Rapid pasting label manufacturing method and system based on intelligent algorithm

Abstract

The invention relates to the technical field of label manufacturing, and particularly discloses a rapid pasting label manufacturing method and system based on an intelligent algorithm, and the method comprises the steps: obtaining all types of manufacturing control parameters and all types of label performance parameters of all reference label manufacturing batches; obtaining a control plane and a performance plane of each reference label manufacturing batch, and obtaining a control normal and a performance normal of each reference label manufacturing batch; obtaining a control standard normal and a performance standard normal of the current label manufacturing batch, obtaining a control analysis matrix of the current label manufacturing batch, and obtaining a control adjustment matrix of the current label manufacturing batch; and obtaining the optimal control values of all types of manufacturing control parameters of the current label manufacturing batch, and obtaining a label manufacturing result of the current label manufacturing batch. The production efficiency is improved, and the reject ratio and the production cost are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of label making, in particular to a fast pasting label making method and system based on intelligent algorithm. BACKGROUND

[0002] Currently, in the label making industry, for label making batches of different product models, manual experience or simple parameter templates are usually used to set the production control parameters. When switching product models, the operator needs to refer to historical production records, manually adjust various parameters, or directly use the parameters of similar models for trial production, and then repeatedly modify the parameters according to the performance of the trial production labels until a qualified label making result is obtained.

[0003] However, manual adjustment is prone to cause unstable label quality due to experience differences, and trial production is time-consuming and costly; template application is difficult to adapt to the subtle needs of different vehicle models, and may cause problems such as poor adhesion and blurred information, affecting production efficiency and product image.

[0004] Therefore, the present application provides a fast pasting label making method and system based on intelligent algorithm. SUMMARY

[0005] The present application provides a fast pasting label making method and system based on intelligent algorithm, which analyzes historical data through intelligent algorithm to accurately determine the best label making parameters, reduces manual intervention and trial and error costs, improves label quality stability, quickly adapts to different batch requirements, improves production efficiency, and reduces the rejection rate and production cost.

[0006] The present application provides a fast pasting label making method based on intelligent algorithm, comprising: S1: based on the product model information of the current label making batch, obtaining all types of production control parameters and all types of label performance parameters of all reference label making batches; S2: based on each type of production control parameter and all types of label performance parameters of each reference label making batch, obtaining the control plane and performance plane of each reference label making batch, and based on the control plane and performance plane of each reference label making batch, obtaining the control normal line and performance normal line of each reference label making batch; S3: based on the control normal line and the performance normal line of all reference label production batches, obtaining the control standard normal line and the performance standard normal line of the current label production batch, and based on the control normal line of all reference label production batches and the control standard normal line of the current label production batch, obtaining the control analysis matrix of the current label production batch, based on the performance normal line of all reference label production batches and the performance standard normal line of the current label production batch, obtaining the performance analysis matrix of the current label production batch, based on the control analysis matrix and the performance analysis matrix of the current label production batch, obtaining the control adjustment matrix of the current label production batch; S4: based on the control adjustment matrix of the current label production batch, obtaining the optimal control value of all class production control parameters of the current label production batch, and based on the optimal control value of all class production control parameters of the current label production batch, obtaining the label production result of the current label production batch.

[0007] Preferably, based on the product model information of the current label production batch, obtaining all class production control parameters and all class label performance parameters of all reference label production batches, including: Receiving the product model information of the current label production batch, and taking the label production batches in the preset industrial database whose product model information is the same as that of the current label production batch as reference label production batches; Based on all reference label production batches and the preset industrial database, obtaining all class production control parameters and all class label performance parameters of all reference label production batches.

[0008] Preferably, based on all reference label production batches and the preset industrial database, obtaining all class production control parameters and all class label performance parameters of all reference label production batches, including: Extracting all class production control parameters of each reference label production batch from the preset industrial database, wherein the all class production control parameters include font size parameters, element spacing parameters and print DPI parameters; Extracting all class label performance parameters of each reference label production batch from the preset industrial database, wherein the all class label performance parameters include single label cost parameters, single label time-consuming parameters and quality inspection unqualified rate parameters.

[0009] Preferably, based on each class production control parameter and all class label performance parameter of each reference label production batch, obtaining the control plane and the performance plane of each reference label production batch, including: The value of the font size parameter of each reference label manufacturing batch is taken as the abscissa value, the value of the element spacing parameter corresponding to the reference label manufacturing batch is taken as the ordinate value, and 0 is taken as the vertical coordinate value to obtain a first set point of each reference label manufacturing batch. The value of the font size parameter of each reference label manufacturing batch is taken as the abscissa value, 0 is taken as the ordinate value, and the value of the print DPI parameter corresponding to the reference label manufacturing batch is taken as the vertical coordinate value to obtain a second set point of each reference label manufacturing batch. 0 is taken as the abscissa value, the value of the element spacing parameter of each reference label manufacturing batch is taken as the ordinate value, and the value of the print DPI parameter corresponding to the reference label manufacturing batch is taken as the vertical coordinate value to obtain a third set point of each reference label manufacturing batch. Based on the first set point, the second set point and the third set point of each reference label manufacturing batch, a control plane of each reference label manufacturing batch is obtained. The value of the single label cost parameter of each reference label manufacturing batch is taken as the abscissa value, the value of the single label time consumption parameter corresponding to the reference label manufacturing batch is taken as the ordinate value, and 0 is taken as the vertical coordinate value to obtain a fourth set point of each reference label manufacturing batch. The value of the single label cost parameter of each reference label manufacturing batch is taken as the abscissa value, 0 is taken as the ordinate value, and the value of the quality inspection unqualified rate parameter corresponding to the reference label manufacturing batch is taken as the vertical coordinate value to obtain a fifth set point of each reference label manufacturing batch. 0 is taken as the abscissa value, the value of the single label time consumption parameter of each reference label manufacturing batch is taken as the ordinate value, and the value of the quality inspection unqualified rate parameter corresponding to the reference label manufacturing batch is taken as the vertical coordinate value to obtain a sixth set point of each reference label manufacturing batch. Based on the fourth set point, the fifth set point and the sixth set point of each reference label manufacturing batch, a performance plane of each reference label manufacturing batch is obtained.

