Book borrowing management method and system

Abstract

The invention is suitable for the field of book management, and provides a book borrowing management method and system, and the method comprises the steps: collecting user behavior data in multiple dimensions; establishing a dynamic credit evaluation system based on the user behavior data; docking an external credit platform, and updating the dynamic credit evaluation system; generating a stepped authority management strategy based on the dynamic credit evaluation system; the user behavior data specifically comprises renewal data, overdue data and book returning integrity data, and the method has the advantages that accurate portraying of the borrowing behaviors of the user is achieved through multi-dimensional data fusion and dynamic evaluation, the book resource circulation efficiency and integrity are improved through a stepped permission strategy, the evaluation authority is enhanced through external credit linkage, and the user experience is improved. The manual verification cost is reduced, a personalized mechanism gives consideration to the user experience and the management efficiency, and an efficient, intelligent and extensible innovative solution is provided for book borrowing management.

Description

Technical Field

[0001] The present invention belongs to the field of book management, and in particular relates to a book lending management method and system. Background Art

[0002] In the traditional book lending management model, the management methods for the book lending process are relatively simple and extensive. In terms of renewal, libraries often only set fixed renewal times and deadlines, and lack an assessment of the rationality of user renewal behavior. Existing book loan management methods lack a comprehensive evaluation system based on multiple dimensions, such as renewal, overdue payments, and completeness of book returns. This makes it impossible to provide personalized management based on users' actual borrowing behavior. This results in inefficient use of library resources and a poorly optimized borrowing experience for readers. Therefore, a new book loan management method that can address these issues is urgently needed. Summary of the Invention

[0003] The purpose of the present invention is to provide a book lending management method and system, aiming to solve the problems raised in the above background technology.

[0004] The present invention is achieved by, on the one hand, providing a book lending management method, the method comprising: Collect user behavior data from multiple dimensions; Establish a dynamic credit evaluation system based on user behavior data; Connect with external credit platforms and update the dynamic credit evaluation system; Generate a tiered authority management strategy based on a dynamic credit evaluation system; The user behavior data specifically includes: Renewal data, overdue data, and book return integrity data.

[0005] As a further solution of the present invention, the multi-dimensional collection of user behavior data specifically includes: Use RFID chips or barcode recognition technology to record borrowing time and return time in real time and generate renewal data; The renewal data includes: renewal application time, renewal times, and whether the renewal application was submitted within 48 hours before the expiration of the original loan period; Obtain overdue data from the library's existing database; The overdue data include: cumulative overdue days, the longest single overdue days, and the proportion of overdue books; Based on AI visual algorithms, the system scans the entire process of users returning books and generates book return integrity data; The book return integrity data includes: outer surface damage, inner page abnormality, and attachment integrity.

[0006] As a further solution of the present invention, the AI visual algorithm is used to perform a full-process scan of the user when returning the book to generate the book return integrity data, which specifically includes: A circular HD camera array is deployed at the library's return counter, covering the book cover, back cover, spine, and inner page. Books are transported at a constant speed using a high-precision conveyor belt, and infrared sensors trigger the camera to capture and generate a time-stamped HD image sequence. Based on the YOLOv8 object detection model, a damage feature library is trained to identify three types of surface defects of targets; The three types of target surface defects include: Tearing and chipping defects: detect irregular edge areas and calculate the damaged area. , triggering a point deduction; Severe contamination defects: Detecting stained areas through color space conversion. If the stained area accounts for more than 5% of the area, a point deduction will be triggered. Indentation and crease defects: Based on the image gradient analysis algorithm, continuous crease lines are identified. If the crease length is greater than 5cm and the depth is greater than 2 pixels, a point deduction will be triggered. Set up an independent RFID tag reading area for the book accessories, and use a fixed RFID reader to detect whether the accessories are missing; If the book is a set of multiple volumes, the completeness rate of the returned sets will be verified through ISBN identification. If the completeness rate is less than 100%, a point deduction will be triggered; Using OCR technology to identify inner page numbers, a continuous page number sequence is generated. The image stitching algorithm is used to combine the unfolded inner page images into a complete book block. The images are then compared with the standard page number range to detect missing or skipped pages. If missing or skipped pages occur, a point deduction will be triggered. Use the U-Net semantic segmentation model to distinguish text content from graffiti and calculate the percentage of pixels in the non-printed area. If the percentage of pixels in the non-printed area is greater than 3%, it is determined and marked as valid graffiti. When an effective graffiti area is identified and marked, spectral analysis of the oil and water stains is performed based on the color channel difference algorithm to distinguish between new and old pollution. When new pollution is identified, point deduction is triggered.

