CN117475562B - Intelligent book archiving management method and system - Google Patents
Intelligent book archiving management method and system Download PDFInfo
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- G07G1/0045—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader
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Abstract
The invention discloses an intelligent book archiving management method and system, which relate to the technical field of data processing, and the method comprises the following steps: activating an RFID (radio frequency identification) identifier of a self-service book borrowing and returning machine, and collecting and returning the RFID tag information of the books; when the first book depreciation degree is larger than or equal to a first depreciation degree threshold value, fixing the returned book on the automatic page turning element; collecting non-page-turned book images and page-turned book images; activating a depreciation degree identification channel of the visual detection element to generate a second book depreciation degree; and when the second book depreciation degree is smaller than the second depreciation degree threshold, updating the first book depreciation degree into the second book depreciation degree, and scheduling the book carrying robot to file the books according to the book categories. The invention solves the technical problems that the analysis load of depreciation degree is large, the management cost is high and books cannot be rapidly and accurately archived when the books are restored and archived in the prior art, and achieves the technical effects of improving the archiving management accuracy of the books, reducing the management cost and improving the management efficiency.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent book archiving management method and system.
Background
When books in the library are borrowed for many times, the books can have certain depreciation, and some books have higher depreciation degree and need to be replaced. In conventional book management, an administrator periodically screens the depreciation degree of books, and determines whether to replace the books according to experience or a certain index. However, in the face of libraries with huge borrowing volumes, more book management costs are required. The prior art has the technical problems that the analysis load of the depreciation degree is large when the books are restored and archived, the management cost is high, and the books cannot be rapidly and accurately archived.
Disclosure of Invention
The application provides an intelligent book archiving management method and system, which are used for solving the technical problems that in the prior art, the analysis load of depreciation degree is large, the management cost is high, and books cannot be archived rapidly and accurately when the books are restored and archived.
In view of the above problems, the present application provides an intelligent book archiving management method and system.
In a first aspect of the present application, there is provided an intelligent book archiving management method, the method comprising:
activating an RFID (radio frequency identification) identifier of a self-service book borrowing and returning machine, and collecting returned book RFID tag information, wherein the returned book RFID tag information comprises book categories and first book depreciation;
When the first book depreciation degree is larger than or equal to a first depreciation degree threshold, the book moving robot is scheduled to transport the returned books to a book depreciation degree evaluation component, and the book depreciation degree evaluation component comprises an automatic page turning component and a visual detection component;
fixing the return book to the automatic page turning element;
when the automatic page turning element is in a closed state, an image sensor of the visual detection element is activated to collect images of the book which is not turned;
when the automatic page turning element is in an opening state, the image sensor of the visual detection element is activated to collect page turning book images;
activating a depreciation degree identification channel of the visual detection element, and detecting the non-page-turned book image and the page-turned book image to generate a second book depreciation degree;
and when the second book depreciation degree is smaller than a second depreciation degree threshold, updating the first book depreciation degree of the returned book RFID label information into the second book depreciation degree, and scheduling the book moving robot to file books according to the book category, wherein the second depreciation degree threshold is larger than the first depreciation degree threshold.
In a second aspect of the present application, there is provided an intelligent book archiving management system, the system comprising:
The tag information acquisition module is used for activating an RFID identifier of the self-service book borrowing and returning machine and acquiring RFID tag information of returned books, wherein the RFID tag information of the returned books comprises book categories and first book depreciation;
the book returning and transporting module is used for scheduling the book moving robot to transport the returned books to a book depreciation degree evaluation component when the first book depreciation degree is larger than or equal to a first depreciation degree threshold value, and the book depreciation degree evaluation component comprises an automatic page turning element and a visual detection element;
the return book fixing module is used for fixing the return book to the automatic page turning element;
the book image acquisition module is used for activating the image sensor of the visual detection element to acquire an image of a book which is not turned when the automatic page turning element is in a closed state;
the page-turning book image acquisition module is used for activating the image sensor of the visual detection element to acquire page-turning book images when the automatic page-turning element is in an open state;
the second book depreciation degree generation module is used for activating a depreciation degree identification channel of the visual detection element, detecting the non-page-turned book image and the page-turned book image and generating a second book depreciation degree;
And the book archiving module is used for updating the first book depreciation degree of the returned book RFID label information into the second book depreciation degree when the second book depreciation degree is smaller than a second depreciation degree threshold, and scheduling the book moving robot to conduct book archiving according to the book category, wherein the second depreciation degree threshold is larger than the first depreciation degree threshold.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the method, the RFID identifier of the self-service book borrowing and returning machine is activated, the RFID tag information of the returned books is collected, the RFID tag information of the returned books comprises book categories and first book depreciation degree, then when the first book depreciation degree is larger than or equal to a first depreciation degree threshold value, the returned books are transported to a book depreciation degree evaluation component through a dispatching moving book robot, the book depreciation component comprises an automatic page turning element and a visual detection element, the returned books are fixed to the automatic page turning element, when the automatic page turning element is in a closed state, an image sensor of the visual detection element is activated to collect images of the non-page-turning books, then when the automatic page turning element is in an open state, an image sensor of the visual detection element is activated to collect images of the page-turning books, detection is performed on the images of the non-page-turning books and the page-turning books, a second book depreciation degree is generated, then when the second book depreciation degree is smaller than the second depreciation degree threshold value, the first book depreciation degree of the RFID tag information of the returned books is updated to the second depreciation degree according to the second depreciation degree threshold value, and the moving book depreciation degree is carried out by the dispatching machine, and the moving book depreciation degree is high. The technical effects of effectively reducing the workload of book archiving management and improving the management quality are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an intelligent book archiving management method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of obtaining a page-turning book image in the intelligent book archiving and managing method according to the embodiment of the present application;
fig. 3 is a schematic flow chart of constructing a second book depreciation degree in the method for managing archiving and archiving intelligent books according to the embodiment of the present application;
fig. 4 is a schematic structural diagram of an intelligent book archiving management system according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a label information acquisition module 11, a return book transportation module 12, a return book fixing module 13, a book image acquisition module 14, a page turning book image acquisition module 15, a second book depreciation degree generation module 16 and a book archiving module 17.
