CN118626804B - Reading analysis processing method for electronic book - Google Patents
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Abstract
The invention discloses a reading analysis processing method of an electronic book, which belongs to the technical field of reading analysis processing of electronic books and comprises the steps of firstly obtaining an electronic book history reading record of each user stored by a platform, setting each user reading class based on the electronic book history reading record of each user, secondly obtaining the electronic book reading record of each user in real time, dynamically updating each user reading class based on the electronic book reading record of each user, thirdly carrying out real-time development evaluation based on each user reading class to obtain a corresponding development state curve, fourthly carrying out real-time early warning analysis on platform development according to the development state curve to obtain a corresponding early warning analysis result and carrying out corresponding early warning processing according to the early warning analysis result, realizing multi-dimensional reading feature extraction and intelligent classification by the method, accurately grasping the reading requirements and preferences of various users, reasonably configuring platform resources based on the user classification and the development state analysis, and improving the resource utilization efficiency.
Description
Technical Field
The invention belongs to the technical field of reading analysis processing of electronic books, and particularly relates to a reading analysis processing method of an electronic book.
Background
In the rapidly-developed digital reading era, an electronic book reading platform has become an important channel for people to acquire knowledge, entertainment and leisure. The platform side is used as a content provider and a service operator and faces multiple challenges of precisely grasping user demands, optimizing resource allocation, improving user experience and the like, if a large amount of user reading record data are accumulated on the platform, but effective analysis tools and methods are lacked, the potential value of the data cannot be fully mined, so that the platform side cannot know the dynamic change condition of the user in time, judgment is carried out only through benefits, the problem of user fluctuation covered by stage benefits is easy to occur, and in order to cope with the challenges and the problems, the platform side is urgent to need an efficient user analysis processing method to realize deep insight and precise management of user reading behaviors.
In order to solve the above problems, the present invention provides a reading analysis processing method for an electronic book.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a reading analysis processing method of an electronic book.
The aim of the invention can be achieved by the following technical scheme:
a reading analysis processing method of an electronic book comprises the following steps:
step one, acquiring an electronic book history reading record of each user stored in a platform, and setting each user reading class based on the electronic book history reading record of each user;
further, the setting method of the user reading class comprises the following steps:
step SA1, setting user reading characteristics of corresponding users according to the history reading records of the electronic book, calculating the similarity among the user reading characteristics, classifying the users with the similarity larger than a threshold value X1 into unit classes, and setting classification representative characteristics;
step SA2, carrying out demand evaluation on the unit features of each unit class based on each classification representative feature to obtain each unit feature meeting each classification representative feature;
step SA3, marking each classification representative feature corresponding to the unit feature as a classification feature to be selected;
Step SA4, calculating a matching value between the unit feature and each class feature to be selected, and distributing the unit class corresponding to the unit feature to the class corresponding to the class representative feature with the highest matching value;
and step SA5, the step SA4 is circulated until all the unit classes are allocated, and the class corresponding to each class representative feature is marked as a user reading class.
Further, in step SA2, the method for performing demand assessment on the unit features of each unit class based on each classification representative feature is as follows:
establishing a demand evaluation model, wherein the expression of the demand evaluation model is as follows:
;
Wherein s is input data, the input data is unit characteristic and classification representative characteristic, and the output data is a demand evaluation value XQ(s);
Analyzing the classification representative features and the unit features through a demand evaluation model to obtain corresponding demand evaluation values;
and determining each unit characteristic meeting each classification representative characteristic according to the demand evaluation value.
Further, the matching value in step SA4 is the similarity.
Step two, acquiring electronic book reading records of users in real time, and dynamically updating the reading classes of the users based on the electronic book reading records of the users;
Thirdly, carrying out real-time development evaluation based on the reading class of each user to obtain a corresponding development state curve;
further, the method for performing real-time development evaluation based on the reading class of each user comprises the following steps:
determining state targets of all user reading classes, and calculating user classification values corresponding to all user reading classes based on all the state targets;
According to the formula Calculating a development state value corresponding to the time;
wherein PK is a development state value, j represents a corresponding user reading class, PHj is a user classification value of the corresponding user reading class, and ηj is a weight coefficient of the corresponding user reading class;
Generating a development state curve according to the development state values corresponding to the times, and supplementing the user classification values corresponding to the reading classes of the users to the development state curve for display.
