence coefficient Different weights are assigned to various material property indices to more accurately reflect the actual contribution of each factor to the coding quality. For example, if the laser absorption rate has the greatest impact on the coding effect, then... It will be assigned a higher value, making it dominant in the overall evaluation. This weighting mechanism ensures the comprehensiveness and specificity of the material property evaluation.

[0039] Building on this, the model further introduces a penalty term. This penalty item uses standard deviation. To measure , , The degree of dispersion among the three indices. When there are large differences among these three indices, indicating high inconsistency in material properties, the standard deviation... This will increase, leading to a decrease in the value of the penalty term, and consequently affecting the final material property index. Decrease. Conversely, when material properties are highly consistent, the standard deviation is small, the penalty term is close to 1, and the effect is... The impact is relatively small. Penalty coefficient This is used to adjust the intensity of the penalty. This design cleverly solves the problem that traditional assessments do not consider the correlation or dispersion between factors, and avoids giving overly optimistic assessment results when material properties are not uniform, thereby enhancing the robustness and accuracy of material property assessment.

[0040] By combining weighted summation with dispersion penalty, the material property evaluation module can output a more accurate and reliable material property index. This index not only comprehensively considers multiple key properties of the material, but also reflects the consistency between these properties, providing high-quality input for subsequent processing accuracy confidence assessment modules, auxiliary gas pressure control modules, and laser pulse frequency control modules. This enables the entire self-adhesive label production coding device to perform more intelligent and adaptive parameter control.

[0041] In a preferred embodiment of the present invention, in the laser status evaluation module: the difference between the actual laser output power and the rated output power of the laser is taken as the absolute value and then divided by the rated output power to obtain the laser output power deviation index. Divide the actual galvanometer positioning error value by the maximum allowable positioning error upper limit to obtain the galvanometer positioning error index; The actual beam quality factor and laser temperature value are successively substituted into the maximum-minimum normalization formula for processing, and the beam quality factor index and laser temperature index are generated in sequence.

[0042] In this embodiment, the laser output power deviation index is used to quantify the degree of deviation between the actual output power and the rated power of the laser. This can be achieved by monitoring the laser's output power in real time, comparing it to a preset rated output power, calculating the absolute value of the difference, and then dividing it by the rated output power. Alternatively, a photodetector or power meter can be used to measure the laser output power in real time and transmit the measured value to the controller for calculation.

[0043] The galvanometer positioning error index characterizes the deviation between the actual positioning of the galvanometer system and the target positioning during laser marking. This can be achieved by acquiring actual positioning data through the encoder or position sensor built into the galvanometer system, comparing it with the commanded positioning data, calculating the error, and then comparing it with the maximum permissible positioning error upper limit. Another approach is to use a vision inspection system to analyze the marked area, deduce the actual positioning accuracy of the galvanometer, and compare it with the preset maximum permissible positioning error upper limit.

[0044] The beam quality factor index reflects the quality of a laser beam; the closer the beam quality factor is to 1, the better the beam quality. This index can be measured in real time using a beam quality analyzer and then normalized. Alternatively, it can be estimated based on the laser type and operating parameters using tables or empirical formulas, and then normalized.

[0045] The laser temperature index reflects the operating temperature of a laser; excessively high or low temperatures can affect the laser's performance and stability. This can be achieved by using temperature sensors (such as thermistors or thermocouples) installed on the core components of the laser to monitor the laser temperature in real time and then normalizing the readings. Alternatively, the temperature of the coolant or the laser casing can be monitored, estimated using a thermal model, and then normalized.

[0046] In a preferred embodiment of the present invention, the laser state evaluation model includes: ; in The power deviation influence coefficient is... This is the beam quality influence coefficient. The galvanometer positioning influence coefficient is... This is the laser temperature influence coefficient. , , as well as All are greater than 0. This is the laser output power deviation index. The beam quality factor index. The galvanometer positioning error index. The laser temperature index. This is the laser state index.

[0047] In this embodiment, the laser state evaluation model aims to comprehensively evaluate various performance indicators of the laser and output a quantified laser state index. The exponential function form in the model. Ensuring the output laser state index The value is always positive, and typically between 0 and 1, to facilitate processing by subsequent modules. The larger the negative exponent value within the parentheses, the more severely the laser performance deviates from its normal state, thus leading to… The smaller the value, the greater the value. The higher the value, the more directly it reflects the health status of the laser.

[0048] In the model The power deviation influence coefficient is... This is the beam quality influence coefficient. The galvanometer positioning influence coefficient is... These are laser temperature influence coefficients, all greater than 0, used to adjust the weights of various performance indicators in the model to reflect their relative importance to the overall state of the laser. These coefficients can be set and optimized based on actual application scenarios, historical data analysis, or expert experience.

