Children book sharing management method and system

Abstract

The invention provides a child book sharing management method and system, and relates to the technical field of book resource management. The method comprises the steps of collecting historical reading record data and education stage data of multiple platform users, extracting group borrowing features of each reference education stage from multiple groups of historical reading record data, and performing stage distribution analysis to generate a book borrowing evolution strategy of each reference education stage; obtaining target reading record data of a target user, and performing group evolution offset detection on the target user to obtain group borrowing offset data of the target user; performing popularity influence stripping on the target user, and extracting popularity filtering preference data of the target user; and fusing the group borrowing offset data and the popularity filtering preference data of the target user, and generating a book borrowing matching strategy of the target user in the target reading record data, which is used for optimizing a book sharing strategy of the child book sharing platform about the target user. According to the invention, book resource recommendation can be accurately carried out for children.

Description

Technical Field

[0001] The present invention relates to the technical field of book resource management, and in particular to a method and system for sharing and managing children's books. Background Art

[0002] The book sharing platform is a digital system based on borrowing and sharing, which can connect users with book resources and effectively improve the sharing efficiency of paper and digital books. For example, the e-book borrowing platforms of public libraries and educational institutions provide support for children's personalized learning through resource management and technical means, and help parents and educators configure book resources according to learning needs.

[0003] Some platforms recommend books through information such as borrowing records and rating data. However, since children's cognitive abilities, learning progress, and interest preferences are prone to change dynamically with age and grade levels, these changes cannot be fully considered, which may lead to the recommended books not matching the real needs of children. In addition, short-term popularity effects such as short-term teaching activity designs often affect the borrowing frequency of books in a short period of time, but this short-term popularity trend does not reflect children's long-term interests, easily leading to interest deviation and affecting the accuracy of recommendations. Moreover, even among children in the same grade, there are differences in knowledge mastery progress and cognitive abilities under the influence of different factors. For example, some children may learn ahead while others may lag behind. Systems that recommend only based on grade or age range cannot effectively cope with this individual difference, resulting in the recommended books may not meet the actual learning needs of each child, and the book resource sharing strategy for children needs to be optimized to better make accurate recommendations of book resources according to children's actual book borrowing behaviors. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a method and system for sharing and managing children's books, which are used to realize personalized book resource recommendations for children.

[0005] The first aspect of the present invention provides a method for sharing and managing children's books, including: Collecting historical reading record data and educational stage data of multiple platform users on the children's book sharing platform, and determining multiple reference educational stages according to the educational stage data; Extracting the group borrowing characteristics of each reference educational stage from multiple groups of historical reading record data, performing stage distribution analysis on the multiple group borrowing characteristics, and generating a book borrowing evolution strategy for each reference educational stage; Obtaining the target reading record data of the target user, determining the target educational stage to which the target user currently belongs, and performing group evolution deviation detection on the target user according to multiple book borrowing evolution strategies to obtain the group borrowing deviation data of the target user; Obtain the borrowing popularity reference data of the target reading record data on the children's book sharing platform. According to the borrowing popularity reference data and the target reading record data, perform heat influence stripping on the target users, and extract the heat filtering preference data of the target users; Integrate the group borrowing deviation data and the heat filtering preference data of the target users to generate a book borrowing matching strategy for the target users in the target reading record data, and optimize the book sharing strategy of the children's book sharing platform for the target users through the book borrowing matching strategy.

[0006] Preferably, extract the group borrowing characteristics of each reference education stage from multiple groups of historical reading record data, perform stage distribution analysis on multiple group borrowing characteristics, and generate a book borrowing evolution strategy for each reference education stage, including: Determine multiple groups of group borrowing popularity data for different books under each reference education stage according to multiple groups of historical reading record data, extract the local borrowing characteristics of each book in each group of group borrowing popularity data, perform feature fusion based on heat time series distribution on multiple local borrowing characteristics of each book, generate group borrowing characteristics for multiple books under the reference education stage, determine the time period distribution data for multiple books in the group borrowing characteristics of each reference education stage according to multiple groups of historical reading record data, and generate a book borrowing evolution strategy for each reference education stage, including the group borrowing preference data for each local stage in the reference education stage.

