An intelligent printing layout method and system based on printed content

Abstract

The present invention discloses an intelligent printing layout method and system based on printed content, which relates to the technical field of printing layout. Specifically, it discloses establishing an image module library, where the image module library includes several image modules, performing feature analysis on historical printing patterns in a historical printing pattern set to determine several feature factor parameter groups, combining combinations representing keywords into a representative keyword group, and based on the representative keyword group, selecting the feature factor parameter group with the highest adaptation degree among different feature factor parameter groups. Based on the reference feature factor parameter group, screening and analyzing the feature label groups of each image template in the image template library, and randomly combining the determined image modules to generate several reference printing templates. Through the above technical solutions, the present invention realizes the automatic generation of printing templates, which not only saves labor but also improves the efficiency of constructing printing templates, providing a variety of reference printing templates for printing work.

Description

Technical Field

[0001] The present invention relates to the technical field of printing layout, and particularly to an intelligent printing layout method and system based on printed content. Background Art

[0002] In today's printing industry, the layout design of printed materials is an important and time-consuming process. Traditional printing layout methods usually rely on the intuition and experience of designers. They need to manually select images, colors, fonts, and layouts to create visually appealing printed materials. This method is not only time-consuming but also may not fully utilize the potential information of the printed content, thus affecting the quality and effect of the printed materials. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system capable of automatically performing printing layout based on printed content.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions:

[0005] An intelligent printing layout method based on printed content, comprising:

[0006] Establish an image module library, the image module library includes a number of image modules, and each image block is set with a specific feature tag group;

[0007] Obtain a historical printing pattern set, and perform feature analysis on the historical printing patterns in the historical printing pattern set to determine a number of feature factor parameter groups, where the feature factor parameter groups are the parameter performances of different feature factors in the same printing pattern;

[0008] Using natural language processing technology, perform semantic analysis on the content to be printed, determine a number of representative keywords, combine the representative keywords into a representative keyword group, and select the feature factor parameter group with the highest adaptation degree based on the representative keyword group in different feature factor parameter groups, and record it as the reference feature factor parameter group;

[0009] Based on the reference feature factor parameter group, perform screening analysis on the feature tag groups of each image template in the image template library, and randomly combine the determined image modules to generate a number of reference printing templates.

[0010] In some embodiments disclosed by the present invention, the feature tag group of the image block includes: color feature, shape feature, relative position block on the printing template, copy type, and a keyword library associated with each copy type.

[0011] In some embodiments disclosed by the present invention, the method for performing feature analysis on the historical printing patterns in the historical printing pattern set includes:

[0012] Mark the image modules on the historical printed pattern, and analyze the color features, shape features, and the location blocks to which they belong of the marked image modules;

[0013] Analyze the text within a preset range beside the delimited image modules to determine the location block of the text relative to the image modules and the type of the text;

[0014] Parametrize the color features, shape features, the location blocks of the image modules, and the location blocks of the text respectively for each image module on the historical printed pattern, and combine them with the text type combination to obtain a set of characteristic factor parameters.

[0015] In some embodiments disclosed by the present invention, the method for parametrizing the color features and shape features of the image modules includes:

[0016] Construct a mapping point array for the historical printed pattern, the mapping point array includes a number of mapping points uniformly set on the historical printed pattern, and set mapping point coordinates for each mapping point;

[0017] Trigger and record the mapping points in the area delimited for each image module, denoted as trigger mapping points, and associate and combine the trigger mapping points belonging to the same image module area to obtain a group of trigger mapping points;

[0018] Randomly select a number of trigger mapping points from the group of trigger mapping points, and calculate the average trigger mapping point between the trigger mapping points;

[0019] Taking the average trigger mapping point as the rotation center, construct a virtual scanning line for successive rotational scanning. When it is necessary to parametrize the color features:

[0020] Record the trigger mapping points passed by the scanning line during each rotational scanning, denoted as color trigger mapping points, record the color and color parameters corresponding to each color trigger mapping point, and calculate the color proportion of the color trigger mapping points of different colors among all the color trigger mapping points, and classify the color proportion and color parameters corresponding to different colors into the set of characteristic factor parameters;

[0021] When it is necessary to parametrize the shape features of the image modules, the method includes:

[0022] Record the opposite boundary trigger mapping points scanned during each rotational scanning, denoted as a group of boundary trigger mapping points, calculate the distance between the two boundary trigger mapping points in each group of boundary trigger mapping points, sort the distances between different groups of boundary trigger mapping points to obtain a sequence of distances between boundaries, and classify the sequence of distances between boundaries into the set of characteristic factor parameters.