[0049] This is the laser output power deviation index. The beam quality factor index. The galvanometer positioning error index. These are laser temperature indices, which are quantified values ​​after preprocessing (e.g., by methods such as maximum-minimum normalization as described in some of the embodiments above), representing the laser output power, beam quality, galvanometer positioning error, and the degree of deviation of the laser temperature, respectively.

[0050] The solution in this application achieves precise quantitative evaluation of the laser's state by introducing the aforementioned exponential function model. During the laser marking process, the laser state evaluation module continuously acquires real-time data such as laser output power deviation, beam quality factor, galvanometer positioning error, and laser temperature. These raw data are preprocessed to generate the corresponding laser output power deviation index. Beam quality factor index Galvanometer positioning error index and laser temperature index Subsequently, these indices were substituted into the aforementioned laser condition assessment model. The squared terms in the model are designed as follows: , , and This approach amplifies deviations in various performance indicators, making the model more sensitive to even minor degradations in laser performance and thus more accurately reflecting the declining trend in laser performance. Furthermore, by using an exponential function, these weighted squared deviations are synthesized into a single laser state index. The index It can dynamically and non-linearly reflect the overall health and performance stability of the laser. This is achieved by adjusting the influence coefficient. , , as well as The model can flexibly adjust the weights of various factors in the evaluation based on the characteristics of different self-adhesive label materials or specific coding process requirements, thereby enhancing the model's adaptability and accuracy. The final output is the laser state index. As an important input parameter for the machining accuracy confidence assessment module, it provides a more reliable and refined data foundation for subsequent machining accuracy confidence assessment, thereby supporting the auxiliary gas pressure control module and the laser pulse frequency control module to make more precise parameter adjustments.

[0051] In a preferred embodiment of the present invention, the machining accuracy confidence evaluation model includes: ; in This is the material penalty coefficient. This is the content complexity penalty coefficient. This is the feedback value of the previous machining accuracy. Dimensionless, and ; The complexity of the content to be censored. A larger value indicates a more complex coded message. Dimensionless, and ; This is the laser state index. This is a material property index. Confidence level for machining accuracy.

[0052] In this embodiment, the technical solution solves the problem of inaccurate evaluation by introducing a specific mathematical model to accurately calculate the confidence level of machining accuracy. Specifically, the confidence level of machining accuracy... This indicates the likelihood or reliability that the current coding task can achieve the expected level of accuracy. The higher the value, the more guaranteed the processing accuracy. It also serves as an important basis for subsequent parameter adjustments by the auxiliary gas pressure control module and the laser pulse frequency control module.

[0053] Previous machining accuracy feedback value This refers to the actual processing accuracy data obtained through online monitoring after the completion of the previous coding task or batch. Its purpose is to incorporate historical data, giving the current confidence assessment continuity and dynamic correction capabilities, avoiding reliance solely on instantaneous states. This feedback value can be obtained by scanning the coded labels with an image recognition system, analyzing indicators such as label clarity, edge smoothness, and character integrity, and quantifying them into values ​​between 0 and 1; or by using a machine vision system to detect defects in the coded characters or patterns, and calculating a feedback value reflecting the previous processing quality based on the number, type, and severity of defects, combined with preset quality standards.

[0054] Target content complexity The complexity of the content to be encoded (such as text, graphics, QR codes, etc.) is measured. Higher complexity places greater demands on encoding accuracy and presents greater challenges, thus requiring it as a crucial factor in assessing confidence. This complexity can be calculated by analyzing the vector or bitmap data of the encoded content, determining metrics such as edge length, number of inflection points, minimum feature size, and pixel density, and then normalizing these metrics to a complexity index between 0 and 1. Alternatively, it can be evaluated using a lookup table or expert system based on predefined rules such as the type of encoded content, character size, and line thickness, generating a corresponding complexity index.

[0055] Laser State Index The comprehensive index reflecting the current working performance and health status of the laser is calculated by the laser status evaluation module based on a model constructed from parameters such as laser output power deviation, beam quality factor, galvanometer positioning error, and laser temperature. It is used to quantify the impact of laser status on processing accuracy.

[0056] Material property index The comprehensive index reflecting the laser energy absorption and response characteristics of self-adhesive label materials is calculated by the material property evaluation module based on parameters such as the material's laser absorption rate, coating color depth, and cleanliness. It is used to quantify the impact of material properties on processing accuracy.

[0057] Material penalty coefficient and content complexity penalty coefficient Weighting factors are used to adjust the negative impact of poor material properties and high content complexity on the confidence level of processing accuracy. This allows the system to flexibly adjust the importance of different factors in confidence assessment based on actual production needs and experience. These coefficients can be initially determined through historical production data analysis, expert experience setting, or machine learning algorithm training, and fine-tuned based on feedback during actual operation. Alternatively, a set of coefficient ranges can be preset, allowing operators to select appropriate coefficients based on material type, product quality requirements, etc., or the system can automatically select coefficients based on a preset rule base.

