Book borrowing optimization method, system and device

Abstract

The invention provides a book borrowing optimization method, system and device, and relates to the technical field of library management, and the method mainly comprises the steps: collecting VOC concentration data of a tested book, and carrying out the preprocessing; the temperature and the relative humidity of the library to be tested are collected and preprocessed; extracting odor characteristics from the VOC concentration data; on the basis of the odor characteristics, through a trained aging prediction model, predicting the residual life of the tested book paper; and based on the residual life, through a dynamic damage-value balance equation, a book borrowing scheme is optimized and solved. According to the method, the VOC concentration data of the tested book is collected in a non-contact manner, and the temperature and the relative humidity are combined, so that the residual life of the tested book can be accurately predicted, and an optimal borrowing scheme can be planned, and therefore, the knowledge propagation value of the book can be guaranteed, and the residual life of the book can be maintained or prolonged as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of library management, and in particular to a book borrowing optimization method, system and device. Background Art

[0002] Libraries often preserve some precious books that are very old, rare in stock, and difficult to reproduce. As time goes by, more ordinary books will become precious books.

[0003] Due to environmental factors (light, temperature and humidity, etc.) and human borrowing factors, precious books usually undergo irreversible aging phenomena such as yellowing and falling pages, thus shortening the service life of precious books; but putting precious books on the shelf will greatly reduce their effectiveness in spreading knowledge.

[0004] How to effectively predict the remaining lifespan of books and formulate scientific borrowing plans accordingly, so as to achieve the technical goal of maintaining or extending the remaining lifespan of precious books and effectively disseminating the important knowledge contained in precious books, is a major issue that needs to be urgently addressed in the industry. Summary of the Invention

[0005] The object of the present invention is to provide a method, system and device for optimizing book borrowing, so as to solve at least one of the above-mentioned technical problems existing in the prior art.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a book borrowing optimization method, comprising the following steps: Step 1: Collect and pre-process the VOC (volatile organic compound) concentration data of the tested books; collect and pre-process the temperature and relative humidity of the tested library; Step 2: extracting odor characteristics from the VOC concentration data, wherein the odor characteristics include an environmentally corrected degradation index, which is used to evaluate the aging rate of the paper; Step 3: Based on the odor characteristics, the remaining life of the tested book paper is predicted using a trained aging prediction model; the remaining life refers to the number of years from the current state of a single sheet of paper until its folding endurance exceeds a preset failure threshold; the aging prediction model includes a corresponding relationship between the odor characteristics and the folding endurance of a single sheet of paper; Step 4: Based on the remaining lifespan, the dynamic damage-value balance equation is used to optimize the book borrowing plan; the borrowing plan includes the number of borrowing times per year, the borrowing duration, and the borrowing environment; the borrowing environment includes temperature and relative humidity; In this way, through the above method, we can take advantage of the fact that paper produces volatile organic compounds at different rates during the degradation process of oxidation, hydrolysis, acid hydrolysis, etc., and collect key data that can reflect the life of the tested books - VOC concentration data - in a non-contact, zero-damage manner without damaging the books. Through the trained aging prediction model, we can accurately predict the remaining life of the paper of the tested books, and construct a dynamic damage-value balance equation to quantify the amount of damage and value, and then seek to solve the optimal borrowing plan, so that books can be borrowed and their remaining life can be maintained or extended.

[0007] In one feasible embodiment, the VOC concentration data in step 1 is collected using a (semiconductor) gas sensor (e.g., with a detection range of 1-500 ppm and a resolution of 0.1 ppm) at a distance of 3-5 cm from the top of the book being tested. This minimizes physical and light contact with the book being tested, while also avoiding areas of the book that are frequently touched and easily contaminated by borrowers, such as the edges and base of the book, thereby improving the reliability of the data source. Of course, if more comprehensive VOC concentration data is needed, gas can be collected from other parts of the tested books and the weighted average VOC concentration value can be calculated.

[0008] In a feasible embodiment, the VOC includes organic acids and aldehydes, etc.; the organic acids include acetic acid and formic acid, etc.; the aldehydes include furfural, benzaldehyde and vanillin, etc.

[0009] In a feasible embodiment, if the book being tested is made of handmade paper from ancient books, the VOC may be acetic acid; this is because acetic acid is the main product of the acetylation reaction of cellulose / hemicellulose and the acidolysis of lignin β-aryl ether bonds in handmade paper from ancient books, and has the highest concentration, thereby simplifying the collection process and data processing volume.

[0010] In a feasible embodiment, if the books being tested are made of mechanical wood pulp paper, the VOCs may be formic acid and furfural; this is because formic acid and furfural are the main products of lignin side chain oxidative breakage and pentosan degradation in mechanical wood pulp paper, and have the highest concentration, thereby simplifying the collection process and data processing volume.

[0011] In a feasible implementation, the method for preprocessing VOC concentration data is sliding average filtering, and the specific formula includes: ; in, Indicates the VOC concentration value after filtering; Indicates the sliding window size, that is, the total number of data points involved in the average calculation, specifically ; Indicates the window radius (can be 5); Indicates the The original VOC concentration value at each time point; Indicates the current time point, that is, the central moment that needs to be filtered.

[0012] In a feasible implementation manner, the temperature and relative humidity in step 1 are collected by a temperature and humidity sensor arranged at the center of the storage area of ​​the books to be measured; in this way, more accurate and reasonable data can be obtained.

