Book borrowing data acquisition and processing method and its device, downloader, and equipment

Abstract

The present application discloses a method for acquiring and processing book borrowing data and its apparatus, downloader, and device. The method for acquiring book borrowing data includes: downloading and acquiring book borrowing business data from one or more book borrowing systems; classifying and combining each book borrowing business data into book borrowing feature mapping data based on a preset book borrowing feature mapping classification rule, and classifying and storing the book borrowing feature mapping data in a book borrowing feature management library; performing data preprocessing on the book borrowing business data of each book borrowing feature mapping data in the book borrowing feature management library; opening a data access interface of the book borrowing feature management library to a target book borrowing system, and the target book borrowing service obtains the book borrowing feature mapping data from the book borrowing feature management library through the data access interface. The present application can centrally manage the book borrowing business data of different book borrowing systems for use to optimize book borrowing services and enhance users' book borrowing experience.

Description

Technical Field

[0001] The present application relates to the field of data collection and processing, and in particular to a method for acquiring book borrowing data and a method for processing book borrowing data, and also to corresponding devices, downloaders and equipment for the method. Background Art

[0002] Existing libraries have their own book lending systems so that they can provide book lending services to book borrowers through the book lending systems and facilitate the library to manage the book borrowing records in the library. However, as the user needs of borrowers increase, some libraries will build book lending services in addition to traditional book lending services in their book lending systems, such as book lending services such as borrowing book recommendations or popular borrowing book activities, to improve the book borrowing experience of borrowers. However, for borrowers or libraries with a small number of books in their collections, there is only a small amount of book lending business data in their book lending systems. Without a sufficient amount of book lending business data for reference, it is impossible to build or improve book lending services such as borrowing data recommendations. As a result, the book lending systems of such libraries are unable to provide additional book lending services to borrowers except for electronic management of book borrowing records, and hinder the future development of the library.

[0003] In view of the problem caused by the lack of book borrowing business data in the existing book borrowing system, the applicant has made corresponding explorations in order to solve this problem. Summary of the Invention

[0004] One of the purposes of this application is to provide a method for obtaining book borrowing data, which includes:

[0005] Downloading and acquiring book lending service data from one or more book lending systems, wherein the book lending service data includes book lending data or book lending user data;

[0006] Based on the preset book borrowing feature mapping classification rules, classify and combine the book borrowing business data into multiple types of book borrowing feature mapping data, and classify and store the book borrowing feature mapping data into a book borrowing feature management library;

[0007] Performing data preprocessing on the book borrowing business data in each of the book borrowing feature mapping data in the book borrowing feature management library, wherein the data preprocessing includes data completion processing, data error correction processing, data standardization processing or data conversion processing;

[0008] The data access interface of the book borrowing feature management library is opened to the target book borrowing system. The target book borrowing service obtains the book borrowing feature mapping data from the book borrowing feature management library through the data access interface to construct the corresponding book borrowing service.

[0009] In a further embodiment, the step of downloading and obtaining book lending service data from one or more book lending systems, wherein the book lending service data includes book lending data or book lending user data, includes:

[0010] Accessing a book lending service database of a book lending system to obtain book lending service data stored in the book lending service database;

[0011] When access to the book lending service database fails, entering the book lending service page of the book lending system, traversing multiple page element objects that construct the book lending service page, and obtaining page element data of each of the page element objects;

[0012] According to the preset book borrowing business data extraction rules, the target page element data corresponding to the target extraction data type preset in the book borrowing business data extraction rules are extracted from each of the page element data, and each of the target page element data is used as the book borrowing business data.

[0013] In a further embodiment, based on a preset book borrowing feature mapping classification rule, the steps of classifying and combining each of the book borrowing business data into multiple categories of book borrowing feature mapping data, and classifying and storing each of the book borrowing feature mapping data in a book borrowing feature management library include:

[0014] Obtaining book borrowing business data downloaded from one or more book borrowing systems, and obtaining different book borrowing feature mapping templates in a preset book borrowing feature mapping classification rule;

[0015] According to the multiple target feature data types preset in each of the book borrowing feature mapping templates, the target book borrowing business data corresponding to each of the target feature data types of each of the book borrowing feature mapping templates is matched from the latest acquired book borrowing business data;

[0016] Encapsulate the target book borrowing business data belonging to the same book borrowing feature mapping template, generate book borrowing feature mapping data corresponding to each book borrowing feature mapping template, and store each book borrowing feature mapping data in the storage location corresponding to the book borrowing feature mapping template in the book borrowing feature management library, wherein the book borrowing feature mapping data uses book name data, book standard number data or borrowing user basic feature data as key data.

[0017] In a further embodiment, data preprocessing is performed on the book borrowing business data in each of the book borrowing feature mapping data in the book borrowing feature management library, and the data preprocessing includes the steps of data completion processing, data error correction processing, data standardization processing or data conversion processing, including:

[0018] Traversing the book borrowing feature mapping data stored in the book borrowing feature management library, detecting incomplete book borrowing feature mapping data in each of the book borrowing feature mapping data, and obtaining the book borrowing business data missing from the incomplete book borrowing feature mapping data for data completion;

[0019] Traversing the book borrowing feature mapping data stored in the book borrowing feature management library, detecting the erroneous book borrowing feature mapping data with data errors in each of the book borrowing feature mapping data, and performing data error correction according to the data correction rule corresponding to the data type of the erroneous book borrowing business data in the erroneous book borrowing feature mapping data;

[0020] According to the standard data format rules acting on the book borrowing feature management library, the data format of the book borrowing business data corresponding to the book borrowing feature mapping data stored in the book borrowing feature management library is modified to perform data standardization;

[0021] Obtain one or more data conversion rules, query the target book borrowing feature mapping data corresponding to each data conversion rule in the book borrowing feature management library, determine the book borrowing business data to be converted corresponding to the data conversion rule in each target book borrowing feature mapping data, and perform data conversion on the book borrowing business data to be converted according to the data conversion rule.

[0022] A book borrowing data acquisition device is proposed to meet one of the purposes of this application, and includes:

[0023] A borrowing data downloading and obtaining module is used to download and obtain book borrowing business data from one or more book borrowing systems, wherein the book borrowing business data includes book borrowing data or book borrowing user data;

[0024] A borrowing data classification storage module is used to classify and combine the book borrowing business data into multiple types of book borrowing feature mapping data based on a preset book borrowing feature mapping classification rule, and classify and store the book borrowing feature mapping data into a book borrowing feature management library;

[0025] A borrowing data pre-processing module is used to perform data pre-processing on the book borrowing business data in each of the book borrowing feature mapping data in the book borrowing feature management library, wherein the data pre-processing includes data completion processing, data error correction processing, data standardization processing or data conversion processing;

[0026] The borrowing data open access module is used to open the data access interface of the book borrowing feature management library to the target book borrowing system. The target book borrowing service obtains the book borrowing feature mapping data from the book borrowing feature management library through the data access interface to construct the corresponding book borrowing service.

[0027] In a further embodiment, the borrowing data downloading and obtaining module includes:

[0028] The business database access submodule is used to access the book lending business database of the book lending system and obtain the book lending business data stored in the book lending business database;

[0029] A page element data acquisition submodule is used to, when access to the book lending service database fails, enter the book lending service page of the book lending system, traverse multiple page element objects that construct the book lending service page, and obtain page element data of each of the page element objects;

[0030] The book borrowing business data generation submodule is used to extract the target page element data corresponding to the target extraction data type preset in the book borrowing business data extraction rule from each page element data according to the preset book borrowing business data extraction rule, and use each target page element data as the book borrowing business data.

[0031] In a further embodiment, the borrowing data classification storage module includes:

[0032] The feature mapping template acquisition submodule is used to obtain book borrowing business data downloaded from one or more book borrowing systems, and obtain different book borrowing feature mapping templates in the preset book borrowing feature mapping classification rules;

[0033] A target business data acquisition submodule is used to match the target book lending business data corresponding to each target feature data type of each book lending feature mapping template from the latest acquired book lending business data according to the multiple target feature data types preset in each book lending feature mapping template;

[0034] The feature mapping data storage submodule is used to encapsulate the target book borrowing business data belonging to the same book borrowing feature mapping template, generate book borrowing feature mapping data corresponding to each book borrowing feature mapping template, and store each book borrowing feature mapping data in the storage location corresponding to the book borrowing feature mapping template in the book borrowing feature management library, wherein the book borrowing feature mapping data uses book name data, book standard number data or borrowing user basic feature data as key data.

[0035] In a further embodiment, the borrowing data pre-processing module includes:

[0036] A business data completion submodule is used to traverse the book borrowing feature mapping data stored in the book borrowing feature management library, detect incomplete book borrowing feature mapping data in each of the book borrowing feature mapping data, obtain the missing book borrowing business data in the incomplete book borrowing feature mapping data, and complete the data;

[0037] A business data error correction submodule is used to traverse the book borrowing feature mapping data stored in the book borrowing feature management library, detect the erroneous book borrowing feature mapping data with data errors in each of the book borrowing feature mapping data, and perform data error correction according to the data correction rule corresponding to the data type of the erroneous book borrowing business data in the erroneous book borrowing feature mapping data;

[0038] A business data standardization submodule is used to correct the data format of the book borrowing business data corresponding to the book borrowing feature mapping data stored in the book borrowing feature management library according to the standard data format rules acting on the book borrowing feature management library to perform data standardization;

[0039] The business data conversion submodule is used to obtain one or more data conversion rules, query the target book borrowing feature mapping data corresponding to each data conversion rule in the book borrowing feature management library, determine the book borrowing business data to be converted corresponding to the data conversion rule in each target book borrowing feature mapping data, and perform data conversion on the book borrowing business data to be converted according to the data conversion rule.

[0040] A book borrowing data downloader is proposed to meet the purpose of the book borrowing data acquisition method of one of the purposes of this application, which includes a central processing unit and a memory, wherein the central processing unit is used to call and run a computer program stored in the memory to execute the steps of the book borrowing data acquisition method.