[0058] The solution in this application uses the aforementioned machining accuracy confidence evaluation model to evaluate the previous machining accuracy feedback value. Laser state index Material property index and the complexity of the target censored content Multiple dynamic factors are organically integrated to form a comprehensive evaluation system. The molecular part of the model... This demonstrates the positive correlation between confidence level and historical processing performance, current equipment health status, and material suitability, ensuring the comprehensiveness of the assessment.

[0059] Among them, the previous machining accuracy feedback value The introduction of this feature makes the evaluation continuous, allowing for the correction of the current state using historical data; laser state index This reflects in real time the impact of laser performance fluctuations on coding accuracy; material property index This takes into account the crucial role of the material itself in laser energy absorption efficiency, enabling the model to adapt to variations in different batches of materials. (Denominator part) This introduces a penalty mechanism, where the material property index... A low value indicates poor material properties, which is addressed by the material penalty coefficient. The confidence level is penalized by lowering its value to address potential accuracy risks caused by material defects; this applies when the complexity of the target data being masked increases. When the content complexity is high, the penalty coefficient is applied. The confidence level is penalized because highly complex content is more prone to graphic distortion or blurry edges. This design improves the confidence level of manufacturing accuracy. The model can dynamically and quantitatively reflect the comprehensive risks faced by the current coding task. It works closely with the material property evaluation module and the laser condition assessment module, utilizing the material property indices output by these modules. and laser state index As input, it enables intelligent assessment of the confidence level of coding accuracy, thereby providing accurate decision-making basis for the subsequent auxiliary gas pressure control module and laser pulse frequency control module, enabling the entire coding device to transform from passive adjustment to active prediction and adaptive control.

[0060] In a preferred embodiment of the present invention, the auxiliary gas pressure regulation model includes: ; in The confidence level weighting coefficient is... For material properties, weighting coefficients , For the rated auxiliary gas pressure, To determine the confidence level for machining accuracy. This is a material property index. This is for auxiliary gas pressure regulation.

[0061] In this embodiment, the auxiliary gas pressure regulation model is a mathematical framework for calculating the auxiliary gas pressure regulation amount. Its function is to determine key parameters in the current coding process, such as the confidence level of processing accuracy. and material property index Dynamically determine the pressure adjustment amount required for the auxiliary gas system. This model can be integrated into the central controller of the coding device, for example, through software algorithms or through real-time calculations using a dedicated digital signal processor. The above formula is the core algorithm for auxiliary gas pressure regulation, which will adjust the rated auxiliary gas pressure... As a benchmark, and combined with the confidence level of machining accuracy and material property index The required pressure adjustment is calculated by weighted summation based on the deviation. This formula can be implemented in an embedded system using a programming language, or it can be rapidly calculated using hardware logic circuits. This is the confidence weighting coefficient, used to measure the confidence level of machining accuracy. The influence of the auxiliary gas pressure regulation is determined by adjusting its contribution to the total regulation based on the degree of importance attached to the confidence level of machining accuracy. For example, this coefficient can be determined through experimental data fitting, expert experience setting, or machine learning algorithm optimization. These are material property weighting coefficients, used to measure material property indices. The influence of the auxiliary gas pressure regulation is determined by adjusting its contribution to the total regulation based on the importance attached to material properties. The method for determining this coefficient is similar to... Similarly, calibration can be performed based on the coding characteristics of different materials. Confidence weighting coefficients. and material property weighting coefficient The value range is limited to [0, 0.5]. This limitation ensures that the adjustment amount will not be too large or too small, thereby avoiding drastic fluctuations in the auxiliary gas pressure and ensuring the stability and safety of the system. These coefficients can be preset when the device leaves the factory, or they can be fine-tuned through the user interface during actual production.

[0062] The rated auxiliary gas pressure is the preset auxiliary gas pressure value under ideal or standard coding conditions. It serves as a reference point for pressure adjustment and is usually set based on empirical parameters such as coding material type and laser power. This value can be stored in the device's non-volatile memory and loaded at the start of each coding task. The processing accuracy confidence level is a parameter that reflects the expected processing accuracy level of the current coding task. The higher the value, the higher the expected accuracy. This parameter is calculated by the processing accuracy confidence assessment module and provided as input to the auxiliary gas pressure control module.

[0063] The material property index is a comprehensive indicator that reflects the laser absorption rate, coating color depth, and cleanliness of current self-adhesive label materials. The higher the value, the more favorable the material properties are for laser marking. This parameter is calculated by the material property evaluation module and provided as input to the auxiliary gas pressure control module.