[0023] In some embodiments disclosed by the present invention, the color parameters include color brightness, color hue, color saturation, and color temperature.

[0024] In some embodiments disclosed by the present invention, the method for selecting the most suitable set of characteristic factor parameters based on the representative keyword group among different sets of characteristic factor parameters includes:

[0025] Establish a copywriting type table, combine the copywriting types in the copywriting type table pairwise to obtain several copywriting type groups, and determine the correlation degree between the copywriting types in each copywriting type group based on the intersection of keywords.

[0026] Determine the main copywriting type to which the content to be printed belongs, and use the main copywriting type and the copywriting types whose correlation degree with the main copywriting type is greater than or equal to a preset value as the screening criteria to screen out several sets of characteristic factor parameters.

[0027] Mark the representative keywords mapped from the representative keywords in the representative keyword group to the corresponding keyword library in the set of characteristic factor parameters as the mapped representative keywords, and determine the adaptation degree between the representative keyword group and the set of characteristic factor parameters based on the keyword proportion of the mapped representative keywords in the representative keyword group.

[0028] Among them, the expression for calculating the adaptation degree is:

[0029]

[0030] Among them, P is the adaptation degree, R i is the correlation degree adjustment coefficient corresponding to the i-th copywriting type whose correlation degree with the main copywriting type is greater than or equal to the preset value, δ i is the keyword proportion of the mapped representative keywords in the representative keyword group in the keyword library of the set of characteristic factor parameters corresponding to the i-th copywriting type, n is the total number of the main copywriting type and the copywriting types whose correlation degree with the main copywriting type is greater than or equal to the preset value, and b is the adaptation degree adjustment constant.

[0031] In some embodiments disclosed by the present invention, the method for screening and analyzing the characteristic tag group of each image template in the image template library includes:

[0032] Analyze the reference set of characteristic factor parameters to determine the color characteristics, shape characteristics, and relative position blocks on the printing template corresponding to different image blocks.

[0033] Based on whether the difference amount between color characteristics is within the preset range, perform a first screening on the characteristic tag group of the image template, and based on whether the difference amount between shape characteristics is within the preset range, perform a second screening on the characteristic tags of the image template after the first screening.

[0034] Based on the difference amount of color features and the difference amount of shape features, determine the degree of coincidence between the reference feature factor parameter group and the feature label group of the image template, and screen out the feature label groups of the image templates whose degree of coincidence is greater than or equal to the preset value.

[0035] In some embodiments disclosed in the present invention, the expression for calculating the degree of coincidence is:

[0036]

[0037] W is the degree of coincidence, K1 is the color weight coefficient, K2 is the shape weight coefficient, μ x is the color proportion of the x-th color, β(x) is the coincidence judgment function of the color parameter of the x-th color, β(x) outputs a specific degree of coincidence value according to the preset range to which the difference amount of the color parameter belongs, c is the color parameter coincidence adjustment constant, N is the total number of color types, α(q) is the coincidence judgment function of the distance between the q-th boundaries in the image module, α(q) outputs a specific degree of coincidence according to the preset range to which the difference amount of the distance between the boundaries belongs, and Q is the total number of distances between the boundaries to be compared.

[0038] In some embodiments disclosed in the present invention, there is also disclosed an intelligent printing layout system based on printed content, including:

[0039] The first module is used to establish an image module library. The image module library includes a number of image modules, and each image block is set with a specific feature label group;

[0040] The second module is used to obtain a set of historical printed patterns and perform feature analysis on the historical printed patterns in the set of historical printed patterns to determine a number of feature factor parameter groups. The feature factor parameter groups are the parameter performances of different feature factors in the same printed pattern;

[0041] The third module is used to use natural language processing technology to perform semantic analysis on the content to be printed, determine a number of representative keywords, combine the representative keywords into a representative keyword group, and select the feature factor parameter group with the highest adaptation degree from different feature factor parameter groups based on the representative keyword group, and record it as the reference feature factor parameter group;

[0042] The fourth module is used to perform screening analysis on the feature label groups of each image template in the image template library based on the reference feature factor parameter group, and randomly combine the determined image modules to generate a number of reference printing templates.