Detailed Description
The application provides an intelligent book archiving management method and system, which are used for solving the technical problems that in the prior art, the analysis load of depreciation degree is large, the management cost is high, and books cannot be archived rapidly and accurately when the books are restored and archived.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides an intelligent book archiving management method, which includes:
s100: activating an RFID (radio frequency identification) identifier of a self-service book borrowing and returning machine, and collecting returned book RFID tag information, wherein the returned book RFID tag information comprises book categories and first book depreciation;
s200: when the first book depreciation degree is larger than or equal to a first depreciation degree threshold, the book moving robot is scheduled to transport the returned books to a book depreciation degree evaluation component, and the book depreciation degree evaluation component comprises an automatic page turning component and a visual detection component;
further, step S200 in the embodiment of the present application further includes:
and when the first book depreciation degree is smaller than the first depreciation degree threshold, scheduling the book carrying robot to file books according to the book categories.
In one possible embodiment, when the Chinese angelica book is placed on the book returning table of the self-service book borrowing and returning machine, the RFID identifier of the self-service book borrowing and returning machine is activated, radio waves are emitted to the REID tag of the return book by the RFID, information stored in the tag is obtained, and RFID tag information of the return book is generated. Wherein the return book RFID tag information includes a book category and a first book fold. The books are classified into the categories of returning books, including literary novel, character biography, historical literature, natural science and the like. The first book depreciation is used for describing the degree of book depreciation. And providing basis for subsequent book archiving management by acquiring the RFID tag information of the returned books.
In one embodiment, when the first book depreciation degree is greater than or equal to the first depreciation degree threshold, the depreciation degree of the returned books is higher, and deep evaluation is needed, and the book carrying robot is scheduled to carry the books placed on the book returning table of the self-service book borrowing and returning machine to the book depreciation degree evaluation component. The book depreciation degree evaluation component is used for reliably analyzing the book depreciation degree from the dimension of image acquisition of the book and comprises an automatic page turning component and a visual detection component. The automatic page turning element is used for automatically opening the page of the closed returned book. The visual detection element is used for collecting images of the opened returned book pages, and optionally comprises a digital camera, a scanner and other devices.
In one embodiment, a certain degree of damage is caused to the book during the process of using the book, and the degree of damage caused to the book during the use increases with the length of time the book is used and the degree to which the book has been damaged. The first fold threshold set by a person skilled in the art is used for carrying out primary screening on the fold degree of the returned books, the books in the RFID label information of the returned books with the first book fold degree smaller than the first fold threshold are transmitted to a control system of the book carrying robot, the book carrying robot is scheduled, and the returned books are archived on corresponding bookshelves. The first depreciation threshold is a first book depreciation minimum value set by a person skilled in the art when the returned books can be directly archived. By comparing the first book depreciation degree with the first depreciation degree threshold, the book depreciation degree analysis flow is optimized, books which do not need deep evaluation are directly archived, management quantity is reduced, and management efficiency is improved.
S300: fixing the return book to the automatic page turning element;
in an embodiment of the present application, the automatic page turning element includes a page turning table, a vacuum element, a cam mechanism, a slide bar, and the like. The return book is fixed on the page turning table of the automatic page turning element, the vacuum element is used for sucking the page of the return book, and then the cam mechanism is used for pushing the slide bar to move, so that the automatic page turning action is completed. The aim of providing support for the subsequent image acquisition by using the visual detection element is achieved by fixing the return book on the automatic page turning element.
S400: when the automatic page turning element is in a closed state, an image sensor of the visual detection element is activated to collect images of the book which is not turned;
in one embodiment, when the automatic page turning element is in a closed state, the number of times of returning the book is in a non-page turning state, an image sensor of the visual detection element is activated, and image acquisition is carried out on the data in the non-page turning state to obtain an image of the non-page turning book. Wherein the non-flipped book image reflects an appearance state of the returned book. The technical effect of providing basic analysis data for analyzing the depreciation degree according to the appearance state of the book is achieved by acquiring the non-page-turned book image.
S500: when the automatic page turning element is in an opening state, the image sensor of the visual detection element is activated to collect page turning book images;
further, as shown in fig. 2, when the automatic page turning element is in an on state, the image sensor of the visual detection element is activated to collect the image of the page turning book, and step S500 in this embodiment of the present application further includes:
when the automatic page turning element is in an opening state, starting the automatic page turning element to turn the returned book on a first page, and starting the automatic page turning element to turn the returned book on a second page after the image sensor is activated to collect a front image and a back image of the first page;
activating the image sensor to acquire an N-th front image and an N-th back image until the returned book is turned over, wherein N is the total number of pages of the book, and N is an integer;
and adding the first page front image and the first page back image to the nth page front image and the nth page back image to the page-turning book image.
