Online book resource management method and system
By obtaining user information and personalized push, as well as evaluating and dynamic monitoring of user uploaded resources, the problems of resource sharing and quality assurance in traditional online book resource management are solved, and efficient and accurate book resource management and push are achieved.
Patent Information
- Application Number
- CN202411273903.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-12
- Publication Date
- 2024-12-20
- Estimated Expiration
- 2044-09-12
AI Technical Summary
Traditional online book resource management lacks user upload function, making it difficult to share book resource, and fails to meet the needs of different age levels, resulting in inaccurate resource push and difficult to guarantee the quality of resources uploaded by users.
By obtaining the target user's age rating, book preferences and sharing information, use the user's book resource push model for personalized push; at the same time, the online book resource evaluation model is used to analyze and evaluate the resources uploaded by users to ensure resource quality. And update user information through dynamic monitoring models to improve push accuracy.
It realizes personalized, precise push and high-quality screening of book resources, improves user satisfaction and resource utilization efficiency, and ensures the quality and dynamic optimization of resources.
Smart Images
Figure CN118797171B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of book resource management, and in particular to an online book resource management method and system. Background Art
[0002] Traditional online book resource management is often limited to book resources added by professionals. The variety is incomplete and cannot include all published online book resources, which need to be constantly supplemented. Since there is no function to add user uploaded resources, it is difficult to share book resources among users, which limits the effective use of book resources. In addition, traditional online book resource management often does not take into account the needs of readers of different age groups and lacks targeted age classification, which may lead to accurate push that does not meet the user's age group. At the same time, book resources are of varying quality. When users upload book resources by themselves, they also need to be professionally evaluated to ensure the quality of online book resources. Summary of the invention
[0003] The present invention aims to provide an online book resource management method and system, which can improve the accuracy of online book resource push and evaluate the book resources before warehousing.
[0004] An online book resource management method comprises the following steps:
[0005] S1. Online book resource push
[0006] Obtain target user information; target user information includes user age classification information, user book preference information and user book resource sharing information; analyze based on target user information and user book resource push model to obtain user pushed book resources; user book resource push model is used to push exclusive book resources to different users based on target user information, so that the online book resources pushed to target users are personalized and accurate;
[0007] S2. Evaluation of online book resource sharing
[0008] Get the book resources uploaded by the user and obtain the uploaded book resource information to be analyzed; record the uploaded book resource information to be analyzed as the uploaded book resource information to be analyzed X, where X={X1, X2, …, X n , …, X N}, X n Indicates uploading book resource information by item X n, N represents the total number of uploaded book resource information items; based on the uploaded book resource information X to be analyzed and the online book resource evaluation model, the comprehensive analysis information of the uploaded book resources is obtained; the online book resource evaluation model is used to perform big data analysis and related content analysis on the uploaded book resource information to be analyzed, evaluate the practicality and recommendation degree of the book resources uploaded by the user, and avoid the situation where the online book resources are uneven;
[0009] Review the user's uploaded book resources according to the uploaded book resource comprehensive analysis information to determine whether the user's uploaded book resources have passed the review. If they have passed the review, the user's uploaded book resources and the corresponding uploaded book resource comprehensive analysis information are added to the online book resource management library, and the user book resource sharing information corresponding to the user's uploaded book resources is updated; otherwise, an abnormal review report is sent, and the user book resource sharing information corresponding to the user's uploaded book resources is updated;
[0010] S3. Online book resource push updates
[0011] The target user information is updated at preset time intervals to obtain updated target user information; based on the analysis of the updated target user information and the online book resource dynamic monitoring model, an online book resource management dynamic update plan is obtained; the online book resource dynamic monitoring model is used to periodically update user information, which helps to improve the accuracy of subsequent online book resource push; according to the online book resource management dynamic update plan, the online book resource management of the target user is optimized.
[0012] As a preferred technical solution of the present invention, the user book resource push model in step S1 includes an age classification information feature analysis layer, an online book resource push analysis layer and a result output layer;
[0013] The age classification information feature analysis layer is used to extract features of user age classification information to obtain first book resource push features; based on the first book resource push features, screening is performed to obtain a first book resource push strategy;
[0014] The online book resource push analysis layer is used to extract features of user book preference information to obtain the second book resource push features, extract features of user online resource sharing information to obtain the third book resource push features; perform feature fusion on the second book resource push features and the third book resource push features to obtain the online book resource push features; perform screening based on the first book resource push strategy based on the online book resource push features to obtain the second book resource push strategy;
[0015] The result output layer is used to screen all online book resources according to the second book resource push strategy to obtain user-pushed book resources.
