Reading material typesetting construction method and system based on analysis of excellent children's books

Abstract

The present invention discloses a method and system for constructing a typesetting of reading materials based on the analysis of excellent children's books, and relates to the technical field of generating typesetting strategies for reading materials. First, excellent children's book materials are collected and split into independent content units, and then the text content is identified, semantic keywords are extracted, and a relationship tree is constructed; the image content is identified, image description keywords are extracted, and a mapping relationship is established with the semantic keyword group relationship tree; the scheme also divides and locates the decorative components, and sets decorative component detection points to form a detection point array template, which is associated with the semantic keyword group relationship tree; these elements are combined into an excellent typesetting data reference group, and the reading material type is configured to construct an excellent typesetting data reference library; the current reading material typesetting strategy is evaluated for value to determine the optimal strategy. By systematically analyzing the typesetting of excellent reading materials, the present invention provides a scientific basis and effective guidance for the typesetting design of children's books, which helps to improve the reading experience and appeal of children's books.

Description

Technical Field

[0001] The present invention relates to the technical field of book typesetting strategy generation, and in particular to a book typesetting construction method and system based on analysis of excellent children's books. Background Art

[0002] In the field of children's reading promotion and publishing, the layout design of children's books must closely align with the laws of children's cognitive development. Children of different ages exhibit significant differences in text recognition, image comprehension, and color perception, requiring that layout strategies be carefully designed based on the cognitive characteristics of the target age group. As a key factor in enhancing the reading experience and stimulating children's interest in reading, the importance of book layout strategies is becoming increasingly prominent. However, faced with the vast amount of children's book resources, how to effectively utilize past excellent book examples and select layout strategies that both align with children's cognitive characteristics and stimulate reading interest has become a technical challenge that needs to be solved urgently. Summary of the Invention

[0003] The purpose of the present invention is to provide a reading material typesetting construction method and system capable of performing preferential evaluation on the reading material typesetting.

[0004] The present invention discloses a method for constructing a typesetting of reading materials based on analysis of excellent children's books, comprising:

[0005] Acquire a number of excellent children's book materials, and separate the excellent children's book materials into independent content to obtain a number of independent content units;

[0006] Identify the text content of each independent content unit to obtain the paragraph content, perform semantic recognition on the paragraph content, determine several semantic keywords, and combine the semantic keywords according to their original order to obtain semantic keyword groups. Based on the sentences to which different semantic keywords belong, group and label the semantic keyword groups to obtain several semantic sub-keyword groups. Based on the combination of paragraph content, construct a semantic keyword group relationship tree;

[0007] Recognize the image content of each independent content unit, output the recognition result in the form of image text description to obtain image description information, combine the image description keywords in the image description information to obtain image description keyword groups, and establish a mapping relationship between the image description keyword groups and the semantic keyword group relationship tree based on the correspondence between the image content and the text content;

[0008] Divide and locate the decoration components in the independent content unit, and evenly set a number of decoration component detection points for each decoration component, each decoration component detection point is set with a position coordinate and a color parameter, and all the decoration component detection points are combined according to the original position to form a decoration component detection point array template, and establish an association relationship between the decoration component detection point array template and the semantic keyword group relationship tree;

[0009] The combination of the semantic keyword group relationship tree, the image description keyword and the decoration component exploration point array template is recorded as an excellent typesetting data reference group, and the excellent typesetting data reference group is configured with a reading type to obtain an excellent typesetting data reference library;

[0010] Use the excellent typesetting data reference library to conduct a value assessment on several currently constructed reading material typesetting strategies, and determine the optimal reading material typesetting strategy based on the value assessment.

[0011] In some embodiments disclosed herein, a method for constructing a semantic keyword phrase relationship tree includes:

[0012] According to the original order of the paragraph content, a paragraph content sequence is constructed, and based on the paragraph content sequence, a trunk of a semantic keyword group key tree is constructed, wherein each semantic keyword group corresponds to a tree node;

[0013] Analyze the contents of each paragraph and its corresponding contents in other paragraphs, and extend a tree branch at the corresponding tree node to form a semantic keyword relationship tree.

[0014] In some embodiments disclosed herein, a method for performing semantic recognition on a text content and determining a number of semantic keywords includes:

[0015] A focus keyword library is set for excellent children's books. The focus keyword library includes several types of focus keyword sets, and each type of focus keyword set includes several focus keywords;

[0016] Preprocess the text content, including word segmentation, stop word removal, and part-of-speech tagging, to obtain several candidate keywords, and perform attention level analysis on the candidate keywords, including determining the frequency of occurrence and relative relationship characteristics of the candidate keywords, and based on the frequency of occurrence and relative relationship characteristics, determine the attention level of the candidate keywords, and screen out several candidate keywords based on the attention level of the candidate keywords;

[0017] Based on the pre-marked type of the text content, the focus keyword set called in the focus keyword library is determined, and several alternative keywords are brought into the focus keyword set for comparison. If there is a mapped focus keyword in the focus keyword set, the corresponding alternative keyword is identified as a semantic keyword.

[0018] In some embodiments disclosed herein, the method for determining the attention level of candidate keywords includes:

[0019] Determine the relative position of the candidate keyword in the sentence, and based on the determined relative position, configure an initial attention parameter for the candidate keyword, and modify the initial attention parameter based on the frequency of occurrence of the candidate keyword to obtain the attention level;

[0020] The expression for calculating the degree of attention is:

[0021] ;

[0022] in, For the level of attention, is the attention conversion coefficient, is the initial focus parameter, is the frequency of occurrence of alternative keywords, is the frequency impact adjustment coefficient, Adjust constants for frequency effects;

[0023] The method for determining the relative position of the candidate keywords in the sentence includes performing syntactic structure analysis on the sentence, identifying the subject, predicate and object components, and determining the position of the candidate keywords in the syntactic structure.

[0024] In some embodiments disclosed herein, a method for identifying image content of each independent content unit includes:

[0025] Divide the content units in the image content, including objects, people, animals and scenes in the image content, and determine the directional relationship between the objects, people and animals;

[0026] Constructing an image description information group based on the image content, the image description information group including a plurality of object description units, a plurality of person description units, a plurality of animal description units, and a scene description unit, wherein the object description unit includes a plurality of object description keywords, the person description unit includes a plurality of person description keywords, the animal description unit includes a plurality of animal description keywords, and the scene description unit includes a plurality of scene description keywords;

[0027] Based on the pointing relationship between objects, people and animals, a unit relationship sequence is constructed for the corresponding object description units, person description units and animal description units. The unit relationship sequence includes other object description units, person description units or animal description units with pointing relationships.