[0043] The present invention discloses an intelligent printing layout method and system based on printed content, which relates to the technical field of printing layout. Specifically, it discloses establishing an image module library. The image module library includes a number of image modules, performing feature analysis on the historical printing patterns in the historical printing pattern set, determining a number of feature factor parameter groups, combining the combinations representing keywords into a representative keyword group, and based on the representative keyword group, selecting the feature factor parameter group with the highest adaptation degree in different feature factor parameter groups. Based on the reference feature factor parameter group, screening and analyzing the feature label groups of each image template in the image template library, and randomly combining the determined image modules to generate a number of reference printing templates. Through the above technical solutions, the present invention realizes the automatic generation of printing templates, which not only saves labor but also improves the efficiency of constructing printing templates, providing a variety of reference printing templates for printing work. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is the method steps of an intelligent printing layout method based on printed content disclosed in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0046] To achieve the above object, the present invention adopts the following technical solutions:

[0047] An intelligent printing layout method based on printed content, refer to Figure 1 , including:

[0048] Step S100, establishing an image module library. The image module library includes a number of image modules, and each image block is set with a specific feature label group.

[0049] In this step, a database containing multiple image modules is established. Each image module is an independent visual element that may be used in printing design, such as icons, pictures, decorative borders, etc.; each image module is associated with a set of feature labels that define the attributes of the module, such as color, shape, size, style, etc. These labels will be used in the subsequent screening and matching processes.

[0050] In some embodiments disclosed by the present invention, the method for performing feature analysis on the historical printing patterns in the historical printing pattern set includes:

[0051] Step S101, marking the image modules on the historical printing patterns, and analyzing the color features, shape features, and the location blocks to which they belong of the marked image modules.

[0052] In this step, the goal is to identify and classify the image modules in the historical printed patterns. This involves using image processing techniques to identify different visual elements in the patterns and label them as independent image modules. Then, each image module is analyzed to extract its color features (such as the main color, color distribution, etc.), shape features (such as geometric shapes, edge features, etc.), and the position information of the image module in the printed pattern.

[0053] Step S102: Analyze the text beside the delimited image module within a preset range to determine the position block of the text relative to the image module and the type of the text.

[0054] The text is an important part of the printed pattern and is usually closely related to the image module. In this step, the focus is on identifying and analyzing the text beside the image module. This includes determining the text content of the text, the positional relationship (such as whether the text is above, below, beside the image, etc.), and the type of the text (such as title, body text, label, etc.); by analyzing the relative position and type of the text and the image module, the information hierarchy and visual flow in the design can be better understood.

[0055] Step S103: Parametrize the color features, shape features, the position block of the image module, and the position block of the text on the historical printed pattern respectively, and combine them with the text type combination to obtain a set of characteristic factor parameters.

[0056] In order to enable the features of the historical printed pattern to be processed and understood by a computer, these features need to be converted into numerical forms, that is, parametrized. This step involves converting the color features, shape features, the position block of the image module, and the position block of the text into a series of numerical indicators. For example, the color features may include numerical representations of the brightness, saturation, hue, etc. of the color; the shape features may include numerical values of geometric attributes such as the area, perimeter, angle, etc. of the shape. Finally, these parametrized features are combined to form a set of characteristic factor parameters, and these sets will be used as the input of the intelligent layout system to generate new printed designs. Through parametrization, the system can perform pattern recognition and decision-making based on the numerical features, thereby achieving more accurate and efficient layout design.

[0057] In some embodiments disclosed by the present invention, the method for parametrizing the color features and shape features of the image module includes:

[0058] Step S1031: Construct a mapping point array for the historical printed pattern, where the mapping point array includes a number of mapping points uniformly set on the historical printed pattern, and set mapping point coordinates for each mapping point.

[0059] In this step, an array of mapping points is created, which is evenly distributed on the historical printed pattern. Each mapping point has a coordinate to locate its position on the pattern. This array provides the basis for subsequent steps to accurately record and analyze the color and shape characteristics of the image module.