[0064] The auxiliary gas pressure adjustment amount is the pressure value that the auxiliary gas system needs to adjust. This value can be positive (increase pressure) or negative (decrease pressure) to compensate for the deviation in marking effect caused by changes in machining accuracy confidence or material properties. This adjustment amount is sent to the actuator of the auxiliary gas system, such as a proportional valve or pressure controller, to achieve the actual pressure adjustment.

[0065] The auxiliary gas pressure control model in this application introduces a processing accuracy confidence level. and material property index This enables intelligent and adaptive adjustment of the auxiliary gas pressure. During the laser marking process, the material property evaluation module constructs a material property evaluation model based on the material's laser absorption rate, coating color depth, and cleanliness, and outputs a material property index. This index reflects the adaptability of current materials to laser marking. Simultaneously, the laser condition assessment module constructs a laser condition assessment model based on the laser output power deviation, beam quality factor, galvanometer positioning error, and laser temperature, and outputs the laser condition index. Subsequently, the processing accuracy confidence assessment module evaluates the processing accuracy based on the previous processing accuracy feedback value, the complexity of the target marking content, and the material property assessment index. In addition, a confidence assessment model for machining accuracy is constructed using the laser state index, and the confidence level of machining accuracy is output. This confidence level quantifies the expected success rate of this coding task. Based on this, the auxiliary gas pressure control module receives these real-time evaluated parameters and inputs them into a preset auxiliary gas pressure control model. This model uses the rated auxiliary gas pressure... Based on the formula Accurately calculate the auxiliary gas pressure regulation amount Specifically, when the confidence level of machining accuracy... A lower value indicates that the blurring effect may be poor. As the value increases, the model will correspondingly increase the auxiliary gas pressure adjustment to enhance the slag removal effect and improve the marking clarity. Similarly, when the material property index... At a lower level, it indicates that the material's response to laser light is not ideal. As the value increases, the model will also increase the auxiliary gas pressure adjustment to compensate for the adverse effects of material properties. Confidence weighting coefficient and material property weighting coefficient The introduction of this feature allows the system to be flexibly adjusted according to actual needs. and The contribution ratio to the final adjustment amount, with its value limited to [0, 0.5], ensures the stability and appropriateness of the adjustment. Finally, the calculated adjustment amount... The gas is sent to the auxiliary gas system, which controls the corresponding pressure regulation to achieve dynamic optimization of the auxiliary gas pressure. This closed-loop control mechanism based on multi-dimensional real-time evaluation enables the auxiliary gas pressure to accurately match the current material state and processing conditions, significantly improving the stability and quality of coding.

[0066] In a preferred embodiment of the present invention, the laser pulse frequency control module specifically constructs a laser pulse frequency control model based on the rated pulse frequency, laser scanning speed, preset dot spacing, processing accuracy confidence level, and actual auxiliary gas pressure, outputs the laser pulse frequency adjustment amount, and controls the laser pulser to adjust the frequency.

[0067] In this embodiment, the laser pulse frequency control module is a functional unit within the marking device, responsible for calculating and outputting laser pulse frequency adjustment commands based on specific input parameters. This module can be an independent microcontroller unit with embedded frequency control algorithms and communication interfaces, used to receive data and send control signals to the laser pulser; alternatively, it can be a software module integrated into the main control system, executing specific program code to calculate the frequency adjustment amount and communicating with the laser pulser via a digital-to-analog converter or pulse width modulation signal interface. The rated pulse frequency refers to the reference pulse frequency set by the manufacturer or preset by the system under standard or ideal operating conditions. It serves as a reference point for frequency adjustment and can be stored as a system parameter in the device's non-volatile memory and loaded upon system startup, or manually input and configured by the user or technician through the operating interface based on the laser model and material characteristics. Laser scanning speed refers to the speed at which the laser spot moves across the material surface. It directly affects the duration of laser energy's effect per unit area. This speed can be monitored and fed back in real-time by a galvanometer control system. For example, the angular velocity of the galvanometer can be acquired via an encoder or position sensor and converted into the linear velocity of the laser spot. Alternatively, it can be used as a preset scanning path parameter, set by the coding software based on the graphic complexity and coding requirements. Preset dot spacing refers to the desired distance between the markers formed by two adjacent laser pulses on the material surface during coding. It is a key parameter affecting the precision and integrity of the coding pattern. It can be automatically calculated and set by the coding software based on the precision requirements of the target coding content, or manually input and adjusted by the operator based on experience or the coding effect on specific materials. Processing accuracy confidence is a quantitative indicator output by the processing accuracy confidence assessment module. It reflects the probability or reliability of the current coding process achieving the expected precision. It can be a dimensionless value between 0 and 1, where 1 represents the highest confidence level, or a percentage value. Actual auxiliary gas pressure refers to the gas pressure actually supplied to the processing area by the auxiliary gas system during laser marking. This pressure can be monitored and acquired in real time by pressure sensors installed on the auxiliary gas pipeline, or by the current set pressure value fed back from the auxiliary gas system's own controller. The laser pulse frequency control model is a mathematical model or algorithm used to calculate the adjustment amount of the laser pulse frequency by comprehensively considering multiple input parameters. It can be a mathematical model based on empirical formulas or physical principles, calculated by substituting parameters, or a model trained using machine learning or artificial intelligence algorithms, predicting the adjustment amount through input features. The laser pulse frequency adjustment amount refers to the frequency value that needs to be increased or decreased based on the rated pulse frequency to optimize the marking effect. It can be a positive or negative value representing an increase or decrease in frequency, or a scaling factor used to multiply by the rated frequency to obtain the new operating frequency.A laser pulser is a component of a laser, responsible for generating and controlling the emission frequency of laser pulses. It can be an independent pulse generator that adjusts the frequency of its output pulses by receiving external control signals, or it can be a drive circuit integrated inside the laser that adjusts the laser pulse frequency by changing its drive parameters.