[0028] In some embodiments disclosed herein, a method for evaluating the value of a currently constructed reading material typesetting strategy using an excellent typesetting data reference library includes:

[0029] Analyze the layout strategy of the reading material, identify the semantic keywords in the strategy, and construct the semantic sub-keyword groups in the strategy based on the semantic keywords in the strategy;

[0030] Analyze the typesetting strategy of the reading material, determine image description information of the image content in the strategy, determine the detection point array template of the decorative component in the strategy, and construct an image description information group in the strategy based on the image description information;

[0031] The semantic sub-keyword group in the strategy, the image description information group in the strategy, and the decorative component detection point array template in the strategy corresponding to the reading material typesetting strategy form a typesetting data reference group in the strategy;

[0032] Several retrieval factors are determined for the typesetting data reference group in the strategy, and based on the retrieval factors, several excellent typesetting data reference groups are retrieved from the excellent typesetting data reference library. The retrieved excellent typesetting data reference groups are compared with the typesetting data reference group in the strategy, and based on the comparison results, the value assessment value of the currently constructed reading material typesetting strategy is determined.

[0033] In some embodiments disclosed herein, a method for determining a plurality of search factors in a layout data reference group in a strategy includes:

[0034] Randomly selecting a number of first initial retrieval factors from the semantic sub-keyword group in the strategy, randomly selecting a number of second initial retrieval factors from the image description information group in the strategy, and randomly selecting a number of third initial retrieval factors from the decoration component probe point array template in the strategy;

[0035] The first initial retrieval factor, the second initial retrieval factor and the third initial retrieval factor are respectively substituted into the excellent typesetting data reference library, and the reference group mapping number of the excellent typesetting data reference group mapped by each initial retrieval factor is calculated respectively. If the reference group mapping number is greater than or equal to the preset value, the corresponding initial retrieval factor is retained, and the retained initial retrieval factor is identified as the retrieval factor for the final search in the excellent data reference library.

[0036] In some embodiments disclosed herein, a method for comparing the retrieved excellent typesetting data reference group with the typesetting data reference group in the strategy includes:

[0037] A first mapping parameter mapped in the semantic keyword group relationship tree of the semantic sub-keyword group relative to the excellent typesetting data reference group in the analysis strategy, a second mapping parameter mapped in the image description keyword group relative to the image description keyword group of the excellent typesetting data reference group in the analysis strategy, and a third mapping parameter mapped in the decoration component detection point array template relative to the decoration component detection point array template of the excellent typesetting data reference group in the analysis strategy;

[0038] Determining the degree of equivalence between each retrieved excellent typesetting data reference group and the typesetting data reference group in the strategy based on the first mapping parameter, the second mapping parameter, and the third mapping parameter, and determining the value evaluation value of the reading material typesetting strategy based on the corresponding equivalence degrees of all retrieved excellent typesetting data reference groups;

[0039] The expression for calculating the degree of equivalence between the excellent typesetting data reference group and the typesetting data reference group in the strategy is:

[0040] ;

[0041] in, To the same extent, is the first mapping parameter weight adjustment coefficient, is the second mapping parameter weight adjustment coefficient, is the third mapping parameter weight adjustment coefficient, The semantic sub-keyword group in the strategy is mapped to the calculation function, which is used to determine whether the semantic sub-keyword group in the i1th strategy is mapped to the semantic keyword group relationship tree. If the mapping is determined, then Output 1, otherwise output 0, wherein the method of determining whether the semantic sub-keyword group in the policy is mapped to the semantic keyword group relationship tree includes judging whether the proportion of the mapped semantic sub-keywords in the policy in the semantic sub-keyword group is greater than or equal to a preset value, and if it is greater than or equal to the preset value, then it is determined that the semantic sub-keyword group in the policy is mapped to the semantic keyword group relationship tree, is the semantic sub-keyword group mapping impact adjustment coefficient in the strategy, is the semantic sub-keyword group mapping impact adjustment constant in the strategy, n1 is the number of semantic sub-keyword groups in the strategy, The image description keyword in the strategy is mapped to the calculation function, which is used to determine whether the image description keyword in the i2th strategy is mapped to the image description keyword group of the excellent typesetting data reference group. If the mapping is determined, then Output 1, otherwise output 0. The image description keyword mapping impact adjustment coefficient in the strategy, is the mapping adjustment constant of the image description keyword group in the strategy, n2 is the number of image description keywords in the strategy image description keyword group, The mapping calculation function for the decoration component probe point array template in the strategy is used to determine whether the decoration component probe point in the i3th strategy is mapped to the decoration component probe point array template of the excellent typesetting data reference group. If the mapping is determined, then Output 1, otherwise output 0. The adjustment coefficient for the detection point mapping effect of the decoration component in the strategy, Adjust the constant for the detection point mapping effect of the decoration component in the strategy, The number of probe points in the probe point array template of the decoration component in the strategy.

[0042] In some embodiments disclosed herein, a method for determining the value evaluation value of a reading material typesetting strategy based on the corresponding equivalent levels of all retrieved excellent typesetting data reference groups is disclosed, including:

[0043] Determine the number of first reference groups of the retrieved excellent typesetting data reference groups, and judge whether the degree of equality corresponding to each excellent typesetting data reference group is greater than or equal to a preset value, and if so, mark the reading material typesetting strategy once;

[0044] Calculate the reference group quantity ratio of the second reference group quantity to the first reference group quantity of the marked reading material typesetting strategy, analyze the preset reference group quantity ratio interval to which the reference group quantity ratio belongs, and identify the value assessment value corresponding to the preset reference group quantity ratio interval as the value assessment value corresponding to the reading material typesetting strategy.

