Demand Forecasting and Intelligent Allocation Method and System for Library Collection Optimization

Abstract

The present invention discloses a demand prediction and intelligent allocation method and system for library collection optimization, which relates to the field of book management technology. The demand prediction and intelligent allocation method for library collection optimization includes the following steps: S1, book borrowing evaluation; S2, user recommendation evaluation; S3, book allocation evaluation. The present invention determines whether to perform new book inventory management based on the obtained book borrowing index of the specified book, then determines whether to optimize the book recommendation strategy based on the obtained user borrowing index of the specified user, and finally determines whether to optimize the book allocation based on the obtained allocation index of the specified book, thereby achieving the effect of improving the accuracy of the library's new book allocation, and solving the problem of insufficient data in the existing technology when performing demand prediction and allocation analysis on new books in the library.

Description

Technical Field

[0001] The present invention relates to the field of book management technology, and in particular to a demand prediction and intelligent allocation method and system for library collection optimization. Background Art

[0002] With the rapid development of information technology and the advent of the digital age, libraries are not only repositories of knowledge and culture, but also key platforms for promoting learning and social interaction. However, traditional library operations face many challenges, particularly in resource allocation and demand management. This is particularly true in areas such as improving library efficiency in collection management, resource allocation, and meeting user needs. Library collections may be over-accumulated or under-resourced, particularly with popular books in high demand while less popular titles struggle to be borrowed. Rationally allocating library resources to avoid waste and reduce costs is a pressing issue.

[0003] Existing technologies can predict book borrowing demand and trends by analyzing large amounts of historical borrowing data, user behavior data, book types and other data, provide accurate demand forecasts, and improve resource allocation efficiency.

[0004] For example, the invention patent announcement with announcement number: CN109785212B discloses a book management method and device for a community library based on big data, including: adding tag information to each book and obtaining reading behavior data of each book, and then judging whether to retain the book based on the reading behavior data of the book, and calculating the similarity of retained books between adjacent community libraries, and recommending books with a similarity greater than a second threshold to adjacent community libraries.

[0005] For example, the patent application with publication number CN119228271A discloses a library inventory management method and system, which includes: obtaining the library's metadata, including book information and user borrowing records, building a knowledge graph based on these data, and using a heterogeneous graph matching algorithm to extract user query intentions to obtain a target node set. Then, the semantic representation information of the user's intention is extracted based on multi-resolution wavelet transform and frequency domain analysis. The sparse Bayesian learning algorithm is used to predict future borrowing demand and generate demand forecast results. Through a multi-objective planning algorithm and a convex optimization method, combined with inventory allocation and logistics costs, an inventory allocation plan is generated, and the plan is optimized through the Lagrangian relaxation method. Finally, the dynamic system state estimation method is combined to adjust the inventory allocation plan in real time.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, it is difficult to predict the demand for new books that have just been put on the shelves due to the lack of historical borrowing records. In addition, for new users and users with few borrowing times, there is a lack of sufficient borrowing history, which affects the accuracy of personalized recommendations. There is also a problem of insufficient data when conducting demand forecasting and allocation analysis for new books in the library. Summary of the Invention

[0008] The embodiments of the present application solve the problem of insufficient data in the prior art for demand forecasting and allocation analysis of new books in libraries by providing a demand forecasting and intelligent allocation method and system for library collection optimization, thereby improving the accuracy of new book allocation in libraries.

[0009] An embodiment of the present application provides a demand forecasting and intelligent allocation method for library collection optimization, comprising the following steps: S1, obtaining a book borrowing index of a specified book based on the actual number of borrowers and book forecast data obtained, determining whether to perform new book inventory management based on the book borrowing index, and the book borrowing index is used to quantitatively evaluate the borrowing capacity of the specified book in the library demand forecast; S2, if new book inventory management is performed, obtaining a user borrowing index of the specified user based on the obtained new user recommendation data, determining whether to perform book recommendation strategy optimization based on the user borrowing index, and the user borrowing index is used to quantitatively evaluate the effectiveness of recommending books to the specified user in the library demand forecast; S3, if book recommendation strategy optimization is performed, obtaining an allocation index of the specified book based on the obtained book allocation data, determining whether to perform book allocation optimization based on the allocation index, and the allocation index is used to quantitatively evaluate the efficiency of book allocation in the library intelligent allocation.

[0010] Furthermore, the book prediction data is obtained by analyzing the historical borrowing data, and the predicted number of new book borrowings is obtained by predicting the historical borrowing data in the demand forecasting system; the historical borrowing data includes the book category and the number of borrowing users; the actual borrowing data includes the specified book category, the actual number of borrowers and the total number of new books; the book prediction data includes the number of similar books, the borrowing prediction accuracy, the predicted borrowing rate and the book similarity; the new user recommendation data includes the book recommendation accuracy coefficient, the book recommendation coverage coefficient, the user click rate and the new book recommendation coefficient; the book allocation data includes the user borrowing waiting time, the user's actual borrowing time, the user's borrowing return rate and the book borrowing coefficient; the borrowing prediction accuracy is obtained by ratioing the absolute value of the difference between the predicted borrowing quantity and the actual borrowing quantity with the predicted borrowing quantity; the predicted borrowing The reading rate is obtained by performing a ratio calculation on the predicted borrowing number and the total number of new books; the book similarity is expressed by the maximum value of the cosine similarity between the specified book category and author and the similarity between the specified book and other books calculated by the book category and author; the book recommendation accuracy coefficient is obtained by performing a ratio calculation on the number of recommended books borrowed by the specified user and the total number of recommended books; the book recommendation coverage coefficient is obtained by performing a ratio calculation on the number of recommended books borrowed by the specified user and the total number of books borrowed by the specified user; the new book recommendation coefficient is obtained by performing a ratio calculation on the difference between the actual maximum borrowing number of the specified book in the recommended books of the new user and the actual maximum borrowing number of the specified book in the non-recommended books and the total number of books borrowed by the user; the book borrowing coefficient is obtained by performing a ratio calculation on the actual borrowing number of books in the preset time period and the total number of books in the library.

[0011] Furthermore, the specific method for evaluating and obtaining the book borrowing index of a specified book based on the obtained actual number of borrowers and book prediction data is as follows:

[0012] The book borrowing deviation, book similarity, borrowing weight obtained from the preset database, and quantity factor of similar books of the specified book are processed to obtain the book borrowing index; the book borrowing deviation includes the actual number of borrowers deviation, the predicted borrowing rate deviation and the borrowing prediction accuracy deviation; the actual number of borrowers deviation is used to reflect the degree of deviation between the actual number of borrowers and the preset number of borrowers data; the predicted borrowing rate deviation is used to reflect the degree of deviation between the predicted borrowing rate and the preset borrowing rate; the borrowing prediction accuracy deviation is used to reflect the degree of deviation between the borrowing prediction accuracy and the preset borrowing prediction accuracy; the borrowing weight includes the borrowing number weight, the borrowing rate weight, the borrowing accuracy weight and the similarity weight.

[0013] Furthermore, the specific method for determining whether to perform new book inventory management based on the book borrowing index is as follows: determining whether the book borrowing index of the specified book is less than a preset book borrowing threshold obtained from a preset database: the preset book borrowing threshold includes a first preset book borrowing threshold and a second preset book borrowing threshold; if the book borrowing index of the specified book is less than the first preset book borrowing threshold obtained from the preset database, the inventory of the specified book is reduced step by step; if the book borrowing index of the specified book is not less than the first preset book borrowing threshold obtained from the preset database, and is not greater than the second preset book borrowing threshold obtained from the preset database, new book inventory management is not performed; if the book borrowing index of the specified book is greater than the second preset book borrowing threshold obtained from the preset database, the inventory of the specified book is increased step by step.