In one possible embodiment, when the automatic page turning element is in an on state, the automatic page turning element indicates that the returned book is in a page turning state under the operation of the automatic page turning element, and the image sensor of the visual detection element is activated to collect images of the page turned book, so that the image of the page turned book is obtained, and the aim of providing basis for analyzing depreciation degree from the page appearance state inside the book is fulfilled.
In one embodiment, when the automatic page turning element is in an on state, the automatic page turning element is started to turn the returned book open to a first page, and at this time, the image sensor of the visual detection element is used for collecting a first page front image and a first page back image, so as to obtain the first page front image and the first page back image. After the image acquisition is completed, the automatic page turning element is started to turn the returned book open a second page. At the moment, the image sensor of the visual detection element is activated to respectively acquire images of the front side and the back side of the second page, so as to obtain a front image of the second page and a back image of the second page. And activating the image sensor of the visual detection element to respectively acquire images of the front side and the back side of the nth page until the returned book is turned over the nth page, so as to obtain an nth page front image and an nth page back image, wherein N is the total number of pages of the book, and N is an integer. And adding the first page front image and the first page back image to the N page front image and the N page back image into the page turning book image, thereby realizing the aim of collecting the front and back images of each page of the returned book. Wherein the page-turning book image reflects the surface appearance status of all pages of the return book.
S600: activating a depreciation degree identification channel of the visual detection element, and detecting the non-page-turned book image and the page-turned book image to generate a second book depreciation degree;
further, as shown in fig. 3, the depreciation degree identifying channel of the visual detection element is activated, and detection is performed on the non-page-turned book image and the page-turned book image to generate a second book depreciation degree, where step S600 further includes:
the depreciation degree identification channel comprises a non-page-turning book detection network and a page-turning book detection network;
according to the book category, matching a health electronic tag of the returned book, wherein the health electronic tag comprises a non-page-turned book health image and a page-turned book health image;
activating the non-page-turned book detection network, receiving the non-page-turned book image and the non-page-turned book health image, and comparing the non-page-turned book image and the non-page-turned book health image to generate a first old folding factor;
activating the page-turning book detection network, receiving the page-turning book image and the page-turning book health image, and comparing to generate a second old factor;
and constructing the second book depreciation degree according to the first depreciation factor and the second depreciation factor.
Further, according to the first fold factor and the second fold factor, the second book fold is constructed, and step S600 in the embodiment of the present application further includes:
when the first depreciation factor is larger than a first depreciation factor threshold, or/and the second depreciation factor is larger than a second depreciation factor threshold, setting the depreciation degree of the second book to be 1;
when the first old factor is smaller than or equal to the first old factor threshold and the second old factor is smaller than or equal to the second old factor threshold, configuring a first weight for the first old factor and a second weight for the second old factor;
and combining the first weight and the second weight, solving a weighted average of the first depreciation factor and the second depreciation factor, and generating the second book depreciation degree.
In one possible embodiment, the second book depreciation degree is obtained by activating a depreciation degree identification channel of the visual detection element, and analyzing image depreciation degrees of each page of the returned book by taking the non-page-turned book image and the page-turned book image as identification data. Wherein the second book depreciation reflects a degree of depreciation of the page appearance state of the return book. The depreciation degree identification channel is used for intelligently analyzing the depreciation degree of the book from the appearance state of the book which is not turned and the appearance state of the page after the book is turned.
In one embodiment, the depreciation recognition channel includes a non-paged book detection network and a paged book detection network. The non-page-turned book detection network is used for intelligently analyzing the non-page-turned book image, so that a first folding factor is obtained. The page turning book detection network is used for intelligently analyzing page turning book images so as to obtain second old factors. And taking the book category as an index, and retrieving the health electronic tag of the returned book from the returned book database. The health electronic tag is used for describing appearance conditions of the returned books in a health state when the books are not used, and comprises a non-page-turned book health image and a page-turned book health image. Activating the non-page-turned book detection network, and comparing and analyzing the non-page-turned book image and the non-page-turned book health image to obtain a first fold factor. And further, activating the page-turning book detection network, and comparing and analyzing the page-turning book image and the page-turning book health image to obtain a second old factor.
In a possible embodiment, after obtaining the first and second invaliding factors, performing a comprehensive analysis, comparing and analyzing with a first and a second invaliding factor threshold set by a person skilled in the art, respectively, when the first invaliding factor is greater than the first invaliding factor threshold or the second invaliding factor threshold is greater than the second invaliding factor threshold; and setting the second book depreciation degree to 1 when the first depreciation factor is larger than the first depreciation factor threshold and the second depreciation factor threshold is larger than the second depreciation factor threshold. When the first depreciation factor is less than or equal to the first depreciation factor threshold and the second depreciation factor is less than or equal to the second depreciation factor threshold, then a person skilled in the art configures a first weight for the first depreciation factor and a second weight for the second depreciation factor. Preferably, the first weight and the second weight are added to be 1. And combining the first weight and the second weight, carrying out weighted calculation on the first fold factor and the second fold factor, and taking the calculation result as the second book fold degree.