[0045] In some embodiments disclosed in the present invention, a reading material typesetting and construction system based on analysis of excellent children's books is also disclosed, including:

[0046] The first module is used to obtain a number of excellent children's books and split the excellent children's books into independent content units;

[0047] The second module is used to identify the text content of each independent content unit to obtain the paragraph content, and perform semantic recognition on the paragraph content to determine a number of semantic keywords. The semantic keywords are combined according to the original order to obtain semantic keyword groups. The semantic keyword groups are grouped and labeled based on the sentences to which different semantic keywords belong to obtain a number of semantic sub-keyword groups. Based on the combination of paragraph content, a semantic keyword group relationship tree is constructed;

[0048] The third module is used to identify the image content of each independent content unit, output the identification result as an image text description to obtain image description information, combine the image description keywords in the image description information to obtain image description keyword groups, and establish a mapping relationship between the image description keyword groups and the semantic keyword group relationship tree based on the correspondence between the image content and the text content;

[0049] The fourth module is used to divide and locate the decoration components in the independent content units, and evenly set a number of decoration component detection points for each decoration component, each decoration component detection point is set with a position coordinate and color parameters, and all the decoration component detection points are combined according to their original positions to form a decoration component detection point array template, and an association relationship is established between the decoration component detection point array template and the semantic keyword group relationship tree;

[0050] The fifth module is used to record the combination of the semantic keyword group relationship tree, the image description keyword and the decoration component exploration point array template as an excellent typesetting data reference group, and configure the reading type for the excellent typesetting data reference group to obtain an excellent typesetting data reference library;

[0051] The sixth module is used to use the excellent typesetting data reference library to conduct value assessment on several currently constructed reading material typesetting strategies, and determine the optimal reading material typesetting strategy based on the value assessment value.

[0052] The present invention discloses a method and system for constructing a typesetting of reading materials based on the analysis of excellent children's books, and relates to the technical field of generating typesetting strategies for reading materials. First, excellent children's book materials are collected and split into independent content units, and then the text content is identified, semantic keywords are extracted, and a relationship tree is constructed; the image content is identified, image description keywords are extracted, and a mapping relationship is established with the semantic keyword group relationship tree; the scheme also divides and locates the decorative components, and sets decorative component detection points to form a detection point array template, which is associated with the semantic keyword group relationship tree; these elements are combined into an excellent typesetting data reference group, and the reading material type is configured to construct an excellent typesetting data reference library; the current reading material typesetting strategy is evaluated for value to determine the optimal strategy. By systematically analyzing the typesetting of excellent reading materials, the present invention provides a scientific basis and effective guidance for the typesetting design of children's books, which helps to improve the reading experience and appeal of children's books.

[0053] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a diagram of the steps of a method for constructing a reading material typesetting based on analysis of excellent children's books disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0056] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solutions of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should have the common meanings understood by those skilled in the art of the present invention.

[0057] Example:

[0058] The present invention discloses a method for constructing a typesetting of reading materials based on the analysis of excellent children's books. Figure 1,include:

[0059] Step S100: Acquire a number of excellent children's book materials, and separate the excellent children's book materials into independent content to obtain a number of independent content units.

[0060] The core of this step is to collect and organize excellent children's books as a foundation for subsequent analysis. To gain a deeper understanding of the typographical strategies within these books, we first break each book down into several independent content units. These units can be story chapters, knowledge points, illustrations, and other essential elements of a book. This breakdown allows for a more detailed analysis of the typographical elements within each unit, such as text, images, and decorative elements, providing clear analysis targets for subsequent steps.

[0061] In step S200, the text content of each independent content unit is identified to obtain the paragraph content, and the paragraph content is semantically identified to determine a number of semantic keywords, and the semantic keywords are combined in the original order to obtain semantic keyword groups, and the semantic keyword groups are grouped and marked based on the sentences to which different semantic keywords belong to obtain a number of semantic sub-keyword groups. In view of the fact that the paragraph content can be combined, a semantic keyword group relationship tree is constructed.

[0062] In this step, the text of each individual content unit is first identified to extract the paragraph content. Subsequently, semantic recognition technology is used to conduct in-depth analysis of these paragraphs to identify several semantic keywords. These keywords are the core and essence of the paragraph content. Next, these keywords are combined according to their original sequence to form semantic keyword groups. Based on the sentences to which different keywords belong, the keyword groups are further grouped and labeled to obtain several semantic sub-keyword groups. Finally, based on the logical relationships between the paragraph content, a semantic keyword group relationship tree is constructed to intuitively display the hierarchical structure and association relationships of the paragraph content.

[0063] In some embodiments disclosed herein, a method for constructing a semantic keyword phrase relationship tree includes:

[0064] Step S201 : constructing a paragraph content sequence according to the original order of the paragraph content, and constructing a trunk of a semantic keyword group key tree based on the paragraph content sequence, wherein each semantic keyword group corresponds to a tree node.

[0065] In some embodiments disclosed herein, the first step in constructing a semantic keyword relationship tree is to process the sequential nature of the textual content. Based on the original order of the textual content, i.e., the order in which it appears in the reading material, a textual content sequence is constructed. This sequence provides a clear timeline or logical line, helping to understand the connections and hierarchies between textual segments.

[0066] Next, we construct the trunk of the semantic keyword tree based on this passage's content sequence. During this process, we treat each semantic keyword as a separate tree node, and arrange these nodes on the trunk sequentially according to the passage's content. This trunk reflects the basic flow and key points of the passage's content, providing a solid foundation for subsequent analysis and presentation.

[0067] In step S202 , each paragraph content is analyzed for other paragraph contents that can be paralleled, and a tree branch is extended from the corresponding tree node to form a semantic keyword relationship tree.

[0068] After constructing the main trunk of the semantic keyword tree, we need to further analyze the parallel relationships between paragraphs. Parallel relationships refer to the relationships between paragraphs that are independent in content, theme, or logic, but together form a complete story or knowledge point. By identifying these parallel relationships, we can gain a deeper understanding of the internal connections and hierarchical structure between paragraphs.

[0069] After identifying parallel relationships, one or more tree branches extend from the corresponding tree nodes to represent these parallel relationships. Each branch represents a parallel text content or semantic keyword group. These branches connect to the nodes on the trunk, forming a semantic keyword group relationship tree. This relationship tree not only reflects the sequential nature of the text content but also reveals the parallel and hierarchical relationships between them, providing strong support for the subsequent analysis and optimization of the reading material layout strategy.

[0070] In some embodiments disclosed herein, a method for performing semantic recognition on a text content and determining a number of semantic keywords includes:

[0071] In step S203, a focus keyword library is set for excellent children's books. The focus keyword library includes several types of focus keyword sets, and each type of focus keyword set includes several focus keywords.

[0072] In some embodiments disclosed in the present invention, in order to more accurately identify semantic keywords in the content of a passage, a focus keyword library is first set for excellent children's books. This keyword library is pre-constructed and contains multiple types of focus keyword sets. Each type of keyword set is carefully selected and organized for a specific theme, field or content type. For example, for story-telling books, the focus keyword set may include character names, place names, event keywords, etc.; for popular science books, it may include keywords such as scientific terms and concept explanations. Such a setting can ensure that in subsequent analysis, the core and key information in the passage content can be captured in a targeted manner.