[0041] One of the purposes of this application is to provide a method for processing book borrowing data, which includes:

[0042] Acquire book borrowing feature mapping data from a book borrowing feature management library through a data access interface, wherein the book borrowing feature mapping data includes book borrowing feature mapping data and book borrowing user feature mapping data;

[0043] Using each of the book borrowing feature mapping data as a training sample to train a book borrowing rate prediction model, and using the book borrowing rate prediction model trained to convergence to predict future book borrowing rates of multiple book objects in multiple book categories in the current book borrowing system;

[0044] Determining historical book borrowing rates of a plurality of book objects in a plurality of book categories in a current book borrowing system according to the book borrowing feature mapping data;

[0045] Determining a borrowing user profile of each borrowing user based on borrowing user feature information of multiple borrowing users in the current book borrowing system and the book borrowing feature mapping data;

[0046] According to the borrowing user portrait of each borrowing user, combined with the future borrowing rate of each book and the historical borrowing rate of each book, the recommended borrowing book set information of each borrowing user is generated.

[0047] In a further embodiment, the step of calling the book borrowing rate prediction model trained to convergence and predicting the future borrowing rates of multiple book objects in multiple book categories based on the book borrowing feature mapping data includes:

[0048] Obtaining a plurality of book borrowing feature mapping data and a plurality of book borrowing user feature mapping data, and calculating the book borrowing rate of the book object corresponding to each of the book borrowing feature mapping data according to the borrowing user's historical borrowing record in each of the book borrowing user feature mapping data;

[0049] Using the book borrowing feature mapping data of each of the book objects and its book borrowing rate as training samples, and using each of the borrowing rate training samples to train a book borrowing rate prediction model until convergence;

[0050] The book feature information of multiple book objects in multiple book categories in the current book lending system is input into the book lending rate prediction model, and the future book lending rate of each book object output by the book lending rate prediction model is obtained.

[0051] In a further embodiment, the step of generating the recommended borrowing book set information for each borrowing user based on the borrowing user portrait of each borrowing user, combined with the future borrowing rate of each book and the borrowing rate of each book, includes:

[0052] Based on the future borrowing rates and historical borrowing rates of a plurality of book objects in a plurality of book categories in the current book borrowing system, and using preset borrowing rate weight information, determining a borrowing rate ranking of each of the book objects;

[0053] According to the borrowing user portrait of the borrowing user, determining a plurality of pre-borrowed book objects corresponding to the borrowing user from each of the book objects;

[0054] The target book objects with higher borrowing rate rankings are screened out from the pre-borrowed book objects, the book borrowing feature mapping data and book feature information corresponding to each target book object are encapsulated, and recommended borrowing book set information for the borrowing user is generated.

[0055] A book lending data processing device is proposed to process book lending data in accordance with one of the purposes of this application, and includes:

[0056] A feature management library access module is used to obtain book borrowing feature mapping data from the book borrowing feature management library through a data access interface, wherein the book borrowing feature mapping data includes book borrowing feature mapping data and book borrowing user feature mapping data;

[0057] a future borrowing rate prediction module, configured to train a book borrowing rate prediction model using each of the book borrowing feature mapping data as a training sample, and to use the book borrowing rate prediction model trained to convergence to predict future borrowing rates of a plurality of book objects in a plurality of book categories in a current book borrowing system;

[0058] A historical borrowing rate determination module is used to determine the historical borrowing rates of multiple book objects in multiple book categories in the current book borrowing system based on the book borrowing feature mapping data;

[0059] A borrowing user portrait determination module determines a borrowing user portrait of each borrowing user based on borrowing user feature information of multiple borrowing users in the current book borrowing system and the book borrowing feature mapping data;

[0060] The recommended book information generation module is used to generate the recommended borrowing book set information for each borrowing user based on the borrowing user portrait of each borrowing user, combined with the future borrowing rate of each book and the historical borrowing rate of each book.

[0061] In a further embodiment, the future borrowing rate prediction module includes:

[0062] The book borrowing rate statistics submodule is used to obtain a plurality of book borrowing feature mapping data and a plurality of book borrowing user feature mapping data, and calculate the book borrowing rate of the book object corresponding to each of the book borrowing feature mapping data according to the borrowing user's historical borrowing records in each of the book borrowing user feature mapping data;

[0063] A prediction model training submodule is configured to use the book borrowing feature mapping data of each of the book objects and its book borrowing rate as training samples, and to train the book borrowing rate prediction model to a convergence state using the borrowing rate training samples;

[0064] The book borrowing rate prediction submodule is used to input the book feature information of multiple book objects in multiple book categories in the current book borrowing system into the book borrowing rate prediction model, and obtain the future book borrowing rate of each book object output by the book borrowing rate prediction model.

[0065] In a further embodiment, the recommended book information generating module includes:

[0066] A book borrowing ranking determination submodule is configured to determine a borrowing rate ranking of each of a plurality of book objects in a plurality of book categories in the current book borrowing system based on the future borrowing rates and the historical borrowing rates of each of the plurality of book objects and using preset borrowing rate weight information;

[0067] A pre-borrowed book object determination submodule is used to determine a plurality of pre-borrowed book objects corresponding to the borrowing user from the book objects according to the borrowing user portrait of the borrowing user;

[0068] The recommended borrowing book determination submodule is used to filter out the target book objects with higher borrowing rate rankings from the pre-borrowed book objects, encapsulate the book borrowing feature mapping data and book feature information corresponding to each target book object, and generate recommended borrowing book set information for the borrowing user.

[0069] In order to solve the above technical problems, an embodiment of the present application also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the above-mentioned book borrowing data acquisition method or the book borrowing data processing method.

[0070] In order to solve the above technical problems, an embodiment of the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the above-mentioned book borrowing data acquisition method or the above-mentioned book borrowing data processing method.

[0071] In order to solve the above technical problems, an embodiment of the present application also provides a computer program product, including a computer program and computer instructions. When the computer program and computer instructions are executed by a processor, the processor executes the steps of the above-mentioned book borrowing data acquisition method or the above-mentioned book borrowing data processing method.

[0072] Compared with the prior art, the advantages of this application are as follows:

[0073] This application collects book borrowing business data from different book borrowing systems, classifies and stores a large amount of collected book borrowing business data by features, manages the collected book borrowing business data, and builds a book borrowing data collection and management system. It also provides each book borrowing system with a data access interface for the book borrowing business data collected and managed from the book borrowing data collection and management system, thereby forming a book borrowing business data sharing network between different book borrowing systems. For a small book borrowing system with a very small number of registered borrowing users or a small number of borrowable books, it can obtain the book borrowing feature mapping data classified and stored by the book borrowing business data in other book borrowing systems from the book borrowing data collection and management system to form a sufficient book borrowing data set, so that the small book borrowing system can use the sufficient book borrowing feature mapping data to build or optimize the corresponding book borrowing service. For example, by obtaining sufficient book borrowing feature mapping data, a book recommendation borrowing service for recommending borrowed books to borrowing users, a popular borrowing book service for generating a popular borrowing book form, or the administrator of the target book borrowing system can use sufficient book borrowing feature data to analyze the recently popular borrowed books to enrich the library's collection of books and provide other books for borrowing users to borrow; it can be seen that this application constructs a book borrowing data collection and management system. For borrowing users or library books with a small number of books in the library, its book borrowing system can obtain sufficient book borrowing business data through the book borrowing business data sharing network, and construct or improve additional book borrowing services such as borrowing data recommendation for borrowing users in addition to recording book borrowing information, thereby improving the borrowing user's book borrowing experience and contributing to the future development of such libraries. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0075] Figure 1 A schematic diagram of a typical network deployment architecture for implementing the technical solution of this application;

[0076] Figure 2 This is a flow chart of a typical embodiment of the method for obtaining book borrowing data of the present application;

[0077] Figure 3 This is a flowchart of a specific implementation method for downloading and obtaining book lending service data from a book lending system in this application;

[0078] Figure 4 This is a flowchart of a specific implementation method of classifying and combining book borrowing business data into book borrowing feature mapping data, and classifying and storing the book borrowing feature mapping data in a book borrowing feature management library in this application;

[0079] Figure 5 This is a flowchart of a specific implementation method for preprocessing book borrowing feature mapping data in a book borrowing feature management library in this application;

[0080] Figure 6 This is a functional block diagram of a typical embodiment of the book borrowing data acquisition device of the present application;

[0081] Figure 7 A flowchart of a typical embodiment of the book borrowing data processing method of the present application;

[0082] Figure 8 A flowchart of a specific implementation method of using book borrowing feature mapping data to train a book borrowing rate prediction model, and using the book borrowing rate prediction model trained to convergence to predict future book borrowing rates in this application;

[0083] Figure 9 This is a flowchart of a specific implementation method for generating recommended borrowing book set information for borrowing users in this application;

[0084] Figure 10 This is a functional block diagram of a typical embodiment of the book borrowing data processing device of the present application;

[0085] Figure 11 This is a basic structural block diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0086] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0087] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0088] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0089] It will be understood by those skilled in the art that the terms "client," "terminal," and "terminal device" as used herein include both devices that are wireless signal receivers, i.e., devices that only have wireless signal receivers without transmission capabilities, and devices that have receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such devices may include: cellular or other communication devices such as personal computers and tablet computers, which have single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service), which may combine voice, data processing, fax, and / or data communication capabilities; PDA (Personal Digital Assistant), which may include a radio frequency receiver, a pager, Internet / Intranet access, a web browser, a notepad, a calendar, and / or a GPS (Global Positioning System) receiver; and conventional laptop and / or palmtop computers or other devices, which have and / or include a radio frequency receiver. As used herein, the terms "client," "terminal," or "terminal device" may be portable, transportable, or installed in a vehicle (air, sea, and / or land), or may be adapted and / or configured to operate locally, and / or in a distributed manner, at any other location on Earth and / or in space. As used herein, the terms "client," "terminal," or "terminal device" may also refer to a communication terminal, an Internet terminal, or a music / video playback terminal, such as a PDA, an MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or may include a smart TV, a set-top box, or other device.

[0090] The hardware referred to by names such as "server", "client", and "work node" in this application is essentially an electronic device with capabilities equivalent to those of a personal computer. It is a hardware device that has the necessary components revealed by the von Neumann principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. Computer programs are stored in its memory, and the central processing unit calls the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0091] It should be noted that the concept of "server" referred to in this application can also be extended to server clusters. Based on the network deployment principles understood by those skilled in the art, the servers described should be logically divided. In physical space, these servers can be independent of each other but callable through interfaces, or integrated into a single physical computer or a set of computers. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method of this application.

