Intelligent management method of library warehouse information system

Abstract

The application discloses an intelligent management method of a library warehouse information system, and relates to the technical field of library management, which comprises the following steps: establishing the association relationship between the book category and the books, and calculating the score reference index based on the user data; establishing the fitting equation of the time and the total number of book demands, and combining the comprehensive proportion index of the books in their categories to calculate the first predicted total number of demands of each book and the error; based on the score calculation strategy, establishing the fitting equation of the comprehensive score and the total number of demands to calculate the second predicted total number of demands of the next week and the error; combining the two predicted total numbers of demands to obtain the comprehensive predicted total number of demands, and executing the replenishment judgment strategy to judge whether the books need to be replenished, so that the accuracy and timeliness of the inventory management are ensured.

Description

Technical Field

[0001] This invention relates to the field of book management technology, and in particular to an intelligent management method for a book warehouse information system. Background Technology

[0002] With the continuous development of the book industry, the management of book warehouses has become increasingly complex. Traditional book warehouse management mainly relies on human experience and simple statistical data for decision-making, which is inadequate when faced with a large number of books and ever-changing market demands. Therefore, the intelligent information management system for book warehouses has emerged, aiming to optimize warehouse management and improve operational efficiency through information technology and data analysis.

[0003] Existing book warehouse information system management methods typically include basic functions such as book receiving, issuing, inventory query, and sales statistics. These methods can help managers understand the book inventory and sales situation in the warehouse to a certain extent, providing some data support for decision-making. However, these methods still have obvious shortcomings in predicting book availability.

[0004] Existing methods often lack effective demand forecasting mechanisms. They can only make simple inferences about future demand based on historical sales data, and cannot fully consider the impact of book category and user reviews on book demand. In addition, existing methods may not be able to effectively integrate information from different data sources and lack the ability to conduct in-depth data analysis. Therefore, they cannot accurately determine whether replenishment operations need to be carried out in advance. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the technical problems in the background art, this invention proposes an intelligent management method for a book warehouse information system. This method involves fitting the relationship between time and the total number of books needed, and combining this with the comprehensive proportion index of each book within its category to obtain the first predicted total demand for each book. Based on a scoring strategy, the method calculates the comprehensive score for each book and establishes a fitting equation between the comprehensive score and the total demand to predict the second predicted total demand for the following week. Finally, it combines the first and second predicted total demands to determine replenishment needs, thereby solving the technical problems described in the background art.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] An intelligent management method for a book warehouse information system includes:

[0010] Acquire book category data and all book data under each category, and establish the relationship between book categories and books; create several data tables, and calculate the rating reference index for each user based on the data in the user data table;

[0011] Establish a fitting equation between time and total book demand, calculate the predicted total book demand for each book category in the next week; calculate the comprehensive proportion index of each book to its respective book category, and combine it with the predicted total book demand for the corresponding book category to obtain the first predicted total demand for each book in the next week; calculate the first prediction error for each book.

[0012] The overall score of each book is calculated based on the scoring strategy; the overall score of each book and the total actual demand are combined from the first to the fourth week to establish a fitting equation between the overall score and the total demand for each book, and the second predicted total demand for each book in the next week is calculated; the second prediction error for each book is calculated.

[0013] Calculate the weighting factors for the first and second total forecasted demand respectively; combine the first and second total forecasted demand with the corresponding weighting factors to obtain the total total forecasted demand for each book in the following week; execute the replenishment judgment strategy to determine whether each book needs to be replenished.

[0014] Specifically, retrieve all book data under each book category, record all books under each book category as associated books of the corresponding book category, and record the corresponding book category as the associated book category of each associated book;

[0015] Establish several data tables, including a user data table, a book inventory table, a book borrowing and returning table, a book sales table, and a book review table;

[0016] Based on each user's book borrowing and returning data and book purchase data in the user data table, calculate each user's rating reference index. The expression is: ,in, , These represent the number of books borrowed and the number of books purchased by each user in the library, respectively. This indicates the preset threshold for the total number of books.