[0013] In a feasible implementation, the specific method for pre-processing the temperature is to take a 24-hour average, and the specific formula includes: ; in, represents the daily average temperature; Indicates the Temperature values ​​for hours.

[0014] In a feasible implementation, the specific method for pre-processing the relative humidity is to take the 24-hour average value, and the specific formula includes: ; in, represents the daily average relative humidity; Indicates the Relative humidity value for one hour.

[0015] In a feasible embodiment, in step 2, the specific calculation formula of the environmental correction degradation index includes: ; in, represents the environmentally corrected degradation index; Indicates the standard ambient temperature, which can be set to 20°C; Indicates standard ambient humidity, which can be 50%RH; It represents the temperature influence coefficient, which can be 0.05 / ℃; Represents the relative humidity influence coefficient, which can be 0.03 / %RH; represents a natural constant; It represents the rate of change of VOC concentration. The specific formula is: ; in, is the sampling interval, which can be set to 1 hour; In this way, the environmentally corrected degradation index can be calculated quickly and concisely.

[0016] In a feasible implementation, the calculation formula of the environmentally corrected degradation index can also take into account the superposition effect of temperature and humidity, and revise the calculation formula into a nonlinear form, specifically: ; in, Indicates the superimposed influence coefficient of temperature and relative humidity.

[0017] In a feasible embodiment, the aging prediction model is a two-stage exponential decay model to approximately simulate the characteristics of paper aging, which is accelerated in the early stage (oxidation stage) and stabilized in the later stage (hydrolysis stage). The specific formula includes: ; in, Indicates the current paper folding resistance, in times, which can be obtained from the measurement value of MIT folding resistance meter; Indicates the preset failure threshold, which can be 5 times. If it is lower than this value, the paper is considered to have reached the end of its life. It represents the decay rate parameter of the first stage (oxidation stage), which can be taken as 0.15; It represents the decay rate parameter of the second stage (hydrolysis stage), which can be taken as 0.02; It represents the threshold value of the degradation stage, which can be 0.8; Indicates the remaining life.

[0018] In a feasible implementation manner, the training method of the aging prediction model is: Step a1: Select paper samples of different materials and conduct accelerated aging tests on each. The accelerated aging test includes a temperature aging test, and specific reference can be made to the international standard "Accelerated Dry Heat Aging of Paper at 105±2°C for 72 hours." Based on a time series, VOC concentration values ​​of various paper samples are collected using a gas sensor. The dominant VOC concentration value is determined using the TopK (maximum number selection) method. Folding endurance measurements of various paper samples at corresponding time series are collected using an MIT folding endurance meter. The VOC concentration values ​​and folding endurance measurements are stored in correspondence as training samples. Step a2: Count the VOC concentration values ​​and find the degradation stage boundary threshold that minimizes the error between the first stage and the second stage. ; Step a3: In the first stage data, train , specifically including: Model linearization formula: ; Optimal solution calculation formula: ; in, Indicates the number of training samples in the first stage; Indicates the The true remaining life value of the training samples; Indicates the The folding resistance of the paper of the training samples; Step a4: In the second stage data, train , specifically including: The formula for the second-stage loss function is: ; in, Indicates the number of training samples in the second stage; The second stage gradient descent formula is as follows: ; Step a5: Evaluate the aging prediction model through a cross-validation strategy, using the mean absolute error (MAE) and accuracy (ACC) as evaluation indicators. In this way, through staged training, the problem of the conventional overall loss function (the mean square error loss of the difference between the predicted value and the true value) being discontinuous (non-differentiable) at the threshold of the degradation stage, which makes it impossible to use the gradient descent method as a whole, is avoided.

[0019] In a feasible implementation, the dynamic damage-value balance equation includes: The objective function, the specific formula is: ; in, It represents the value time decay factor, that is, the future income discount rate, which can be 0.05; Indicates the damage penalty coefficient, which is used to balance the value and loss of the tested books, and can be set to 2; Indicates the total number of years that can be borrowed; Indicates the Year's knowledge value is used to calculate the The knowledge dissemination value generated by book borrowing in a year is calculated as follows: ; in, The value of the book's knowledge can be defined based on its scarcity and content, with a value range of 1-10. For example, a rare copy of a classic book can have a value of 10, while a common, general-purpose book can have a value of 1. Indicates the The number of borrowings per year. A greater number of borrowings often means a wider range of knowledge dissemination; Indicates the The average duration of a single loan in the year, in days. This formula reflects the diminishing marginal value gain of the loan duration (for example, if a book is read frequently in a short period of time, the value gain is significant, that is, from 1 day to 5 days; if a book is read many times in a long period of time, the value gain is limited, that is, from 20 days to 25 days). Indicates the Years of life lost are used to quantify the Years of loss of book life due to borrowing.

[0020] In one possible implementation, when books are only borrowed in the library, The specific formula is: ; in, Indicates the first borrowing basic damage amount, that is, the longer the borrowing time, the greater the damage, and the value can be 0.001; The second basic damage of borrowing is the fixed basic damage of each borrowing, including damage caused by page turning, transportation, light, temperature and humidity changes, etc. The value can be 0.0005; represents the environmental damage coefficient, and the specific formula is: ; in, represents the annual mean temperature; Indicates the annual average relative humidity.