[0060] Step S1032, trigger records are made for the mapping points in the area delimited for each image module, denoted as trigger mapping points, and the trigger mapping points belonging to the same image module area are associated and combined to obtain a trigger mapping point group.

[0061] For each image module, record the triggered mapping points within its area, which represent the color and shape information of the image module; combine these triggered mapping points to form a trigger mapping point group, and each group is associated with a specific image module.

[0062] Step S1033, randomly select several trigger mapping points from the trigger mapping point group and calculate the average trigger mapping point between the trigger mapping points.

[0063] Randomly select several points from the trigger mapping point group and calculate the average value between these points; this average value represents a central point of the color or shape characteristics of the image module and can be used as a reference point for subsequent analysis.

[0064] Step S1034, using the average trigger mapping point as the rotation center, construct a virtual scan line for successive rotational scanning. When it is necessary to parameterize the color characteristics:

[0065] Record the trigger mapping points passed by the scan line during each rotational scanning, denoted as color trigger mapping points, record the color and color parameters corresponding to each color trigger mapping point, calculate the color proportion of the color trigger mapping points of different colors among all color trigger mapping points, and classify the color proportion and color parameters corresponding to different colors into the characteristic factor parameter group.

[0066] The methods for parameterizing the shape characteristics of the image module include:

[0067] Record the opposite boundary trigger mapping points scanned during each rotational scanning, denoted as the boundary trigger mapping point group, calculate the distance between the two boundary trigger mapping points in each boundary trigger mapping point group, sort the distances between different boundary trigger mapping point groups to obtain a boundary distance sequence, and classify the boundary distance sequence into the characteristic factor parameter group.

[0068] In this step, the average trigger mapping point is used as the rotation center to construct a virtual scan line, and the scan is rotated successively. For color features, the trigger mapping points passed by each scan line, i.e., color trigger mapping points, are recorded, and the color and its parameters (such as hue, saturation, etc.) corresponding to each point are recorded; for shape features, the boundary trigger mapping points scanned are recorded, and the distances between these boundary points are calculated; in this way, a distance sequence between boundaries can be obtained, which represents the shape features of the image module.

[0069] In some embodiments disclosed by the present invention, the color parameters include color brightness, color hue, color saturation, and color temperature.

[0070] Step S200: Obtain a set of historical printing patterns, perform feature analysis on the historical printing patterns in the set of historical printing patterns, and determine a number of characteristic factor parameter groups, where the characteristic factor parameter groups are the parameter performances of different characteristic factors in the same printing pattern.

[0071] Collect a series of historical printing patterns, which can come from past successful cases or industry standard designs; then, analyze these patterns to identify and quantify the characteristic factors that affect the printing effect; these characteristic factors may include color combinations, image layouts, text formats, etc., and each factor has its specific performance in the pattern, such as color proportion, image position, etc.

[0072] Step S300: Use natural language processing technology to perform semantic analysis on the content to be printed, determine a number of representative keywords, combine the representative keywords into a representative keyword group, and select the characteristic factor parameter group with the highest adaptation degree based on the representative keyword group in different characteristic factor parameter groups, and record it as the reference characteristic factor parameter group.

[0073] Perform natural language processing on the content to be printed to extract key information and understand its semantic meaning; this involves identifying important keywords and phrases, which reflect the core theme and emotional tendency of the content; then, combine these keywords into a representative keyword group to guide the design decision of the printing layout.

[0074] Among them, the detailed steps of performing semantic analysis using natural language processing technology can be

[0075] Content extraction: First, extract key information from the text content to be printed, which may include titles, subtitles, body text, tags, notes, etc.; this information is the basis for semantic analysis; Word segmentation and tokenization: Segment the text content, that is, split the text into words or phrases and tokenize these words or phrases for subsequent analysis; Keyword identification: Use natural language processing techniques such as TF-IDF (Term Frequency-Inverse Document Frequency), text clustering, or topic modeling (such as LDA) to identify keywords in the text; these keywords are the core of the text content and can represent the main themes and intentions of the text; Semantic analysis: Conduct semantic analysis on the extracted keywords to understand their meanings in the context. This may involve sentiment analysis, entity recognition, relationship extraction, etc. to comprehensively understand the text content; Formation of representative keyword groups: Combine the identified keywords into representative keyword groups. This combination can be based on the importance, frequency of the keywords, or their distribution in the text.

