A management method and system for electronic books

Abstract

The present invention relates to the technical field of e-book management, and discloses a management method and system for e-books. A collection unit determines an initial evaluation value of each e-book based on text features and a feature model. A judgment unit generates a reading mark according to each reading data, and determines a reading identifier based on the number of marks of the reading mark. A first management unit compares the number of marks with a historical data set, determines a target evaluation value according to an evaluation adjustment factor and the initial evaluation value, performs cluster analysis on all e-books according to text features, compares each target evaluation value in a first book set with a target evaluation threshold, and constructs a second book set according to the comparison results. A second management unit determines a backup mode for the first book set and the second book set according to the book storage capacity. The present invention ensures the reliability and intelligence of e-book management by analyzing reading data and text data.

Description

Technical Field

[0001] The present invention relates to the technical field of e-book management, and in particular to a method and system for managing e-books. Background Art

[0002] E-books are digitized books, which generally store text content in electronic text or image format for users to read on electronic reading devices. With the development of the information industry, the increasing popularity of electronic devices and the Internet, the demand for reading e-books on the Internet and mobile terminals is growing. However, existing e-book platforms have certain drawbacks in storage management. On the one hand, the types of e-books are rich and diverse and cover many different fields. The e-book platform lacks an effective classification system and cannot distinguish different types of e-books. On the other hand, users' reading behavior on the platform embodies their reading habits and interest tendencies. The e-book platform has not established a matching analysis and classification management mechanism, and cannot classify and manage e-books based on reading data. Ultimately, the e-book platform stores e-books in a chaotic manner and cannot meet users' reading needs.

[0003] Therefore, it is necessary to design a management method and system for e-books to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for managing e-books, aiming to solve the above-mentioned problems.

[0005] In one aspect, the present invention provides a management system for e-books, comprising:

[0006] a collection unit configured to obtain reading data and text data of all e-books, collect text features of each text data, and determine an initial evaluation value of each e-book based on the text features and a feature model;

[0007] a judgment unit configured to generate a reading mark according to each reading data, determine a reading identification based on the number of marks of the reading mark, and judge whether to adjust the initial evaluation value according to the reading identification;

[0008] a first management unit configured to, when determining to adjust the initial evaluation value, compare the number of tags with a historical data set, determine an evaluation adjustment factor based on the comparison result, determine a target evaluation value based on the evaluation adjustment factor and the initial evaluation value, perform cluster analysis on all e-books based on the text features, divide the first book set based on the clustering results, compare each target evaluation value in the first book set with a target evaluation threshold, and construct a second book set based on the comparison result;

[0009] The second management unit is configured to store the first book set and the second book set and determine access rights, and determine a backup mode of the first book set and the second book set according to a book storage capacity.

[0010] Furthermore, when collecting text features of each text data, it includes:

[0011] The collection unit analyzes the text data of all e-books based on natural language processing, performs word segmentation, removes stop words, and extracts stems for each text data, and uses a dependency syntactic analysis model to parse the relationship between words to determine book phrases, and uses the book phrases that conform to the syntactic structure as keywords;

[0012] All keywords of each text data and the text data size are used as the text features.

[0013] Furthermore, when determining the initial evaluation value of each e-book based on the text features and the feature model, the method includes:

[0014] Obtain the feature data set and sample it according to the sampling ratio to obtain the training set and test set, use grid search to find the model's hyperparameters, and establish a random forest model;

[0015] Using the training set to train the random forest model, substituting the test set into the trained random forest model and determining the accuracy of model prediction, when the accuracy is greater than or equal to an accuracy threshold, determining the trained random forest model as the feature model; otherwise, continuing to train the random forest model until it is greater than or equal to the accuracy threshold;

[0016] The text features are substituted into the feature model to determine the initial evaluation value of each electronic book.