[0068] The core of the laser pulse frequency control module in this application lies in constructing a laser pulse frequency control model. This model comprehensively considers multiple key parameters to achieve dynamic and adaptive adjustment of the laser pulse frequency. Specifically, the module first obtains the rated pulse frequency as a benchmark, which is the standard operating frequency of the laser under ideal conditions. Simultaneously, it receives the real-time laser scanning speed, which directly affects the residence time of the laser spot on the material, thus affecting the energy density. The preset dot spacing provides the basis for the dot density requirements of the coding content. Furthermore, the processing accuracy confidence level output by the processing accuracy confidence assessment module provides predictive information on the current coding quality for frequency control, enabling the system to make forward-looking adjustments based on predictions of future coding effects. The actual auxiliary gas pressure reflects the stability of the processing environment, as the auxiliary gas is crucial for slag removal and thermal effect control. The laser pulse frequency control model takes all the above parameters as input and, through internal calculation logic, comprehensively evaluates the potential impact of these factors on the coding effect. For example, when the scanning speed increases, the model may calculate the need to increase the pulse frequency to maintain sufficient energy density and dot coverage; when the processing accuracy confidence level is low, the model may fine-tune the frequency to try to optimize the coding quality; when the actual auxiliary gas pressure deviates from the ideal value, the model may also compensate for its impact on the processing by adjusting the frequency. The laser pulse frequency adjustment amount calculated by the model is then sent to the laser pulser. Based on the received adjustment amount, the laser pulser increases or decreases its rated frequency accordingly, thereby changing the emission frequency of the laser pulse. In this way, the laser pulse frequency control module is no longer simply setting a fixed frequency, but forming a closed-loop or semi-closed-loop intelligent control mechanism. It works closely with the material property evaluation module, laser status evaluation module, and processing accuracy confidence level evaluation module in the coding device for self-adhesive label production, together forming an intelligent coding system that can sense changes in materials, equipment, and environment. The processing accuracy confidence level, as an indicator that integrates material property index and laser status index, allows the frequency control to more comprehensively reflect the actual needs and potential risks of the current coding process. This comprehensive consideration and dynamic adjustment of multiple parameters enables the laser pulse frequency to accurately match the current coding conditions, avoiding the problem of overly sparse or dense coding points caused by adjusting a single parameter or setting based on experience. This significantly improves the graphic integrity, edge quality, and overall stability and consistency of the coding.

[0069] In a preferred embodiment of the present invention, the laser pulse frequency modulation model includes: ; in The confidence level influence coefficient. This is the gas pressure influence coefficient. , The rated pulse frequency, The laser scanning speed index, The dot spacing index, It is a constant that is very small and greater than 0. To determine the confidence level for machining accuracy. This refers to the actual auxiliary gas pressure index. This refers to the laser pulse frequency adjustment amount; , , The calculation method is as follows: the laser scanning speed, the preset dot spacing, and the actual auxiliary gas pressure are successively substituted into the maximum-minimum normalization formula for processing, and the laser scanning speed index, dot spacing index, and actual auxiliary gas pressure index are generated in sequence.