[0017] Specifically, sum the total number of borrowed and sold books of all related books belonging to the same book category for each week within the first four weeks. Then, sum the total number of borrowed and sold books belonging to the same book category for the same week. This gives the total demand for books in each book category for the first four weeks. The expression is: ;

[0018] in, Indicates the previous number week, Values These correspond to the first week, the second week, the third week, and the fourth week, respectively. Indicates the first The first related book is in the first... Total number of borrowings during the week; Indicates the first The first related book is in the first... Total sales during the week Indicates the first The total number of related books in each book category;

[0019] Based on the total demand for books in each book category over the previous four weeks Establish a fitting equation between time and total book demand; substitute the values ​​and calculate the predicted total book demand for each book category for the following week.

[0020] Furthermore, calculate the total actual demand for each book in each previous week, and its proportion to the total demand for books in the related book categories. The expression is: The total actual demand refers to the sum of the total number of loans and the total number of sales.

[0021] By combining the total actual demand for each book over the first four weeks with the proportion of the total demand for books in their respective related book categories, we obtain the comprehensive proportion index of each book to its related book category. The expression is: ;

[0022] The overall proportion index of each book to its associated book category is combined with the predicted total demand for the corresponding book category in the following week to obtain the first predicted total demand for each book in the following week. .

[0023] Specifically, the scoring calculation strategy includes:

[0024] For the For each book, obtain the rating from each evaluation data point. And the rating index for evaluating users. ,in, Indicates the first One evaluation data point;

[0025] From multiple review data for the same book, the rating reference index of the reviewing users. Perform normalization to obtain the unit average reference index for each evaluation data point. The expression is: ,in, Indicates the first The total number of reviews for each book;

[0026] The score in each evaluation data And the rating index for evaluating users. Combined, a comprehensive rating is obtained for the corresponding book. The expression is: .

[0027] Furthermore, based on the comprehensive rating of each book from the first to the fourth week, and the total demand for each book from the first to the fourth week, a fitting equation is established between the comprehensive rating of each book and the total demand.

[0028] Substitute the overall rating of each book To obtain the second forecast total demand for each book in the following week. .

[0029] Specifically, obtain the first and second predicted total demand for each book in the first to fourth weeks of the previous period, and obtain the actual total demand for each book in the first to fourth weeks of the previous period.

[0030] The difference between the total actual demand for each previous week and the total predicted demand for the first week and the total predicted demand for the second week is recorded as the first error and the second error for each book in each previous week.

[0031] The first prediction error for each book is obtained by averaging the first and second errors over the first four weeks. Second prediction error .

[0032] Specifically, the first prediction error With the second prediction error Combined, calculate the weighting factors for the first and second total forecasted demand, respectively. and The expression is: ;

[0033] The total forecast demand for each book in the first week. Second forecast total demand Combined with the corresponding weighting factors, the total predicted demand for each book in the following week is obtained. The expression is: .

[0034] Specifically, the replenishment determination strategy includes:

[0035] Retrieve the inventory quantity data of each book in the warehouse from the book inventory table. ,when At that time, the book will be restocked;

[0036] when At that time, the book will not be restocked.

[0037] (III) Beneficial Effects

[0038] This invention provides an intelligent management method for a book warehouse information system, which has the following beneficial effects:

[0039] 1. By systematically collecting and organizing book categories, book data, user behavior data, and evaluation data, a comprehensive data foundation was established; this provided accurate and complete data support for subsequent predictions and decisions, ensuring that the system could make accurate analyses and judgments based on actual data, thereby improving the reliability and effectiveness of the entire management system;

[0040] 2. By analyzing historical borrowing and sales data, a fitting equation was established between time and total book demand, thereby predicting the book demand for each book category in the coming week. This helps libraries understand the demand trends of various types of books in advance, providing a scientific basis for inventory management and replenishment decisions, effectively reducing inventory backlog and stockouts, and improving the utilization and satisfaction rate of books.

[0041] 3. Based on user review data, a comprehensive rating for each book was calculated, and a fitting equation was established between the comprehensive rating of books and the total demand, thereby predicting the demand for each book in the coming week; user feedback information was fully utilized, making the predictions closer to the actual needs of users and improving the accuracy and relevance of the predictions; by comprehensively considering the popularity of books and market demand, it provides strong support for the library's acquisition and recommendation strategies.

