Book resource sharing and management platform based on cloud computing

Abstract

The invention discloses a book resource sharing and management platform based on cloud computing, relates to the technical field of book resource management, and realizes scientific quantification and durability dynamic analysis of book loss by combining a plurality of algorithms in a data processing module and a cloud computing analysis module. The data processing module constructs an environmental influence factor EHF and an annual aging factor AF through data cleaning and normalization processing, and effectively evaluates the long-term influence of humidity and time on books. Meanwhile, texture and color feature extraction is performed on the image data based on a visual algorithm, and a cover loss score CS and an internal paper wear score IS are generated. And the cloud computing and analyzing module dynamically computes a single borrowing loss rate Ds, an accumulated loss value Dt and a durability score Sr by using the comprehensive loss data set. The module generates a maintenance prompt through the evaluation of the dynamic loss rate and the long-term loss trend, and preliminarily evaluates the use condition of the book.

Description

Technical Field

[0001] The present invention relates to the technical field of book resource management, and in particular to a book resource sharing and management platform based on cloud computing. Background Art

[0002] The book resource sharing and management platform based on cloud computing belongs to the field of book resource management. This field is committed to realizing the intelligent management and optimization of book resources through technical means such as cloud computing, big data and the Internet of Things. In the specific application of smart library management, the focus is on solving the storage, circulation and protection problems of book resources. "Book durability data analysis" is a key technology that aims to use data analysis and evaluation technology to dynamically monitor the status of books, optimize resource allocation, and extend the service life of books. The platform provides scientific maintenance recommendations and priority decision support by intelligently collecting and processing book borrowing behavior, physical loss and environmental data.

[0003] At present, libraries mainly rely on manual inspection or periodic maintenance plans to manage book loss, which has many defects: first, manual inspection is inefficient and cannot achieve real-time monitoring of the status of large-scale books; second, unified periodic maintenance ignores the differences between books, which may cause resource waste or maintenance delays; finally, for popular or precious books, their loss rate is faster, but there is a lack of dynamic response mechanism under the traditional management model. These problems lead to inefficient management of books, shortened service life, and affect readers' reading experience. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a book resource sharing and management platform based on cloud computing, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a book resource sharing and management platform based on cloud computing, including a front-end data acquisition module, a data processing module, a cloud computing and analysis module, a book durability analysis and evaluation module, and a priority analysis module;

[0006] The front-end data collection module accesses the self-service library management system and the meteorological system by setting WebAPIs, collects book borrowing data and image data, and builds a cloud computing platform to transmit the collected book borrowing data and image data to the cloud computing platform;

[0007] The data processing module obtains borrowing loss data by pre-processing the book borrowing data in the cloud computing platform, and then extracts features from the image data to obtain physical loss data; integrates the borrowing loss data with the physical loss data to obtain a comprehensive loss data set, and constructs a non-relational database, and stores the comprehensive loss data set in the non-relational database;

[0008] The cloud computing analysis module constructs a loss analysis algorithm model in the cloud computing platform, extracts the comprehensive loss data set and inputs it into the loss analysis algorithm model, calculates and outputs a single borrowing loss rate Ds, and then calculates and outputs a cumulative loss value Dt based on the single borrowing loss rate Ds;

[0009] The book durability analysis and evaluation module calculates and outputs the durability score Sr of the book according to the cumulative loss value Dt, and performs a preliminary evaluation of the durability of the current book based on the output result of the durability score Sr, and generates a maintenance reminder according to the preliminary evaluation result;

[0010] After generating the maintenance reminder, the priority analysis module calculates and outputs the maintenance priority MP of all books according to the durability score Sr and the cumulative loss value Dt of the books, and presets the priority threshold Tmp. Then, the maintenance priority MP is compared and evaluated with the priority threshold Tmp for the second time to analyze the timeliness of book maintenance.

[0011] Preferably, the front-end data collection module includes a borrowing loss collection unit and a data transmission unit;

[0012] The borrowing loss collection unit is connected to the self-service library management system by setting WebAPIs to collect the borrowing behavior data and image data of each book in the self-service library in real time;

[0013] At the same time, the meteorological system is connected through WebAPIs to collect environmental loss data in real time;

[0014] Integrate borrowing behavior data and environmental loss data to obtain book borrowing data;

[0015] The book borrowing data includes the borrowing times BT, the borrowing duration DL, the borrowing return interval GI, the user rating DF, the humidity RH and the book publishing year PY;

[0016] The image data includes a book cover image and a book side page paper image;

[0017] The data transmission unit constructs a cloud computing platform, wirelessly connects WebAPIs to the cloud computing platform through the Internet of Things IoT gateway, transmits the collected book borrowing data and image data to the cloud computing platform, and uses TLS / SSL encryption protocol to encrypt the transmission of book borrowing data and image data.