[0014] Furthermore, the specific method for evaluating the obtained new user recommendation data to obtain the user borrowing index of a specified user is as follows: obtaining a book recommendation precision coefficient deviation based on the relative relationship between the book recommendation precision coefficient and the preset book recommendation precision coefficient obtained from the preset database, the preset book recommendation precision coefficient includes a first preset book recommendation precision coefficient and a second preset book recommendation precision coefficient, and the book recommendation precision coefficient deviation is used to reflect the degree of deviation between the book recommendation precision coefficient and the preset book recommendation precision coefficient; obtaining a book recommendation coverage coefficient deviation based on the relative relationship between the book recommendation coverage coefficient and the preset book recommendation coverage coefficient obtained from the preset database, the preset book recommendation coverage coefficient includes a first preset book recommendation coverage coefficient and a second preset book recommendation coverage coefficient, and the book recommendation coverage coefficient deviation is used to reflect the degree of deviation between the book recommendation coverage coefficient and the preset book recommendation coverage coefficient. The degree of deviation of the number; the new book recommendation coefficient deviation is obtained according to the relative relationship between the new book recommendation coefficient and the preset new book recommendation coefficient obtained from the preset database, the preset new book recommendation coefficient includes a first preset new book recommendation coefficient and a second preset new book recommendation coefficient, and the new book recommendation coefficient deviation is used to reflect the degree of deviation between the new book recommendation coefficient and the preset new book recommendation coefficient; the user click rate deviation is obtained according to the relative relationship between the user click rate and the preset user click rate obtained from the preset database, and the user click rate deviation is used to reflect the degree of deviation between the user click rate and the preset user click rate; the book recommendation precision coefficient deviation, the book recommendation coverage coefficient deviation, the new book recommendation coefficient deviation, the user click rate deviation and the user borrowing weight obtained from the preset database are processed to obtain the user borrowing index; the user borrowing weight includes recommendation precision weight, recommendation coverage weight, new book recommendation weight and user click weight.

[0015] Furthermore, the specific method for determining whether to optimize the book recommendation strategy based on the user borrowing index is as follows: determine whether the user borrowing index of the specified user is less than the preset user borrowing threshold obtained from the preset database: if the user borrowing index of the specified user is not less than the preset user borrowing threshold obtained from the preset database, the book recommendation strategy is not optimized; if the user borrowing index of the specified user is less than the preset user borrowing threshold obtained from the preset database, the book recommendation strategy is optimized.

[0016] Furthermore, the specific method for evaluating the allocation index of a specified book based on the obtained book allocation data is as follows: obtaining a user borrowing waiting time deviation based on the relative relationship between the user borrowing waiting time and the preset user borrowing waiting time obtained from the preset database, and the user borrowing waiting time deviation is used to reflect the degree of deviation between the user borrowing waiting time and the preset user borrowing waiting time; obtaining a user actual borrowing time deviation based on the relative relationship between the user's actual borrowing time and the preset user book borrowing time obtained from the preset database, and the user actual borrowing time deviation is used to reflect the degree of deviation between the user's actual borrowing time and the preset user book borrowing time; obtaining a user borrowing return rate deviation based on the relative relationship between the user borrowing return rate and the preset user borrowing return rate obtained from the preset database, and the The user borrowing and returning rate deviation is used to reflect the degree of deviation between the user borrowing and returning rate and the preset user borrowing and returning rate; the book borrowing coefficient deviation is obtained according to the relative relationship between the book borrowing coefficient and the preset book borrowing coefficient obtained from the preset database, and the book borrowing coefficient deviation is used to reflect the degree of deviation between the book borrowing coefficient and the preset book borrowing coefficient; when the user borrowing and returning rate is not less than the preset user borrowing and returning rate, and the book borrowing coefficient is not less than the preset book borrowing coefficient, the user borrowing waiting time deviation, the user actual borrowing time deviation, the user borrowing and returning rate deviation, the book borrowing coefficient deviation and the allocation weight obtained from the preset database are processed to obtain an allocation index, otherwise the allocation index is recorded as 0; the allocation weight includes the borrowing waiting weight, the borrowing time weight, the borrowing return rate weight and the borrowing coefficient weight.

[0017] Furthermore, the specific method for determining whether to perform book allocation optimization based on the allocation index is as follows: determining whether the library's allocation index is less than a preset allocation threshold obtained from a preset database: if the library's allocation index is not less than the preset allocation threshold obtained from the preset database, book allocation optimization is not performed; if the library's allocation index is less than the preset allocation threshold obtained from the preset database, book allocation optimization is performed; the book allocation optimization includes dynamic resource scheduling and inventory management optimization; the dynamic resource scheduling means dynamically adjusting the location of books based on historical borrowing data; the inventory management optimization means adjusting the inventory of books whose number of borrowers is not within the preset borrowing user number range.

[0018] Furthermore, the book allocation optimization also includes optimization judgment, which is as follows: judging whether the allocation index obtained after the dynamic resource adjustment is less than the preset allocation threshold obtained from the preset database; if the allocation index obtained after the dynamic resource adjustment is less than the preset allocation threshold obtained from the preset database, then performing inventory management optimization, otherwise ending the book allocation optimization; judging whether the allocation index obtained after the inventory management optimization is less than the preset allocation threshold obtained from the preset database; if the allocation index obtained after the inventory management optimization is less than the preset allocation threshold obtained from the preset database, then providing feedback, otherwise ending the book allocation optimization.

[0019] An embodiment of the present application provides a demand forecasting and intelligent allocation system for library collection optimization, including: a book borrowing evaluation module, a user recommendation evaluation module, and a book allocation evaluation module; wherein the book borrowing evaluation module is used to evaluate the actual number of borrowers and book forecast data to obtain a book borrowing index of a specified book, and determine whether to perform new book inventory management based on the book borrowing index. The book borrowing index is used to quantitatively evaluate the borrowing capacity of the specified book in the library demand forecast; the user recommendation evaluation module is used to evaluate the user borrowing index of the specified user based on the obtained new user recommendation data after new book inventory management is performed, and determine whether to perform book recommendation strategy optimization based on the user borrowing index. The user borrowing index is used to quantitatively evaluate the effectiveness of recommending books to the specified user in the library demand forecast; the book allocation evaluation module is used to evaluate the allocation index of the specified book based on the obtained book allocation data after book recommendation strategy optimization is performed, and determine whether to perform book allocation optimization based on the allocation index. The allocation index is used to quantitatively evaluate the efficiency of book allocation in the library intelligent allocation.

[0020] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0021] 1. The book borrowing index of the specified book is obtained to determine whether to conduct new book inventory management. Then, the user borrowing index of the specified user is obtained based on the obtained new user recommendation data to determine whether to optimize the book recommendation strategy. Finally, the allocation index of the specified book is obtained based on the obtained book allocation data to determine whether to optimize the book allocation. This optimizes the library's collection inventory and further improves the accuracy of the library's new book allocation, effectively solving the problem of insufficient data in the existing technology for demand forecasting and allocation analysis of new books in the library.