Further, the non-page-turned book detection network is activated, the non-page-turned book image and the non-page-turned book health image are received and compared, and a first fold factor is generated, and step S600 in the embodiment of the present application further includes:
the non-page-turning book detection network comprises a first feature extraction channel and a first feature comparison channel;
activating a first sub-channel of the first feature extraction channel to extract first size feature information of the non-page-turned book image;
activating a second sub-channel of the first feature extraction channel to extract second size feature information of the non-page-turned book health image, wherein the first sub-channel and the second sub-channel are mutually twinned networks;
and activating the first characteristic comparison channel to calculate a first size deviation of the second size characteristic information and the first size characteristic information, and setting the ratio of the first size deviation to a size deviation threshold value as the first depreciation factor.
In one embodiment, the non-paged book detection network includes a first feature extraction channel and a first feature alignment channel. The first feature extraction channel is used for extracting the size features of the returned books in the non-page-turning state and comprises a first sub-channel and a second sub-channel. The first sub-channel is used for extracting the size characteristics of the returned book reflected in the non-page-turned book image in the non-page-turned state, so that first size characteristic information is obtained. And the second sub-channel is used for extracting the size characteristics in the healthy image of the returned book in the non-page-turning state. The dimensional features include book length, book width, book thickness, and the like. Preferably, the first sub-channel after training is obtained by acquiring a plurality of sample non-page-turning book images and a plurality of sample first size characteristic information as a first training data set, and performing supervised training on a frame constructed based on a convolutional neural network by using the first training data set until output reaches convergence. Based on the same construction principle, acquiring a plurality of sample non-page-turned book health images and a plurality of sample second size characteristic information as a second training data set, and performing supervised training on a framework constructed based on a convolutional neural network by using the second training data set until output reaches convergence, so as to acquire the second sub-channel after training is completed. The plurality of sample non-page-turned book images and the plurality of sample non-page-turned book health images are in one-to-one correspondence. And connecting the first sub-channel and the second sub-channel which are subjected to training in parallel, so as to obtain the first feature extraction channel. The first sub-channel and the second sub-channel are mutually twinned, that is, the network topology structures, the types, the weights, the offsets and the like of the neurons of the first sub-channel and the second sub-channel are the same, but the weights and the offsets of all the neurons are not necessarily the same.
Preferably, the first feature comparison channel is used for performing intelligent comparison analysis on the degree of dimensional deviation between the first dimensional feature information and the second dimensional feature information. Optionally, the training data set is equally divided into n parts according to the first size characteristic information of a plurality of samples and the second size characteristic information of a plurality of samples and the first folding factors of a plurality of samples calculated by a person skilled in the art as training data sets, the network layer constructed based on the convolutional neural network is trained in sequence, and the super parameters of the network layer trained by the next training data set are adjusted according to the training output result of the last training data set until the output reaches convergence, so as to obtain the first characteristic comparison channel after training. And connecting the output layers of the first sub-channel and the second sub-channel of the first characteristic comparison channel with the input layer of the first characteristic comparison channel, thereby constructing the non-page-turned book detection network. The method and the device realize the aim of intelligently comparing and analyzing the images of the books which are not turned and the healthy images of the books which are not turned and determining the first folding factors.
In one possible embodiment, the non-page-turned book image and the non-page-turned book health image are respectively input into a first sub-channel and a second sub-channel of the first feature extraction channel to perform feature extraction, the extracted first size feature information and second size feature information are input into the first feature comparison channel, a ratio of a first size deviation to a size deviation threshold is calculated, and the first folding factor is output. The size deviation threshold is a maximum size deviation value which is set by a person skilled in the art and meets the depreciation requirement after the books are used.
Further, the page-turning book detection network is activated, the page-turning book image and the page-turning book health image are received and compared, and a second old factor is generated, and step S600 in this embodiment of the present application further includes:
the page turning book detection network comprises a second feature extraction channel and a second feature comparison channel;
activating a third sub-channel of the second feature extraction channel to extract third size feature information and third text semantic feature information of the page-turning book image;
activating a fourth sub-channel of the second feature extraction channel to extract fourth size feature information and fourth text semantic feature information of the page-turning book health image, wherein the third sub-channel and the fourth sub-channel are mutually a twin network;
activating the second signature alignment channel:
calculating a ratio of a second size deviation of the third size characteristic information and the fourth size characteristic information to a size deviation threshold value, and setting the ratio as a size depreciation factor;
calculating the text semantic deviation of the third text semantic feature information and the fourth text semantic feature information, and setting the ratio of the text semantic deviation to a text semantic deviation threshold value as a semantic depreciation factor, wherein the text semantic deviation is a hamming distance;
When the size depreciation factor of the ith page is larger than a size depreciation factor threshold, or/and the semantic depreciation factor is larger than a semantic depreciation factor threshold, marking the ith page as an abnormal page, wherein N is larger than or equal to i and larger than or equal to 1;
and calculating the ratio of the number of abnormal pages to N, and setting the ratio as the second old-folding factor.