[0042] 4. Combining the first and second total predicted demand and their weighting factors, the total predicted demand for each book in the following week is calculated, and a decision is made on whether restocking is necessary. This comprehensive consideration of multiple forecasting factors improves the scientific nature and accuracy of restocking decisions. By monitoring inventory and predicted demand in real time, potential stockout risks can be identified and addressed promptly, ensuring an adequate supply of books and improving user satisfaction and service quality. The forecasting and restocking operations are transformed into an algorithmic model for learning, further optimizing the system's forecasting capabilities and decision-making efficiency. Attached Figure Description

[0043] Figure 1 The present invention provides a flowchart of the steps of an intelligent management method for a book warehouse information system. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] refer to Figure 1 This invention provides an intelligent management method for a book warehouse information system, comprising:

[0046] Step 1: Obtain book category data and all book data under each category, and establish the relationship between book categories and books; create user data table, book inventory table, book borrowing and returning table, book sales table, and book review table; calculate the rating reference index for each user based on the data in the user data table;

[0047] Step one includes the following steps:

[0048] Step 101: Obtain data on all book categories in the library, including book categories such as philosophy and religion, politics and law, military affairs, and economics;

[0049] Retrieve all book data under each book category, record all books under each book category as associated books of the corresponding book category, and record the corresponding book category as the associated book category of each associated book; each book category contains several associated books, and each type of book belongs to only one book category;

[0050] Step 102: Create a user data table, a book inventory table, a book borrowing and returning table, a book sales table, and a book review table;

[0051] The user data table stores relevant data for all users in the library, including multiple book borrowing and returning data, book purchase data, and book evaluation data.

[0052] The book inventory table stores data on the quantity of all books in the library in the warehouse;

[0053] The book borrowing and returning table stores the borrowing and returning data for each book. Each piece of data includes user data, data for one or more books (i.e., the user borrowed multiple books at once), and data for the borrowing time and the required return time.

[0054] The book sales table stores the sales data for each book sale. A single data entry may contain data for one or more books, meaning that a user may have purchased multiple books at once.

[0055] The book review table stores the review data for the corresponding books, including reviewers, ratings, and comments. Users can review the books after borrowing or purchasing them.

[0056] The user data table is linked to the book borrowing and returning table, the book sales table, and the book review table through user name data;

[0057] Step 103: Based on each user's book borrowing and returning data and book purchase data in the user data table, calculate the rating reference index for each user. The expression is: ,in, , These represent the number of books borrowed and the number of books purchased by each user in the library, respectively. This represents the preset threshold for the total number of books. This threshold is used to determine whether each user's rating is meaningful and to prevent malicious ratings. The specific value is set by the library administrator.

[0058] By systematically collecting and organizing book categories, book data, user behavior data, and evaluation data, a comprehensive data foundation was established. This provided accurate and complete data support for subsequent predictions and decision-making, ensuring that the system could conduct precise analysis and judgment based on actual data, thereby improving the reliability and effectiveness of the entire management system.

[0059] Step 2: Obtain the total number of borrowed and sold books for each book category in each of the previous four weeks; establish a fitting equation between time and total book demand, and calculate the predicted total book demand for each book category in the next week; calculate the comprehensive proportion index of each book to its respective book category, and combine it with the predicted total book demand for the corresponding book category to obtain the first predicted total demand for each book in the next week; calculate the first prediction error for each book.

[0060] Step two includes the following steps:

[0061] Step 201: Filter out the associated books for each book category; obtain the historical borrowing and sales records for each associated book under each book category from the book borrowing and return table and the book sales table, and calculate the total borrowing and sales of each associated book for each week in the previous four weeks; sum the total borrowing and sales of all associated books belonging to the same book category for each week in the previous four weeks, and then add the total borrowing and sales of books belonging to the same book category in the same week to obtain the total demand for books in each book category in the first, second, third, and fourth weeks respectively. The expression is: ;

[0062] in, Indicates the previous number week, Values These correspond to the first week, the second week, the third week, and the fourth week, respectively. Indicates the first The first related book is in the first... Total number of borrowings during the week; Indicates the first The first related book is in the first... Total sales during the week Indicates the first The total number of related books in each book category;

[0063] Step 202: Calculate the total demand for books in each book category over the previous four weeks. The data is placed into a time series graph for the corresponding book category. The horizontal axis of the time series graph represents time in weeks, and the vertical axis represents the total demand for books. The relationship between time (i.e., number of weeks) and the total demand for books is fitted using a linear fitting method.