[0017] Furthermore, when generating a reading mark according to each of the reading data and determining a reading identifier based on the number of marks of the reading mark, the method includes:

[0018] Each text data corresponds to a reading data;

[0019] The judgment unit obtains the interval time between each reading and the previous reading in the reading data, generates the reading mark for the interval time greater than the interval time threshold, and counts the number of the marks generated;

[0020] Presetting a first preset number of marks and a second preset number of marks, wherein the first preset number of marks is greater than the second preset number of marks;

[0021] When the number of the marks is greater than or equal to the first preset number of marks, determining that the reading mark is a high-frequency reading mark;

[0022] When the number of marks is less than the first preset number of marks and greater than the second preset number of marks, determining that the reading mark is a medium frequency reading mark;

[0023] When the number of marks is less than or equal to a second preset number of marks, the reading mark is determined to be a low-frequency reading mark.

[0024] Furthermore, when determining whether to adjust the initial evaluation value according to the reading identifier, the method includes:

[0025] When the high-frequency reading mark is identified, the judgment unit determines to adjust the initial evaluation value; otherwise, the judgment unit determines not to adjust the initial evaluation value and determines the initial evaluation value as the target evaluation value.

[0026] Furthermore, when comparing the number of tags with the historical data set, determining the evaluation adjustment factor according to the comparison result, and determining the target evaluation value according to the evaluation adjustment factor and the initial evaluation value, the method includes:

[0027] The historical data set includes a number of historical marker quantities and a number of historical evaluation adjustment factors, each historical marker quantity corresponds to a historical evaluation adjustment factor;

[0028] When there are a number of historical marks in the historical data set whose similarity to the high-frequency reading identifier is greater than a similarity threshold, the first management unit uses the historical evaluation adjustment factor corresponding to the number of historical marks with the maximum similarity as the evaluation adjustment factor; otherwise, the first management unit determines the evaluation adjustment factor according to a pre-trained evaluation model;

[0029] The target evaluation value is the product of the evaluation adjustment factor and the initial evaluation value.

[0030] Furthermore, when clustering all the e-books according to the text features and dividing the first book set according to the clustering results, the method includes:

[0031] The first management unit normalizes each parameter in the text feature to form a text feature vector, combines the text feature vectors of all e-books into a feature matrix, randomly selects K cluster centers, and determines a cluster distance from each text feature vector to each cluster center;

[0032] S1: Assign the e-books to the set corresponding to the cluster center with the closest cluster distance;

[0033] S2: Re-determine the position of each cluster center;

[0034] S3: Repeat S1 and S2 until the position of the cluster center no longer changes or the preset number of iterations is reached, and divide all e-books into K first book sets.

[0035] Furthermore, when comparing each target evaluation value in the first book set with the target evaluation threshold and constructing the second book set according to the comparison result, the method includes:

[0036] The first management unit extracts electronic books with target evaluation values ​​greater than or equal to the target evaluation threshold from the first book set, constructs the second book set, and constructs the remaining electronic books in the first book set into a third book set.

[0037] Furthermore, when storing the first book set and the second book set and determining access rights, and determining backup modes of the first book set and the second book set according to the book storage capacity, the method includes:

[0038] The second management unit establishes a first storage library and a second storage library;

[0039] The second management unit stores the entire second book set in the first storage repository and sets a first access permission for the first storage repository, stores the entire third book set in the second storage repository and sets a second access permission for the second storage repository, wherein the level of the first access permission is greater than the level of the second access permission;

[0040] obtaining a first book storage capacity of the first storage library, and when the first book storage capacity is greater than or equal to half of the storage capacity of the first storage library, determining the backup mode of the first storage library to be incremental backup;

[0041] When the first book storage capacity is less than half of the storage capacity of the first storage library, the backup mode of the first storage library is determined to be full backup;

[0042] The backup mode of the second storage library is determined to be periodic backup.

[0043] Compared with the prior art, the present invention has the following advantages: 1. Optimizing the classification system: acquiring e-book text data and extracting text features, and combining this with a feature model to determine an initial evaluation value, thereby ensuring the reliability of quantification for each e-book. 2. Cluster analysis of all e-books enables orderly classification of e-books covering diverse fields and a wide variety of categories based on their text features, thus avoiding the chaotic e-book storage caused by the lack of an effective classification system. 3. A first management unit determines an evaluation adjustment factor through comparison and integrates user reading data into the e-book evaluation system, enabling e-book classification management to accurately reflect users' reading habits and interests. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81.