In one possible embodiment, the page-turning book detection network includes a second feature extraction channel and a second feature alignment channel. The second feature extraction channel comprises a third sub-channel and a fourth sub-channel which are respectively used for extracting the size features and the semantic features of the page-turning book image and the page-turning book health image. After the book is turned over, when the semantic features have larger difference from the semantic features of the health image of the turned book, the book pages are indicated to be damaged by dirt, unfilled corners, bending and the like. Based on the same construction principle as the first sub-channel and the second sub-channel, acquiring a plurality of sample page-turning book images, a plurality of sample third dimension characteristic information and a plurality of sample third text semantic characteristic information as training data, and performing supervision training on a framework constructed based on a convolutional neural network until output reaches convergence, so as to acquire the third sub-channel after training is completed. And similarly, acquiring a plurality of sample page-turning book health images, a plurality of sample fourth size characteristic information and a plurality of sample fourth text semantic characteristic information as training data, and performing supervised training on a framework constructed based on a convolutional neural network until output reaches convergence, so as to acquire the fourth sub-channel after training is completed. Wherein the third sub-channel and the fourth sub-channel are twinned with each other. And the second feature comparison channel is constructed by acquiring a plurality of sample third-size feature information and a plurality of sample fourth-size feature information, a plurality of sample third text semantic feature information and a plurality of sample fourth text semantic feature information, and a plurality of sample size depreciation factors and a plurality of sample semantic depreciation factors as training data. And connecting the output layers of the third sub-channel and the fourth sub-channel with the input layer of the second characteristic comparison channel to obtain the page turning book detection network.
Preferably, the size depreciation factor and the semantic depreciation factor are obtained through network identification analysis by respectively inputting the page-turning book image and the page-turning book health image into a third sub-channel and a fourth sub-channel in a second feature extraction channel of the page-turning book detection network. The size depreciation factor is a ratio of the size deviation of the book calculated in the third size characteristic information and the fourth size characteristic information to a ratio of the second size deviation to a size deviation threshold. The semantic depreciation factor is the deviation between the text semantics respectively described by the third text semantic feature information and the fourth text semantic feature information, namely the text semantic deviation, and the ratio of the text semantic deviation to a text semantic deviation threshold is compared. The text semantic deviation is the number of codes adjusted after the page text codes of the page turning book images after feature extraction are replaced by the page text codes of the page turning book images after feature extraction, namely the Hamming distance.
In one possible embodiment, when the size depreciation factor of the ith page is greater than a size depreciation factor threshold or the semantic depreciation factor is greater than a semantic depreciation factor threshold, the ith page is identified as an outlier, and N.gtoreq.i.gtoreq.1 when the size depreciation factor of the ith page is greater than a size depreciation factor threshold and the semantic depreciation factor is greater than a semantic depreciation factor threshold. That is, when any one dimension of the size and the semantics cannot meet the corresponding threshold requirement, the corresponding page is an abnormal page. And further, counting the ratio of the number of the abnormal pages to N, and determining the proportion of the abnormal pages to the total number of pages of the returned book to obtain the second old-fold factor.
S700: and when the second book depreciation degree is smaller than a second depreciation degree threshold, updating the first book depreciation degree of the returned book RFID label information into the second book depreciation degree, and scheduling the book moving robot to file books according to the book category, wherein the second depreciation degree threshold is larger than the first depreciation degree threshold.
In one embodiment, when the second book depreciation is smaller than the second depreciation threshold, the first book depreciation of the returned book RFID tag information is updated to the second book depreciation, that is, the book depreciation is still within an acceptable range, and the first book depreciation needs to be updated according to the more accurate second book depreciation. And after updating, according to the book category, the book carrying robot is scheduled to file the returned books into the corresponding bookshelf. Preferably, the second fold threshold is greater than the first fold threshold. The technical effect of improving management accuracy is achieved on the basis of ensuring the management efficiency of book archiving.
In summary, the embodiments of the present application have at least the following technical effects:
According to the method, the RFID identifier of the self-service book borrowing and returning machine is activated to collect RFID tag information of the returned books, the purpose of collecting basic conditions of returned data is achieved, then when the first book fold is larger than or equal to a first fold threshold, the book moving robot is scheduled to transport the returned books to the book fold evaluation assembly, the book fold evaluation assembly comprises an automatic page turning element and a visual detection element, the purpose of analyzing whether the returned books need further deep fold evaluation is achieved, the returned books are fixed to the automatic page turning element, when the automatic page turning element is in a closed state, an image sensor of the visual detection element is activated to collect images of the non-page-turned books, then when the automatic page turning element is in an open state, the image sensor of the visual detection element is activated to collect images of the non-page-turned books, the purpose of providing data support for book fold analysis is achieved, the non-page-turned images and the images of the books are detected through a fold recognition channel of the activation visual detection element, the intelligent book fold recognition channel is achieved, the accuracy of the non-page-turned images of the books is improved, the purpose of using the books is achieved, the purpose of using the automatic page turning machine is achieved, the accuracy is improved, and when the first fold information is smaller than the first fold threshold, and the second fold information is according to the first fold threshold. The technical effects of effectively reducing the workload of book archiving management and improving the management quality are achieved.
Example two
Based on the same inventive concept as the method for managing the archiving of the smart book in the foregoing embodiments, as shown in fig. 4, the present application provides a system for managing the archiving of the smart book, and the embodiments of the system and method in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the tag information acquisition module 11 is used for activating an RFID identifier of the self-service book borrowing and returning machine and acquiring the RFID tag information of the returned books, wherein the RFID tag information of the returned books comprises the book category and the first book depreciation;
a return book transport module 12 for scheduling a book moving robot to transport a return book to a book depreciation assembly when the first book depreciation is greater than or equal to a first depreciation threshold, the book depreciation assembly comprising an automatic page turning element and a visual detection element;
a return book fixing module 13 for fixing the return book to the automatic page turning element;
a book image acquisition module 14, configured to activate the image sensor of the visual detection element to acquire an image of a book that is not turned when the automatic page turning element is in a closed state;
the page-turning book image acquisition module 15 is used for activating the image sensor of the visual detection element to acquire page-turning book images when the automatic page-turning element is in an open state;
A second book depreciation degree generating module 16, configured to activate a depreciation degree identifying channel of the visual detecting element, perform detection on the non-page-turned book image and the page-turned book image, and generate a second book depreciation degree;
and the book archiving module 17 is configured to update the first book fold of the returned book RFID tag information to the second book fold when the second book fold is smaller than a second fold threshold, and schedule the book moving robot to archive the books according to the book category, where the second fold threshold is greater than the first fold threshold.