[0021] In a possible implementation, when books are not only borrowed in the library but also checked out of the library, The specific formula is: ; in, It represents the life loss of each scene item. The specific expression is: ; in, Indicates the The number of borrowings in similar scenarios, and meeting ; The scenarios include in-library borrowing, controllable off-library borrowing and uncontrollable off-library borrowing, a total of 3 categories; the controllable off-library borrowing means that readers can ensure that the temperature and relative humidity of off-library borrowing are equivalent to those in the library, can ensure that they do not smoke or eat during the borrowing process, and sign an environmental protection agreement; the uncontrollable off-library borrowing means that it is not possible to ensure that the temperature and relative humidity of off-library borrowing are equivalent to those in the library, cannot ensure that they do not smoke or eat during the borrowing process, and has not signed an environmental protection agreement; It represents the additional loss of transshipment, and the specific expression is: ; in, Indicates the number of borrowing times in off-site controllable borrowing scenarios; Indicates the number of borrowing times in uncontrolled borrowing scenarios outside the library; Indicates the The average duration of a single borrowing in similar scenarios; Indicates the Environmental damage coefficients in similar scenarios, including: In the reading scene in the library: ; In the off-site controlled borrowing scenario: ; In the case of uncontrolled borrowing outside the library: ; in, Indicates inherent damage outside the museum, that is, the high-risk environment benchmark value, which can be 2; Represents the regional climate factor, used to evaluate the average summer humidity of the reader's location in the past five years, and can be obtained by collecting reader information; Indicates the behavioral safety factor, which is used to evaluate the reader's behavior and can be obtained by collecting reader information; The specific expression is: ; in, For the Relative humidity in summer at the reader's location; The specific expression is: ; in, Indicates the extent to which readers smoke while reading; Indicates the extent to which readers eat while reading; in this way, the damage caused by smoke, food, etc. contaminating fingers and then contaminating paper through fingers in uncontrolled borrowing scenarios outside the library can be quantified.

[0022] In a feasible implementation, the dynamic damage-value balance equation further includes a constraint condition: The cumulative life loss limit is expressed as follows: ; in, It represents the safety redundancy factor, which can be 0.8; The specific expression for the single borrowing time limit is: ; in, Indicates the minimum duration of a single borrowing; The maximum duration of a single loan.

[0023] In a feasible implementation, in step 4, the method for optimizing the book borrowing plan is a constrained particle swarm optimization (PSO) method, which specifically includes: Step b1, define the particle position formula, the specific expression is: ; in, Indicates the Number of borrowings per year; Indicates the The average duration of a single loan in 2017; Indicates the the average annual temperature of the year; Indicates the The annual average relative humidity of the year; represents the particle position vector; Define the formula for updating particle velocity. The specific expression is: ; in, Indicates the inertia weight, which can be 0.7; and They are the first acceleration constant and the second acceleration constant, both of which can be 1.5; Indicates the Particles in dimension On, at the moment speed; represent the first random factor and the second random factor respectively; Indicates the Particles in dimension The best historical position; Indicates that the entire population is in dimension The best historical position; Indicates the Particles in dimension Current location; Step b2, initializing the particle swarm; Step b3: Calculate the fitness and determine whether it has converged. If so, output the optimal borrowing plan. If not, proceed to step b4. Step b4: Update and ; Update the particle velocity by updating the particle velocity formula; update the particle position by updating the particle position formula; Step b5: After applying constraint correction, execute step b3; the constraint correction includes: like ,but , thereby achieving a rebound; among them, Indicates the minimum position boundary.

[0024] In the second aspect, based on the same inventive concept, the present application also provides a book borrowing optimization system, including a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive the VOC concentration data of the tested books and the temperature and relative humidity of the tested library; The data processing module includes a pre-processing unit, an odor characterization unit, an aging prediction unit, and a borrowing plan unit; The pre-processing unit is used to pre-process the VOC concentration data, temperature and relative humidity respectively; The odor characteristic unit is used to extract odor characteristics from VOC concentration data, and the odor characteristics include an environmentally corrected degradation index for evaluating the aging rate of paper; The aging prediction unit predicts the remaining life of the paper of the book under test based on the odor characteristics using a trained aging prediction model; the remaining life refers to the number of years from the current state of a single sheet of paper until its folding endurance exceeds a preset failure threshold; the aging prediction model includes a corresponding relationship between the odor characteristics and the folding endurance of a single sheet of paper; The borrowing plan unit optimizes and solves the book borrowing plan based on the remaining lifespan and the dynamic damage-value balance equation; the borrowing plan includes the number of borrowing times per year, the borrowing duration, and the borrowing environment; the borrowing environment includes temperature and relative humidity; The result generation module is used to send out the remaining lifespan and borrowing plan of the tested books.

[0025] On the third aspect, based on the same inventive concept, the present application also provides a book borrowing optimization device, including a processor, a memory and a bus, wherein the memory stores instructions and data read by the processor, and the processor is used to call the instructions and data in the memory to execute the book borrowing optimization method as described above, and the bus connects the functional components for transmitting information.

[0026] In a possible embodiment, the device further includes a gas sensor.

[0027] By adopting the above technical solution, the present invention has the following beneficial effects: The present invention provides a book borrowing optimization method, system, and device. By non-contactly collecting VOC concentration data of the tested books and combining it with temperature and relative humidity, the remaining lifespan of the tested books can be relatively accurately predicted, and an optimal borrowing plan can be planned accordingly, thereby ensuring the knowledge dissemination value of the books while maintaining or extending their remaining lifespan as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 A flow chart of a book borrowing optimization method provided by an embodiment of the present invention; Figure 2 A flow chart of a training method for an aging prediction model provided by an embodiment of the present invention; Figure 3 A flow chart of a method for optimizing a book borrowing solution provided by an embodiment of the present invention; Figure 4 A diagram of a book borrowing optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] 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.