[0007] Preferably, perform group evolution deviation detection on the target users according to multiple book borrowing evolution strategies to obtain the group borrowing deviation data of the target users, including: Extract the individual borrowing preference data of the target users at each local stage from the target reading record data. According to the target education stage to which the target users currently belong, determine multiple target borrowing evolution strategies associated with the target users among multiple book borrowing evolution strategies, and match the multiple groups of individual borrowing preference data of the target users with the multiple target borrowing evolution strategies, including performing group evolution deviation detection on the target users according to the group borrowing preference data and the individual borrowing preference data at each local stage to obtain the group borrowing deviation characteristics of the target users at each local stage, and integrating the group borrowing deviation characteristics to obtain the group borrowing deviation data of the target users.

[0008] Preferably, perform heat influence stripping on the target users and extract the heat filtering preference data of the target users, including: Determine multiple local book preference features of the target user in the target reading record data, extract multiple global book preference parameters for each local book preference feature from the borrowing popularity reference data, calculate the popularity deviation factor for each local book preference feature based on the multiple global book preference parameters, and perform popularity influence stripping on the local book preference features of the target user to obtain the popularity-filtered preference data of the target user regarding multiple local book preference features.

[0009] Preferably, fuse the group borrowing deviation data and the popularity-filtered preference data of the target user to generate a book borrowing matching strategy of the target user in the target reading record data, including: Determine the target local stage to which the user belongs, and in combination with the target education stage to which the target user belongs, extract the local borrowing evolution strategy associated with the target local stage from the book borrowing evolution strategies of the target education stage; Determine multiple strategy fusion weights regarding the local borrowing evolution strategy according to the group borrowing deviation data, perform popularity analysis on the local borrowing evolution strategy through the strategy fusion weights, and calculate the first popularity parameters of multiple books; Perform correction processing on the multiple first popularity parameters through the popularity-filtered preference data, calculate the second popularity parameters corresponding to each first popularity parameter, and generate a book borrowing matching strategy of the target user in the target reading record data according to the second popularity parameters of multiple books.

[0010] Preferably, perform group evolution deviation detection on the target user to obtain the group borrowing deviation characteristics of the target user in each local stage, including: Extract the borrowing preference feature vectors corresponding to the group borrowing preference data and the individual borrowing preference data respectively, perform matching analysis on the borrowing preference feature vectors corresponding to the group borrowing preference data and the individual borrowing preference data in each local stage, calculate the similarity parameters in each local stage, and generate the group borrowing deviation characteristics of the target user in each local stage according to the multiple similarity parameters.

[0011] The second aspect of the present invention provides a children's book sharing management system for implementing the above-mentioned children's book sharing management method, including: A reading data preprocessing module for collecting the historical reading record data and education stage data of multiple platform users on the children's book sharing platform, and determining multiple reference education stages according to the education stage data; A book borrowing evolution analysis module for extracting the group borrowing characteristics of each reference education stage from multiple groups of historical reading record data, performing stage distribution analysis on the multiple group borrowing characteristics, and generating a book borrowing evolution strategy for each reference education stage; The group borrowing deviation analysis module is used to obtain the target reading record data of the target user, determine the target education stage to which the target user currently belongs, perform group evolution deviation detection on the target user according to multiple book borrowing evolution strategies, and obtain the group borrowing deviation data of the target user; The heat influence stripping module is used to obtain the borrowing heat reference data of the children's book sharing platform regarding the target reading record data, perform heat influence stripping on the target user according to the borrowing heat reference data and the target reading record data, and extract the heat filtering preference data of the target user; The book sharing strategy generation module is used to fuse the group borrowing deviation data and the heat filtering preference data of the target user, generate a book borrowing matching strategy for the target user in the target reading record data, and optimize the book sharing strategy of the children's book sharing platform for the target user through the book borrowing matching strategy.