[0092] See also Figure 1 The hardware foundation required for the implementation of the relevant technical solutions of this application can be deployed according to the architecture shown in the figure. The book borrowing data downloader described in this application is as follows: Figure 1 The book borrowing data downloader 101 shown, the book borrowing data downloader 101 downloads and obtains book borrowing business data from the book borrowing system device 102 shown and other book borrowing system devices shown, wherein the book borrowing system device 102 shown and other book borrowing system devices shown serve different book borrowing systems respectively, and the book borrowing data downloader 101 downloads the book borrowing business data by accessing the book borrowing business database of the book borrowing system device 101 and other book borrowing system devices shown, or downloads and obtains the book borrowing business data from the book borrowing service page that the book borrowing system device 101 and other book borrowing system devices shown are responsible for for their book borrowing systems, and after the book borrowing data downloader 101 and other book borrowing data downloaders shown download and obtain the corresponding book borrowing business data, the book borrowing business data is pushed to the book borrowing data constructed by the server or server cluster. In the collection and management system 103, the book borrowing business data downloaded by each shown book borrowing data downloader is classified into different book borrowing feature mapping data categories and stored in the book borrowing feature management library of the shown book borrowing data collection and management system 103, and the shown book borrowing data collection and management system 103 will perform data pre-processing such as data completion processing, data error correction processing, data standardization processing or data conversion processing on the book borrowing feature mapping data in the book borrowing feature management library, and then open a data access interface for accessing the book borrowing feature management library to the book borrowing system device 104 where the target book borrowing system is located, so that the shown target book borrowing system device 104 can obtain the corresponding book borrowing feature mapping data from the book borrowing feature management library of the shown book borrowing data collection and management system 103 through the data access interface, build or optimize the corresponding book borrowing service, and provide the corresponding book borrowing service for the borrowing user at the shown terminal device 105.

[0093] For the server, the book borrowing data acquisition method and the book-based data processing method described in this application will usually be constructed as an application, and the corresponding program interface will be opened for the book borrowing data downloader and the book borrowing data collection and management system to call and run the application on the server to implement the corresponding service process. The relevant technical solutions in this application that are suitable for running on the server can be implemented in the server in this way.

[0094] The application mentioned above refers to an application running on a server or terminal device. This application implements the relevant technical solutions of the present application in a programming manner. Its program code can be stored in a non-volatile storage medium that can be recognized by the computer in the form of computer-executable instructions, and can be loaded into the memory by the central processing unit for execution. The relevant device of the present application is constructed by the operation of this application on the computer.

[0095] Those skilled in the art should be aware that although the various methods of this application are described based on the same concept and thus exhibit commonality, unless otherwise specified, these methods can be independently executed. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept. Therefore, concepts with the same expression, as well as concepts that are appropriately transformed for convenience despite different expression, should be understood as equivalent.

[0096] See also Figure 2 In a typical embodiment of the present invention, a method for obtaining book borrowing data includes the following steps:

[0097] Step S11: Download and obtain book lending service data from one or more book lending systems. The book lending service data includes book lending data or book lending user data.

[0098] The book lending system refers to the system used by the library to provide electronic book lending services to borrowers. The library enters the books in its library into the book lending system so that borrowers can browse the books in the library in the book lending system for borrowing books. For example, after the borrower logs in to the book lending system, he can browse the library's books on the book lending service page of the book lending system and select the books he wants to borrow for borrowing.

[0099] The book borrowing system generally includes book borrowing data corresponding to books borrowed and entered, as well as book borrowing user data of borrowers registered in the system for book borrowing. The book borrowing data generally includes the book standard number data (ISBN data) of the book, the book name data, the book sub-title data, the book author name data, other responsible person data of the book, the book series title data, the book classification number data, the book publisher data, the book publication time data, the book type number data, the book distinction number data, the book call number data, the book physical carrier data (such as book binding, book page number, book size or book publication times, etc.), the book language data, the book subject word data, the book description information, the book additional information data or the book borrowing record data and other data types. Of course, those skilled in the art can flexibly design the data types included in the book borrowing data, which will not be elaborated here.

[0100] Correspondingly, the book borrowing user data includes the borrowing user's basic characteristic data (such as the borrowing user's name, the borrowing user's gender, the borrowing user's user number, the borrowing user's student number, the borrowing user's date of birth or the borrowing user's age, etc.), the borrowing user's educational background data (such as the borrowing user's academic qualifications, the borrowing user's grade, the borrowing user's class, the borrowing user's major or the borrowing user's club, etc.), the borrowing user's reading preference data, the borrowing user's reading habit data (such as the average reading time in a unit time period, the frequent reading place or the frequent reading time period, etc.), the borrowing user's contact information data, the borrowing user's interest data, the borrowing user's reading language data or the borrowing user's book borrowing record data and other data types. Of course, those skilled in the art can flexibly design the data types included in the book borrowing user data, which will not be elaborated here.

[0101] In the book lending system, the book lending books and book lending user books in the system are generally used as book lending business data, and the book lending business books are stored in the book lending business database for continuous storage, so as to obtain the book lending business books from the book lending business database to build a book lending service webpage for borrowing users to browse the books in the library for borrowing, and record the borrowing records of each book in the library and the book borrowing records of the borrowing users, thereby building a complete electronic book lending function for borrowing users.

[0102] When downloading and obtaining corresponding book lending business data from one or more book lending systems, generally, the book lending business database of each book lending system is accessed to obtain the book lending business data in each book lending system. If the book lending system does not open its book lending business database, the corresponding page element data will be downloaded from the book lending service page of the book lending system through the book lending business data downloading method built based on Scrapy, so as to extract the target page elements of the data type included in the book lending business data from the downloaded page element data, and use these target page data as the book lending business data. Specifically, access the book lending business database. The system obtains the book lending business database of the book lending business data stored in the book lending business database. When access to the book lending business database fails, the book lending business data downloading method will be used to enter the book lending service page of the book lending system, traverse the multiple page element objects that construct the book lending service page, obtain the page element data of each of the page element objects, and then according to the preset book lending business data extraction rule, extract the target page element data corresponding to the target extraction data type preset in the book lending business data extraction rule from each of the page element data, and use each of the target page element data as the book lending business data.

[0103] Regarding the implementation device for downloading and obtaining the corresponding book borrowing business data from each book borrowing system, generally, the device downloads and obtains the book borrowing business data from each book borrowing system through the book borrowing data downloader, such as Figure 1 The book borrowing data downloader 101 shown downloads book borrowing service data from one or more book borrowing systems by deploying one or more of the book borrowing data downloaders.

[0104] Step S12: Based on the preset book borrowing feature mapping classification rules, classify and combine the book borrowing business data into multiple types of book borrowing feature mapping data, and classify and store the book borrowing feature mapping data into a book borrowing feature management library.

[0105] After obtaining the book borrowing business data in one or more book borrowing systems, each of the image borrowing business data will be classified into various types of book borrowing feature mapping data based on the preset book borrowing feature mapping classification rules, and then each of the image borrowing feature mapping data will be classified and stored in the book borrowing feature management library to perform feature classification on the downloaded book borrowing business data, and classified and stored in the book borrowing feature management library for centralized data management.

[0106] The book borrowing feature mapping classification rule has multiple book borrowing feature mapping templates, and each of the book borrowing feature mapping templates has its own target feature data type, so as to extract the target book borrowing business data corresponding to the target feature data type in each of the book borrowing feature mapping templates from the downloaded book borrowing business data, and then combine the target book borrowing business data corresponding to each of the book borrowing feature mapping templates into book borrowing feature mapping data, and use the book borrowing business data corresponding to a certain target feature data type in the book borrowing feature mapping data as key data, so as to facilitate the classification and storage of the book borrowing feature mapping data into the book borrowing feature management library. In the corresponding storage location, for example, when the book borrowing feature mapping template is a book basic feature mapping template, the book borrowing feature mapping template has a target feature data class of book standard number data type, book name data type and author name data type, then the book standard number data, book name data and author name data of the same book object will be determined from the downloaded book borrowing business data, and the standard number book, the book name data and the author name data will be encapsulated as the book borrowing feature mapping data of the book basic feature mapping data, and the standard number data, the book name data or the author name data in the book basic feature mapping data will be used as key data.

[0107] The book borrowing feature management library has storage locations corresponding to different types of book borrowing feature mapping data to classify and store different types of book borrowing feature mapping data. For example, the book borrowing feature management library may have storage locations for different book borrowing feature mapping types, such as book basic feature mapping type, borrowing user basic feature type, book borrowing record type, borrowing user borrowing record type, borrowing user reading habit type, etc., to store book borrowing feature mapping data of corresponding book borrowing feature mapping type in each of the storage locations.

[0108] Step S13, performing data preprocessing on the book borrowing business data in each of the book borrowing feature mapping data in the book borrowing feature management library, wherein the data preprocessing includes data completion processing, data error correction processing, data standardization processing or data conversion processing:

[0109] The downloaded book borrowing business data are classified into different book borrowing feature mapping data, and each of the book borrowing feature mapping data is stored in the book borrowing feature management library. Data preprocessing is performed on each of the book borrowing feature mapping data stored in the book borrowing feature management library to organize the book borrowing business data contained in each of the book borrowing feature mapping data, so that a book borrowing system having a data access interface of the book borrowing feature management library can obtain complete, accurate and uniformly formatted book borrowing business data for constructing or optimizing book borrowing services.

[0110] The data preprocessing generally includes: data completion processing for detecting missing book borrowing business data in the book borrowing feature mapping data and completing it, data error correction processing for detecting book borrowing business data with data errors in the book borrowing feature mapping data and correcting them, data standardization processing for standardizing the data format of the corresponding book borrowing business data in the book borrowing feature mapping data, and data conversion processing for converting the corresponding book borrowing business data in the book borrowing feature mapping data into the corresponding data type.