[0016] As a preferred technical solution of the present invention, the online book resource evaluation model in step S2 includes an evaluation feature classification layer, a book resource term search layer, a book resource external term evaluation layer, a book resource internal feature evaluation layer, a book resource comprehensive evaluation layer and a comprehensive information output layer;
[0017] The evaluation feature classification layer is used to identify the features of the uploaded book resource information X to be analyzed, and divide it into the internal information Y of the uploaded book resource to be analyzed and the external information Z of the uploaded book resource to be analyzed; for the internal information Y of the uploaded book resource to be analyzed, Y={Y1, Y2, …, Y i , …, Y I},Y i Represented as the internal information association item Y of the book resource i , I is the total number of related items of internal information of book resources; for the external information Z of uploaded book resources to be analyzed, Z={Z1, Z2, …, Z j , …, Z J}, Z j Represented as the book resource external information association item Z j , J is the total number of external information related items of book resources;
[0018] The book resource entry search layer is used to associate item Z with external information of book resources. j Perform big data search to obtain the set of terms related to external information of book resources Z j '; Traverse all book resource external information related items Z j ;
[0019] The external term evaluation layer of book resources is used to associate the term set Z with the external information of book resources. j 'Perform external term evaluation and obtain the term evaluation index W of the book resource external information related item j ; Evaluate all the terms related to external information of book resources W j Perform mean calculation to obtain the external entry evaluation index W of the book resources j ';
[0020] The internal feature evaluation layer of book resources is used to associate item Y with the internal information of book resources. i Conduct internal term evaluation to obtain the term evaluation index U of the internal information related items of the book resources i ; Evaluate all the information related items in the book resources with the entry index U i Perform mean calculation to obtain the internal entry evaluation index U of the book resource i ';
[0021] The comprehensive evaluation layer of book resources is used to use the formula S=A1*W j'+A2*U i 'Calculate the comprehensive evaluation index S of book resources, A1 and A2 represent the external term evaluation index W of book resources respectively j ' and the internal entry evaluation index U of the book resources i 'Calculation weight;
[0022] The comprehensive information output layer is used to generate the entry set Z based on the external information of all book resources. j 'Generate summary text with the internal information Y of the uploaded book resources to be analyzed, and obtain the summary information of the uploaded book resources; combine the summary information of the uploaded book resources with the comprehensive evaluation index S of the book resources to obtain the comprehensive analysis information of the uploaded book resources, and output the comprehensive analysis information of the uploaded book resources.
[0023] As a preferred technical solution of the present invention, the specific steps of training the external term evaluation layer of book resources and the internal feature evaluation layer of book resources include:
[0024] For the external entry evaluation layer of book resources:
[0025] Collecting several groups of external term evaluation training samples, each group of external term evaluation training samples includes an external term set and a corresponding external term evaluation index; combining several groups of external term evaluation training samples to obtain an external term evaluation training set; using the external term evaluation training set to perform model training on the external term evaluation layer of book resources to obtain the external term evaluation layer of book resources;
[0026] For the internal feature evaluation layer of book resources:
[0027] Collect several groups of internal feature evaluation training samples, each group of internal feature evaluation training samples contains internal information management items and corresponding internal feature evaluation indexes; combine several groups of internal feature evaluation training samples to obtain internal feature evaluation training sets; use the internal feature evaluation training sets to perform model training on the internal feature evaluation layer of book resources to obtain the internal feature evaluation layer of book resources.
[0028] As a preferred technical solution of the present invention, the specific steps of determining the specific values of A1 and A2 in the comprehensive evaluation layer of book resources include:
[0029] Construct an initial weight group, which contains K weight individuals G k ; Each weighted individual G k contains a set of specific values of A1 and A2; the position of the optimal weighted individual is set to L k , the number of iterations is denoted as t, and the maximum number of iterations is denoted as T max ,t=1,2,…,T,…,T max ;
[0030] When the number of iterations t=1, according to the weighted individual G in the initial weight group k Perform simulation calculation to obtain fitness B k At this time, L k G is the weighted individual with the largest fitness in the initial weight group k Position; According to the formula R(t) = (D1-D2)*((T max -t) / T max ) E The population update factor R (1) is calculated, and the initial weight population is updated according to the population update factor R (1) to obtain the updated weight population F (1); D1 is the initial value of the population update factor, D2 is the final value of the population update factor, E is a nonlinear adjustment system, and the value range of E is 0-1;
[0031] When the number of iterations t=T, according to the updated weighted individual G in the weighted population F(T-1) k Perform simulation calculation to obtain fitness B k ; According to the formula R(t)=(D1-D2)*((T max -t) / T max ) E The population update factor R(T) is calculated, and the updated weight population F(T-1) is updated according to the population update factor R(T) to obtain the updated weight population F(T); at this time, L k To update the weighted individual G with the largest fitness in the weighted population F (T-1) k location;
[0032] The steps for simulation calculation are:
[0033] Collect several groups of index evaluation training samples; each group of index evaluation training samples contains any online book resource and a corresponding book resource score; combine several groups of index evaluation training samples to obtain an index evaluation training set;
[0034] Based on the weighted individual G k Set up a simulated book resource comprehensive evaluation layer, deploy the simulated book resource comprehensive evaluation layer to the online book resource evaluation model, and obtain a simulated online book resource evaluation model; input the index evaluation training set into the simulated online book resource evaluation model for analysis to obtain a simulated online resource evaluation score; perform Euclidean distance calculation on the simulated online resource evaluation score and the corresponding book resource score in the index evaluation training set to obtain the simulated evaluation difference C k , the simulated evaluation difference C k As fitness B k ;
[0035] When the number of iterations t=T max When the current L k The individual at the corresponding position is the optimal weighted individual; the specific values of A1 and A2 corresponding to the optimal weighted individual are used as the specific values of A1 and A2 in the comprehensive evaluation layer of book resources.
[0036] As a preferred technical solution of the present invention, the specific steps of step S3 include:
[0037] S31. Obtain user book resource sharing information corresponding to the book resources uploaded by all target users updated within a preset time interval, and obtain updated target user book resource sharing information;
[0038] S32. Obtaining online book resource reading information and online book resource search information of the target user within a preset time interval; updating the user's book preference information for online book resource reading information and online book resource search information to obtain updated target user book preference information;
[0039] S33. Update the target user information based on the updated target user book resource sharing information and the updated target user book preference information to obtain updated target user information; input the updated target user information into the online book resource dynamic monitoring model for analysis to obtain an online book resource management dynamic update plan.