[0018] Preferably, the data processing module includes a borrowing data processing unit, an image feature extraction unit and a data storage unit;

[0019] The borrowing data processing unit performs preprocessing based on the received book borrowing data, wherein the preprocessing method includes data cleaning and normalization processing, and at the same time, based on the humidity RH and the book publishing year PY in the book borrowing data, performs calculation processing to obtain the environmental impact factor EHF and the annual aging factor AF, and summarizes them with the remaining parameters in the book borrowing data to obtain the borrowing loss data;

[0020] The environmental impact factor EHF is obtained by calculating and processing the humidity RH. The specific algorithm formula is: EHF = 1-e -k·(RH-50) , k represents the sensitive factor of humidity on books, and e represents the exponential function;

[0021] The annual aging factor AF is obtained by calculating the book publishing year PY, and the specific algorithm formula is: Where PZ represents the current year.

[0022] Preferably, the image feature extraction unit processes the book cover image based on the received image data using the visual algorithm OpenCV to extract texture and color change loss features; and identifies scratches, stains and damage features of the book cover image; and then uses a convolutional neural network CNN to classify and score the scratches, stains and damage features in the book cover image to obtain a cover loss score CS; the range of the loss score is 0-100, 0 means no loss, and 100 means complete damage;

[0023] Then, by applying edge detection and texture analysis algorithms, the creases, damages and incomplete features in the images of the side pages of the books are identified, and the wear types of the images of the side pages of the books are scored using the machine learning classifier SVM to obtain the internal paper wear score IS.

[0024] The cover loss score CS and the internal paper wear score IS are then integrated to obtain physical loss data; the score range is 0-100, 0 means no loss, and 100 means complete damage;

[0025] The data storage unit obtains a comprehensive loss data set by integrating the acquired borrowing loss data with the physical loss data, and constructs a non-relational database. At the same time, a write port, a write-out port, an initial data table and a processing data table are set, and the book borrowing data and image data are stored in the initial data table through the write port, and the comprehensive loss data set is stored in the processing data table.

[0026] Preferably, the cloud computing analysis module includes a single borrowing analysis unit and a cumulative loss unit;

[0027] The single borrowing analysis unit extracts the comprehensive loss data set through the write port, inputs it into the loss analysis algorithm model, calculates and outputs the single borrowing loss rate Ds of each book, and analyzes the loss of books caused by single borrowing;

[0028] The single borrowing loss rate Ds is calculated and output by the following loss analysis algorithm model;

[0029]

[0030] In the formula, PL represents the theoretical life of paper, and TR represents the cover loss transfer factor.

[0031] Preferably, the cumulative loss unit calculates and outputs a cumulative loss value Dt based on the single borrowing loss rate Ds and the borrowing times BT, so as to analyze the overall loss of the accumulated borrowing of books;

[0032] The cumulative loss value Dt is calculated and output by the following algorithm formula:

[0033]

[0034] Where, DS i It represents the single borrowing loss rate of the book caused by the i-th borrowing.

[0035] Preferably, the book durability analysis and evaluation module includes a durability analysis unit and a durability evaluation unit;

[0036] The durability analysis unit uses the accumulated loss value Dt, combined with the environmental impact factor EHF and the annual aging factor AF to perform comprehensive calculation and output the durability score Sr, and dynamically analyzes the usage status of the book;

[0037] The durability score Sr is calculated and outputted by the following algorithm formula;

[0038]

[0039] In the formula, max represents the upper limit value, and max(Dt) represents the upper limit value of the accumulated loss value of books.

[0040] Preferably, the durability evaluation unit performs a preliminary evaluation of the durability of the current book based on the output result of the durability score Sr. The specific evaluation content is as follows:

[0041] When the durability score Sr>80, it means that the book is in normal condition and does not require maintenance;

[0042] When 50≤durability score Sr≤80, it means that the book is worn out, and a first maintenance instruction is generated and sent to the self-service library machine management system to prompt the book to be repaired;

[0043] When the durability score Sr is less than 50, it indicates that the book is in an abnormal state. At this time, a second maintenance instruction is generated and sent to the self-service library management system, prompting the user to replace the current book within 24 hours.

[0044] Preferably, the priority analysis module includes a maintenance priority analysis unit and a maintenance priority evaluation unit;

[0045] The maintenance priority analysis unit calculates the maintenance priority MP of each book based on the durability scores Sr of all books combined with the borrowing times BT and the accumulated loss value Dt of each book when regenerating the first maintenance instruction, so as to measure the current maintenance needs of the book;

[0046] The maintenance priority MP is calculated and output by the following algorithm formula:

[0047]

[0048] In the formula, represents partial derivative.