[0070] In this embodiment, the laser pulse frequency control model is a mathematical framework used to dynamically calculate the adjustment amount of the laser pulse frequency. Its core function is to comprehensively consider multiple real-time parameters affecting coding quality (such as laser scanning speed, dot spacing, processing accuracy confidence level, and actual auxiliary gas pressure), thereby achieving intelligent and adaptive adjustment of the laser pulse frequency to optimize the coding effect. Rated pulse frequency This refers to the preset reference pulse frequency of the laser pulser under standard or ideal operating conditions. It is the starting point for frequency adjustment; all adjustments are made based on this reference frequency to adapt to different processing requirements and environmental changes. Laser scanning speed index. This is a dimensionless value obtained by standardizing the actual laser scanning speed. It is generated by substituting the real-time acquired laser scanning speed into a maximum-minimum normalization formula. This index reflects the speed at which the laser beam moves across the material surface and is a key parameter affecting the duration of laser energy's effect per unit area. Dot Spacing Index This is a dimensionless value obtained by standardizing the preset dot spacing. It is generated by substituting the preset dot spacing into a maximum-minimum normalization formula. This index represents the distance between adjacent laser dots and directly relates to the continuity, precision, and edge quality of the coding pattern. It is a constant with a minimum value greater than 0. Introduced into mathematical models to ensure the denominator is not zero, thus avoiding mathematical singularities or errors during calculations, and guaranteeing the numerical stability of the model under various parameter combinations. Machining accuracy confidence level. This is a comprehensive indicator that assesses the potential accuracy level of the current coding process. Its value is typically calculated by the machining accuracy confidence assessment module based on previous machining accuracy feedback, the complexity of the target coding content, the material property evaluation index, and the laser state index. This confidence level reflects the system's degree of confidence in achieving high-precision coding under current processing conditions. Actual auxiliary gas pressure index. This is a dimensionless value obtained by standardizing the actual auxiliary gas pressure. It is generated by substituting the real-time monitored auxiliary gas pressure into a maximum-minimum normalization formula. This index reflects the pressure level of the auxiliary gas in the marking area. The auxiliary gas is mainly used to remove slag, cool materials, and protect optical components; its pressure directly affects the clarity of the marking and the material ablation effect. (Confidence level influence coefficient) This coefficient is used to quantify the influence of processing accuracy confidence level on laser pulse frequency adjustment. By adjusting this coefficient, the system's response strength to frequency adjustment can be controlled when the prediction accuracy is high, thereby achieving finer frequency control while ensuring marking quality. Gas pressure influence coefficient. This coefficient is used to quantify the impact of actual auxiliary gas pressure on laser pulse frequency adjustment. By adjusting this coefficient, the system's response to frequency adjustment can be controlled when the auxiliary gas pressure deviates from the ideal value, thus compensating for the potential impact of gas pressure changes on the coding effect. Laser pulse frequency adjustment amount This is a value calculated by the laser pulse frequency control model, used to adjust the frequency based on the rated pulse frequency. This adjustment can be positive (increasing the frequency) or negative (decreasing the frequency). Its purpose is to dynamically match the laser pulse frequency to the current processing conditions to achieve the best marking effect. The maximum-minimum normalization formula is a data preprocessing method used to convert raw data of different dimensions or ranges into dimensionless values ​​within a uniform range (e.g., between 0 and 1). Its function is to eliminate the influence of differences in the dimensions and numerical ranges of different parameters on the model's calculation results, enabling the model to fairly process and compare various input parameters. The laser pulser is one of the core components of a laser marking device, responsible for generating and outputting laser pulses with specific frequencies and energy. Its frequency adjustment capability is the foundation for achieving dynamic marking control. Controlling the laser pulser to adjust its frequency refers to adjusting the laser pulse frequency calculated by the laser pulse frequency control model. Applied to laser pulsers, this allows the output frequency to be increased from the rated pulse frequency. Adjusted to This is typically achieved by sending commands or signals to the control unit of the laser pulser to ensure that the laser pulse frequency can respond to the model calculation results in real time and accurately.

[0071] This application's solution achieves intelligent and adaptive adjustment of the laser pulse frequency during the coding process in self-adhesive label production by constructing a precise laser pulse frequency control model. This model uses a rated pulse frequency... Based on this, the laser scanning speed, preset dot spacing, and processing accuracy confidence level were comprehensively considered. and actual auxiliary gas pressure Several key parameters are considered. During operation, the system acquires raw data in real time, including laser scanning speed, preset dot spacing, and actual auxiliary gas pressure. To eliminate differences in the dimensions and numerical ranges of these parameters, this raw data is sequentially substituted into a maximum-minimum normalization formula to generate a dimensionless laser scanning speed index. Dot spacing index and actual auxiliary gas pressure index These standardized indices, along with the machining accuracy confidence level provided by the machining accuracy confidence assessment module, and the preset confidence level influence coefficient and gas pressure influence coefficient These, together, serve as inputs to the laser pulse frequency modulation model. The model contains constants that are minimally greater than 0. This ensures computational stability and avoids the case where the denominator is zero. The core of this model lies in its dynamic balancing mechanism: it calculates an adjustment factor that considers the matching relationship between scanning speed and dot spacing, and adjusts it based on the confidence level of machining accuracy. Enhance it, while adjusting according to the actual auxiliary gas pressure. This was then corrected. Finally, the model outputs a laser pulse frequency adjustment amount. This adjustment is used to control the laser pulser, causing its output frequency to change from... Adjusted to In this way, the proposed solution overcomes the limitations of traditional coding devices that rely on experience for frequency adjustment, achieving precise matching between the laser pulse frequency and real-time processing conditions. For example, when the laser scanning speed increases or the dot spacing decreases, the model correspondingly increases the pulse frequency to ensure dot density and pattern continuity; when the processing accuracy confidence level is high, the model can more actively optimize the frequency to improve coding quality; and when the auxiliary gas pressure fluctuates, the model can also adjust the frequency in a timely manner to maintain a stable coding effect. This dynamic and adaptive frequency control mechanism allows the coding process to better adapt to changes in material properties, equipment status, and the complexity of the coding content, thereby significantly improving the coding accuracy, clarity, and consistency of self-adhesive labels.