[0040] As a preferred technical solution of the present invention, the online book resource dynamic monitoring model includes a data preprocessing layer, a data analysis layer and a result output layer;
[0041] The data preprocessing layer is used to obtain target user information, preprocess the target user information to obtain preprocessed target user information; preprocess the updated target user information to obtain preprocessed updated target user information;
[0042] The data analysis layer is used to perform difference analysis based on the pre-processed target user information and the pre-processed updated target user information to obtain dynamic difference information of the target user information; it is used to perform solution matching based on the dynamic difference information of the target user information to obtain a dynamic update solution for online book resource management;
[0043] The result output layer is used to output the dynamic update plan of online book resource management;
[0044] The specific steps of training the data analysis layer include:
[0045] Collect several groups of historical update difference training samples; each group of historical update difference training samples contains historical update difference information and corresponding update plans; combine several groups of historical update difference training samples to obtain a historical update difference training set; use the historical update difference training set to train the data analysis layer with the update plan as the goal to obtain the data analysis layer.
[0046] An online book resource management system, comprising:
[0047] The book resource push module is used to obtain target user information; the target user information includes user age classification information, user book preference information and user book resource sharing information; based on the target user information and the user book resource push model, analysis is performed to obtain user pushed book resources; the user book resource push model is used to push exclusive book resources to different users based on the target user information, so that the online book resources pushed to the target user are personalized and accurate;
[0048] The book resource evaluation module is used to obtain the book resources uploaded by the user and obtain the uploaded book resource information to be analyzed; the uploaded book resource information to be analyzed is recorded as the uploaded book resource information to be analyzed X, where X={X1, X2, …, X n , …, X N}, X n Indicates uploading book resource information by item X n , N represents the total number of uploaded book resource information sub-items; based on the uploaded book resource information X to be analyzed and the online book resource evaluation model, the analysis is performed to obtain the comprehensive analysis information of the uploaded book resources; the online book resource evaluation model is used to perform big data analysis and related content analysis on the uploaded book resource information to be analyzed, evaluate the practicality and recommendation degree of the user-uploaded book resources, and avoid the situation where the online book resources are of varying quality; based on the uploaded book resource comprehensive analysis information, the user-uploaded book resources are reviewed to determine whether the user-uploaded book resources have passed the review. If they have passed the review, the user-uploaded book resources and the corresponding uploaded book resource comprehensive analysis information are added to the online book resource management library, and the user book resource sharing information corresponding to the user-uploaded book resources is updated; otherwise, an abnormal review report is sent, and the user book resource sharing information corresponding to the user-uploaded book resources is updated;
[0049] The book resource push update module is used to update the target user information at preset time intervals to obtain updated target user information; based on the analysis of the updated target user information and the online book resource dynamic monitoring model, an online book resource management dynamic update plan is obtained; the online book resource dynamic monitoring model is used to periodically update user information, which helps to improve the accuracy of subsequent online book resource push; and the online book resource management of the target user is optimized according to the online book resource management dynamic update plan.
[0050] The present invention has the following advantages:
[0051] 1. The present invention can provide personalized book resource push to meet the specific needs of different users by obtaining detailed information of target users, including age classification, book preferences and resource sharing information; the application of the user book resource push model ensures that the book resources pushed to users are highly relevant and accurate, thereby improving user satisfaction and resource utilization efficiency; the online book resource evaluation model is used to perform big data analysis and content evaluation on the uploaded book resources, which can effectively screen out high-quality book resources and ensure the high quality of online resources; user information is regularly updated and an online book resource dynamic monitoring model is applied, so that book resource management can adapt to changes in user needs and realize dynamic optimization and updating of resources.
[0052] 2. The present invention applies the evaluation feature classification layer in the online book resource evaluation model to refine the book resource information into internal information and external information, making the evaluation more refined and improving the pertinence and accuracy of the evaluation; the book resource term search layer comprehensively searches the external information of book resources through big data analysis to ensure the comprehensiveness of the evaluation; the book resource external term evaluation layer and the book resource internal feature evaluation layer conduct in-depth evaluations on the external and internal information respectively, improving the depth and quality of the evaluation; the book resource comprehensive evaluation layer obtains a comprehensive book resource evaluation index through weighted calculation of external and internal evaluation indexes, making the evaluation results more comprehensive and objective. BRIEF DESCRIPTION OF THE DRAWINGS
[0053] Figure 1 The figure is a schematic diagram of the structure of an online book resource management system adopted in an embodiment of the present invention.
[0054] Figure 2 The figure is a flow chart of an online book resource management method adopted in an embodiment of the present invention. DETAILED DESCRIPTION
[0055] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.