[0111] Regarding the implementation method of performing the data completion processing on the book borrowing business data possessed by the book borrowing feature mapping data, specifically, the book borrowing feature mapping data stored in the book borrowing feature management library is traversed to detect the incomplete book borrowing feature mapping data with incomplete data in each of the book borrowing feature mapping data, and then the book borrowing business data missing from the incomplete book borrowing feature mapping data is obtained, and the obtained missing book borrowing business data is stored in the incomplete book borrowing feature mapping data to complete the data completion processing of the incomplete book borrowing feature mapping data. The missing book borrowing business data can generally be obtained from the book business system, or by using the book borrowing business data existing in the incomplete book borrowing feature mapping data, searching and downloading network data that is associated with each of the book borrowing business data and meets the data type of the missing book borrowing business data on the network, and using the network data as the missing book borrowing business data in the incomplete book borrowing feature mapping data for data completion.

[0112] Regarding the implementation method of performing the data error correction processing on the book borrowing business data possessed by the book borrowing feature mapping data, specifically, the book borrowing feature mapping data stored in the book borrowing feature management library is traversed, and the erroneous book borrowing feature mapping data with data errors in each of the book borrowing feature mapping data are detected. Data error correction processing is performed according to the data correction rules corresponding to the data type of the book borrowing business data with data errors in the erroneous book borrowing feature mapping data. The data error correction rules are generally corresponding typo correction rules, borrowing start and end time correction rules or book publication time rules, etc., which are rules for performing data error correction on book borrowing business data of different data types.

[0113] Regarding the implementation method of performing the data standardization processing on the book borrowing business data possessed by the book borrowing feature mapping data, specifically, according to the standard data format rules acting on the book borrowing feature management library, the data format of the corresponding book borrowing business data in the book borrowing feature mapping data stored in the book borrowing feature management library is corrected to perform data standardization. For example, when the standard data format rule is the standard time data format rule, the time-type book borrowing business data in the book borrowing feature mapping data (such as the publication time data of the book, the time when the book is borrowed, the birth date of the borrowing user, the time when the borrowing user borrows the book, etc.) are uniformly corrected to the corresponding time format (such as correcting November 12 12:33 to 11 / 12 12:33). In addition to this, there may also be standard data format rules such as standard gender data format rules, standard age data format rules, standard book classification number format rules, etc. Of course, those skilled in the art can flexibly design different standard data format rules, which will not be elaborated here.

[0114] Regarding the implementation method of performing the data conversion processing on the book borrowing business data possessed by the book borrowing feature mapping data, specifically, one or more data conversion rules are obtained, the target book borrowing feature mapping data corresponding to each of the data conversion rules in the book borrowing feature management library is queried, and the book borrowing business data to be converted corresponding to the data conversion rule in each of the target book borrowing feature mapping data is determined, so as to perform data conversion on the book borrowing business data to be converted according to the data conversion rule. For example, when the data conversion rule is a borrowing user reading frequency conversion rule, the target book borrowing feature mapping data whose book borrowing feature mapping type is a borrowing user borrowing record type is queried in the book borrowing feature management library, and multiple target book borrowing business data whose data types are book borrowing start time and book borrowing end time in the target book borrowing feature mapping data of the borrowing user borrowing record type are determined. Data conversion is performed based on each of the multiple target book borrowing business data to generate borrowing user reading frequency data and store it in the target book borrowing feature mapping data. Of course, those skilled in the art can flexibly design different data conversion rules, which will not be described in detail here.

[0115] Step S14: Open the data access interface of the book borrowing feature management library to the target book borrowing system. The target book borrowing service obtains the book borrowing feature mapping data from the book borrowing feature management library through the data access interface to construct the corresponding book borrowing service.

[0116] The book borrowing feature management library has a data access interface. By providing the data access interface to the target book borrowing system, the target book borrowing system can obtain book borrowing feature mapping data from the book borrowing feature management library through the data access interface, so that the target book borrowing system can use each of the book borrowing feature mapping data to construct or optimize the corresponding book borrowing service. For example, by obtaining a sufficient amount of book borrowing feature mapping data, a book recommendation borrowing service for recommending borrowed books to borrowing users, a popular borrowing book service for generating a popular borrowing book form, or the management personnel of the target book borrowing system can use a sufficient amount of book borrowing feature data to analyze the recently popular borrowed books to enrich the library's collection of books and provide other books for borrowing users. Of course, those skilled in the art can flexibly design the target book borrowing system to obtain a sufficient amount of book borrowing feature mapping data through the data access interface to construct or optimize the corresponding book borrowing service, which will not be elaborated here.

[0117] The above exemplary embodiments and their variations fully disclose the implementation scheme of the book borrowing data acquisition method of the present application. However, various variations of the method can be derived by changing and expanding some technical means. Other embodiments are briefly described below:

[0118] In one embodiment, please refer to Figure 3 The step of downloading and obtaining book lending service data from one or more book lending systems, wherein the book lending service data includes book lending data or book lending user data, includes:

[0119] Step S111: accessing the book lending service database of the book lending system and obtaining the book lending service data stored in the book lending service database:

[0120] The book lending system opens its book lending business database storing book lending business data, and the book lending data collection and management system can directly download and obtain the book lending business data from the book lending business database.

[0121] Step S112: When access to the book lending service database fails, enter the book lending service page of the book lending system, traverse multiple page element objects that construct the book lending service page, and obtain page element data of each page element object:

[0122] For a book lending system that does not open its book lending business database, the book lending data collection and management system will use a book lending business data downloading method built based on Scrapy to download and obtain page element data from the book lending service page of the book lending system through the book lending service, so as to extract the book lending business data from the downloaded page element data.

[0123] The book borrowing service page of the book borrowing system is generally composed of multiple types of page element objects, and each of the page element objects is composed of different page element data. For example, the book borrowing service page generally has a book page element object composed of basic book data such as book pictures, book name data, author name data, etc., so that the book page element object composed of the basic book data of the books in the library is output to the book borrowing service page for display, so that borrowing users can browse the library's borrowable book objects through the book page element objects in the book borrowing service page. In addition, the book borrowing service page generally also has a borrowing user page element object composed of the borrowing user's user information or a borrowing book list page element object composed of a borrowing ranking list of the number of borrowing times of borrowed books, etc.

[0124] By using the book borrowing service data downloading method, the page element data in the book borrowing service page is traversed to download the page element data of each page element data.

[0125] Step S113: According to the preset book lending business data extraction rule, target page element data corresponding to the target extraction data type preset in the book lending business data extraction rule is extracted from each of the page element data, and each of the target page element data is used as the book lending business data:

[0126] The book borrowing business data extraction rules preset multiple target extraction data types to extract target page element data corresponding to each target extraction data type from the downloaded page element data, and then use the extracted target page element data as book borrowing business data. For example, the target extraction data type can be book standard number data, book name data, sub-book name data, author name data, other responsible person data, series title data, classification number data, publishing house data, publication time data or book type number data and other book borrowing data types, or the target extraction data type can be book borrowing user data types such as basic feature data of the borrowing user, educational background data of the borrowing user, reading preference data or reading habit data.

[0127] The implementation of this embodiment is generally executed by the book borrowing data downloader described in this application, or by the server cluster of the book borrowing data collection and management system running the service process constructed based on this embodiment.

[0128] In this embodiment, book borrowing business data is downloaded and obtained from the book borrowing system through two data downloading and obtaining methods, so that the book borrowing data collection and management system can collect sufficient book borrowing business data, and provide sufficient data for the book borrowing system with insufficient book borrowing business data to construct and optimize the book borrowing service, thereby improving the borrowing experience of book borrowing users.

[0129] In one embodiment, please refer to Figure 4 The steps of classifying and combining the book borrowing business data into multiple types of book borrowing feature mapping data based on a preset book borrowing feature mapping classification rule, and classifying and storing the book borrowing feature mapping data into a book borrowing feature management library include:

[0130] Step S121: Obtain book borrowing business data downloaded from one or more book borrowing systems, and obtain different book borrowing feature mapping templates in the preset book borrowing feature mapping classification rules:

[0131] After downloading and obtaining the book borrowing business data from the book borrowing system, different book borrowing feature mapping templates in the preset book borrowing feature mapping classification rules will be obtained. Different book borrowing feature mapping templates have their own corresponding book borrowing feature mapping types, and each of the book borrowing feature mapping templates is preset with multiple target feature data types.

[0132] Step S122: According to the multiple target feature data types preset in each of the book borrowing feature mapping templates, target book borrowing business data corresponding to each of the target feature data types of each of the book borrowing feature mapping templates is matched from the latest acquired book borrowing business data:

[0133] After obtaining each of the book borrowing feature mapping templates, the target book borrowing business data of each target feature data type will be matched from the downloaded book borrowing business data according to the multiple target feature data types preset in each of the book borrowing feature mapping templates. For example, when the book borrowing feature mapping template is a book basic feature mapping template, the book borrowing feature mapping template has a target feature data class of standard number data type, book name data type and author name data type, then the book standard number data, book name data and author name data of the same book object will be matched from the downloaded book borrowing business data.

[0134] Step S123: encapsulate the target book borrowing business data belonging to the same book borrowing feature mapping template, generate book borrowing feature mapping data corresponding to each book borrowing feature mapping template, and store each book borrowing feature mapping data in the storage location corresponding to the book borrowing feature mapping template in the book borrowing feature management library, wherein the book borrowing feature mapping data uses the book name data, the book standard number data, or the borrowing user basic feature data as key data:

[0135] After matching the target book borrowing business data corresponding to each target feature data type of the book borrowing feature mapping template, the target book borrowing business data corresponding to each target feature data type belonging to the same data source are encapsulated into a book borrowing feature mapping data. For example, taking the book basic feature mapping template as an example, after obtaining the standard number data, book name data and author name data belonging to the same book object, the standard number data, the book name data and the author name data are encapsulated as the book borrowing feature mapping data corresponding to the book object, and the book name data or the book standard number data is used as the book borrowing feature mapping data. key data so that the book borrowing feature mapping data corresponds to the book object. In addition, for the book borrowing feature mapping template being a borrowing user feature mapping template, after obtaining the target book borrowing business data corresponding to each target feature data type of the same borrowing user object of the borrowing user feature mapping template and encapsulating it into book borrowing feature mapping data, for example, the borrowing user's basic feature data (such as the borrowing user's name, the borrowing user's gender, the borrowing user's user number, the borrowing user's student number, the borrowing user's date of birth or the borrowing user's age, etc.) is used as the key data of the book borrowing feature mapping data, so that the book borrowing feature mapping data corresponds to the borrowing user object.