[0012] The present invention has the following beneficial effects: By extracting group characteristics and analyzing stage distributions of the borrowing behaviors of the children's group, the present invention constructs book borrowing evolution strategies for different education stages. Based on the target reading record data of the target user, it performs group evolution deviation detection on the target user, analyzes the matching nature between the target user and the group reading behavior, further considers the interference effect of short-term hotspots on individual interests, quantitatively analyzes the impact of short-term hotspots on individuals from dimensions such as behavior attenuation and local interest matching, and finally fuses the deviation characteristics of group preferences with the individual reading preferences after filtering the influence of short-term hotspots to generate a book borrowing matching strategy for the target user regarding the target reading record data, effectively distinguishing the stable demands driven by cognitive development from the temporary preferences induced by the external environment, improving the accuracy of children's book recommendations in aspects such as knowledge connection and interest matching, and achieving precise personalized book recommendations for different individuals. Description of the Drawings

[0013] Figure 1 It is a flowchart of an exemplary children's book sharing management method in an embodiment of the present invention.

[0014] Figure 2 It is a structural diagram of an exemplary children's book sharing management system in an embodiment of the present invention. Detailed Embodiments

[0015] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] Figure 1The flowchart of an exemplary children's book sharing management method in an embodiment of the present invention is shown as follows. Please refer to Figure 1 , a children's book sharing management method, comprising: Step S10: Collect the historical reading record data and educational stage data of multiple platform users on the children's book sharing platform, and determine multiple reference educational stages according to the educational stage data.

[0017] In this embodiment, for the historical reading record data and educational stage data of different platform users collected from the children's book sharing platform, the historical reading records include behavioral data such as the types of books read or borrowed by platform users, borrowing frequencies, borrowing durations, etc. It should be noted that different book sharing platforms may involve relevant records of online or offline books at the same time. Those skilled in the art can think that this application is not limited to the analysis of relevant record data of only one type of online or offline book resources. The educational stage data covers information such as the school year, grade, or age of the user. By analyzing these data, multiple reference educational stages are determined. For example, the reference educational stages can be divided into the first grade of primary school, the second grade, etc., or further divided into the upper and lower semesters of different grades, as well as different periods in each semester. Under the education of the school, there will be certain commonalities in the cognitive abilities, learning needs, and reading interests of children in each reference educational stage. At the same time, affected by factors such as family background and individual differences, there will also be individual needs that are somewhat different from the grade or age of the children.

[0018] Step S20: Extract the group borrowing characteristics of each reference educational stage from multiple groups of historical reading record data, perform stage distribution analysis on the multiple group borrowing characteristics, and generate a book borrowing evolution strategy for each reference educational stage.

[0019] In this embodiment, group characteristic information mining is performed on the historical reading record data of multiple platform users, including extracting the group borrowing characteristics of each reference educational stage to reflect some common interests of children when borrowing books at different reference educational stages. For example, lower-grade primary school students may tend to read picture books and children's literature works, and become more fond of more professional books such as science and technology and history as the grade gradually increases. At the same time, aiming at this change in group interests, further explore the evolution of detailed preferences within the reference educational stage, and generate corresponding book borrowing evolution strategies to identify the group behavior characteristics of different educational stages and reflect the changes in children's interests and the evolution of reading habits when borrowing books at different educational stages.

[0020] In some implementation processes, for the extraction of group borrowing characteristics and the generation of book borrowing evolution strategies for reference educational stages, it specifically includes: Step S201: Determine, based on multiple sets of historical reading record data, the multiple sets of group borrowing popularity data for different books at each reference education stage, extract the local borrowing characteristics of each book in each set of group borrowing popularity data, and perform feature fusion based on the popularity time series distribution on the multiple local borrowing characteristics of each book to generate the group borrowing characteristics for multiple books at the reference education stage.

[0021] Specifically, determine the group borrowing popularity data at each reference education stage through multiple sets of historical reading record data, including the overall popularity data of different book types among groups at the reference education stage. For example, evaluate the popularity through borrowing frequency, borrowing duration, etc., and quantify the popularity data of different book types through the distribution of the overall borrowing frequency or borrowing duration. For each reference education stage, there will be a corresponding set of group borrowing popularity data at the same education stage at different time periods. Extract the borrowing-related characteristics of each book in each set of group borrowing popularity data to obtain the local borrowing characteristics of each book in each set of group borrowing popularity data. Specifically, it can be the popularity characteristics corresponding to different types of books in the group borrowing popularity data after normalization as a whole.