[0056] Example 1, an online book resource management method, see Figure 2 As shown, the following steps are included:
[0057] S1. Online book resource push
[0058] Obtain target user information; target user information includes user age rating information, user book preference information and user book resource sharing information; analyze based on target user information and user book resource push model to obtain user pushed book resources; user book resource push model is used to push exclusive book resources to different users based on target user information, so that the online book resources pushed to target users are personalized and accurate; user age rating information includes basic user information, such as age and personal information, which can limit the user's reading range; user book preference information includes user's daily search records and reading records, which can reflect the user's reading preferences; user book resource sharing information includes the number of times the user shares resources, the type of book resources and the success rate, which can reflect the user's expertise and activity;
[0059] The user book resource push model in step S1 includes an age classification information feature analysis layer, an online book resource push analysis layer, and a result output layer;
[0060] The age classification information feature analysis layer is used to extract features of user age classification information to obtain the first book resource push features; based on the first book resource push features, screening is performed to obtain the first book resource push strategy; the first book resource push strategy defines an age range for book push, for example, if the target user is 10 years old, then push less economic books, and push interesting mathematics and science popular science book resources suitable for this age group;
[0061] The online book resource push analysis layer is used to extract features of user book preference information to obtain the second book resource push features, extract features of user online resource sharing information to obtain the third book resource push features; perform feature fusion of the second book resource push features and the third book resource push features to obtain the online book resource push features; perform screening based on the first book resource push strategy based on the online book resource push features to obtain the second book resource push strategy; the second book resource push strategy is based on the first book resource push strategy and starts from the user's personal preference and expertise to perform accurate push, meeting the personalized standards of different users;
[0062] The result output layer is used to filter all online book resources according to the second book resource push strategy to obtain user-pushed book resources;
[0063] S2. Evaluation of online book resource sharing
[0064] Get the book resources uploaded by the user and obtain the uploaded book resource information to be analyzed; record the uploaded book resource information to be analyzed as the uploaded book resource information to be analyzed X, where X={X1, X2, …, X n , …, X N}, X n Indicates uploading book resource information by item X n , N represents the total number of uploaded book resource information items; based on the uploaded book resource information X to be analyzed and the online book resource evaluation model, the comprehensive analysis information of the uploaded book resources is obtained; the online book resource evaluation model is used to perform big data analysis and related content analysis on the uploaded book resource information to be analyzed, evaluate the practicality and recommendation degree of the book resources uploaded by the user, and avoid the situation where the online book resources are uneven;
[0065] The online book resource evaluation model in step S2 includes an evaluation feature classification layer, a book resource term search layer, a book resource external term evaluation layer, a book resource internal feature evaluation layer, a book resource comprehensive evaluation layer and a comprehensive information output layer;
[0066] The evaluation feature classification layer is used to identify the features of the uploaded book resource information X to be analyzed, and divide it into the internal information Y of the uploaded book resource to be analyzed and the external information Z of the uploaded book resource to be analyzed; for the internal information Y of the uploaded book resource to be analyzed, Y={Y1, Y2, …, Y i , …, Y I},Y i Represented as the internal information association item Y of the book resource i , I is the total number of related items of internal information of book resources; for the external information Z of uploaded book resources to be analyzed, Z={Z1, Z2, …, Z j , …, Z J}, Z j Represented as the book resource external information association item Z j , J is the total number of items related to external information of book resources; the internal information of the uploaded book resources to be analyzed may include the version information, publisher information, typesetting information, cover information and other information that affect the reading experience of the book resources; the external information of the uploaded book resources to be analyzed may include the creativity, evaluation information, readability and other information of the book resources, which can reflect the basic value of the book resources;
[0067] The book resource entry search layer is used to associate item Z with external information of book resources. j Perform big data search to obtain the set of terms related to external information of book resources Z j '; Traverse all book resource external information related items Z j; For example, searching for keywords related to the book resource on the website, obtaining search results as evaluations of the book resource, extracting entries from the evaluations as associated items, and obtaining an associated item entry set; by analyzing the evaluation index of the associated item entry set, the basic value of the book resource can be reflected;
[0068] The external term evaluation layer of book resources is used to associate the term set Z with the external information of book resources. j 'Perform external term evaluation and obtain the term evaluation index W of the book resource external information related item j ; Evaluate all the terms related to external information of book resources W j Perform mean calculation to obtain the external entry evaluation index W of the book resources j ';
[0069] The internal feature evaluation layer of book resources is used to associate item Y with the internal information of book resources. i Conduct internal term evaluation to obtain the term evaluation index U of the internal information related items of the book resources i ; Evaluate all the information related items in the book resources with the entry index U i Perform mean calculation to obtain the internal entry evaluation index U of the book resource i ';
[0070] The comprehensive evaluation layer of book resources is used to use the formula S=A1*W j '+A2*U i 'Calculate the comprehensive evaluation index S of book resources, A1 and A2 represent the external term evaluation index W of book resources respectively j ' and the internal entry evaluation index U of the book resources i 'Calculation weight;
[0071] The comprehensive information output layer is used to generate the entry set Z based on the external information of all book resources. j 'Generate summary text with the internal information Y of the uploaded book resources to be analyzed, and obtain the summary information of the uploaded book resources; combine the summary information of the uploaded book resources with the comprehensive evaluation index S of the book resources to obtain the comprehensive analysis information of the uploaded book resources, and output the comprehensive analysis information of the uploaded book resources; the summary text generation is based on the generative language model, and the summary is generated by using the term set of the external information related items of the book resources and the internal information of the uploaded book resources to be analyzed, so as to quickly understand the basic information of the book resources and improve the speed of obtaining the book resource information;
[0072] By applying the evaluation feature classification layer in the online book resource evaluation model, the book resource information is refined into internal information and external information, making the evaluation more refined and improving the pertinence and accuracy of the evaluation; the book resource term search layer comprehensively searches the external information of book resources through big data analysis to ensure the comprehensiveness of the evaluation; the book resource external term evaluation layer and the book resource internal feature evaluation layer conduct in-depth evaluations of external and internal information respectively, improving the depth and quality of the evaluation; the book resource comprehensive evaluation layer obtains a comprehensive book resource evaluation index by weighted calculation of external and internal evaluation indexes, making the evaluation results more comprehensive and objective, and at the same time, the push weight of high-quality book resources can be increased according to the evaluation index;