Further, the return book transportation module 12 is configured to perform the following steps:
and when the first book depreciation degree is smaller than the first depreciation degree threshold, scheduling the book carrying robot to file books according to the book categories.
Further, the image acquisition module 15 for a page-turning book is configured to execute the following steps:
when the automatic page turning element is in an opening state, starting the automatic page turning element to turn the returned book on a first page, and starting the automatic page turning element to turn the returned book on a second page after the image sensor is activated to collect a front image and a back image of the first page;
Activating the image sensor to acquire an N-th front image and an N-th back image until the returned book is turned over, wherein N is the total number of pages of the book, and N is an integer;
and adding the first page front image and the first page back image to the nth page front image and the nth page back image to the page-turning book image.
Further, the second book depreciation generating module 16 is configured to perform the following steps:
the depreciation degree identification channel comprises a non-page-turning book detection network and a page-turning book detection network;
according to the book category, matching a health electronic tag of the returned book, wherein the health electronic tag comprises a non-page-turned book health image and a page-turned book health image;
activating the non-page-turned book detection network, receiving the non-page-turned book image and the non-page-turned book health image, and comparing the non-page-turned book image and the non-page-turned book health image to generate a first old folding factor;
activating the page-turning book detection network, receiving the page-turning book image and the page-turning book health image, and comparing to generate a second old factor;
and constructing the second book depreciation degree according to the first depreciation factor and the second depreciation factor.
Further, the second book depreciation generating module 16 is configured to perform the following steps:
when the first depreciation factor is larger than a first depreciation factor threshold, or/and the second depreciation factor is larger than a second depreciation factor threshold, setting the depreciation degree of the second book to be 1;
when the first old factor is smaller than or equal to the first old factor threshold and the second old factor is smaller than or equal to the second old factor threshold, configuring a first weight for the first old factor and a second weight for the second old factor;
and combining the first weight and the second weight, solving a weighted average of the first depreciation factor and the second depreciation factor, and generating the second book depreciation degree.
Further, the second book depreciation generating module 16 is configured to perform the following steps:
the non-page-turning book detection network comprises a first feature extraction channel and a first feature comparison channel;
activating a first sub-channel of the first feature extraction channel to extract first size feature information of the non-page-turned book image;
activating a second sub-channel of the first feature extraction channel to extract second size feature information of the non-page-turned book health image, wherein the first sub-channel and the second sub-channel are mutually twinned networks;
And activating the first characteristic comparison channel to calculate a first size deviation of the second size characteristic information and the first size characteristic information, and setting the ratio of the first size deviation to a size deviation threshold value as the first depreciation factor.
Further, the second book depreciation generating module 16 is configured to perform the following steps:
the page turning book detection network comprises a second feature extraction channel and a second feature comparison channel;
activating a third sub-channel of the second feature extraction channel to extract third size feature information and third text semantic feature information of the page-turning book image;
activating a fourth sub-channel of the second feature extraction channel to extract fourth size feature information and fourth text semantic feature information of the page-turning book health image, wherein the third sub-channel and the fourth sub-channel are mutually a twin network;
activating the second signature alignment channel:
calculating a ratio of a second size deviation of the third size characteristic information and the fourth size characteristic information to a size deviation threshold value, and setting the ratio as a size depreciation factor;
calculating the text semantic deviation of the third text semantic feature information and the fourth text semantic feature information, and setting the ratio of the text semantic deviation to a text semantic deviation threshold value as a semantic depreciation factor, wherein the text semantic deviation is a hamming distance;
When the size depreciation factor of the ith page is larger than a size depreciation factor threshold, or/and the semantic depreciation factor is larger than a semantic depreciation factor threshold, marking the ith page as an abnormal page, wherein N is larger than or equal to i and larger than or equal to 1;
and calculating the ratio of the number of abnormal pages to N, and setting the ratio as the second old-folding factor.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (7)
1. An intelligent book archiving management method, characterized by comprising:
activating an RFID (radio frequency identification) identifier of a self-service book borrowing and returning machine, and collecting returned book RFID tag information, wherein the returned book RFID tag information comprises book categories and first book depreciation;
when the first book depreciation degree is larger than or equal to a first depreciation degree threshold, the book moving robot is scheduled to transport the returned books to a book depreciation degree evaluation component, and the book depreciation degree evaluation component comprises an automatic page turning component and a visual detection component;
fixing the return book to the automatic page turning element;
when the automatic page turning element is in a closed state, an image sensor of the visual detection element is activated to collect images of the book which is not turned;
When the automatic page turning element is in an opening state, the image sensor of the visual detection element is activated to collect page turning book images;
activating a depreciation degree identification channel of the visual detection element, and detecting the non-page-turned book image and the page-turned book image to generate a second book depreciation degree;
when the second book depreciation degree is smaller than a second depreciation degree threshold, updating the first book depreciation degree of the returned book RFID tag information into the second book depreciation degree, and scheduling the book moving robot to file books according to the book category, wherein the second depreciation degree threshold is larger than the first depreciation degree threshold; and when the first book depreciation degree is smaller than the first depreciation degree threshold, scheduling the book carrying robot to file books according to the book categories.