[0032] 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.

[0033] The present invention will be further explained below with reference to specific embodiments.

[0034] It should also be noted that the following specific embodiments or specific implementations are a series of optimized settings listed in the present invention to further explain the specific content of the invention, and these settings can be combined or used in association with each other.

[0035] Example 1: like Figure 1 As shown, this embodiment provides a book borrowing optimization method, including the following steps: Step 1: Collect and pre-process the VOC (volatile organic compound) concentration data of the tested books; collect and pre-process the temperature and relative humidity of the tested library; Step 2: extracting odor characteristics from the VOC concentration data, wherein the odor characteristics include an environmentally corrected degradation index, which is used to evaluate the aging rate of the paper; Step 3: Based on the odor characteristics, the remaining life of the tested book paper is predicted using a trained aging prediction model; the remaining life refers to the number of years from the current state of a single sheet of paper until its folding endurance exceeds a preset failure threshold; the aging prediction model includes a corresponding relationship between the odor characteristics and the folding endurance of a single sheet of paper; Step 4: Based on the remaining lifespan, the dynamic damage-value balance equation is used to optimize the book borrowing plan; the borrowing plan includes the number of borrowing times per year, the borrowing duration, and the borrowing environment; the borrowing environment includes temperature and relative humidity; In this way, through the above method, we can take advantage of the fact that paper produces volatile organic compounds at different rates during the degradation process of oxidation, hydrolysis, acid hydrolysis, etc., and collect key data that can reflect the life of the tested books - VOC concentration data - in a non-contact, zero-damage manner without damaging the books. Through the trained aging prediction model, we can accurately predict the remaining life of the paper of the tested books, and construct a dynamic damage-value balance equation to quantify the amount of damage and value, and then seek to solve the optimal borrowing plan, so that books can be borrowed and their remaining life can be maintained or extended.

[0036] Furthermore, the VOC concentration data in step 1 is collected by a (semiconductor) gas sensor (e.g., with a detection range of 1-500 ppm and a resolution of 0.1 ppm) at a distance of 3-5 cm from the top of the tested book. This minimizes physical and light contact with the tested book, while also avoiding areas of the book that are frequently touched by borrowers, such as the edges and base of the book, which are prone to contamination, thereby improving the reliability of the data source.

[0037] Furthermore, the VOC includes organic acids and aldehydes, etc.; the organic acids include acetic acid and formic acid, etc.; the aldehydes include furfural, benzaldehyde and vanillin, etc.

[0038] Furthermore, the method for preprocessing VOC concentration data is sliding average filtering, and the specific formula includes: ; in, Indicates the VOC concentration value after filtering; Indicates the sliding window size, that is, the total number of data points involved in the average calculation, specifically ; Indicates the window radius (can be 5); Indicates the The original VOC concentration value at each time point; Indicates the current time point, that is, the central moment that needs to be filtered.

[0039] Furthermore, the temperature and relative humidity in step 1 are collected by a temperature and humidity sensor arranged at the center of the storage area of ​​the books to be measured; thus, more accurate and reasonable data can be obtained.

[0040] Furthermore, the specific method for pre-processing the temperature is to take the 24-hour average value. The specific formula includes: ; in, represents the daily average temperature; Indicates the Temperature values ​​for hours.

[0041] Furthermore, the specific method for pre-processing the relative humidity is to take the 24-hour average value. The specific formula includes: ; in, represents the daily average relative humidity; Indicates the Relative humidity value for one hour.

[0042] Furthermore, in step 2, the specific calculation formula of the environmentally corrected degradation index includes: ; in, represents the environmentally corrected degradation index; Indicates the standard ambient temperature, which can be set to 20°C; Indicates standard ambient humidity, which can be 50%RH; It represents the temperature influence coefficient, which can be 0.05 / ℃; Represents the relative humidity influence coefficient, which can be 0.03 / %RH; represents a natural constant; It represents the rate of change of VOC concentration. The specific formula is: ; in, is the sampling interval, which can be set to 1 hour; In this way, the environmentally corrected degradation index can be calculated quickly and concisely.

[0043] Furthermore, the aging prediction model is a two-stage exponential decay model, so as to approximately simulate the characteristics of paper aging, which is accelerated in the early stage (oxidation stage) and stabilized in the later stage (hydrolysis stage). The specific formula includes: ; in, Indicates the current paper folding resistance, in times, which can be obtained from the measurement value of MIT folding resistance meter; Indicates the preset failure threshold, which can be 5 times. If it is lower than this value, the paper is considered to have reached the end of its life. It represents the decay rate parameter of the first stage (oxidation stage), which can be taken as 0.15; It represents the decay rate parameter of the second stage (hydrolysis stage), which can be taken as 0.02; It represents the threshold value of the degradation stage, which can be 0.8; Indicates the remaining life.