[0076] In some embodiments disclosed by the present invention, the method for selecting the most suitable set of characteristic factor parameters based on representative keyword groups in different sets of characteristic factor parameters includes:

[0077] Step S301, establish a copywriting type table, pair up the copywriting types in the copywriting type table to obtain several copywriting type groups, and determine the correlation degree between the copywriting types in each copywriting type group based on the cross-correlation of keywords.

[0078] Step S302, determine the main copywriting type to which the content to be printed belongs, and use the main copywriting type and the copywriting types whose correlation degree with the main copywriting type is greater than or equal to a preset value as the screening criteria to screen out several sets of characteristic factor parameters.

[0079] Step S303, mark the representative keywords mapped from the representative keywords in the representative keyword group to the corresponding keyword library in the set of characteristic factor parameters as the mapped representative keywords, and determine the adaptation degree between the representative keyword group and the set of characteristic factor parameters based on the keyword proportion of the mapped representative keywords in the representative keyword group.

[0080] Among them, the expression for calculating the adaptation degree is:

[0081]

[0082] Among them, P is the adaptation degree, R i is the correlation degree adjustment coefficient corresponding to the i-th copywriting type whose correlation degree with the main copywriting type is greater than or equal to the preset value, δ iis the proportion of keywords in the keyword library corresponding to the characteristic factor parameter group of the i-th copywriting type that are mapped to representative keywords in the representative keyword group. n is the total number of the main copywriting types and the copywriting types whose degree of relevance to the main copywriting types is greater than or equal to a preset value. b is an adaptation degree adjustment constant.

[0083] Step S400: Based on the reference characteristic factor parameter group, screen and analyze the characteristic label groups of each image template in the image template library, and randomly combine the determined image template blocks to generate several reference printing templates.

[0084] Based on the reference characteristic factor parameter group and the representative keyword group determined in the previous steps, screen the image templates in the image template library; the basis for screening is the matching degree between the characteristic label group of the image template and the reference characteristic factor parameter group; the screened image modules are then randomly combined to generate multiple possible printing templates; these templates can be further evaluated through a certain evaluation mechanism (such as user feedback or an automatic scoring system) to determine the best printing template.

[0085] In some embodiments disclosed in the present invention, the method for screening and analyzing the characteristic label group of each image template in the image template library includes:

[0086] Step S401: Analyze the reference characteristic factor parameter group to determine the color characteristics, shape characteristics, and relative position blocks on the printing template corresponding to different image blocks.

[0087] Step S402: Based on whether the difference amount between color characteristics is within a preset range, perform a first screening on the characteristic label group of the image template, and based on whether the difference amount between shape characteristics is within a preset range, perform a second screening on the characteristic labels of the image template after the first screening.

[0088] Step S403: Based on the difference amount of color characteristics and the difference amount of shape characteristics, determine the degree of coincidence between the reference characteristic factor parameter group and the characteristic label group of the image template, and screen out the characteristic label groups of the image templates whose degree of coincidence is greater than or equal to a preset value.

[0089] In some embodiments disclosed in the present invention, the expression for calculating the degree of coincidence is:

[0090]

[0091] W is the degree of coincidence, K1 is the color weight coefficient, K2 is the shape weight coefficient, μ xLet $\omega(x)$ be the color proportion of the $x$-th color, $\beta(x)$ be the conformity judgment function of the color parameter of the $x$-th color. $\beta(x)$ outputs a specific conformity degree value according to the preset range to which the difference amount of the color parameter belongs. $c$ is the color parameter conformity adjustment constant, $N$ is the total number of color types, $\alpha(q)$ is the conformity judgment function of the distance between the $q$-th boundaries in the image module. $\alpha(q)$ outputs a specific conformity degree according to the preset range to which the difference amount of the distance between the boundaries belongs. $Q$ is the total number of distances between the boundaries used for comparison.

[0092] In some embodiments disclosed by the present invention, the feature tag group of the image plate includes: color features, shape features, relative position blocks on the printing template, copy types, and keyword libraries associated with each copy type.