Further, the method for calculating the user classification value comprises the following steps:
Marking data corresponding to each index in the state target as an index standard, and marking the indexes as i, i=1, 2, and the number of the indexes is a positive integer;
Identifying state data corresponding to the user reading class in real time, namely data related to the state target, extracting characteristics of the state data, obtaining operation indexes corresponding to all index standards, and marking the operation indexes as YLi;
According to the formula Calculating a corresponding user classification value;
Wherein PH is a user classification value, e is a natural constant, lambdai is a proportionality coefficient, and the value range is 0< lambdai is less than or equal to 1.
And fourthly, carrying out real-time early warning analysis on the platform development according to the development state curve to obtain a corresponding early warning analysis result, and carrying out corresponding early warning processing according to the early warning analysis result.
Further, the method for carrying out real-time early warning analysis on the platform development according to the development state curve comprises the following steps:
establishing a first early warning model, wherein the expression of the first early warning model is as follows:
;
wherein PHtj is a user classification value corresponding to the user reading class at the corresponding time, and the output data is a first early warning value YD (PHtj);
The method comprises the steps of carrying out real-time analysis on user classification values of all user reading classes through a first early warning model to obtain first early warning values of all user reading classes at corresponding time;
Supplementing a corresponding curve slope change curve in the classification value graph;
estimating target completion time according to the first coordinate graph and the classification value graph, and performing first early warning evaluation based on the target completion time to obtain a first evaluation result corresponding to the corresponding user reading class;
Calculating a corresponding development growth rate in real time according to the development state curve, and performing first early warning evaluation according to the obtained development growth rate to obtain a corresponding second evaluation result;
And integrating the first evaluation result and the second evaluation result into an early warning analysis result.
Further, the early warning processing comprises the step of managing the bookshelf of the user, and the management method comprises the following steps:
Acquiring bookshelf management requirements of a user, extracting features of the bookshelf management requirements to acquire all schoolbag management features, generating corresponding schoolbags according to all the schoolbag management features, and marking all the schoolbag attributes;
Placing each electronic book on the bookshelf into a corresponding schoolbag based on the schoolbag attribute of each schoolbag;
when the user does not have bookshelf management requirements, identifying user reading classes to which the user belongs, acquiring all schoolbag management templates corresponding to the user reading classes, selecting applied schoolbag management templates by the user, and generating all schoolbags according to the applied schoolbag management templates.
Further, the method for setting the schoolbag management template of each user reading class comprises the following steps:
identifying schoolbags set by each user in the user reading class and corresponding schoolbag attributes, and integrating the schoolbags and the corresponding schoolbag attributes into a schoolbag template to be selected;
The method comprises the steps of carrying out evaluation screening on all the to-be-selected schoolbag templates to obtain the priority of all the to-be-selected schoolbag templates, sorting all the to-be-selected schoolbag templates according to the order of the priority to obtain a display list, marking all the F to-be-selected schoolbag templates in the display list as schoolbag management templates before sorting, and enabling F to be a positive integer.
Compared with the prior art, the invention has the beneficial effects that:
The multi-dimensional reading feature extraction and intelligent classification are realized, the reading requirements and preferences of various users are accurately grasped, platform resources are reasonably configured based on user classification and development state analysis, the resource utilization efficiency is improved, products and service strategies are timely adjusted according to the user requirement change, the user experience and satisfaction are improved, and the platform side can timely learn about market change and user trend through real-time dynamic analysis and decision support, and establish effective competition strategies to enhance market competitiveness.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a method for reading, analyzing and processing an electronic book includes:
step one, acquiring an electronic book history reading record of each user stored in a platform, and setting each user reading class based on the electronic book history reading record of each user;
the platform refers to an electronic book reading platform.
The setting method of the user reading class comprises the following steps:
Step SA1, counting related data such as reading quantity, subject material occupation ratio, payment expenditure and the like of a user on each electronic book according to the historical reading record of the electronic books, and identifying the electronic book classification, subject material definition of contents and the like according to the actual setting of a platform, wherein the classification, subject material and the like of each electronic book, such as each subject material of a womb, a stricken, a seed field and the like in the historical classification, are preset on the platform, corresponding characteristic data are extracted, and each characteristic data are integrated into user reading characteristics;
Calculating the similarity between the reading characteristics of all users, wherein the similarity is greater than a threshold value X1, and the users are classified into unit types, wherein the threshold value X1 is generally greater than 90 percent, such as 95 percent, 98 percent and the like, and is used for classifying the users which can be regarded as the same reading characteristics into one type, reducing the subsequent data processing amount and improving the processing efficiency;
the platform side sets each classification representative feature according to the management requirement, namely, the subsequent classification representative feature corresponds to one user reading class, is the lowest standard of the user reading class, and mainly aims at the user reading class with the requirement of the platform side on charge expenditure;
Step SA2, carrying out demand evaluation on the unit features of each unit class based on each classification representative feature to obtain each unit feature meeting each classification representative feature, wherein the condition that one unit feature meets a plurality of classification representative features possibly occurs;
step SA3, marking each classification representative feature corresponding to the unit feature as a classification feature to be selected;
Step SA4, calculating a matching value between the unit feature and each class feature to be selected, and distributing the unit class corresponding to the unit feature to the class corresponding to the class representative feature with the highest matching value;
and step SA5, the step SA4 is circulated until all the unit classes are allocated, and the class corresponding to each class representative feature is marked as a user reading class.