[0022] For the multiple local borrowing characteristics of each book, that is, the local borrowing characteristics of a certain type of book in different sessions, consider the evolution over time. For example, for a certain grade, there may be differences in the popularity of a certain type of book between the previous students and the current students. Extract the feature parameters of the popularity time series distribution from the multiple local borrowing characteristics of the book as the group borrowing characteristics of different types of books. It can be characterized by calculating the product of the mean and standard deviation of the popularity of a certain type of book in multiple sessions. The larger the popularity mean, the higher the overall popularity of this type of book on the whole. The larger the popularity standard deviation, the more evenly distributed the overall popularity level of this type of book over time. Thus, it characterizes the overall borrowing popularity performance of different types of books at a specific reference education stage.

[0023] Step S202: Determine the time period distribution data for multiple books in the group borrowing characteristics of each reference education stage based on multiple sets of historical reading record data, and generate the book borrowing evolution strategy for each reference education stage.

[0024] Specifically, the group borrowing characteristics in the reference education stage include the overall popularity performance of different types of books in a specific education stage. For example, the popularity of different types of books among the group in the first semester of the second grade. In this case, further analyze the time period distribution characteristics of each type of book in this education stage based on multiple sets of historical reading record data. Specifically, extract from multiple sets of historical reading record data the overall distribution performance of the popularity of each type of book in a specific education stage. For example, whether it gradually increases or decreases from the beginning to the end of the semester, or the overall popularity is relatively average throughout the semester, so as to formulate the book borrowing evolution strategy for each reference education stage. For example, books with relatively average popularity distribution and high overall popularity can be recommended at different times throughout the semester, while books with high overall popularity but only showing high popularity at the end of the semester can reduce the recommendation priority in the first and middle periods of the semester. In this way, obtain the book borrowing evolution strategy for multiple books under each reference education stage, which can specifically include the group borrowing preference data for each local stage in the reference education stage. For example, in a certain semester, the group borrowing preference data for different book types from the overall perspective in the first, middle, and late periods of the semester, showing the borrowing reference popularity of different types of books. In the actual scenario, at the beginning of the semester, it may be biased towards some relatively pleasant fairy tales or fable stories, etc. As the semester progresses and the cognitive ability of students gradually improves, it may be more suitable to read some science popularization readings with relatively complex content, etc., to reflect the dynamic change characteristics of the popularity of different types of books in the education stage.

[0025] Step S30: Obtain the target reading record data of the target user, determine the target education stage to which the target user currently belongs, and perform group evolution offset detection on the target user according to multiple book borrowing evolution strategies to obtain the group borrowing offset data of the target user.

[0026] In this embodiment, for the target reading record data of the target user, which is the individual to be analyzed, it includes the reading record data of the target user on different types of books in a specific education stage. For example, the relevant reading record data of the target user in the previous three months, which includes the overall book reading preference habits of the user in the current corresponding education stage, that is, the education stage to which the target reading record data belongs. The preference evolution offset characteristics between the target user and the group rules can be mined through the target reading record data, and analyze whether the target user shows a deviation in book reading habits due to differences in knowledge mastery or cognitive ability from the group.

[0027] In some implementation processes, the group evolution offset detection of the target user specifically includes: Extract the individual borrowing preference data of the target user in each local stage from the target reading record data. For example, the book borrowing-related data corresponding to the first, middle, and last periods of a certain semester reflect the changes in the individual's reading habits at different times within the semester. And according to the target education stage to which the target user currently belongs, determine the multiple target borrowing evolution strategies associated with the target user among multiple book borrowing evolution strategies.

[0028] Specifically, the multiple target borrowing evolution strategies associated with the target user can specifically be the target education stage to which the target user currently belongs, and the target borrowing evolution strategies corresponding to several adjacent education stages before and after the target education stage. Then, match the multiple groups of individual borrowing preference data of the target user with the multiple target borrowing evolution strategies to achieve the detection of the group evolution deviation of the target user, and obtain the group borrowing deviation characteristics of the target user in each local stage.