[0073] In step S204, the text content is preprocessed, including word segmentation, removal of stop words and part-of-speech tagging, to obtain several alternative keywords, and the attention level of the alternative keywords is analyzed, including determining the frequency of occurrence and relative relationship characteristics of the alternative keywords, and based on the frequency of occurrence and relative relationship characteristics, determining the attention level of the alternative keywords, and based on the attention level of the alternative keywords, screening out several alternative keywords.

[0074] Next, the text content is preprocessed to convert the original text into a form more suitable for analysis. The preprocessing steps include word segmentation, stop word removal, and part-of-speech tagging. Through these operations, a series of alternative keywords can be obtained. Then, the attention level of these alternative keywords is analyzed, mainly examining their frequency of occurrence and relative relationship characteristics. The frequency of occurrence reflects the importance and significance of the keywords in the text, while the relative relationship characteristics help understand the association and hierarchy between keywords. Based on these analysis results, the attention level of each alternative keyword can be determined, and a number of alternative keywords with high attention levels can be screened as candidates for subsequent analysis.

[0075] In step S205, based on the pre-marked type of the text content, the focus keyword set called in the focus keyword library is determined, and several candidate keywords are brought into the focus keyword set for comparison. If there is a mapped focus keyword in the focus keyword set, the corresponding candidate keyword is identified as a semantic keyword.

[0076] After completing the screening of candidate keywords, the corresponding focus keyword set is retrieved from the focus keyword library based on the pre-marked type of the passage content. This step ensures that the analysis closely matches the actual characteristics and needs of the passage content. The screened candidate keywords are then brought into the focus keyword set for comparison. If a matching focus keyword is found in the focus keyword set, the candidate keyword is identified as a semantic keyword. This identification process not only considers the characteristics and importance of the keywords themselves, but also fully considers their relevance and representativeness within specific types and topics, thereby ensuring the accuracy and effectiveness of semantic keyword identification.

[0077] In some embodiments disclosed herein, the method for determining the attention level of candidate keywords includes:

[0078] Step S2041, determine the relative position of the candidate keyword in the sentence, and configure an initial attention parameter for the candidate keyword based on the determined relative position, and modify the initial attention parameter based on the occurrence frequency of the candidate keyword to obtain the attention level.

[0079] The expression for calculating the degree of attention is:

[0080] .

[0081] in, For the level of attention, is the attention conversion coefficient, is the initial focus parameter, is the frequency of occurrence of alternative keywords, is the frequency impact adjustment coefficient, Adjust constants for frequency effects;

[0082] The method for determining the relative position of the candidate keywords in the sentence includes performing syntactic structure analysis on the sentence, identifying the subject, predicate and object components, and determining the position of the candidate keywords in the syntactic structure.

[0083] Step S300: Identify the image content of each independent content unit, output the identification result in the form of image text description to obtain image description information, combine the image description keywords in the image description information to obtain image description keyword groups, and establish a mapping relationship between the image description keyword groups and the semantic keyword group relationship tree based on the correspondence between the image content and the text content.

[0084] This step focuses on the identification and analysis of image content. For each image in an independent content unit, image recognition technology is used to convert it into an image text description, thereby extracting the key information in the image. Next, the keywords in these image descriptions are combined to form image description keyword groups. In order to deeply understand the connection between image and text content, a mapping relationship between image description keyword groups and semantic keyword group relationship trees is established based on the correspondence between image content and text content. This step helps to grasp the layout and presentation of images in typesetting, and how they echo the text content.

[0085] In some embodiments disclosed herein, a method for identifying image content of each independent content unit includes:

[0086] Step S301 : dividing the content units in the image content into objects, people, animals and scenes in the image content, and determining the directional relationships among the objects, people and animals.

[0087] When identifying the image content of each independent content unit, the image is first carefully segmented to identify and distinguish the various content units within the image. These units may include objects, people, animals, and scenes. The purpose of this step is to break down complex image content into basic elements that are easier to understand and analyze. Next, the interrelationships between these content units are analyzed in depth, especially the directional relationships between objects, people, and animals, such as who is using what object and the interactions between people and animals. These directional relationships are crucial for understanding the story lines, emotional expression, and visual focus of the image content.

[0088] Step S302, constructing an image description information group based on the image content, the image description information group includes a number of object description units, a number of character description units, a number of animal description units and a scene description unit, wherein the object description unit includes a number of object description keywords, the character description unit includes a number of character description keywords, the animal description unit includes a number of animal description keywords, and the scene description unit includes a number of scene description keywords.

[0089] After completing the division of image content units, we begin constructing the image description information group. This information group provides a comprehensive and detailed description of the image content, including several object description units, person description units, animal description units, and scene description units. Each description unit contains a series of descriptive keywords, which accurately summarize the characteristics, attributes, or status of the corresponding content unit. By constructing such a description information group, we can more systematically understand and express the image content, providing a rich information foundation for subsequent analysis and presentation.

[0090] Step S303: Based on the pointing relationships among objects, people and animals, a unit relationship sequence is constructed for the corresponding object description units, person description units and animal description units. The unit relationship sequence includes other object description units, person description units or animal description units having pointing relationships.

[0091] Finally, based on the pointing relationships between objects, people, and animals determined in step S301, the corresponding description units are arranged in order to construct a unit relationship sequence. This sequence not only reflects the association and interaction between the various elements in the image content, but also reveals the hierarchy and order between them. For example, a person description unit may point to an object description unit, indicating that the person is using the object; and an animal description unit may follow a person description unit, indicating that the animal is part of or the result of the person's action. By constructing such a unit relationship sequence, the story clues and emotional expressions in the image content can be presented more clearly, providing strong support for the typesetting and presentation of reading materials.

[0092] In step S400, the decorative components in the independent content unit are divided and positioned, and a number of decorative component probe points are evenly set for each decorative component. Each decorative component probe point is set with position coordinates and color parameters. All decorative component probe points are combined according to their original positions to form a decorative component probe point array template, and an association relationship is established between the decorative component probe point array template and the semantic keyword group relationship tree.

[0093] In this step, we focus on decorative components within the material, such as borders, backgrounds, and icons. First, we divide and locate these decorative components, clarifying their position and function within the layout. Next, we evenly assign several decorative component detection points to each decorative component, assigning position coordinates and color parameters to each detection point. These detection points form a detection point array template for the decorative component, which comprehensively reflects its morphological and color characteristics. Finally, we associate the detection point array template with the semantic keyword group relationship tree, allowing for comprehensive consideration of the coordination and integrity of the decorative component with the text and image content in subsequent analysis.