[0044] On the other hand, the present application also provides a method for managing e-books, which is applied to the above-mentioned management system for e-books, comprising:

[0045] Obtaining reading data and text data of all e-books, collecting text features of each text data, and determining an initial evaluation value of each e-book based on the text features and feature model;

[0046] generating a reading mark according to each reading data, determining a reading identification based on the number of marks of the reading mark, and determining whether to adjust the initial evaluation value according to the reading identification;

[0047] When it is determined that the initial evaluation value is to be adjusted, the number of tags is compared with the historical data set, an evaluation adjustment factor is determined based on the comparison result, a target evaluation value is determined based on the evaluation adjustment factor and the initial evaluation value, all e-books are clustered according to the text features, a first book set is divided according to the clustering results, each target evaluation value in the first book set is compared with a target evaluation threshold, and a second book set is constructed based on the comparison results;

[0048] The first book set and the second book set are stored and access rights are determined, and backup modes of the first book set and the second book set are determined according to the book storage capacity.

[0049] It is understandable that the above-mentioned method and system for managing e-books have the same beneficial effects, and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0051] Figure 1 A functional block diagram of a management system for e-books provided by an embodiment of the present invention;

[0052] Figure 2 The present invention provides a flowchart of a method for managing e-books. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0054] In some embodiments of the present application, see Figure 1 As shown, a management system for electronic books includes:

[0055] The collection unit is configured to obtain reading data and text data of all e-books, collect text features of each text data, and determine an initial evaluation value of each e-book based on the text features and feature models.

[0056] The judgment unit is configured to generate a reading mark according to each reading data, determine a reading identification based on the number of the reading marks, and judge whether to adjust the initial evaluation value according to the reading identification.

[0057] The first management unit is configured to, when determining to adjust the initial evaluation value, compare the number of tags with the historical data set, determine the evaluation adjustment factor based on the comparison result, determine the target evaluation value based on the evaluation adjustment factor and the initial evaluation value, perform cluster analysis on all e-books based on text features, divide the first book set based on the clustering results, compare each target evaluation value in the first book set with the target evaluation threshold, and construct the second book set based on the comparison result.

[0058] The second management unit is configured to store the first book set and the second book set and determine access rights, and determine a backup mode of the first book set and the second book set according to the book storage capacity.

[0059] Specifically, the collection unit uses web crawlers and sniffing tools to call the platform's server to obtain reading data and text data for all e-books. Each e-book corresponds to a set of reading data and text data. Reading data includes parameters such as clicking on an e-book to start reading and pause reading. Text data includes parameters such as the number of words in an e-book and the related fields it represents. Text features reflect keywords related to the text data, such as words such as crime, mystery, and clues in suspense novels. The text features are combined with the established feature model to determine the initial evaluation value of each e-book. The initial evaluation value reflects the content quality and characteristics of an e-book. The higher the initial evaluation value, the wider the coverage of its related keywords. The judgment unit generates reading tags based on the reading data. For example: if a user frequently reads an e-book, a reading mark can be established for the user's reading behavior, and the reading mark is determined based on the number of these reading marks. A large number of marks indicates that the user is very interested in it, and the reading mark tends to be "popular" or "high attention". Whether the initial evaluation value needs to be adjusted is determined based on the reading mark. When the reading mark shows that a book has a high degree of attention, its initial evaluation value needs to be adjusted. When it is determined that the initial evaluation value needs to be adjusted, the first management unit compares the number of marks with the historical data set to determine the evaluation adjustment factor, thereby avoiding blind adjustment of the initial evaluation value and obtaining the target evaluation value. At the same time, all e-books are clustered based on text features, and e-books with similar text features are divided into a first book set. For example, all martial arts e-books are grouped into one category. All e-books are divided into multiple categories or sets through clustering, so that similar or related e-books are grouped into the same set, forming a stable and reliable classification management foundation. The target evaluation value of each e-book in the first book set is compared with the target evaluation threshold to construct a second book set. The second book set represents a category of e-books that are similar or related and are relatively "popular" or "highly-regarded". The second management unit is responsible for storing the first book set and the second book set and determining the relevant access rights. At the same time, the backup mode is determined based on the book storage capacity. For popular and large-capacity e-books, a more complex backup mode is required to prevent the leakage of some exclusive e-books, thereby ensuring the data security of the e-books.