[0010] Preferably, based on the control plane and the performance plane of each reference label manufacturing batch, a control normal line and a performance normal line of each reference label manufacturing batch are obtained, comprising: The normal line of the control plane of each reference label manufacturing batch is taken as the control normal line corresponding to the reference label manufacturing batch, and the normal line of the performance plane of each reference label manufacturing batch is taken as the performance normal line corresponding to the reference label manufacturing batch.

[0011] Preferably, based on the control normal line and the performance normal line of all reference label manufacturing batches, a control standard normal line and a performance standard normal line of the current label manufacturing batch are obtained, comprising: The vector average of the control normal lines of all reference label manufacturing batches is calculated, and the vector average is taken as the control standard normal line of the current label manufacturing batch.

[0012] Preferably, based on the control normal line of all reference label production batches and the control standard normal line of the current label production batch, a control analysis matrix of the current label production batch is obtained, including: A vector difference between the control normal line of each reference label production batch and the control standard normal line is calculated to obtain a deviation vector of each reference batch in the control dimension; The deviation vectors of all reference batches are arranged in a preset order to form an initial matrix; The original values of the production control parameters corresponding to each reference batch are supplemented into the initial matrix to constitute the control analysis matrix of the current label production batch.

[0013] Preferably, based on the performance normal line of all reference label production batches and the performance standard normal line of the current label production batch, a performance analysis matrix of the current label production batch is obtained, including: The vector module length between the performance normal line of each reference label production batch and the performance standard normal line is calculated; The vector module length, performance normal line of each reference batch and the measured value of the label performance parameter of the corresponding batch are arranged in the same dimension to form the performance analysis matrix of the current label production batch.

[0014] Preferably, based on the control analysis matrix and the performance analysis matrix of the current label production batch, a control adjustment matrix of the current label production batch is obtained, including: The control parameter deviation amount in the control analysis matrix of the current label production batch is obtained; The performance index deviation amount in the performance analysis matrix of the current label production batch is obtained; A mapping relationship between the control parameter deviation amount in the control analysis matrix and the performance index deviation amount in the performance analysis matrix of the current label production batch is established to determine the key control parameter affecting the performance; According to the influence weight of the key control parameter, the control analysis matrix is weighted and corrected, and then compared with the performance analysis matrix element by element to obtain a control parameter adjustment coefficient; The adjustment coefficient is classified and arranged according to the preset type of production control parameter to form the control adjustment matrix of the current label production batch.

[0015] The application provides a fast label pasting production system based on an intelligent algorithm, including: A parameter acquisition module: based on the product model information of the current label production batch, all types of production control parameters and all types of label performance parameters of all reference label production batches are obtained; Parameter analysis module: based on each reference label production batch of each type of production control parameter and all type label performance parameter, obtain the control plane and performance plane of each reference label production batch, and based on the control plane and performance plane of each reference label production batch, obtain the control normal line and performance normal line of each reference label production batch; Difference analysis module: based on the control normal line and performance normal line of all reference label production batches, obtain the control standard normal line and performance standard normal line of the current label production batch, and based on the control normal line of all reference label production batches and the control standard normal line of the current label production batch, obtain the control analysis matrix of the current label production batch, based on the performance normal line of all reference label production batches and the performance standard normal line of the current label production batch, obtain the performance analysis matrix of the current label production batch, based on the control analysis matrix and the performance analysis matrix of the current label production batch, obtain the control adjustment matrix of the current label production batch; Result acquisition module: based on the control adjustment matrix of the current label production batch, obtain the optimal control value of all types of production control parameters of the current label production batch, and based on the optimal control value of all types of production control parameters of the current label production batch, obtain the label production result of the current label production batch.

[0016] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and attained by the structure particularly pointed out in the written description and claims hereof.

[0017] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application together with the embodiments, and explain the present application, and do not constitute a limitation on the present application. In the drawings: Figure 1 It is a flowchart of a kind of quick pasting label production method based on intelligent algorithm in the embodiment of the present application; Figure 2 It is a structure schematic view of a kind of quick pasting label production system based on intelligent algorithm in the embodiment of the present application. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present application will be described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described here are only used to illustrate and explain the present application, and not to limit the present application.

[0020] Embodiment 1: The present application provides a smart algorithm-based fast label making method, comprising: S1: Based on the product model information of the current label making batch, obtaining all class making control parameters and all class label performance parameters of all reference label making batches; S2: Based on each class of making control parameters and all class label performance parameters of each reference label making batch, obtaining the control plane and performance plane of each reference label making batch, and based on the control plane and performance plane of each reference label making batch, obtaining the control normal line and performance normal line of each reference label making batch; S3: Based on the control normal line and performance normal line of all reference label making batches, obtaining the control standard normal line and performance standard normal line of the current label making batch, and based on the control normal line of all reference label making batches and the control standard normal line of the current label making batch, obtaining the control analysis matrix of the current label making batch, based on the performance normal line of all reference label making batches and the performance standard normal line of the current label making batch, obtaining the performance analysis matrix of the current label making batch, and based on the control analysis matrix and performance analysis matrix of the current label making batch, obtaining the control adjustment matrix of the current label making batch; S4: Based on the control adjustment matrix of the current label making batch, obtaining the optimal control value of all class making control parameters of the current label making batch, and based on the optimal control value of all class making control parameters of the current label making batch, obtaining the label making result of the current label making batch.