[0094] Step S500: record the combination of the semantic keyword group relationship tree, image description keywords and decoration component detection point array template as an excellent typesetting data reference group, and configure the reading type for the excellent typesetting data reference group to obtain an excellent typesetting data reference library.

[0095] After the previous steps, the typesetting elements in excellent children's books have been comprehensively analyzed and extracted. In this step, the semantic keyword group relationship tree, image description keywords, and decorative component exploration point array templates are combined to form an excellent typesetting data reference group. These data reference groups are a concentrated embodiment of the typesetting strategy of excellent reading materials. Next, each data reference group is configured with a corresponding reading material type, such as story, popular science, picture book, etc., so that it can be matched and referenced more accurately in subsequent applications. Finally, all excellent typesetting data reference groups are combined to construct an excellent typesetting data reference library, which provides rich data support for the subsequent evaluation and optimization of reading material typesetting strategies.

[0096] Step S600: Use an excellent typesetting data reference library to perform value evaluation on several currently constructed reading material typesetting strategies, and determine the optimal reading material typesetting strategy based on the value evaluation value.

[0097] In this step, we leverage a library of excellent typography data to evaluate the value of several currently developed layout strategies. Through comparison and analysis, we assess the pros and cons of each strategy in terms of content presentation, visual effects, and reading experience, and assign corresponding value assessments. Finally, based on these assessments, we determine the optimal layout strategy for children's books, providing scientific and effective guidance for the publication and promotion of children's books.

[0098] In some embodiments disclosed herein, a method for evaluating the value of a currently constructed reading material typesetting strategy using an excellent typesetting data reference library includes:

[0099] Step S601 : Analyze the typesetting strategy of the reading material, determine the semantic keywords in the strategy, and construct the semantic sub-keyword groups in the strategy based on the semantic keywords in the strategy.

[0100] The initial stage of evaluating the value of a currently constructed book layout strategy requires an in-depth analysis of the strategy. The core of this step is to identify and extract the semantic keywords within the strategy. These keywords are the core elements of the book's content and are crucial for understanding the book's theme, plot, and key points. After extracting the semantic keywords, these keywords are further combined according to their inherent logical relationships and hierarchical structure to form semantic sub-keyword groups within the strategy. This step aims to simplify the complex content of the book into a form that is easier to analyze and compare, providing a clear foundation for subsequent value assessment.

[0101] Step S602 : Analyze the reading material typesetting strategy, determine image description information of the image content in the strategy, determine the decorative component detection point array template in the strategy, and construct an image description information group in the strategy based on the image description information.

[0102] Next, we analyze the layout strategy for the publication, focusing on the image content and decorative components within the strategy. For image content, we extract and organize image description information, including key elements such as objects, people, animals, and scenes within the image, to form the image description information groups within the strategy. These information groups intuitively reflect the role and effect of images in the publication layout and are crucial for evaluating the visual appeal and information delivery capabilities of the publication. We also identify the detection point array templates for the decorative components within the strategy. These templates describe the position, form, and color characteristics of the decorative components within the layout, providing a crucial basis for evaluating the aesthetics and overall integrity of the publication layout.

[0103] Step S603 : forming a strategy layout data reference group by combining the strategy semantic sub-keyword group, the strategy image description information group, and the strategy decoration component detection point array template corresponding to the reading material layout strategy.

[0104] After analyzing semantic keywords, image descriptions, and decorative components, these elements were integrated into a typographic data reference group within the strategy. This reference group provides a comprehensive and detailed description of the publication's typographic strategy, encompassing the core elements of the content, key visual features, and detailed information about the typographic design. By constructing this reference group, we can more systematically understand and evaluate the overall effectiveness and value of the publication's typographic strategy.

[0105] In step S604, several retrieval factors are determined for the typesetting data reference group in the strategy, and based on the retrieval factors, several excellent typesetting data reference groups are retrieved from the excellent typesetting data reference library, and the retrieved excellent typesetting data reference groups are compared with the typesetting data reference group in the strategy, and based on the comparison results, the value assessment value of the currently constructed reading material typesetting strategy is determined.

[0106] Finally, based on the characteristics and requirements of the typesetting data reference group in the strategy, several retrieval factors are determined. These retrieval factors are the basis for searching for similar or related typesetting data in the excellent typesetting data reference library. Through retrieval, one or more groups of excellent typesetting data reference groups similar to the currently constructed reading material typesetting strategy can be found. Next, these retrieved excellent typesetting data reference groups are compared and analyzed in detail with the typesetting data reference groups in the strategy to evaluate their pros and cons in terms of content presentation, visual effects, reading experience, etc. Finally, based on these comparison results, the value assessment value of the currently constructed reading material typesetting strategy can be determined. This value reflects the relative position and level of the strategy in excellent typesetting practices, providing clear guidance and basis for subsequent optimization and improvement.

[0107] In some embodiments disclosed herein, a method for determining a plurality of search factors in a layout data reference group in a strategy includes:

[0108] Step S6041: randomly select a number of first initial retrieval factors from the semantic sub-keyword group in the strategy, randomly select a number of second initial retrieval factors from the image description information group in the strategy, and randomly select a number of third initial retrieval factors from the decoration component detection point array template in the strategy.

[0109] The process of determining a number of retrieval factors within the strategy's typographic data reference set first involves randomly selecting initial retrieval factors. This step aims to randomly select a certain number of keywords, descriptions, or probe point features from the strategy's semantic subkeyword group, image description information group, and decorative component probe point array template as initial retrieval factors. These initial retrieval factors will serve as the starting point for subsequent searches for similar or related typographic data within the excellent typographic data reference library.

[0110] Specifically, several first initial retrieval factors are randomly selected from the semantic subkeyword groups in the strategy. These factors may be core words or phrases in the content of the reading material and are crucial for understanding the theme and plot. Simultaneously, several second initial retrieval factors are randomly selected from the image description information group in the strategy. These factors describe key elements or features in the image, such as objects, people, animals, or scenes. Furthermore, several third initial retrieval factors are randomly selected from the decorative component exploration point array template in the strategy. These factors represent specific characteristics of decorative components in the layout, such as position, shape, or color.

[0111] Step S6042: Substitute the first initial retrieval factor, the second initial retrieval factor, and the third initial retrieval factor into the excellent typesetting data reference library respectively, and calculate the reference group mapping number of the excellent typesetting data reference group mapped by each initial retrieval factor respectively. If the reference group mapping number is greater than or equal to the preset value, retain the corresponding initial retrieval factor, and identify the retained initial retrieval factor as the retrieval factor for the final search in the excellent data reference library.