[0060] It can be understood that through the clustering analysis of text features and the adjustment of the initial evaluation value based on reading data, the system can classify e-books of different types and different reading levels into corresponding sets respectively, avoiding a chaotic storage state, improving the search efficiency of users when looking for specific books, determining the backup mode according to the storage capacity of the books, ensuring the security of e-book data, avoiding data loss caused by storage failures, and at the same time, setting relevant access permissions to protect the book copyright, thus realizing the orderly management of e-books.

[0061] In some embodiments of the present application, when collecting the text features of each text data, it includes: the collection unit analyzes the text data of all e-books based on natural language processing, performs word segmentation, stop word removal, and stemming on each text data, and uses a dependency parsing model to analyze the relationships between words to determine book phrases, and takes the book phrases that conform to the syntactic structure as keywords, and takes all the keywords of each text data and the size of the text data as text features.

[0062] Specifically, when collecting the text features of the text data, first analyze the text data of all e-books through natural language processing (NLP) technology. The analysis includes the following three main steps: Word segmentation: Cut the continuous text in the text data into independent words or phrases for subsequent processing. Stop word removal: Remove the common words that do not affect the actual semantics in the text data (such as "of", "is", etc.) to improve the effectiveness of the analysis. Stemming: Restore the words to their root forms (such as "suspense" and "suspenseful" unified as "suspense") to reduce semantic redundancy. Use a dependency parsing model to analyze the grammatical and semantic relationships between words and extract noun phrases from the document. Dependency parsing can accurately identify noun phrases (such as "ancient legend", "space exploration"), which reflect the themes of e-books and are the key basis for keyword extraction. The dependency parsing model is mature and lengthy, and will not be described in detail here. By combining NLP technology and dependency parsing, the extraction and effective recognition of text features are achieved, thus improving the intelligent level of e-book management.

[0063] In some embodiments of the present application, when determining the initial evaluation value of each e-book based on text features and a feature model, it includes: obtaining a feature data set and sampling it according to a sampling ratio to obtain a training set and a test set, using grid search to find the hyperparameters of the model, establishing a random forest model, using the training set to train the random forest model, substituting the test set into the trained random forest model and determining the accuracy rate predicted by the model. When the accuracy rate is greater than or equal to the accuracy rate threshold, determine the trained random forest model as the feature model; otherwise, continue to train the random forest model until it is greater than or equal to the accuracy rate threshold, and substitute the text features into the feature model to determine the initial evaluation value of each e-book.

[0064] Specifically, the feature dataset contains all keyword data and the data evaluation values ​​matched by the data samples. The feature dataset is divided into a training set and a test set. The split ratio is typically 4:1, ensuring that both the training and test sets contain diverse data to improve the model's generalization ability. Grid search exhaustively searches for hyperparameters in the parameter space, such as the number of trees and the maximum tree depth. The training set is used to train a random forest model. Random forests improve model accuracy and stability by integrating multiple decision trees and averaging their predictions. During training, the model attempts to learn patterns and relationships in the data to improve its prediction or classification capabilities. The test set is fed into the trained random forest model to calculate the model's accuracy. Accuracy reflects the model's performance on unseen data and is an important metric for evaluating model performance. Once the model's accuracy exceeds or equals a threshold, it is considered to have stably approached the global optimal solution. The trained random forest model is then designated as the feature model, which outputs the initial evaluation value. If the model fails to reach the threshold, the random forest model is trained again until it reaches or equals the threshold. The accuracy threshold in this embodiment is preferably 80%. The text features are substituted into the feature model to determine the initial evaluation value of each e-book, and the machine learning model is used for prediction, thereby ensuring the reliability and stability of e-book classification management.

[0065] In some embodiments of the present application, when generating a reading mark according to each reading data and determining a reading identifier based on the number of marks of the reading mark, it includes: each text data corresponds to a reading data, a judgment unit obtains the interval time between each reading and the previous reading in the reading data, and generates a reading mark for the interval time greater than the interval time threshold, statistically generates the number of marks for the reading mark, pre-sets a first preset mark number and a second preset mark number, the first preset mark number is greater than the second preset mark number, when the mark number is greater than or equal to the first preset mark number, the reading identifier is determined to be a high-frequency reading identifier, when the mark number is less than the first preset mark number and greater than the second preset mark number, the reading identifier is determined to be a medium-frequency reading identifier, and when the mark number is less than or equal to the second preset mark number, the reading identifier is determined to be a low-frequency reading identifier.