[0064] It should be noted that line fitting is a commonly used data analysis method used to find the best-fitting line to describe the trend relationship between data points;

[0065] Assume there is a linear relationship between time and the total demand for books, i.e. The independent variable for the first four weeks is designated as 1, the independent variable for the first three weeks as 2, and so on. The parameters for each linear relationship are calculated using a linear regression algorithm. and This leads to a linear relationship between time and the total demand for books in each category, i.e., a fitting equation for time versus total book demand; Substituting the linear relationship, we can calculate the total predicted demand for books in each book category for the following week;

[0066] Step 203: Calculate the sum of the total number of borrowed books and the total number of sold books for each book in the previous week, i.e., the total actual demand, and calculate its proportion to the total demand for books in the related book category. The expression is: ;

[0067] Based on the sum of the total number of borrowed and sold books in the first four weeks, and combining this with the total demand for books in their respective related book categories, we obtain the comprehensive proportion index of each book to its related book category. The expression is: ;

[0068] The overall proportion index of each book to its associated book category is combined with the predicted total demand for the corresponding book category in the following week to obtain the first predicted total demand for each book in the following week. ;

[0069] Step 204: Obtain the total predicted demand for each book from the first to the fourth week prior. This is achieved by combining the total borrowing and sales of each book from the fifth to the eighth week prior, and performing the operations in steps 201-203. Also, obtain the total actual demand for each book from the first to the fourth week prior. Record the difference between the total actual demand and the total predicted demand for each week as the first error for each book in each week prior. Average the first errors for each book over the first four weeks to obtain the first predicted error for each book. .

[0070] By analyzing historical borrowing and sales data, a fitting equation was established between time and total book demand, thereby predicting the demand for each book category in the coming week. This helps libraries understand the demand trends of various types of books in advance, providing a scientific basis for inventory management and replenishment decisions, effectively reducing inventory backlog and stockouts, and improving the utilization and satisfaction rate of books.

[0071] Step 3: Calculate the overall score of each book based on the scoring calculation strategy; obtain the overall score of each book from the first week to the fourth week in advance, and combine it with the total actual demand for each book from the first week to the fourth week in advance to establish a fitting equation between the overall score and the total demand for each book; calculate the second predicted total demand for each book in the next week based on the overall score of each book in advance; calculate the second prediction error for each book.

[0072] Step three includes the following steps:

[0073] Step 301: Collect and obtain all the evaluation data for each book from the book evaluation table, including the users who evaluated each book and their ratings, and execute the rating calculation strategy.

[0074] Execute the scoring calculation strategy: For the first For each book, obtain the rating from each evaluation data point. And the rating index for evaluating users. ,in, Indicates the first One evaluation data point;

[0075] From multiple review data for the same book, the rating reference index of the reviewing users. Perform normalization to obtain the unit average reference index for each evaluation data point. The expression is: ,in, Indicates the first The total number of reviews for each book;

[0076] The score in each evaluation data And the rating index for evaluating users. Combined, a comprehensive rating is obtained for the corresponding book. The expression is: ;

[0077] Step 302: Obtain all evaluation data for each book except for the first week, and execute the scoring calculation strategy according to Step 301 to obtain the comprehensive score of each book in the first week; Obtain all evaluation data for each book except for the first and second weeks, and execute the scoring calculation strategy to obtain the comprehensive score of each book in the first second week; Obtain all evaluation data for each book except for the first to third weeks, and execute the scoring calculation strategy to obtain the comprehensive score of each book in the first third week; Obtain all evaluation data for each book except for the first to fourth weeks, and execute the scoring calculation strategy to obtain the comprehensive score of each book in the first fourth week;

[0078] It should be noted that the overall score for the first week represents the overall score of each book at the beginning of the first week; the overall scores for the second, third, and fourth weeks have the same meaning.

[0079] Obtain the total actual demand for each book in the first, second, third, and fourth weeks prior to the deadline;

[0080] Step 303: Based on the linear regression equation The overall rating of each book from the first to the fourth week was fitted, along with the total demand for each book from the first to the fourth week. This represents the overall rating of each book from the first to the fourth week. This represents the total demand for each book from the first to the fourth week prior; the slope of the fitted line is calculated using the least squares method. and intercept This leads to the fitting equation of the overall rating of each book minus the total demand.