[0073] Review the user's uploaded book resources according to the uploaded book resource comprehensive analysis information to determine whether the user's uploaded book resources have passed the review. If they have passed the review, the user's uploaded book resources and the corresponding uploaded book resource comprehensive analysis information are added to the online book resource management library, and the user book resource sharing information corresponding to the user's uploaded book resources is updated; otherwise, an abnormal review report is sent, and the user book resource sharing information corresponding to the user's uploaded book resources is updated;
[0074] Automatically determine whether the evaluation value of the book resource exceeds the preset evaluation index threshold based on the comprehensive analysis information of the uploaded book resources; if it exceeds the preset evaluation index threshold, it passes the review; otherwise, it fails the review; but regardless of whether it is reviewed or not, the book resource sharing information is retained for the user and updated, which can provide help for subsequent push; for example, when the book resource is a teaching test question resource, the teaching test questions in different regions are regional, and the user can upload the relevant teaching test question resources by himself. After a basic evaluation of the teaching test question resources, it may fail to pass the review due to printing and scanning and other issues; but retain the record of user resource upload, because when the user shares, it can be inferred that the user is an active user, and different levels of reward measures can be set for active users, thereby promoting the sharing of online book resources;
[0075] The specific steps of training the external term evaluation layer of book resources and the internal feature evaluation layer of book resources include:
[0076] For the external entry evaluation layer of book resources:
[0077] Collecting several groups of external term evaluation training samples, each group of external term evaluation training samples includes an external term set and a corresponding external term evaluation index; combining several groups of external term evaluation training samples to obtain an external term evaluation training set; using the external term evaluation training set to perform model training on the external term evaluation layer of book resources to obtain the external term evaluation layer of book resources;
[0078] For the internal feature evaluation layer of book resources:
[0079] Collecting several groups of internal feature evaluation training samples, each group of internal feature evaluation training samples contains internal information management items and corresponding internal feature evaluation indexes; combining several groups of internal feature evaluation training samples to obtain internal feature evaluation training sets; using the internal feature evaluation training sets to perform model training on the internal feature evaluation layer of book resources to obtain the internal feature evaluation layer of book resources;
[0080] The specific steps for determining the specific values of A1 and A2 in the comprehensive evaluation layer of book resources include:
[0081] Construct an initial weight group, which contains K weight individuals G k ; Each weighted individual G k contains a set of specific values of A1 and A2; the position of the optimal weighted individual is set to L k , the number of iterations is denoted as t, and the maximum number of iterations is denoted as T max ,t=1,2,…,T,…,T max ;The maximum number of iterations is set by professional technicians based on actual conditions;
[0082] When the number of iterations t=1, according to the weighted individual G in the initial weight group k Perform simulation calculation to obtain fitness B k At this time, L k G is the weighted individual with the largest fitness in the initial weight group k Position; According to the formula R(t) = (D1-D2)*((T max -t) / T max ) E The population update factor R (1) is calculated, and the initial weight population is updated according to the population update factor R (1) to obtain the updated weight population F (1); D1 is the initial value of the population update factor, D2 is the final value of the population update factor, E is a nonlinear adjustment system, and the value range of E is 0-1;
[0083] When the number of iterations t=T, according to the updated weighted individual G in the weighted population F(T-1) k Perform simulation calculation to obtain fitness B k ; According to the formula R(t)=(D1-D2)*((T max -t) / T max ) E The population update factor R(T) is calculated, and the updated weight population F(T-1) is updated according to the population update factor R(T) to obtain the updated weight population F(T); at this time, L k To update the weighted individual G with the largest fitness in the weighted population F (T-1)k location;
[0084] The steps for simulation calculation are:
[0085] Collect several groups of index evaluation training samples; each group of index evaluation training samples contains any online book resource and a corresponding book resource score; combine several groups of index evaluation training samples to obtain an index evaluation training set;
[0086] Based on the weighted individual G k Set up a simulated book resource comprehensive evaluation layer, deploy the simulated book resource comprehensive evaluation layer to the online book resource evaluation model, and obtain a simulated online book resource evaluation model; input the index evaluation training set into the simulated online book resource evaluation model for analysis to obtain a simulated online resource evaluation score; perform Euclidean distance calculation on the simulated online resource evaluation score and the corresponding book resource score in the index evaluation training set to obtain the simulated evaluation difference C k , the simulated evaluation difference C k As fitness B k ;
[0087] When the number of iterations t=T max When the current L k The individual at the corresponding position is the optimal weighted individual; the specific values of A1 and A2 corresponding to the optimal weighted individual are used as the specific values of A1 and A2 in the comprehensive evaluation layer of book resources;
[0088] Through the iterative process and fitness evaluation, the optimal weight combination A1 and A2 can be effectively selected, thereby improving the accuracy of the comprehensive evaluation of book resources; the introduction of the population update factor R (t) enables the weight update process to have the ability to dynamically adjust, and the update strategy can be flexibly adjusted according to the iteration progress and fitness changes; through simulation calculation and fitness evaluation, it can ensure that the evaluation model better adapts to different book resource characteristics and improves the generalization ability of the model; the selection of the optimal weight makes the comprehensive evaluation of book resources more in line with actual needs, thereby improving the quality of resource recommendations based on the evaluation results and user satisfaction;
[0089] S3. Online book resource push updates
[0090] The target user information is updated at preset time intervals to obtain updated target user information; based on the analysis of the updated target user information and the online book resource dynamic monitoring model, an online book resource management dynamic update plan is obtained; the online book resource dynamic monitoring model is used to periodically update the user information, which helps to improve the accuracy of subsequent online book resource push; the online book resource management of the target user is optimized according to the online book resource management dynamic update plan; the target user's online book resource management is adjusted according to the feedback of the online book resource management dynamic update plan when pushing books. For example, if the user's preference information changes from mathematics to English within the preset time interval, the push frequency of English book resources can be appropriately increased when the target user uses it next time;
[0091] The specific steps of step S3 include:
[0092] S31. Obtain user book resource sharing information corresponding to the book resources uploaded by all target users updated within a preset time interval, and obtain updated target user book resource sharing information;
[0093] S32. Obtaining online book resource reading information and online book resource search information of the target user within a preset time interval; updating the user's book preference information for online book resource reading information and online book resource search information to obtain updated target user book preference information;
[0094] S33. Update the target user information based on the target user's book resource sharing information and update the target user's book preference information to obtain the updated target user information; update the target user information input into the online book resource dynamic monitoring model for analysis to obtain the online book resource management dynamic update program;
[0095] By updating user information within a preset time interval, the library resource management system can timely reflect the latest needs and preferences of users, thus improving the real-time nature of services. Updating user book preference information enables the system to more accurately capture the personalized needs of users, thereby providing more personalized book recommendations and services. By analyzing user reading and search behaviors, the configuration of book resources can be optimized to ensure that high-demand resources are better promoted and utilized. The use of the online book resource dynamic monitoring model can dynamically monitor and respond to changes in user behavior, thus achieving dynamic optimization of online book resource management.