[0007] As a further solution of the present invention, establishing a dynamic credit evaluation system based on user behavior data specifically includes: A three-level 100-point evaluation system is established based on renewal data, overdue data, and book return completeness data; The three-level percentage evaluation system includes: Renewal compliance indicator, with a weight of 30%, adds 5 points for each renewal before the due date, deducts 10 points for each forced renewal after the due date, and deducts 3 points for each renewal exceeding 5 times per year; Overdue default index, with a weight of 40%, is scored based on the number of days overdue: 5 points will be deducted per day for 1-3 days, 10 points will be deducted per day for 4-7 days, and 20 points will be deducted per day for more than 7 days. An additional 20 points will be deducted for more than 3 overdue events per year. The return integrity index, with a weight of 30%, is based on the degree of damage: 10 points will be deducted for minor damage, 30 points for severe damage, 50 points for missing pages, and 100 points for missing attachments, and a compensation notice will be generated; Introducing a time decay factor, the time decay function includes: the weight coefficient of behavior in the past 6 months is 1.5, the weight coefficient of behavior in 6-12 months is 1, and the weight coefficient of behavior over 12 months is 0.5; Calculate the user's real-time credit score based on the three-level evaluation system and time decay factor ; The calculation process of the user's real-time credit score is as follows: ; Where, is the indicator weight value, is the behavior data value, is the time decay factor, .

[0008] As a further solution of the present invention, the connection to the external credit platform and updating of the dynamic credit evaluation system specifically includes: Obtain user credit authorization permissions and obtain real-time credit data from third-party platforms; The real-time credit data obtained from the third-party platform is converted into a plus-minus score format that can be recognized by the three-level percentage evaluation system.

[0009] As a further solution of the present invention, the generation of a stepped authority management strategy based on a dynamic credit evaluation system specifically includes: Based on the user's real-time credit score, the user's borrowing rating is divided into: A-level high-quality users, real-time credit score of users ; B-level good users, real-time credit score of users ; C-level warning users, real-time credit score of users ; D-level risk users, real-time credit score of users ; Generate basic borrowing permission strategies and overdue integrity management strategies based on user borrowing ratings; The basic borrowing permission strategy includes: A-level premium users can borrow 20 books, with a single borrowing period of 30 days, three renewals, and a 30-day renewal period. Popular book reservation priority is in the priority queue. B-level premium users: 15 books can be borrowed, single borrowing period: 21 days, renewal times: 2 times, renewal period: 21 days each, popular book reservation priority: normal queue; C-level premium users: 10 books can be borrowed, single borrowing period: 14 days, number of renewals: 1, renewal period: 14 days each, popular book reservation priority: normal queue; D-level premium users: 5 books can be borrowed, single borrowing period: 7 days, renewal times: 0, and reservation of popular books is prohibited; The overdue integrity management strategy includes: A-level premium users have a 7-day grace period for overdue payments. Overdue fines are waived during the grace period. Return integrity verification is free. Deposit requirements are waived. For Class B premium users, the overdue grace period is 3 days, the overdue fine is 0.3 yuan per book per day, the completeness verification mechanism for returned books is automatic verification by the device, and the security deposit requirement is 0.00 yuan per day. C-level premium users: 0 days of overdue grace period, 0.5 yuan per book per day of overdue fine, manual verification of returned books, and a 100 yuan deposit. For D-level premium users, the overdue grace period is 0 days, the overdue penalty standard is 1 yuan / book / day, the book return integrity verification mechanism is: manual verification + AI verification, and the deposit requirement is 100 yuan.

[0010] As a further embodiment of the present invention, in another aspect, a book lending management system is provided, the system comprising: Multi-dimensional collection module, used to collect user behavior data in multiple dimensions; Dynamic credit evaluation system module, used to establish a dynamic credit evaluation system based on user behavior data; The docking module is used to connect to external credit platforms and update the dynamic credit evaluation system; The generation module is used to generate a stepped authority management strategy based on a dynamic credit evaluation system.