[0022] 2. The actual number of borrowers is deviated from the data of the actual number of borrowers and the preset number of borrowers. Then, the predicted borrowing rate deviation is obtained based on the predicted borrowing rate and the preset borrowing rate. Then, the borrowing prediction accuracy deviation is obtained based on the borrowing prediction accuracy and the preset borrowing prediction accuracy. Finally, the book borrowing deviation, book similarity and borrowing weight are processed to obtain the book borrowing index, thereby quantitatively evaluating the borrowing capacity of the specified books in the library demand forecast, and then realizing the adjustment of the library's newly introduced collection inventory.

[0023] 3. The user borrowing waiting time deviation is obtained by the user borrowing waiting time and the preset user borrowing waiting time, and then the user actual borrowing time deviation is obtained according to the user actual borrowing time and the preset user book borrowing time. Finally, the user borrowing waiting time deviation, the user actual borrowing time deviation, the user borrowing return rate, the book borrowing coefficient, the preset book borrowing coefficient, the preset user borrowing return rate and the allocation weight are processed to obtain the allocation index, thereby quantitatively evaluating the efficiency of book allocation in the library's intelligent allocation, and thus achieving the improvement of the intelligent allocation efficiency of the library collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of a demand forecasting and intelligent allocation method for library collection optimization provided in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of the structure of a demand forecasting and intelligent allocation system for library collection optimization provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application solve the problem of insufficient data in the prior art for demand forecasting and allocation analysis of new books in the library by providing a demand forecasting and intelligent allocation method and system for library collection optimization. The method obtains the actual number of borrowers and book forecast data for evaluation to obtain the book borrowing index of the specified book, and determines whether to perform new book inventory management based on the book borrowing index. Then, the method obtains the user borrowing index of the specified user based on the obtained new user recommendation data, and determines whether to optimize the book recommendation strategy based on the user borrowing index. Finally, the method obtains the allocation index of the specified book based on the obtained book allocation data, and determines whether to optimize the book allocation based on the allocation index, thereby improving the accuracy of new book allocation in the library.

[0027] The technical solution in the embodiment of the present application is to solve the problem of insufficient data when performing demand forecasting and allocation analysis for new books in the library. The overall idea is as follows:

[0028] Whether to carry out new book inventory management is determined by the book borrowing index of the specified book, and then whether to optimize the book recommendation strategy is determined by the user borrowing index of the specified user obtained based on the new user recommendation data. Finally, whether to optimize the book allocation is determined by the allocation index of the specified book obtained based on the book allocation data, thereby achieving the effect of improving the accuracy of the library's new book allocation.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] like Figure 1 As shown, it is a flowchart of the demand forecasting and intelligent allocation method for library collection optimization provided by an embodiment of the present application, and the method includes the following steps: S1, book borrowing evaluation: the book borrowing index of the specified book is obtained based on the actual number of borrowers and book forecast data obtained, and whether new book inventory management is performed is determined based on the book borrowing index. The book borrowing index is used to quantitatively evaluate the borrowing capacity of the specified book in the library demand forecast; S2, user recommendation evaluation: if new book inventory management is performed, the user borrowing index of the specified user is obtained based on the obtained new user recommendation data, and whether book recommendation strategy optimization is performed is determined based on the user borrowing index. The user borrowing index is used to quantitatively evaluate the effectiveness of recommending books to the specified user in the library demand forecast; S3, book allocation evaluation: if book recommendation strategy optimization is performed, the allocation index of the specified book is obtained based on the obtained book allocation data, and whether book allocation optimization is performed is determined based on the allocation index. The allocation index is used to quantitatively evaluate the efficiency of book allocation in the library intelligent allocation.

[0031] Among them, the book prediction data is obtained by analyzing the predicted number of new book borrowings and the actual borrowing data. The predicted number of new book borrowings is obtained by predicting the historical borrowing data in the demand forecasting system; the historical borrowing data includes book categories and the number of borrowing users; the actual borrowing data includes the specified book categories, the actual number of borrowers and the total number of new books; the book prediction data includes the number of similar books, the borrowing prediction accuracy, the predicted borrowing rate and the book similarity; the new user recommendation data includes the book recommendation accuracy coefficient, the book recommendation coverage coefficient, the user click rate and the new book recommendation coefficient; the book allocation data package The system includes the user's borrowing waiting time, the user's actual borrowing time, the user's borrowing return rate and the book borrowing coefficient; the historical borrowing data and the actual borrowing data are obtained through the library management system; the borrowing prediction accuracy is obtained by calculating the ratio of the absolute value of the difference between the predicted borrowing quantity and the actual borrowing quantity to the predicted borrowing quantity; the predicted borrowing rate is obtained by calculating the ratio of the predicted borrowing quantity to the total number of new books; the book similarity is expressed by the maximum value of the cosine similarity between the specified book category and author and the similarity between the specified book and other books calculated by the book category and author; the number of books of the same type is calculated by the statistical The book recommendation accuracy coefficient is obtained by calculating the ratio of the number of recommended books borrowed by the specified user to the total number of recommended books; the book recommendation coverage coefficient is obtained by calculating the ratio of the number of recommended books borrowed by the specified user to the total number of books borrowed by the specified user; the user click rate is obtained by counting the number of times the specified user clicks on a book in the book recommendation system; the new book recommendation coefficient is obtained by calculating the ratio of the difference between the actual maximum borrowing number of the specified book in the recommended books of the new user and the actual maximum borrowing number of the specified book in the non-recommended books and the total number of books borrowed by the user; the user borrowing waiting time is obtained by recording the time difference between the user's request to borrow a book and the successful borrowing; the user's actual borrowing time is obtained by recording the time difference between the return time and the borrowing time of the book; the user borrowing return rate is obtained by recording the number of books returned on time in the library within a preset time period and the total number of borrowed books; the book borrowing coefficient is obtained by recording the actual borrowing number of books in the preset time period and the total number of books in the library; the user borrowing return rate and the book borrowing coefficient are data obtained by the library based on the borrowing data of all users.

[0032] In this embodiment, the designated books represent new books introduced by the library; the designated users represent users who have no historical borrowing data in the library; the demand forecast uses an existing demand forecasting system to predict borrowing demand, helping the library to manage inventory. For example, the demand forecasting system uses an autoregressive integrated moving average (ARIMA) model to predict the number of new book borrowings based on the number of books in the same category as the new book and the number of users who borrow books in the same category; the existing book recommendation system provides book recommendations for the designated users, thereby improving the designated users' borrowing experience.

[0033] Through the above steps, the library's borrowing efficiency is improved, thereby achieving an improvement in the accuracy of the library's new book allocation.

[0034] Furthermore, the specific method for evaluating the book borrowing index of a specified book based on the actual number of borrowers and the book prediction data is as follows: the actual number of borrowers deviation (i.e. ), the preset borrowing number data includes the preset minimum borrowing number and the preset maximum borrowing number; the predicted borrowing rate deviation (i.e. ); The borrowing prediction accuracy deviation (i.e. ); the book borrowing deviation, book similarity, borrowing weight obtained from the preset database, and the number factor of similar books of the specified book are processed to obtain the book borrowing index; the book borrowing deviation includes the actual number of borrowers deviation, the predicted borrowing rate deviation and the borrowing prediction accuracy deviation; the actual number of borrowers deviation is used to reflect the degree of deviation between the actual number of borrowers and the preset number of borrowers; the predicted borrowing rate deviation is used to reflect the degree of deviation between the predicted borrowing rate and the preset borrowing rate; the borrowing prediction accuracy deviation is used to reflect the degree of deviation between the borrowing prediction accuracy and the preset borrowing prediction accuracy; the borrowing weight includes the borrowing number weight, the borrowing rate weight, the borrowing accuracy weight and the similarity weight.