[0029] In this process, specifically based on the group borrowing preference data and individual borrowing preference data in each local stage, perform the detection of the group evolution deviation of the target user. Specifically, it can analyze the differences between the user and the group borrowing preferences in a specific local stage. Exemplarily, extract the borrowing preference feature vectors representing the preference characteristics of different types of books from the group borrowing preference data and individual borrowing preference data respectively. Each element in the vector represents the preference quantization value of a certain type of book. By analyzing and matching the borrowing preference feature vectors of the user individual in each local stage with the borrowing preference feature vectors of the group in this local stage and the adjacent local stages before and after, the corresponding matching values, such as similarity parameters, are characterized by, for example, the Euclidean distance, to determine whether the borrowing preference of the user in the current local stage conforms to the overall situation, or shows phenomena such as being ahead, lagging behind, or conforming to the group law, and quantitatively record different phenomena, so as to obtain the group borrowing deviation characteristics of the target user in each local stage. Finally, fuse the multiple group borrowing deviation characteristics of the target user, which can be to calculate the overall proportion of different deviation states. For example, it is analyzed that 60% of the multiple local stages show conformity with the borrowing preferences of the group in the same local stage, and 40% show a high degree of matching with the borrowing preferences of the next local stage. In this way, generate the group borrowing deviation data of the target user.

[0030] Step S40: Obtain the borrowing popularity reference data of the children's book sharing platform regarding the target reading record data. According to the borrowing popularity reference data and the target reading record data, perform heat influence stripping on the target user and extract the heat-filtered preference data of the target user.

[0031] In this embodiment, the key objective of stripping the heat influence from the target user is to identify the influence of the heat effect on the borrowing behavior of the target user, and strip these external interference factors, and finally obtain the true book preferences of the user. In this process, first obtain the borrowing heat reference data of the target reading record data on the children's book sharing platform, that is, under the time period of the target reading record data, the overall borrowing heat related data of different types of books in this time period on the platform, and further analyze whether some of the user's preferred book types are caused by short-term heat mutations existing in the platform during the current time period, so as to reduce the influence of short-term heat mutation events existing in the platform on the current preferences of the target user, and obtain heat-filtered preference data that can more truly represent the current preferences of the user.

[0032] In some implementation processes, stripping the heat influence from the target user specifically includes: Determine multiple local book preference characteristics of the target user in the target reading record data, extract multiple global book preference parameters for each local book preference characteristic from the borrowing heat reference data, and calculate the heat deviation factor for each local book preference characteristic according to the multiple global book preference parameters.

[0033] Specifically, the local book preference characteristics can specifically be some book types that exceed the preset heat threshold in the overall heat of different book types in the target reading record data. For these local book preference characteristics, corresponding heat parameters can be quantified according to the borrowing frequency, etc., and further analyze the borrowing heat reference data to calculate multiple global book preference parameters for each local book preference characteristic.

[0034] In this embodiment, through the local heat intensity parameter, the behavior attenuation parameter, and the local interest matching parameter, it is characterized whether the book type preference of the user in the target reading record data is affected by local heat abnormal events in the platform. Among them, the local heat intensity parameter is characterized by the ratio between the borrowing frequency of a certain type of book and all types of books in the borrowing heat reference data. The local interest matching parameter is characterized by the ratio between the borrowing frequency of a certain type of book and all types of books in the target reading record data.

[0035] The behavior attenuation parameter is characterized by the ratio between the borrowing frequencies corresponding to a certain type of book within the first target window period and the second target window period in the target reading record data of the target user. Among them, the window size of the target window period can be specifically set according to the time length involved in the target reading record data. For example, a quarter is taken to set the target sliding window, and then the borrowing popularity reference data is traversed through the target sliding window. The window with the highest total borrowing frequency of a specific type of book in multiple sliding windows is recorded as the second target window period, and the next event window of the second target window period is recorded as the first target window period. For example, if the total borrowing frequency of a certain type of book in the second week in the borrowing popularity reference data is the highest, the second week is recorded as the second target window period, and the corresponding time window in the third week is recorded as the first target window period. After positioning the target window period through the borrowing popularity reference data, the target reading record data is analyzed through the target window period to measure whether the user's short-term preference is affected by the popularity event.