[0096] The dynamic monitoring model of online book resources includes data preprocessing layer, data analysis layer and result output layer;
[0097] The data preprocessing layer is used to obtain target user information, preprocess the target user information to obtain preprocessed target user information; preprocess the updated target user information to obtain preprocessed updated target user information;
[0098] The data analysis layer is used to perform difference analysis based on the pre-processed target user information and the pre-processed updated target user information to obtain dynamic difference information of the target user information; it is used to perform solution matching based on the dynamic difference information of the target user information to obtain a dynamic update solution for online book resource management;
[0099] The result output layer is used to output the dynamic update plan of online book resource management;
[0100] The specific steps of training the data analysis layer include:
[0101] Collecting several groups of historical update difference training samples; each group of historical update difference training samples contains historical update difference information and corresponding update schemes; combining several groups of historical update difference training samples to obtain a historical update difference training set; using the historical update difference training set to train the data analysis layer with the update scheme as the target, to obtain the data analysis layer;
[0102] By training with historical update difference training samples, the data analysis layer can continuously learn and improve, so that the model can adapt to new user behavior patterns and market changes; through precise dynamic update plans, it can provide book resources that are more in line with user needs, thereby enhancing user satisfaction and loyalty; it can identify the specific needs and preference changes of different users, provide support for personalized services, and make the push of book resources more accurate.
[0103] Example 2, an online book resource management system, see Figure 1 As shown, including:
[0104] The book resource push module is used to obtain target user information; the target user information includes user age classification information, user book preference information and user book resource sharing information; based on the target user information and the user book resource push model, analysis is performed to obtain user pushed book resources; the user book resource push model is used to push exclusive book resources to different users based on the target user information, so that the online book resources pushed to the target user are personalized and accurate;
[0105] The book resource evaluation module is used to obtain the book resources uploaded by the user and obtain the uploaded book resource information to be analyzed; the uploaded book resource information to be analyzed is recorded as the uploaded book resource information to be analyzed X, where X={X1, X2, …, X n , …, X N}, X n Indicates uploading book resource information by item Xn , N represents the total number of uploaded book resource information sub-items; based on the uploaded book resource information X to be analyzed and the online book resource evaluation model, the analysis is performed to obtain the comprehensive analysis information of the uploaded book resources; the online book resource evaluation model is used to perform big data analysis and related content analysis on the uploaded book resource information to be analyzed, evaluate the practicality and recommendation degree of the user-uploaded book resources, and avoid the situation where the online book resources are of varying quality; based on the uploaded book resource comprehensive analysis information, the user-uploaded book resources are reviewed to determine whether the user-uploaded book resources have passed the review. If they have passed the review, the user-uploaded book resources and the corresponding uploaded book resource comprehensive analysis information are added to the online book resource management library, and the user book resource sharing information corresponding to the user-uploaded book resources is updated; otherwise, an abnormal review report is sent, and the user book resource sharing information corresponding to the user-uploaded book resources is updated;
[0106] The book resource push update module is used to update the target user information at preset time intervals to obtain updated target user information; based on the analysis of the updated target user information and the online book resource dynamic monitoring model, an online book resource management dynamic update plan is obtained; the online book resource dynamic monitoring model is used to periodically update user information, which helps to improve the accuracy of subsequent online book resource push; and the online book resource management of the target user is optimized according to the online book resource management dynamic update plan.
[0107] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.