[0081] The overall score for each book will be calculated based on all the evaluation data. Substituting the comprehensive rating of the corresponding book into the total demand fitting equation, we obtain the second predicted total demand for each book in the following week. ;

[0082] Step 304: Obtain the total predicted demand for each book from the first to the fourth week prior, specifically by combining the evaluation data of each book from the fifth to the eighth week prior and performing the operations in steps 301-303; also obtain the total actual demand for each book from the first to the fourth week prior, and record the difference between the total actual demand and the total predicted demand for each week as the second error for each book in each week prior; average the second errors for each book over the first four weeks to obtain the second prediction error for each book. .

[0083] Based on user review data, a comprehensive rating for each book was calculated, and a fitting equation was established between the comprehensive rating of books and the total demand, thereby predicting the demand for each book in the coming week. This fully utilizes user feedback information, making the predictions more closely reflect actual user needs and improving the accuracy and relevance of the predictions. By comprehensively considering the popularity of books and market demand, this provides strong support for the library's acquisition and recommendation strategies.

[0084] Step 4: Calculate the weighting factors for the first and second total forecasted demand respectively; combine the first and second total forecasted demand with the corresponding weighting factors to obtain the total total forecasted demand for each book in the next week; execute the replenishment judgment strategy to determine whether each book needs to be replenished.

[0085] Step four includes the following steps:

[0086] Step 401: Based on the book title, align the first and second forecast total demand metrics for each book in the following week to obtain the first forecast total demand for each book in the following week. Second forecast total demand First prediction error and the second prediction error ;

[0087] The first prediction error With the second prediction error Combined, calculate the weighting factors for the first and second total forecasted demand, respectively. and The expression is: ;

[0088] It should be noted that the weighting factors for calculating the first and second total forecasted demand are... and It is used to optimize the accuracy and reliability of prediction models by comparing and analyzing the differences between predicted demand and actual demand for books.

[0089] Step 402: Calculate the total forecast demand for each book in the first week. Second forecast total demand Combined with the corresponding weighting factors, the total predicted demand for each book in the following week is obtained. The expression is: ;

[0090] Step 403: At the start of the next week, implement the replenishment judgment strategy, specifically by retrieving the inventory quantity data of each book in the warehouse from the book inventory table. Based on the total forecast demand for each book in the following week First prediction error and the second prediction error , and inventory quantity data Determine whether the books need to be restocked.

[0091] when When this happens, it indicates that the book is out of stock and a restocking operation will be performed.

[0092] when When this happens, it means that the book is in sufficient stock and no restocking will be performed.

[0093] It should be noted that the first prediction error and the second prediction error Total demand forecast The combination is used to more accurately assess whether book inventory meets future demand, in order to prevent unforeseen events from causing insufficient book inventory and thus avoid stockouts.

[0094] Step 404: Input the first and second prediction operations for the demand of each book in the next week and the replenishment judgment operation into the machine for learning. These operations can be transformed into an algorithm model, and historical data can be used to train the model so that the model can learn the prediction pattern of book demand and accurately determine whether books need to be replenished.

[0095] By combining the total predicted demand from the first and second forecasts and their weighting factors, the total predicted demand for each book in the following week is calculated, and a decision is made on whether restocking is necessary. This comprehensive consideration of multiple forecasting factors improves the scientific rigor and accuracy of restocking decisions. By monitoring inventory and predicted demand in real time, potential stockout risks can be identified and addressed promptly, ensuring an adequate supply of books and enhancing user satisfaction and service quality. Furthermore, by transforming forecasting and restocking operations into an algorithmic model for learning, the system's forecasting capabilities and decision-making efficiency are further optimized.

[0096] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer storage medium or transmitted through a computer storage medium.