[0072] A coding method for producing self-adhesive labels is applied to the aforementioned coding device for producing self-adhesive labels. During the laser coding process, the auxiliary gas pressure and the laser pulse frequency are adjusted through the aforementioned device.

[0073] In this embodiment, "during the laser marking process" refers to real-time or near-real-time monitoring, evaluation, and adjustment of relevant parameters throughout the entire time period in which the laser actually emits laser light and marks the self-adhesive label. This ensures that the marking parameters dynamically match the current operating conditions, rather than being based on preset or static parameters. For example, this can be achieved by immediately initiating a parameter monitoring and adjustment cycle after laser marking begins, or by periodically checking and adjusting at preset time intervals or event triggering methods during the marking process.

[0074] "Through the aforementioned device" refers to using the coding device for producing self-adhesive labels disclosed in this application. This device integrates intelligent components such as a material property evaluation module, a laser status evaluation module, a processing accuracy confidence evaluation module, an auxiliary gas pressure control module, and a laser pulse frequency control module. This device can be a centralized control system, for example, with an industrial computer or high-performance embedded controller as the core processing unit, responsible for receiving input data from various sensors, performing evaluation model calculations, and sending control commands to the actuators. Alternatively, the device can also be a distributed control system, where each module acts as an independent intelligent unit, exchanging data and working collaboratively through an industrial bus or network.

[0075] "Achieving adjustment of auxiliary gas pressure and laser pulser frequency" refers to precisely adjusting the parameters of the auxiliary gas system and laser pulser based on the device's intelligent evaluation results. For auxiliary gas pressure adjustment, a high-precision proportional valve or servo control valve can be used, combined with a pressure sensor to form a closed-loop control, changing the gas flow rate or pressure in real time according to the adjustment amount output by the control module. For laser pulser frequency adjustment, the digital signal processor or field-programmable gate array inside the laser control system receives frequency commands from the control module and precisely controls the repetition frequency of the laser pulses to adapt to different coding requirements.

[0076] This application's solution addresses the problems of traditional methods, such as reliance on experience, long setup times, and unstable quality, by dynamically applying intelligent devices during the laser marking process to adjust the auxiliary gas pressure and laser pulse frequency. Specifically, this feature ensures real-time and synchronous adjustment during laser marking, avoiding delays caused by manual intervention or post-processing adjustments, and seamlessly integrating marking operation with control. This feature is achieved through intelligent components such as a material property evaluation module, a laser status evaluation module, and a processing accuracy confidence assessment module. Based on real-time generated evaluation indices such as material property indices, laser status indices, and processing accuracy confidence, adjustment commands are dynamically generated and executed, rather than relying on subjective experience. This demonstrates the adaptive advantages of multi-factor comprehensive evaluation. The adjustment of auxiliary gas pressure and laser pulse frequency specifically targets the core aspects of marking quality. The auxiliary gas pressure adjustment is generated based on the material property evaluation index and processing accuracy confidence to ensure effective slag removal and clear marking. Simultaneously, the laser pulse frequency adjustment is generated based on parameters such as laser scanning speed and dot spacing to optimize dot density and graphic integrity, thereby synergistically improving the stability and consistency of marking. This approach, which combines intelligent assessment with real-time dynamic adjustment, enables the coding process to respond quickly to changes in materials, equipment, and the environment, significantly improving the adaptability and robustness of coding.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A coding device for producing self-adhesive labels, comprising a belt conveyor and a coding machine body, characterized in that, The coding machine body is equipped with a controller, which includes: The material property evaluation module is used to construct a material property evaluation model based on the material's laser absorption rate, coating color depth, and cleanliness, and output the material property evaluation index. The laser condition assessment module is used to construct a laser condition assessment model based on the laser output power deviation, beam quality factor, galvanometer positioning error, and laser temperature, and output the laser condition index. The machining accuracy confidence assessment module is used to construct a machining accuracy confidence assessment model based on the previous machining accuracy feedback value, the complexity of the target marking content, the material property assessment index, and the laser state index, and output the machining accuracy confidence. The auxiliary gas pressure control module is used to construct an auxiliary gas pressure control model based on the rated auxiliary gas pressure, processing accuracy confidence level and material property evaluation index, output the auxiliary gas pressure adjustment amount and control the auxiliary gas system to perform pressure regulation. The laser pulse frequency control module is used to output the laser pulse frequency adjustment amount and control the laser pulser to adjust the frequency.