[0066] In some embodiments of the present application, when determining whether to adjust the initial evaluation value based on a reading mark, it includes: when a high-frequency reading mark is identified, the judgment unit determines to adjust the initial evaluation value; otherwise, it determines not to adjust the initial evaluation value, and determines the initial evaluation value as the target evaluation value.

[0067] Specifically, each e-book corresponds to a reading data and a text data. The judgment unit focuses on the interval time between each reading and the previous reading. This indicator can effectively reflect the user's reading habits and interest intensity in books. The interval time threshold is preferably 60s to avoid short-term pauses caused by users switching screens or clicking on advertisements, thereby affecting the statistical results. When the interval time exceeds the preset interval time threshold, a reading mark is generated for the interval, and the abstract reading behavior is converted into statistically quantifiable data. By counting the number of reading marks and comparing them with the preset first preset mark number and second preset mark number, the reading behavior is divided into three levels: high-frequency reading mark, medium-frequency reading mark and low-frequency reading mark. The first preset mark number is preferably 10, and the second preset mark number is preferably 5. A high-frequency reading mark means that the user has a strong interest in the e-book, and repeated reading is an intuitive reflection of the attractiveness of the e-book. A medium-frequency reading mark represents that the user maintains a certain degree of attention to the book, but the interest intensity is slightly weaker, while a low-frequency reading mark indicates that the user has a low willingness to read. When a high-frequency reading mark is identified, it is determined that the initial evaluation value needs to be adjusted. This is because the user's reading behavior verifies the actual value of the e-book and its initial evaluation value needs to be changed. In other cases, the initial evaluation value is maintained, so that the scattered reading data is structured, ensuring the intelligent level of classification and management of e-books.

[0068] In some embodiments of the present application, when comparing the number of tags with the historical data set, determining the evaluation adjustment factor based on the comparison result, and determining the target evaluation value based on the evaluation adjustment factor and the initial evaluation value, it includes: the historical data set includes a number of historical tags and a number of historical evaluation adjustment factors, each historical tag number corresponds to a historical evaluation adjustment factor, when there is a number of historical tags in the historical data set whose similarity with the high-frequency reading mark is greater than the similarity threshold, the first management unit uses the historical evaluation adjustment factor of the historical tag number corresponding to the maximum similarity as the evaluation adjustment factor, otherwise, the first management unit determines the evaluation adjustment factor based on the pre-trained evaluation model, and the target evaluation value is the product of the evaluation adjustment factor and the initial evaluation value.

[0069] Specifically, a similarity threshold is used to determine the degree of match between the current number of tags and historical conditions. When historical data with a high degree of similarity is found, this data can be directly used to determine the evaluation adjustment factor, thereby ensuring the reliability and consistency of the adjustment operation. In cases where the current number of tags does not fully match the historical data, the evaluation adjustment factor is determined through a pre-trained evaluation model. In response to changes in the number of tags, data-driven automated adjustments are made, thereby improving the system's automation level and the accuracy of e-book classification. By comprehensively utilizing a large amount of historical data, rich reference information is provided for the determination of the evaluation adjustment factor, and it can learn and optimize from historical experience, thereby continuously improving the accuracy of the evaluation adjustment factor. The evaluation model is obtained through training based on different data samples and the evaluation adjustment factors that match the data samples. The specific training process is consistent with the feature model and will not be repeated here. The greater the number of tags, the larger the corresponding evaluation adjustment factor.

[0070] It can be understood that the initial evaluation value is adjusted according to the evaluation adjustment factor. When the number of marks is larger, the corresponding evaluation adjustment factor will increase accordingly. By establishing a multiplication relationship between the evaluation adjustment factor and the initial evaluation value, the control of the initial evaluation value is achieved, and the intelligent level of e-book classification management is improved.