In one embodiment, the method for performing the demand assessment on the unit features of each unit class based on the representative features of each category in step SA2 is as follows:
establishing a demand evaluation model, wherein the demand evaluation model is used for evaluating input unit characteristics and classification representative characteristics and judging whether the input unit characteristics and classification representative characteristics meet classification requirements or not, and the expression of the demand evaluation model is as follows S is input data, the input data is unit characteristics and classification representative characteristics, and the output data is a demand evaluation value XQ(s);
Analyzing the classification representative features and the unit features through a demand evaluation model to obtain corresponding demand evaluation values;
and determining each unit characteristic meeting each classification representative characteristic according to the demand evaluation value.
In one embodiment, the matching value in step SA4 is similarity.
In one embodiment, the matching value in step SA4 may be other characteristics that can be measured for distance, which can be calculated based on the prior art.
Step two, acquiring electronic book reading records of users in real time, and dynamically updating the reading classes of the users based on the electronic book reading records of the users;
Thirdly, carrying out real-time development evaluation based on the reading class of each user to obtain a corresponding development state curve;
the method for carrying out real-time development evaluation based on the reading class of each user comprises the following steps:
The platform side is generally set according to the development target, if the platform side does not set, the state of the previous user reading class is increased by a preset amplitude to serve as the state target, such as 1%;
Marking data corresponding to each index in the state target as an index standard, and marking the indexes as i, i=1, 2, and the number of the indexes is a positive integer;
Identifying state data corresponding to the user reading class in real time, namely data related to the state target, extracting characteristics of the state data, obtaining operation indexes corresponding to all index standards, and marking the operation indexes as YLi;
According to the formula Calculating a corresponding user classification value;
wherein PH is a user classification value, e is a natural constant, lambdai is a proportionality coefficient, and the value range is 0< lambdai is less than or equal to 1;
synchronously setting weight coefficients of reading classes of all users when setting the classification characteristics to be selected by a platform side;
According to the formula Calculating a development state value corresponding to the time;
wherein PK is a development state value, j represents a corresponding user reading class, PHj is a user classification value of the corresponding user reading class, and ηj is a weight coefficient of the corresponding user reading class;
Generating a development state curve according to the development state values corresponding to the times, and supplementing the user classification values corresponding to the reading classes of the users to the development state curve for display.
And fourthly, carrying out real-time early warning analysis on the development of the platform according to the development state curve, obtaining a corresponding early warning analysis result, and carrying out corresponding early warning processing according to the obtained early warning analysis result.
The method for carrying out real-time early warning analysis on the platform development according to the development state curve comprises the following steps:
Establishing a first early warning model which is used for analyzing the classification value of the corresponding user and judging whether the development of the user reading class meets the requirement or not, wherein the development is a process and can not meet the requirement in a period of time, and the expression of the first early warning model is as follows Wherein PHtj is a user classification value corresponding to the user reading class at the corresponding time, and the output data is a first early warning value YD (PHtj);
the method comprises the steps of carrying out real-time analysis on user classification values of all user reading classes through a first early warning model to obtain first early warning values of all user reading classes at corresponding time, respectively generating a corresponding first coordinate graph and a corresponding classification value graph based on the first early warning values and the user classification values corresponding to all time, namely, taking the horizontal axis as time, and taking the vertical axis as the first early warning values and the user classification values respectively;
Supplementing a corresponding curve slope change curve in the classification value diagram, namely determining a corresponding curve slope according to the user classification value of the corresponding time, and further generating a corresponding curve slope change curve;
Estimating target completion time, namely time with a first early warning value of 1, according to the first coordinate graph and the classification value graph, carrying out first early warning evaluation based on the target completion time, and comparing the target completion time with preset completion target time;
Calculating a corresponding development growth rate in real time according to the development state curve, and carrying out first early warning evaluation according to the obtained development growth rate, namely that the development growth rate cannot be lower than a preset value;
And integrating the first evaluation result and the second evaluation result into an early warning analysis result.