[0035] Among them, the specific method of obtaining the book borrowing index is:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] In the formula, i represents the number of the specified book type, , N represents the total number of types of specified books, represents the borrowing index of the i-th specified book, XT represents the quantity factor of the same type of books, represents the deviation of the actual number of borrowers of the i-th specified book, represents the predicted borrowing rate deviation of the i-th specified book, represents the borrowing prediction accuracy deviation of the i-th specified book, represents the book similarity of the i-th specified book, Indicates the default borrowing rate. Indicates the preset borrowing prediction accuracy. represents the actual number of borrowers of the i-th specified book, Indicates the preset minimum number of borrowers. Indicates the preset maximum number of borrowers. represents the predicted borrowing rate of the i-th specified book, represents the borrowing prediction accuracy of the i-th specified book, Indicates the weight of borrowers. represents the borrowing rate weight, Indicates the exact borrowing weight, Represents the similarity weight.

[0041] In this embodiment, the preset borrowing rate is represented by the average value of the predicted borrowing rate in the historical time period; the preset borrowing prediction accuracy is represented by the average value of the borrowing prediction accuracy in the historical time period; the preset minimum number of borrowers is represented by the minimum number of actual borrowers in the historical time period; and the preset maximum number of borrowers is represented by the maximum number of actual borrowers in the historical time period.

[0042] The borrowing weight is obtained from the preset database. The borrower number weight indicates the influence of the actual borrower number on the book borrowing index, the borrowing rate weight indicates the influence of the predicted borrowing rate on the book borrowing index, the borrowing accuracy weight indicates the influence of the borrowing prediction accuracy on the book borrowing index, and the similarity weight indicates the influence of the book similarity on the book borrowing index. The sum of the four is 1. For example, the actual borrower number and the preset book borrowing index form a mapping set, and the real-time actual borrower number is input into the mapping set to obtain the corresponding borrower number weight; the predicted borrowing rate and the preset book borrowing index form a mapping set, and the real-time predicted borrowing rate is input into the mapping set to obtain the corresponding borrowing rate weight; the borrowing prediction accuracy and the preset book borrowing index form a mapping set, and the real-time borrowing prediction accuracy is input into the mapping set to obtain the corresponding borrowing accuracy weight; the book similarity and the preset book borrowing index form a mapping set, and the real-time book similarity is input into the mapping set to obtain the corresponding borrowing accuracy weight. The mapping relationship can be one-to-one or many-to-one.

[0043] The factor for the number of similar books is obtained from the database. It indicates the degree of influence of the number of similar books on the book borrowing index. For example, based on the relationship between the historical number of similar books and book prediction data (such as borrowing prediction accuracy and predicted borrowing rate), a mapping set is constructed between the number of similar books and their corresponding impact factors. The real-time number of similar books is then input into the mapping set to obtain the corresponding number of similar books factor.

[0044] The book borrowing index in this algorithm involves processing multiple independent variables (actual number of borrowers, borrowing prediction accuracy, predicted borrowing rate and book similarity), and there is a mutual influence relationship between these independent variables; a higher predicted borrowing rate may lead to an increase in the actual number of borrowers; a higher book similarity may lead to an increase in the predicted borrowing rate and the actual number of borrowers; a lower borrowing prediction accuracy may lead to insufficient inventory of designated books and a decrease in the predicted borrowing rate, which may lead to a decrease in the actual borrowing rate and the actual number of borrowers of the designated books; a designated book with a higher predicted borrowing rate usually has more actual borrowers, so the predicted borrowing rate is one of the key factors affecting the actual number of borrowers.

[0045] In this algorithm, the book borrowing index is positively correlated with the actual number of borrowers, borrowing prediction accuracy, predicted borrowing rate and book similarity.

[0046] Taking the factor of the number of similar books as 1, and the weight of the number of borrowers, the weight of the borrowing rate, the weight of the borrowing accuracy, and the weight of the similarity as 0.25, 0.2, 0.3, and 0.25 respectively, the statistical table of changes in the book borrowing index is shown in Table 1:

[0047] Table 1 Statistics of changes in book borrowing index

[0048]

[0049] From the first and second groups of data in the table, it can be seen that the book borrowing index increases with the increase of the deviation of the actual number of borrowers; from the second and third groups of data, it can be seen that the book borrowing index increases with the increase of the deviation of the predicted borrowing rate; from the third and fourth groups, it can be seen that the book borrowing index increases with the increase of the deviation of the borrowing prediction accuracy; from the fourth and fifth groups of data, it can be seen that the book borrowing index increases with the increase of book similarity.

[0050] Through the above steps, the borrowing capacity of the specified books in the library demand forecast is quantitatively evaluated, and the inventory of the newly introduced collections in the library is adjusted.

[0051] Furthermore, a specific method for determining whether to perform new book inventory management based on the book borrowing index is as follows: determine whether the book borrowing index of a specified book is less than a preset book borrowing threshold obtained from a preset database: the preset book borrowing threshold includes a first preset book borrowing threshold and a second preset book borrowing threshold; if the book borrowing index of the specified book is less than the first preset book borrowing threshold obtained from the preset database, the inventory of the specified book is reduced step by step; if the book borrowing index of the specified book is not less than the first preset book borrowing threshold obtained from the preset database, and is not greater than the second preset book borrowing threshold obtained from the preset database, new book inventory management is not performed; if the book borrowing index of the specified book is greater than the second preset book borrowing threshold obtained from the preset database, the inventory of the specified book is increased step by step.

[0052] In this embodiment, the first preset book borrowing threshold is represented by the difference between the average value and three times the variance of the qualified book borrowing index in the historical time period; the second preset book borrowing threshold is represented by the sum of the average value and three times the variance of the qualified book borrowing index in the historical time period.

[0053] Reduce the inventory of designated books step by step: reduce the original inventory of designated books by 10% each time; increase the inventory of designated books step by step: increase the original inventory of designated books by 10% each time.

[0054] Through the above steps, the borrowing capacity of the specified books in the library demand forecast is quantitatively evaluated, and the inventory of the newly introduced collections in the library is adjusted.

[0055] Furthermore, the specific method for evaluating the user borrowing index of a specified user based on the acquired new user recommendation data is as follows: the book recommendation accuracy coefficient deviation (i.e. ), the preset book recommendation accuracy coefficient includes a first preset book recommendation accuracy coefficient and a second preset book recommendation accuracy coefficient. The book recommendation accuracy coefficient deviation is used to reflect the degree of deviation between the book recommendation accuracy coefficient and the preset book recommendation accuracy coefficient; the book recommendation coverage coefficient deviation (i.e. ), the preset book recommendation coverage coefficient includes a first preset book recommendation coverage coefficient and a second preset book recommendation coverage coefficient, and the book recommendation coverage coefficient deviation is used to reflect the degree of deviation between the book recommendation coverage coefficient and the preset book recommendation coverage coefficient; the new book recommendation coefficient deviation (i.e. ), the preset new book recommendation coefficient includes a first preset new book recommendation coefficient and a second preset new book recommendation coefficient, and the new book recommendation coefficient deviation is used to reflect the degree of deviation between the new book recommendation coefficient and the preset new book recommendation coefficient; the user click rate deviation (i.e. ), the user click rate deviation is used to reflect the degree of deviation between the user click rate and the preset user click rate; the book recommendation precision coefficient deviation, book recommendation coverage coefficient deviation, new book recommendation coefficient deviation, user click rate deviation and the user borrowing weight obtained from the preset database are processed to obtain the user borrowing index; the user borrowing weight includes the recommendation precision weight, recommendation coverage weight, new book recommendation weight and user click weight.