[0136] Each of the book borrowing feature mapping data is stored in a storage location corresponding to the book borrowing feature mapping template in a book borrowing feature management library, so as to classify and store each of the book borrowing feature mapping data in the book borrowing feature management library.

[0137] In this embodiment, the downloaded book borrowing business data is classified and combined into different types of book borrowing feature mapping data through the book borrowing feature mapping classification rules, and the data is classified and stored in the book borrowing feature management library, so that the book borrowing business data is classified into different types of mapping relationship data storage for data management, and it is convenient for the book borrowing system to query the corresponding type of book borrowing business data from the book borrowing feature management library to build and optimize the book borrowing service.

[0138] In one embodiment, please refer to Figure 5 , performing data preprocessing on the book borrowing business data in each of the book borrowing feature mapping data in the book borrowing feature management library, wherein the data preprocessing includes the steps of data completion processing, data error correction processing, data standardization processing or data conversion processing, including:

[0139] Step S131, traverse the book borrowing feature mapping data stored in the book borrowing feature management library, detect incomplete book borrowing feature mapping data in each of the book borrowing feature mapping data, obtain the missing book borrowing business data of the incomplete book borrowing feature mapping data, and complete the data:

[0140] Traverse the book borrowing feature mapping data stored in the book borrowing feature management library to detect the incomplete book borrowing feature mapping data in each of the book borrowing feature mapping data, and then obtain the book borrowing business data missing from the incomplete book borrowing feature mapping data, and store the obtained missing book borrowing business data in the incomplete book borrowing feature mapping data to complete the data completion processing of the incomplete book borrowing feature mapping data. For example, when the incomplete book borrowing feature mapping data is book feature mapping data, and the book borrowing business data missing from the book feature mapping data is publisher data, the book name data contained in the book feature mapping data and "publisher" can be used as search terms to search for the publisher data corresponding to the book name data from the Internet, and use the publisher data to complete the book feature mapping data, or download the corresponding publisher data from the book borrowing system through the book name data to complete the book feature mapping data.

[0141] Step S132: traverse the book borrowing feature mapping data stored in the book borrowing feature management library, detect the erroneous book borrowing feature mapping data with data errors in each of the book borrowing feature mapping data, and perform data error correction according to the data correction rule corresponding to the data type of the erroneous book borrowing business data in the erroneous book borrowing feature mapping data:

[0142] Traverse the book borrowing feature mapping data stored in the book borrowing feature management library, detect the erroneous book borrowing feature mapping data with data errors in each of the book borrowing feature mapping data, and perform data correction processing according to the data correction rules corresponding to the data type of the book borrowing business data with data errors in the erroneous book borrowing feature mapping data. The data correction rules are generally corresponding typo correction rules, borrowing start and end time correction rules or book publication time rules, etc., which are rules for data correction of book borrowing business data of different data types.

[0143] Step S133: According to the standard data format rules acting on the book borrowing feature management library, the data format of the book borrowing business data corresponding to the book borrowing feature mapping data stored in the book borrowing feature management library is modified to perform data standardization:

[0144] According to the standard data format rules acting on the book borrowing feature management library, the data format of the corresponding book borrowing business data in the book borrowing feature mapping data stored in the book borrowing feature management library is corrected to perform data standardization. For example, when the standard data format rule is the standard time data format rule, the time-type book borrowing business data in the book borrowing feature mapping data (such as the publication time data of the book, the time when the book is borrowed, the borrower's date of birth, the time when the borrower borrows the book, etc.) are uniformly corrected to the corresponding time format (such as correcting November 12, 12:33 to 11 / 1212:33). In addition, there may also be standard data format rules such as standard gender data format rules, standard age data format rules, and standard book classification number format rules.

[0145] Step S134: Obtain one or more data conversion rules, query the target book borrowing feature mapping data corresponding to each data conversion rule in the book borrowing feature management library, determine the book borrowing business data to be converted corresponding to the data conversion rule in each target book borrowing feature mapping data, and perform data conversion on the book borrowing business data to be converted according to the data conversion rule:

[0146] Obtain one or more data conversion rules, query the target book borrowing feature mapping data corresponding to each of the data conversion rules in the book borrowing feature management library, determine the book borrowing business data to be converted corresponding to the data conversion rule in each of the target book borrowing feature mapping data, and perform data conversion on the book borrowing business data to be converted according to the data conversion rule. For example, when the data conversion rule is a borrowing user reading frequency conversion rule, query the target book borrowing feature mapping data whose book borrowing feature mapping type is a borrowing user borrowing record type in the book borrowing feature management library, determine multiple target book borrowing business data whose data types are book borrowing start time and book borrowing end time in the target book borrowing feature mapping data of the borrowing user borrowing record type, perform data conversion based on each of the multiple target book borrowing business data to generate borrowing user reading frequency data and store it in the target book borrowing feature mapping data.

[0147] In this embodiment, data completion processing, data error correction processing, data standardization processing or data conversion processing are performed on the book borrowing feature mapping data stored in the book borrowing feature management library to organize the book borrowing business data contained in each of the book borrowing feature mapping data, so that a book borrowing system having a data access interface of the book borrowing feature management library can obtain complete, accurate and uniformly formatted book borrowing business data for constructing or optimizing book borrowing services.

[0148] Furthermore, by functionalizing each step in the method for obtaining book borrowing data disclosed in the above embodiments, a client visiting reporting device of the present application can be constructed. Figure 6 In a typical embodiment, the device includes: a borrowing data downloading and obtaining module 11, which is used to download and obtain book borrowing business data from one or more book borrowing systems, wherein the book borrowing business data includes book borrowing data or book borrowing user data; a borrowing data classification and storage module 12, which is used to classify and combine each of the book borrowing business data into multiple categories of book borrowing feature mapping data based on a preset book borrowing feature mapping classification rule, and classify and store each of the book borrowing feature mapping data into a book borrowing feature management library; a borrowing data pre-processing module 13, which is used to perform data pre-processing on the book borrowing business data in each of the book borrowing feature mapping data in the book borrowing feature management library, wherein the data pre-processing includes data completion processing, data error correction processing, data standardization processing or data conversion processing; a borrowing data open access module 14, which is used to open a data access interface of the book borrowing feature management library to a target book borrowing system, wherein the target book borrowing service obtains the book borrowing feature mapping data from the book borrowing feature management library through the data access interface to construct a corresponding book borrowing service.

[0149] In one embodiment, the borrowing data download and acquisition module 11 includes: a business database access submodule, which is used to access the book borrowing business database of the book borrowing system and obtain the book borrowing business data stored in the book borrowing business database; a page element data acquisition submodule, which is used to enter the book borrowing service page of the book borrowing system when access to the book borrowing business database fails, traverse multiple page element objects that construct the book borrowing service page, and obtain page element data of each page element object; a book borrowing business data generation submodule, which is used to extract target page element data corresponding to the target extraction data type preset in the book borrowing business data extraction rule from each page element data according to a preset book borrowing business data extraction rule, and use each target page element data as book borrowing business data.

[0150] In one embodiment, the borrowing data classification storage module 12 includes: a feature mapping template acquisition submodule, which is used to acquire book borrowing business data downloaded from one or more book borrowing systems, and acquire different book borrowing feature mapping templates in the preset book borrowing feature mapping classification rules; a target business data acquisition submodule, which is used to match the target book borrowing business data corresponding to each target feature data type of each book borrowing feature mapping template from the latest acquired book borrowing business data according to multiple target feature data types preset in each book borrowing feature mapping template; a feature mapping data storage submodule, which is used to encapsulate the target book borrowing business data belonging to the same book borrowing feature mapping template, generate book borrowing feature mapping data corresponding to each book borrowing feature mapping template, and store each book borrowing feature mapping data in the storage location corresponding to the book borrowing feature mapping template in the book borrowing feature management library, wherein the book borrowing feature mapping data uses book name data, book standard number data or borrowing user basic feature data as key data.

[0151] In one embodiment, the borrowing data pre-processing module 13 includes: a business data completion submodule, which is used to traverse the book borrowing feature mapping data stored in the book borrowing feature management library, detect the incomplete book borrowing feature mapping data with incomplete data in each of the book borrowing feature mapping data, obtain the book borrowing business data missing from the incomplete book borrowing feature mapping data for data completion; a business data error correction submodule, which is used to traverse the book borrowing feature mapping data stored in the book borrowing feature management library, detect the erroneous book borrowing feature mapping data with erroneous data in each of the book borrowing feature mapping data, and correct the data according to the data type corresponding to the erroneous book borrowing business data in the erroneous book borrowing feature mapping data. Data error correction is performed according to the correction rules; a business data standardization submodule is used to correct the data format of the book borrowing business data corresponding to the book borrowing feature mapping data stored in the book borrowing feature management library according to the standard data format rules acting on the book borrowing feature management library to perform data standardization; a business data conversion submodule is used to obtain one or more data conversion rules, query the target book borrowing feature mapping data corresponding to each of the data conversion rules in the book borrowing feature management library, determine the book borrowing business data to be converted corresponding to the data conversion rule in each of the target book borrowing feature mapping data, and perform data conversion on the book borrowing business data to be converted according to the data conversion rules.

[0152] See also Figure 7 The present application provides a method for processing book borrowing data. In a typical embodiment, the method comprises the following steps:

[0153] Step S21: obtaining book borrowing feature mapping data from a book borrowing feature management library through a data access interface, wherein the book borrowing feature mapping data includes book borrowing feature mapping data and book borrowing user feature mapping data:

[0154] When the book lending system has a data access interface opened by the book lending data collection and management system, the book lending system can obtain the book lending feature mapping data from the book lending feature management library of the book lending data collection and management system through the data access interface.

[0155] The book borrowing feature mapping data refers to the book borrowing feature mapping data having book borrowing business data as book borrowing data. Correspondingly, the book borrowing user feature mapping data refers to the book borrowing feature mapping data having book borrowing business data as book borrowing user data.