[0011] As a further solution of the present invention, the multi-dimensional acquisition module specifically includes: The first generation unit uses RFID chips or barcode recognition technology to record borrowing time and return time in real time and generate renewal data; The acquisition unit is used to obtain overdue data from the library's existing database; The second generation unit is used to perform a full-process scan of users returning books based on AI visual algorithms to generate book return integrity data.

[0012] The present invention provides a book borrowing management method and system, which realizes accurate profiling of user borrowing behavior through multi-dimensional data fusion and dynamic evaluation. The stepped permission strategy improves the efficiency and integrity of book resource circulation, external credit linkage enhances the authority of evaluation and reduces manual verification costs. The personalized mechanism takes into account both user experience and management efficiency, providing an efficient, intelligent and scalable innovative solution for book borrowing management. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a main flow chart of a book borrowing management method.

[0014] Figure 2 The present invention is a flowchart for collecting user behavior data in multiple dimensions in a book borrowing management method.

[0015] Figure 3 It is a flow chart of a book borrowing management method that uses AI visual algorithms to perform full-process scanning of users when returning books to generate book return integrity data.

[0016] Figure 4 The present invention is a flowchart of establishing a dynamic credit evaluation system based on user behavior data in a book borrowing management method.

[0017] Figure 5 The present invention is a flowchart of a book borrowing management method for connecting to an external credit platform to update a dynamic credit evaluation system.

[0018] Figure 6 It is a main structure diagram of a book borrowing management system.

[0019] Figure 7 It is a structural diagram of a multi-dimensional collection module in a book lending management system. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0022] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0023] The present invention will be further explained below with reference to specific embodiments.

[0024] The present invention provides a book borrowing management method and system, which solves the technical problems in the background technology.

[0025] like Figure 1 FIG. 1 is a main flow chart of a book borrowing management method provided by an embodiment of the present invention, wherein the book borrowing management method includes: Step S100: Collect user behavior data from multiple dimensions; Step S200: Establishing a dynamic credit evaluation system based on user behavior data; Step S300: Connecting to an external credit platform and updating the dynamic credit evaluation system; Step S400: Generate a step-by-step authority management strategy based on the dynamic credit evaluation system; The user behavior data specifically includes: Renewal data, overdue data, and book return integrity data.

[0026] When applied in this embodiment, the book borrowing management method provided by the present invention uses RFID, AI visual algorithms and other technologies to collect user renewal, overdue, and book return completeness behavior data in multiple dimensions, combines reading preferences extracted by natural language processing and blockchain evidence storage technology, and constructs a dynamic credit evaluation system that includes renewal compliance, overdue default rate, and book return completeness. It introduces time decay functions and machine learning to achieve dynamic adjustment of indicator weights and real-time calculation of credit ratings, and connects to a third-party credit reporting platform to achieve external credit data integration; generates a stepped permission management strategy based on credit ratings, differentially configures basic permissions such as the number of borrowed books, number of renewals, and overdue grace period, and provides supporting value-added services and constraints. Through the rule engine, it realizes full-process automated execution and data-driven system self-optimization, forming an intelligent closed-loop management system of "behavior collection-credit evaluation-permission management-strategy iteration".

[0027] like Figure 2 As shown, as a preferred embodiment of the present invention, the multi-dimensional collection of user behavior data specifically includes: Step S101: Using RFID chips or barcode recognition technology to record borrowing time and return time in real time and generate renewal data; The renewal data includes: renewal application time, renewal times, and whether the renewal application was submitted within 48 hours before the expiration of the original loan period; Step S102: Obtain overdue data from the library's existing database; The overdue data include: cumulative overdue days, the longest single overdue days, and the proportion of overdue books; Step S103: Scan the entire process of the user returning the book based on the AI visual algorithm to generate the book return integrity data; The book return integrity data includes: outer surface damage, inner page abnormality, and attachment integrity.