The specific early warning processing mode is preset according to the management requirement of the platform side, and the matching processing is carried out subsequently.
In one embodiment, the early warning process is to solve the development problem of the corresponding user reading class, such as adding investment of the corresponding user reading class, conducting drainage, adding excellent homework conforming to the scope of the user reading class, but the processing can also be performed from the reading experience of the user, because the current many platforms are inconvenient for the user to manage the electronic books added with the bookshelf, the user can perform operations of classifying management, top setting, and the like of the bookshelf, but the user can adjust the operations by himself, and the user can be hard to manage a plurality of read electronic books with the lapse of time, based on the following early warning process mode is provided in the embodiment:
The method comprises the steps of acquiring bookshelf management requirements of a user, namely inputting information such as the number of books, book definitions, inclusion ranges, display modes and the like which need to be established on a platform, wherein each electronic book is stored in the book and can be checked only by clicking the book, and stealth forms, namely, the electronic books belonging to the book can be directly checked, and stealth of the book are displayed in the bookshelf according to the sequence in the book, so that the position of the electronic book is convenient to adjust, and the stealth book is set as a top;
The method comprises the steps of carrying out feature extraction on bookshelf management requirements to obtain all schoolbag management features, wherein the schoolbag management features are related features such as schoolbag definition, inclusion range, display modes and the like;
Placing each electronic book on the bookshelf into a corresponding schoolbag based on the schoolbag attribute of each schoolbag;
When the user does not have bookshelf management requirements, identifying user reading classes to which the user belongs, acquiring all schoolbag management templates corresponding to the user reading classes, selecting the schoolbag management templates for application by the user, and generating all schoolbags according to the applied schoolbag management templates.
In one embodiment, the schoolbag management template of each user reading class may be directly specified by the platform side.
In one embodiment, the method for setting the schoolbag management template of each user reading class comprises the following steps:
identifying schoolbags set by each user in the user reading class and corresponding schoolbag attributes, and integrating the schoolbags and the corresponding schoolbag attributes into a schoolbag template to be selected;
The method comprises the steps of carrying out evaluation screening on all schoolbag templates to be selected to obtain priorities of all schoolbag templates to be selected, determining corresponding priorities according to various modes such as using quantity, evaluation, similarity distribution and the like, namely firstly carrying out similarity classification to form a plurality of classifications of different types, selecting one best from each classification to carry out participation, and carrying out evaluation by using the existing priority evaluation mode, wherein the priority of the selected schoolbag templates is lowest, sorting all schoolbag templates to be selected according to the order of the priorities to obtain a display list, marking F all schoolbag templates to be selected before sorting in the display list as schoolbag management templates, and F is a positive integer.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (6)
1. The reading analysis processing method of the electronic book is characterized by comprising the following steps of:
step one, acquiring an electronic book history reading record of each user stored in a platform, and setting each user reading class based on the electronic book history reading record of each user;
the setting method of the user reading class comprises the following steps:
step SA1, setting user reading characteristics of corresponding users according to the history reading records of the electronic book, calculating the similarity among the user reading characteristics, classifying the users with the similarity larger than a threshold value X1 into unit classes, and setting classification representative characteristics;
step SA2, carrying out demand evaluation on the unit features of each unit class based on each classification representative feature to obtain each unit feature meeting each classification representative feature;
step SA3, marking each classification representative feature corresponding to the unit feature as a classification feature to be selected;
Step SA4, calculating a matching value between the unit feature and each class feature to be selected, and distributing the unit class corresponding to the unit feature to the class corresponding to the class representative feature with the highest matching value;
Step SA5, the step SA4 is circulated until all the unit classes are allocated, and the class corresponding to each class representative feature is marked as a user reading class;
Step two, acquiring electronic book reading records of users in real time, and dynamically updating the reading classes of the users based on the electronic book reading records of the users;
Thirdly, carrying out real-time development evaluation based on the reading class of each user to obtain a corresponding development state curve;
Performing real-time early warning analysis on platform development according to the development state curve to obtain a corresponding early warning analysis result, and performing corresponding early warning treatment according to the early warning analysis result;
the method for carrying out real-time development evaluation based on the reading class of each user comprises the following steps:
determining state targets of all user reading classes, and calculating user classification values corresponding to all user reading classes based on all the state targets;
According to the formula Calculating a development state value corresponding to the time;
wherein PK is a development state value, j represents a corresponding user reading class, PHj is a user classification value of the corresponding user reading class, and ηj is a weight coefficient of the corresponding user reading class;
Generating a development state curve according to the development state values corresponding to each time, and supplementing the user classification values corresponding to the reading classes of each user into the development state curve for display;
the method for calculating the user classification value comprises the following steps:
Marking data corresponding to each index in the state target as an index standard, and marking the indexes as i, i=1, 2, and the number of the indexes is a positive integer;
Identifying state data corresponding to the user reading class in real time, namely data related to the state target, extracting characteristics of the state data, obtaining operation indexes corresponding to all index standards, and marking the operation indexes as YLi;
According to the formula Calculating a corresponding user classification value;
Wherein PH is a user classification value, e is a natural constant, lambdai is a proportionality coefficient, and the value range is 0< lambdai is less than or equal to 1.