[0156] Step S22: using each of the book borrowing feature mapping data as a training sample to train a book borrowing rate prediction model, and using the book borrowing rate prediction model trained to convergence to predict the future borrowing rates of multiple book objects in multiple book categories in the current book borrowing system:

[0157] The book borrowing system obtains book borrowing feature mapping data from the book borrowing feature management library as training samples to train the book borrowing rate prediction model, which is a model constructed based on a neural network algorithm.

[0158] After the book lending system obtains multiple book borrowing feature mapping data and multiple book borrowing user feature mapping data, it will obtain the borrowing user's historical borrowing record in each of the book borrowing user feature mapping data to obtain the borrowed book objects and the borrowing times of the book objects contained in each of the borrowing user's historical borrowing records, determine the total borrowing times of each of the book objects based on the borrowing times of each of the book objects, and then calculate the book borrowing rate of each of the book objects based on the total borrowing times of each of the book objects, and determine the target book borrowing feature mapping data of each of the book objects from the book borrowing feature mapping data, and then obtain the book borrowing rates of each of the target books and the book objects to which they belong as training samples, and train the book borrowing rate prediction model to a convergence state.

[0159] After the book borrowing rate prediction model is trained to a convergence state, book feature information of multiple book objects in multiple book categories in the book borrowing system can be obtained, and the book feature information of each book can be input into the book borrowing rate prediction model to obtain the future book borrowing rate of each book object output by the book borrowing rate prediction model.

[0160] In addition to predicting the future borrowing rates of existing book objects in the book lending system, the book borrowing rate prediction model can also predict the future borrowing rates of book objects that are not yet available in the book lending system, so that library managers of the book lending system can determine the books that need to be purchased by the library based on the future borrowing rates of the non-existent book objects, so as to provide new borrowing books to borrowers. Of course, those skilled in the art can flexibly use the book borrowing rate prediction model to assist related businesses in the book lending system, which will not be elaborated here.

[0161] Step S23, determining the historical borrowing rates of multiple book objects in multiple book categories in the current book borrowing system based on the book borrowing feature mapping data:

[0162] After the book lending system obtains the book borrowing feature mapping data from the book borrowing feature management library, it can refer to the book borrowing feature mapping data to count the historical borrowing rates of the book objects used for borrowing in each book category in the book lending system, which can optimize the situation where the historical borrowing rates of books with reference significance for each book object cannot be counted due to the small amount of borrowing data in the book lending system.

[0163] The target book objects corresponding to the downloaded book borrowing feature mapping data of each book are queried from each book object of each book category in the book borrowing system. For example, the book standard number data or book name data of each book borrowing feature mapping data is obtained to query the target book objects corresponding to each book standard number data or each book name data from each book object in the book borrowing system.

[0164] After determining each of the target book objects, the historical borrowing records of the borrowing users in the feature mapping data of each book borrowing user are obtained to determine the historical borrowing records of the target borrowing users whose borrowed book objects are the target book objects from the historical borrowing records of each of the borrowing users. Then, based on the historical borrowing records of each of the target borrowing users, the total number of borrowings of the target book objects is determined. Then, based on the total number of borrowings of each of the target book objects, the historical borrowing rate of each of the book objects is calculated.

[0165] Step S24: Determine a borrower profile of each borrower based on borrower feature information of multiple borrowers in the current book borrowing system and the book borrowing feature mapping data:

[0166] Obtain the book borrowing user feature mapping data of each of the book borrowing feature mapping data, and construct a borrowing user portrait template for different borrowing user types acting on the book borrowing service based on the borrowing user basic feature data (borrowing user gender, borrowing user date of birth or borrowing user age, etc.) contained in each of the book borrowing user feature mapping data, the borrowing user's educational background data (such as borrowing user academic qualifications, borrowing user grade, borrowing user required major or borrowing user community, etc.), the borrowing user's reading preference data (frequently borrowed book categories, frequently borrowed book authors, frequently borrowed book series or frequently borrowed book publishers, etc.), the borrowing user's reading habit data (such as average reading time in a unit time period, frequently read places or frequently read time periods, etc.), the borrowing user's interest and hobby data, the borrowing user's reading language data, etc. The borrowing user portrait template comprehensively considers the borrowing user's basic feature data in the book borrowing feature mapping data, Based on the borrowing user data (such as distinguishing the gender of the borrowing user by male or female, and determining different user age ranges according to the age of the borrowing user), reading preference data, reading habit data, interest data, and reading language data, the borrowing user book borrowing preferences, reading habits, and reading languages ​​of borrowing users of different genders and age groups are classified, and then the borrowing user portrait templates hit by different borrowing users in the book borrowing system are judged, and then the borrowing user feature data corresponding to the borrowing user are filled in the hit borrowing user portrait template to generate the borrowing user portrait corresponding to the borrowing user. It can be seen that referring to the borrowing users in other book borrowing systems to assist in the generation of the borrowing user portrait of the borrowing users in the book borrowing system can make the borrowing user portrait of the borrowing user richer in information for the book borrowing system with fewer borrowing users, which is equivalent to predicting the borrowing preferences and reading habits of the borrowing users, thereby improving the accuracy of recommending borrowed books to the borrowing users.

[0167] After generating each type of borrowing user portrait template, the borrowing user characteristic information of each borrowing user registered in the book borrowing system will be obtained to determine the target borrowing user portrait template hit by the borrowing user characteristic data such as the borrowing user gender, borrowing user age, borrowing user grade, borrowing user study major, borrowing user reading language, borrowing user hobbies or borrowing book records contained in the borrowing user characteristic information of each borrowing user in the borrowing user portrait template, and then the borrowing user name, borrowing user student number or borrowing user name and other data that can represent the borrowing user's personal identity in the borrowing user characteristic information of each borrowing user are filled into the borrowing user portrait template it hits to generate a borrowing user portrait of each borrowing user.

[0168] Step S25: Generate recommended borrowing book set information for each borrowing user based on the borrowing user portrait of each borrowing user, combined with the future borrowing rate of each book and the historical borrowing rate of each book:

[0169] After generating the borrowing user portrait of each borrowing user in the book borrowing system, books with higher borrowing rates will be determined for each borrowing user as recommended borrowing books based on the borrowing user portrait of each user and combined with the future borrowing rate and historical borrowing rate of the books corresponding to each book object in the book borrowing system. That is, books that are more popular and in line with the borrowing preferences and habits of the borrowing users will be determined as recommended borrowing books.

[0170] Based on the future borrowing rates and historical borrowing rates of multiple book objects in multiple book categories in the current book lending system, and using preset borrowing rate weight information, the borrowing rate ranking of each book object is determined. Specifically, when the borrowing rate weight information includes the borrowing rate weight of the future borrowing rate of the book, it can be set to 0.4, and the borrowing rate weight of the historical borrowing rate of the book can be set to 0.6. Then, the borrowing rate weight information is used to calculate the comprehensive borrowing rate of each book object, and then the borrowing objects are sorted according to the comprehensive borrowing rates to determine the borrowing rate ranking among the book objects.

[0171] Based on the borrowing user portrait of each borrowing user in the book borrowing system, multiple pre-borrowed book objects for each borrowing user will be determined from the book borrowing system. For example, when the borrowing user's borrowing user portrait indicates that his borrowing preference is science fiction books and his reading habit is to read for a long time, book objects with a large number of pages and that have not been borrowed by the borrowing user can be determined from the science fiction book category in the book borrowing system, and such book objects will be used as the pre-borrowed book objects of the borrowing user.

[0172] After determining the pre-borrowed book objects of the borrowing user in the book borrowing system, and screening out the target book objects with higher borrowing rate rankings from the pre-borrowed book objects, the book borrowing feature mapping data and book feature information corresponding to each target book object are encapsulated to generate recommended borrowing book set information for the borrowing user.

[0173] In addition to using the book borrowing feature mapping data obtained from the book borrowing feature management library to construct a borrowing book recommendation service, the book borrowing system can also construct other types of book borrowing services, for example, constructing a borrowing book wish list service. Specifically, based on the book borrowing feature mapping data of each book, the target book objects that are popular among the book objects in other book borrowing systems and not in this book borrowing system are determined, and a list of the target book objects is generated and pushed to the borrowing users in this book borrowing system, so that the borrowing users can select the book objects they want to borrow from the target book objects, generate the borrowing user's borrowing book wish list, and then enable this book borrowing system to include the book objects in the borrowing book wish list, so that the borrowing users can borrow the book objects in their borrowing book wish list from this book borrowing system, etc.; of course, those skilled in the art can flexibly design or optimize the book borrowing service constructed or optimized using the book borrowing feature mapping data obtained from the book borrowing feature management library, which will not be elaborated here.

[0174] The above exemplary embodiments and their variations fully disclose the implementation scheme of the book borrowing data processing method of the present application. However, various variations of the method can be derived by changing and expanding some technical means. Other embodiments are briefly described below:

[0175] In one embodiment, please refer to Figure 8 The step of calling the book borrowing rate prediction model trained to convergence and predicting the future borrowing rates of multiple book objects in multiple book categories based on the book borrowing feature mapping data includes:

[0176] Step S221: Obtain multiple book borrowing feature mapping data and multiple book borrowing user feature mapping data, and calculate the book borrowing rate of the book object corresponding to each of the book borrowing feature mapping data according to the borrowing user's historical borrowing records in each of the book borrowing user feature mapping data:

[0177] The borrowing history records of the borrowing users contain the book objects borrowed by the borrowing users in the past. By obtaining the book objects contained in the borrowing history records of each borrowing user, the total number of borrowing times of each book object is calculated, and then the book borrowing rate of each book object is calculated based on the total number of borrowing times of each book object.

[0178] Step S222: Using the book borrowing feature mapping data of each book object and its book borrowing rate as training samples, and using each borrowing rate training sample to train a book borrowing rate prediction model until convergence:

[0179] The target book borrowing feature mapping data of each book object is determined from the book borrowing feature mapping data, and then the book borrowing rates of each target book and the book object to which it belongs are obtained as training samples, and the book borrowing rate prediction model is trained to a convergence state.