[0028] When this embodiment is applied, the Internet of Things technology and AI algorithm are used to capture three types of core behavioral data: RFID chips or barcode recognition technology are used to record the borrowing time and return time of books in real time, and renewal data including the renewal application time, the number of renewals and whether the renewal application is submitted within 48 hours before the expiration of the original borrowing period is generated based on the timestamp, accurately distinguishing between compliant renewal (application in advance) and overdue renewal (application after the expiration); extracting multi-dimensional overdue data such as the cumulative overdue days, the longest single overdue days, and the proportion of overdue books from the library's existing database to comprehensively reflect the user's default frequency, duration and scope of impact. The library's existing database adopts a relational database architecture, in which the overdue data fields include user ID, The book's ISBN, borrowing time, due date, actual return date, and overdue days mark (the difference between the due date and the actual return date can be automatically calculated) can dynamically generate the cumulative overdue days (the sum of all overdue days in the user's history), the longest single overdue days (the longest consecutive number of days a single book has been overdue), and the proportion of overdue books (the proportion of overdue books among the current unreturned books). With the help of AI visual algorithms, users are scanned throughout the entire process when returning books. The AI visual algorithm can automatically generate book return integrity data including external damage (torn cover, missing corners, degree of contamination), internal page abnormalities (missing pages, graffiti area, water stains), and accessory integrity (missing supporting CDs and manuals), to achieve digital detection and graded evaluation of the physical condition of books.

[0029] like Figure 3 As shown in FIG. 1 , as a preferred embodiment of the present invention, the AI visual algorithm is used to perform a full-process scan of the user when returning the book to generate the book return integrity data, which specifically includes: Step S1031: A circular high-definition camera array is deployed at the library return counter, covering the book cover, back cover, spine, and inner page unfolding surface. Books are transported at a constant speed using a high-precision conveyor belt, and infrared sensors are used to trigger the capture, generating a high-definition image sequence with a time stamp. Step S1032: Train the damage feature library based on the YOLOv8 target detection model to identify three types of target surface defects. The three types of target appearance defects include: Tearing and chipping defects: detect irregular edge areas and calculate the damaged area. , triggering a point deduction; Severe contamination defects: Detecting stained areas through color space conversion. If the stained area accounts for more than 5% of the area, a point deduction will be triggered. Indentation and crease defects: Based on the image gradient analysis algorithm, continuous crease lines are identified. If the crease length is greater than 5cm and the depth is greater than 2 pixels, a point deduction will be triggered. Set up an independent RFID tag reading area for the book accessories, and use a fixed RFID reader to detect whether the accessories are missing; If the book is a set of multiple volumes, the completeness rate of the returned sets will be verified through ISBN identification. If the completeness rate is less than 100%, a point deduction will be triggered; Step S1033: Identify the inner page numbers using OCR technology, generate a continuous page number sequence, and use an image stitching algorithm to synthesize the unfolded inner page images into a complete book block. Compare the images to the standard page number range to detect missing pages or page skips. If missing pages or page skips occur, a point deduction is triggered. Step S1034: Using a U-Net semantic segmentation model to distinguish text content from graffiti, the pixel ratio of the non-printed area is calculated. If the pixel ratio of the non-printed area is greater than 3%, it is determined and marked as valid graffiti. Step S1035: When a valid graffiti area is identified and marked, a spectral analysis is performed on the oil and water stain area based on a color channel difference algorithm to distinguish between new and old contamination. If new contamination is identified, a point deduction is triggered; In application, this embodiment deploys a circular high-definition camera array (covering the book cover, back cover, spine, and inner page unfold) at the library return counter. Combined with a high-precision conveyor belt and an infrared sensor-triggered image acquisition system, this generates a multi-angle high-definition image sequence with a time stamp. This allows for full-process scanning and inspection of book returns based on AI vision algorithms and IoT technologies. First, the YOLOv8 target detection algorithm is used. The YOLOv8 target detection algorithm can quickly locate specific targets in an image and identify surface damage. For tears and chipped corners, the damaged area is calculated using edge contour detection (a penalty is triggered if the image is >5mm²). For severe soiling, the stain area ratio is detected using color space conversion (RGB to HSV) (a penalty is triggered if the image is >5%). For indentations and creases, continuous creases with a length >5cm and a depth >2 pixels are identified using an image gradient analysis algorithm. Simultaneously, RFID technology (radio frequency identification, contactless automatic identification of accessory tags) is used to detect the integrity of accompanying accessories. For multiple books, ISBN recognition technology (barcode recognition, rapid reading of the book's unique identifier) is used to verify the completeness of the returned sets (<100%). In the inner page detection stage, OCR technology (optical character recognition technology, which can convert image text into editable text) is used to identify page numbers and generate a continuous sequence. The book block is synthesized by combining the image stitching algorithm (a technology that synthesizes complete images through feature matching). The standard page number range is compared to detect missing or skipped pages (triggering point deductions). The U-Net semantic segmentation algorithm (U-Net semantic segmentation algorithm is a deep learning model for image segmentation that can accurately distinguish the semantic information of different regions) is used to calculate the pixel ratio of the non-printed area (>3% is marked as valid graffiti), and the color channel difference algorithm (a technology that analyzes the difference in RGB channel pixel values) is used to distinguish new contamination such as oil stains and water stains (triggering additional point deductions).