[0093] In some embodiments disclosed by the present invention, there is also disclosed an intelligent printing layout system based on printed content, including:

[0094] A first module for establishing an image module library. The image module library includes a number of image modules, and each image block is set with a specific feature tag group;

[0095] A second module for obtaining a set of historical printing patterns and performing feature analysis on the historical printing patterns in the set of historical printing patterns to determine a number of feature factor parameter groups. The feature factor parameter groups are the parameter performances of different feature factors in the same printing pattern;

[0096] A third module for using natural language processing technology to perform semantic analysis on the content to be printed, determining a number of representative keywords, combining the representative keywords into a representative keyword group, and selecting the feature factor parameter group with the highest adaptation degree from different feature factor parameter groups based on the representative keyword group, and recording it as a reference feature factor parameter group;

[0097] A fourth module for performing screening analysis on the feature tag group of each image template in the image template library based on the reference feature factor parameter group, and randomly combining the determined image blocks to generate a number of reference printing templates.

[0098] The present invention discloses an intelligent printing layout method and system based on printed content, relating to the technical field of printing layout. Specifically, it discloses establishing an image module library, which includes several image modules, analyzing the features of historical printing patterns in a historical printing pattern set to determine several characteristic factor parameter groups, combining combinations representing keywords into a representative keyword group, and selecting the characteristic factor parameter group with the highest adaptation degree based on the representative keyword group in different characteristic factor parameter groups. Based on the reference characteristic factor parameter group, screening and analyzing the characteristic label groups of each image template in the image template library, and randomly combining the determined image modules to generate several reference printing templates. Through the above technical solutions, the present invention realizes the automatic generation of printing templates, which not only saves labor but also improves the efficiency of constructing printing templates, providing a variety of reference printing templates for printing work.

[0099] As mentioned above, the above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, making equivalent substitutions or changes should be covered within the protection scope of the present invention.

Claims

1. An intelligent printing layout method based on printed content, characterized in that Including: Establish an image module library, which includes several image modules, and each image block is set with a specific feature tag group; Obtain a historical printing pattern set, perform feature analysis on the historical printing patterns in the historical printing pattern set, and determine several feature factor parameter groups, where the feature factor parameter groups are the parameter performances of different feature factors in the same printing pattern; Using natural language processing technology, perform semantic analysis on the content to be printed, determine several representative keywords, combine the representative keywords into a representative keyword group, and select the feature factor parameter group with the highest adaptation degree based on the representative keyword group in different feature factor parameter groups, and record it as the reference feature factor parameter group; Based on the reference feature factor parameter group, perform screening analysis on the feature tag groups of each image template in the image template library, and randomly combine the determined image modules to generate several reference printing templates; The method for selecting the feature factor parameter group with the highest adaptation degree based on the representative keyword group in different feature factor parameter groups includes: Establish a copywriting type table, combine the copywriting types in the copywriting type table in pairs to obtain several copywriting type groups, and determine the correlation degree between the copywriting types in each copywriting type group based on the cross - property of the keywords; Determine the copywriting type to which the content to be printed belongs, and use the copywriting type and the copywriting types with a correlation degree greater than or equal to the preset value as the screening criteria to screen out several feature factor parameter groups; Mark the representative keywords mapped by the representative keywords in the representative keyword group in the corresponding keyword library of the feature factor parameter group as the mapped representative keywords, and determine the adaptation degree between the representative keyword group and the feature factor parameter group based on the keyword proportion of the mapped representative keywords in the representative keyword group; Among them, the expression for calculating the adaptation degree is: Among them, P is the adaptation degree, is the correlation degree adjustment coefficient corresponding to the i-th copywriting type whose correlation degree with the main copywriting type is greater than or equal to the preset value, is the proportion of the keywords represented by the mapped keywords in the keyword library of the characteristic factor parameter group corresponding to the i-th copywriting type in the representative keyword group. n is the total number of copywriting types and the copywriting types whose correlation degree with the copywriting type is greater than or equal to the preset value, and b is the adaptation degree adjustment constant.

2. The intelligent printing layout method based on printed content according to claim 1, wherein, The feature tag group of each image block includes: color feature, shape feature, relative position block on the printing template, copywriting type, and the keyword library associated with each copywriting type.