[0180] Step S223: Inputting the book feature information of multiple book objects in multiple book categories in the current book lending system into the book lending rate prediction model, and obtaining the future book lending rate of each book object output by the book lending rate prediction model:

[0181] After the book borrowing rate prediction model is trained to a convergence state, book feature information of multiple book objects in multiple book categories in the book borrowing system can be obtained, and the book feature information of each book can be input into the book borrowing rate prediction model to obtain the future book borrowing rate of each book object output by the book borrowing rate prediction model.

[0182] In this embodiment, the book borrowing rate prediction model is trained to convergence by using book borrowing feature mapping data and book borrowing user feature mapping data collected from other book borrowing systems, and the future book borrowing rate of book objects in the book borrowing system is predicted using the book borrowing rate prediction model trained to convergence, thereby improving the accuracy of predicting the future book borrowing rate of book borrowing systems with a small amount of borrowing business data, so that accurate future book borrowing rate data can be used to build or optimize corresponding book borrowing services.

[0183] In one embodiment, please refer to Figure 9 The step of generating the recommended borrowing book set information for each borrowing user based on the borrowing user portrait of each borrowing user, combined with the future borrowing rate of each book and the borrowing rate of each book, includes:

[0184] Step S251: Based on the future borrowing rates and historical borrowing rates of multiple book objects in multiple book categories in the current book lending system, and using preset borrowing rate weight information, determine the borrowing rate ranking of each of the book objects:

[0185] After generating the borrowing user portrait of each borrowing user in the book borrowing system, books with higher borrowing rates will be determined for each borrowing user as recommended borrowing books based on the borrowing user portrait of each user and combined with the future borrowing rate and historical borrowing rate of the books corresponding to each book object in the book borrowing system. That is, books that are more popular and in line with the borrowing preferences and habits of the borrowing users will be determined as recommended borrowing books.

[0186] Based on the future borrowing rates and historical borrowing rates of multiple book objects in multiple book categories in the current book lending system, and using preset borrowing rate weight information, the borrowing rate ranking of each book object is determined. Specifically, when the borrowing rate weight information includes the borrowing rate weight of the future borrowing rate of the book, it can be set to 0.4, and the borrowing rate weight of the historical borrowing rate of the book can be set to 0.6. Then, the borrowing rate weight information is used to calculate the comprehensive borrowing rate of each book object, and then the borrowing objects are sorted according to the comprehensive borrowing rates to determine the borrowing rate ranking among the book objects.

[0187] Step S252: According to the borrowing user portrait of the borrowing user, a plurality of pre-borrowed book objects corresponding to the borrowing user are determined from the book objects:

[0188] Based on the borrowing user portrait of each borrowing user in the book borrowing system, multiple pre-borrowed book objects for each borrowing user are determined from the book borrowing system. For example, when the borrowing user's borrowing user portrait indicates that his borrowing preference is science fiction books and his reading habit is to read for a long time, book objects with a large number of pages and that have not been borrowed by the borrowing user can be determined from the science fiction book category in the book borrowing system, and such book objects can be used as the pre-borrowed book objects of the borrowing user.

[0189] Step S253: Filter out the target book objects with higher borrowing rate rankings from the pre-borrowed book objects, encapsulate the book borrowing feature mapping data and book feature information corresponding to each target book object, and generate recommended borrowing book set information for the borrowing user:

[0190] After determining the pre-borrowed book objects of the borrowing user in the book borrowing system, and screening out the target book objects with higher borrowing rate rankings from the pre-borrowed book objects, the book borrowing feature mapping data and book feature information corresponding to each target book object are encapsulated to generate recommended borrowing book set information for the borrowing user.

[0191] In this embodiment, a recommended borrowing book service is provided to borrowing users through the borrowing user portrait and the borrowing rate ranking of each book object, so that borrowing users can learn about the book objects that they have not borrowed and that meet their borrowing preferences for borrowing, thereby improving the borrowing user's book borrowing experience.

[0192] Furthermore, by functionalizing each step in the book borrowing data processing method disclosed in the above embodiments, a client visiting reporting device of the present application can be constructed. Figure 10In a typical embodiment, the device includes: a feature management library access module 21, which is used to obtain book borrowing feature mapping data from a book borrowing feature management library through a data access interface, wherein the book borrowing feature mapping data includes book borrowing feature mapping data and book borrowing user feature mapping data; a future borrowing rate prediction module 22, which is used to train a book borrowing rate prediction model using each of the book borrowing feature mapping data as a training sample, so as to use the book borrowing rate prediction model trained to convergence to predict the future borrowing rates of multiple book objects in multiple book categories in the current book borrowing system; a history The borrowing rate determination module 23 is used to determine the historical borrowing rates of multiple book objects in multiple book categories in the current book borrowing system based on the book borrowing feature mapping data; the borrowing user portrait determination module 24 is used to determine the borrowing user portrait of each borrowing user in the current book borrowing system based on the borrowing user feature information of multiple borrowing users and the book borrowing feature mapping data; the recommended book information generation module 25 is used to generate the recommended borrowing book set information of each borrowing user based on the borrowing user portrait of each borrowing user, combined with the future borrowing rate of each book and the historical borrowing rate of each book.

[0193] In one embodiment, the future borrowing rate prediction module 22 includes: a book borrowing rate statistics submodule, which is used to obtain multiple book borrowing feature mapping data and multiple book borrowing user feature mapping data, and calculate the book borrowing rate of the book object corresponding to each of the book borrowing feature mapping data according to the borrowing user's historical borrowing records in each of the book borrowing user feature mapping data; a prediction model training submodule, which is used to use the book borrowing feature mapping data of each of the book objects and its book borrowing rate as training samples, and use each of the borrowing rate training samples to train the book borrowing rate prediction model to a convergence state; a book borrowing rate prediction submodule, which is used to input the book feature information of multiple book objects in multiple book categories in the current book borrowing system into the book borrowing rate prediction model, and obtain the future book borrowing rate of each book object output by the book borrowing rate prediction model.

[0194] In one embodiment, the recommended book information generation module 25 includes: a book borrowing ranking determination submodule, which is used to determine the borrowing rate ranking of each of the multiple book objects in multiple book categories in the current book borrowing system based on the future borrowing rate and the historical borrowing rate of each of the multiple book objects, and using preset borrowing rate weight information; a pre-borrowed book object determination submodule, which is used to determine multiple pre-borrowed book objects corresponding to the borrowing user from each of the book objects according to the borrowing user portrait of the borrowing user; and a recommended borrowing book determination submodule, which is used to filter out the target book objects with higher borrowing rate rankings from each of the pre-borrowed book objects, encapsulate the book borrowing feature mapping data and book feature information corresponding to each of the target book objects, and generate recommended borrowing book set information for the borrowing user.

[0195] In order to solve the above technical problems, the embodiment of the present application further provides a computer device for running a computer program implemented according to the book borrowing data acquisition method or the book borrowing data processing method. Figure 11 , Figure 11 This is a basic structural block diagram of the computer device in this embodiment.

[0196] like Figure 11 As shown, a schematic diagram of the internal structure of a computer device. The computer device includes a processor, a non-volatile storage medium, a memory and a network interface connected via a system bus. Among them, the non-volatile storage medium of the computer device stores an operating system, a database and computer-readable instructions, and the database may store a control information sequence. When the computer-readable instructions are executed by the processor, the processor may implement a method for acquiring book borrowing data or a method for processing book borrowing data. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor may execute a method for acquiring book borrowing data or a method for processing book borrowing data. The network interface of the computer device is used to connect and communicate with a terminal. Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0197] In this embodiment, the processor is used to execute the specific functions of each module / submodule in the book lending data acquisition device or the book lending data processing device of this application, and the memory stores the program code and various data required to execute the above modules. The network interface is used to transmit data between user terminals or servers. The memory in this embodiment stores the program code and data required to execute all modules / submodules in the book lending data acquisition device or the book lending data processing device. The server can call the server's program code and data to execute the functions of all submodules.

[0198] The present application also provides a non-volatile storage medium, wherein the book borrowing data acquisition method or the book borrowing data processing method is written into a computer program and stored in the storage medium in the form of computer-readable instructions. When the computer-readable instructions are executed by one or more processors, it means that the program is running in the computer, thereby enabling one or more processors to execute the steps of the book borrowing data acquisition method or the book borrowing data processing method implemented in any of the above embodiments.

[0199] Those skilled in the art will appreciate that all or part of the processes in the above-described embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-described method for acquiring book borrowing data or the above-described method for processing book borrowing data. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0200] In summary, this application can centrally manage the book borrowing business data of different book borrowing systems for use in order to optimize the book borrowing service and enhance the user's book borrowing experience.

[0201] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0202] Those skilled in the art will appreciate that the steps, measures, and schemes in the various operations, methods, and processes discussed in this application may be interchanged, modified, combined, or deleted. Furthermore, other steps, measures, and schemes in the various operations, methods, and processes discussed in this application may also be interchanged, modified, rearranged, decomposed, combined, or deleted. Furthermore, steps, measures, and schemes in the prior art that are similar to those disclosed in this application may also be interchanged, modified, rearranged, decomposed, combined, or deleted.