[0112] Next, the selected initial search factors need to be verified to determine whether they are suitable as a basis for searching in the excellent typesetting data reference library. The core of this step is to calculate the number of reference group mappings of excellent typesetting data reference groups mapped by each initial search factor in the excellent typesetting data reference library. If the number of reference group mappings of an initial search factor is greater than or equal to the preset value (this preset value can be set according to actual conditions, such as the scale, diversity or search requirements of the typesetting data), it means that this search factor has a certain degree of representativeness and universality in the excellent typesetting data, and can help find similar or related typesetting data. Therefore, this initial search factor is retained as a valid search factor.

[0113] Through this step of screening and verification, a set of representative and effective search factors can be identified. These factors will serve as the basis for subsequent searches for similar or related typographic data in excellent typographic data reference libraries. This method can ensure a more accurate and efficient search process, thereby finding more excellent typographic data reference groups that are similar or relevant to the currently constructed reading material typographic strategy, providing strong support for the value assessment and optimization of the strategy.

[0114] In some embodiments disclosed herein, a method for comparing the retrieved excellent typesetting data reference group with the typesetting data reference group in the strategy includes:

[0115] Step S6043, analyzing the first mapping parameter mapped in the semantic sub-keyword group relationship tree relative to the excellent typesetting data reference group in the strategy, analyzing the second mapping parameter of the image description keyword group relative to the image description keyword group of the excellent typesetting data reference group in the strategy, analyzing the third mapping parameter of the decorative component probe point array template relative to the decorative component probe point array template of the excellent typesetting data reference group in the strategy.

[0116] Step S6044, based on the first mapping parameter, the second mapping parameter and the third mapping parameter, determine the degree of equivalence between each retrieved excellent typesetting data reference group and the typesetting data reference group in the strategy, and based on the corresponding equivalence of all retrieved excellent typesetting data reference groups, determine the value assessment value of the reading material typesetting strategy.

[0117] The expression for calculating the degree of equivalence between the excellent typesetting data reference group and the typesetting data reference group in the strategy is:

[0118] .

[0119] in, To the same extent, is the first mapping parameter weight adjustment coefficient, is the second mapping parameter weight adjustment coefficient, is the third mapping parameter weight adjustment coefficient, The semantic sub-keyword group in the strategy is mapped to the calculation function, which is used to determine whether the semantic sub-keyword group in the i1th strategy is mapped to the semantic keyword group relationship tree. If the mapping is determined, then Output 1, otherwise output 0, wherein the method of determining whether the semantic sub-keyword group in the policy is mapped to the semantic keyword group relationship tree includes judging whether the proportion of the mapped semantic sub-keywords in the policy in the semantic sub-keyword group is greater than or equal to a preset value, and if it is greater than or equal to the preset value, then it is determined that the semantic sub-keyword group in the policy is mapped to the semantic keyword group relationship tree, is the semantic sub-keyword group mapping impact adjustment coefficient in the strategy, is the semantic sub-keyword group mapping impact adjustment constant in the strategy, n1 is the number of semantic sub-keyword groups in the strategy, The image description keyword in the strategy is mapped to the calculation function, which is used to determine whether the image description keyword in the i2th strategy is mapped to the image description keyword group of the excellent typesetting data reference group. If the mapping is determined, then Output 1, otherwise output 0. The image description keyword mapping impact adjustment coefficient in the strategy, is the mapping adjustment constant of the image description keyword group in the strategy, n2 is the number of image description keywords in the strategy image description keyword group, The mapping calculation function for the decoration component probe point array template in the strategy is used to determine whether the decoration component probe point in the i3th strategy is mapped to the decoration component probe point array template of the excellent typesetting data reference group. If the mapping is determined, then Output 1, otherwise output 0. The adjustment coefficient for the detection point mapping effect of the decoration component in the strategy, Adjust the constant for the detection point mapping effect of the decoration component in the strategy, The number of probe points in the probe point array template of the decoration component in the strategy.

[0120] In some embodiments disclosed herein, a method for determining the value evaluation value of a reading material typesetting strategy based on the corresponding equivalent levels of all retrieved excellent typesetting data reference groups is disclosed, including:

[0121] Step S60441, determine the first reference group number of the retrieved excellent typesetting data reference group, and judge whether the degree of equivalence corresponding to each excellent typesetting data reference group is greater than or equal to a preset value. If it is greater than or equal to the preset value, mark the reading material typesetting strategy once.

[0122] Step S60442, calculate the reference group quantity ratio of the second reference group quantity of the marked reading material typesetting strategy to the first reference group quantity, analyze the preset reference group quantity ratio interval to which the reference group quantity ratio belongs, and identify the value assessment value corresponding to the preset reference group quantity ratio interval as the value assessment value corresponding to the reading material typesetting strategy.

[0123] In some embodiments disclosed in the present invention, a reading material typesetting and construction system based on analysis of excellent children's books is also disclosed, including:

[0124] The first module is used to obtain a number of excellent children's reading materials and split the excellent children's reading materials into independent content to obtain a number of independent content units.

[0125] The second module is used to identify the text content of each independent content unit to obtain the paragraph content, and perform semantic recognition on the paragraph content to determine a number of semantic keywords, and combine the semantic keywords in the original order to obtain semantic keyword groups, and group and mark the semantic keyword groups based on the sentences to which different semantic keywords belong to obtain a number of semantic sub-keyword groups. In view of the fact that the paragraph content can be combined, a semantic keyword group relationship tree is constructed.

[0126] The third module is used to identify the image content of each independent content unit, output the identification result in the form of image text description to obtain image description information, combine the image description keywords in the image description information to obtain image description keyword groups, and establish a mapping relationship between the image description keyword groups and the semantic keyword group relationship tree based on the correspondence between the image content and the text content.

[0127] The fourth module is used to divide and locate the decoration components in the independent content units, and evenly set a number of decoration component detection points for each decoration component, each decoration component detection point is set with a position coordinate and color parameters, and all the decoration component detection points are combined according to their original positions to form a decoration component detection point array template, and an association relationship is established between the decoration component detection point array template and the semantic keyword group relationship tree;

[0128] The fifth module is used to record the combination of semantic keyword group relationship tree, image description keyword and decoration component exploration point array template as an excellent typesetting data reference group, and configure the reading type for the excellent typesetting data reference group to obtain an excellent typesetting data reference library.

[0129] The sixth module is used to use the excellent typesetting data reference library to conduct value assessment on several currently constructed reading material typesetting strategies, and determine the optimal reading material typesetting strategy based on the value assessment value.