[0056] The specific method for obtaining the user borrowing index is as follows:

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] In the formula, m represents the number of the specified user, , M represents the total number of specified users, Indicates the user borrowing index of the mth specified user, Indicates the deviation of the book recommendation accuracy coefficient for the mth specified user, represents the book recommendation coverage coefficient deviation of the mth specified user, Indicates the click rate of the mth specified user. Indicates the new book recommendation coefficient deviation of the mth specified user, Indicates the preset user click rate, Indicates the book recommendation accuracy coefficient for the mth specified user, Indicates the first preset book recommendation accuracy coefficient, Indicates the second preset book recommendation accuracy coefficient, represents the book recommendation coverage coefficient for the mth specified user, represents the first preset book recommendation coverage coefficient, represents the second preset book recommendation coverage coefficient, Indicates the new book recommendation coefficient for the mth specified user, Indicates the first preset new book recommendation coefficient, Indicates the second preset new book recommendation coefficient, Indicates the recommended precision weight, Indicates the recommended coverage weight, Indicates the user click weight, Indicates the weight of new book recommendation.

[0062] In this embodiment, the preset user click-through rate is represented by the average value of the user click-through rate in the historical time period; the first preset book recommendation precision coefficient is represented by the difference between the average value and three times the variance of the book recommendation precision coefficient in the historical time period, and the second preset book recommendation precision coefficient is represented by the sum of the average value and three times the variance of the book recommendation precision coefficient in the historical time period; the first preset book recommendation coverage coefficient is represented by the difference between the average value and three times the variance of the book recommendation coverage coefficient in the historical time period, and the second preset book recommendation coverage coefficient is represented by the sum of the average value and three times the variance of the book recommendation coverage coefficient in the historical time period; the first preset new book recommendation coefficient is represented by the difference between the average value and three times the variance of the new book recommendation coefficient in the historical time period, and the second preset new book recommendation coefficient is represented by the sum of the average value and three times the variance of the new book recommendation coefficient in the historical time period.

[0063] The user borrowing weight is obtained from the preset database, the recommendation precision weight indicates the influence of the book recommendation precision coefficient on the user borrowing index, the recommendation coverage weight indicates the influence of the book recommendation coverage coefficient on the user borrowing index, the new book recommendation weight indicates the influence of the new book recommendation coefficient on the user borrowing index, and the user click weight indicates the influence of the user click rate on the user borrowing index; the sum of the four is 1, for example, the book recommendation precision coefficient and the preset user borrowing index form a mapping set, and the real-time book recommendation precision coefficient is input into the mapping set to obtain the corresponding recommendation precision weight; the book recommendation coverage coefficient and the preset user borrowing index form a mapping set, and the real-time book recommendation coverage coefficient is input into the mapping set to obtain the corresponding recommendation coverage weight; the new book recommendation coefficient and the preset user borrowing index form a mapping set, and the real-time new book recommendation coefficient is input into the mapping set to obtain the corresponding new book recommendation weight; the user click rate and the preset user borrowing index form a mapping set, and the real-time user click rate is input into the mapping set to obtain the corresponding user click weight; the mapping relationship can be one-to-one or many-to-one.

[0064] The user borrowing index in this algorithm involves processing multiple independent variables (book recommendation precision coefficient, book recommendation coverage coefficient, user click-through rate and new book recommendation coefficient), and there is a mutual influence relationship between these independent variables; the higher the book recommendation precision coefficient, the more likely the designated user is to be interested in the recommended books, thereby increasing the corresponding user click-through rate and book recommendation coverage coefficient; if the book recommendation system meets user needs more accurately, the corresponding user click-through rate will be higher, and the number of successfully recommended books will increase, thereby improving the book recommendation precision coefficient and book recommendation coverage coefficient for the corresponding designated user; a higher new book recommendation coefficient may lead to an increase in the book recommendation precision coefficient, book recommendation coverage coefficient and user click-through rate for the corresponding designated user.

[0065] In this algorithm, the user borrowing index is positively correlated with the book recommendation accuracy coefficient, book recommendation coverage coefficient, user click rate and new book recommendation coefficient.

[0066] Through the above steps, the effectiveness of recommending books to designated users in library demand forecasting is quantitatively evaluated, thereby improving the accuracy of book recommendations in the library.

[0067] Furthermore, a specific method for determining whether to optimize the book recommendation strategy based on the user borrowing index is as follows: determining whether the user borrowing index of a specified user is less than a preset user borrowing threshold value obtained from a preset database: if the user borrowing index of the specified user is not less than the preset user borrowing threshold value obtained from the preset database, the book recommendation strategy is not optimized; if the user borrowing index of the specified user is less than the preset user borrowing threshold value obtained from the preset database, the book recommendation strategy is optimized; book recommendation strategy optimization means adjusting the recommendation strategy in real time according to the basic information and user behavior of the new user.

[0068] In this embodiment, the preset user borrowing threshold is represented by the average value of the qualified user borrowing indexes in a historical time period.

[0069] Use the basic information of new users (such as age, subject, interests, etc.) to make preliminary book recommendations: For example, a new user registers a library account and fills in basic information, indicating that he or she is a college student majoring in computer science. The book recommendation system can recommend some popular books and classic textbooks in computer science to the user based on this basic information, such as "Introduction to Algorithms" and "Data Structure and Algorithm Analysis". It can also make recommendations based on market hotspots, such as new works by Nobel Prize winners in literature.

[0070] Adjust the book recommendation strategy based on the user's click history and search history. For example, if a new user starts borrowing books about machine learning, the book recommendation system can identify the user's interest in machine learning and recommend more machine learning-related books to the user, such as "Python Machine Learning".

[0071] Through the above steps, the accuracy of book recommendations for new users in the library is improved.