[0049] Preferably, the maintenance priority evaluation unit extracts the maintenance priority MP of all book histories in the fully self-service library machine, calculates and outputs a priority threshold Tmp, and the specific algorithm formula of the priority threshold Tmp is: Tmp=μMP+α·σMP, where μMP represents the mean value of the maintenance priority MP of the book history, α represents the adjustment factor, and σMP represents the standard deviation of the maintenance priority MP of all book histories;

[0050] When the first maintenance instruction is received again, a second comparative evaluation is performed based on the priority threshold Tmp and the maintenance priority MP to analyze the time priority of the book maintenance and the specific evaluation content;

[0051] When the maintenance priority MP>priority threshold Tmp, it means that the current book is abnormally worn. At this time, the first prompt warning is generated and sent to the self-service library management system, prompting the current book to be repaired within 3 hours;

[0052] When the maintenance priority MP ≤ the priority threshold Tmp, it indicates that the wear of the current book is normal. At this time, a second prompt warning is generated and sent to the self-service library machine management system, prompting the current book to be repaired within 24 hours.

[0053] The present invention provides a book resource sharing and management platform based on cloud computing. It has the following beneficial effects:

[0054] (1) The platform realizes the comprehensive collection and management of book data through modular design. The front-end data collection module uses the self-service library management system and the meteorological system to collect borrowing behavior data, environmental loss data and image data in real time by setting WebAPIs. Borrowing data covers dynamic information such as the number of borrowings BT, borrowing time DL, and return interval GI. Image data includes cover images and side page paper images. Environmental data quantifies the impact of the environment on books through factors such as humidity RH. Through the Internet of Things IoT gateway and TLS / SSL encryption protocol, the system securely transmits the collected data to the cloud platform. This module realizes the efficient collection and precise integration of multi-dimensional data, significantly improves the comprehensiveness of book status monitoring, and lays a solid data foundation for subsequent analysis.

[0055] (2) The platform combines multiple algorithms in the data processing module and the cloud computing and analysis module to achieve scientific quantification of book loss and dynamic durability analysis. The data processing module constructs the environmental impact factor EHF and the annual aging factor AF through data cleaning and normalization, effectively evaluating the long-term impact of humidity and time on books. At the same time, the texture and color features of the image data are extracted based on the visual algorithm to generate the cover loss score CS and the internal paper wear score IS. The cloud computing and analysis module uses a comprehensive loss data set to dynamically calculate the single borrowing loss rate Ds, the cumulative loss value Dt and the durability score Sr. This module generates maintenance prompts and preliminarily evaluates the usage status of books by evaluating the dynamic loss rate and long-term loss trend, making up for the shortcomings of traditional book management that relies on manual experience and judgment, and greatly improving the scientificity and accuracy of book management.

[0056] (3) The platform uses the priority analysis module to intelligently sort the maintenance needs of books by combining the durability score Sr and the cumulative loss value Dt with the maintenance priority MP of the book. The maintenance priority analysis unit dynamically generates the priority threshold Tmp based on the maintenance priority MP and the historical data of the book, and accurately evaluates the maintenance timeliness of the book through a secondary comparison with the maintenance priority MP. When the maintenance priority MP> the priority threshold Tmp, the system generates the first maintenance reminder, suggesting that the books with abnormal loss be repaired within 3 hours; when the maintenance priority MP≤ the priority threshold Tmp, the second reminder is generated, suggesting that the maintenance be completed within 24 hours. Through the intelligent analysis of the maintenance priority evaluation unit, the system effectively avoids the accelerated loss of popular books due to high-frequency use, and at the same time discovers and alleviates the potential threats to books from environmental factors. The intelligence, timeliness and pertinence of this maintenance strategy not only improve the efficiency of book management, but also greatly reduce resource waste, providing libraries with better resource sharing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of the book resource sharing and management platform based on cloud computing of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Example 1

[0060] See also Figure 1 The present invention provides a book resource sharing and management platform based on cloud computing. To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0061] The book resource sharing and management platform based on cloud computing includes front-end data collection module, data processing module, cloud computing analysis module, book durability analysis and evaluation module and priority analysis module;

[0062] The front-end data collection module accesses the self-service library management system and the meteorological system by setting up WebAPIs, collects book borrowing data and image data, and builds a cloud computing platform to transmit the collected book borrowing data and image data to the cloud computing platform;

[0063] The data processing module obtains borrowing loss data by preprocessing the book borrowing data in the cloud computing platform, and then extracts features from the image data to obtain physical loss data; integrates the borrowing loss data with the physical loss data to obtain a comprehensive loss data set, and constructs a non-relational database to store the comprehensive loss data set in the non-relational database;

[0064] The cloud computing analysis module constructs a loss analysis algorithm model in the cloud computing platform, extracts a comprehensive loss data set and inputs it into the loss analysis algorithm model to calculate and output a single borrowing loss rate Ds, and then calculates and outputs a cumulative loss value Dt based on the single borrowing loss rate Ds;

[0065] The book durability analysis and evaluation module calculates and outputs the durability score Sr of the book based on the cumulative loss value Dt, and based on the output result of the durability score Sr, performs a preliminary evaluation of the durability of the current book, and generates maintenance prompts based on the preliminary evaluation results;

[0066] After generating maintenance reminders, the priority analysis module calculates and outputs the maintenance priority MP of all books based on the durability score Sr and the cumulative loss value Dt of the books, presets the priority threshold Tmp, and then conducts a secondary comparative evaluation of the maintenance priority MP and the priority threshold Tmp to analyze the timeliness of book maintenance.