[0030] like Figure 4 As shown in FIG. 1 , as a preferred embodiment of the present invention, establishing a dynamic credit evaluation system based on user behavior data specifically includes: Step S201: Establish a three-level percentage evaluation system based on renewal data, overdue data, and book return completeness data; The three-level percentage evaluation system includes: Renewal compliance indicator, with a weight of 30%, adds 5 points for each renewal before the due date, deducts 10 points for each forced renewal after the due date, and deducts 3 points for each renewal exceeding 5 times per year; Overdue default index, with a weight of 40%, is scored based on the number of days overdue: 5 points will be deducted per day for 1-3 days, 10 points will be deducted per day for 4-7 days, and 20 points will be deducted per day for more than 7 days. An additional 20 points will be deducted for more than 3 overdue events per year. The return integrity index, with a weight of 30%, is based on the degree of damage: 10 points will be deducted for minor damage, 30 points for severe damage, 50 points for missing pages, and 100 points for missing attachments, and a compensation notice will be generated; Introducing a time decay factor, the time decay function includes: the weight coefficient of behavior in the past 6 months is 1.5, the weight coefficient of behavior in 6-12 months is 1, and the weight coefficient of behavior over 12 months is 0.5; Step S202: Calculate the user's real-time credit score based on the three-level evaluation system and the time decay factor ; The calculation process of the user's real-time credit score is as follows:

[0031] Where, is the indicator weight value, is the behavior data value, is the time decay factor, ; It should be understood that a multi-dimensional behavior quantitative assessment is achieved based on renewal data, overdue data, and book return integrity data: the renewal compliance index has a weight of 30%, with a base score of 100 points. Compliance renewal includes applying before the overdue date, which adds 5 points each time, and forced renewal after the overdue date is deducted 10 points each time. If the annual cumulative renewal exceeds 5 times, 3 points will be deducted for each additional time, which encourages users to plan renewal time reasonably and avoid exceeding the limit and occupying resources; the overdue default index has a weight of 40%. The weighting is also based on 100 points. Strict deductions are implemented based on the number of days overdue: 5 points per day for 1-3 days, 10 points per day for 4-7 days, and 20 points per day for more than 7 days. An additional 20 points will be deducted for more than three overdue incidents per year, strengthening the control over repeated and long-term overdue behavior. The return integrity indicator, with a 30% weighting, is carefully scored based on the physical condition of the book: 10 points will be deducted for minor damage, 30 points for major damage, and 50 points for missing pages. Missing attachments will result in a direct deduction of 100 points and a compensation notice. This gradient deduction mechanism ensures the physical integrity of the book. The system introduces a time decay factor: a weight of 1.5 for behavior in the past six months, 1.0 for behavior in the past six to 12 months, and 0.5 for behavior over 12 months. Each indicator score is weighted and adjusted based on the time of the behavior to calculate a real-time credit score, achieving a focused assessment of the user's recent behavior and a reasonable decay of historical behavior.

[0032] like Figure 5 As shown, as a preferred embodiment of the present invention, the connection to the external credit platform and the update of the dynamic credit evaluation system specifically include: Step S301: Obtain user credit authorization authority and obtain real-time credit data from a third-party platform; Step S302: Convert the real-time credit data obtained by the third-party platform into a plus-minus score format recognizable by the three-level percentage evaluation system.