[0071] In some embodiments of the present application, when all e-books are clustered and analyzed according to text features and the first book set is divided according to the clustering results, it includes: the first management unit normalizes each parameter in the text features to form a text feature vector, and combines the text feature vectors of all e-books into a feature matrix, randomly selects K cluster centers, and determines the clustering distance from each text feature vector to each cluster center.

[0072] S1: Assign the e-books to the set corresponding to the cluster center with the closest cluster distance.

[0073] S2: Re-determine the position of each cluster center.

[0074] S3: Repeat S1 and S2 until the position of the cluster center no longer changes or the preset number of iterations is reached, and all e-books are divided into K first book sets.

[0075] Specifically, each parameter in the text features (such as size and all keywords) is normalized to ensure that all feature values ​​are within the same magnitude range (e.g., [0, 1]). This prevents distortion in clustering results due to differences in feature vector scale. Using the k-means clustering algorithm, reliable and stable e-book classification management is achieved. Based on multidimensional feature vectors and normalization, similarities between text features are effectively captured, ensuring the accuracy of the classification results. The cluster center K and the number of iterations are adjusted based on the specific e-book application scenario. The resulting collection of text features provides data support for subsequent storage. The method for determining cluster distance is lengthy and mature, so we will not describe it in detail here.

[0076] In some embodiments of the present application, when comparing each target evaluation value in the first book set with the target evaluation threshold and constructing the second book set based on the comparison results, it includes: the first management unit extracts the e-books in the first book set whose target evaluation values ​​are greater than or equal to the target evaluation threshold, and constructs the second book set, and constructs the remaining e-books in the first book set into a third book set.

[0077] In some embodiments of the present application, when storing a first book set and a second book set and determining access rights, and determining a backup mode of the first book set and the second book set based on the book storage capacity, it includes: a second management unit establishing a first repository and a second repository, the second management unit storing all second book sets in the first repository and setting a first access right for the first repository, storing all third book sets in the second repository and setting a second access right for the second repository, the level of the first access right is greater than the level of the second access right, obtaining the first book storage capacity of the first repository, when the first book storage capacity is greater than or equal to half of the storage capacity of the first repository, determining the backup mode of the first repository as incremental backup, when the first book storage capacity is less than half of the storage capacity of the first repository, determining the backup mode of the first repository as full backup, and determining the backup mode of the second repository as periodic backup.

[0078] Specifically, the first management unit compares the target evaluation value with the target evaluation threshold, achieving secondary screening and grading of e-books. The target evaluation value comprehensively considers the e-book's textual features and user reading data, and is a quantitative reflection of the book's quality and popularity. E-books with a target evaluation value greater than or equal to the target evaluation threshold are extracted to form a second book collection. The e-books in the second book collection have a wider keyword coverage, relatively comprehensive content, and a high degree of repetitive reading. The remaining e-books in the first book collection form a third book collection, which has relatively lower content and popularity than the second book collection. This classification is based on the target evaluation value, achieving refined classification management of e-books. Since all e-books are divided into K first book collections during clustering, multiple second book collections and multiple third book collections are also formed when extracting e-books. The second management unit establishes a first repository and a second repository. The first and second repositories are established based on MongoDB or Firebase, and users can traverse and search for required e-books in the first and second repositories. The second book collection is stored in the first repository and a higher first access permission is set. These e-books are of high value and popularity and are suitable for opening to specific users to avoid the risk of exclusive e-books being leaked. The third book collection is stored in the second repository and a lower second access permission is set to balance resource allocation and user needs.

[0079] It is understood that the first book storage capacity represents the actual capacity of the second book collection. The backup mode is dynamically determined based on the book storage capacity of the first repository. When the first book storage capacity is greater than or equal to one-half of the first repository's storage capacity, incremental backup is used, backing up only newly added or modified e-books, thereby improving backup efficiency and conserving storage resources. When the capacity is less than one-half, full backup is used to ensure the integrity and reliability of the e-book backup. For the second repository, periodic backup is used, backing up e-books at fixed intervals. This ensures secure e-book management while reducing backup costs and resource burden, thereby enhancing the level of intelligent e-book management.