[0097] Computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0098] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this invention should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent management method for a book warehouse information system, characterized in that: include: Acquire book category data, as well as data on all books within each category, and establish the relationship between book categories and books; Create several data tables and calculate a rating reference index for each user based on the data in the user data tables; Specifically: Establish several data tables, including a user data table, a book inventory table, a book borrowing and returning table, a book sales table, and a book review table; Based on each user's book borrowing and returning data and book purchase data in the user data table, calculate each user's rating reference index. The expression is: ,in, , These represent the number of books borrowed and the number of books purchased by each user in the library, respectively. This indicates the preset threshold for the total number of books; Establish a fitting equation between time and total book demand, calculate the predicted total book demand for each book category in the next week; calculate the comprehensive proportion index of each book to its respective book category, and combine it with the predicted total book demand for the corresponding book category to obtain the first predicted total demand for each book in the next week; calculate the first prediction error for each book. The overall score for each book is calculated based on the scoring strategy. A fitting equation is established between the overall score and the total demand for each book, combining the overall scores and the total actual demand for each book from the first to the fourth week. The second predicted total demand for each book in the following week is then calculated. Finally, the second prediction error for each book is calculated. Where: The scoring calculation strategy includes: For the For each book, obtain the rating from each evaluation data point. And the rating index for evaluating users. ,in, Indicates the first One evaluation data point; From multiple review data for the same book, the rating reference index of the reviewing users. Perform normalization to obtain the unit average reference index for each evaluation data point. The expression is: ,in, Indicates the first The total number of reviews for each book; The score in each evaluation data And the rating index for evaluating users. Combined, a comprehensive rating is obtained for the corresponding book. The expression is: ; Calculate the weighting factors for the first and second total forecasted demand respectively; combine the first and second total forecasted demand with the corresponding weighting factors to obtain the total total forecasted demand for each book in the following week; execute the replenishment judgment strategy to determine whether each book needs to be replenished.

2. The intelligent management method for a book warehouse information system as described in claim 1, characterized in that: Retrieve all book data under each book category, record all books under each book category as associated books of the corresponding book category, and record the corresponding book category as the associated book category of each associated book.

3. The intelligent management method for a book warehouse information system as described in claim 1, characterized in that: Sum the total borrowing and sales of all related books belonging to the same book category for each of the previous four weeks. Then, sum the total borrowing and sales of all related books belonging to the same book category for the same week. This gives the total demand for books in each book category for the first four weeks. The expression is: ; in, Indicates the previous number week, Values These correspond to the first week, the second week, the third week, and the fourth week, respectively. Indicates the first The first related book is in the first... Total number of borrowings during the week; Indicates the first The first related book is in the first... Total sales during the week Indicates the first The total number of related books in each book category; Based on the total demand for books in each book category over the previous four weeks Establish a fitting equation between time and total book demand; substitute the values ​​and calculate the predicted total book demand for each book category for the following week.

4. The intelligent management method for a book warehouse information system as described in claim 3, characterized in that: Calculate the total actual demand for each book in each previous week, and its proportion to the total demand for books in the related book categories. The expression is: The total actual demand refers to the sum of the total number of loans and the total number of sales. By combining the total actual demand for each book over the first four weeks with the proportion of the total demand for books in their respective related book categories, we obtain the comprehensive proportion index of each book to its related book category. The expression is: ; The overall proportion index of each book to its associated book category is combined with the predicted total demand for the corresponding book category in the following week to obtain the first predicted total demand for each book in the following week. .

5. The intelligent management method for a book warehouse information system as described in claim 4, characterized in that: Based on the overall rating of each book from the first to the fourth week, and the total demand for each book from the first to the fourth week, a fitting equation is established between the overall rating of each book and the total demand. Substitute the overall rating of each book To obtain the second forecast total demand for each book in the following week. .

6. The intelligent management method for a book warehouse information system as described in claim 1, characterized in that: Obtain the first and second predicted total demand for each book from the first week to the fourth week, and obtain the actual total demand for each book from the first week to the fourth week. The difference between the total actual demand for each previous week and the total predicted demand for the first week and the total predicted demand for the second week is recorded as the first error and the second error for each book in each previous week. The first prediction error for each book is obtained by averaging the first and second errors over the first four weeks. Second prediction error .

7. The intelligent management method for a book warehouse information system as described in claim 1, characterized in that: Based on the book title, the metrics for the first and second forecast total demand for each book in the following week are aligned to obtain the first forecast total demand for each book in the following week. Second forecast total demand First prediction error and the second prediction error ; The first prediction error With the second prediction error Combined, calculate the weighting factors for the first and second total forecasted demand, respectively. and The expression is: ; The total forecast demand for each book in the first week. Second forecast total demand Combined with the corresponding weighting factors, the total predicted demand for each book in the following week is obtained. The expression is: .

8. The intelligent management method for a book warehouse information system as described in claim 7, characterized in that: The replenishment determination strategy includes: Retrieve the inventory quantity data of each book in the warehouse from the book inventory table. ,when At that time, the book will be restocked; when At that time, the book will not be restocked.

Images