[0072] Furthermore, a specific method for evaluating the allocation index of a specified book based on the acquired book allocation data is as follows: obtaining a user borrowing waiting time deviation (i.e., JYP) based on the relative relationship between the user borrowing waiting time and the preset user borrowing waiting time obtained from the preset database, and the user borrowing waiting time deviation is used to reflect the degree of deviation between the user borrowing waiting time and the preset user borrowing waiting time; obtaining a user actual borrowing time deviation (i.e., JTP) based on the relative relationship between the user's actual borrowing time and the preset user book borrowing time obtained from the preset database, and the user actual borrowing time deviation is used to reflect the degree of deviation between the user's actual borrowing time and the preset user book borrowing time; obtaining a user borrowing return rate deviation (i.e., JTP) based on the relative relationship between the user's borrowing return rate and the preset user borrowing return rate obtained from the preset database. ), the user borrowing and returning rate deviation is used to reflect the deviation between the user borrowing and returning rate and the preset user borrowing and returning rate; the book borrowing coefficient deviation (i.e. ), the book borrowing coefficient deviation is used to reflect the degree of deviation between the book borrowing coefficient and the preset book borrowing coefficient; when the user borrowing return rate is not less than the preset user borrowing return rate, and the book borrowing coefficient is not less than the preset book borrowing coefficient, the user borrowing waiting time deviation, the user actual borrowing time deviation, the user borrowing return rate deviation, the book borrowing coefficient deviation and the allocation weight obtained from the preset database are processed to obtain the allocation index, otherwise the allocation index is recorded as 0; the allocation weight includes the borrowing waiting weight, the borrowing time weight, the borrowing return rate weight and the borrowing coefficient weight; the specific method for obtaining the allocation index is:

[0073] ;

[0074] ;

[0075] ;

[0076] In the formula, i represents the number of the specified book, , N represents the total number of types of specified books, m represents the number of specified users, , M represents the total number of designated users, TP represents the library's allocation index, JYP represents the deviation of users' waiting time for borrowing, JTP represents the deviation of users' actual borrowing time, JGH represents the user's borrowing return rate, and JYX represents the book borrowing coefficient. Indicates the preset user borrowing return rate. Indicates the preset book borrowing coefficient, Indicates the waiting time for the mth designated user to borrow the ith designated book. Indicates the actual borrowing time of the mth designated user who borrowed the i-th designated book. Indicates the preset user borrowing waiting time. Indicates the preset borrowing time of user books. Indicates the borrowing waiting weight, Indicates the borrowing time weight, represents the borrowing return rate weight, Represents the borrowing coefficient weight.

[0077] In this embodiment, the preset user borrowing and returning rate is represented by the average value of the user borrowing and returning rates in the historical time period; the preset book borrowing coefficient is represented by the average value of the book borrowing coefficient in the historical time period; the preset user borrowing and returning rate and the preset book borrowing coefficient are data comprehensively obtained by the library based on the borrowing data of all users.

[0078] The allocation weight is obtained from a preset database. The borrowing waiting weight indicates the degree of influence of the user's borrowing waiting time on the allocation index. The borrowing time weight indicates the degree of influence of the user's actual borrowing time on the allocation index. The borrowing return rate weight indicates the degree of influence of the user's borrowing return rate on the allocation index. The borrowing coefficient weight indicates the degree of influence of the book borrowing coefficient on the allocation index. The sum of the four is 1. For example, the user's borrowing waiting time and the preset allocation index form a mapping set, and the real-time user's borrowing waiting time is input into the mapping set to obtain the corresponding borrowing waiting weight; the user's actual borrowing time and the preset allocation index form a mapping set, and the real-time user's actual borrowing time is input into the mapping set to obtain the corresponding borrowing time weight; the user's borrowing return rate and the preset allocation index form a mapping set, and the real-time user's borrowing return rate is input into the mapping set to obtain the corresponding borrowing return rate weight; the book borrowing coefficient and the preset allocation index form a mapping set, and the real-time book borrowing coefficient is input into the mapping set to obtain the corresponding borrowing coefficient weight. The mapping relationship can be one-to-one or many-to-one.

[0079] The allocation index in this algorithm is derived by processing multiple independent variables (user borrowing wait time, actual borrowing time, user borrowing return rate, and book borrowing coefficient). These independent variables interact with each other. A longer borrowing wait time may reduce the willingness of any library user to borrow books, causing them to give up borrowing books, which in turn reduces the book borrowing coefficient. A higher borrowing return rate may increase user borrowing wait time. A higher book borrowing coefficient indicates higher library resource utilization and a higher borrowing index. A lower book borrowing coefficient may mean that a large number of books are neglected or not borrowed in a timely manner, which affects the book borrowing index. A shorter actual borrowing time may also lead to a shorter user borrowing wait time.

[0080] In this algorithm, when the user's borrowing waiting time is not greater than the preset user borrowing waiting time, the shorter the user's borrowing waiting time, the greater the allocation index; when the user's actual book borrowing time is not greater than the preset user book borrowing time, the shorter the user's actual book borrowing time, the greater the allocation index; the allocation index is positively correlated with the user's borrowing return rate and the book borrowing coefficient.

[0081] Taking the preset user borrowing return rate as 0.3, the preset book borrowing coefficient as 0.3, the borrowing waiting weight, borrowing time weight, borrowing return rate weight, and borrowing coefficient weight as 0.25, 0.2, 0.25, and 0.3 respectively as an example, the statistical table of the change of the allocation index is shown in Table 2:

[0082] Table 2 Statistics of changes in the allocation index

[0083]

[0084] From the first and second groups of data in the table, it can be seen that the allocation index increases with the increase of the deviation of the user's borrowing waiting time; from the second and third groups of data, it can be seen that the allocation index increases with the increase of the deviation of the user's actual borrowing time; from the third and fourth groups, it can be seen that the allocation index increases with the increase of the user's borrowing return rate; from the fourth and fifth groups of data, it can be seen that the allocation index increases with the increase of the book borrowing coefficient.

[0085] Through the above steps, the efficiency of book allocation in library intelligent allocation is quantitatively evaluated, thereby achieving an improvement in the efficiency of intelligent allocation of library collections.

[0086] Furthermore, a specific method for determining whether to perform book allocation optimization based on the allocation index is as follows: determining whether the library's allocation index is less than a preset allocation threshold value obtained from a preset database: if the library's allocation index is not less than the preset allocation threshold value obtained from the preset database, book allocation optimization is not performed; if the library's allocation index is less than the preset allocation threshold value obtained from the preset database, book allocation optimization is performed; book allocation optimization includes dynamic resource scheduling and inventory management optimization; dynamic resource scheduling means dynamically adjusting the location of books based on historical borrowing data; inventory management optimization means adjusting the inventory of books whose number of borrowers is not within the preset borrowing user number range.

[0087] It should be added that the book allocation optimization also includes optimization judgment, which is as follows: judging whether the allocation index obtained after the dynamic resource adjustment is less than the preset allocation threshold obtained from the preset database; if the allocation index obtained after the dynamic resource adjustment is less than the preset allocation threshold obtained from the preset database, then inventory management optimization is performed, otherwise the book allocation optimization is ended; judging whether the allocation index obtained after the inventory management optimization is less than the preset allocation threshold obtained from the preset database; if the allocation index obtained after the inventory management optimization is less than the preset allocation threshold obtained from the preset database, then feedback is given to the preset personnel, otherwise the book allocation optimization is ended.

[0088] In this embodiment, the preset allocation threshold is represented by an average value of qualified allocation indexes in a historical time period.

[0089] Dynamic resource adjustment: The demand forecasting system predicts borrowing trends based on historical borrowing data. For example, during exam season, it is predicted that the borrowing volume of professional books will increase significantly. Therefore, professional books can be allocated to popular areas (such as the library's study room or reading room) in advance to meet readers' needs.

[0090] Inventory management optimization: The preset range of the number of borrowing users is represented by the sum of the difference between the average number of borrowing users and three times the variance in the historical time period; the preset minimum number of borrowing users is represented by the difference between the average number of borrowing users and three times the variance in the historical time period; the preset maximum range of the number of borrowing users is represented by the sum of the average number of borrowing users and three times the variance in the historical time period; when the number of borrowing users of any book is less than the preset minimum number of borrowing users, the inventory of the corresponding book will be reduced; when the number of borrowing users of any book is greater than the preset maximum number of borrowing users, the inventory of the corresponding book will be increased.