3. The intelligent printing layout method based on printed content according to claim 1, wherein The method for performing feature analysis on the historical printing patterns in the historical printing pattern set includes: Mark the image modules on the historical printing pattern, and perform color feature, shape feature, and analysis of the location block to which they belong on the marked image modules; Analyze the copywriting within a preset range beside the delimited image module, and determine the position block of the copywriting relative to the image module and the copywriting type; Parametrize the color feature, shape feature, the position block of the image module, and the position block of the copywriting on each historical printing pattern respectively, and combine them with the copywriting type to obtain the feature factor parameter group.

4. An intelligent printing layout method based on printed content according to claim 3, characterized in that, The method for parametrizing the color feature and shape feature of the image module includes: Construct a mapping point array for the historical printing pattern, where the mapping point array includes several mapping points evenly set on the historical printing pattern, and set mapping point coordinates for each mapping point; Trigger records are made for the mapping points in the area delimited for each image module, denoted as trigger mapping points, and the trigger mapping points belonging to the same image module area are associated and combined to obtain a trigger mapping point group; Randomly select several trigger mapping points from the trigger mapping point group, and calculate the average trigger mapping point between the trigger mapping points; Taking the average trigger mapping point as the rotation center, construct a virtual scan line for successive rotational scanning. When it is necessary to parameterize the color feature: Record the trigger mapping points passed by the scan line during each rotational scanning, which are denoted as color trigger mapping points, record the corresponding color and color parameters for each color trigger mapping point, calculate the color proportion of the color trigger mapping points of different colors in all color trigger mapping points, and classify the color proportion and color parameters corresponding to different colors into the feature factor parameter group; When it is necessary to parameterize the shape feature of the image module, the methods include: Record the relative boundary trigger mapping points scanned during each rotational scanning, which are denoted as the boundary trigger mapping point group, calculate the distance between the two boundary trigger mapping points in each boundary trigger mapping point group, sort the distances between different boundary trigger mapping point groups to obtain a boundary distance sequence, and classify the boundary distance sequence into the feature factor parameter group.

5. The intelligent printing layout method based on printed content according to claim 4, characterized in that The color parameters include color brightness, color hue, color saturation, and color temperature.

6. The intelligent printing layout method based on printed content according to claim 1, characterized in that The method for screening and analyzing the feature label group of each image template in the image template library includes: Analyze the reference feature factor parameter group to determine the color features, shape features, and relative position blocks on the printing template corresponding to different image plates; Based on whether the difference amount between color features is within a preset range, perform the first screening on the feature label group of the image template. Based on whether the difference amount between shape features is within a preset range, perform the second screening on the feature labels of the image template after the first screening; Based on the difference amount of color features and the difference amount of shape features, determine the matching degree between the reference feature factor parameter group and the feature label group of the image template, and screen out the feature label groups of the image templates whose matching degree is greater than or equal to the preset value.

7. An intelligent printing layout method based on printed content according to claim 6, characterized in that, The expression for calculating the matching degree is: W is the degree of fit, is the color weight coefficient, is the shape weight coefficient, is the color proportion of the xth color, is the matching judgment function of the color parameter of the xth color, According to the preset range of the difference in color parameters, a specific matching degree value is output, c is the color parameter matching adjustment constant, N is the total number of color types, is the matching judgment function of the qth boundary distance in the image module, According to the preset range to which the difference between the boundary distances belongs, a specific degree of matching is output, and Q is the total number of boundary distances used for comparison.

8. An intelligent printing layout system based on printed content, characterized in that, The intelligent printing layout method for executing any one of claims 1-7 includes: The first module is used to establish an image module library, which includes several image modules, and each image block is set with a specific feature label group; The second module is used to obtain a historical printing pattern set, perform feature analysis on the historical printing patterns in the historical printing pattern set, and determine several feature factor parameter groups, where the feature factor parameter group is the parameter performance of different feature factors in the same printing pattern; The third module is used to use natural language processing technology to perform semantic analysis on the content to be printed, determine several representative keywords, combine the representative keywords into a representative keyword group, and select the feature factor parameter group with the highest adaptation degree from different feature factor parameter groups based on the representative keyword group, and denote it as the reference feature factor parameter group; The fourth module is used to screen and analyze the feature label group of each image template in the image template library based on the reference feature factor parameter group, and randomly combine the determined image blocks to generate several reference printing templates.

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