[0033] When this embodiment is applied, when the user agrees to obtain external credit authorization permission, real-time credit data can be obtained from a third-party platform, such as Alipay's Sesame Credit score, and the real-time credit data obtained from the third-party platform is converted into a plus-minus score format that can be recognized by the three-level percentage evaluation system. A Sesame Credit score of 750 or higher corresponds to an additional 10 points, a score of 600-749 corresponds to no additional points, and a score of less than 600 corresponds to a deduction of 20 points.

[0034] As a preferred embodiment of the present invention, the generation of a stepped authority management strategy based on a dynamic credit evaluation system specifically includes: Based on the user's real-time credit score, the user's borrowing rating is divided into: A-level high-quality users, real-time credit score of users ; B-level good users, real-time credit score of users ; C-level warning users, real-time credit score of users ; D-level risk users, real-time credit score of users ; Generate basic borrowing permission strategies and overdue integrity management strategies based on user borrowing ratings; The basic borrowing permission strategy includes: A-level premium users can borrow 20 books, with a single borrowing period of 30 days, three renewals, and a 30-day renewal period. Popular book reservation priority is in the priority queue. B-level premium users: 15 books can be borrowed, single borrowing period: 21 days, renewal times: 2 times, renewal period: 21 days each, popular book reservation priority: normal queue; C-level premium users: 10 books can be borrowed, single borrowing period: 14 days, number of renewals: 1, renewal period: 14 days each, popular book reservation priority: normal queue; D-level premium users: 5 books can be borrowed, single borrowing period: 7 days, renewal times: 0, and reservation of popular books is prohibited; The overdue integrity management strategy includes: A-level premium users have a 7-day grace period for overdue payments. Overdue fines are waived during the grace period. Return integrity verification is free. Deposit requirements are waived. For Class B premium users, the overdue grace period is 3 days, the overdue fine is 0.3 yuan per book per day, the completeness verification mechanism for returned books is automatic verification by the device, and the security deposit requirement is 0.00 yuan per day. C-level premium users: 0 days of overdue grace period, 0.5 yuan per book per day of overdue fine, manual verification of returned books, and a 100 yuan deposit. For D-level premium users, the overdue grace period is 0 days, the overdue penalty standard is 1 yuan / book / day, the book return integrity verification mechanism is: manual verification + AI verification, and the deposit requirement is 100 yuan.

[0035] like Figure 6 As shown, as another preferred embodiment of the present invention, on the other hand, a book lending management system includes: Multi-dimensional collection module 100, used for collecting user behavior data in multiple dimensions; Dynamic credit evaluation system module 200, used to establish a dynamic credit evaluation system based on user behavior data; The docking module 300 is used to connect to the external credit platform and update the dynamic credit evaluation system; The generation module 400 is used to generate a stepped authority management strategy based on the dynamic credit evaluation system.

[0036] When this embodiment is applied, the multi-dimensional collection module 100 collects user behavior data in multiple dimensions. Based on the user behavior data, the dynamic credit evaluation system module 200 establishes a dynamic credit evaluation system. The docking module 300 docks with the external credit platform and updates the dynamic credit evaluation system. Based on the dynamic credit evaluation system, the generation module 400 generates a stepped permission management strategy.

[0037] like Figure 7 As shown, as another preferred embodiment of the present invention, the multi-dimensional acquisition module 100 specifically includes: The first generating unit 101 uses RFID chips or barcode recognition technology to record borrowing time and return time in real time and generate renewal data; An acquisition unit 102 is used to acquire overdue data from an existing database of the library; The second generating unit 103 is used to perform a full-process scan of the user returning the book based on the AI visual algorithm to generate book return integrity data.

[0038] When this embodiment is applied, RFID chips or barcode recognition technology are used to record borrowing time and return time in real time. The first generation unit 101 generates renewal data, the acquisition unit 102 obtains overdue data from the library's existing database, and based on the AI visual algorithm, the user is scanned throughout the entire process when returning the book. The second generation unit 103 generates book return integrity data.