[0091] Through the above steps, the efficiency of intelligent allocation of library collections is improved.

[0092] like Figure 2 As shown, it is a structural schematic diagram of the demand forecasting and intelligent allocation system for library collection optimization provided by an embodiment of the present application. The demand forecasting and intelligent allocation system for library collection optimization provided by an embodiment of the present application includes: a book borrowing evaluation module, a user recommendation evaluation module and a book allocation evaluation module; wherein the book borrowing evaluation module is used to evaluate the actual number of borrowers and book forecast data to obtain a book borrowing index of a specified book, and determine whether to perform new book inventory management based on the book borrowing index. The book borrowing index is used to quantitatively evaluate the borrowing capacity of the specified book in the library demand forecast; the user recommendation evaluation module is used to evaluate the user borrowing index of the specified user based on the obtained new user recommendation data after new book inventory management is performed, and determine whether to optimize the book recommendation strategy based on the user borrowing index. The user borrowing index is used to quantitatively evaluate the effectiveness of recommending books to the specified user in the library demand forecast; the book allocation evaluation module is used to evaluate the allocation index of the specified book based on the obtained book allocation data after book recommendation strategy optimization is performed, and determine whether to perform book allocation optimization based on the allocation index. The allocation index is used to quantitatively evaluate the efficiency of book allocation in the library intelligent allocation.

[0093] In this embodiment, the book borrowing evaluation module uses the demand forecasting system to predict borrowing demand based on historical borrowing data, obtains the book borrowing index of the specified book, and determines whether new book inventory management is needed for the specified book. For example, the library may decide to replenish the inventory of the book; through the user recommendation evaluation module, by analyzing the user's borrowing preferences and borrowing frequency, the book recommendation system can more accurately recommend books that may be of interest to users, thereby improving the user's borrowing experience; the book allocation evaluation module evaluates the efficiency of book allocation, so that the library can dynamically adjust the location or quantity of books to ensure maximum resource utilization, reduce readers' waiting time, and improve the overall operating efficiency of the library; through quantitative indicators, the library's resource allocation and user experience are evaluated and optimized to achieve efficient management of the library's collection.

[0094] To summarize, the embodiment of the present application determines whether to perform new book inventory management by obtaining the book borrowing index of the specified book, then determines whether to optimize the book recommendation strategy based on the user borrowing index of the specified user obtained based on the new user recommendation data, and finally determines whether to optimize the book allocation by obtaining the allocation index of the specified book based on the obtained book allocation data, thereby optimizing the library's collection inventory and further improving the accuracy of the library's new book allocation, effectively solving the problem of insufficient data in the existing technology for demand forecasting and allocation analysis of new books in the library.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0100] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A demand forecasting and intelligent allocation method for library collection optimization, characterized by: The following steps are involved: S1, evaluating the actual number of borrowers and book forecast data to obtain a book borrowing index for a specified book, and determining whether to perform new book inventory management based on the book borrowing index. The book borrowing index is used to quantitatively evaluate the borrowing capacity of the specified book in the library demand forecast; S2: After new book inventory management is performed, the user borrowing index of the specified user is evaluated based on the acquired new user recommendation data, and whether to optimize the book recommendation strategy is determined based on the user borrowing index. The user borrowing index is used to quantitatively evaluate the effectiveness of recommending books to the specified user in the library demand forecast; S3: After optimizing the book recommendation strategy, an evaluation is performed based on the acquired book allocation data to obtain an allocation index for the specified book. Based on the allocation index, it is determined whether to optimize the book allocation. The allocation index is used to quantitatively evaluate the efficiency of book allocation in intelligent library allocation. The specific method for evaluating and obtaining the book borrowing index of a specified book based on the actual number of borrowers and book forecast data is as follows: The borrowing deviation, similarity, borrowing weight and quantity factor of similar books of the specified book are processed to obtain the borrowing index; The book borrowing deviation includes the actual number of borrowers deviation, the predicted borrowing rate deviation and the borrowing prediction accuracy deviation; The actual number of borrowers deviation is used to reflect the degree of deviation between the actual number of borrowers and the preset number of borrowers data; The predicted borrowing rate deviation is used to reflect the degree of deviation between the predicted borrowing rate and the preset borrowing rate; The borrowing prediction accuracy deviation is used to reflect the degree of deviation between the borrowing prediction accuracy and the preset borrowing prediction accuracy; The borrowing weights include the borrower number weight, borrowing rate weight, borrowing accuracy weight and similarity weight.

2. The demand forecasting and intelligent allocation method for library collection optimization according to claim 1, characterized in that: The book prediction data is obtained by analyzing the predicted number of new book borrowings and the actual borrowing data. The predicted number of new book borrowings is obtained by predicting the number of historical borrowing data. The historical borrowing data includes book categories and the number of borrowing users; The actual borrowing data includes the designated book category, the actual number of borrowers and the total number of new books; The book prediction data includes the number of similar books, borrowing prediction accuracy, predicted borrowing rate and book similarity; The new user recommendation data includes book recommendation accuracy coefficient, book recommendation coverage coefficient, user click rate and new book recommendation coefficient; The book allocation data includes the user's borrowing waiting time, the user's actual borrowing time, the user's borrowing return rate and the book borrowing coefficient; The borrowing prediction accuracy is obtained by calculating the ratio of the absolute value of the difference between the predicted borrowing quantity and the actual borrowing quantity to the predicted borrowing quantity; The predicted borrowing rate is obtained by calculating the ratio of the predicted borrowing quantity to the total number of new books; The book similarity is represented by the maximum value of the cosine similarity between the specified book and the author and the similarity between the specified book and other books calculated by the book category and the author; The book recommendation accuracy coefficient is obtained by calculating the ratio of the number of recommended books borrowed by the specified user to the total number of recommended books; The book recommendation coverage coefficient is obtained by calculating the ratio of the number of recommended books borrowed by the designated user to the total number of books borrowed by the designated user; The new book recommendation coefficient is obtained by calculating the ratio of the difference between the actual maximum borrowing quantity of the specified book in the recommended books of the new user and the actual maximum borrowing quantity of the specified book in the non-recommended books and the total number of books borrowed by the user; The book borrowing coefficient is obtained by recording the actual number of books borrowed in a preset time period and performing a ratio calculation with the total number of books in the library.

3. The demand forecasting and intelligent allocation method for library collection optimization according to claim 1, characterized in that: The specific method for determining whether to perform new book inventory management based on the book borrowing index is as follows: Determine whether the book borrowing index of the specified book is less than the preset book borrowing threshold obtained from the preset database: The preset book borrowing threshold includes a first preset book borrowing threshold and a second preset book borrowing threshold; If the book borrowing index of the specified book is less than the first preset book borrowing threshold obtained from the preset database, the inventory of the specified book is reduced step by step; If the book borrowing index of the specified book is not less than the first preset book borrowing threshold obtained from the preset database, and is not greater than the second preset book borrowing threshold obtained from the preset database, new book inventory management is not performed; If the book borrowing index of the specified book is greater than a second preset book borrowing threshold obtained from the preset database, the inventory of the specified book is increased step by step.