2. The method of claim 1, wherein said activating said image sensor of said visual detection element to acquire a page-flip book image when said automatic page-flip element is in an on state comprises:
when the automatic page turning element is in an opening state, starting the automatic page turning element to turn the returned book on a first page, activating the image sensor to collect a first page front image and a first page back image, starting the automatic page turning element to turn the returned book on a second page, and activating the image sensor to collect a second page front image and a second page back image;
Activating the image sensor to acquire an N-th front image and an N-th back image until the returned book is turned over, wherein N is the total number of pages of the book, and N is an integer;
and adding the first page front image and the first page back image to the nth page front image and the nth page back image to the page-turning book image.
3. The method of claim 2, wherein said activating the depreciation level identification channel of the visual detection element to perform detection on the non-flipped book image and the flipped book image to generate a second book depreciation level comprises:
the depreciation degree identification channel comprises a non-page-turning book detection network and a page-turning book detection network;
according to the book category, matching a health electronic tag of the returned book, wherein the health electronic tag comprises a non-page-turned book health image and a page-turned book health image;
activating the non-page-turned book detection network, receiving the non-page-turned book image and the non-page-turned book health image, and comparing the non-page-turned book image and the non-page-turned book health image to generate a first old folding factor;
activating the page-turning book detection network, receiving the page-turning book image and the page-turning book health image, and comparing to generate a second old factor;
And constructing the second book depreciation degree according to the first depreciation factor and the second depreciation factor.
4. The method of claim 3, wherein said constructing said second book depreciation based on said first depreciation factor and said second depreciation factor comprises:
when the first depreciation factor is larger than a first depreciation factor threshold, or/and the second depreciation factor is larger than a second depreciation factor threshold, setting the depreciation degree of the second book to be 1;
when the first old factor is smaller than or equal to the first old factor threshold and the second old factor is smaller than or equal to the second old factor threshold, configuring a first weight for the first old factor and a second weight for the second old factor;
and combining the first weight and the second weight, solving a weighted average of the first depreciation factor and the second depreciation factor, and generating the second book depreciation degree.
5. The method of claim 3, wherein the activating the non-flipped book detection network, receiving the non-flipped book image and the non-flipped book health image for comparison, generating a first fold factor, comprises:
The non-page-turning book detection network comprises a first feature extraction channel and a first feature comparison channel;
activating a first sub-channel of the first feature extraction channel to extract first size feature information of the non-page-turned book image;
activating a second sub-channel of the first feature extraction channel to extract second size feature information of the non-page-turned book health image, wherein the first sub-channel and the second sub-channel are mutually twinned networks;
and activating the first characteristic comparison channel to calculate a first size deviation of the second size characteristic information and the first size characteristic information, calculating the ratio of the first size deviation to a size deviation threshold, and setting the ratio of the first size deviation to the size deviation threshold as the first depreciation factor.
6. The method of claim 3, wherein the activating the page-turning book detection network, receiving the page-turning book image and the page-turning book health image for comparison, generating a second fold factor, comprises:
the page turning book detection network comprises a second feature extraction channel and a second feature comparison channel;
activating a third sub-channel of the second feature extraction channel to extract third size feature information and third text semantic feature information of the page-turning book image;
Activating a fourth sub-channel of the second feature extraction channel to extract fourth size feature information and fourth text semantic feature information of the page-turning book health image, wherein the third sub-channel and the fourth sub-channel are mutually a twin network;
activating the second signature alignment channel:
calculating a second size deviation of the third size characteristic information and the fourth size characteristic information, calculating a ratio of the second size deviation to a size deviation threshold, and setting the ratio of the second size deviation to the size deviation threshold as a size depreciation factor;
calculating text semantic deviation of the third text semantic feature information and the fourth text semantic feature information, calculating the ratio of the text semantic deviation to a text semantic deviation threshold, and setting the ratio of the text semantic deviation to the text semantic deviation threshold as a semantic depreciation factor, wherein the text semantic deviation is a hamming distance;
when the size depreciation factor of the ith page is larger than a size depreciation factor threshold, or/and the semantic depreciation factor is larger than a semantic depreciation factor threshold, marking the ith page as an abnormal page, wherein N is larger than or equal to i and larger than or equal to 1;
and calculating the ratio of the number of abnormal pages to N, and setting the ratio as the second old-folding factor.