[0021] In this embodiment, all class making control parameters and all class label performance parameters of all reference label making batches refer to key parameters and corresponding performance during making of the same model or similar model batch of the current product label; for example, the making control parameters can be that the font size of a certain model product label is 12pt, the element spacing is 5mm, and the print DPI is 300, and the performance parameters can be that the single label cost is 0.5 yuan, the single label time consumption is 2 seconds, and the quality inspection unqualified rate is 0.3%; optionally, the making control parameters can also include ink thickness parameters, and the performance parameters can also include label temperature resistance parameters, etc.

[0022] In this embodiment, the control plane refers to a plane determined by at least three making control parameters of the same reference batch in a three-dimensional coordinate system, used to represent the correlation between the making control parameters of the batch.

[0023] Specifically, the control plane is determined by the manufacturing control parameters of the same reference label manufacturing batch, such as the font size parameter, the element spacing parameter, and the printing DPI parameter, in a three-dimensional coordinate system, and is specifically constructed by three set points of the batch: the first set point: the font size parameter value as the horizontal coordinate, the element spacing parameter value as the vertical coordinate, and 0 as the vertical coordinate; the second set point: the font size parameter value as the horizontal coordinate, 0 as the vertical coordinate, and the printing DPI parameter value as the vertical coordinate; and the third set point: 0 as the horizontal coordinate, the element spacing parameter value as the vertical coordinate, and the printing DPI parameter value as the vertical coordinate.

[0024] In this embodiment, the performance plane is a plane determined by the label performance parameters of the same reference label manufacturing batch, such as the single-label cost parameter, the single-label time consumption parameter, and the quality inspection unqualified rate parameter, in a three-dimensional coordinate system, and is specifically constructed by three set points of the batch: the fourth set point: the single-label cost parameter value as the horizontal coordinate, the single-label time consumption parameter value as the vertical coordinate, and 0 as the vertical coordinate; the fifth set point: the single-label cost parameter value as the horizontal coordinate, 0 as the vertical coordinate, and the quality inspection unqualified rate parameter value as the vertical coordinate; and the sixth set point: 0 as the horizontal coordinate, the single-label time consumption parameter value as the vertical coordinate, and the quality inspection unqualified rate parameter value as the vertical coordinate, for representing the correlation between the performance parameters of the labels of the batch.

[0025] In this embodiment, the control normal line is a straight line perpendicular to the control plane of the same reference label manufacturing batch, that is, the normal line of the control plane.

[0026] In this embodiment, the performance normal line is a straight line perpendicular to the performance plane of the same reference label manufacturing batch, that is, the normal line of the performance plane.

[0027] In this embodiment, the control standard normal line is a vector average value calculated based on the control normal lines of all reference label manufacturing batches, as a reference vector of the current label manufacturing batch in the control dimension, for reflecting the overall law of the correlation of the manufacturing control parameters of all reference batches.

[0028] In this embodiment, the performance standard normal line is a vector average value calculated based on the performance normal lines of all reference label manufacturing batches, as a reference vector of the current label manufacturing batch in the performance dimension, for reflecting the overall law of the correlation of the label performance parameters of all reference batches.

[0029] In this embodiment, the optimal control value refers to the specific values of the manufacturing control parameters that can make the label manufacturing of the current product reach the optimal performance; for example, for a new product label, the optimal control value may be font size 14pt, element spacing 4mm, and printing DPI 350; optionally, different performance focuses can be adjusted to obtain the optimal control value combination with lower cost when more attention is paid to cost.

[0030] Specifically, based on the current label production batch control adjustment matrix, the optimal control value of all class production control parameters of the current label production batch is obtained, for example: "if the adjustment coefficient of font size in the control adjustment matrix is +0.2, then the optimal control value = reference batch average font size x (1+0.2)".

[0031] In this embodiment, the label production result of the current label production batch refers to the label produced according to the optimal control value, which meets the requirements and performance standards of the article to be pasted; for example, the produced article label has clear handwriting, firm pasting, and the cost and time consumption are within the expected range; optionally, it can also include related production information such as the production quantity and production time of the label.

[0032] The beneficial effects of the above technical solutions are: through intelligent algorithm analysis of historical data, accurate determination of label production optimal parameters, reduction of manual intervention and trial and error cost, improvement of label quality stability, rapid adaptation to different batch requirements, improvement of production efficiency, reduction of unqualified rate and production cost.

[0033] Embodiment 2: The present application provides a kind of based on intelligent algorithm's quick pasting label production method, based on the commodity model information of current label production batch, obtain all class production control parameters and all class label performance parameters of all reference label production batch, including: Receive the commodity model information of current label production batch, and the label production batch in all preset industrial databases, the commodity model information of the same label production batch as the commodity model information of current label production batch is regarded as reference label production batch; Based on all reference label production batch and preset industrial database, obtain all class production control parameters and all class label performance parameters of all reference label production batch.

[0034] In this embodiment, the preset industrial database refers to a database that stores detailed information of past production batches of various commodity labels, including commodity model, production control parameter, performance parameter and other data, which is used to provide reference for new label production batch;For example, this database may store historical production data of different commodity labels such as household appliances and food, each data record the type of corresponding label, font size during production, single label cost and other information;Optionally, the database can also include label raw material information, equipment running state and other auxiliary data.