[0067] In this embodiment, the platform uses WebAPIs to access the self-service library management system and meteorological system through the front-end data collection module, collects book borrowing behavior data, environmental loss data and image data in real time, and transmits the data securely to the cloud through the IoT gateway and encryption protocol, providing multi-dimensional and high-precision data support for subsequent processing. The data processing module cleans, normalizes and extracts features of the collected data, generates borrowing loss data and physical loss data, and builds a non-relational database to store the comprehensive loss data set to ensure the structured and efficient data integration. The cloud computing and analysis module dynamically calculates the single borrowing loss rate Ds and the cumulative loss value Dt based on the loss analysis algorithm model, and realizes the quantitative analysis of the book loss process. The book durability analysis and evaluation module evaluates the use status of the book through the durability score Sr, generates maintenance prompts, and preliminarily determines whether the book needs maintenance or replacement. Finally, the priority analysis module combines the durability score Sr and the cumulative loss value Dt to calculate the maintenance priority MP of each book, generates the maintenance time priority through secondary comparison with the priority threshold Tmp, and provides clear repair or replacement suggestions. This system, through modular design and cloud computing technology, fully realizes dynamic monitoring, accurate evaluation and intelligent maintenance of book status. Its implementation not only optimizes the library resource management process, but also significantly improves the life cycle and resource utilization efficiency of books. Through scientific loss analysis and priority sorting, the system effectively avoids the accelerated damage of books due to excessive use or adverse environment, while rationally allocating maintenance resources and reducing manpower and time costs.

[0068] Example 2

[0069] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the front-end data collection module includes a borrowing loss collection unit and a data transmission unit;

[0070] The borrowing loss collection unit is connected to the self-service library management system by setting WebAPIs to collect the borrowing behavior data and image data of each book in the self-service library in real time;

[0071] At the same time, the meteorological system is connected through WebAPIs to collect environmental loss data in real time;

[0072] Integrate borrowing behavior data and environmental loss data to obtain book borrowing data;

[0073] Book borrowing data includes borrowing times BT, borrowing duration DL, borrowing return interval GI, user rating DF, humidity RH and book publishing year PY;

[0074] The image data includes a book cover image and a book side page paper image;

[0075] The data transmission unit builds a cloud computing platform, wirelessly connects WebAPIs to the cloud computing platform through the Internet of Things (IoT) gateway, and transmits the collected book borrowing data and image data to the cloud computing platform. At the same time, the TLS / SSL encryption protocol is used to encrypt the transmission of book borrowing data and image data.

[0076] In this embodiment, the front-end data collection module of the platform realizes the efficient collection and secure transmission of multi-dimensional data of books through the collaborative work of the borrowing loss collection unit and the data transmission unit. The borrowing loss collection unit connects with the self-service library management system through WebAPIs to obtain the borrowing behavior data of each book in real time; at the same time, it connects to the meteorological system to collect environmental loss data in real time, integrates multi-source data into book borrowing data, and lays the foundation for subsequent processing. The data transmission unit connects WebAPIs with the cloud computing platform through the Internet of Things IoT gateway, uses TLS / SSL encryption protocol to ensure transmission security, and uploads the collected data to the cloud computing platform in real time for storage and analysis. Through the implementation of this module, the system realizes the comprehensive collection of dynamic usage data and environmental data of books, ensuring the integrity, real-time and security of the data. This not only provides accurate data support for subsequent book loss analysis, but also significantly improves the efficiency and accuracy of information collection in library management. At the same time, the modular data transmission design optimizes the resource sharing process and reduces the delay and security risks of data transmission.

[0077] Example 3

[0078] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the data processing module includes a borrowing data processing unit, an ,image feature extraction unit and a data storage unit;

[0079] The borrowing data processing unit performs preprocessing based on the received book borrowing data, and the preprocessing method includes data cleaning and normalization processing. At the same time, based on the humidity RH and the book publishing year PY in the book borrowing data, it performs calculation processing to obtain the environmental impact factor EHF and the annual aging factor AF, and summarizes them with the remaining parameters in the book borrowing data to obtain the borrowing loss data;

[0080] The environmental impact factor EHF is obtained by calculating the humidity RH. The specific algorithm formula is: EHF = 1-e -k·(RH-50) , k represents the sensitivity factor of humidity on books, which is set based on the paper type, and e represents the exponential function;

[0081] The annual aging factor AF is obtained by calculating the book publishing year PY. The specific algorithm formula is: Where PZ represents the current year.