7. An intelligent book archiving management system, the system comprising:
the tag information acquisition module is used for activating an RFID identifier of the self-service book borrowing and returning machine and acquiring RFID tag information of returned books, wherein the RFID tag information of the returned books comprises book categories and first book depreciation;
the book returning and transporting module is used for scheduling the book moving robot to transport the returned books to a book depreciation degree evaluation component when the first book depreciation degree is larger than or equal to a first depreciation degree threshold value, and the book depreciation degree evaluation component comprises an automatic page turning element and a visual detection element;
the return book fixing module is used for fixing the return book to the automatic page turning element;
the book image acquisition module is used for activating the image sensor of the visual detection element to acquire an image of a book which is not turned when the automatic page turning element is in a closed state;
the page-turning book image acquisition module is used for activating the image sensor of the visual detection element to acquire page-turning book images when the automatic page-turning element is in an open state;
the second book depreciation degree generation module is used for activating a depreciation degree identification channel of the visual detection element, detecting the non-page-turned book image and the page-turned book image and generating a second book depreciation degree;
The book archiving module is used for updating the first book depreciation degree of the returned book RFID tag information into the second book depreciation degree when the second book depreciation degree is smaller than a second depreciation degree threshold, and scheduling the book moving robot to conduct book archiving according to the book category, wherein the second depreciation degree threshold is larger than the first depreciation degree threshold; and when the first book depreciation degree is smaller than the first depreciation degree threshold, scheduling the book carrying robot to file books according to the book categories.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6202925B1 (en) * | 1997-09-05 | 2001-03-20 | Hitachi, Ltd. | System for management of object carrying |
| RU2224221C2 (en) * | 1999-09-01 | 2004-02-20 | Общество с ограниченной ответственностью Фирма "Дата-Центр" | Procedure establishing authenticity, denomination and degree of decrepitude of banknotes and machine for their sorting and counting |
| JP2005275872A (en) * | 2004-03-25 | 2005-10-06 | Matsushita Electric Ind Co Ltd | Information collection system, information collection method and information collection program |
| CN106596582A (en) * | 2016-11-23 | 2017-04-26 | 重庆向日葵机器人有限公司 | Automatic evaluation robot for book quality and control method thereof |
| CN106596583A (en) * | 2016-11-23 | 2017-04-26 | 重庆向日葵机器人有限公司 | Book quality evaluation robot based on cross mechanical arm and control method |
| CN106779912A (en) * | 2016-11-23 | 2017-05-31 | 陶震宇 | Second-hand book transaction assessment system and method based on mobile Internet |
| FR3067846A1 (en) * | 2017-06-15 | 2018-12-21 | Nedap France | ANTI-THEFT MANAGEMENT SYSTEM AND ASSOCIATED METHOD |
| CN109271839A (en) * | 2018-07-23 | 2019-01-25 | 广东数相智能科技有限公司 | A kind of books defect detection method, system and storage medium |
| CN110286772A (en) * | 2019-06-30 | 2019-09-27 | 上海萃钛智能科技有限公司 | A kind of intelligent bookmark managing device, system and method |
| CN110929821A (en) * | 2019-12-02 | 2020-03-27 | 辽宁科技学院 | Automatic book arrangement device and book arrangement method thereof |
| CN111862442A (en) * | 2020-07-24 | 2020-10-30 | 常州工学院 | Book management device integrating book recovery and donation and management method thereof |
| JP2022184094A (en) * | 2021-05-31 | 2022-12-13 | 株式会社オービック | Fixed asset system, depreciation cost calculation method, and depreciation cost calculation program |
| CN117046748A (en) * | 2023-08-23 | 2023-11-14 | 河南易斯莱盾信息科技有限公司 | Automatic book recycling equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2395826A (en) * | 2002-11-27 | 2004-06-02 | Ds Ltd | Library checking system |
-
2023
- 2023-12-27 CN CN202311815620.6A patent/CN117475562B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6202925B1 (en) * | 1997-09-05 | 2001-03-20 | Hitachi, Ltd. | System for management of object carrying |
| RU2224221C2 (en) * | 1999-09-01 | 2004-02-20 | Общество с ограниченной ответственностью Фирма "Дата-Центр" | Procedure establishing authenticity, denomination and degree of decrepitude of banknotes and machine for their sorting and counting |
| JP2005275872A (en) * | 2004-03-25 | 2005-10-06 | Matsushita Electric Ind Co Ltd | Information collection system, information collection method and information collection program |
| CN106596582A (en) * | 2016-11-23 | 2017-04-26 | 重庆向日葵机器人有限公司 | Automatic evaluation robot for book quality and control method thereof |
| CN106596583A (en) * | 2016-11-23 | 2017-04-26 | 重庆向日葵机器人有限公司 | Book quality evaluation robot based on cross mechanical arm and control method |
| CN106779912A (en) * | 2016-11-23 | 2017-05-31 | 陶震宇 | Second-hand book transaction assessment system and method based on mobile Internet |
| FR3067846A1 (en) * | 2017-06-15 | 2018-12-21 | Nedap France | ANTI-THEFT MANAGEMENT SYSTEM AND ASSOCIATED METHOD |
| CN109271839A (en) * | 2018-07-23 | 2019-01-25 | 广东数相智能科技有限公司 | A kind of books defect detection method, system and storage medium |
| CN110286772A (en) * | 2019-06-30 | 2019-09-27 | 上海萃钛智能科技有限公司 | A kind of intelligent bookmark managing device, system and method |
| CN110929821A (en) * | 2019-12-02 | 2020-03-27 | 辽宁科技学院 | Automatic book arrangement device and book arrangement method thereof |
| CN111862442A (en) * | 2020-07-24 | 2020-10-30 | 常州工学院 | Book management device integrating book recovery and donation and management method thereof |
| JP2022184094A (en) * | 2021-05-31 | 2022-12-13 | 株式会社オービック | Fixed asset system, depreciation cost calculation method, and depreciation cost calculation program |
| CN117046748A (en) * | 2023-08-23 | 2023-11-14 | 河南易斯莱盾信息科技有限公司 | Automatic book recycling equipment |
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|---|---|
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