[0035] In this embodiment, all class production control parameters and all class label performance parameters of all reference label production batches refer to production control parameters used by past production batches of the same model as the current label extracted from the preset industrial database and the label performance of these parameters; for example, the production control parameters include font size 10pt, element spacing 3mm, and print DPI 200 for a certain food label, and the performance parameters include single label cost 0.3 yuan, single label time 1.5 seconds, and quality inspection unqualified rate 0.2%; optionally, the production control parameters can include printing pressure parameters, and the performance parameters can include label humidity resistance parameters, etc.

[0036] The beneficial effects of the above technical solutions are: by accurately matching the same model reference batch data, quickly obtaining effective production parameters and performance information, reducing data screening time, and improving parameter acquisition efficiency. Relying on the preset database to ensure data accuracy, laying a reliable foundation for subsequent parameter optimization, thereby improving production efficiency and reducing the unqualified rate and production cost caused by inaccurate parameters.

[0037] Embodiment 3: The present application provides a fast label pasting production method based on intelligent algorithm, based on all reference label production batches and a preset industrial database, obtaining all class production control parameters and all class label performance parameters of all reference label production batches, including: Extracting all class production control parameters of each reference label production batch from the preset industrial database, wherein the all class production control parameters include font size parameters, element spacing parameters, and print DPI parameters; Extracting all class label performance parameters of each reference label production batch from the preset industrial database, wherein the all class label performance parameters include single label cost parameters, single label time parameters, and quality inspection unqualified rate parameters.

[0038] In this embodiment, the quality inspection unqualified rate parameter refers to the proportion of the number of labels judged as unqualified in the total production quantity in the reference label production batch, which is used to reflect the quality level of label production; for example, 5 out of 1000 labels in a certain reference batch are judged as unqualified due to blurred characters, poor adhesion, etc., and the quality inspection unqualified rate parameter is 0.5%; optionally, the unqualified types can be further divided, such as appearance unqualified rate, performance unqualified rate, etc.

[0039] The beneficial effects of the above technical solutions are: clearly extracting key production control parameters and performance parameters from the preset industrial database to ensure data pertinence and accuracy and reduce irrelevant data interference. Focus on core parameters to improve data processing efficiency and provide accurate basis for subsequent analysis to help quickly determine the optimal production scheme, thereby improving production efficiency and reducing the unqualified rate and cost.

[0040] Embodiment 4: The application provides a smart algorithm-based fast label making method, based on each reference label making batch, each type of making control parameter and all types of label performance parameters, obtaining the control plane and performance plane of each reference label making batch, including: The numerical value of the font size parameter of each reference label making batch is taken as the horizontal coordinate value, the numerical value of the element spacing parameter corresponding to the reference label making batch is taken as the vertical coordinate value, 0 is taken as the vertical coordinate value, to obtain the first set point of each reference label making batch, the numerical value of the font size parameter of each reference label making batch is taken as the horizontal coordinate value, 0 is taken as the vertical coordinate value, and the numerical value of the print DPI parameter corresponding to the reference label making batch is taken as the vertical coordinate value, to obtain the second set point of each reference label making batch, 0 is taken as the horizontal coordinate value, the numerical value of the element spacing parameter of each reference label making batch is taken as the vertical coordinate value, and the numerical value of the print DPI parameter corresponding to the reference label making batch is taken as the vertical coordinate value, to obtain the third set point of each reference label making batch, and based on the first set point, the second set point and the third set point of each reference label making batch, the control plane of each reference label making batch is obtained. The numerical value of the single label cost parameter of each reference label making batch is taken as the horizontal coordinate value, the numerical value of the single label time-consuming parameter corresponding to the reference label making batch is taken as the vertical coordinate value, and 0 is taken as the vertical coordinate value, to obtain the fourth set point of each reference label making batch, the numerical value of the single label cost parameter of each reference label making batch is taken as the horizontal coordinate value, 0 is taken as the vertical coordinate value, and the numerical value of the quality inspection unqualified rate parameter corresponding to the reference label making batch is taken as the vertical coordinate value, to obtain the fifth set point of each reference label making batch, 0 is taken as the horizontal coordinate value, the numerical value of the single label time-consuming parameter of each reference label making batch is taken as the vertical coordinate value, and the numerical value of the quality inspection unqualified rate parameter corresponding to the reference label making batch is taken as the vertical coordinate value, to obtain the sixth set point of each reference label making batch, and based on the fourth set point, the fifth set point and the sixth set point of each reference label making batch, the performance plane of each reference label making batch is obtained.

[0041] In this embodiment, the first set point refers to a point determined in a three-dimensional coordinate system with the numerical value of the font size parameter of the reference label making batch as the horizontal coordinate, the numerical value of the element spacing parameter as the vertical coordinate, and 0 as the vertical coordinate, which is used to reflect the correlation between the two control parameters; for example, the font size parameter of a certain reference batch is 12pt, and the element spacing parameter is 4mm, then the coordinates of the first set point are (12pt, 4mm, 0); optionally, the coordinate unit can be adjusted according to the actual parameter situation, such as taking mm as the unit of font size.

[0042] In this embodiment, the second set point refers to a point determined in a three-dimensional coordinate system with the font size parameter value of the reference label making batch as the horizontal coordinate, 0 as the vertical coordinate, and the print DPI parameter value as the vertical coordinate, used to reflect the correlation between the font size and the print DPI two control parameters; for example, the font size parameter of a certain reference batch is 10pt, and the print DPI parameter is 300, then the coordinates of the second set point are (10pt, 0, 300); optionally, if there are other related control parameters, the coordinate system dimension can be extended for representation.