[0036] Finally, the following method is adopted to calculate the popularity deviation factor of any local book preference feature: In the formula, represents the popularity deviation factor of the local book preference feature, represents the local popularity intensity parameter of the local book preference feature, represents the behavior attenuation parameter of the local book preference feature, represents the local interest matching parameter of the local book preference feature. When the popularity of a certain type of book on the platform is relatively high in the short term, and after the user borrows this type of book, the subsequent interest decays quickly, and at the same time, the user's preference for this type of book is relatively small in general, it means that there is a large deviation between the user's popularity for this type of book and their own habits, and the larger the popularity deviation factor, the more likely it is that the user is affected by the short-term popularity event. If a certain type of book has a high short-term popularity on the platform, but the user's interest in this type of book is relatively stable within a certain period of time and has good subsequent persistence, that is, there is no too serious attenuation phenomenon, it means that it can be more truly used as the user's interest preference.

[0037] Finally, the heat influence of the local book preference characteristics of the target user is stripped according to the heat deviation factor. In this process, for multiple local book preference characteristics, the greater the corresponding heat deviation factor, the lower the reference value. The deviation influence weight obtained after normalizing multiple heat deviation factors can be used to calculate the ratio between the heat parameter corresponding to the local book preference characteristic and the deviation influence weight obtained after normalization as the final heat parameter for each local book preference characteristic, so as to obtain the heat-filtered preference data of the target user for multiple local book preference characteristics. By this means, the borrowing data affected by short-term popularity factors is stripped to ensure that the remaining data can better reflect the overall interest preferences of users within a specific period.

[0038] Step S50: Integrate the group borrowing deviation data and heat-filtered preference data of the target user to generate a book borrowing matching strategy for the target user in the target reading record data, and optimize the book sharing strategy of the children's book sharing platform for the target user through the book borrowing matching strategy.

[0039] In this embodiment, for the group borrowing deviation data and heat-filtered preference data of the target user, the group borrowing deviation data can reflect the reading behavior differences of the user relative to the group in the same education stage, such as being ahead, lagging behind, or meeting expectations. Although the heat influence factors are not filtered, it can reveal the objective state of the user's knowledge mastery progress and can also objectively reflect the common laws of the education stage. Even if some books are widely borrowed by the group due to popularity, but the cognitive needs at the education stage are the same. For example, a certain math picture book becomes a short-term hot topic due to teacher recommendation, this kind of popularity is reasonable and should be retained. Or because of the influence of a certain teaching activity, students in a certain grade are recommended to read a book on natural observation in class. Although they are all short-term hot topics, they are reasonable and necessary to retain because they conform to the teaching activity, which can more truly reflect the dynamic characteristics of the education stage. The heat-filtered preference data can more essentially reflect the long-term preference law of the individual by stripping the interference of short-term hot topics.

[0040] Therefore, for the fusion process of group borrowing deviation data and popularity filtering preference data, the target local stage and target education stage to which the user currently belongs can be determined first. For example, it is the mid-term of the second semester of the third grade. And according to the group borrowing deviation data, the local borrowing evolution strategy associated with the target local stage can be determined, including being extracted from the book borrowing evolution strategy of the target education stage. Specifically, it is the target local stage, and the local strategy information corresponding to the book borrowing evolution strategy in the target education stage for the previous or next local stage adjacent to the target local stage determined according to the leading or lagging phenomenon in the group borrowing deviation. And according to the group borrowing deviation data, multiple strategy fusion weights regarding the local borrowing evolution strategy are determined. For example, if the group borrowing deviation data shows 70% compliance with the group law and 30% leading phenomenon, then for the target local stage and the next local stage adjacent to the target local stage, the strategy fusion weights regarding multiple types of books in the local borrowing evolution strategy are 0.7 and 0.3 respectively. The borrowing reference popularity regarding multiple types in the local borrowing evolution strategy is weighted and calculated through the strategy fusion weights to obtain the first popularity parameter for each type of book. And further based on the popularity filtering preference data, the first popularity parameter for each type of book is corrected to obtain the second popularity parameter for each type of book. Finally, according to the second popularity parameter of the type of book, a book borrowing matching strategy for the target user regarding the target reading record data is generated. In this process, the real-time popularity parameter of the target user regarding different types of books under the target local stage can be determined first, and then the difference between the second popularity parameter and the real-time popularity parameter of each type of book is analyzed. If the second popularity parameter is higher than the real-time popularity parameter, the recommendation priority of the corresponding type of book is increased, otherwise it is decreased. Finally, the book sharing strategy of the children's book sharing platform regarding the target user is optimized through the book borrowing matching strategy.