[0039] The above embodiment of the present invention provides a book borrowing management method and a book borrowing management system. The book borrowing management method provided by the present invention collects user renewal, overdue, book return integrity and other behavioral data in multiple dimensions through technologies such as RFID and AI visual algorithms, combines reading preferences extracted by natural language processing and blockchain evidence storage technology, and constructs a dynamic credit evaluation system including renewal compliance, overdue default rate, and book return integrity. It introduces time decay function and machine learning to achieve dynamic adjustment of indicator weights and real-time calculation of credit ratings, and connects to a third-party credit reporting platform to achieve external credit data integration; generates a stepped permission management strategy based on credit ratings, differentially configures basic permissions such as the number of borrowed books, number of renewals, and overdue grace period, and provides supporting value-added services and constraints. It realizes full-process automated execution and data-driven system self-optimization through a rule engine, forming a "behavior collection-credit evaluation-permission management-strategy iteration" system. An intelligent closed-loop management system; this method and system achieves accurate profiling of user borrowing behavior through multi-dimensional data fusion and dynamic evaluation, the stepped permission strategy improves the efficiency and integrity of book resource circulation, the external credit linkage enhances the authority of evaluation, reduces the cost of manual verification, and the personalized mechanism takes into account both user experience and management efficiency, providing an efficient, intelligent and scalable innovative solution for book borrowing management.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A book borrowing management method, characterized in that: The method comprises: Collect user behavior data from multiple dimensions; Establish a dynamic credit evaluation system based on user behavior data; Connect with external credit platforms and update the dynamic credit evaluation system; Generate a tiered authority management strategy based on a dynamic credit evaluation system; The user behavior data specifically includes: Renewal data, overdue data, and book return integrity data.

2. The book borrowing management method according to claim 1, characterized in that: The multi-dimensional collection of user behavior data specifically includes: Use RFID chips or barcode recognition technology to record borrowing time and return time in real time and generate renewal data; The renewal data includes: renewal application time, renewal times, and whether the renewal application was submitted within 48 hours before the expiration of the original loan period; Obtain overdue data from the library's existing database; The overdue data include: cumulative overdue days, the longest single overdue days, and the proportion of overdue books; Based on AI visual algorithms, the system scans the entire process of users returning books and generates book return integrity data; The book return integrity data includes: outer surface damage, inner page abnormality, and attachment integrity.

3. The book borrowing management method according to claim 2, characterized in that: The AI vision algorithm is used to perform a full-process scan of users when returning books, and the generation of book return integrity data specifically includes: A circular HD camera array is deployed at the library's return counter, covering the book cover, back cover, spine, and inner page. Books are transported at a constant speed using a high-precision conveyor belt, and infrared sensors trigger the camera to capture and generate a time-stamped HD image sequence. Based on the YOLOv8 object detection model, a damage feature library is trained to identify three types of surface defects of targets; The three types of target surface defects include: Tearing and chipping defects: detect irregular edge areas and calculate the damaged area. , triggering a point deduction; Severe contamination defects: Detecting stained areas through color space conversion. If the stained area accounts for more than 5% of the area, a point deduction will be triggered. Indentation and crease defects: Based on the image gradient analysis algorithm, continuous crease lines are identified. If the crease length is greater than 5cm and the depth is greater than 2 pixels, a point deduction will be triggered. Set up an independent RFID tag reading area for the book accessories, and use a fixed RFID reader to detect whether the accessories are missing; If the book is a set of multiple volumes, the completeness rate of the returned sets will be verified through ISBN identification. If the completeness rate is less than 100%, a point deduction will be triggered; Using OCR technology to identify inner page numbers, a continuous page number sequence is generated. The image stitching algorithm is used to combine the unfolded inner page images into a complete book block. The images are then compared with the standard page number range to detect missing or skipped pages. If missing or skipped pages occur, a point deduction will be triggered. Use the U-Net semantic segmentation model to distinguish text content from graffiti and calculate the percentage of pixels in the non-printed area. If the percentage of pixels in the non-printed area is greater than 3%, it is determined and marked as valid graffiti. When an effective graffiti area is identified and marked, spectral analysis of the oil and water stains is performed based on the color channel difference algorithm to distinguish between new and old pollution. When new pollution is identified, point deduction is triggered.