[0080] In summary, the present invention has the following beneficial effects: The classification system is optimized by acquiring e-book text data, extracting text features, and combining them with a feature model to determine an initial evaluation value, ensuring the reliability of quantification for each e-book. Cluster analysis is performed on all e-books, enabling orderly classification of e-books covering diverse fields and a wide variety of categories based on their text features, thus avoiding the chaotic e-book storage caused by the lack of an effective classification system. The first management unit determines the evaluation adjustment factor through comparison and integrates the user's reading data into the e-book evaluation system, enabling e-book classification management to accurately reflect the user's reading habits and interests. The second management unit stores the first and second book collections, determines access rights, and dynamically determines the backup mode based on the book storage capacity. This not only ensures the security of e-book storage and prevents data loss, but also dynamically plans backup strategies based on actual storage needs, improving the utilization efficiency of storage resources and ensuring the reliability and intelligence of e-book management.

[0081] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a method for managing e-books, which is used to apply the above-mentioned management system for e-books, including:

[0082] S100: Obtain reading data and text data of all e-books, collect text features of each text data, and determine an initial evaluation value of each e-book based on the text features and feature model.

[0083] S200: generating a reading mark according to each reading data, determining a reading identification based on the number of the reading marks, and determining whether to adjust the initial evaluation value according to the reading identification.

[0084] S300: When it is determined to adjust the initial evaluation value, the number of tags is compared with the historical data set, and the evaluation adjustment factor is determined based on the comparison result. The target evaluation value is determined according to the evaluation adjustment factor and the initial evaluation value, and all e-books are clustered according to the text features. The first book set is divided according to the clustering results, and each target evaluation value in the first book set is compared with the target evaluation threshold. The second book set is constructed according to the comparison result.

[0085] S400: storing the first book set and the second book set and determining access rights, and determining backup modes of the first book set and the second book set according to the book storage capacity.

[0086] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A management system for electronic books, characterized in that: include: a collection unit configured to obtain reading data and text data of all e-books, collect text features of each text data, and determine an initial evaluation value of each e-book based on the text features and a feature model; a judgment unit configured to generate a reading mark according to each reading data, determine a reading identification based on the number of marks of the reading mark, and judge whether to adjust the initial evaluation value according to the reading identification; a first management unit configured to, when determining to adjust the initial evaluation value, compare the number of tags with a historical data set, determine an evaluation adjustment factor based on the comparison result, determine a target evaluation value based on the evaluation adjustment factor and the initial evaluation value, perform cluster analysis on all e-books based on the text features, divide the first book set based on the clustering results, compare each target evaluation value in the first book set with a target evaluation threshold, and construct a second book set based on the comparison result; a second management unit configured to store the first book set and the second book set, determine access permissions, and determine a backup mode for the first book set and the second book set according to a book storage capacity; When collecting text features of each text data, including: The collection unit analyzes the text data of all e-books based on natural language processing, performs word segmentation, removes stop words, and extracts stems for each text data, and uses a dependency syntactic analysis model to parse the relationship between words to determine book phrases, and uses the book phrases that conform to the syntactic structure as keywords; Taking all keywords and text data size of each text data as the text features; When determining the initial evaluation value of each e-book based on the text features and the feature model, the method includes: Obtain the feature data set and sample it according to the sampling ratio to obtain the training set and test set, use grid search to find the model's hyperparameters, and establish a random forest model; Using the training set to train the random forest model, substituting the test set into the trained random forest model and determining the accuracy of model prediction, when the accuracy is greater than or equal to an accuracy threshold, determining the trained random forest model as the feature model; otherwise, continuing to train the random forest model until it is greater than or equal to the accuracy threshold; The text features are substituted into the feature model to determine the initial evaluation value of each electronic book.

2. The electronic book management system according to claim 1, characterized in that: When generating a reading mark according to each of the reading data and determining a reading identifier based on the number of marks of the reading mark, the method includes: Each text data corresponds to a reading data; The judgment unit obtains the interval time between each reading and the previous reading in the reading data, generates the reading mark for the interval time greater than the interval time threshold, and counts the number of the marks generated; Presetting a first preset number of marks and a second preset number of marks, wherein the first preset number of marks is greater than the second preset number of marks; When the number of the marks is greater than or equal to the first preset number of marks, determining that the reading mark is a high-frequency reading mark; When the number of marks is less than the first preset number of marks and greater than the second preset number of marks, determining that the reading mark is a medium frequency reading mark; When the number of marks is less than or equal to a second preset number of marks, the reading mark is determined to be a low-frequency reading mark.