[0041] It should be noted that due to the differences between children and adults in terms of cognitive abilities, learning progress, and interest preferences, for example, children's interests and learning needs are more likely to change significantly with age and grade levels. Some platforms recommend shared book resources through methods such as borrowing records and rating systems. Due to the continuous changes in children's cognitive development, they are more likely to have different preferences for book theme types at different times. If the dynamic nature of children's interest changes is not fully considered, it is very likely that some recommended books do not fully meet the real needs of children at different learning stages. At the same time, factors such as heat events that occur in the short term and individual differences in children's mastery of teaching knowledge will also affect children's needs. The present invention comprehensively considers the impacts of the above factors on children's real preferences, formulates book sharing strategies for different users, enables the platform to achieve precise personalized recommendations for different individuals among a wide range of children, improves the usage efficiency and sharing effect of books, and considers flexible reconciliation based on interest drive under the overall teaching requirements or teaching directions to enhance the reading experience of child users.

[0042] Figure 2 The following is a structural diagram of an exemplary children's book sharing management system in an embodiment of the present invention. Please refer to Figure 2 Based on the concept of the above-mentioned children's book sharing management method, a children's book sharing management system includes: A reading data preprocessing module, configured to collect historical reading record data and educational stage data of multiple platform users on a children's book sharing platform, and determine multiple reference educational stages according to the educational stage data; A book borrowing evolution analysis module, configured to extract the group borrowing characteristics of each reference educational stage from multiple groups of historical reading record data, perform stage distribution analysis on multiple group borrowing characteristics, and generate a book borrowing evolution strategy for each reference educational stage; A group borrowing deviation analysis module, configured to obtain the target reading record data of a target user, determine the target educational stage to which the target user currently belongs, perform group evolution deviation detection on the target user according to multiple book borrowing evolution strategies, and obtain the group borrowing deviation data of the target user; A heat impact stripping module, configured to obtain the borrowing heat reference data of the children's book sharing platform regarding the target reading record data, perform heat impact stripping on the target user according to the borrowing heat reference data and the target reading record data, and extract the heat-filtered preference data of the target user; A book sharing strategy generation module, configured to fuse the group borrowing deviation data and the heat-filtered preference data of the target user, generate a book borrowing matching strategy of the target user for the target reading record data, and optimize the book sharing strategy of the children's book sharing platform regarding the target user through the book borrowing matching strategy.

[0043] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The parts not described in detail in this specification belong to the prior art well known to those skilled in the art.

Claims

1. A children's book sharing management method, characterized in that: include: Collect historical reading record data and education stage data of multiple platform users on the children's book sharing platform, and determine multiple reference education stages based on the education stage data; Extract the group borrowing characteristics of each reference education stage from multiple groups of historical reading record data, perform stage distribution analysis on multiple group borrowing characteristics, and generate the book borrowing evolution strategy for each reference education stage; Obtain the target reading record data of the target user, determine the target education stage to which the target user currently belongs, perform group evolution deviation detection on the target user according to multiple book borrowing evolution strategies, and obtain the group borrowing deviation data of the target user; Obtain borrowing heat reference data about target reading record data on the children's book sharing platform, and perform heat influence stripping on the target user based on the borrowing heat reference data and the target reading record data, and extract heat filtering preference data of the target user; By integrating the target user's group borrowing deviation data and heat filtering preference data, a book borrowing matching strategy for the target user in the target reading record data is generated. The book sharing strategy for the target user on the children's book sharing platform is optimized through the book borrowing matching strategy.

2. A children's book sharing management method according to claim 1, characterized in that: The borrowing characteristics of each reference education stage are extracted from multiple groups of historical reading record data, and the stage distribution analysis of multiple borrowing characteristics of each group is performed to generate the book borrowing evolution strategy for each reference education stage, including: Based on multiple groups of historical reading record data, multiple groups of group borrowing heat data about different books in each reference education stage are determined, and the local borrowing features of each book in each group of group borrowing heat data are extracted. The multiple local borrowing features of each book are fused based on the time series distribution of heat to generate group borrowing features for multiple books in the reference education stage. Based on multiple groups of historical reading record data, the time period distribution data about multiple books in the group borrowing features of each reference education stage are determined to generate the book borrowing evolution strategy for each reference education stage, including the group borrowing preference data in each local stage of the reference education stage.