[0043] In this embodiment, the third set point refers to a point determined in a three-dimensional coordinate system with 0 as the horizontal coordinate, the element spacing parameter value of the reference label making batch as the vertical coordinate, and the print DPI parameter value as the vertical coordinate, used to reflect the correlation between the element spacing and the print DPI two control parameters; for example, the element spacing parameter of a certain reference batch is 5mm, and the print DPI parameter is 250, then the coordinates of the third set point are (0, 5mm, 250); optionally, the distribution of the third set points of multiple batches can be used to observe the variation law of the element spacing and the print DPI.

[0044] In this embodiment, the fourth set point refers to a point determined in a three-dimensional coordinate system with the single label cost parameter value of the reference label making batch as the horizontal coordinate, the single label time consumption parameter value as the vertical coordinate, and 0 as the vertical coordinate, used to reflect the correlation between the cost and the time consumption two performance parameters; for example, the single label cost of a certain reference batch is 0.4 yuan, and the single label time consumption is 1.8 seconds, then the coordinates of the fourth set point are (0.4 yuan, 1.8 seconds, 0); optionally, the fourth set points of multiple batches can be combined to analyze the balance relationship between the cost and the time consumption.

[0045] The beneficial effects of the above technical solutions are: by converting the control parameters and performance parameters into three-dimensional coordinate points and constructing a plane, the correlation law between the parameters is intuitively presented. The control and performance characteristics of the reference batch are visualized, which facilitates the accurate extraction of the control normal line and the performance normal line, lays a reliable foundation for subsequent standard normal line calculation, and improves the scientificity and efficiency of parameter analysis.

[0046] Embodiment 5: The present application provides a fast label making method based on intelligent algorithm, based on the control plane and the performance plane of each reference label making batch, the control normal line and the performance normal line of each reference label making batch are obtained, including: The normal line of the control plane of each reference label making batch is taken as the control normal line of the corresponding reference label making batch, and the normal line of the performance plane of each reference label making batch is taken as the performance normal line of the corresponding reference label making batch.

[0047] The beneficial effects of the above technical solutions are: by taking the normal lines of the control plane and the performance plane as the control normal line and the performance normal line of the corresponding batch, the directional characteristics and parameter association nature of the plane are captured. The conversion process from the plane to the normal line is simplified, the data coherence and accuracy are guaranteed, a reliable foundation is provided for subsequent standard normal line and matrix analysis, and the overall parameter optimization efficiency is improved.

[0048] Embodiment 6: The present application provides a fast label making method based on intelligent algorithm, based on the control normal line and the performance normal line of all reference label making batches, obtaining the control standard normal line and the performance standard normal line of the current label making batch, comprising: Calculate the vector average value of the control normal line of all reference label making batches, and take the vector average value as the control standard normal line of the current label making batch.

[0049] In this embodiment, calculating the vector average value of the control normal line of all reference label making batches means that the vector components of the control normal line of each reference batch are averaged respectively to obtain a comprehensive vector as a standard vector reflecting the control law of all reference batches; for example, the control normal line vectors of 3 reference batches are (2, 3, 4), (4, 5, 6) and (6, 7, 8), and the vector average value is (4, 5, 6).

[0050] Optionally, the weighted vector average value can be calculated for each reference batch according to the weight, and the weight is set according to the batch similarity.

[0051] The beneficial effects of the above technical solutions are: by calculating the vector average value of the reference batch control normal line to obtain the control standard normal line, the control characteristics of all reference batches are integrated to provide an objective and unified control reference for the current batch. This method reduces the influence of individual batch deviation and improves the reliability of the standard, lays a precise foundation for subsequent control analysis matrix construction, and helps efficient optimization of production parameters.

[0052] Embodiment 7: The present application provides a fast label making method based on intelligent algorithm, based on the control normal line of all reference label making batches and the control standard normal line of the current label making batch, obtaining the control analysis matrix of the current label making batch, comprising: Calculate the vector difference between the control normal line of each reference label making batch and the control standard normal line, to obtain the deviation vector of each reference batch in the control dimension; Arrange the deviation vectors of all reference batches in a predetermined order to form an initial matrix; Supplement the original values of the production control parameters corresponding to each reference batch to the initial matrix to form the control analysis matrix of the current label making batch.

[0053] In this embodiment, the deviation vector of each reference batch in the control dimension refers to the vector difference between the control normal of each reference batch and the control standard normal in the three-dimensional space, which is used to quantify the deviation degree between the individual reference batch and the overall standard; for example, the control normal of a reference batch is (5, 6, 7), and the control standard normal is (4, 5, 6), and the deviation vector is (1, 1, 1); optionally, the deviation degree can be further intuitively reflected by the vector length.

[0054] In this embodiment, the preset order refers to a fixed rule followed when arranging the deviation vectors of all reference batches, aiming to ensure the consistency and repeatability of the matrix structure; for example, arranging according to the production time sequence of the reference batches, the deviation vector corresponding to the first produced batch is arranged in the front.

[0055] Optionally, the batches can also be sorted from high to low according to the similarity with the current batch.

[0056] In this embodiment, the original value of the production control parameter corresponding to the reference batch refers to the specific value of each control parameter actually used in the production process of the reference batch, which is the original data without processing; for example, the font size parameter of a reference batch is 12pt, the element spacing parameter is 5mm, and the print DPI parameter is 300, which are the original values of the production control parameters; optionally, it can also include the ink density, printing speed and other original control parameters at that time.

[0057] In this embodiment, supplementing the original value of the production control parameter corresponding to each reference batch to the initial matrix refers to adding the original value of the production control parameter of each reference batch to the matrix according to the corresponding position on the basis of the initial matrix composed of deviation vectors, enriching the information dimension of the matrix; for example, the initial matrix is [[1, 1, 1], [2, 2, 2]] (two rows correspond to the deviation vectors of two reference batches), after supplementing the original parameters (12pt, 5mm, 300) of the first batch and the original parameters (10pt, 4mm, 280) of the second batch, the matrix becomes [[1, 1, 1, 12pt, 5mm, 300], [2, 2, 2, 10pt, 4mm, 280]].