3. The electronic book management system according to claim 2, characterized in that: When determining whether to adjust the initial evaluation value according to the reading identifier, the method includes: When the high-frequency reading mark is identified, the judgment unit determines to adjust the initial evaluation value; otherwise, the judgment unit determines not to adjust the initial evaluation value and determines the initial evaluation value as the target evaluation value.

4. The electronic book management system according to claim 3, characterized in that: When comparing the number of markers with the historical data set, determining the evaluation adjustment factor according to the comparison result, and determining the target evaluation value according to the evaluation adjustment factor and the initial evaluation value, the method includes: The historical data set includes a number of historical marker quantities and a number of historical evaluation adjustment factors, each historical marker quantity corresponds to a historical evaluation adjustment factor; When there are a number of historical marks in the historical data set whose similarity to the high-frequency reading identifier is greater than a similarity threshold, the first management unit uses the historical evaluation adjustment factor corresponding to the number of historical marks with the maximum similarity as the evaluation adjustment factor; otherwise, the first management unit determines the evaluation adjustment factor according to a pre-trained evaluation model; The target evaluation value is the product of the evaluation adjustment factor and the initial evaluation value.

5. The electronic book management system according to claim 4, characterized in that: When clustering all the e-books according to the text features and dividing the first book set according to the clustering results, the method includes: The first management unit normalizes each parameter in the text feature to form a text feature vector, combines the text feature vectors of all e-books into a feature matrix, randomly selects K cluster centers, and determines a cluster distance from each text feature vector to each cluster center; S1: Assign the e-books to the set corresponding to the cluster center with the closest cluster distance; S2: Re-determine the position of each cluster center; S3: Repeat S1 and S2 until the position of the cluster center no longer changes or the preset number of iterations is reached, and divide all e-books into K first book sets.

6. The electronic book management system according to claim 5, characterized in that: When comparing each target evaluation value in the first book set with the target evaluation threshold and constructing the second book set according to the comparison result, the method includes: The first management unit extracts electronic books with target evaluation values ​​greater than or equal to the target evaluation threshold from the first book set, constructs the second book set, and constructs the remaining electronic books in the first book set into a third book set.

7. The electronic book management system according to claim 6, characterized in that: When the first book set and the second book set are stored and access rights are determined, and a backup mode of the first book set and the second book set is determined according to the book storage capacity, the method includes: The second management unit establishes a first storage library and a second storage library; The second management unit stores the entire second book set in the first storage repository and sets a first access permission for the first storage repository, stores the entire third book set in the second storage repository and sets a second access permission for the second storage repository, wherein the level of the first access permission is greater than the level of the second access permission; obtaining a first book storage capacity of the first storage library, and when the first book storage capacity is greater than or equal to half of the storage capacity of the first storage library, determining the backup mode of the first storage library to be incremental backup; When the first book storage capacity is less than half of the storage capacity of the first storage library, the backup mode of the first storage library is determined to be full backup; The backup mode of the second storage library is determined to be periodic backup.

8. A method for managing e-books, for applying the e-book management system according to any one of claims 1 to 7, characterized in that: include: Obtaining reading data and text data of all e-books, collecting text features of each text data, and determining an initial evaluation value of each e-book based on the text features and feature model; generating a reading mark according to each reading data, determining a reading identification based on the number of marks of the reading mark, and determining whether to adjust the initial evaluation value according to the reading identification; When it is determined that the initial evaluation value is to be adjusted, the number of tags is compared with the historical data set, an evaluation adjustment factor is determined based on the comparison result, a target evaluation value is determined based on the evaluation adjustment factor and the initial evaluation value, all e-books are clustered according to the text features, a first book set is divided according to the clustering results, each target evaluation value in the first book set is compared with a target evaluation threshold, and a second book set is constructed based on the comparison results; The first book set and the second book set are stored and access rights are determined, and backup modes of the first book set and the second book set are determined according to the book storage capacity.

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