[0130] The present invention discloses a method and system for constructing a typesetting of reading materials based on the analysis of excellent children's books, and relates to the technical field of generating typesetting strategies for reading materials. First, excellent children's book materials are collected and split into independent content units, and then the text content is identified, semantic keywords are extracted, and a relationship tree is constructed; the image content is identified, image description keywords are extracted, and a mapping relationship is established with the semantic keyword group relationship tree; the scheme also divides and locates the decorative components, and sets decorative component detection points to form a detection point array template, which is associated with the semantic keyword group relationship tree; these elements are combined into an excellent typesetting data reference group, and the reading material type is configured to construct an excellent typesetting data reference library; the current reading material typesetting strategy is evaluated for value to determine the optimal strategy. By systematically analyzing the typesetting of excellent reading materials, the present invention provides a scientific basis and effective guidance for the typesetting design of children's books, which helps to improve the reading experience and appeal of children's books.

[0131] Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented via hardware or via software combined with a necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product. This software product can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or external hard drive) and includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various implementation scenarios of the present invention.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for constructing a reading material layout based on analysis of excellent children's books, characterized in that: include: Acquire a number of excellent children's book materials, and separate the excellent children's book materials into independent content to obtain a number of independent content units; Identify the text content of each independent content unit to obtain the paragraph content, perform semantic recognition on the paragraph content, determine several semantic keywords, and combine the semantic keywords according to their original order to obtain semantic keyword groups. Based on the sentences to which different semantic keywords belong, group and label the semantic keyword groups to obtain several semantic sub-keyword groups. Based on the combination of paragraph content, construct a semantic keyword group relationship tree; Recognize the image content of each independent content unit, output the recognition result in the form of image text description to obtain image description information, combine the image description keywords in the image description information to obtain image description keyword groups, and establish a mapping relationship between the image description keyword groups and the semantic keyword group relationship tree based on the correspondence between the image content and the text content; Divide and locate the decoration components in the independent content unit, and evenly set a number of decoration component detection points for each decoration component, each decoration component detection point is set with a position coordinate and a color parameter, and all the decoration component detection points are combined according to the original position to form a decoration component detection point array template, and establish an association relationship between the decoration component detection point array template and the semantic keyword group relationship tree; The combination of the semantic keyword group relationship tree, the image description keyword and the decoration component exploration point array template is recorded as an excellent typesetting data reference group, and the excellent typesetting data reference group is configured with a reading type to obtain an excellent typesetting data reference library; Use the excellent typesetting data reference library to conduct a value assessment on several currently constructed reading material typesetting strategies, and determine the optimal reading material typesetting strategy based on the value assessment.

2. The method for constructing a reading material typesetting based on analysis of excellent children's books according to claim 1, characterized in that: Methods for constructing a semantic keyword phrase relationship tree include: According to the original order of the paragraph content, a paragraph content sequence is constructed, and based on the paragraph content sequence, a trunk of a semantic keyword group key tree is constructed, wherein each semantic keyword group corresponds to a tree node; Analyze the contents of each paragraph and its corresponding contents in other paragraphs, and extend a tree branch at the corresponding tree node to form a semantic keyword relationship tree.

3. The method for constructing a typesetting of reading materials based on analysis of excellent children's books according to claim 1, characterized in that: Methods for performing semantic recognition on text content and determining a number of semantic keywords include: A focus keyword library is set for excellent children's books. The focus keyword library includes several types of focus keyword sets, and each type of focus keyword set includes several focus keywords; Preprocess the text content, including word segmentation, stop word removal, and part-of-speech tagging, to obtain several candidate keywords, and perform attention level analysis on the candidate keywords, including determining the frequency of occurrence and relative relationship characteristics of the candidate keywords, and based on the frequency of occurrence and relative relationship characteristics, determine the attention level of the candidate keywords, and screen out several candidate keywords based on the attention level of the candidate keywords; Based on the pre-marked type of the text content, the focus keyword set called in the focus keyword library is determined, and several alternative keywords are brought into the focus keyword set for comparison. If there is a mapped focus keyword in the focus keyword set, the corresponding alternative keyword is identified as a semantic keyword.

4. The method for constructing a typesetting of reading materials based on analysis of excellent children's books according to claim 3, characterized in that: Methods for determining the popularity of candidate keywords include: Determine the relative position of the candidate keyword in the sentence, and based on the determined relative position, configure an initial attention parameter for the candidate keyword, and modify the initial attention parameter based on the frequency of occurrence of the candidate keyword to obtain the attention level; The expression for calculating the degree of attention is: ； in, For the level of attention, is the attention conversion coefficient, is the initial focus parameter, is the frequency of occurrence of alternative keywords, is the frequency impact adjustment coefficient, Adjust constants for frequency effects; The method for determining the relative position of the candidate keywords in the sentence includes performing syntactic structure analysis on the sentence, identifying the subject, predicate and object components, and determining the position of the candidate keywords in the syntactic structure.

5. The method for constructing a typesetting of reading materials based on analysis of excellent children's books according to claim 1, characterized in that: The method for identifying the image content of each independent content unit includes: Divide the content units in the image content, including objects, people, animals and scenes in the image content, and determine the directional relationship between the objects, people and animals; Constructing an image description information group based on the image content, the image description information group including a plurality of object description units, a plurality of person description units, a plurality of animal description units, and a scene description unit, wherein the object description unit includes a plurality of object description keywords, the person description unit includes a plurality of person description keywords, the animal description unit includes a plurality of animal description keywords, and the scene description unit includes a plurality of scene description keywords; Based on the pointing relationship between objects, people and animals, a unit relationship sequence is constructed for the corresponding object description units, person description units and animal description units. The unit relationship sequence includes other object description units, person description units or animal description units with pointing relationships.

6. The method for constructing a typesetting of reading materials based on analysis of excellent children's books according to claim 5, characterized in that: Methods for evaluating the value of currently constructed reading material typesetting strategies using an excellent typesetting data reference library include: Analyze the layout strategy of the reading material, identify the semantic keywords in the strategy, and construct the semantic sub-keyword groups in the strategy based on the semantic keywords in the strategy; Analyze the typesetting strategy of the reading material, determine image description information of the image content in the strategy, determine the detection point array template of the decorative component in the strategy, and construct an image description information group in the strategy based on the image description information; The semantic sub-keyword group in the strategy, the image description information group in the strategy, and the decorative component detection point array template in the strategy corresponding to the reading material typesetting strategy form a typesetting data reference group in the strategy; Several retrieval factors are determined for the typesetting data reference group in the strategy, and based on the retrieval factors, several excellent typesetting data reference groups are retrieved from the excellent typesetting data reference library. The retrieved excellent typesetting data reference groups are compared with the typesetting data reference group in the strategy, and based on the comparison results, the value assessment value of the currently constructed reading material typesetting strategy is determined.