[0203] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for obtaining book borrowing data, characterized in that: include: Downloading and acquiring book lending service data from one or more book lending systems, wherein the book lending service data includes book lending data or book lending user data; Based on the preset book borrowing feature mapping classification rules, classify and combine the book borrowing business data into multiple types of book borrowing feature mapping data, and classify and store the book borrowing feature mapping data into a book borrowing feature management library; Performing data preprocessing on the book borrowing business data in each of the book borrowing feature mapping data in the book borrowing feature management library, wherein the data preprocessing includes data completion processing, data error correction processing, data standardization processing or data conversion processing; Opening a data access interface of the book borrowing feature management library to a target book borrowing system, wherein the target book borrowing system obtains book borrowing feature mapping data from the book borrowing feature management library through the data access interface to construct a corresponding book borrowing service; Acquire book borrowing feature mapping data from a book borrowing feature management library through a data access interface, wherein the book borrowing feature mapping data includes book borrowing feature mapping data and book borrowing user feature mapping data; Using each of the book borrowing feature mapping data as a training sample to train a book borrowing rate prediction model, and using the book borrowing rate prediction model trained to convergence to predict future book borrowing rates of multiple book objects in multiple book categories in the current book borrowing system; Determining historical book borrowing rates of a plurality of book objects in a plurality of book categories in a current book borrowing system according to the book borrowing feature mapping data; Determining a borrowing user profile of each borrowing user based on borrowing user feature information of multiple borrowing users in the current book borrowing system and the book borrowing feature mapping data; Generate recommended borrowing book set information for each borrowing user based on the borrowing user portrait of each borrowing user, combined with the future borrowing rate of each book and the historical borrowing rate of each book; Wherein, data preprocessing is performed on the book borrowing business data in each of the book borrowing feature mapping data in the book borrowing feature management library, and the data preprocessing includes the steps of data completion processing, data error correction processing, data standardization processing or data conversion processing, including: Traversing the book borrowing feature mapping data stored in the book borrowing feature management library, detecting incomplete book borrowing feature mapping data in each of the book borrowing feature mapping data, and obtaining the book borrowing business data missing from the incomplete book borrowing feature mapping data for data completion; Traversing the book borrowing feature mapping data stored in the book borrowing feature management library, detecting the erroneous book borrowing feature mapping data with data errors in each of the book borrowing feature mapping data, and performing data error correction according to the data correction rule corresponding to the data type of the erroneous book borrowing business data in the erroneous book borrowing feature mapping data; According to the standard data format rules acting on the book borrowing feature management library, the data format of the book borrowing business data corresponding to the book borrowing feature mapping data stored in the book borrowing feature management library is modified to perform data standardization; Obtain one or more data conversion rules, query the target book borrowing feature mapping data corresponding to each data conversion rule in the book borrowing feature management library, determine the book borrowing business data to be converted corresponding to the data conversion rule in each target book borrowing feature mapping data, and perform data conversion on the book borrowing business data to be converted according to the data conversion rule.

2. The method for obtaining book borrowing data according to claim 1, wherein: The step of downloading and obtaining book lending service data from one or more book lending systems, wherein the book lending service data includes book lending data or book lending user data, includes: Accessing a book lending service database of a book lending system to obtain book lending service data stored in the book lending service database; When access to the book lending service database fails, entering the book lending service page of the book lending system, traversing multiple page element objects that construct the book lending service page, and obtaining page element data of each of the page element objects; According to the preset book borrowing business data extraction rules, the target page element data corresponding to the target extraction data type preset in the book borrowing business data extraction rules are extracted from each of the page element data, and each of the target page element data is used as the book borrowing business data.

3. The method for obtaining book borrowing data according to claim 1, wherein: Based on a preset book borrowing feature mapping classification rule, classifying and combining each of the book borrowing business data into multiple categories of book borrowing feature mapping data, and classifying and storing each of the book borrowing feature mapping data in a book borrowing feature management library, the steps include: Obtaining book borrowing business data downloaded from one or more book borrowing systems, and obtaining different book borrowing feature mapping templates in a preset book borrowing feature mapping classification rule; According to the multiple target feature data types preset in each of the book borrowing feature mapping templates, the target book borrowing business data corresponding to each of the target feature data types of each of the book borrowing feature mapping templates is matched from the latest acquired book borrowing business data; Encapsulate the target book borrowing business data belonging to the same book borrowing feature mapping template, generate book borrowing feature mapping data corresponding to each book borrowing feature mapping template, and store each book borrowing feature mapping data in the storage location corresponding to the book borrowing feature mapping template in the book borrowing feature management library, wherein the book borrowing feature mapping data uses book name data, book standard number data or borrowing user basic feature data as key data.

4. The method for obtaining book borrowing data according to claim 1, wherein: The step of calling the book borrowing rate prediction model trained to convergence and predicting the future borrowing rates of multiple book objects in multiple book categories based on the book borrowing feature mapping data includes: Obtaining a plurality of book borrowing feature mapping data and a plurality of book borrowing user feature mapping data, and calculating the book borrowing rate of the book object corresponding to each of the book borrowing feature mapping data according to the borrowing user's historical borrowing record in each of the book borrowing user feature mapping data; Using the book borrowing feature mapping data of each of the book objects and its book borrowing rate as training samples, and using each of the borrowing rate training samples to train a book borrowing rate prediction model until convergence; The book feature information of multiple book objects in multiple book categories in the current book lending system is input into the book lending rate prediction model, and the future book lending rate of each book object output by the book lending rate prediction model is obtained.

5. The method for processing book borrowing data according to claim 1, wherein: The step of generating the recommended borrowing book set information for each borrowing user based on the borrowing user portrait of each borrowing user, combined with the future borrowing rate of each book and the borrowing rate of each book, includes: Based on the future borrowing rates and historical borrowing rates of a plurality of book objects in a plurality of book categories in the current book borrowing system, and using preset borrowing rate weight information, determining a borrowing rate ranking of each of the book objects; According to the borrowing user portrait of the borrowing user, a plurality of pre-borrowed book objects corresponding to the borrowing user are determined from the book objects; The target book objects with higher borrowing rate rankings are screened out from the pre-borrowed book objects, the book borrowing feature mapping data and book feature information corresponding to each target book object are encapsulated, and recommended borrowing book set information for the borrowing user is generated.

6. A device for acquiring book borrowing data, characterized in that: include: A borrowing data downloading and obtaining module is used to download and obtain book borrowing business data from one or more book borrowing systems, wherein the book borrowing business data includes book borrowing data or book borrowing user data; A borrowing data classification storage module is used to classify and combine the book borrowing business data into multiple types of book borrowing feature mapping data based on a preset book borrowing feature mapping classification rule, and classify and store the book borrowing feature mapping data into a book borrowing feature management library; A borrowing data pre-processing module is used to perform data pre-processing on the book borrowing business data in each of the book borrowing feature mapping data in the book borrowing feature management library, wherein the data pre-processing includes data completion processing, data error correction processing, data standardization processing or data conversion processing; A borrowing data open access module, configured to open a data access interface of the book borrowing feature management library to a target book borrowing system, so that the target book borrowing system obtains book borrowing feature mapping data from the book borrowing feature management library through the data access interface to construct a corresponding book borrowing service; A feature management library access module is used to obtain book borrowing feature mapping data from the book borrowing feature management library through a data access interface, wherein the book borrowing feature mapping data includes book borrowing feature mapping data and book borrowing user feature mapping data; a future borrowing rate prediction module, configured to train a book borrowing rate prediction model using each of the book borrowing feature mapping data as a training sample, and to use the book borrowing rate prediction model trained to convergence to predict future borrowing rates of a plurality of book objects in a plurality of book categories in a current book borrowing system; A historical borrowing rate determination module is used to determine the historical borrowing rates of multiple book objects in multiple book categories in the current book borrowing system based on the book borrowing feature mapping data; A borrowing user portrait determination module determines a borrowing user portrait of each borrowing user based on borrowing user feature information of multiple borrowing users in the current book borrowing system and the book borrowing feature mapping data; A recommended book information generation module is used to generate the recommended borrowing book set information of each borrowing user based on the borrowing user portrait of each borrowing user, combined with the future borrowing rate of each book and the historical borrowing rate of each book; The borrowing data pre-processing module includes: A business data completion submodule is used to traverse the book borrowing feature mapping data stored in the book borrowing feature management library, detect incomplete book borrowing feature mapping data in each of the book borrowing feature mapping data, obtain the missing book borrowing business data in the incomplete book borrowing feature mapping data, and complete the data; A business data error correction submodule is used to traverse the book borrowing feature mapping data stored in the book borrowing feature management library, detect the erroneous book borrowing feature mapping data with data errors in each of the book borrowing feature mapping data, and perform data error correction according to the data correction rule corresponding to the data type of the erroneous book borrowing business data in the erroneous book borrowing feature mapping data; A business data standardization submodule is used to correct the data format of the book borrowing business data corresponding to the book borrowing feature mapping data stored in the book borrowing feature management library according to the standard data format rules acting on the book borrowing feature management library to perform data standardization; The business data conversion submodule is used to obtain one or more data conversion rules, query the target book borrowing feature mapping data corresponding to each data conversion rule in the book borrowing feature management library, determine the book borrowing business data to be converted corresponding to the data conversion rule in each target book borrowing feature mapping data, and perform data conversion on the book borrowing business data to be converted according to the data conversion rule.

7. The book borrowing data acquisition device according to claim 6, characterized in that: The future borrowing rate prediction module includes: The book borrowing rate statistics submodule is used to obtain a plurality of book borrowing feature mapping data and a plurality of book borrowing user feature mapping data, and calculate the book borrowing rate of the book object corresponding to each of the book borrowing feature mapping data according to the borrowing user's historical borrowing records in each of the book borrowing user feature mapping data; A prediction model training submodule is configured to use the book borrowing feature mapping data of each of the book objects and its book borrowing rate as training samples, and to train the book borrowing rate prediction model to a convergence state using the borrowing rate training samples; The book borrowing rate prediction submodule is used to input the book feature information of multiple book objects in multiple book categories in the current book borrowing system into the book borrowing rate prediction model, and obtain the future book borrowing rate of each book object output by the book borrowing rate prediction model.

8. The book borrowing data acquisition device according to claim 6, characterized in that: The recommended book information generation module includes: A book borrowing ranking determination submodule is configured to determine a borrowing rate ranking of each of a plurality of book objects in a plurality of book categories in the current book borrowing system based on the future borrowing rates and the historical borrowing rates of each of the plurality of book objects and using preset borrowing rate weight information; A pre-borrowed book object determination submodule is used to determine a plurality of pre-borrowed book objects corresponding to the borrowing user from the book objects according to the borrowing user portrait of the borrowing user; The recommended borrowing book determination submodule is used to filter out the target book objects with higher borrowing rate rankings from the pre-borrowed book objects, encapsulate the book borrowing feature mapping data and book feature information corresponding to each target book object, and generate recommended borrowing book set information for the borrowing user.

9. A book borrowing data downloader, comprising a central processing unit and a memory, characterized in that: The central processing unit is used to call and run the computer program stored in the memory to execute the steps of the book borrowing data acquisition method according to any one of claims 1 to 5.

10. A computer device comprising a central processing unit and a memory, characterized in that: The central processing unit is configured to call and run a computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 5.

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