4. The demand forecasting and intelligent allocation method for library collection optimization according to claim 1, characterized in that: The specific method of evaluating the obtained new user recommendation data to obtain the user borrowing index of a specified user is as follows: Obtaining a book recommendation precision coefficient deviation based on a relative relationship between the book recommendation precision coefficient and a preset book recommendation precision coefficient obtained from a preset database, wherein the preset book recommendation precision coefficient includes a first preset book recommendation precision coefficient and a second preset book recommendation precision coefficient, and the book recommendation precision coefficient deviation is used to reflect the degree of deviation between the book recommendation precision coefficient and the preset book recommendation precision coefficient; Obtaining a book recommendation coverage coefficient deviation based on a relative relationship between the book recommendation coverage coefficient and a preset book recommendation coverage coefficient obtained from a preset database, wherein the preset book recommendation coverage coefficient includes a first preset book recommendation coverage coefficient and a second preset book recommendation coverage coefficient, and the book recommendation coverage coefficient deviation is used to reflect the degree of deviation between the book recommendation coverage coefficient and the preset book recommendation coverage coefficient; Obtaining a new book recommendation coefficient deviation based on a relative relationship between the new book recommendation coefficient and a preset new book recommendation coefficient obtained from a preset database, wherein the preset new book recommendation coefficient includes a first preset new book recommendation coefficient and a second preset new book recommendation coefficient, and the new book recommendation coefficient deviation is used to reflect the degree of deviation between the new book recommendation coefficient and the preset new book recommendation coefficient; Obtaining a user click rate deviation based on a relative relationship between the user click rate and a preset user click rate obtained from a preset database, wherein the user click rate deviation is used to reflect a degree of deviation between the user click rate and the preset user click rate; The book recommendation accuracy coefficient deviation, book recommendation coverage coefficient deviation, new book recommendation coefficient deviation, user click rate deviation and user borrowing weight obtained from the preset database are processed to obtain the user borrowing index; The user borrowing weight includes recommendation accuracy weight, recommendation coverage weight, new book recommendation weight and user click weight.

5. The demand forecasting and intelligent allocation method for library collection optimization according to claim 1, characterized in that: The specific method for determining whether to optimize the book recommendation strategy based on the user borrowing index is as follows: Determine whether the user borrowing index of a specified user is less than the preset user borrowing threshold obtained from the preset database: If the user borrowing index of the specified user is not less than the preset user borrowing threshold obtained from the preset database, the book recommendation strategy will not be optimized; If the user borrowing index of the specified user is less than the preset user borrowing threshold obtained from the preset database, the book recommendation strategy is optimized.

6. The demand forecasting and intelligent allocation method for library collection optimization according to claim 1, characterized in that: The specific method of evaluating the obtained book allocation data to obtain the allocation index of a specified book is as follows: Obtaining a user borrowing waiting time deviation based on a relative relationship between the user borrowing waiting time and a preset user borrowing waiting time obtained from a preset database, wherein the user borrowing waiting time deviation is used to reflect a degree of deviation between the user borrowing waiting time and the preset user borrowing waiting time; The actual borrowing time deviation of the user is obtained according to the relative relationship between the actual borrowing time of the user and the preset borrowing time of the user book obtained from the preset database, wherein the actual borrowing time deviation of the user is used to reflect the degree of deviation between the actual borrowing time of the user and the preset borrowing time of the user book; Obtaining a user borrowing return rate deviation based on a relative relationship between the user borrowing return rate and a preset user borrowing return rate obtained from a preset database, wherein the user borrowing return rate deviation is used to reflect the degree of deviation between the user borrowing return rate and the preset user borrowing return rate; Obtaining a book borrowing coefficient deviation according to a relative relationship between the book borrowing coefficient and a preset book borrowing coefficient obtained from a preset database, wherein the book borrowing coefficient deviation is used to reflect a degree of deviation between the book borrowing coefficient and the preset book borrowing coefficient; When the user borrowing return rate is not less than the preset user borrowing return rate, and the book borrowing coefficient is not less than the preset book borrowing coefficient, the user borrowing waiting time deviation, the user actual borrowing time deviation, the user borrowing return rate deviation, the book borrowing coefficient deviation and the allocation weight obtained from the preset database are processed to obtain the allocation index; otherwise, the allocation index is recorded as 0; The allocation weights include borrowing waiting weight, borrowing duration weight, borrowing return rate weight and borrowing coefficient weight.

7. The demand forecasting and intelligent allocation method for library collection optimization according to claim 1, characterized in that: The specific method for determining whether to perform book allocation optimization based on the allocation index is as follows: Determine whether the library's allocation index is less than the preset allocation threshold obtained from the preset database: If the library's allocation index is not less than the preset allocation threshold obtained from the preset database, book allocation optimization will not be performed; If the library's allocation index is less than the preset allocation threshold obtained from the preset database, book allocation optimization is performed; The book allocation optimization includes dynamic resource scheduling and inventory management optimization; The dynamic resource scheduling means dynamically adjusting the book location based on historical borrowing data; The inventory management optimization refers to adjusting the inventory of books whose number of borrowers is not within a preset range of the number of borrowers.

8. The demand forecasting and intelligent allocation method for library collection optimization according to claim 7, characterized in that: The book allocation optimization also includes optimization judgment, which is as follows: Determining whether an allocation index obtained after dynamic resource adjustment is less than a preset allocation threshold obtained from a preset database; If the allocation index obtained after dynamic resource adjustment is less than the preset allocation threshold obtained from the preset database, inventory management optimization is performed; otherwise, book allocation optimization is terminated; Determine whether the allocation index obtained after inventory management optimization is less than a preset allocation threshold obtained from a preset database; If the allocation index obtained after inventory management optimization is less than the preset allocation threshold obtained from the preset database, feedback is given; otherwise, the book allocation optimization is terminated.

9. The demand forecasting and intelligent allocation system for library collection optimization is characterized by: include: Book borrowing evaluation module, user recommendation evaluation module and book allocation evaluation module; The book borrowing evaluation module is used to evaluate the actual number of borrowers and book forecast data to obtain a book borrowing index for a specified book, and to determine whether to perform new book inventory management based on the book borrowing index. The book borrowing index is used to quantitatively evaluate the borrowing capacity of a specified book in the library demand forecast. The user recommendation evaluation module is used to evaluate the user borrowing index of a specified user based on the acquired new user recommendation data after new book inventory management is performed, and to determine whether to optimize the book recommendation strategy based on the user borrowing index. The user borrowing index is used to quantitatively evaluate the effectiveness of recommending books to specified users in library demand forecasting; The book allocation evaluation module is used to evaluate the allocation index of a specified book based on the acquired book allocation data after optimizing the book recommendation strategy, and to determine whether to optimize the book allocation based on the allocation index. The allocation index is used to quantitatively evaluate the efficiency of book allocation in intelligent library allocation. The specific method for evaluating and obtaining the book borrowing index of a specified book based on the actual number of borrowers and book forecast data is as follows: The borrowing deviation, similarity, borrowing weight and quantity factor of similar books of the specified book are processed to obtain the borrowing index; The book borrowing deviation includes the actual number of borrowers deviation, the predicted borrowing rate deviation and the borrowing prediction accuracy deviation; The actual number of borrowers deviation is used to reflect the degree of deviation between the actual number of borrowers and the preset number of borrowers data; The predicted borrowing rate deviation is used to reflect the degree of deviation between the predicted borrowing rate and the preset borrowing rate; The borrowing prediction accuracy deviation is used to reflect the degree of deviation between the borrowing prediction accuracy and the preset borrowing prediction accuracy; The borrowing weights include the borrower number weight, borrowing rate weight, borrowing accuracy weight and similarity weight.

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