Claims
1. An online book resource management method, characterized in that: The following steps are involved: S1. Online book resource push Get target user information; The target user information includes user age rating information, user book preference information and user book resource sharing information; Analyze the target user information and the user book resource push model to obtain the user pushed book resources; The user book resource push model is used to push exclusive book resources to different users based on target user information, so that the online book resources pushed to the target users are personalized and accurate; S2. Evaluation of online book resource sharing Get the book resources uploaded by the user and obtain the uploaded book resource information to be analyzed; record the uploaded book resource information to be analyzed as the uploaded book resource information to be analyzed X, where X={X1, X2, …, X n , …, X N }, X n Indicates uploading book resource information by item X n , N represents the total number of uploaded book resource information items; based on the uploaded book resource information X to be analyzed and the online book resource evaluation model, the comprehensive analysis information of the uploaded book resources is obtained; the online book resource evaluation model is used to perform big data analysis and related content analysis on the uploaded book resource information to be analyzed, evaluate the practicality and recommendation degree of the book resources uploaded by users, and avoid the situation where online book resources are uneven; Review the user's uploaded book resources according to the uploaded book resource comprehensive analysis information to determine whether the user's uploaded book resources have passed the review. If they have passed the review, the user's uploaded book resources and the corresponding uploaded book resource comprehensive analysis information are added to the online book resource management library, and the user book resource sharing information corresponding to the user's uploaded book resources is updated; otherwise, an abnormal review report is sent, and the user book resource sharing information corresponding to the user's uploaded book resources is updated; S3. Online book resource push updates The target user information is updated at preset time intervals to obtain updated target user information; based on the updated target user information and the online book resource dynamic monitoring model, an analysis is performed to obtain a dynamic update plan for online book resource management; the online book resource dynamic monitoring model is used to periodically update user information, which helps to improve the accuracy of subsequent online book resource push; the online book resource management of the target user is optimized according to the online book resource management dynamic update plan; The online book resource evaluation model in step S2 includes an evaluation feature classification layer, a book resource term search layer, a book resource external term evaluation layer, a book resource internal feature evaluation layer, a book resource comprehensive evaluation layer and a comprehensive information output layer; The evaluation feature classification layer is used to identify the features of the uploaded book resource information X to be analyzed, and divide it into the internal information Y of the uploaded book resource to be analyzed and the external information Z of the uploaded book resource to be analyzed; for the internal information Y of the uploaded book resource to be analyzed, Y={Y1, Y2, …, Y i , …, Y I },Y i Represented as the internal information association item Y of the book resource i , I is the total number of related items of internal information of book resources; for the external information Z of uploaded book resources to be analyzed, Z={Z1, Z2, …, Z j , …, Z J }, Z j Represented as the book resource external information association item Z j , J is the total number of external information related items of book resources; The book resource entry search layer is used to associate item Z with external information of book resources. j Perform big data search to obtain the set of terms related to external information of book resources Z j '; Traverse all book resource external information related items Z j ; The external entry evaluation layer of book resources is used to associate the entry set Z with the external information of book resources. j 'Perform external term evaluation and obtain the term evaluation index W of the external information related items of the book resources j ; Evaluate all the terms related to external information of book resources W j Perform mean calculation to obtain the external entry evaluation index W of the book resources j '; The internal feature evaluation layer of book resources is used to associate item Y with the internal information of book resources. i Conduct internal term evaluation to obtain the term evaluation index U of the internal information related items of the book resources i ; Evaluate all the information related items in the book resources with the entry index U i Perform mean calculation to obtain the internal entry evaluation index U of the book resource i '; The comprehensive evaluation layer of book resources is used to use the formula S=A1*W j '+A2*U i 'Calculate the comprehensive evaluation index S of book resources, A1 and A2 represent the external term evaluation index W of book resources respectively j ' and the internal entry evaluation index U of the book resources i 'Calculation weight; The comprehensive information output layer is used to generate the entry set Z based on the external information of all book resources. j 'Generate summary text with the internal information Y of the uploaded book resources to be analyzed, and obtain the summary information of the uploaded book resources; combine the summary information of the uploaded book resources with the comprehensive evaluation index S of the book resources to obtain the comprehensive analysis information of the uploaded book resources, and output the comprehensive analysis information of the uploaded book resources.
2. An online book resource management method according to claim 1, characterized in that: The user book resource push model in step S1 includes an age classification information feature analysis layer, an online book resource push analysis layer, and a result output layer; The age classification information feature analysis layer is used to extract features of the user's age classification information to obtain the first book resource push features; Screening is performed based on the push characteristics of the first book resource to obtain a push strategy for the first book resource; The online book resource push analysis layer is used to extract features from user book preference information to obtain a second book resource push feature, and to extract features from user online resource sharing information to obtain a third book resource push feature; The second book resource push feature and the third book resource push feature are integrated to obtain the online book resource push feature; Based on the online book resource push characteristics, the first book resource push strategy is screened to obtain a second book resource push strategy; The result output layer is used to screen all online book resources according to the second book resource push strategy to obtain user-pushed book resources.
3. An online book resource management method according to claim 2, characterized in that: The specific steps of training the external term evaluation layer of book resources and the internal feature evaluation layer of book resources include: For the external entry evaluation layer of book resources: Collecting several groups of external term evaluation training samples, each group of external term evaluation training samples includes an external term set and a corresponding external term evaluation index; combining several groups of external term evaluation training samples to obtain an external term evaluation training set; using the external term evaluation training set to perform model training on the external term evaluation layer of book resources to obtain the external term evaluation layer of book resources; For the internal feature evaluation layer of book resources: Collect several groups of internal feature evaluation training samples, each group of internal feature evaluation training samples contains internal information management items and corresponding internal feature evaluation indexes; combine several groups of internal feature evaluation training samples to obtain internal feature evaluation training sets; use the internal feature evaluation training sets to perform model training on the internal feature evaluation layer of book resources to obtain the internal feature evaluation layer of book resources.