[0082] The image feature extraction unit processes the book cover image based on the received image data using the visual algorithm OpenCV to extract the texture and color change loss features; and identifies the scratches, stains and damage features of the book cover image; and then uses the convolutional neural network CNN to classify and score the scratches, stains and damage features in the book cover image to obtain the cover loss score CS; the loss score ranges from 0 to 100, 0 means no loss, and 100 means complete damage;

[0083] Then, by applying edge detection and texture analysis algorithms, the creases, damages and incomplete features in the images of the side pages of the books are identified, and the wear types of the images of the side pages of the books are scored using the machine learning classifier SVM to obtain the internal paper wear score IS.

[0084] The cover loss score CS and the internal paper wear score IS are then integrated to obtain the physical loss data; the score range is 0-100, 0 means no loss, and 100 means complete damage;

[0085] The data storage unit obtains a comprehensive loss data set by integrating the acquired borrowing loss data with the physical loss data, and constructs a non-relational database. At the same time, a write port, a write-out port, an initial data table and a processing data table are set, and the book borrowing data and image data are stored in the initial data table through the write port, and the comprehensive loss data set is stored in the processing data table.

[0086] In this embodiment, the data processing module of the platform realizes efficient preprocessing and integration of book borrowing data and image data through the organic combination of the borrowing data processing unit, the image feature extraction unit and the data storage unit. The borrowing data processing unit performs data cleaning and normalization processing on the collected book borrowing data to ensure data quality and consistency; at the same time, based on the humidity RH and the book publishing year PY, it dynamically calculates the environmental impact factor EHF and the annual aging factor AF, and generates borrowing loss data by summarizing with the borrowing behavior data. The image feature extraction unit uses visual algorithms and convolutional neural networks CNN to extract texture, color change and loss features of the book cover and side page paper images, generates a cover loss score CS and an internal paper wear score IS, and quantitatively evaluates the physical loss degree of the book. The data storage unit integrates the borrowing loss data and the physical loss data to construct a comprehensive loss data set, and uses a non-relational database for efficient storage. At the same time, it sets write and write ports to store the initial data and processed data in layers to ensure the efficiency and security of data access. This module comprehensively improves the ability to quantify the loss status of books through multi-dimensional data processing and feature extraction. It not only provides accurate data support for loss analysis and durability assessment, but also optimizes the data management process through standardized and intelligent data storage methods.

[0087] Example 4

[0088] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the cloud computing and analysis module includes a single borrowing ,analysis unit and a cumulative loss unit;

[0089] The single borrowing analysis unit extracts the comprehensive loss data set through the write port, inputs it into the loss analysis algorithm model, calculates and outputs the single borrowing loss rate Ds of each book, and analyzes the loss of books caused by a single borrowing;

[0090] The single-borrowing loss rate Ds is calculated and output by the following loss analysis algorithm model;

[0091]

[0092] In the formula, PL represents the theoretical life of paper, which is the theoretical durable life set according to the book paper type. Ordinary paper is about 30 years, and high-quality paper is about 50 years. TR represents the cover loss transfer factor, which indicates the proportion of cover loss transferred to the internal paper. The default value is 0.4.

[0093] The cumulative loss unit calculates and outputs the cumulative loss value Dt based on the single borrowing loss rate Ds and the number of borrowing times BT, and analyzes the overall loss of the accumulated borrowing of books;

[0094] The accumulated loss value Dt is calculated and output by the following algorithm formula;

[0095]

[0096] Where, DS i It represents the single borrowing loss rate of the book caused by the i-th borrowing.

[0097] In this embodiment, the cloud computing and analysis module of the platform realizes the accurate calculation and dynamic evaluation of the loss of books through the coordinated operation of the single borrowing analysis unit and the cumulative loss unit. The single borrowing analysis unit extracts relevant data from the comprehensive loss data set, inputs the loss analysis algorithm model, and calculates the single borrowing loss rate Ds of each book based on the theoretical life of the book paper PL, the cover loss score CS, the internal paper wear score IS and other dynamic parameters, and quantitatively analyzes the impact of single borrowing behavior on the immediate loss of books. On this basis, the cumulative loss unit combines the number of borrowings BT to comprehensively evaluate the cumulative loss value Dt of each book in a specific use cycle, and dynamically analyzes the overall loss of books in long-term use by considering all historical borrowing losses and introducing a time increment factor. Through the instant calculation of the loss rate Ds and the dynamic evaluation of the cumulative loss value Dt, this module not only provides accurate quantification of the loss status of books, but also fully reflects the durability and deterioration trend of books under different use conditions. Through the cloud deployment of the automated algorithm model, the subjective error of manual evaluation is greatly reduced, and the efficiency and accuracy of data analysis are improved. At the same time, the module can capture changes in book loss in real time, providing a reliable basis for subsequent durability analysis and maintenance priority assessment.