[0058] Optionally, only the key part of the original parameters can be supplemented according to the analysis requirements.

[0059] The beneficial effects of the above technical solutions are: by calculating the deviation vector, arranging in order and supplementing the original parameters to construct the control analysis matrix, the system integrates the control differences and original data of the reference batches and the standard. The matrix clearly presents the parameter deviation rule, providing a structured basis for subsequent adjustment, improving the accuracy and efficiency of control parameter analysis, and assisting in quickly deriving the optimal production scheme.

[0060] Embodiment 8: The application provides a smart algorithm-based fast label making method, based on performance normal lines of all reference label making batches and a performance standard normal line of a current label making batch, a performance analysis matrix of the current label making batch is obtained, including: calculating a vector module length between the performance normal line of each reference label making batch and the performance standard normal line; arranging the vector module length, the performance normal line and the measured value of the label performance parameter of each reference batch according to the same dimension to form the performance analysis matrix of the current label making batch.

[0061] In this embodiment, the vector module length between the performance normal line of each reference label making batch and the performance standard normal line is calculated by first calculating the difference vector of the two vectors, and then calculating the length of the difference vector, which is used to quantify the deviation degree of the performance characteristics of a single reference batch from the overall standard performance characteristics; for example, the performance normal line of a certain reference batch is (3, 4, 5), the performance standard normal line is (1, 2, 3), the difference vector is (2, 2, 2), and the vector module length is 3.46.

[0062] Optionally, the module length can be normalized to make the deviation degrees of different batches more easily comparable.

[0063] In this embodiment, arranging the vector module length, the performance normal line and the measured value of the label performance parameter of each reference batch according to the same dimension means arranging the three types of data of each reference batch as a row of the matrix, and the position of each type of data in each row is consistent, forming a structured matrix; for example, two reference batches, the first batch has a vector module length of 3.46, a performance normal line of (3, 4, 5), and a measured value of (0.5 yuan, 2 seconds, 0.3%), and the second batch has a vector module length of 2.24, a performance normal line of (2, 3, 4), and a measured value of (0.4 yuan, 1.8 seconds, 0.2%), and after arrangement, the matrix is [[3.46, 3, 4, 5, 0.5 yuan, 2 seconds, 0.3%], [2.24, 2, 3, 4, 0.4 yuan, 1.8 seconds, 0.2%]].

[0064] Optionally, the data arrangement order can be adjusted according to the analysis focus, such as placing the measured value of the performance parameter in front.

[0065] The beneficial effects of the above technical solutions are: the performance deviation degree is quantified by calculating the vector module length, the performance normal line and the measured value are arranged according to the dimension to form a matrix, and the performance related data is integrated by the system. The matrix clearly presents the differences between the performance characteristics of each batch and the standard, providing a structured basis for analyzing the performance fluctuation reasons, improving the performance analysis efficiency and accuracy, and assisting in optimizing the label making performance.

[0066] Embodiment 9: The application provides a smart algorithm-based fast label making method, based on a control analysis matrix and a performance analysis matrix of a current label making batch, obtaining a control adjustment matrix of the current label making batch, comprising: obtaining a control parameter deviation in the control analysis matrix of the current label making batch; obtaining a performance index deviation in the performance analysis matrix of the current label making batch; establishing a mapping relationship between the control parameter deviation in the control analysis matrix and the performance index deviation in the performance analysis matrix of the current label making batch, and determining key control parameters affecting performance; According to the influence weight of the key control parameter, the control analysis matrix is weighted and corrected, and then compared and operated with the performance analysis matrix element by element to obtain a control parameter adjustment coefficient; The adjustment coefficient is classified and arranged according to the category of the preset making control parameter, and a control adjustment matrix of the current label making batch is formed.

[0067] In this embodiment, the control parameter deviation in the control analysis matrix of the current label making batch refers to the deviation value of the making control parameter of each reference batch compared with the standard parameter extracted from the control analysis matrix, which is used to reflect the abnormality degree of the control parameter; for example, the font size of a certain reference batch deviates from the standard value by 2pt, and the element spacing deviates by 1mm, these values are the control parameter deviation; optionally, the absolute value or relative value of the deviation can be extracted, which is selected according to the analysis requirement.

[0068] In this embodiment, the performance index deviation in the performance analysis matrix of the current label making batch refers to the deviation value of the label performance index of each reference batch compared with the standard index extracted from the performance analysis matrix, which reflects the performance level; for example, the cost of a single label of a certain reference batch exceeds the standard by 0.1 yuan, and the unqualified rate of quality inspection exceeds 0.2%, these values are the performance index deviation; optionally, only the deviation of the performance that does not meet the standard can be extracted, focusing on the part that needs to be improved.

[0069] In this embodiment, the mapping relationship between the control parameter deviation in the control analysis matrix and the performance index deviation in the performance analysis matrix of the current label making batch refers to finding the correlation rule between the control parameter deviation and the performance index deviation through data analysis, and determining which control parameter deviation will cause which performance index abnormality; for example, it is found that when the print DPI parameter deviation increases, the quality inspection unqualified rate deviation also increases, that is, a positive correlation mapping relationship between the two is established; optionally, the strength of the mapping relationship can be quantified by regression analysis and other algorithms.