3. A children's book sharing management method according to claim 2, characterized in that: According to multiple book borrowing evolution strategies, group evolution deviation detection is performed on the target user to obtain the group borrowing deviation data of the target user, including: The individual borrowing preference data of the target user in each local stage is extracted from the target reading record data, and according to the target education stage to which the target user currently belongs, multiple target borrowing evolution strategies associated with the target user in multiple book borrowing evolution strategies are determined, and multiple groups of individual borrowing preference data of the target user are matched with the multiple target borrowing evolution strategies, including performing group evolution deviation detection on the target user according to the group borrowing preference data and individual borrowing preference data in each local stage, obtaining the group borrowing deviation characteristics of the target user in each local stage, and fusing the group borrowing deviation characteristics to obtain the group borrowing deviation data of the target user.

4. A children's book sharing management method according to claim 3, characterized in that: The target user's heat influence is stripped off to extract the target user's heat filtering preference data, including: Determine multiple local book preference features of the target user in the target reading record data, extract multiple global book preference parameters of each local book preference feature from the borrowing heat reference data, calculate the heat deviation factor of each local book preference feature according to the multiple global book preference parameters, and perform heat influence stripping on the local book preference features of the target user according to the heat deviation factor to obtain the heat filtering preference data of the target user regarding the multiple local book preference features.

5. A children's book sharing management method according to claim 4, characterized in that: The target user's group borrowing deviation data and heat filtering preference data are integrated to generate a book borrowing matching strategy for the target user in the target reading record data, including: Determine the target local stage to which the user belongs, combine the target education stage to which the target user belongs, and extract the local borrowing evolution strategy associated with the target local stage from the book borrowing evolution strategy of the target education stage; Determine multiple strategy fusion weights of the local borrowing evolution strategy according to the group borrowing deviation data, perform heat analysis on the local borrowing evolution strategy through the strategy fusion weights, and calculate the first heat parameters of multiple books; The plurality of first heat parameters are modified and processed by heat filtering preference data, and the second heat parameter corresponding to each first heat parameter is calculated, and a book borrowing matching strategy for the target user in the target reading record data is generated according to the second heat parameters of the plurality of books.

6. A children's book sharing management method according to claim 4, characterized in that: Perform group evolution deviation detection on the target user to obtain the group borrowing deviation characteristics of the target user in each local stage, including: The borrowing preference feature vectors corresponding to the group borrowing preference data and the individual borrowing preference data are extracted respectively, and the borrowing preference feature vectors corresponding to the group borrowing preference data and the individual borrowing preference data in each local stage are matched and analyzed to calculate the similarity parameters in each local stage, and the group borrowing offset characteristics of the target user in each local stage are generated according to multiple similarity parameters.

7. A children's book sharing management system, characterized in that: The system is used to implement a children's book sharing management method as described in any one of claims 1 to 6, comprising: A reading data preprocessing module is used to collect historical reading record data and education stage data of multiple platform users on the children's book sharing platform, and determine multiple reference education stages based on the education stage data; The book borrowing evolution analysis module is used to extract the group borrowing characteristics of each reference education stage from multiple groups of historical reading record data, perform stage distribution analysis on multiple group borrowing characteristics, and generate the book borrowing evolution strategy for each reference education stage; The group borrowing deviation analysis module is used to obtain the target reading record data of the target user, determine the target education stage to which the target user currently belongs, perform group evolution deviation detection on the target user according to multiple book borrowing evolution strategies, and obtain the group borrowing deviation data of the target user; The heat impact stripping module is used to obtain the borrowing heat reference data of the target reading record data on the children's book sharing platform, and strip the heat impact of the target user based on the borrowing heat reference data and the target reading record data, and extract the heat filtering preference data of the target user; The book sharing strategy generation module is used to integrate the target user's group borrowing offset data and heat filtering preference data, generate the target user's book borrowing matching strategy in the target reading record data, and optimize the children's book sharing platform's book sharing strategy for the target user through the book borrowing matching strategy.

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