2. The coding device for producing self-adhesive labels according to claim 1, characterized in that, In the material property evaluation module: the acquired laser absorptivity, coating color depth, and cleanliness data of the material are successively substituted into the maximum-minimum normalization formula for processing, and the laser absorptivity index, coating color depth index, and cleanliness index are generated in sequence.

3. The coding device for self-adhesive label production according to claim 2, characterized in that, In the material property evaluation model: The material property evaluation index is obtained by multiplying the laser absorption rate index, coating color depth index, and cleanliness index by their respective preset weighting coefficients, summing the results, and then multiplying by a penalty compensation term. The penalty compensation item is 1 minus (the product of the standard deviations of the laser absorption rate index, coating color depth index, and cleanliness index and the preset penalty coefficient); the sum of each preset weight coefficient is 1, and all are greater than zero; the penalty coefficient in the penalty compensation item is greater than zero.

4. The coding device for producing self-adhesive labels according to claim 1, characterized in that, In the laser condition assessment module: the difference between the actual laser output power and the rated output power is taken, the absolute value is then divided by the rated output power to obtain the laser output power deviation index; Divide the actual galvanometer positioning error value by the maximum allowable positioning error upper limit to obtain the galvanometer positioning error index; The actual beam quality factor and laser temperature value are successively substituted into the maximum-minimum normalization formula for processing, and the beam quality factor index and laser temperature index are generated in sequence.

5. The coding device for producing self-adhesive labels according to claim 4, characterized in that, In the laser condition assessment model: The laser state index is calculated by taking an exponential function with the natural constant e as the base. The exponent of this exponential function is negative and is the sum of the squares of the laser output power deviation index, the squares of the beam quality factor index, the squares of the galvanometer positioning error index, and the squares of the laser temperature index, each multiplied by a corresponding preset influence coefficient. All influence coefficients are greater than zero.

6. The coding device for producing self-adhesive labels according to claim 1, characterized in that, In the confidence assessment model for machining accuracy: The processing accuracy confidence level is obtained by multiplying the previous processing accuracy feedback value, the laser state index, and an enhancement factor related to the material property index, and then dividing by a factor that includes a penalty term for insufficient material property index and a penalty term for the complexity of the coding content; the previous processing accuracy feedback value and the coding content complexity are both dimensionless values ​​between 0 and 1; the material penalty coefficient and the content complexity penalty coefficient are both constants greater than zero.

7. The coding device for producing self-adhesive labels according to claim 1, characterized in that, In the auxiliary gas pressure control model: The auxiliary gas pressure adjustment amount is obtained by multiplying the rated auxiliary gas pressure by an adjustment factor; The adjustment factor is composed of the degree of insufficient confidence in processing accuracy (1 minus the confidence in processing accuracy) multiplied by a preset confidence weight coefficient, and the degree of insufficient material property index (1 minus the material property index) multiplied by a preset material property weight coefficient, and the sum of the two. The confidence level weighting coefficient and the material property weighting coefficient are both values ​​between 0 and 0.

5.

8. The coding device for producing self-adhesive labels according to claim 1, characterized in that, Specifically, the laser pulse frequency control module constructs a laser pulse frequency control model based on the rated pulse frequency, laser scanning speed, preset dot spacing, processing accuracy confidence level, and actual auxiliary gas pressure, outputs the laser pulse frequency adjustment amount, and controls the laser pulser to adjust the frequency.

9. The coding device for producing self-adhesive labels according to claim 1, characterized in that, In the laser pulse frequency modulation model: The laser pulse frequency adjustment amount is calculated by multiplying the rated pulse frequency by the adjustment scale, which is the difference between the product of the two terms and 1. The first product term is the ratio of the laser scanning speed exponent to (the sum of the dot pitch exponent and the minimum constant); The second product term is: 1 plus the machining accuracy confidence factor, and then minus the gas pressure factor. Among them, the confidence factor of processing accuracy is itself multiplied by the preset confidence factor, and the gas pressure factor is the difference between 1 and the actual auxiliary gas pressure index, and then multiplied by the preset gas pressure factor. The confidence level influence coefficient and the gas pressure influence coefficient are both values ​​between 0 and 0.5; the laser scanning speed index, the dot spacing index, and the actual auxiliary gas pressure index are obtained by normalizing the corresponding physical quantities through maximum-minimum value processing.

10. A coding method for producing self-adhesive labels, applied to the coding apparatus for producing self-adhesive labels according to any one of claims 1-9, characterized in that, During the laser marking process, the auxiliary gas pressure and the laser pulser frequency are adjusted using the aforementioned device.

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