[0098] Example 5

[0099] This embodiment is explained in Example 4. Please refer to Figure 1 ,Specifically: the book durability analysis and evaluation module includes a durability analysis unit and a durability evaluation unit;

[0100] The durability analysis unit uses the accumulated loss value Dt, combined with the environmental impact factor EHF and the annual aging factor AF to perform a comprehensive calculation to output the durability score Sr, and dynamically analyzes the usage status of the books;

[0101] The durability score Sr is calculated and output by the following algorithm formula;

[0102]

[0103] In the formula, max represents the upper limit value, and max(Dt) represents the upper limit value of the accumulated loss value of books.

[0104] The durability evaluation unit conducts a preliminary evaluation of the durability of the current book based on the output result of the durability score Sr. The specific evaluation contents are as follows:

[0105] When the durability score Sr>80, it means that the book is in normal condition and does not require maintenance;

[0106] When 50≤durability score Sr≤80, it means that the book is worn out, and a first maintenance instruction is generated and sent to the self-service library machine management system to prompt the book to be repaired;

[0107] When the durability score Sr is less than 50, it indicates that the book is in an abnormal state. At this time, a second maintenance instruction is generated and sent to the self-service library management system, prompting the user to replace the current book within 24 hours.

[0108] In this embodiment, the book durability analysis and evaluation module of the platform fully realizes the dynamic evaluation of book durability and the intelligent generation of maintenance prompts through the efficient collaboration of the durability analysis unit and the durability evaluation unit. The durability analysis unit uses the cumulative loss value Dt, combined with the environmental impact factor EHF and the annual aging factor AF, to dynamically calculate the durability score Sr to quantify the current usage status and comprehensive durability of the book. The durability score Sr is evaluated in three levels, and the durability evaluation unit automatically generates maintenance instructions based on the scoring results: books in normal status do not require maintenance; books with wear and tear generate the first maintenance instruction to prompt repair; books in abnormal status generate the second maintenance instruction, and it is recommended to replace them within 24 hours. This process realizes the timely transmission and execution of maintenance information through linkage with the self-service library machine management system. Through the combination of dynamic analysis and hierarchical evaluation, this module can not only accurately reflect the current status of the book, but also provide targeted and timely maintenance suggestions, avoiding resource waste and maintenance delays caused by human subjective judgment. At the same time, the durability scoring model ensures the comparability of the status of different books through the normalization of the upper limit value max(Dt), making management decisions more scientific. The implementation of the module has significantly improved the library's maintenance efficiency and book resource utilization, providing solid technical support for extending the life cycle of books, optimizing resource allocation and improving user satisfaction.

[0109] Example 6

[0110] This embodiment is explained in Example 5. Please refer to Figure 1 ,Specifically: the priority analysis module includes a maintenance priority analysis unit and a ,maintenance priority evaluation unit;

[0111] When the maintenance priority analysis unit regenerates the first maintenance instruction, it calculates the maintenance priority MP of each book based on the durability score Sr of all books combined with the borrowing times BT and the accumulated loss value Dt of each book, so as to measure the current maintenance demand of the book;

[0112] The maintenance priority MP is calculated and output by the following algorithm formula;

[0113]

[0114] In the formula, represents partial derivative.

[0115] The maintenance priority evaluation unit extracts the maintenance priority MP of all book histories in the self-service library machine, calculates and outputs the priority threshold Tmp. The specific algorithm formula of the priority threshold Tmp is: Tmp = μMP + α·σMP, where μMP represents the mean value of the maintenance priority MP of the book history, α represents the adjustment factor, and σMP represents the standard deviation of the maintenance priority MP of all book histories;

[0116] When the first maintenance instruction is received again, a second comparative evaluation is performed based on the priority threshold Tmp and the maintenance priority MP to analyze the time priority of the book maintenance and the specific evaluation content;

[0117] When the maintenance priority MP>priority threshold Tmp, it means that the current book is abnormally worn. At this time, the first prompt warning is generated and sent to the self-service library management system, prompting the current book to be repaired within 3 hours;

[0118] When the maintenance priority MP ≤ the priority threshold Tmp, it indicates that the wear of the current book is normal. At this time, a second warning is generated and sent to the self-service library machine management system, prompting the current book to be repaired within 24 hours.