[0044] Further, if Figure 2 As shown, the training method of the aging prediction model is: Step a1: Select paper samples of different materials and conduct accelerated aging tests on each. The accelerated aging test includes a temperature aging test, and specific reference can be made to the international standard "Accelerated Dry Heat Aging of Paper at 105±2°C for 72 hours." Based on a time series, VOC concentration values ​​of various paper samples are collected using a gas sensor. The dominant VOC concentration value is determined using the TopK (maximum number selection) method. Folding endurance measurements of various paper samples at corresponding time series are collected using an MIT folding endurance meter. The VOC concentration values ​​and folding endurance measurements are stored in correspondence as training samples. Step a2: Count the VOC concentration values ​​and find the degradation stage boundary threshold that minimizes the error between the first stage and the second stage. ; Step a3: In the first stage data, train , specifically including: Model linearization formula: ; Optimal solution calculation formula: ; in, Indicates the number of training samples in the first stage; Indicates the The true remaining life value of the training samples; Indicates the The folding resistance of the paper of the training samples; Step a4: In the second stage data, train , specifically including: The formula for the second-stage loss function is: ; in, Indicates the number of training samples in the second stage; The second stage gradient descent formula is as follows: ; Step a5: Evaluate the aging prediction model using a conventional cross-validation strategy. Evaluation metrics include mean absolute error (MAE) and accuracy (ACC). In this way, through staged training, the problem of the conventional overall loss function (the mean square error loss of the difference between the predicted value and the true value) being discontinuous (non-differentiable) at the threshold of the degradation stage, which makes it impossible to use the gradient descent method as a whole, is avoided.

[0045] Furthermore, the dynamic damage-value balance equation includes: The objective function, the specific formula is: ; in, It represents the value time decay factor, that is, the future income discount rate, which can be 0.05; Indicates the damage penalty coefficient, which is used to balance the value and loss of the tested books, and can be set to 2; Indicates the total number of years that can be borrowed; Indicates the Year's knowledge value is used to calculate the The knowledge dissemination value generated by book borrowing in a year is calculated as follows: ; in, The value of the book's knowledge can be defined based on its scarcity and content, with a value range of 1-10. For example, a rare copy of a classic book can have a value of 10, while a common, general-purpose book can have a value of 1. Indicates the The number of borrowings per year. A greater number of borrowings often means a wider range of knowledge dissemination; Indicates the The average duration of a single loan in the year, in days. This formula reflects the diminishing marginal value gain of the loan duration (for example, if a book is read frequently in a short period of time, the value gain is significant, that is, from 1 day to 5 days; if a book is read many times in a long period of time, the value gain is limited, that is, from 20 days to 25 days). Indicates the Years of life lost are used to quantify the Years of loss of book life due to borrowing.

[0046] Furthermore, when books are only available for borrowing within the library, The specific formula is: ; in, Indicates the first borrowing basic damage amount, that is, the longer the borrowing time, the greater the damage, and the value can be 0.001; The second basic damage of borrowing is the fixed basic damage of each borrowing, including damage caused by page turning, transportation, light, temperature and humidity changes, etc. The value can be 0.0005; represents the environmental damage coefficient, and the specific formula is: ; in, represents the annual mean temperature; Indicates the annual average relative humidity.

[0047] Furthermore, when books are not only borrowed in the library but also checked out of the library, The specific formula is: ; in, It represents the life loss of each scene item. The specific expression is: ; in, Indicates the The number of borrowings in similar scenarios, and meeting ; The scenarios include in-library borrowing, controllable off-library borrowing and uncontrollable off-library borrowing, a total of 3 categories; the controllable off-library borrowing means that readers can ensure that the temperature and relative humidity of off-library borrowing are equivalent to those in the library, can ensure that they do not smoke or eat during the borrowing process, and sign an environmental protection agreement; the uncontrollable off-library borrowing means that it is not possible to ensure that the temperature and relative humidity of off-library borrowing are equivalent to those in the library, cannot ensure that they do not smoke or eat during the borrowing process, and has not signed an environmental protection agreement; It represents the additional loss of transshipment, and the specific expression is: ; in, Indicates the number of borrowing times in off-site controllable borrowing scenarios; Indicates the number of borrowing times in uncontrolled borrowing scenarios outside the library; Indicates the The average duration of a single borrowing in similar scenarios; Indicates the Environmental damage coefficients in similar scenarios, including: In the reading scene in the library: ; In the off-site controlled borrowing scenario: ; In the case of uncontrolled borrowing outside the library: ; in, Indicates inherent damage outside the museum, that is, the high-risk environment benchmark value, which can be 2; Represents the regional climate factor, used to evaluate the average summer humidity of the reader's location in the past five years, and can be obtained by collecting reader information; Indicates the behavioral safety factor, which is used to evaluate the reader's behavior and can be obtained by collecting reader information; The specific expression is: ; in, For the Relative humidity in summer at the reader's location; The specific expression is: ; in, Indicates the extent to which readers smoke while reading; Indicates the extent to which readers eat while reading; in this way, the damage caused by smoke, food, etc. contaminating fingers and then contaminating paper through fingers in uncontrolled borrowing scenarios outside the library can be quantified.

[0048] Furthermore, the dynamic damage-value balance equation also includes constraints: The cumulative life loss limit is expressed as follows: ; in, It represents the safety redundancy factor, which can be 0.8; The specific expression for the single borrowing time limit is: ; in, Indicates the minimum duration of a single borrowing; The maximum duration of a single loan.