2. The method for reading, analyzing and processing an electronic book according to claim 1, wherein in step SA2, the method for performing a demand evaluation on the unit features of each unit class based on the representative features of each category comprises:
establishing a demand evaluation model, wherein the expression of the demand evaluation model is as follows:
;
Wherein s is input data, the input data is unit characteristic and classification representative characteristic, and the output data is a demand evaluation value XQ(s);
Analyzing the classification representative features and the unit features through a demand evaluation model to obtain corresponding demand evaluation values;
and determining each unit characteristic meeting each classification representative characteristic according to the demand evaluation value.
3. The method of claim 1, wherein the matching value in step SA4 is similarity.
4. The method for reading, analyzing and processing an electronic book according to claim 1, wherein the method for performing real-time early warning analysis on platform development according to a development state curve comprises the following steps:
establishing a first early warning model, wherein the expression of the first early warning model is as follows:
;
wherein PHtj is a user classification value corresponding to the user reading class at the corresponding time, and the output data is a first early warning value YD (PHtj);
The method comprises the steps of carrying out real-time analysis on user classification values of all user reading classes through a first early warning model to obtain first early warning values of all user reading classes at corresponding time;
Supplementing a corresponding curve slope change curve in the classification value graph;
estimating target completion time according to the first coordinate graph and the classification value graph, and performing first early warning evaluation based on the target completion time to obtain a first evaluation result corresponding to the corresponding user reading class;
Calculating a corresponding development growth rate in real time according to the development state curve, and performing first early warning evaluation according to the obtained development growth rate to obtain a corresponding second evaluation result;
And integrating the first evaluation result and the second evaluation result into an early warning analysis result.
5. The method for reading and analyzing an electronic book according to claim 1, wherein the early warning process includes managing a bookshelf of a user, the method including:
Acquiring bookshelf management requirements of a user, extracting features of the bookshelf management requirements to acquire all schoolbag management features, generating corresponding schoolbags according to all the schoolbag management features, and marking all the schoolbag attributes;
Placing each electronic book on the bookshelf into a corresponding schoolbag based on the schoolbag attribute of each schoolbag;
when the user does not have bookshelf management requirements, identifying user reading classes to which the user belongs, acquiring all schoolbag management templates corresponding to the user reading classes, selecting applied schoolbag management templates by the user, and generating all schoolbags according to the applied schoolbag management templates.
6. The method for reading, analyzing and processing an electronic book according to claim 5, wherein the method for setting a schoolbag management template of each user reading class comprises:
identifying schoolbags set by each user in the user reading class and corresponding schoolbag attributes, and integrating the schoolbags and the corresponding schoolbag attributes into a schoolbag template to be selected;
The method comprises the steps of carrying out evaluation screening on all the to-be-selected schoolbag templates to obtain the priority of all the to-be-selected schoolbag templates, sorting all the to-be-selected schoolbag templates according to the order of the priority to obtain a display list, marking all the F to-be-selected schoolbag templates in the display list as schoolbag management templates before sorting, and enabling F to be a positive integer.
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| CN117540101A (en) * | 2023-12-04 | 2024-02-09 | 深圳市二一教育科技有限责任公司 | Online bookend management method and system based on artificial intelligence |
| CN117952796A (en) * | 2024-02-02 | 2024-04-30 | 广州数字出版有限公司 | Reading teaching quality assessment method and system based on data analysis |
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| CN118193845B (en) * | 2024-04-11 | 2024-11-05 | 中国标准化研究院 | Standard intelligent library knowledge recommendation method and system based on big data |
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