[0119] In this embodiment, the priority analysis module of the platform realizes the intelligent sorting and dynamic adjustment of book maintenance requirements through the efficient collaboration of the maintenance priority analysis unit and the maintenance priority evaluation unit. After the first maintenance instruction is generated, the maintenance priority analysis unit combines the durability score Sr, the number of borrowing times BT and the accumulated loss value Dt of all books to calculate the maintenance priority MP of each book. Dynamically capture the loss growth rate and accurately quantify the status changes and maintenance needs of books. The maintenance priority evaluation unit calculates the priority threshold Tmp based on the maintenance priority MP of all books, and analyzes the time priority of maintenance by performing a secondary comparison with the MP value of the current book: if MP>Tmp, the system generates the first prompt warning and recommends that the repair be completed within 3 hours; if MP≤Tmp, a second prompt warning is generated and it is recommended that the repair be completed within 24 hours. By linking with the self-service library management system, the warning information can be communicated in a timely manner and guide the actual operation. The implementation of this module enables the library to dynamically grasp the maintenance needs of all books and allocate maintenance resources according to priority, avoiding the situation where popular books lose their circulation value due to accelerated loss and untimely maintenance, while reducing the risk of resource waste and maintenance delays. The threshold Tmp calculated by the maintenance priority model combined with historical data improves the scientific nature of decision-making and the timeliness of maintenance.

[0120] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A book resource sharing and management platform based on cloud computing, characterized by: It includes front-end data collection module, data processing module, cloud computing and analysis module, book durability analysis and evaluation module and priority analysis module; The front-end data collection module accesses the self-service library management system and the meteorological system by setting WebAPIs, collects book borrowing data and image data, and builds a cloud computing platform to transmit the collected book borrowing data and image data to the cloud computing platform; The data processing module obtains borrowing loss data by pre-processing the book borrowing data in the cloud computing platform, and then extracts features from the image data to obtain physical loss data; integrates the borrowing loss data with the physical loss data to obtain a comprehensive loss data set, and constructs a non-relational database, and stores the comprehensive loss data set in the non-relational database; The cloud computing analysis module constructs a loss analysis algorithm model in the cloud computing platform, extracts the comprehensive loss data set and inputs it into the loss analysis algorithm model, calculates and outputs a single borrowing loss rate Ds, and then calculates and outputs a cumulative loss value Dt based on the single borrowing loss rate Ds; The book durability analysis and evaluation module calculates and outputs the durability score Sr of the book according to the cumulative loss value Dt, and performs a preliminary evaluation of the durability of the current book based on the output result of the durability score Sr, and generates a maintenance reminder according to the preliminary evaluation result; After generating the maintenance reminder, the priority analysis module calculates and outputs the maintenance priority MP of all books according to the durability score Sr and the cumulative loss value Dt of the books, and presets the priority threshold Tmp. Then, the maintenance priority MP is compared and evaluated with the priority threshold Tmp for the second time to analyze the timeliness of book maintenance.

2. The book resource sharing and management platform based on cloud computing according to claim 1 is characterized by: The front-end data collection module includes a borrowing loss collection unit and a data transmission unit; The borrowing loss collection unit is connected to the self-service library management system by setting WebAPIs to collect the borrowing behavior data and image data of each book in the self-service library in real time; At the same time, the meteorological system is connected through WebAPIs to collect environmental loss data in real time; Integrate borrowing behavior data and environmental loss data to obtain book borrowing data; The book borrowing data includes the borrowing times BT, the borrowing duration DL, the borrowing return interval GI, the user rating DF, the humidity RH and the book publishing year PY; The image data includes a book cover image and a book side page paper image; The data transmission unit constructs a cloud computing platform, wirelessly connects WebAPIs to the cloud computing platform through the Internet of Things IoT gateway, transmits the collected book borrowing data and image data to the cloud computing platform, and uses TLS / SSL encryption protocol to encrypt the transmission of book borrowing data and image data.

3. The book resource sharing and management platform based on cloud computing according to claim 2 is characterized by: The data processing module includes a borrowing data processing unit, an image feature extraction unit and a data storage unit; The borrowing data processing unit performs preprocessing based on the received book borrowing data, wherein the preprocessing method includes data cleaning and normalization processing, and at the same time, based on the humidity RH and the book publishing year PY in the book borrowing data, performs calculation processing to obtain the environmental impact factor EHF and the annual aging factor AF, and summarizes them with the remaining parameters in the book borrowing data to obtain the borrowing loss data; The environmental impact factor EHF is obtained by calculating and processing the humidity RH. The specific algorithm formula is: EHF = 1-e -k· ( RH-50 ), k represents the sensitive factor of humidity on books, and e represents the exponential function; The annual aging factor AF is obtained by calculating and processing the book publishing year PY, and the specific algorithm formula is: Where PZ represents the current year.