[0070] In this embodiment, the control analysis matrix is weighted and corrected according to the influence weight of the key control parameter, and then is subjected to element-by-element comparison operation with the performance analysis matrix to obtain the control parameter adjustment coefficient. That is, a higher weight is given to the key control parameter that has a greater influence on the performance, the proportion of the corresponding parameter in the control analysis matrix is adjusted, and then the element at the corresponding position in the corrected control analysis matrix and the performance analysis matrix is calculated to obtain the coefficient for adjusting the control parameter. For example, the print DPI is a key parameter, the weight is set to 0.6, the corresponding matrix element value is enlarged after correction, and then the print DPI adjustment coefficient is obtained by calculating the corresponding element in the performance analysis matrix.

[0071] The beneficial effects of the above technical solutions are as follows: the key control parameter is accurately located by associating the control parameter with the performance index deviation, the adjustment coefficient is obtained by combining the weight correction matrix and operation, and the control adjustment matrix is formed. This process focuses on the key parameters, improves the adjustment pertinence and accuracy, helps to quickly determine the optimal control parameter, improves the production efficiency, and reduces the cost and the unqualified rate.

[0072] Embodiment 10: The present application provides a fast label making system based on intelligent algorithm, comprising: The parameter acquisition module obtains all types of production control parameters and all types of label performance parameters of all reference label making batches based on the product model information of the current label making batch. The parameter analysis module obtains the control plane and the performance plane of each reference label making batch based on each type of production control parameter and all types of label performance parameters of each reference label making batch, and obtains the control normal line and the performance normal line of each reference label making batch based on the control plane and the performance plane of each reference label making batch. The difference analysis module obtains the control standard normal line and the performance standard normal line of the current label making batch based on the control normal line and the performance normal line of all reference label making batches, and obtains the control analysis matrix of the current label making batch based on the control normal line of all reference label making batches and the control standard normal line of the current label making batch, and obtains the performance analysis matrix of the current label making batch based on the performance normal line of all reference label making batches and the performance standard normal line of the current label making batch, and obtains the control adjustment matrix of the current label making batch based on the control analysis matrix and the performance analysis matrix of the current label making batch. The result acquisition module obtains the best control value of all types of production control parameters of the current label making batch based on the control adjustment matrix of the current label making batch, and obtains the label making result of the current label making batch based on the best control value of all types of production control parameters of the current label making batch.

[0073] The above technical scheme has the beneficial effects that: historical data is analyzed by an intelligent algorithm to accurately determine the best parameters for label making, manual intervention and trial and error costs are reduced, label quality stability is improved, different batch requirements are quickly adapted, production efficiency is improved, and unqualified rate and production cost are reduced.

[0074] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application, and the present application also intends to include these modifications and variations. In the embodiment, the control analysis matrix is weighted and corrected according to the influence weight of the key control parameter, and then the element-by-element comparison operation is performed with the performance analysis matrix to obtain the control parameter adjustment coefficient, that is, a higher weight is given to the key control parameter that has a greater influence on the performance, the proportion of the corresponding parameter in the control analysis matrix is adjusted, then the elements at the corresponding positions in the corrected control analysis matrix and the performance analysis matrix are calculated to obtain the coefficient for adjusting the control parameter; for example, the print DPI is a key parameter, the weight is set to 0.6, the corresponding matrix element value is enlarged after correction, and then the corresponding element in the performance analysis matrix is calculated to obtain the adjustment coefficient of the print DPI, which is +50; optionally, the comparison operation can adopt various ways such as difference and ratio, and the selection is based on the actual demand.

Claims

1. A method for making quick adhesive labels based on an intelligent algorithm, characterized in that: include: S1: Based on the product model information of the current label production batch, obtain all class production control parameters and all class label performance parameters of all reference label production batches; S2: Based on the per-class production control parameters and all-class label performance parameters of each reference label production batch, obtain the control plane and performance plane of each reference label production batch, and based on the control plane and performance plane of each reference label production batch, obtain the control normal and performance normal of each reference label production batch; S3: Based on the control normals and performance normals of all reference label production batches, obtain the control standard normal and performance standard normal of the current label production batch, and based on the control normals of all reference label production batches and the control standard normal of the current label production batch, obtain the control analysis matrix of the current label production batch based on the performance normals of all reference label production batches and the performance standard normal of the current label production batch, and based on the control analysis matrix and performance analysis matrix of the current label production batch, obtain the control adjustment matrix of the current label production batch; S4: Based on the control adjustment matrix of the current label production batch, the optimal control values ​​of all class production control parameters of the current label production batch are obtained, and based on the optimal control values ​​of all class production control parameters of the current label production batch, the label production result of the current label production batch is obtained.

2. The method for making a quick adhesive label based on an intelligent algorithm according to claim 1, characterized in that: S1: Based on the product model information of the current label production batch, obtain all class production control parameters and all class label performance parameters of all reference label production batches, including: Receive product model information of a current label production batch, and use a label production batch in a preset industrial database whose product model information is the same as the product model information of the current label production batch as a reference label production batch; Based on all reference label production batches and a preset industrial database, all class production control parameters and all class label performance parameters of all reference label production batches are obtained.

3. The method for making a quick adhesive label based on an intelligent algorithm according to claim 2, characterized in that: Based on all reference label production batches and the preset industrial database, all class production control parameters and all class label performance parameters of all reference label production batches are obtained, including: Extracting all production control parameters for each reference label production batch from a preset industrial database, wherein all production control parameters include font size parameters, element spacing parameters, and printing DPI parameters; All class label performance parameters of each reference label production batch are extracted from the preset industrial database, where all class label performance parameters include single label cost parameters, single label time parameters and quality inspection failure rate parameters.