7. The method for constructing a typesetting of reading materials based on analysis of excellent children's books according to claim 6, characterized in that: Methods for determining several search factors in the data reference group layout in the strategy include: Randomly selecting a number of first initial retrieval factors from the semantic sub-keyword group in the strategy, randomly selecting a number of second initial retrieval factors from the image description information group in the strategy, and randomly selecting a number of third initial retrieval factors from the decoration component probe point array template in the strategy; The first initial retrieval factor, the second initial retrieval factor and the third initial retrieval factor are respectively substituted into the excellent typesetting data reference library, and the reference group mapping number of the excellent typesetting data reference group mapped by each initial retrieval factor is calculated respectively. If the reference group mapping number is greater than or equal to the preset value, the corresponding initial retrieval factor is retained, and the retained initial retrieval factor is identified as the retrieval factor for the final search in the excellent data reference library.

8. The method for constructing a typesetting of reading materials based on analysis of excellent children's books according to claim 6, characterized in that: The method for comparing the retrieved excellent typesetting data reference group with the typesetting data reference group in the strategy includes: A first mapping parameter mapped in the semantic keyword group relationship tree of the semantic sub-keyword group relative to the excellent typesetting data reference group in the analysis strategy, a second mapping parameter mapped in the image description keyword group relative to the image description keyword group of the excellent typesetting data reference group in the analysis strategy, and a third mapping parameter mapped in the decoration component detection point array template relative to the decoration component detection point array template of the excellent typesetting data reference group in the analysis strategy; Determining the degree of equivalence between each retrieved excellent typesetting data reference group and the typesetting data reference group in the strategy based on the first mapping parameter, the second mapping parameter, and the third mapping parameter, and determining the value evaluation value of the reading material typesetting strategy based on the corresponding equivalence degrees of all retrieved excellent typesetting data reference groups; The expression for calculating the degree of equivalence between the excellent typesetting data reference group and the typesetting data reference group in the strategy is: ； in, To the same extent, is the first mapping parameter weight adjustment coefficient, is the second mapping parameter weight adjustment coefficient, is the third mapping parameter weight adjustment coefficient, The semantic sub-keyword group in the strategy is mapped to the calculation function, which is used to determine whether the semantic sub-keyword group in the i1th strategy is mapped to the semantic keyword group relationship tree. If the mapping is determined, then Output 1, otherwise output 0, wherein the method of determining whether the semantic sub-keyword group in the policy is mapped to the semantic keyword group relationship tree includes judging whether the proportion of the mapped semantic sub-keywords in the policy in the semantic sub-keyword group is greater than or equal to a preset value, and if it is greater than or equal to the preset value, then it is determined that the semantic sub-keyword group in the policy is mapped to the semantic keyword group relationship tree, is the semantic sub-keyword group mapping impact adjustment coefficient in the strategy, is the semantic sub-keyword group mapping impact adjustment constant in the strategy, n1 is the number of semantic sub-keyword groups in the strategy, The image description keyword in the strategy is mapped to the calculation function, which is used to determine whether the image description keyword in the i2th strategy is mapped to the image description keyword group of the excellent typesetting data reference group. If the mapping is determined, then Output 1, otherwise output 0. The image description keyword mapping impact adjustment coefficient in the strategy, is the mapping adjustment constant of the image description keyword group in the strategy, n2 is the number of image description keywords in the strategy image description keyword group, The mapping calculation function for the decoration component probe point array template in the strategy is used to determine whether the decoration component probe point in the i3th strategy is mapped to the decoration component probe point array template of the excellent typesetting data reference group. If the mapping is determined, then Output 1, otherwise output 0. The adjustment coefficient for the detection point mapping effect of the decoration component in the strategy, Adjust the constant for the detection point mapping effect of the decoration component in the strategy, The number of probe points in the probe point array template of the decoration component in the strategy.

9. The method for constructing a typesetting of reading materials based on analysis of excellent children's books according to claim 8, characterized in that: Based on the degree of equivalence corresponding to all retrieved excellent typesetting data reference groups, the method for determining the value assessment value of the reading material typesetting strategy includes: Determine the number of first reference groups of the retrieved excellent typesetting data reference groups, and judge whether the degree of equality corresponding to each excellent typesetting data reference group is greater than or equal to a preset value, and if so, mark the reading material typesetting strategy once; Calculate the reference group quantity ratio of the second reference group quantity to the first reference group quantity of the marked reading material typesetting strategy, analyze the preset reference group quantity ratio interval to which the reference group quantity ratio belongs, and identify the value assessment value corresponding to the preset reference group quantity ratio interval as the value assessment value corresponding to the reading material typesetting strategy.

10. A reading material typesetting construction system based on analysis of excellent children's books, characterized by: include: The first module is used to obtain a number of excellent children's books and split the excellent children's books into independent content units; The second module is used to identify the text content of each independent content unit to obtain the paragraph content, and perform semantic recognition on the paragraph content to determine a number of semantic keywords. The semantic keywords are combined according to the original order to obtain semantic keyword groups. The semantic keyword groups are grouped and labeled based on the sentences to which different semantic keywords belong to obtain a number of semantic sub-keyword groups. Based on the combination of paragraph content, a semantic keyword group relationship tree is constructed; The third module is used to identify the image content of each independent content unit, output the identification result as an image text description to obtain image description information, combine the image description keywords in the image description information to obtain image description keyword groups, and establish a mapping relationship between the image description keyword groups and the semantic keyword group relationship tree based on the correspondence between the image content and the text content; The fourth module is used to divide and locate the decoration components in the independent content units, and evenly set a number of decoration component detection points for each decoration component, each decoration component detection point is set with a position coordinate and color parameters, and all the decoration component detection points are combined according to their original positions to form a decoration component detection point array template, and an association relationship is established between the decoration component detection point array template and the semantic keyword group relationship tree; The fifth module is used to record the combination of the semantic keyword group relationship tree, the image description keyword and the decoration component exploration point array template as an excellent typesetting data reference group, and configure the reading type for the excellent typesetting data reference group to obtain an excellent typesetting data reference library; The sixth module is used to use the excellent typesetting data reference library to conduct value assessment on several currently constructed reading material typesetting strategies, and determine the optimal reading material typesetting strategy based on the value assessment value.

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