4. The book borrowing management method according to claim 1, characterized in that: The establishment of a dynamic credit evaluation system based on user behavior data specifically includes: A three-level 100-point evaluation system is established based on renewal data, overdue data, and book return completeness data; The three-level percentage evaluation system includes: Renewal compliance indicator, with a weight of 30%, adds 5 points for each renewal that complies with regulations, including applications before the due date. 10 points are deducted for each forced renewal after the due date. 3 points are deducted for each renewal exceeding 5 times per year. Overdue default index, with a weight of 40%, is scored based on the number of days overdue: 5 points will be deducted per day for 1-3 days, 10 points will be deducted per day for 4-7 days, and 20 points will be deducted per day for more than 7 days. An additional 20 points will be deducted for more than 3 overdue events per year. The return integrity index, with a weight of 30%, is based on the degree of damage: 10 points will be deducted for minor damage, 30 points for severe damage, 50 points for missing pages, and 100 points for missing attachments, and a compensation notice will be generated; Introducing a time decay factor, the time decay function includes: the weight coefficient of behavior in the past 6 months is 1.5, the weight coefficient of behavior in 6-12 months is 1, and the weight coefficient of behavior over 12 months is 0.5; Calculate the user's real-time credit score based on the three-level evaluation system and time decay factor ; The calculation process of the user's real-time credit score is as follows: ； Where, is the indicator weight value, is the behavior data value, is the time decay factor, .

5. The book borrowing management method according to claim 1, characterized in that: The connection to the external credit platform and the update of the dynamic credit evaluation system specifically include: Obtain user credit authorization permissions and obtain real-time credit data from third-party platforms; The real-time credit data obtained from the third-party platform is converted into a plus-minus score format that can be recognized by the three-level percentage evaluation system.

6. The book borrowing management method according to claim 4, characterized in that: The generation of a step-by-step authority management strategy based on the dynamic credit evaluation system specifically includes: Based on the user's real-time credit score, the user's borrowing rating is divided into: A-level high-quality users, real-time credit score of users ; B-level good users, real-time credit score of users ; C-level warning users, real-time credit score of users ; D-level risk users, real-time credit score of users ; Generate basic borrowing permission strategies and overdue integrity management strategies based on user borrowing ratings; The basic borrowing permission strategy includes: A-level premium users can borrow 20 books, with a single borrowing period of 30 days, three renewals, and a 30-day renewal period. Popular book reservation priority is in the priority queue. B-level premium users: 15 books can be borrowed, single borrowing period: 21 days, renewal times: 2 times, renewal period: 21 days each, popular book reservation priority: normal queue; C-level premium users: 10 books can be borrowed, single borrowing period: 14 days, number of renewals: 1, renewal period: 14 days each, popular book reservation priority: normal queue; D-level premium users: 5 books can be borrowed, single borrowing period: 7 days, renewal times: 0, and reservation of popular books is prohibited; The overdue integrity management strategy includes: A-level premium users have a 7-day grace period for overdue payments. Overdue fines are waived during the grace period. Return integrity verification is free. Deposit requirements are waived. For Class B premium users, the overdue grace period is 3 days, the overdue fine is 0.3 yuan per book per day, the completeness verification mechanism for returned books is automatic verification by the device, and the security deposit requirement is 0.00 yuan per day. C-level premium users: 0 days of overdue grace period, 0.5 yuan per book per day of overdue fine, manual verification of returned books, and a 100 yuan deposit. For D-level premium users, the overdue grace period is 0 days, the overdue penalty standard is 1 yuan / book / day, the book return integrity verification mechanism is: manual verification + AI verification, and the deposit requirement is 100 yuan.

7. A book lending management system, characterized in that: Applying the book lending management method according to any one of claims 1 to 6, the system comprises: Multi-dimensional collection module, used to collect user behavior data in multiple dimensions; Dynamic credit evaluation system module, used to establish a dynamic credit evaluation system based on user behavior data; The docking module is used to connect to external credit platforms and update the dynamic credit evaluation system; The generation module is used to generate a stepped authority management strategy based on a dynamic credit evaluation system.

8. The book lending management system according to claim 6, characterized in that: The multi-dimensional acquisition module specifically includes: The first generation unit uses RFID chips or barcode recognition technology to record borrowing time and return time in real time and generate renewal data; The acquisition unit is used to obtain overdue data from the library's existing database; The second generation unit is used to perform a full-process scan of users returning books based on AI visual algorithms to generate book return integrity data.

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