4. The method for making a quick adhesive label based on an intelligent algorithm according to claim 1, characterized in that: Based on the per-class production control parameters and all-class label performance parameters of each reference label production batch, a control plane and a performance plane of each reference label production batch are obtained, including: using the value of the font size parameter of each reference label production batch as the abscissa value, the value of the element spacing parameter of the corresponding reference label production batch as the ordinate value, and 0 as the ordinate value to obtain a first set point for each reference label production batch; using the value of the font size parameter of each reference label production batch as the abscissa value, 0 as the ordinate value, and the value of the printing DPI parameter of the corresponding reference label production batch as the ordinate value to obtain a second set point for each reference label production batch; using 0 as the abscissa value, the value of the element spacing parameter of each reference label production batch as the ordinate value, and the value of the printing DPI parameter of the corresponding reference label production batch as the ordinate value to obtain a third set point for each reference label production batch; and obtaining a control plane for each reference label production batch based on the first set point, the second set point, and the third set point for each reference label production batch; The numerical value of the single label cost parameter of each reference label production batch is used as the horizontal axis value, the numerical value of the single label time consumption parameter of the corresponding reference label production batch is used as the vertical axis value, and 0 is used as the vertical axis value to obtain the fourth setting point of each reference label production batch. The numerical value of the single label cost parameter of each reference label production batch is used as the horizontal axis value, 0 is used as the vertical axis value, and the numerical value of the quality inspection failure rate parameter of the corresponding reference label production batch is used as the vertical axis value to obtain the fifth setting point of each reference label production batch. The numerical value of 0 is used as the horizontal axis value, the numerical value of the single label time consumption parameter of each reference label production batch is used as the vertical axis value, and the numerical value of the quality inspection failure rate parameter of the corresponding reference label production batch is used as the vertical axis value to obtain the sixth setting point of each reference label production batch. Based on the fourth setting point, the fifth setting point and the sixth setting point of each reference label production batch, the performance plane of each reference label production batch is obtained.

5. The method for making a quick adhesive label based on an intelligent algorithm according to claim 4, characterized in that: Based on the control plane and the performance plane of each reference label production batch, a control normal and a performance normal of each reference label production batch are obtained, including: The normal of the control plane of each reference label production batch is regarded as the control normal of the corresponding reference label production batch, and the normal of the performance plane of each reference label production batch is regarded as the performance normal of the corresponding reference label production batch.

6. The method for making a quick adhesive label based on an intelligent algorithm according to claim 1, characterized in that: Based on the control normals and performance normals of all reference label production batches, the control standard normals and performance standard normals of the current label production batch are obtained, including: Calculate the vector average of the control normals of all reference label production batches, and use the vector average as the control standard normal of the current label production batch.

7. The method for making a quick adhesive label based on an intelligent algorithm according to claim 1, characterized in that: Based on the control normals of all reference label production batches and the control standard normals of the current label production batch, a control analysis matrix for the current label production batch is obtained, including: Calculate the vector difference between the control normal and the control standard normal of each reference label production batch to obtain the deviation vector of each reference batch in the control dimension; Arrange the deviation vectors of all reference batches in a preset order to form an initial matrix; The original values ​​of the production control parameters corresponding to each reference batch are added to the initial matrix to form the control analysis matrix of the current label production batch.

8. The method for making a quick adhesive label based on an intelligent algorithm according to claim 1, characterized in that: Based on the performance normals of all reference label production batches and the performance standard normals of the current label production batch, a performance analysis matrix of the current label production batch is obtained, including: Calculate the vector norm between the performance normal and the performance standard normal of each reference label production batch; The vector modulus, performance normal and measured values ​​of label performance parameters of each reference batch are arranged in the same dimension to form a performance analysis matrix for the current label production batch.

9. The method for making a quick adhesive label based on an intelligent algorithm according to claim 1, characterized in that: Based on the control analysis matrix and the performance analysis matrix of the current label production batch, a control adjustment matrix of the current label production batch is obtained, including: Obtain the control parameter deviation in the control analysis matrix of the current label production batch; Obtain the performance indicator deviation in the performance analysis matrix of the current label production batch; Establish a mapping relationship between the control parameter deviation in the control analysis matrix of the current label production batch and the performance indicator deviation in the performance analysis matrix to determine the key control parameters that affect performance; According to the influence weights of key control parameters, the control analysis matrix is ​​weighted and modified, and then compared element by element with the performance analysis matrix to obtain the control parameter adjustment coefficient; The adjustment coefficients are classified and arranged according to the categories of the preset production control parameters to form a control adjustment matrix for the current label production batch.

10. A quick-adhesive label making system based on intelligent algorithm, characterized in that: A method for making a quick adhesive label based on an intelligent algorithm for executing any one of claims 1 to 9, comprising: Parameter acquisition module: based on the product model information of the current label production batch, obtain all class production control parameters and all class label performance parameters of all reference label production batches; Parameter analysis module: based on the production control parameters of each class of each reference label production batch and the performance parameters of all class labels, obtain the control plane and performance plane of each reference label production batch, and based on the control plane and performance plane of each reference label production batch, obtain the control normal and performance normal of each reference label production batch; Difference analysis module: based on the control normals and performance normals of all reference label production batches, obtain the control standard normals and performance standard normals of the current label production batch; based on the control normals of all reference label production batches and the control standard normals of the current label production batch, obtain the control analysis matrix of the current label production batch; based on the performance normals of all reference label production batches and the performance standard normals of the current label production batch, obtain the performance analysis matrix of the current label production batch; based on the control analysis matrix and performance analysis matrix of the current label production batch, obtain the control adjustment matrix of the current label production batch; Result acquisition module: based on the control adjustment matrix of the current label production batch, obtain the optimal control values ​​of all class production control parameters of the current label production batch, and based on the optimal control values ​​of all class production control parameters of the current label production batch, obtain the label production results of the current label production batch.

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