[0049] Further, if Figure 3 As shown, in step 4, the method for finding the optimal solution for the book borrowing plan is a constrained particle swarm optimization (PSO) method, which specifically includes: Step b1, define the particle position formula, the specific expression is: ; in, Indicates the Number of borrowings per year; Indicates the The average duration of a single loan in 2017; Indicates the the average annual temperature of the year; Indicates the The annual average relative humidity of the year; represents the particle position vector; Define the formula for updating particle velocity. The specific expression is: ; in, Indicates the inertia weight, which can be 0.7; and They are the first acceleration constant and the second acceleration constant, both of which can be 1.5; Indicates the Particles in dimension On, at the moment speed; represent the first random factor and the second random factor respectively; Indicates the Particles in dimension The best historical position; Indicates that the entire population is in dimension The best historical position; Indicates the Particles in dimension Current location; Step b2, initializing the particle swarm; Step b3: Calculate the fitness and determine whether it has converged. If so, output the optimal borrowing plan. If not, proceed to step b4. Step b4: Update and ; Update the particle velocity by updating the particle velocity formula; update the particle position by updating the particle position formula; Step b5: After applying constraint correction, execute step b3; the constraint correction includes: like ,but , thereby achieving a rebound; among them, Indicates the minimum position boundary.

[0050] Furthermore, the book borrowing optimization method also includes a book borrowing management method for non-precious books, so as to count the book status and depict the user's credit profile, specifically including: Step S100: Collect user behavior data from multiple dimensions; Step S200: Establish a dynamic credit evaluation system based on user behavior data; Step S300: Connect to an external credit platform and update 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.

[0051] Furthermore, the book borrowing management method collects user renewal, overdue, book return integrity and other behavioral data in multiple dimensions through technologies such as RFID and AI visual algorithms, and combines reading preferences extracted by natural language processing and blockchain evidence storage technology to construct a dynamic credit evaluation system that includes renewal compliance, overdue default rate, and book return integrity. It introduces time decay function and machine learning to realize dynamic adjustment of indicator weights and real-time calculation of credit ratings, and connects to a third-party credit reporting platform to realize 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, overdue grace period, etc., 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".

[0052] Furthermore, the multi-dimensional collection of user behavior data specifically includes: Step S101: Use RFID chip 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 book return integrity data; The book return integrity data includes: outer surface damage, inner page abnormality, and attachment integrity.

[0053] Furthermore, through the Internet of Things technology and AI visual algorithms, three types of core behavioral data can be captured: 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 renewal application time, renewal number and whether the renewal application was submitted within 48 hours before the expiration of the original borrowing period is generated based on the timestamp, accurately distinguishing between compliant renewal and overdue renewal behavior; the cumulative overdue days, the longest single overdue days, and the proportion of overdue books are extracted from the library's existing database to fully reflect the frequency, duration and scope of user defaults. The database adopts a relational database architecture, in which the overdue data fields include user ID, book ISBN, borrowing time, due date, actual return time, and overdue days mark, and dynamically generates cumulative overdue days, the longest single overdue days, and the proportion of overdue books; with the help of AI visual algorithms, the user is 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.

[0054] Furthermore, AI visual algorithms are used to scan the entire process of users returning books, generating return integrity data, including: Step S1031: Deploy a cultural relic-grade LED light source and a ring-shaped high-definition camera array at the library return counter, covering the book cover, back cover, spine, top, bottom, and edge. A high-precision conveyor belt is used to transport books at a constant speed, and infrared sensors are used to trigger capture, generating a high-definition image sequence with a time stamp. Step S1032: Train a damage feature library based on the YOLOv8 target detection model to identify three types of target surface defects. The three types of target surface defects include: Tearing and chipping defects: Detect irregular edge areas and calculate the damaged area. If the damaged area is greater than 1 square centimeter, a point deduction will be triggered; 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, based on the book top, book root and book opening image, whether there is a page number missing is identified through edge detection: if there is a page number missing, trigger the deduction; Step S1034, using the U-Net semantic segmentation model to distinguish between text content and scribble marks, calculate the non-printing area pixel ratio and determine the effective scribble: if the non-printing area pixel ratio is > 3%, it is determined and marked as effective scribble; Step S1035, when identifying and marking as effective scribble area, based on the color channel difference algorithm, the oil and water stains area is analyzed by spectrum, and the new and old pollution is distinguished: when it is identified as new pollution, trigger the deduction; In application, the present embodiment can generate a multi-angle high-definition image sequence containing a time stamp by deploying cultural relic-level LED light sources (3000K high-color rendering LED + ultraviolet / infrared filtering) and ring-shaped high-definition camera arrays (covering book covers, back covers, book spines, book tops, book roots and book openings) at the library return counter, combined with a high-precision conveyor belt and an image acquisition system triggered by an infrared sensor, to realize scanning and detection of the entire process of book return based on AI vision algorithms and Internet of Things technology: first, the YOLOv8 target detection algorithm is used to quickly locate specific targets in the image and identify external damage, including tearing, corner defects, which are detected by edge contour detection to calculate the damage area (> 5mm² triggers a deduction), serious pollution defects are detected by color space conversion (RGB to HSV) to detect the stain area ratio (> 5% triggers a deduction), indentation and crease defects are identified based on image gradient analysis algorithm to identify continuous creases with a length > 5cm and a depth > 2 pixel levels; Simultaneously, the RFID technology (wireless radio frequency identification technology, non-contact automatic identification of accessory labels) is used to detect the integrity of the books and accessories, and the ISBN identification technology (bar code identification technology, fast reading of the unique identification of books) is used to check the return set number integrity rate (< 100% triggers a deduction); In the page missing detection link, edge detection is used to detect page number missing (trigger a deduction); U-Net semantic segmentation algorithm (U-Net semantic segmentation algorithm is a deep learning model for image segmentation, which can accurately distinguish different regional semantic information) is used to calculate the non-printing area pixel ratio (> 3% marked as effective scribble), and the color channel difference algorithm (a technology that analyzes the difference between RGB channel pixel values) is used to distinguish new pollution such as oil and water stains (trigger additional deductions).