4. The online book resource management method according to claim 3, characterized in that: The specific steps for determining the specific values of A1 and A2 in the comprehensive evaluation layer of book resources include: Construct an initial weight group, which contains K weight individuals G k ; Each weighted individual G k contains a set of specific values of A1 and A2; the position of the optimal weighted individual is set to L k , the number of iterations is denoted as t, and the maximum number of iterations is denoted as T max ,t=1,2,…,T,…,T max ; When the number of iterations t=1, according to the weighted individual G in the initial weight group k Perform simulation calculation to obtain fitness B k At this time, L k G is the weighted individual with the largest fitness in the initial weight group k Position; According to the formula R(t)=(D1-D2)*((T max -t) / T max ) E The population update factor R (1) is calculated, and the initial weight population is updated according to the population update factor R (1) to obtain the updated weight population F (1); D1 is the initial value of the population update factor, D2 is the final value of the population update factor, E is a nonlinear adjustment system, and the value range of E is 0-1; When the number of iterations t=T, according to the updated weighted individual G in the weighted population F(T-1) k Perform simulation calculation to obtain fitness B k ; According to the formula R(t)=(D1-D2)*((T max -t) / T max ) E The population update factor R(T) is calculated, and the updated weight population F(T-1) is updated according to the population update factor R(T) to obtain the updated weight population F(T); at this time, L k To update the weighted individual G with the largest fitness in the weighted population F (T-1) k location; The steps for simulation calculation are: Collect several groups of index evaluation training samples; each group of index evaluation training samples contains any online book resource and a corresponding book resource score; combine several groups of index evaluation training samples to obtain an index evaluation training set; Based on the weighted individual G k Set up a simulated book resource comprehensive evaluation layer, deploy the simulated book resource comprehensive evaluation layer to the online book resource evaluation model, and obtain a simulated online book resource evaluation model; input the index evaluation training set into the simulated online book resource evaluation model for analysis to obtain a simulated online resource evaluation score; perform Euclidean distance calculation on the simulated online resource evaluation score and the corresponding book resource score in the index evaluation training set to obtain the simulated evaluation difference C k , the simulated evaluation difference C k As fitness B k ; When the number of iterations t=T max When the current L k The individual at the corresponding position is the optimal weighted individual; the specific values of A1 and A2 corresponding to the optimal weighted individual are used as the specific values of A1 and A2 in the comprehensive evaluation layer of book resources.
5. An online book resource management method according to claim 4, characterized in that: The specific steps of step S3 include: S31. Obtain user book resource sharing information corresponding to the book resources uploaded by all target users updated within a preset time interval, and obtain updated target user book resource sharing information; S32. Obtaining online book resource reading information and online book resource search information of the target user within a preset time interval; updating the user's book preference information for online book resource reading information and online book resource search information to obtain updated target user book preference information; S33. Update the target user information based on the updated target user book resource sharing information and the updated target user book preference information to obtain updated target user information; input the updated target user information into the online book resource dynamic monitoring model for analysis to obtain an online book resource management dynamic update plan.
6. An online book resource management method according to claim 5, characterized in that: The dynamic monitoring model of online book resources includes data preprocessing layer, data analysis layer and result output layer; The data preprocessing layer is used to obtain target user information, preprocess the target user information, and obtain preprocessed target user information; Preprocessing the update target user information to obtain preprocessed update target user information; The data analysis layer is used to perform difference analysis based on the pre-processed target user information and the pre-processed updated target user information to obtain dynamic difference information of the target user information; Used to match solutions based on dynamic difference information of target user information to obtain dynamic update solutions for online book resource management; The result output layer is used to output the dynamic update plan of online book resource management; The specific steps of training the data analysis layer include: Collecting several groups of historical update difference training samples; each group of historical update difference training samples contains historical update difference information and corresponding update schemes; A plurality of groups of historical update difference training samples are combined to obtain a historical update difference training set; the historical update difference training set is used to train the data analysis layer with the update scheme as a target to obtain the data analysis layer.
7. An online book resource management system, characterized in that: The system applies an online book resource management method according to any one of claims 1 to 6, including: The book resource push module is used to obtain target user information; the target user information includes user age classification information, user book preference information and user book resource sharing information; based on the target user information and the user book resource push model, analysis is performed to obtain user pushed book resources; the user book resource push model is used to push exclusive book resources to different users based on the target user information, so that the online book resources pushed to the target user are personalized and accurate; The book resource evaluation module is used to obtain the book resources uploaded by the user and obtain the uploaded book resource information to be analyzed; the uploaded book resource information to be analyzed is recorded as the uploaded book resource information to be analyzed X, where X={X1, X2, …, X n , …, X N }, X n Indicates uploading book resource information by item X n , N represents the total number of uploaded book resource information sub-items; based on the uploaded book resource information X to be analyzed and the online book resource evaluation model, the analysis is performed to obtain the comprehensive analysis information of the uploaded book resources; the online book resource evaluation model is used to perform big data analysis and related content analysis on the uploaded book resource information to be analyzed, evaluate the practicality and recommendation degree of the user-uploaded book resources, and avoid the situation where the online book resources are of varying quality; based on the uploaded book resource comprehensive analysis information, the user-uploaded book resources are reviewed to determine whether the user-uploaded book resources have passed the review. If they have passed the review, the user-uploaded book resources and the corresponding uploaded book resource comprehensive analysis information are added to the online book resource management library, and the user book resource sharing information corresponding to the user-uploaded book resources is updated; otherwise, an abnormal review report is sent, and the user book resource sharing information corresponding to the user-uploaded book resources is updated; The book resource push update module is used to update the target user information at preset time intervals to obtain updated target user information; based on the analysis of the updated target user information and the online book resource dynamic monitoring model, an online book resource management dynamic update plan is obtained; the online book resource dynamic monitoring model is used to periodically update user information, which helps to improve the accuracy of subsequent online book resource push; and the online book resource management of the target user is optimized according to the online book resource management dynamic update plan.