4. The book resource sharing and management platform based on cloud computing according to claim 3 is characterized by: The image feature extraction unit processes the book cover image based on the received image data using the visual algorithm OpenCV to extract texture and color change loss features; And identify the scratches, stains and damage features of the book cover image; then use the convolutional neural network CNN to classify and score the scratches, stains and damage features in the book cover image to obtain the cover loss score CS; the range of the loss score is 0-100, 0 means no loss, and 100 means complete damage; Then, by applying edge detection and texture analysis algorithms, the creases, damages and incomplete features in the images of the side pages of the books are identified, and the wear types of the images of the side pages of the books are scored using the machine learning classifier SVM to obtain the internal paper wear score IS. The cover loss score CS and the internal paper wear score IS are then integrated to obtain physical loss data; the score range is 0-100, 0 means no loss, and 100 means complete damage; The data storage unit obtains a comprehensive loss data set by integrating the acquired borrowing loss data with the physical loss data, and constructs a non-relational database. At the same time, a write port, a write-out port, an initial data table and a processing data table are set, and the book borrowing data and image data are stored in the initial data table through the write port, and the comprehensive loss data set is stored in the processing data table.

5. The book resource sharing and management platform based on cloud computing according to claim 4 is characterized in that: The cloud computing and analysis module includes a single borrowing analysis unit and a cumulative loss unit; The single borrowing analysis unit extracts the comprehensive loss data set through the write port, inputs it into the loss analysis algorithm model, calculates and outputs the single borrowing loss rate Ds of each book, and analyzes the loss of books caused by single borrowing; The single borrowing loss rate Ds is calculated and output by the following loss analysis algorithm model; In the formula, PL represents the theoretical life of paper, and TR represents the cover loss transfer factor.

6. The book resource sharing and management platform based on cloud computing according to claim 5 is characterized by: The cumulative loss unit calculates and outputs a cumulative loss value Dt based on the single borrowing loss rate Ds and the number of borrowing times BT, and analyzes the overall loss of the accumulated borrowing of books; The cumulative loss value Dt is calculated and output by the following algorithm formula: Where, DS i It represents the single borrowing loss rate of the book caused by the i-th borrowing.

7. The book resource sharing and management platform based on cloud computing according to claim 6 is characterized by: The book durability analysis and evaluation module includes a durability analysis unit and a durability evaluation unit; The durability analysis unit uses the accumulated loss value Dt, combined with the environmental impact factor EHF and the annual aging factor AF to perform comprehensive calculation and output the durability score Sr, and dynamically analyzes the usage status of the book; The durability score Sr is calculated and outputted by the following algorithm formula; In the formula, max represents the upper limit value, and max(Dt) represents the upper limit value of the accumulated loss value of books.

8. The book resource sharing and management platform based on cloud computing according to claim 7 is characterized by: The durability evaluation unit performs a preliminary evaluation of the durability of the current book based on the output result of the durability score Sr. The specific evaluation content is as follows: When the durability score Sr>80, it means that the book is in normal condition and does not require maintenance; When 50≤durability score Sr≤80, it means that the book is worn out, and a first maintenance instruction is generated and sent to the self-service library machine management system to prompt the book to be repaired; When the durability score Sr is less than 50, it indicates that the book is in an abnormal state. At this time, a second maintenance instruction is generated and sent to the self-service library management system, prompting the user to replace the current book within 24 hours.

9. The book resource sharing and management platform based on cloud computing according to claim 8 is characterized by: The priority analysis module includes a maintenance priority analysis unit and a maintenance priority evaluation unit; The maintenance priority analysis unit calculates the maintenance priority MP of each book based on the durability scores Sr of all books combined with the borrowing times BT and the accumulated loss value Dt of each book when regenerating the first maintenance instruction, so as to measure the current maintenance needs of the book; The maintenance priority MP is calculated and output by the following algorithm formula: In the formula, represents partial derivative.

10. The book resource sharing and management platform based on cloud computing according to claim 9 is characterized in that: The maintenance priority evaluation unit extracts the maintenance priority MP of all book histories in the fully self-service library machine, calculates and outputs a priority threshold Tmp, and the specific algorithm formula of the priority threshold Tmp is: Tmp=μMP+α·σMP, where μMP represents the mean value of the maintenance priority MP of the book history, α represents the adjustment factor, and σMP represents the standard deviation of the maintenance priority MP of all book histories; When the first maintenance instruction is received again, a second comparative evaluation is performed based on the priority threshold Tmp and the maintenance priority MP to analyze the time priority of the book maintenance and the specific evaluation content; When the maintenance priority MP>priority threshold Tmp, it means that the current book is abnormally worn. At this time, the first prompt warning is generated and sent to the self-service library management system, prompting the current book to be repaired within 3 hours; When the maintenance priority MP ≤ the priority threshold Tmp, it indicates that the wear of the current book is normal. At this time, a second warning is generated and sent to the self-service library machine management system, prompting the current book to be repaired within 24 hours.

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