[0055] Further, based on user behavior data, a dynamic credit evaluation system is established, which specifically includes: Step S201, based on the data of renewing, overdue and book return integrity, a three-level percentage evaluation system is established; 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 indicator, 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 user's real-time credit score The specific calculation process is: ; in, Indicates the The weight of each indicator; Indicates the Behavioral data value of each indicator; represents the time decay factor; Indicates the total number of indicators; In this way, the scores of each indicator can be weighted and adjusted according to the time when the behavior occurred, and the real-time credit score can be calculated, thereby achieving a focused assessment of the user's recent behavior and a reasonable attenuation of historical behavior.

[0056] Furthermore, connecting to external credit platforms and updating 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.

[0057] Furthermore, based on the dynamic credit evaluation system, a step-by-step authority management strategy is generated, specifically including: Based on the user's real-time credit score, the user's borrowing rating is divided into: A-level users, real-time credit score of users: ; B-level users, real-time credit score of users: ; C-level users, real-time credit score of users: ; D-level 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: For Level A users, the number of books that can be borrowed is 20, the single borrowing period is 30 days, the number of renewals is 3 times, the renewal period is 30 days each time, and the priority for popular book reservations is the priority queue. Level B users can borrow 15 books, with a single borrowing period of 21 days, two renewals, and a 21-day renewal period. Popular book reservation priority is in the normal queue. For Class C users, the number of books that can be borrowed is 10, the single borrowing period is 14 days, the number of renewals is 1, the renewal period is 14 days each time, and the reservation priority for popular books is the normal queue. Level D users can borrow 5 books, with a single borrowing period of 7 days and 0 renewals. They are prohibited from reserving popular books. The overdue integrity management strategy includes: Level A users: Overdue grace period is 7 days; Book return integrity verification mechanism: No verification required; Class B users: Overdue grace period is 3 days; Book return integrity verification mechanism: Automatic verification by the device; Level C users: Overdue grace period is 0 days, and the return integrity verification mechanism is manual verification; For Class D users, the overdue grace period is 0 days, and the book return integrity verification mechanism includes manual verification + automatic verification by the device.

[0058] Example 2: like Figure 4 As shown, this embodiment provides a book borrowing optimization system, including a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive the VOC concentration data of the tested books and the temperature and relative humidity of the tested library; The data processing module includes a pre-processing unit, an odor characterization unit, an aging prediction unit, and a borrowing plan unit; The pre-processing unit is used to pre-process the VOC concentration data, temperature and relative humidity respectively; The odor characteristic unit is used to extract odor characteristics from VOC concentration data, and the odor characteristics include an environmentally corrected degradation index for evaluating the aging rate of paper; The aging prediction unit predicts the remaining life of the paper of the book under test based on the odor characteristics using a trained aging prediction model; the remaining life refers to the number of years from the current state of a single sheet of paper until its folding endurance exceeds a preset failure threshold; the aging prediction model includes a corresponding relationship between the odor characteristics and the folding endurance of a single sheet of paper; The borrowing plan unit optimizes and solves the book borrowing plan based on the remaining lifespan and the dynamic damage-value balance equation; the borrowing plan includes the number of borrowing times per year, the borrowing duration, and the borrowing environment; the borrowing environment includes temperature and relative humidity; The result generation module is used to send out the remaining lifespan and borrowing plan of the tested books.

[0059] Example 3: This embodiment provides a book lending optimization device, including a processor, a memory, and a bus. The memory stores instructions and data read by the processor. The processor is used to call the instructions and data in the memory to execute the book lending optimization method described above. The bus connects the functional components to transmit information.

[0060] Furthermore, the device also includes a gas sensor.

[0061] In another embodiment, this solution can also be implemented in the form of an integrated device, which can include corresponding modules for performing each or several steps in each of the above embodiments. The modules can be one or more hardware modules specifically configured to perform the corresponding steps, or implemented by a processor configured to perform the corresponding steps, or stored in a computer-readable medium for implementation by the processor, or implemented by some combination.

[0062] The processor performs the various methods and processes described above. For example, the method implementation in this solution can be implemented as a software program, which is tangibly contained in a machine-readable medium, such as a memory. In some embodiments, part or all of the software program can be loaded and / or installed via a memory and / or a communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps in the method described above can be performed. Alternatively, in other embodiments, the processor can be configured to perform one of the above methods by any other appropriate means (e.g., by means of firmware).

[0063] The device can be implemented using a bus architecture. The bus architecture can include any number of interconnecting buses and bridges, depending on the specific application and overall design constraints of the hardware. The bus connects various circuits including one or more processors, memories, and / or hardware modules. The bus can also connect various other circuits such as peripherals, voltage regulators, power management circuits, external antennas, etc.

[0064] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0065] 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 optimization method, characterized in that: include: Step 1: Collect VOC concentration data of the tested books and perform preprocessing; collect temperature and relative humidity of the tested library and perform preprocessing; Step 2: extracting odor characteristics from the VOC concentration data, wherein the odor characteristics include an environmentally corrected degradation index; Step 3: Based on the odor characteristics, the remaining life of the tested book paper is predicted using a trained aging prediction model; the remaining life refers to the number of years from the current state of a single sheet of paper until its folding endurance exceeds a preset failure threshold; the aging prediction model includes a corresponding relationship between the odor characteristics and the folding endurance of a single sheet of paper; Step 4: Based on the remaining lifespan, the book borrowing plan is optimized through the dynamic damage-value balance equation; the borrowing plan includes the number of borrowings per year, the borrowing duration, and the borrowing environment; the borrowing environment includes temperature and relative humidity.

2. The method according to claim 1, characterized in that The VOC concentration data in step 1 is collected by a gas sensor at a distance of 3-5 cm from the top of the measured book.

3. The method according to claim 1, characterized in that The method for preprocessing VOC concentration data is sliding average filtering. The specific formula includes: ； in, Indicates the VOC concentration value after filtering; Indicates the sliding window size, specifically ; Indicates the window radius; Indicates the The original VOC concentration value at each time point; Indicates the current time point.

4. The method according to claim 3, characterized in that The specific method for preprocessing temperature is to take the 24-hour average value. The specific formula includes: ； in, represents the daily average temperature; Indicates the Temperature value in hours; The specific method for pre-processing relative humidity is to take the 24-hour average value. The specific formula includes: ； in, represents the daily average relative humidity; Indicates the Relative humidity value for one hour.

5. The method according to claim 4, characterized in that In step 2, the specific calculation formula of the environmental correction degradation index includes: ； in, represents the environmentally corrected degradation index; Indicates standard ambient temperature; Indicates standard ambient humidity; represents the temperature influence coefficient; Represents the relative humidity influence coefficient; represents a natural constant; It represents the rate of change of VOC concentration. The specific formula is: ； in, is the sampling interval.

6. The method according to claim 5, characterized in that The aging prediction model is a two-stage exponential decay model, and the specific formula includes: ； in, Indicates the current paper folding resistance; Indicates the preset failure threshold; represents the decay rate parameter of the first stage; represents the decay rate parameter of the second stage; Indicates the degradation stage demarcation threshold; Indicates the remaining life.

7. The method according to claim 6, characterized in that The dynamic damage-value balance equation includes: The objective function, the specific formula is: ； in, represents the value time decay factor; represents the damage penalty coefficient; Indicates the total number of years that can be borrowed; Indicates the years of life lost; Indicates the Year's knowledge value is used to calculate the The knowledge dissemination value generated by book borrowing in a year is calculated as follows: ； in, Indicates the knowledge value weight of the book; Indicates the Number of borrowings per year; Indicates the The average duration of a single loan in 2017; Constraints: The cumulative life loss limit is expressed as follows: ； in, represents the safety redundancy factor; The specific expression for the single borrowing time limit is: ； in, Indicates the minimum duration of a single borrowing; The maximum duration of a single loan.

8. The method according to claim 7, characterized in that In step 4, the method for optimizing the book borrowing plan specifically includes: Step b1, define the particle position formula, the specific expression is: ； in, Indicates the Number of borrowings per year; Indicates the The average duration of a single loan in 2017; Indicates the the average annual temperature of the year; Indicates the The annual average relative humidity of the year; represents the particle position vector; Define the formula for updating particle velocity. The specific expression is: ； in, represents the inertia weight; and are the first acceleration constant and the second acceleration constant, respectively; Indicates the Particles in dimension On, at the moment speed; represent the first random factor and the second random factor respectively; Indicates the Particles in dimension The best historical position; Indicates that the entire population is in dimension The best historical position; Indicates the Particles in dimension Current location; Step b2, initializing the particle swarm; Step b3: Calculate the fitness and determine whether it has converged. If so, output the optimal borrowing plan. If not, proceed to step b4. Step b4: Update and ; Update the particle velocity by updating the particle velocity formula; update the particle position by updating the particle position formula; Step b5: After applying constraint correction, execute step b3; the constraint correction includes: like ,but ;in, Indicates the minimum position boundary.

9. A book borrowing optimization system, characterized in that: It includes a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive the VOC concentration data of the tested books and the temperature and relative humidity of the tested library; The data processing module includes a pre-processing unit, an odor characterization unit, an aging prediction unit, and a borrowing plan unit; The pre-processing unit is used to pre-process the VOC concentration data, temperature and relative humidity respectively; The odor feature unit is used to extract odor features from VOC concentration data, wherein the odor features include an environmentally corrected degradation index; The aging prediction unit predicts the remaining life of the paper of the book under test based on the odor characteristics using a trained aging prediction model; the remaining life refers to the number of years from the current state of a single sheet of paper until its folding endurance exceeds a preset failure threshold; the aging prediction model includes a corresponding relationship between the odor characteristics and the folding endurance of a single sheet of paper; The borrowing plan unit optimizes and solves the book borrowing plan based on the remaining lifespan and the dynamic damage-value balance equation; the borrowing plan includes the number of borrowing times per year, the borrowing duration, and the borrowing environment; the borrowing environment includes temperature and relative humidity; The result generation module is used to send out the remaining lifespan and borrowing plan of the tested books.

10. A book borrowing optimization device, characterized in that: It includes a processor, a memory and a bus, the memory stores instructions and data read by the processor, the processor is used to call the instructions and data in the memory to execute the method according to any one of claims 1 to 8, and the bus connects the functional components to transmit information.

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